Seafloor character was derived from interpretations of aerial photograph-derived kelp-distribution data available for Santa Cruz Island in the Santa Barbara Channel, California (Kushner and others 2013). The number of substrate classes was reduced because rugosity could not be derived for all areas.

This Data Release contains GIS data generated by USGS for use in a BOEM funded project to compare natural rockfish nursery habitat to habitat created by manmade structures in the eastern Santa Barbara Channel. The contours were created from published Data Elevation Models of Carignan and others (2009) and Dartnell and others (2012). Contours were generated using the ESRI Contour tool in spatial analyst. The contour interval is 10 meters. The contours were clipped to exclude areas outside the BOEM rockfish nurseries study area.

Seafloor character was derived from interpretations of lidar data available for the mainland coast within the study area from the California State Waters Mapping Program (Johnson and others, 2012; Johnson and others, 2013a; Johnson and others, 2013b; Johnson and others, 2013c). The number of substrate classes was reduced because rugosity could not be derived for all areas. References Cited: Johnson, S.Y., Dartnell, P., Cochrane, G.R., Golden, N.E., Phillips, E.L., Ritchie, A.C., Greene, H.G., Krigsman, L.M., Kvitek, R.G., Dieter, B.E., Endris, C.A., Seitz, G.G., Sliter, R.W., Erdey, M.E., Gutierrez, C.I., Wong, F.L., Yoklavich, M.M., Draut, A.E., Hart, P.E., and Conrad, J.E. (S.Y. Johnson and S.A. Cochran, eds.), 2013a, California State Waters Map Series—Offshore of Santa Barbara, California: U.S. Geological Survey Scientific Investigations Map 3281, 45 p., 11 sheets, scale 1:24,000, https://doi.org/10.3133/sim3281. Johnson, S.Y., Dartnell, P., Cochrane, G.R., Golden, N.E., Phillips, E.L., Ritchie, A.C., Kvitek, R.G., Greene, H.G., Endris, C.A., Seitz, G.G., Sliter, R.W., Erdey, M.D., Wong, F.L., Gutierrez, C.I., Krigsman, L.M., Draut, A.E., and Hart, P.E. (S.Y. Johnson and S.A. Cochran, eds.), 2013b, California State Waters Map Series—Offshore of Carpinteria, California: U.S. Geological Survey Scientific Investigations Map 3261, 42 p., 10 sheets, scale 1:24,000, https://pubs.usgs.gov/sim/3261/. Johnson, S.Y., Dartnell, P., Cochrane, G.R., Golden, N.E., Phillips, E.L., Ritchie, A.C., Kvitek, R.G., Greene, H.G., Krigsman, L.M., Endris, C.A., Clahan, K.B., Sliter, R.W., Wong, F.L., Yoklavich, M.M., and Normark, W.R. (S.Y. Johnson, ed.), 2012, California State Waters Map Series—Hueneme Canyon and Vicinity, California: U.S. Geological Survey Scientific Investigations Map 3225, 41 p., 12 sheets, scale 1:24,000, https://pubs.usgs.gov/sim/3225/. Johnson, S.Y., Dartnell, P., Cochrane, G.R., Golden, N.E., Phillips, E.L., Ritchie, A.C., Kvitek, R.G., Greene, H.G., Krigsman, L.M., Endris, C.A., Seitz, G.G., Gutierrez, C.I., Sliter, R.W., Erdey, M.D., Wong, F.L., Yoklavich, M.M., Draut, A.E., and Hart, P.E. (S.Y. Johnson and S.A. Cochran, eds.), 2013c, California State Waters Map Series—Offshore of Ventura, California: U.S. Geological Survey Scientific Investigations Map 3254, pamphlet 42 p., 11 sheets, scale 1:24,000, https://pubs.usgs.gov/sim/3254/.

Substrate was classified using the method of (Cochrane 2008) for this study multibeam sonar. Sea floor character derived from multibeam sonar data is available for the mainland coast within the study area from the California State Waters Mapping Program (Johnson and others, 2012; Johnson and others, 2013a; Johnson and others, 2013b; Johnson and others, 2013c). The number of substrate classes was reduced because rugosity could not be derived for all areas due to the lack of bathymetry data for other data sets used in the study. References Cited: Cochrane, G.R., 2008, Video-supervised classification of sonar data for mapping seafloor habitat, in Reynolds, J.R., and Greene, H.G., eds., Marine habitat mapping technology for Alaska: Fairbanks, University of Alaska, Alaska Sea Grant College Program, p. 185-194, accessed April 5, 2011, at http://doc.nprb.org/web/research/research%20pubs/615_habitat_mapping_workshop/Individual%20Chapters%20High-Res/Ch13%20Cochrane.pdf. Johnson, S.Y., Dartnell, P., Cochrane, G.R., Golden, N.E., Phillips, E.L., Ritchie, A.C., Greene, H.G., Krigsman, L.M., Kvitek, R.G., Dieter, B.E., Endris, C.A., Seitz, G.G., Sliter, R.W., Erdey, M.E., Gutierrez, C.I., Wong, F.L., Yoklavich, M.M., Draut, A.E., Hart, P.E., and Conrad, J.E. (S.Y. Johnson and S.A. Cochran, eds.), 2013a, California State Waters Map Series—Offshore of Santa Barbara, California: U.S. Geological Survey Scientific Investigations Map 3281, 45 p., 11 sheets, scale 1:24,000, https://doi.org/10.3133/sim3281. Johnson, S.Y., Dartnell, P., Cochrane, G.R., Golden, N.E., Phillips, E.L., Ritchie, A.C., Kvitek, R.G., Greene, H.G., Endris, C.A., Seitz, G.G., Sliter, R.W., Erdey, M.D., Wong, F.L., Gutierrez, C.I., Krigsman, L.M., Draut, A.E., and Hart, P.E. (S.Y. Johnson and S.A. Cochran, eds.), 2013b, California State Waters Map Series—Offshore of Carpinteria, California: U.S. Geological Survey Scientific Investigations Map 3261, 42 p., 10 sheets, scale 1:24,000, https://pubs.usgs.gov/sim/3261/. Johnson, S.Y., Dartnell, P., Cochrane, G.R., Golden, N.E., Phillips, E.L., Ritchie, A.C., Kvitek, R.G., Greene, H.G., Krigsman, L.M., Endris, C.A., Clahan, K.B., Sliter, R.W., Wong, F.L., Yoklavich, M.M., and Normark, W.R. (S.Y. Johnson, ed.), 2012, California State Waters Map Series—Hueneme Canyon and Vicinity, California: U.S. Geological Survey Scientific Investigations Map 3225, 41 p., 12 sheets, scale 1:24,000, https://pubs.usgs.gov/sim/3225/. Johnson, S.Y., Dartnell, P., Cochrane, G.R., Golden, N.E., Phillips, E.L., Ritchie, A.C., Kvitek, R.G., Greene, H.G., Krigsman, L.M., Endris, C.A., Seitz, G.G., Gutierrez, C.I., Sliter, R.W., Erdey, M.D., Wong, F.L., Yoklavich, M.M., Draut, A.E., and Hart, P.E. (S.Y. Johnson and S.A. Cochran, eds.), 2013c, California State Waters Map Series—Offshore of Ventura, California: U.S. Geological Survey Scientific Investigations Map 3254, pamphlet 42 p., 11 sheets, scale 1:24,000, https://pubs.usgs.gov/sim/3254/.

Substrate was classified using the method of (Cochrane and Lafferty, 2002) for this study. Sea floor character derived from towed sidescan sonar data is available for the mainland coast within the study area from USGS online publications (Cochrane and others, 2003; Cochrane and others, 2005). The number of substrate classes was reduced because rugosity could not be derived for all areas due to the lack of bathymetry data for other data sets used in the study. References Cited: Cochrane, G.R., Nasby, N.M., Reid, J.A., Waltenberger, B., Lee, K.M., 2003, Nearshore Benthic Habitat GIS for the Channel Islands National Marine Sanctuary and Southern California State Fisheries Reserves Volume 1: U.S. Geological Survey Open-File Report 03-85, http://pubs.usgs.gov/of/2003/0085/. Cochrane, G.R., and Lafferty, K.D., 2002, Use of acoustic classification of sidescan sonar data for mapping benthic habitat in the Northern Channel Islands, California: Continental Shelf Research, v. 22, p. 683-690. Cochrane, G.R., Conrad, J.E., Reid, J.A., Fangman, S., Golden, N.E., 2005, The Nearshore Benthic Habitat GIS for the Channel Islands National Marine Sanctuary and Southern California State Fisheries Reserves, Volume II; Version 1.0: U.S. Geological Survey Open-File Report 2005-1170, http://pubs.usgs.gov/of/2005/1170/.

This Data Release contains data from the U.S. Geological Survey (USGS) survey of the Oregon outer Continental shelf (OCS) Floating Wind Farm Site in 2014. The backscatter intensity data was collected along with bathymetry data by USGS during the period from August 20 to September 1, 2014, using a Reson 7111 multibeam echosounder. The mapping mission collected bathymetry data from about 163 m to 566 m depths on the Oregon outer continental shelf. The acquisition was funded by the U.S. Bureau of Ocean Energy Management. Within the final imagery, brighter tones indicate higher backscatter intensity, and darker tones indicate lower backscatter intensity. The intensity represents a complex interaction between the acoustic pulse and the seafloor, as well as characteristics within the shallow subsurface, providing a general indication of seafloor texture and composition. Backscatter intensity depends on the acoustic source level; the frequency used to image the seafloor; the grazing angle; the composition and character of the seafloor, including grain size, water content, bulk density, and seafloor roughness; and some biological cover. Harder and rougher bottom types such as rocky outcrops or coarse sediment typically return stronger intensities (high backscatter, lighter tones), whereas softer bottom types such as fine sediment return weaker intensities (low backscatter, darker tones).

This Data Release contains data from the U.S. Geological Survey (USGS) survey of the Oregon outer continental shelf (OCS) Floating Wind Farm Site in 2014. The bathymetry raster was generated from bathymetry data collected by USGS during the period from August 20 to September 1, 2014, using a Reson 7111 multibeam echosounder. The mapping mission collected bathymetry data from about 163 m to 566 m depths on the Oregon outer continental shelf. The acquisition was funded by the U.S. Bureau of Ocean Energy Management.

This data release contains data from the USGS field activity 2014-607-FA, a survey of the Oregon Outer Continental Shelf (OCS) Floating Wind Farm Site in 2014. The bathymetry raster was generated from bathymetry data collected by U.S. Geological Survey (USGS) during the period from August 20 to September 1, 2014 using a Reson 7111 multibeam echosounder. The mapping mission collected bathymetry data from about 163 m to 566 m depths on the Oregon outer continental shelf. The acquisition was funded by the U.S. Bureau of Ocean Energy Management. Contours were generated using the ESRI Contour tool in spatial analyst. The contour interval is 10 meters.

This part of the Oregon Outer Continental Shelf (OCS) Floating Windfarm Suite Data Release presents geological observations from video collected on U.S. Geological Survey (USGS) field activity 2014-607-FA in the Floating Wind Farm survey area. The survey was conducted using 12 hour day operations out of Charleston Harbor near Coos Bay, Oregon. The cruise plan consisted of 23 days on site split between sonar mapping and video ground truth surveying. Activities parsed out to nine days of sonar mapping, three days of video surveying, eight days of no operations due to weather, and three days mobilizing and demobilizing (table 1). Typically the Snavely would transit out to the survey area in an hour at a speed of 20 knots. Marine Mammal observations were made during the multibeam sonar mapping portion of the cruise only. Multibeam sonar operations were conducted on north or south oriented tracklines at a speed of 4 to 5 knots depending on sea state. Observations were also made on the transit out to the floating Windfarm site.

This Data Release contains data from the USGS survey of the Oregon OCS Floating Wind Farm Site in 2014. The shaded-relief raster was generated from bathymetry data collected by USGS during the period from August 20 to September 1, 2014. using a Reson 7111 multibeam echosounder. The mapping mission collected bathymetry data from about 163 m to 566 m depths on the Oregon outer continental shelf. The acquisition was funded by the U.S. Bureau of Ocean Energy Management.

This part of the Oregon OCS Data Release presents marine mammal observations from U.S. Geological Survey (USGS) field activity 2014-607-FA in the Oregon Outer continental Shelf (OCS)Floating Wind Farm survey area. The survey was conducted using 12 hour day operations out of Charleston Harbor near Coos Bay, Oregon. The cruise plan consisted of 23 days on site split between sonar mapping and video ground truth surveying. Activities parsed out to nine days of sonar mapping, three days of video surveying, eight days of no operations due to weather, and three days mobilizing and demobilizing (table 1). Typically the Snavely would transit out to the survey area in an hour at a speed of 20 knots. Marine Mammal observations were made during the multibeam sonar mapping portion of the cruise only. Multibeam sonar operations were conducted on north or south oriented tracklines at a speed of 4 to 5 knots depending on sea state. Observations were also made on the transit out to the Floating Windfarm site.

This data release contains digital video files from the USGS field activity 2014-607-FA, a survey of the Oregon Outer Continental Shelf (OCS) Floating Wind Farm Site in 2014. Video data were collected over 3 days between September 6 and September 9, 2014 using a towed camera sled system. 11.6 hours of video were collected along 18 transects; the mean length of time per transect was 38 minutes. Video operations were conducted by deploying up drift of a target and drifting over it at speeds of 1 knot or slower. The video data were collected in order to ground truth geologic and habitat interpretations of sonar data collected during the same field activity. The video-survey locations were chosen after the sonar mapping to investigate sea-floor features of interest, including bathymetric features such as ridges and depressions, areas that represent the spectrum of backscatter intensity observed in the survey area, and areas that represent the spectrum of water depths surveyed. The camera sled was equipped with both vertical-downward and adjustable oblique-forward facing HD video cameras. Paired lasers set a fixed distance apart are visible in the video and are used to scale features on the seafloor. A fiber optic cable was used to allow real-time on-board viewing of both camera feeds. The sled was also equipped with a forward-scanning sonar system for collision avoidance. Conductivity and temperature were continuously recorded using a SeaBird Seacat 37-SM. Depth and altitude were measured to aid operations but not recorded. A downward facing still camera, designed for small invertebrate identification, was attached to the sled, but insufficient lighting rendered the still images unusable. Real-time observations of the major and minor substrate type were made, as well as occasional comments about organisms, features, or objects of interest.

This data release contains boomer subbottom data collected in June and July of 1996 on the shelf and slope offshore Eureka, California. Subbottom acoustic penetration spans up to several tens of meters, and is variable by location. This data release contains digital SEG-Y data. The data were collected aboard the R/V Wecoma using a Huntec Hydrosonde Deep-Tow system.

This part of the data release contains processed, high-resolution, chirp seismic-reflection profiles that were collected aboard the R/V Bold Horizon in 2018 on U.S. Geological Survey cruise 2018-638-FA offshore Oceanside to San Diego, southern California. Approximately 127 line-kilometers of chirp data were collected offshore Oceanside (BH lines) and 125 line-kilometers were collected offshore Silver Strand, San Diego (SS lines). The data were acquired using an Edgetech 512 Chirp sub-bottom profiling system. These data are divided up and presented by navigation line, as reflected in the individual file names.

This section of the data release contains core logger tabular data of 41 vibracores that were collected aboard the R/V Bold Horizon in 2018 on U.S. Geological Survey Field Activity 2018-638-FA offshore Oceanside to San Diego, southern California. The cores were analyzed for sound velocity (P-wave) and gamma ray density. The logging was performed at 1-cm intervals from the top of each core section. In addition to the core logger data, the location of the cores are available as either a comma-delimited file or a shapefile.

This section of the data release contains photographs of 41 vibracores that were collected aboard the R/V Bold Horizon in 2018 on U.S. Geological Survey Field Activity 2018-638-FA offshore Oceanside to San Diego, southern California.

This section of the data release contains grain-size and total organic carbon (TOC) analyses of 174 samples taken from vibracores that were collected aboard the R/V Bold Horizon in 2018 on U.S. Geological Survey Field Activity 2018-638-FA offshore Oceanside to San Diego, southern California. The samples were analyzed for percent weight of grain size and total organic carbon. The samples were taken at approx. 50 cm intervals from (and including) the core-catcher, which represents the bottom of the core.

Data from various sources, including 2018 and 2019 multibeam bathymetry data collected by the National Oceanic and Atmospheric Administration (NOAA) and the U.S. Geological Survey (USGS) were combined to create a composite 30-m resolution multibeam bathymetry surface of southern Cascadia Margin offshore Oregon and northern California. The data are available as a geoTIFF file.

This polygon shapefile describes the data sources used to create a composite 30-m resolution multibeam bathymetry surface of southern Cascadia Margin offshore Oregon and northern California.

Phosphorites and phosphatized rocks from the summit of the Rio Grande Rise (RGR) in the south Atlantic Ocean were collected via dredge during the oceanographic research cruise RGR1 to the western RGR. The location (latitude, longitude, depth), mineralogy, concentrations of phosphorus and rare earth elements, and 87Sr/86Sr ratios of phosphorites and phosphatized FeMn crusts, ironstones, and carbonates from 10 dredge sites are presented here. These data were used to determine the presence of carbonate fluorapatite in different rock types, the age of carbonate fluorapatite and the characteristics of the phosphatizing fluid.

This section of the data release contains approximately 369 line-kilometers of processed, high-resolution chirp seismic-reflection profiles that were collected aboard the R/V Snavely in 2017 on U.S. Geological Survey cruise 2017-686-FA offshore Oceanside, southern California. The along-shore and across-shore chirp profiles are oriented to assess sand and gravel resources in Federal and State waters for potential use in future beach nourishment projects along stretches of the coast where critical erosion hotspots have been identified. The chirp profiles were acquired using an Edgetech 512 Chirp sub-bottom profiling system.

This part of the data release contains marine magnetic data that were collected aboard the R/V Snavely in 2017 on U.S. Geological Survey cruise 2017-686-FA offshore Oceanside, southern California. The magnetic field data were collected to characterize the surface and subsurface properties of the study area, including unconsolidated sediment thickness and subsurface sediment horizons.

This section of the data release contains grain-size analysis of twenty-six seafloor surface grab samples that were collected aboard the R/V Snavely in 2017 on U.S. Geological Survey cruise 2017-686-FA offshore Oceanside, southern California. The samples were collected at strategic locations along the same transects as seismic-reflection lines oriented to assess sand and gravel resources in Federal and State waters for potential use in future beach nourishment projects along stretches of the coast where critical erosion hotspots have been identified. Samples were collected at 23 locations using a Van Veen sediment sampler along 4 shore-perpendicular transects spaced 5 to 9 km apart, aligned with previously collected seismic profile lines, at depths ranging from about 20 to 80 m. One sample on the outer shelf (BSS-06) was collected to evaluate an area of suspected sand occurrence. Two duplicate samples were collected at one location (BSS-08) in order to evaluate local sample variability.

Coastal managers and ocean engineers rely heavily on projected average and extreme wave conditions for planning and design purposes, but when working on a local or regional scale, are faced with much uncertainty as changes in the global climate impart spatially varying trends. Future storm conditions are likely to evolve in a fashion that is unlike past conditions and is ultimately dependent on the complicated interaction between the Earth’s atmosphere and ocean systems. Despite a lack of available data and tools to address future impacts, consideration of climate change is increasingly becoming a requirement for organizations considering future nearshore and coastal vulnerabilities. To address this need, the USGS used winds from four different atmosphere-ocean coupled general circulation models (AOGCMs) or Global Climate Models (GCMs) and the WaveWatchIII numerical wave model to compute historical and future wave conditions under the influence of two climate scenarios. The GCMs respond to specified, time-varying concentrations of various atmospheric constituents (such as greenhouse gases) and include an interactive representation of the atmosphere, ocean, land, and sea ice. The two climate scenarios are derived from the Coupled Model Inter-Comparison Project, Phase 5 (CMIP5; World Climate Research Programme, 2013) and represent one medium-emission mitigation scenario (RCP4.5; Representative Concentration Pathways) and one high-emissions scenario (RCP8.5). The historical time-period spans the years 1976 through 2005, whereas the two future time-periods encompass the mid (years 2026 through 2045) and end of the 21st century (years 2081 through 2099/2100). Continuous time-series of dynamically downscaled hourly wave parameters (significant wave heights, peak wave periods, and wave directions) and three-hourly winds (wind speed and wind direction) are available for download at discrete deep-water locations along four U.S. coastal regions: • Pacific Islands • West Coast • East Coast • Alaska Coasts The Alaskan region includes a total of 25 model output points. Six output points surround the Arctic coast, eight surround the Aleutian Islands, four are within the shallow region of the Bering Sea, and the remaining seven are within the Gulf of Alaska. The U.S. West Coast region stretches from the U.S.- Mexico border to the U.S.- Canada border and includes open coast areas of California, Oregon, and Washington. The West Coast region includes fifteen model output points. Eight model output points are co-located with observation buoys and are identified by National Oceanic and Atmospheric Administration National Data Buoy Center (NDBC, http://www.ndbc.noaa.gov/) station numbers (N46229, N46213, N46214, N46042, N46028, N46069, N46219, N46047). The U.S. East and Gulf Coasts encompass fifteen coastal states stretching from the Gulf Coast States and Florida in the south to the U.S.-Canada border north of Maine. The region includes seventeen model output points; seven are co-located with NDBC observation buoys (N44011, N44014, N41001, N41002, N41010, N42001, N42055). Data summaries for the U.S. East and Gulf Coast regions are provided from the 1.25° x 1.00° global (NWW3) wave model grid (described in Data and Methods section below). Data summaries for the U.S. West Coast region are available from the NWW3 grid and from the finer resolution 0.25° x 0.25° Eastern North Pacific (ENP) grid nested within the NWW3 grid. Data summaries for the southern coast of Alaska are also available from the ENP grid. In cases where model data exist for both the NWW3 and ENP grids, both sets of data are available for download (http://dx.doi.org/10.5066/F7D798GR). The data and cursory overviews of changing conditions along the coasts are summarized in Storlazzi and others (2015) and Erikson and others (2016). References Cited: Erikson, L.H., Hegermiller, C.A., Barnard, P.L., and Storlazzi, C.D., 2016, Wave projections for United States mainland coasts: U.S. Geological Survey pamphlet to accompany data release, https://doi.org/10.5066/F7D798GR. Erikson, L.H., Hegermiller, C.A., Barnard, P.L., Ruggiero, P., and van Ormondt, M., 2015b, Projected wave conditions in the Eastern North Pacific under the influence of two CMIP5 climate scenarios: Journal of Ocean Modelling, v. 96, p. 171–185, https://doi.org/10.1016/j.ocemod.2015.07.004. Erikson, L.H., Hemer, M.A., Lionello, P., Mendez, F.J., Mori, N., Semedo, A., Wang, X.L., and Wolf, J., 2015a, Projection of wave conditions in response to climate change: A community approach to global and regional wave downscaling: Proceedings Coastal Sediments 2015, 13 p., https://doi.org/10.1142/9789814689977_0243. Meinshausen, M., Smith, S.J., Calvin, K., Daniel, J.S., Kainuma, M.L.T., Lamarque, J-F., Matsumoto, K., Montzka, S.A., Raper, S.C.B., Riahi, K., Thomson, A., Velders, G.J.M., and van Vuuren, D.P.P., 2011, The RCP greenhouse gas concentrations and their extensions from 1765 to 2300: Climate Change, v. 109, p. 213–241, https://doi.org/10.1007/s10584-011-0156-z. Moss, R.H., Edmonds, J.A., Hibbard, K.A., Manning, M.R., Rose, S.K., van Vuuren, D.P., Carter, T.R., Emori, S., Kainuma, M., Kram, T., Meehl, G.A., Mitchell, J.F.B., Nakicenovic, N., Riahi, K., Smith, S.J., Stouffer, R.J., Thomson, A.M., Weyant, J.P., and Wilbanks, T.J., 2010, The next generation of scenarios for climate change research and assessment: Nature, v. 463, p. 747–756, https://doi.org/10.1038/nature08823. Riahi, K., Rao, S., Krey, V., Cho, C., Chirkov, V., Fischer, G., Kindermann, G., Nakicenovic, N., and Rafai, P., 2011, RCP 8.5: Exploring the consequence of high emission trajectories: Climatic Change, v. 109, p. 33–57, https://doi.org/10.1007/s10584-011-0149-y. Storlazzi, C.D., Shope, J.B., Erikson, L.H., Hegermiller, C.A., and Barnard, P.L., 2015, Future wave and wind projections for United States and United States-affiliated Pacific Islands: U.S. Geological Survey Open-File Report 2015–1001, 426 p., https://doi.org/10.3133/ofr20151001. Taylor, K.E., Stouffer, R.J., Meehl, G.A., 2012, An overview of CMIP5 and the experiment design: Bulletin of the American Meteorological Society, v. 93, p. 485–498, https://doi.org/10.1175/BAMS-D-11-00094.1. Thomson, A.M., Calvin, K.V., Smith, S.J., Kyle, G.P., Volke, A., Patel, P., Delgado-Arias, S., Bond-Lamberty, B., Wise, M.A., Clarke, L.E., Edmonds, J.A., 2011, RCP4.5: A pathway for stabilization of radiative forcing by 2100: Climatic Change, v. 109, p. 77–94, https://doi.org/10.1007/s10584-011-0151-4. van Vuuren, D.P., Edmonds, J.A., Kainuma, M., Riahi, K., Thomson, A.M., Hibbard, K., Hurtt, G.C., Kram, T., Krey, V., Lamarque, J-F., Masui, T., Meinshausen, M., Nakicenovic, N., Smith, S.J., and Rose, S., 2011, The representative concentration pathways: an overview: Climatic Change, v. 109, p. 5–31, https://doi.org/10.1007/s10584-011-0148-z.

We present methane data from along the coast of Barter Island, Alaska, collected with an Unmanned Aerial System and an off-the-shelf, cost-effective methane sensor. The data were collected on September 3 and September 5, 2017, as part of a larger Arctic coastal erosion investigation study by the U.S. Geological Survey (USGS). The data contain latitude, longitude and CH4 (ppm), and are presented as tab-delimited text files that have been zipped into one file. In addition, we have included one file of comparative data from Barrow, Alaska that were collected by the National Oceanic and Atmospheric Administration (NOAA) Global Monitoring Division from 1986-2017 as a courtesy to users. The three datasets together accompany Oberle, F.K.J., Gibbs, A.E., Richmond, B.M., Erikson, L.H., Waldrop, M.P., and Swarzenski, P.W., 2019, Towards determining spatial methane distribution on Arctic permafrost bluffs with an unmanned aerial system: SN Applied Sciences, https://doi.org/10.1007/s42452-019-0242-9.

This biotope raster is part of a data release of the Oregon outer continental shelf (OCS) proposed wind farm map site. The biotopes mapped in this area have been numbered to indicate combinations of seafloor hardness, ruggedness and depth associated with biotopes derived by analysis of video data as described in the accompanying Open-File Report (Cochrane and others, 2017). The map was created using video and multibeam echosounder bathymetry and backscatter data collected in 2014 and processed in 2015 (Cochrane and others, 2015).

This polygon shapefile is part of a data release of the Oregon outer continental shelf (OCS) proposed wind farm map site. The polygons have attribute values for Coastal and Marine Ecological Classification Standard (CMECS) geoforms, substrate, and modifiers. CMECS is the U.S. government standard for marine habitat characterization and was developed by representatives from a consortium of federal agencies. The standard provides an ecologically relevant structure for biologic, geologic, chemical, and physical habitat attributes. This map illustrates the geoform and substrate components of the standard. The CMECS classes are documented at https://www.fgdc.gov/standards/projects/FGDC-standards-projects/cmecs-folder/CMECS_Version_06-2012_FINAL.pdf Please refer to Madden and others (2008) for more information regarding the CMECS. The polygons were derived by classifying multibeam echosounder bathymetry and backscatter collected in 2014; details and data are available in Cochrane and others (2015) and Cochrane and others (2017).

This seafloor-character raster is part of a data release of the Oregon outer continental shelf (OCS) proposed wind farm map site. The substrate classes mapped in this area have been numbered to indicate combinations of seafloor hardness and ruggedness. The map was created from multibeam echosounder (MBES) bathymetry and backscatter data collected in 2014 and processed in 2015 (Cochrane and others, 2016) and a video supervised classification method described by Cochrane (2008).

Underwater video imagery was collected in March 2014 in the nearshore waters of Faga'alu Bay on the Island of Tutuila, American Samoa, as part of the U.S. Geological Survey Coastal and Marine Geology Program's Pacific Coral Reefs Project. Included here are 40 video files in .mpg format and an Environmental Systems Research Institute (ESRI) shapefile with location (navigation) points every two seconds.

This data release contains water level and velocity measurements from wave runup experiments performed in a laboratory flume setting. Wave-driven water level variability (and runup at the shoreline) is a significant cause of coastal flooding induced by storms. Wave runup is challenging to predict, particularly along tropical coral reef-fringed coastlines due to the steep bathymetric profiles and large bottom roughness generated by reef organisms. The 2012 University of Western Australia Fringing Reef Experiment (UWAFRE) measured water levels and velocities for sixteen wave and offshore (still) water level conditions on a 1:36 geometric scale fringing reef profile with and without bottom roughness. Experiments were performed in a 55-m long wave flume (Eastern Scheldt Flume) at Deltares, the Netherlands. These data accompany the following publications: Buckley, M.L., Lowe, R.J., Hansen, J.E., and van Dongeren, A.R., 2015, Dynamics of wave setup over a steeply sloping fringing reef: Journal of Physical Oceanography, v. 45, p. 3005-3023, https://doi.org/10.1175/Jpo-D-15-0067.1 Buckley, M.L., Lowe, R.J., Hansen, J.E., and van Dongeren, A.R., 2016, Wave setup over a fringing reef with large bottom roughness: Journal of Physical Oceanography, v. 46, p. 2317-2333, https://doi.org/10.1175/Jpo-D-15-0148.1 Buckley, M.L., Lowe, R.J., Hansen, J.E., van Dongeren, A.R., and Storlazzi, C.D., 2018, Mechanisms of wave-driven water level variability on reef-fringed coastlines: Journal of Geophysical Research-Oceans, https://doi.org/10.1029/2018JC013933.

This dataset consists of long-term (~63 years) shoreline change rates for the north coast of Alaska between the Hulahula River and the Colville River. Rate calculations were computed within a GIS using the Digital Shoreline Analysis System (DSAS) version 4.3, an ArcGIS extension developed by the U.S. Geological Survey. Long-term rates of shoreline change were calculated using a linear regression rate-of-change method based on available shoreline data between 1947 and 2010. A reference baseline was used as the originating point for the orthogonal transects cast by the DSAS software. The transects intersect each shoreline establishing measurement points, which are then used to calculate long-term rates.

This dataset consists of short-term (~31 years) shoreline change rates for the north coast of Alaska between the Hulahula River and the Colville River. Rate calculations were computed within a GIS using the Digital Shoreline Analysis System (DSAS) version 4.3, an ArcGIS extension developed by the U.S. Geological Survey. Short-term rates of shoreline change were calculated using a linear regression rate-of-change method based on available shoreline data between 1979 and 2010. A reference baseline was used as the originating point for the orthogonal transects cast by the DSAS software. The transects intersect each shoreline establishing measurement points, which are then used to calculate short-term rates.

This dataset includes a reference baseline used by the Digital Shoreline Analysis System (DSAS) to calculate rate-of-change statistics for the exposed north coast of Alaska coastal region between the Hulahula River and the Colville River for the time period 1947 to 2010. This baseline layer serves as the starting point for all transects cast by the DSAS application and can be used to establish measurement points used to calculate shoreline-change rates.

This dataset consists of long-term (~63 years) shoreline change rates for the north coast of Alaska between the Hulahula River and the Colville River. Rate calculations were computed within a GIS using the Digital Shoreline Analysis System (DSAS) version 4.3, an ArcGIS extension developed by the U.S. Geological Survey. Long-term rates of shoreline change were calculated using a linear regression rate-of-change method based on available shoreline data between 1947 and 2010. A reference baseline was used as the originating point for the orthogonal transects cast by the DSAS software. The transects intersect each shoreline establishing measurement points, which are then used to calculate long-term rates.

This dataset consists of short-term (~31 years) shoreline change rates for the north coast of Alaska between the Hulahula River and the Colville River. Rate calculations were computed within a GIS using the Digital Shoreline Analysis System (DSAS) version 4.3, an ArcGIS extension developed by the U.S. Geological Survey. Short-term rates of shoreline change were calculated using a linear regression rate-of-change method based on available shoreline data between 1979 and 2010. A reference baseline was used as the originating point for the orthogonal transects cast by the DSAS software. The transects intersect each shoreline establishing measurement points, which are then used to calculate short-term rates.

This dataset includes a reference baseline used by the Digital Shoreline Analysis System (DSAS) to calculate rate-of-change statistics for the sheltered north coast of Alaska coastal region between the Hulahula River and the Colville River for the time period 1947 to 2010. This baseline layer serves as the starting point for all transects cast by the DSAS application and can be used to establish measurement points used to calculate shoreline-change rates.

This dataset includes shorelines from 63 years ranging from 1947 to 2010 for the north coast of Alaska between the Hulahula River and the Colville River. Shorelines were compiled from topographic survey sheets (T-sheets; National Oceanic and Atmospheric Administration (NOAA)), aerial orthophotographs (U.S. Geological Survey (USGS), National Aeronautics and Space Administration (NASA), Conoco-Philips (CP), British Petroleum Alaska (BPXA)), satellite imagery (State of Alaska), and lidar elevation data (USGS). Historical shoreline positions serve as easily understood features that can be used to describe the movement of beaches through time. These data are used to calculate rates of shoreline change for the U.S. Geological Survey's National Assessment of Shoreline Change Project. Rates of long-term and short-term shoreline change were generated in a GIS using the Digital Shoreline Analysis System (DSAS) version 4.3. DSAS uses a measurement baseline method to calculate rate-of-change statistics. Transects are cast from the reference baseline to intersect each shoreline, establishing measurement points used to calculate shoreline change rates.

This dataset consists of long-term (~63 years) shoreline change rates for the north coast of Alaska between the U.S. Canadian Border and the Hulahula River. Rate calculations were computed within a GIS using the Digital Shoreline Analysis System (DSAS) version 4.3, an ArcGIS extension developed by the U.S. Geological Survey. Long-term rates of shoreline change were calculated using a linear regression rate-of-change method based on available shoreline data between 1947 and 2010. A reference baseline was used as the originating point for the orthogonal transects cast by the DSAS software. The transects intersect each shoreline establishing measurement points, which are then used to calculate long-term rates.

This dataset consists of short-term (~32 years) shoreline change rates for the north coast of Alaska between the U.S. Canadian Border and the Hulahula River. Rate calculations were computed within a GIS using the Digital Shoreline Analysis System (DSAS) version 4.3, an ArcGIS extension developed by the U.S. Geological Survey. Short-term rates of shoreline change were calculated using a linear regression rate-of-change method based on available shoreline data between 1978 and 2010. A reference baseline was used as the originating point for the orthogonal transects cast by the DSAS software. The transects intersect each shoreline establishing measurement points, which are then used to calculate short-term rates.

This dataset includes a reference baseline used by the Digital Shoreline Analysis System (DSAS) to calculate rate-of-change statistics for the exposed north coast of Alaska coastal region between the U.S. Canadian Border to the Hulahula River for the time period 1947 to 2010. This baseline layer serves as the starting point for all transects cast by the DSAS application and can be used to establish measurement points used to calculate shoreline-change rates.

This dataset consists of long-term (~63 years) shoreline change rates for the north coast of Alaska between the U.S. Canadian Border and the Hulahula River. Rate calculations were computed within a GIS using the Digital Shoreline Analysis System (DSAS) version 4.3, an ArcGIS extension developed by the U.S. Geological Survey. Long-term rates of shoreline change were calculated using a linear regression rate-of-change method based on available shoreline data between 1947 and 2010. A reference baseline was used as the originating point for the orthogonal transects cast by the DSAS software. The transects intersect each shoreline establishing measurement points, which are then used to calculate long-term rates.

This dataset consists of short-term (~32 years) shoreline change rates for the north coast of Alaska between the U.S. Canadian Border and the Hulahula River. Rate calculations were computed within a GIS using the Digital Shoreline Analysis System (DSAS) version 4.3, an ArcGIS extension developed by the U.S. Geological Survey. Short-term rates of shoreline change were calculated using a linear regression rate-of-change method based on available shoreline data between 1978 and 2010. A reference baseline was used as the originating point for the orthogonal transects cast by the DSAS software. The transects intersect each shoreline establishing measurement points, which are then used to calculate short-term rates.

This dataset includes a reference baseline used by the Digital Shoreline Analysis System (DSAS) to calculate rate-of-change statistics for the sheltered north coast of Alaska coastal region between the U.S. Canadian Border to the Hulahula River for the time period 1947 to 2010. This baseline layer serves as the starting point for all transects cast by the DSAS application and can be used to establish measurement points used to calculate shoreline-change rates.

This dataset includes shorelines from 63 years ranging from 1947 to 2010 for the north coast of Alaska between the U.S. Canadian Border and the Hulahula River. Shorelines were compiled from topographic survey sheets (T-sheets; National Oceanic and Atmospheric Administration (NOAA)), aerial orthophotographs (U.S. Geological Survey (USGS), National Aeronautics and Space Administration (NASA), satellite imagery (U.S. Fish and Wildlife Service (USFWS), State of Alaska), and lidar elevation data (USGS). Historical shoreline positions serve as easily understood features that can be used to describe the movement of beaches through time. These data are used to calculate rates of shoreline change for the U.S. Geological Survey's National Assessment of Shoreline Change Project. Rates of long-term and short-term shoreline change were generated in a GIS using the Digital Shoreline Analysis System (DSAS) version 4.3. DSAS uses a measurement baseline method to calculate rate-of-change statistics. Transects are cast from the reference baseline to intersect each shoreline, establishing measurement points used to calculate shoreline change rates.

This dataset consists of long-term (~65 years) shoreline change rates for the north coast of Alaska between Point Barrow and Icy Cape. Rate calculations were computed within a GIS using the Digital Shoreline Analysis System (DSAS) version 4.3, an ArcGIS extension developed by the U.S. Geological Survey. Long-term rates of shoreline change were calculated using a linear regression rate-of-change method based on available shoreline data between 1947 and 2012. A reference baseline was used as the originating point for the orthogonal transects cast by the DSAS software. The transects intersect each shoreline establishing measurement points, which are then used to calculate long-term rates.

This dataset consists of short-term (~32 years) shoreline change rates for the north coast of Alaska between Point Barrow and Icy Cape. Rate calculations were computed within a GIS using the Digital Shoreline Analysis System (DSAS) version 4.3, an ArcGIS extension developed by the U.S. Geological Survey. Short-term rates of shoreline change were calculated using an end point rate-of-change method based on available shoreline data between 1979 and 2011. A reference baseline was used as the originating point for the orthogonal transects cast by the DSAS software. The transects intersect each shoreline establishing measurement points, which are then used to calculate short-term rates.

This dataset consists of short-term (~31 years) shoreline change rates for the north coast of Alaska between the Point Barrow and Icy Cape. Rate calculations were computed within a GIS using the Digital Shoreline Analysis System (DSAS) version 4.3, an ArcGIS extension developed by the U.S. Geological Survey. Short-term rates of shoreline change were calculated using a linear regression rate-of-change method based on available shoreline data between 1979 and 2010. A reference baseline was used as the originating point for the orthogonal transects cast by the DSAS software. The transects intersect each shoreline establishing measurement points, which are then used to calculate short-term rates.

This dataset includes a reference baseline used by the Digital Shoreline Analysis System (DSAS) to calculate rate-of-change statistics for the exposed north coast of Alaska coastal region between Point Barrow and Icy Cape for the time period 1947 to 2012. This baseline layer serves as the starting point for all transects cast by the DSAS application and can be used to establish measurement points used to calculate shoreline-change rates.

This dataset consists of long-term (~65 years) shoreline change rates for the north coast of Alaska between Point Barrow and Icy Cape. Rate calculations were computed within a GIS using the Digital Shoreline Analysis System (DSAS) version 4.3, an ArcGIS extension developed by the U.S. Geological Survey. Long-term rates of shoreline change were calculated using a linear regression rate-of-change method based on available shoreline data between 1947 and 2012. A reference baseline was used as the originating point for the orthogonal transects cast by the DSAS software. The transects intersect each shoreline establishing measurement points, which are then used to calculate long-term rates.

This dataset consists of short-term (~33 years) shoreline change rates for the north coast of Alaska between Point Barrow and Icy Cape. Rate calculations were computed within a GIS using the Digital Shoreline Analysis System (DSAS) version 4.3, an ArcGIS extension developed by the U.S. Geological Survey. Short-term rates of shoreline change were calculated using an end point rate-of-change method based on available shoreline data between 1979 and 2012. A reference baseline was used as the originating point for the orthogonal transects cast by the DSAS software. Transects intersect each shoreline establishing measurement points, which are then used to calculate short-term rates.

This dataset includes a reference baseline used by the Digital Shoreline Analysis System (DSAS) to calculate rate-of-change statistics for the sheltered north coast of Alaska coastal between Point Barrow and Icy Cape for the time period 1947 to 2012. This baseline layer serves as the starting point for all transects cast by the DSAS application and can be used to establish measurement points used to calculate shoreline-change rates.

This dataset includes shorelines from 65 years ranging from 1947 to 2012 for the north coast of Alaska between Point Barrow and Icy Cape. Shorelines were compiled from topographic survey sheets and Nautical Charts (T-sheet, Nautical Chart; National Oceanic and Atmospheric Administration (NOAA)), aerial orthophotographs (U.S. Geological Survey (USGS), National Aeronautics and Space Administration (NASA), satellite imagery (State of Alaska), and lidar elevation data (USGS). Historical shoreline positions serve as easily understood features that can be used to describe the movement of beaches through time. These data are used to calculate rates of shoreline change for the U.S. Geological Survey's National Assessment of Shoreline Change Project. Rates of long-term and short-term shoreline change were generated in a GIS using the Digital Shoreline Analysis System (DSAS) version 4.3. DSAS uses a measurement baseline method to calculate rate-of-change statistics. Transects are cast from the reference baseline to intersect each shoreline, establishing measurement points used to calculate shoreline change rates.

This dataset consists of long-term (~65 years) shoreline change rates for the north coast of Alaska between the Colville River and Point Barrow. Rate calculations were computed within a GIS using the Digital Shoreline Analysis System (DSAS) version 4.3, an ArcGIS extension developed by the U.S. Geological Survey. Long-term rates of shoreline change were calculated using a linear regression rate-of-change method based on available shoreline data between 1947 and 2012. A reference baseline was used as the originating point for the orthogonal transects cast by the DSAS software. The transects intersect each shoreline establishing measurement points, which are then used to calculate long-term rates.

This dataset consists of short-term (~33 years) shoreline change rates for the north coast of Alaska between the Colville River and Point Barrow. Rate calculations were computed within a GIS using the Digital Shoreline Analysis System (DSAS) version 4.3, an ArcGIS extension developed by the U.S. Geological Survey. Short-term rates of shoreline change were calculated using a linear regression rate-of-change method based on available shoreline data between 1979 and 2012. A reference baseline was used as the originating point for the orthogonal transects cast by the DSAS software. The transects intersect each shoreline establishing measurement points, which are then used to calculate short-term rates.

This dataset includes a reference baseline used by the Digital Shoreline Analysis System (DSAS) to calculate rate-of-change statistics for the exposed north coast of Alaska coastal region between the Colville River and Point Barrow for the time period 1947 to 2012. This baseline layer serves as the starting point for all transects cast by the DSAS application and can be used to establish measurement points used to calculate shoreline-change rates.

This dataset consists of long-term (~65 years) shoreline change rates for the north coast of Alaska between the Colville River and Point Barrow. Rate calculations were computed within a GIS using the Digital Shoreline Analysis System (DSAS) version 4.3, an ArcGIS extension developed by the U.S. Geological Survey. Long-term rates of shoreline change were calculated using a linear regression rate-of-change method based on available shoreline data between 1947 and 2012. A reference baseline was used as the originating point for the orthogonal transects cast by the DSAS software. The transects intersect each shoreline establishing measurement points, which are then used to calculate long-term rates.

This dataset consists of short-term (~33 years) shoreline change rates for the north coast of Alaska between the Colville River and Point Barrow. Rate calculations were computed within a GIS using the Digital Shoreline Analysis System (DSAS) version 4.3, an ArcGIS extension developed by the U.S. Geological Survey. Short-term rates of shoreline change were calculated using a linear regression rate-of-change method based on available shoreline data between 1979 and 2012. A reference baseline was used as the originating point for the orthogonal transects cast by the DSAS software. The transects intersect each shoreline establishing measurement points, which are then used to calculate short-term rates.

This dataset includes a reference baseline used by the Digital Shoreline Analysis System (DSAS) to calculate rate-of-change statistics for the sheltered north coast of Alaska coastal region between the Colville River and Point Barrow for the time period 1947 to 2012. This baseline layer serves as the starting point for all transects cast by the DSAS application and can be used to establish measurement points used to calculate shoreline-change rates.

This dataset includes shorelines from 65 years ranging from 1947 to 2012 for the north coast of Alaska between the Colville River and Point Barrow. Shorelines were compiled from topographic survey sheets (T-sheets; National Oceanic and Atmospheric Administration (NOAA)), aerial orthophotographs (U.S. Geological Survey (USGS), National Aeronautics and Space Administration (NASA), and lidar elevation data(USGS). Historical shoreline positions serve as easily understood features that can be used to describe the movement of beaches through time. These data are used to calculate rates of shoreline change for the U.S. Geological Survey's National Assessment of Shoreline Change Project. Rates of long-term and short-term shoreline change were generated in a GIS using the Digital Shoreline Analysis System (DSAS) version 4.3. DSAS uses a measurement baseline method to calculate rate-of-change statistics. Transects are cast from the reference baseline to intersect each shoreline, establishing measurement points used to calculate shoreline change rates.

Water depth, turbidity, and current velocity time-series data were collected in Little Holland Tract from 2015 to 2017. Depth (from pressure) and velocity were measured in high-frequency (8 Hz) bursts. Burst means represent tidal stage and currents, and burst data can be used to determine wave height, period, direction, and wave-orbital velocity. The turbidity sensors were calibrated to suspended-sediment concentration measured in water samples collected on site. The calibration and fit parameters for all of the turbidity sensors used in the study are tabulated and provided with the data. Data were sequentially added to this data release as they were collected and post-processed. Typically, each zip folder for a deployment period contains one file from a CTD, two files of data from a bursting pressure sensor and two data files from the velocimeter, which includes data from the optical backscatter sensor.

Water depth, turbidity, and current velocity time-series data were collected in Little Holland Tract in 2016. Depth (from pressure) and velocity were measured in high-frequency (8 Hz) bursts. Burst means represent tidal stage and currents, and burst data can be used to determine wave height, period, and direction, and wave-orbital velocity. The turbidity sensors were calibrated to suspended-sediment concentration measured in water samples collected on site. The calibration and fit parameters for all of the turbidity sensors used in the study are tabulated and provided with the data. Data were sequentially added to this data release as they were collected and post-processed. Typically, each zip folder for a deployment period contains two files of data from a bursting pressure sensor and two data files from the velocimeter, which includes data from the optical backscatter sensor.

Water depth, turbidity, and current velocity time-series data were collected in Little Holland Tract in 2016. Depth (from pressure) and velocity were measured in high-frequency (8 Hz) bursts. Burst means represent tidal stage and currents, and burst data can be used to determine wave height, period, direction, and wave-orbital velocity. The turbidity sensors were calibrated to suspended-sediment concentration measured in water samples collected on site. The calibration and fit parameters for all of the turbidity sensors used in the study are tabulated and provided with the data. Data were sequentially added to this data release as they were collected and post-processed. Typically, each zip folder for a deployment period contains two files of data from a bursting pressure sensor and two data files from the velocimeter, which includes data from the optical backscatter sensor.

Water depth and turbidity time-series data were collected in Little Holland Tract (LHT) from 2015 to 2017. Depth (from pressure) was measured in high-frequency (6 or 8 Hz) bursts. Burst means represent tidal stage, and burst data can be used to determine wave height and period. The turbidity sensors were calibrated to suspended-sediment concentration measured in water samples collected on site. The calibration and fit parameters for all of the turbidity sensors used in the study are tabulated and provided with the data. Data were sequentially added to this data release as they were collected and post-processed. Typically, each zip folder for a deployment period contains one file from an optical backscatter sensor and two files of data from a bursting pressure sensor.

Water depth, turbidity, and current velocity time-series data were collected in Liberty Island from 2015 to 2017. Depth (from pressure) and velocity were measured in high-frequency (8 Hz) bursts. Burst means represent tidal stage and currents, and burst data can be used to determine wave height, period, and direction, and wave-orbital velocity. The turbidity sensors were calibrated to suspended-sediment concentration measured in water samples collected on site. The calibration and fit parameters for all of the turbidity sensors used in the study are tabulated and provided with the data. Data were sequentially added to this data release as they were collected and post-processed. Typically, each zip folder for a deployment period contains two files of data from a bursting pressure sensor and two data files from the velocimeter, which includes data from the optical backscatter sensor.

Water depth and turbidity time-series data were collected in Little Holland Tract (LHT) from 2015 to 2017. Depth (from pressure) was measured in high-frequency (6 or 8 Hz) bursts. Burst means represent tidal stage, and burst data can be used to determine wave height and period. The turbidity sensors were calibrated to suspended-sediment concentration measured in water samples collected on site. The calibration and fit parameters for all of the turbidity sensors used in the study are tabulated and provided with the data. Data were sequentially added to this data release as they were collected and post-processed. Typically, each zip folder for a deployment period contains one file from an optical backscatter sensor and two files of data from a bursting pressure sensor.

Water depth, turbidity, and current velocity time-series data were collected in Liberty Island Conservation Bank (WVA) in 2017. The turbidity sensors were not calibrated to suspended-sediment concentration at this location. Typically, each zip folder for a deployment period contains two data files from a velocimeter and one data file from a CTD, each of which include data from an optical backscatter sensor.

This dataset includes navigation data for chirp seismic-reflection data collected in 2014 by the U.S. Geological Survey (USGS) in San Pablo Bay, northern California.

This dataset includes processed, high-resolution chirp seismic-reflection data collected in 2014 by the U.S. Geological Survey (USGS) in San Pablo Bay, northern California.

This dataset includes raw, high-resolution chirp seismic-reflection data collected in 2014 by the U.S. Geological Survey (USGS) in San Pablo Bay, northern California.

This portion of the data release presents topographic data collected at 5 study sites along Elwha River, Washington between 2006 and 2017. Elevations along channel-perpendicular transects were surveyed using a total station and prism rod. Initial geodetic control was established using static global positioning system (GPS) occupations. A total station was subsequently used to expand and maintain the survey control network at each site. All survey data were referenced to the NAD83 datum, using the UTM, zone 10, coordinate reference system. All elevations were referenced to the NAVD88 vertical datum. Based on repeat measurements of points with known positions, we estimated the horizontal and vertical accuracy of the topographic measurements to be within 2 to 3 cm. Topographic data were collected using a total station using survey layout routines to locate data along the same transects during each survey. Wetted portions of the channel were measured primarily by wading with the survey rod. In several locations where wading was not feasible due to water depth, measurements were not taken or they were made by swimming with the survey rod or by deploying the survey rod from an inflatable kayak (necessary in the thalweg of reach 3 between 2006 and 2011). Although the focus of the surveys was to make repeated measurements of elevations along a transect, some additional topographic measurements were within the reaches off of the transects. Additionally, a small side channel within reach 2 was surveyed from 2006 to 2008 along transects perpendicular to that channel and along the channel's thalweg. Two series of figures are provided to aid with visualizing the channel cross-section data. The first series, labeled “Elwha_CrossSectionPlots_FallSurveys_*.png”, shows the channel cross-section profiles from the annual fall surveys, plotted for each transect and each reach. The second series, labeled “Elwha_CrossSectionPlots_Grid_FallSurveys_*.png”, shows the channel cross-section profiles from the annual fall surveys for each reach, with each survey date and transect on a separate set of axes to provide a better means of viewing cross-section changes between survey dates (note that Reach 1B was not plotted this way because it was surveyed less frequently).

This part of the data release presents digital elevation models (DEMs) derived from bathymetry and topography data of northern Monterey Bay, California collected in October 2014. Bathymetry data were collected using two personal watercraft (PWCs), each equipped with single-beam echosounders and survey-grade global navigation satellite system (GNSS) receivers. Topography data were collected on foot with GNSS receivers mounted on backpacks and with an all-terrain vehicle (ATV) using a GNSS receiver mounted at a measured height above the ground. DEM surfaces were produced from all available elevation data using linear interpolation.

This part of the data release presents bathymetry data from northern Monterey Bay, California collected in October 2014 using two personal watercraft (PWCs). The PWCs were equipped with single-beam echosounders and survey-grade global navigation satellite system (GNSS) receivers.

This part of the data release presents topography data from northern Monterey Bay, California collected in October 2014. Topography data were collected on foot with survey-grade global navigation satellite system (GNSS) receivers mounted on backpacks and with an all-terrain vehicle (ATV) using a GNSS receiver mounted at a measured height above the ground.

This part of the data release presents digital elevation models (DEMs) derived from bathymetry and topography data of northern Monterey Bay, California collected in March 2015. Bathymetry data were collected using two personal watercraft (PWCs), each equipped with single-beam echosounders and survey-grade global navigation satellite system (GNSS) receivers. Topography data were collected on foot with GNSS receivers mounted on backpacks and with an all-terrain vehicle (ATV) using a GNSS receiver mounted at a measured height above the ground. Additional topography data were collected with a terrestrial lidar scanner. DEM surfaces were produced from all available elevation data using linear interpolation.

This part of the data release presents bathymetry data from northern Monterey Bay, California collected in March 2015 using two personal watercraft (PWCs). The PWCs were equipped with single-beam echosounders and survey-grade global navigation satellite system (GNSS) receivers.

This part of the data release presents topography data from northern Monterey Bay, California collected in March 2015 with a terrestrial lidar scanner.

This part of the data release presents topography data from northern Monterey Bay, California collected in March 2015. Topography data were collected on foot with survey-grade global navigation satellite system (GNSS) receivers mounted on backpacks and with an all-terrain vehicle (ATV) using a GNSS receiver mounted at a measured height above the ground.

This part of the data release presents digital elevation models (DEMs) derived from bathymetry and topography data of northern Monterey Bay, California collected in September and October 2015. Bathymetry data were collected using two personal watercraft (PWCs), each equipped with single-beam echosounders and survey-grade global navigation satellite system (GNSS) receivers. Topography data were collected on foot with GNSS receivers mounted on backpacks and with an all-terrain vehicle (ATV) using a GNSS receiver mounted at a measured height above the ground. Additional topography data were collected with a terrestrial lidar scanner. DEM surfaces were produced from all available elevation data using linear interpolation.

This part of the data release presents bathymetry data from northern Monterey Bay, California collected in September and October 2015 using two personal watercraft (PWCs). The PWCs were equipped with single-beam echosounders and survey-grade global navigation satellite system (GNSS) receivers.

This part of the data release presents topography data from northern Monterey Bay, California collected in September 2015 with a terrestrial lidar scanner.

This part of the data release presents topography data from northern Monterey Bay, California collected in September and October 2015. Topography data were collected on foot with survey-grade global navigation satellite system (GNSS) receivers mounted on backpacks and with an all-terrain vehicle (ATV) using a GNSS receiver mounted at a measured height above the ground.

This part of the data release presents digital elevation models (DEMs) derived from bathymetry and topography data of northern Monterey Bay, California collected in March 2016. Bathymetry data were collected using two personal watercraft (PWCs), each equipped with single-beam echosounders and survey-grade global navigation satellite system (GNSS) receivers. Topography data were collected on foot with GNSS receivers mounted on backpacks and with an all-terrain vehicle (ATV) using a GNSS receiver mounted at a measured height above the ground. Additional topography data were collected with a terrestrial lidar scanner. DEM surfaces were produced from all available elevation data using linear interpolation.

This part of the data release presents bathymetry data from northern Monterey Bay, California collected in March 2016 using two personal watercraft (PWCs). The PWCs were equipped with single-beam echosounders and survey-grade global navigation satellite system (GNSS) receivers.

This part of the data release presents topography data from northern Monterey Bay, California collected in March 2016 with a terrestrial lidar scanner.

This part of the data release presents topography data from northern Monterey Bay, California collected in March 2016. Topography data were collected on foot with survey-grade global navigation satellite system (GNSS) receivers mounted on backpacks and with an all-terrain vehicle (ATV) using a GNSS receiver mounted at a measured height above the ground.

This part of the data release presents topography data from northern Monterey Bay, California collected in October 2016 with a terrestrial lidar scanner.

This part of the data release presents digital elevation models (DEMs) derived from bathymetry and topography data of northern Monterey Bay, California collected in September and October 2016. Bathymetry data were collected using a personal watercraft (PWC) and small boat, each equipped with single-beam echosounders and survey-grade global navigation satellite system (GNSS) receivers. Topography data were collected on foot with GNSS receivers mounted on backpacks and with an all-terrain vehicle (ATV) using a GNSS receiver mounted at a measured height above the ground. Additional topography data were collected with a terrestrial lidar scanner. DEM surfaces were produced using linear interpolation.

This part of the data release presents bathymetry data from northern Monterey Bay, California collected in September 2016 using a personal watercraft (PWC) and small boat. The survey vessels were equipped with single-beam echosounders and survey-grade global navigation satellite system (GNSS) receivers.

This part of the data release presents topography data from northern Monterey Bay, California collected in September 2016. Topography data were collected on foot with survey-grade global navigation satellite system (GNSS) receivers mounted on backpacks and with an all-terrain vehicle (ATV) using a GNSS receiver mounted at a measured height above the ground.

This part of the data release presents digital elevation models (DEMs) derived from bathymetry and topography data of northern Monterey Bay, California collected in March 2017. Bathymetry data were collected using two personal watercraft (PWCs), each equipped with single-beam echosounders and survey-grade global navigation satellite system (GNSS) receivers. Topography data were collected on foot with GNSS receivers mounted on backpacks and with an all-terrain vehicle (ATV) using a GNSS receiver mounted at a measured height above the ground. Additional topography data were collected with a terrestrial lidar scanner. DEM surfaces were produced from all available elevation data using linear interpolation.

This part of the data release presents bathymetry data from northern Monterey Bay, California collected in March 2017 using personal watercraft (PWC). The survey vessels were equipped with single-beam echosounders and survey-grade global navigation satellite system (GNSS) receivers.

This part of the data release presents topography data from northern Monterey Bay, California collected in March 2017 with a terrestrial lidar scanner.

This part of the data release presents topography data from northern Monterey Bay, California collected in March 2017. Topography data were collected on foot with survey-grade global navigation satellite system (GNSS) receivers mounted on backpacks and with an all-terrain vehicle (ATV) using a GNSS receiver mounted at a measured height above the ground.

This part of the data release presents digital elevation models (DEMs) derived from bathymetry and topography data of northern Monterey Bay, California collected in September 2017. Bathymetry data were collected using two personal watercraft (PWCs), each equipped with single-beam echosounders and survey-grade global navigation satellite system (GNSS) receivers. Topography data were collected on foot with GNSS receivers mounted on backpacks and with an all-terrain vehicle (ATV) using a GNSS receiver mounted at a measured height above the ground. Additional topography data were collected with a terrestrial lidar scanner. DEM surfaces were produced from all available elevation data using linear interpolation.

This part of the data release presents bathymetry data from northern Monterey Bay, California collected in September 2017 using two personal watercraft (PWCs). The PWCs were equipped with single-beam echosounders and survey-grade global navigation satellite system (GNSS) receivers.

This part of the data release presents topography data from northern Monterey Bay, California collected in September 2017 with a terrestrial lidar scanner.

This part of the data release presents topography data from northern Monterey Bay, California collected in September 2017. Topography data were collected on foot with survey-grade global navigation satellite system (GNSS) receivers mounted on backpacks and with an all-terrain vehicle (ATV) using a GNSS receiver mounted at a measured height above the ground.

The ViTexOCR script presents a new method for extracting navigation data from videos with text overlays using optical character recognition (OCR) software. Over the past few decades, it was common for videos recorded during surveys to be overlaid with real-time geographic positioning satellite chyrons including latitude, longitude, date and time, as well as other ancillary data (such as speed, heading, or user input identifying fields). Embedding these data into videos provides them with utility and accuracy, but using the location data for other purposes, such as analysis in a geographic information system, is not possible when only available on the video display. Extracting the text data from imagery using software allows these videos to be located and analyzed in a geospatial context. The script allows a user to select a video, specify the text data types (e.g. latitude, longitude, date, time, or other), text color, and the pixel locations of overlay text data on a sample video frame. The script’s output is a data file containing the retrieved geospatial and temporal data. All functionality is bundled in a Python script that incorporates a graphical user interface and several other software dependencies.

In February 2016 the U.S. Geological Survey, Pacific Coastal and Marine Science Center in cooperation with North Carolina State University and the National Park Service collected multibeam bathymetry and acoustic backscatter data in Lake Crescent located in Olympic National Park, Washington. Data were collected using a Reson 7111 multibeam echosounder pole-mounted to the 36-foot USGS R/V Parke Snavely. These metadata describe the multibeam acoustic-backscatter data file that is included in "LakeCrescent_backscatter_3m_UTM10_NAD83.zip" which is accessible from https://doi.org/10.5066/F7B56GW5.

In February 2016 the U.S. Geological Survey, Pacific Coastal and Marine Science Center in cooperation with North Carolina State University and the National Park Service collected multibeam bathymetry and acoustic-backscatter data in Lake Crescent located in Olympic National Park, Washington. Data were collected using a Reson 7111 multibeam echosounder pole-mounted to the 36-foot USGS R/V Parke Snavely. These metadata describe the multibeam bathymetry raster data file that is included in "LakeCrescent_bathy_3m_UTM10_NAD83_NAVD88.zip" which is accessible from https://doi.org/10.5066/F7B56GW5.

This dataset includes marine magnetic data collected by the U.S. Geological Survey (USGS) in 2011 during field activity B-05-11-CC between Point Sur and Piedras Blancas, central California.

Time-series data of water depth, velocity, turbidity, and temperature were acquired between 5 October 2015 and 21 March 2017 within the Monterey Canyon off of Monterey, CA, USA. In order to better understand the triggering, progression and evolution of turbidity currents in Monterey Submarine Canyon, an experiment was designed to directly measure velocity, suspended sediment and physical water properties (temperature, salinity and density) along the canyon axis during an 18-month period. Three moorings in the upper canyon (MS1, MS2, MS3) containing oceanographic instruments and Anderson- type sediment traps were deployed during three consecutive six-month periods (A: October 2015 - April 2016; B: April - October 2016; C: October 2016 - March 2017). In addition, a bottom platform to the South of the canyon head (MS0) housed instrumentation to measure currents and waves on the adjacent shelf. The mooring diagram image files are a generalized representation of the deployed instrumentation at each site, and are included as a visual aid for understanding the sampling environment. A text file of the specific sensors listing parameters measured is also included.

This part of the data release includes raw computed tomography (CT) images of sediment cores collected in 2009 offshore of Palos Verdes, California. It is one of seven files included in this U.S. Geological Survey data release that include data from a set of sediment cores acquired from the continental slope, offshore Los Angeles and the Palos Verdes Peninsula, adjacent to the Palos Verdes Fault. Gravity cores were collected by the USGS in 2009 (cruise ID S-I2-09-SC; http://cmgds.marine.usgs.gov/fan_info.php?fan=SI209SC), and vibracores were collected with the Monterey Bay Aquarium Research Institute's remotely operated vehicle (ROV) Doc Ricketts in 2010 (cruise ID W-1-10-SC; http://cmgds.marine.usgs.gov/fan_info.php?fan=W110SC). One spreadsheet (PalosVerdesCores_Info.xlsx) contains core name, location, and length. One spreadsheet (PalosVerdesCores_MSCLdata.xlsx) contains Multi-Sensor Core Logger P-wave velocity, gamma-ray density, and magnetic susceptibility whole-core logs. One zipped folder of .bmp files (PalosVerdesCores_Photos.zip) contains continuous core photographs of the archive half of each core. One spreadsheet (PalosVerdesCores_GrainSize.xlsx) contains laser particle grain size sample information and analytical results. One spreadsheet (PalosVerdesCores_Radiocarbon.xlsx) contains radiocarbon sample information, results, and calibrated ages. One zipped folder of DICOM files (PalosVerdesCores_CT.zip) contains raw computed tomography (CT) image files. One .pdf file (PalosVerdesCores_Figures.pdf) contains combined displays of data for each core, including graphic diagram descriptive logs. This particular metadata file describes the information contained in the file PalosVerdesCores_CT.zip. All cores are archived by the U.S. Geological Survey Pacific Coastal and Marine Science Center.

This part of the data release includes graphical representation (figures) of data from sediment cores collected in 2009 offshore of Palos Verdes, California. This file graphically presents combined data for each core (one core per page). Data on each figure are continuous core photograph, CT scan (where available), graphic diagram core description (graphic legend included at right; visual grain size scale of clay, silt, very fine sand [vf], fine sand [f], medium sand [med], coarse sand [c], and very coarse sand [vc]), multi-sensor core logger (MSCL) p-wave velocity (meters per second) and gamma-ray density (grams per cc), radiocarbon age (calibrated years before present) with analytical error (years), and pie charts that present grain-size data as percent sand (white), silt (light gray), and clay (dark gray). This is one of seven files included in this U.S. Geological Survey data release that include data from a set of sediment cores acquired from the continental slope, offshore Los Angeles and the Palos Verdes Peninsula, adjacent to the Palos Verdes Fault. Gravity cores were collected by the USGS in 2009 (cruise ID S-I2-09-SC; http://cmgds.marine.usgs.gov/fan_info.php?fan=SI209SC), and vibracores were collected with the Monterey Bay Aquarium Research Institute's remotely operated vehicle (ROV) Doc Ricketts in 2010 (cruise ID W-1-10-SC; http://cmgds.marine.usgs.gov/fan_info.php?fan=W110SC). One spreadsheet (PalosVerdesCores_Info.xlsx) contains core name, location, and length. One spreadsheet (PalosVerdesCores_MSCLdata.xlsx) contains Multi-Sensor Core Logger P-wave velocity, gamma-ray density, and magnetic susceptibility whole-core logs. One zipped folder of .bmp files (PalosVerdesCores_Photos.zip) contains continuous core photographs of the archive half of each core. One spreadsheet (PalosVerdesCores_GrainSize.xlsx) contains laser particle grain size sample information and analytical results. One spreadsheet (PalosVerdesCores_Radiocarbon.xlsx) contains radiocarbon sample information, results, and calibrated ages. One zipped folder of DICOM files (PalosVerdesCores_CT.zip) contains raw computed tomography (CT) image files. One .pdf file (PalosVerdesCores_Figures.pdf) contains combined displays of data for each core, including graphic diagram descriptive logs. This particular metadata file describes the information contained in the file PalosVerdesCores_Figures.pdf. All cores are archived by the U.S. Geological Survey Pacific Coastal and Marine Science Center.

This part of the data release includes grain-size analysis of sediment cores collected in 2009 offshore of Palos Verdes, California. It is one of seven files included in this U.S. Geological Survey data release that include data from a set of sediment cores acquired from the continental slope, offshore Los Angeles and the Palos Verdes Peninsula, adjacent to the Palos Verdes Fault. Gravity cores were collected by the USGS in 2009 (cruise ID S-I2-09-SC; http://cmgds.marine.usgs.gov/fan_info.php?fan=SI209SC), and vibracores were collected with the Monterey Bay Aquarium Research Institute's remotely operated vehicle (ROV) Doc Ricketts in 2010 (cruise ID W-1-10-SC; http://cmgds.marine.usgs.gov/fan_info.php?fan=W110SC). One spreadsheet (PalosVerdesCores_Info.xlsx) contains core name, location, and length. One spreadsheet (PalosVerdesCores_MSCLdata.xlsx) contains Multi-Sensor Core Logger P-wave velocity, gamma-ray density, and magnetic susceptibility whole-core logs. One zipped folder of .bmp files (PalosVerdesCores_Photos.zip) contains continuous core photographs of the archive half of each core. One spreadsheet (PalosVerdesCores_GrainSize.xlsx) contains laser particle grain size sample information and analytical results. One spreadsheet (PalosVerdesCores_Radiocarbon.xlsx) contains radiocarbon sample information, results, and calibrated ages. One zipped folder of DICOM files (PalosVerdesCores_CT.zip) contains raw computed tomography (CT) image files. One .pdf file (PalosVerdesCores_Figures.pdf) contains combined displays of data for each core, including graphic diagram descriptive logs. This particular metadata file describes the information contained in the file PalosVerdesCores_GrainSize.xlsx. All cores are archived by the U.S. Geological Survey Pacific Coastal and Marine Science Center.

This part of the data release is a spreadsheet including the name, location, and length of sediment cores collected in 2009 offshore from Palos Verdes, California. It is one of seven files included in this U.S. Geological Survey data release that include data from a set of sediment cores acquired from the continental slope, offshore Los Angeles and the Palos Verdes Peninsula, adjacent to the Palos Verdes Fault. Gravity cores were collected by the USGS in 2009 (cruise ID S-I2-09-SC; http://cmgds.marine.usgs.gov/fan_info.php?fan=SI209SC), and vibracores were collected with the Monterey Bay Aquarium Research Institute’s remotely operated vehicle (ROV) Doc Ricketts in 2010 (cruise ID W-1-10-SC; http://cmgds.marine.usgs.gov/fan_info.php?fan=W110SC). One spreadsheet (PalosVerdesCores_Info.xlsx) contains core name, location, and length. One spreadsheet (PalosVerdesCores_MSCLdata.xlsx) contains Multi-Sensor Core Logger P-wave velocity, gamma-ray density, and magnetic susceptibility whole-core logs. One zipped folder of .bmp files (PalosVerdesCores_Photos.zip) contains continuous core photographs of the archive half of each core. One spreadsheet (PalosVerdesCores_GrainSize.xlsx) contains laser particle grain size sample information and analytical results. One spreadsheet (PalosVerdesCores_Radiocarbon.xlsx) contains radiocarbon sample information, results, and calibrated ages. One zipped folder of DICOM files (PalosVerdesCores_CT.zip) contains raw computed tomography (CT) image files. One .pdf file (PalosVerdesCores_Figures.pdf) contains combined displays of data for each core, including graphic diagram descriptive logs. This particular metadata file describes the information contained in the file PalosVerdesCores_Info.xlsx. All cores are archived by the U.S. Geological Survey Pacific Coastal and Marine Science Center.

This part of the data release includes Multi-Sensor Core Logger (MSCL) P-wave velocity, gamma-ray density, and magnetic susceptibility whole-core logs of sediment cores collected in 2009 offshore of Palos Verdes, California. It is one of seven files included in this U.S. Geological Survey data release that include data from a set of sediment cores acquired from the continental slope, offshore Los Angeles and the Palos Verdes Peninsula, adjacent to the Palos Verdes Fault. Gravity cores were collected by the USGS in 2009 (cruise ID S-I2-09-SC; http://cmgds.marine.usgs.gov/fan_info.php?fan=SI209SC), and vibracores were collected with the Monterey Bay Aquarium Research Institute's remotely operated vehicle (ROV) Doc Ricketts in 2010 (cruise ID W-1-10-SC; http://cmgds.marine.usgs.gov/fan_info.php?fan=W110SC). One spreadsheet (PalosVerdesCores_Info.xlsx) contains core name, location, and length. One spreadsheet (PalosVerdesCores_MSCLdata.xlsx) contains Multi-Sensor Core Logger P-wave velocity, gamma-ray density, and magnetic susceptibility whole-core logs. One zipped folder of .bmp files (PalosVerdesCores_Photos.zip) contains continuous core photographs of the archive half of each core. One spreadsheet (PalosVerdesCores_GrainSize.xlsx) contains laser particle grain size sample information and analytical results. One spreadsheet (PalosVerdesCores_Radiocarbon.xlsx) contains radiocarbon sample information, results, and calibrated ages. One zipped folder of DICOM files (PalosVerdesCores_CT.zip) contains raw computed tomography (CT) image files. One .pdf file (PalosVerdesCores_Figures.pdf) contains combined displays of data for each core, including graphic diagram descriptive logs. This particular metadata file describes the information contained in the file PalosVerdesCores_MSCLdata.xlsx. All cores are archived by the U.S. Geological Survey Pacific Coastal and Marine Science Center.

This part of the data release includes continuous core photographs in bmp format of sediment cores collected in 2009 offshore of Palos Verdes, California. It is one of seven files included in this U.S. Geological Survey data release that include data from a set of sediment cores acquired from the continental slope, offshore Los Angeles and the Palos Verdes Peninsula, adjacent to the Palos Verdes Fault. Gravity cores were collected by the USGS in 2009 (cruise ID S-I2-09-SC; http://cmgds.marine.usgs.gov/fan_info.php?fan=SI209SC), and vibracores were collected with the Monterey Bay Aquarium Research Institute's remotely operated vehicle (ROV) Doc Ricketts in 2010 (cruise ID W-1-10-SC; http://cmgds.marine.usgs.gov/fan_info.php?fan=W110SC). One spreadsheet (PalosVerdesCores_Info.xlsx) contains core name, location, and length. One spreadsheet (PalosVerdesCores_MSCLdata.xlsx) contains Multi-Sensor Core Logger P-wave velocity, gamma-ray density, and magnetic susceptibility whole-core logs. One zipped folder of .bmp files (PalosVerdesCores_Photos.zip) contains continuous core photographs of the archive half of each core. One spreadsheet (PalosVerdesCores_GrainSize.xlsx) contains laser particle grain size sample information and analytical results. One spreadsheet (PalosVerdesCores_Radiocarbon.xlsx) contains radiocarbon sample information, results, and calibrated ages. One zipped folder of DICOM files (PalosVerdesCores_CT.zip) contains raw computed tomography (CT) image files. One .pdf file (PalosVerdesCores_Figures.pdf) contains combined displays of data for each core, including graphic diagram descriptive logs. This particular metadata file describes the information contained in the file PalosVerdesCores_Photos.zip. All cores are archived by the U.S. Geological Survey Pacific Coastal and Marine Science Center.

This part of the data release is a spreadsheet including radiocarbon sample information and calibrated ages of sediment cores collected in 2009 offshore of Palos Verdes, California. It is one of seven files included in this U.S. Geological Survey data release that include data from a set of sediment cores acquired from the continental slope, offshore Los Angeles and the Palos Verdes Peninsula, adjacent to the Palos Verdes Fault. Gravity cores were collected by the USGS in 2009 (cruise ID S-I2-09-SC; http://cmgds.marine.usgs.gov/fan_info.php?fan=SI209SC), and vibracores were collected with the Monterey Bay Aquarium Research Institute's remotely operated vehicle (ROV) Doc Ricketts in 2010 (cruise ID W-1-10-SC; http://cmgds.marine.usgs.gov/fan_info.php?fan=W110SC). One spreadsheet (PalosVerdesCores_Info.xlsx) contains core name, location, and length. One spreadsheet (PalosVerdesCores_MSCLdata.xlsx) contains Multi-Sensor Core Logger P-wave velocity, gamma-ray density, and magnetic susceptibility whole-core logs. One zipped folder of .bmp files (PalosVerdesCores_Photos.zip) contains continuous core photographs of the archive half of each core. One spreadsheet (PalosVerdesCores_GrainSize.xlsx) contains laser particle grain size sample information and analytical results. One spreadsheet (PalosVerdesCores_Radiocarbon.xlsx) contains radiocarbon sample information, results, and calibrated ages. One zipped folder of DICOM files (PalosVerdesCores_CT.zip) contains raw computed tomography (CT) image files. One .pdf file (PalosVerdesCores_Figures.pdf) contains combined displays of data for each core, including graphic diagram descriptive logs. This particular metadata file describes the information contained in the file PalosVerdesCores_Radiocarbon.xlsx. All cores are archived by the U.S. Geological Survey Pacific Coastal and Marine Science Center.

These metadata describe the U.S. Geological Survey (USGS), Pacific Coastal and Marine Science Center (PCMSC) 2004 bathymetry data collected in Skagit Bay Washington that is provided as a 1-m resolution TIFF image, as well as a 1-m resolution shaded-relief TIFF image. In 2004, 2005, 2007, and 2010 the USGS, PCMSC collected bathymetry and acoustic backscatter data in Skagit Bay, Washington using an interferometric bathymetric sidescan-sonar system mounded to the USGS R/V Parke Snavely and the USGS R/V Karluk. The research was conducted in coordination with the Swinomish Indian Tribal Community, Skagit River System Cooperative, Skagit Watershed Council, Puget Sound Nearshore Ecosystem Restoration Project, and U.S. Army Corps of Engineers to characterize estuarine habitats and processes, including the sediment budget of the Skagit River and the influence of river-delta channelization on sediment transport. Information quantifying the distribution of habitats and extent that sediment transport influences habitats and the morphology of the delta is useful for planning for salmon recovery, agricultural resilience, flood risk protection, and coastal change associated with sea-level rise.

These metadata describe the U.S. Geological Survey (USGS), Pacific Coastal and Marine Science Center (PCMSC) 2005 bathymetry data collected in Skagit Bay Washington that is provided as a 1-m resolution TIFF image, as well as a 1-m resolution shaded-relief TIFF image. In 2004, 2005, 2007, and 2010 the U.S. Geological Survey (USGS), Pacific Coastal and Marine Science Center (PCMSC) collected bathymetry and acoustic backscatter data in Skagit Bay, Washington using an interferometric bathymetric sidescan sonar system mounded to the USGS R/V Parke Snavely and the USGS R/V Karluk. The research was conducted in coordination with the Swinomish Indian Tribal Community, Skagit River System Cooperative, Skagit Watershed Council, Puget Sound Nearshore Ecosystem Restoration Project, and U.S. Army Corps of Engineers to characterize estuarine habitats and processes, including the sediment budget of the Skagit River and the influence of river-delta channelization on sediment transport. Information quantifying the distribution of habitats and extent that sediment transport influences habitats and the morphology of the delta is useful for planning for salmon recovery, agricultural resilience, flood risk protection, and coastal change associated with sea-level rise.

These metadata describe the U.S. Geological Survey (USGS), Pacific Coastal and Marine Science Center (PCMSC) 2007 bathymetry data collected in Skagit Bay Washington that is provided as a 1-m resolution TIFF image, as well as a 1-m resolution shaded-relief TIFF image. In 2004, 2005, 2007, and 2010 the U.S. Geological Survey (USGS), Pacific Coastal and Marine Science Center (PCMSC) collected bathymetry and acoustic backscatter data in Skagit Bay, Washington using an interferometric bathymetric sidescan sonar system mounded to the USGS R/V Parke Snavely and the USGS R/V Karluk. The research was conducted in coordination with the Swinomish Indian Tribal Community, Skagit River System Cooperative, Skagit Watershed Council, Puget Sound Nearshore Ecosystem Restoration Project, and U.S. Army Corps of Engineers to characterize estuarine habitats and processes, including the sediment budget of the Skagit River and the influence of river-delta channelization on sediment transport. Information quantifying the distribution of habitats and extent that sediment transport influences habitats and the morphology of the delta is useful for planning for salmon recovery, agricultural resilience, flood risk protection, and coastal change associated with sea-level rise.

These metadata describe the U.S. Geological Survey (USGS), Pacific Coastal and Marine Science Center (PCMSC) 2010 bathymetry data collected in Skagit Bay Washington that is provided as a 1-m resolution TIFF image, as well as a 1-m resolution shaded-relief TIFF image. In 2004, 2005, 2007, and 2010 the U.S. Geological Survey (USGS), Pacific Coastal and Marine Science Center (PCMSC) collected bathymetry and acoustic backscatter data in Skagit Bay, Washington using an interferometric bathymetric sidescan sonar system mounded to the USGS R/V Parke Snavely and the USGS R/V Karluk. The research was conducted in coordination with the Swinomish Indian Tribal Community, Skagit River System Cooperative, Skagit Watershed Council, Puget Sound Nearshore Ecosystem Restoration Project, and U.S. Army Corps of Engineers to characterize estuarine habitats and processes, including the sediment budget of the Skagit River and the influence of river-delta channelization on sediment transport. Information quantifying the distribution of habitats and extent that sediment transport influences habitats and the morphology of the delta is useful for planning for salmon recovery, agricultural resilience, flood risk protection, and coastal change associated with sea-level rise.

These metadata describe the U.S. Geological Survey (USGS), Pacific Coastal and Marine Science Center (PCMSC) merged acoustic-backscatter imagery that was collected in 2005, 2007, and 2010 in Skagit Bay Washington that is provided as a 5-m resolution TIFF image. In 2004, 2005, 2007, and 2010 the U.S. Geological Survey (USGS), Pacific Coastal and Marine Science Center (PCMSC) collected bathymetry and acoustic backscatter data in Skagit Bay, Washington using an interferometric bathymetric sidescan sonar system mounded to the USGS R/V Parke Snavely and the USGS R/V Karluk. The research was conducted in coordination with the Swinomish Indian Tribal Community, Skagit River System Cooperative, Skagit Watershed Council, Puget Sound Nearshore Ecosystem Restoration Project, and U.S. Army Corps of Engineers to characterize estuarine habitats and processes, including the sediment budget of the Skagit River and the influence of river-delta channelization on sediment transport. Information quantifying the distribution of habitats and extent that sediment transport influences habitats and the morphology of the delta is useful for planning for salmon recovery, agricultural resilience, flood risk protection, and coastal change associated with sea-level rise.

These metadata describe the U.S. Geological Survey (USGS), Pacific Coastal and Marine Science Center (PCMSC) merged bathymetry digital terrain model comprised of the 2005, 2007, and 2010 bathymetry data collected in Skagit Bay Washington that is provided as a 1-m resolution TIFF image, as well as a 1-m resolution shaded-relief TIFF image. In 2004, 2005, 2007, and 2010 the U.S. Geological Survey (USGS), Pacific Coastal and Marine Science Center (PCMSC) collected bathymetry and acoustic backscatter data in Skagit Bay, Washington using an interferometric bathymetric sidescan sonar system mounded to the USGS R/V Parke Snavely and the USGS R/V Karluk. The research was conducted in coordination with the Swinomish Indian Tribal Community, Skagit River System Cooperative, Skagit Watershed Council, Puget Sound Nearshore Ecosystem Restoration Project, and U.S. Army Corps of Engineers to characterize estuarine habitats and processes, including the sediment budget of the Skagit River and the influence of river-delta channelization on sediment transport. Information quantifying the distribution of habitats and extent that sediment transport influences habitats and the morphology of the delta is useful for planning for salmon recovery, agricultural resilience, flood risk protection, and coastal change associated with sea-level rise.

This dataset includes 63 still images extracted from digital video imagery of sediment grab samples, along with laboratory grain size analysis of the sediment grab samples, taken from the mouth of the Columbia River, OR and WA, USA. Digital video was collected in September 2014 in the mouth of the Columbia River, USA, as part of the U.S. Geological Survey Coastal and Marine Geology Program contribution to the Office of Naval Research funded River and Inlets Dynamics experiment (RIVET II). Still images were extracted from the underwater video footage whenever the camera was resting on the sediment bed and individual sediment grains were visible and in focus. The images were used to calculate the calibration curve through auto-correlation regressed against the results of laboratory-determined median grain size (D50) of the grab samples (Barnard, 2007), provided in an accompanying .csv file.

This dataset includes 2,523 still images extracted from geo-referenced digital video imagery of the seafloor at the mouth of the Columbia River, OR and WA, USA, along with grain size analysis of the surface sediment. Underwater digital video was collected in September 2014 in the mouth of the Columbia River, USA, as part of the U.S. Geological Survey Coastal and Marine Geology Program contribution to the Office of Naval Research funded River and Inlets Dynamics experiment (RIVET II). Still images were extracted from the underwater video footage whenever the camera was resting on the sediment bed and individual sediment grains were visible and in focus. The images are used to calculate the median grain size through an auto-correlation method (Barnard and other 2007), and are provided in an accompanying .csv file.

We characterized seafloor sediment conditions near the mouth of the Elwha River from underwater photographs taken every four hours from September 2011 to December 2013. A digital camera was affixed to a tripod that was deployed in approximately 10 meters of water. Each photograph was qualitatively characterized as one of six categories: (1) base, or no sediment; (2) low sediment; (3) medium sediment; (4) high sediment; (5) turbid; or (6) kelp. For base conditions, no sediment was present on the seafloor. Low sediment conditions were characterized by a light dusting of sediment; medium sediment conditions were characterized by a layer of sediment that covered all rock surfaces but did not obscure the relief of the seafloor; high sediment conditions were characterized by a layer of sediment that covered all rock surfaces and obscured the relief of the seafloor. During turbid conditions, suspended sediment in the water column obscured the view of the seafloor, and during kelp conditions, blades of kelp covered the camera lens, blocking our view of the seafloor.

Abstract: This data release presents modeled time series of nearshore waves along the southern California coast, from Point Conception to the Mexican border, hindcasted for 1980-2010 and projected using global climate model forcing for 1975-2005 and 2012-2100. Details: As part of the Coastal Storm Modeling System (CoSMoS), time series of hindcast, historical, and 21st-century nearshore wave parameters (wave height, period, and direction) were simulated for the southern California coast from Point Conception to the Mexican border. Changes in deep-water wave conditions directly regulate the energy driving coastal processes. However, a number of physical processes, for example, refraction on continental shelves and/or diffraction by islands, transform deep-water waves as they propagate to the nearshore, which complicates large-scale modeling efforts. In this work, a hindcast of nearshore waves was simulated by forcing a numerical wave model with hindcasted intermediate-water waves and reanalysis winds. A lookup table was created by relating corresponding offshore winds and waves with nearshore wave conditions. Using the lookup table, historical and 21st-century nearshore-wave time series were generated for global climate model-forced offshore winds and waves. Three-hourly wave parameters from the U.S. Army Corps of Engineers Wave Information Studies (WIS; http://wis.usace.army.mil/) and near-surface winds (10 m above ground) from the California Reanalysis Downscaling at 10 km (CaRD10; Kanamitsu and Kanamaru, 2007) were used to force the Simulating Waves Nearshore (SWAN) numerical model in stationary mode over a curvilinear grid extending along the coast from Point Conception to the Mexican border and from the shoreline to approximately 25 km offshore to hindcast the time period 1980-2010. The offshore extent of the model domain was defined by the locations of WIS stations used for forcing. Horizontal-grid resolution varies largely depending on bathymetry and shoreline curvature, ranging from 24 to 543 m in the along- and across-shore directions. Bathymetry data are from the 2013 Coastal California TopoBathy Merge Project (National Oceanic and Atmospheric Administration, 2013). Wave spectra were computed with a JONSWAP shape, 10-degree directional resolution, and 34 frequency bands ranging logarithmically from 0.0418 to 1 Hz. Three-hourly nearshore wave parameters (significant wave height [Hs], mean wave period [Tm], peak wave period [Tp], mean wave direction [Dm], and peak wave direction [Dp]) were output from the simulations at the 10-m bathymetric contour approximately every 100 m in the alongshore direction at a total of 4,802 locations in the nearshore and at an additional 23 locations coincident with California Data Information Program (CDIP; Scripps Institute of Oceanography; http://cdip.ucsd.edu) wave buoys. A lookup table was generated by relating offshore wind and deep-water wave conditions at a single offshore point and nearshore wave conditions simulated by the wave hindcast. The open boundary of the SWAN simulation does not represent deep-water wave conditions, as it is located in intermediate water and shoreward of the Channel Islands. Therefore, the NOAA WW3 Climate Forecast System Reanalysis Reforecast (CFSRR; Chawla and others, 2012) wave time series at a single point (CDIP buoy 067, equivalent to National Data Buoy Center station 46219) defined the deep-water end member. The lookup table was based on binning CFSRR deep-water wave parameters (Hs, Tp, Dp) and CaRD10 wind speed (U) at CDIP 067. Significant wave height was binned from 0.5 to 10.25 m at 0.25-m intervals; peak wave period was binned from 3 to 24 s at 3-s intervals; peak wave direction was binned from 5 to 360 degrees at 5-degree intervals; and wind speed was binned from 0 to 24 m/s at 6-m/s intervals. Interval sizes for Hs and Tp were based on the average RMSE for each variable. For each combination of deep-water Hs, Tp, Dp, and U, time indices falling into each bin were identified. For each nearshore location, median Hs, Tp, Tm, Dp, and Dm corresponding to all time indices of a given set of deep-water binned conditions were computed to complete the lookup table. Because swell travel time from offshore to nearshore is on the order of 1.5 h (assuming an average depth of 100 m and Tp of 15 s over a distance of about 120 km) and the model outputs are at three-hourly intervals, we assume no time lag between deep water and nearshore conditions. Historical (1976-2005) and 21st-century (2012-2100) deep-water wave time series at CDIP 067 were derived from the WaveWatch3 wave model over global (1.25 deg x 1.25 deg) and nested eastern North Pacific regional (0.25 deg x 0.25 deg) grids forced by three-hourly near-surface wind fields from a global climate model (GCM; GFDL-ESM2M RCP 4.5). Wind (CaRD10 and GFDL-ESM2M at CDIP 067) and coincident deep-water wave time series were passed through the lookup table to generate historical and 21st-century nearshore wave conditions. Wind and wave conditions that were not present in the lookup table or that had not occurred in the hindcast were filled using quantile relationships. Outputs include: southern California three-hourly, nearshore wave parameters (Hs, Tp, Dp, Tm, Dm) for 4,802 locations approximately 100 m apart along the 10-m bathymetric contour from Point Conception to the Mexican border and for an additional 23 points collocated with CDIP wave buoys. Wave parameters are available for three periods: 1) a validated hindcast (1980-2010) period derived from reanalysis data, 2) a historical (1976-2005) projection derived from GFDL-ESM2M (GCM-historical), and 3) a 21st-century (2012-2100) projection also derived from GFDL-ESM2M. Data are available as NetCDF files packaged by region, with each file containing the time series for roughly 600 locations. The points collocated with wave buoys are within one separate file. References: Chawla, A., Spindler, D., and Tolman, H., 2012, 30 Year Wave Hindcasts using WAVEWATCH III with CFSR winds--Phase 1: National Oceanic and Atmospheric Administration, National Weather Service, Environmental Modeling Center, Marine Modeling and Analysis Branch, Technical note, MMAB Contribution n. 302, 12 p. with Appendices. Kanamitsu, M., and Kanamaru, H., 2007, 57-Year California Reanalysis Downscaling at 10km (CaRD10) Part 1--System Detail and Validation with Observations: Journal of Climate, v. 20, p. 5,527-5,552. National Oceanic and Atmospheric Administration, 2013, 2013 NOAA Coastal California TopoBathy Merge Project, National Oceanic and Atmospheric Administration, National Centers for Environmental Information database, accessed February 28, 2015 at https://coast.noaa.gov/dataviewer/#/lidar/search/where:ID=2612.

To support Coastal Storm Modeling System (CoSMoS) in the San Francisco Bay (v2.1), time series of historical and 21st-century near-surface wind fields (eastward and northward wind arrays) were simulated throughout the Bay. While global climate models (GCMs) provide useful projections of near-surface wind vectors into the 21st century, resolution is not sufficient enough for use in regional wave modeling projects, such as CoSMoS. Short-duration high wind speeds, on the order of hours, are of key importance in wave and subsequent coastal flood modeling. Here we present temporally-downscaled wind data for historical (1975-2004) and projected (2010-2100) time periods, developed using a method similar to constructed analogues (CA), suitable for use in local wave models.

Underwater video was collected in March 2014 in the nearshore waters of Faga`alu Bay on the island of Tutuila, American Samoa, as part of the U.S. Geological Survey Coastal and Marine Geology Program's Pacific Coral Reefs Project. This dataset includes 2,119 still images extracted from the video footage every 10 seconds and an Environmental Systems Research Institute (ESRI) shapefile of individual still-image locations with benthic habitat interpretations for each image.

Time-series of sediment chemistry, including organic biomarker composition and bulk inorganic geochemical analytes, from samples collected over a one-year period in a sediment trap. The sediment traps were deployed at a depth between 603 m to 1318 m, and they were programmed to rotate a 250 mL sample bottle at 30 d intervals, delivering 12 samples during the 1-year deployment between August 2012 and June 2013. In addition, dissolved water column nutrient concentrations and water column trace element particulate concentrations were collected in Baltimore Canyon on the U.S. Mid-Atlantic Bight (MAB).

Spatial surveys of water column acoustic backscatter were performed between May 28 and June 2, 2013, in the mouth of the Columbia River, Oregon and Washington. These data were collected using a Biosonics DTX single-beam echosounder with 430 kHz transducer.

Lagrangian surface currents were measured using drifters equipped with global navigation satellite system (GNSS) receivers. A total of 8 drifter deployments were performed between May 25 and June 8, 2013. For each deployment, drifters were released within the MCR and their positions were recorded until the drifters were recovered. The average duration of the drifter deployments varied between 1.6 h and 17.2 h and the number of drifters released in a deployment ranged between 11 and 84. The initial positions and timing of the release of the drifters relative to the tidal cycle varied throughout the drifter deployments. Digital files containing the drifter data from each deployment are available in NetCDF format.

This portion of the USGS data release presents sediment grain-size data from samples collected from the mouth of the Columbia River, Oregon and Washington, in 2013. Surface sediment was sampled using a small ponar, or 'grab', sampler on May 9, 2013 from the F/V Cape Windy at 3 locations. A handheld global navigation satellite system (GNSS) receiver was used to determine the locations of sediment samples. The grain size distributions of samples were determined using standard techniques developed by the USGS Pacific Coastal and Marine Science Center sediment lab. The grain-size data are provided in a comma-delimited spreadsheet (.csv).

A process-based numerical model of the mouth of the Columbia River (MCR) and estuary, Oregon and Washington, was applied to simulate hydrodynamic conditions for the time period of the Office of Naval Research-funded River and Inlets Dynamics (RIVET II) field experiment conducted between May 9 and June 15, 2013. The model application was constructed using Delft3D, an open-source software package used to solve the unsteady shallow water equations to simulate water motion due to tides, waves, wind, and buoyancy effects. This portion of the USGS data release describes the model application for this experiment and presents input files necessary to run the Delft3D model, as well as selected output from the model simulation.

Time-series data of water surface elevation, wave height, and water column currents, temperature, salinity, and acoustic seabed images were acquired for 38 days between 9 May and 15 June, 2013 in the mouth of the Columbia River, Oregon and Washington.

Spatial surveys of water column currents were performed between June 14 and 16, 2013, in the mouth of the Columbia River, Oregon and Washington. These data were collected using an acoustic-doppler current profiler (ADCP).

Ocean surface current results from a physics-based, 3-dimensional coupled ocean-atmosphere numerical model were generated to understand coral larval dispersal patterns in Maui Nui, Hawaii, USA. The model was used to simulate coral larval dispersal patterns from a number of existing State-managed reefs and large tracks of reefs with high coral coverage that might be good candidates for marine-protected areas (MPAs) during 8 spawning events during 2010-2013. The goal of this effort is to provide geophysical data to help provide guidance to sustain coral health in Maui Nui, Hawaii, USA. Each model output run is available as a netCDF file with self-contained attribute information. Each file name is appended with the model-simulation date in YYYYMMDD format; the file name denotes the beginning of simulation portion of the model run, with the model starting and spinning up over two days before the model-simulation date in the file name.

This dataset presents elevation measurements of two dams on the Elwha River, Washington, during their removal processes from 2008 to 2013. Elevation measurements of the Elwha Dam were taken from October 2008 to March 2012. Elevation measurements of the Glines Canyon dam, which was further upstream than the Elwha Dam, were taken from October 2010 to October 2013. The measurements were by the U.S. Bureau of Reclamation as part of a study investigating the river channel's morphological responses to dam removal.

Sediment inputs to Lake Mills, on the Elwha River, Washington, were measured from 1927 to 2016. These measurements represent the annual total sediment load, in tonnes per year, that were input into Lake Mills and partially trapped by Glines Canyon dam. The sediment was allowed to erode and be transported down-river by the removal of the Glines Canyon and Elwha dams during 2011 to 2014. The measurements were taken as part of a study investigating the river channel's morphological responses to the removal of two large dams - the Elwha River and Glines Canyon dams.

Daily values of discharge and sediment loads were measured and estimated at U.S. Geological Survey gaging station 12046260, on the Elwha River at the diversion near Port Angeles, Washington. Daily data are reported from September 15, 2011 to September 30, 2016. Specific data include (1) date; (2) discharge; (3) suspended-sediment concentration and one standard-deviation bounds; (4) percentage of fine-grained particles (silts and clays) in suspension; (5) loads of total suspended-sediment, fine-grained particles in suspension, and sand in suspension; (6) gauged bedload for particles between 2-16 mm and greater than 16 mm; and (7) estimated bedload for particles smaller than 2 mm.

Bedload sediment transport was calculated on the Elwha River, Washington to measure the amount of sediment transported along the riverbed during the 2016 water year. Bedload was measured using the Elwha bedload impact plate system (Hilldale and others, 2015). Physical bedload sampling by the U.S. Bureau of Reclamation for system calibration took place during November, 2012; March, May, and June 2013; and April 2014 at the Diversion Weir gauge (Magirl and others, 2015). Early in water year 2016 (year 5) the river formed an avulsion channel across the floodplain on river left, preventing a complete measurement of bedload passing the Diversion Weir gauge. As a result, bedload for water year 2016 (year 5 of the larger study) was estimated using a discharge rating curve to obtain monthly values.

This dataset presents 28 georeferenced orthomosaic images of the middle and lower reaches of the Elwha River. Each mosaic image was created by stitching together thousands of individual photographs that were matched based on numerous unique tie points shared by the photographs. The individual photographs were taken by a plane-mounted camera during multiple flights over the study area spanning 2012 to 2017. Because each mosaic is orthogonal to the earth's surface and is georeferenced to real-world coordinates, changes to the river channel and surrounding morphology can be seen and measured, including channel width, river braiding, bar formation, and other metrics to assess responses of the river to the removal of two large dams upstream from the study area.

Digital elevation models (DEMs) of the lower Elwha River, Washington, were created by synthesizing lidar and PlaneCam Structure-from-Motion (SfM) data. Lidar and still digital photographs were collected by airplane during surveys from 2012 to 2016. The digital photographs were used to create a SfM digital surface model. Each DEM represents the ending conditions for that water year (for example, the 2013 DEM represents conditions at approximately September 30, 2013). The final DEMs, presented here, were created from the most recent lidar before September 30 of a given year, supplemented with an error-corrected SfM model from a low-flow summer Elwha PlaneCam flight as close to 30 September as possible. This synthetic data product was created because the aerial lidar data had gaps near the river, which the SfM data were able to close. The georeferenced DEMs were used to assess the river's responses to the removal of the Elwha and the Glines Canyon dams upstream from the study area.

Streamgage levels on the Elwha River were measured from 2011 to 2016. These measurements show the height of the river's water surface, both in meters relative to the stream bed, as well as in meters relative to vertical geographic coordinates. Measurements were collected using a Global Water WL16 battery-operated vented water level logger in a hardened casing. The instrument was installed on October 17, 2011 on the left bank of the Elwha River at a power line crossing above the Elwha Surface Water Intake (at approximately river kilometer 5.6), which is downstream of the (now historical) Elwha Dam site. Data collection ended May 12, 2016. The data were collected as part of a study investigating responses of the Elwha River to the removal of two large dams: the Glines Canyon and Elwha dams. From July 2, 2015 to October 31, 2015 the gauge was buried under a gravel bar, and the data are not reliable. These data are flagged as "BURIED" in the spreadsheet's "FLAG" column. From April 21, 2016 through May 12, 2016 periods of noise and bad data (flagged "NOISE" and "BAD") interrupted valid data values. The gauge was beginning to fail, but comparison with USGS 12046260 stage data allowed identification of valid data. After May 12, 2016, significant deviations from expected values and increased noise appeared. This was interpreted as instrument failure and no further data were marked valid. Data are included for reference purposes but flagged as "BAD."

This data release provides 15-minute data of suspended-sediment concentration and fine (less than 0.0625 mm) suspended-sediment concentration during the removal of 2 large dams on the Elwha River from September 2011 to September 2016. Data are derived from regression relations with turbidity at the USGS gaging station Elwha River at the Diversion (no.12046260).

Spatial surveys of water column physical properties were acquired with a conductivity-temperature-depth (CTD) profiler for four days in February 2015 and one day in July 2015 off the north coast of the island of Tutuila, American Samoa in support of a study on the coastal circulation patterns within and in the vicinity of the National Park of American Samoa.

Satellite-tracked, DGPS-equipped Lagrangian surface-current drifter deployments were conducted over 12 weeks between 14 April and 7 July 2015 at various locations within and offshore of the National Park of American Samoa study area to track surface currents. The drifters internally logged their location every 1 minute, and they transmitted their positions to satellites every 5 minutes. A drogue was attached to the drifters at 1 m below sea level in order to track the currents at that depth.

Time-series data of water surface elevation, wave height, and water column currents, temperature, and salinity were acquired for 150 days between 13 April and 14 July 2015 off the north coast of the island of Tutuila, American Samoa in support of a study on the coastal circulation patterns within and in the vicinity of the National Park of American Samoa.

Spatial surveys of water column currents and surface winds were conducted from February 17 to 20, 2015, off the north coast of the island of Tutuila, American Samoa. These data were collected using an acoustic-doppler current profiler (ADCP) and a meterological sensor in support of a study on the coastal circulation patterns within and in the vicinity of the National Park of American Samoa.

Topographic data were collected by the U.S. Geological Survey (USGS) in 2015 for Little Holland Tract in the Sacramento-San Joaquin River Delta, California. The data were collected on foot using a global positioning system (GPS) backpack platform that consisted of survey-grade Trimble R10 and R7 global navigation satellite system (GNSS) receivers with Zephyr 2 antennas. Orthometric elevations relative to NAVD88 were computed using the National Geodetic Survey Geoid12a, and the final data were projected in Cartesian coordinates using the UTM Zone 10 North (meters) (NAD83[2011]) coordinate system. The mean estimated vertical uncertainty of the 2015 USGS GPS backpack survey is 3.5 cm. 

Bathymetric data were collected by the U.S. Geological Survey (USGS) in 2015 for Little Holland Tract in the Sacramento-San Joaquin River Delta, California. The data were collected using a personal watercraft (PWC) platform that consisted of Trimble R7 Global Navigation Satellite System (GNSS) receivers with Zephyr 2 antennas, combined with Odom Echotrac CV-100 single-beam echosounders and 200 kHz transducers. Data was post-processed to remove spurious data points. Raw depths were converted to ellipsoid elevations in the data acquisition software. Orthometric elevations relative to NAVD88 were computed using National Geodetic Survey Geoid12a offsets, and the final data were projected in Cartesian coordinates using the UTM Zone 10 North (meters) (NAD83[2011]) coordinate system. The mean estimated vertical uncertainty of the 2015 USGS PWC survey is 6.1 cm.

This product is a digital elevation model (DEM) for the Little Holland Tract in the Sacramento-San Joaquin River Delta, California based on U.S. Geological Survey (USGS)-collected elevation data, merged with existing topographic and bathymetric elevation data. The USGS collected topographic and bathymetric elevation data in 2015, using a combination of methods. Topographic and shallow-water bathymetric data were collected on foot using a global positioning system (GPS) backpack platform that consisted of survey-grade Trimble R10, and Trimble R7 global navigation satellite system (GNSS) receivers with Zephyr 2 antennas. Bathymetric data were collected using a personal watercraft (PWC) platform that consisted of Trimble R7 GNSS receivers with Zephyr 2 antennas, combined with Odom Echotrac CV-100 single-beam echosounders and 200 kHz transducers. The USGS elevation data were merged with topographic aerial Light Detection and Ranging (lidar) data collected by California Department of Water Resources (DWR) in 2007 and single-beam bathymetric data collected by Environmental Data Solutions (EDS) in 2009 to generate the final DEM. The GeoTIFF raster and comma-delimited text files are available for download at http://doi.org/10.5066/F7RX9954.

This dataset includes swell-filtered, high-resolution seismic-reflection data, collected by the U.S. Geological Survey (USGS) in 2014, between Point Sal and Refugio State Beach in southern California.

This dataset includes navigation data for marine geophysical data, collected by the U.S. Geological Survey (USGS) in 2014, between Point Sal and Refugio State Beach in southern California.

This dataset includes raw, high-resolution seismic-reflection data, collected by the U.S. Geological Survey (USGS) in 2014, between Point Sal and Refugio State Beach in southern California.

A process-based wave-resolving hydrodynamic model (XBeach Non-Hydrostatic, ‘XBNH’) was used to create a large synthetic database for use in a “Bayesian Estimator for Wave Attack in Reef Environments” (BEWARE), relating incident hydrodynamics and coral reef geomorphology to coastal flooding hazards on reef-lined coasts. Building on previous work, BEWARE improves system understanding of reef hydrodynamics by examining the intrinsic reef and extrinsic forcing factors controlling runup and flooding on reef-lined coasts. The Bayesian estimator has high predictive skill for the XBNH model outputs that are flooding indicators, and was validated for a number of available field cases. BEWARE is a potentially powerful tool for use in early warning systems or risk assessment studies, and can be used to make projections about how wave-induced flooding on coral reef-lined coasts may change due to climate change. These data accompany the following publication: Pearson, S.G., Storlazzi, C.D., van Dongeren, A.R., Tissier, M.F.S., and Reniers, A.J.H.M., 2017, A Bayesian-based system to assess wave-driven flooding hazards on coral reef-lined coasts: Journal of Geophysical Research—Oceans, https://doi.org/10.1002/2017JC013204.

This data release contains approximately 190 line-kilometers of processed, high-resolution multichannel seismic-reflection (MCS) profiles that were collected aboard the R/V Snavely in 2015 on U.S. Geological Survey cruise 2015-617-FA in Monterey Bay, offshore central California. The majority of MCS profiles collected are oriented north-south across the Monterey Canyon head to address marine geohazards and submarine canyon evolution. The MCS profiles were acquired using a 700-Joule minisparker source and a 24-channel digital streamer.

This part of the USGS data release presents acoustic backscatter data for the Columbia River Mouth, Oregon and Washington. The acoustic backscatter data of the Columbia River Mouth, Oregon and Washington were collected by the U.S. Geological Survey (USGS). Mapping was completed in 2013, using a 234-kHz SEA SWATHPlus interferometric system. These data are not intended for navigational purposes.

This part of the USGS data release presents bathymetry data for the Columbia River Mouth, Oregon and Washington. The bathymetry data of the Columbia River Mouth, Oregon and Washington were collected by the U.S. Geological Survey (USGS). Mapping was completed in 2013, using a 234-kHz SEA SWATHPlus interferometric system. These data are not intended for navigational purposes.

Projected future wave-driven flooding depths on Roi-Namur Island on Kwajalein Atoll in the Republic of the Marshall Islands for a range of climate-change scenarios. This study utilized field data to calibrate oceanographic and hydrogeologic models, which were then used with climate-change and sea-level rise projections to explore the effects of sea-level rise and wave-driven flooding on atoll islands and their freshwater resources. The overall objective of this effort, due to the large uncertainty in future emissions (and thus climate change scenarios) that is largely irreducible, was to reduce risk and increase island resiliency by providing model simulations across a range of plausible future conditions. This effort focuses on Roi-Namur Island on Kwajalein Atoll in the Republic of the Marshall Islands (RMI). RMI is home to more than 1,100 low-lying islands on 29 atolls, yet the approach and findings presented in this study can serve as a proxy for atolls around the world, most of which have a similar morphology and structure, including on average, even lower land elevations, and are the home for numerous island nations and hundreds of thousands of people. The primary goal of this investigation was to determine the influence of climate change and sea-level rise on wave-driven flooding and the resulting impacts to infrastructure and freshwater resources on atoll islands. First, we mapped the morphology and benthic habitats of the atoll to determine the influence of spatially-varying bathymetric structure and hydrodynamic roughness on wave propagation over the coral reefs that make up the atoll. Second, we analyzed historic meteorologic and oceanographic data to provide historical context for the limited in-situ data and comparison to previous seawater overwash and flooding events. These data were then used to calibrate and validate physics-based, dynamically-downscaled numerical models to project future atmospheric and oceanic forcing for a range of climate-change scenarios. Third, we made in-situ observations to better understand how changes in meteorologic and oceanographic forcing controlled wave-driven water levels, seawater flooding of the island, and the resulting hydrogeologic response. We then used those data to calibrate and validate a physics-based, numerical hydrodynamic model of the island. The hydrodynamic model was used to forecast future wave-driven island overwash and seawater flooding for a range of climate-change and SLR scenarios. The data provided here are the seawater flooding depths for three Intergovernmental Panel on Climate Change (IPCC) AR5 climate-change scenarios: Representative Concentration Pathways (RCP)4.5 and RCP8.5, representing medium and high greenhouse concentration trajectory scenarios, respectively, and RCP8.5 plus icesheet collapse (RCP8.5i). The climate-change scenarios were incorporated into the model by increasing mean sea level based on the future sea-level rise and wave projections. The modeled time frame ranged from 2035 to 2105 at 10-yr time steps. These data accompany the following publication: Storlazzi, C.D., Gingerich, S.B., van Dongeren, A., Cheriton, O.M., Swarzenski, P.W., Quataert, E., Voss, C.I., Field D.W., Annamalai, H., Piniak G.A., McCall, R., 2018, Most atolls will be uninhabitable by the mid-21st century due to sea-level rise exacerbating wave-driven flooding, Science Advances, https://doi.org/10.1126/sciadv.aap9741.

Surface runoff and submarine groundwater discharge in particular are known vectors to the coastal ocean of elevated nutrients and contaminants leading to eutrophication, algal overgrowth, and coral disease. Freshwater discharging directly from submarine groundwater vents off of Kahekili Beach Park, Kaanapali, in West Maui contains elevated nutrient concentrations and lower pH values. Coral cores were collected in July 2013 from the shallow reef at Kahekili in Kaanapali, West Maui, Hawaii from scleractinian Porites lobata to specifically addresses the relationship between coral reef health and compounding stressors from contaminated submarine groundwater discharge.

Time-series of seawater carbonate chemistry variables, including salinity, dissolved inorganic nutrients, pH, total alkalinity, and dissolved inorganic carbon from sites along Kahekili Beach Park, west Maui near submarine groundwater seeps and living coral reefs. Samples for seawater were collected by pumping bottom water from the seafloor using a peristaltic pump and collecting discrete water samples every 4-hrs over a 6-day period.

This data release contains information on gravity cores that were collected by the U.S. Geological Survey in the area of San Pablo Bay and Carquinez Strait, California in 1990, 1991, and 2000. Ten (10) pdf files describe gravity cores that were split, photographed, and imaged by X-rays, and another pdf file contains a core-log legend. In addition, a shapefile provides sample collection data. Seventy-two gravity cores were collected by the U.S. Geological Survey in 1990, 1991, and 2000 from San Pablo Bay and Carquinez Strait, California. Gravity cores from San Pablo Bay contain bioturbated laminated silts and sandy clays, whole and broken bivalve shells (mostly mussels), fossil tube structures, and fine plant or wood fragments. Gravity cores from the channel wall of Carquinez Strait east of San Pablo Bay consist of sand and clay layers, whole and broken bivalve shells (less than in San Pablo Bay), trace fossil tubes, and minute fragments of plant material.

This part of the data release presents bathymetry data from the Elwha River delta collected in February 2016 using a kayak. The kayak was equipped with a single-beam echosounder and a survey-grade global navigation satellite system (GNSS) receiver.

This part of the data release presents bathymetry data from the Elwha River delta collected in February 2016 using two personal watercraft (PWCs). The PWCs were equipped with single-beam echosounders and survey-grade global navigation satellite system (GNSS) receivers.

A suite of morphological metrics were derived from existing shoreline and elevation datasets for barrier islands and spits located along the north-slope coast of Alaska between Cape Beaufort and the U.S.-Canadian border. This dataset includes barrier polygons attributed with morphological metrics from five time periods: 1950s, 1980s, 2000s, 2010s, and 2020s.

A suite of morphological metrics were derived from existing shoreline and elevation datasets for barrier islands and spits located along the north-slope coast of Alaska between Cape Beaufort and the U.S.-Canadian border. This dataset includes shoreline vectors, including data source and acquisition date, from five time periods: 1950s, 1980s, 2000s, 2010s, and 2020s. The shoreline vectors were combined to produce polygons upon which the metrics were calculated.

Elemental compositions are reported for the fine fraction of surface sediments from Bellingham Bay (June 2017 and March 2019) and in the fine fraction of streambank sediment from the Nooksack River (September 2017, March 2019, September 2019), Squalicum Creek (March and September 2019), Whatcom Creek (March and September 2019), and Padden Creek (March and September 2019). Major oxide percentages are reported in Nooksack River fine sediment collected in September 2017. Ancillary data for sediment collected during 2017 and 2019 from Bellingham Bay, Nooksack River, and small creeks include: percent weights of gravel, sand, and fines; total organic carbon content (TOC); carbonate content (CaCO3); ratios of stable carbon 13/12 (d13C) and nitrogen 15/14 (d15N) isotopes and total carbon to total nitrogen (C:N); and short-lived cosmogenic radionuclide activities (Beryllium-7, Cesium-137, and excess Lead-210).

This dataset contains California shoreline change rates derived from mean high water (MHW) shorelines from 1998 (in Central and Southern California) and 2002 (in Northern California) to 2016. The MHW elevation in each analysis region (Northern, Central, and Southern California) maintained consistency with that of the National Assessment of Shoreline Change. The operational MHW line was extracted from Light Detection and Ranging (LiDAR) digital elevation models (DEMs) using the ArcGIS smoothed contour method. Within the Digital Shoreline Analysis System (DSAS), end-point rates (EPR) of shoreline change were calculated between the 1998/2002 and the 2016 shorelines at a transect spacing of 50 meters to provide a long-term perspective of sandy shoreline behavior along the coast of California.

This dataset contains mean high water (MHW) shorelines for sandy beaches along the coast of California for the years 1998/2002, 2015, and 2016. The MHW elevation in each analysis region (Northern, Central, and Southern California) maintained consistency with that of the National Assessment of Shoreline Change. The operational MHW line was extracted from Light Detection and Ranging (LiDAR) digital elevation models (DEMs) using the ArcGIS smoothed contour method. The smoothed contour line was then quality controlled to remove artifacts, as well as remove any contour tool interpretation of human-made infrastructure (such as jetties, piers, and sea walls), using satellite imagery from ArcGIS.

This dataset contains shoreline change measurements for sandy beaches along the coast of California over the 2015/2016 El Nino winter season. Mean high water (MHW) shorelines were extracted from Light Detection and Ranging (LiDAR) digital elevation models from the fall of 2015 and the spring of 2016 using the ArcGIS smoothed contour method. The MHW elevation in each analysis region (Northern, Central, and Southern California) maintained consistency with that of the National Assessment of Shoreline Change. Within the Digital Shoreline Analysis System (DSAS), the net shoreline movement (NSM) between the pre-El Nino (2015) and post-El Nino (2016) shorelines was calculated at a transect spacing of 50 meters as a proxy for sandy shoreline change throughout the El Nino winter season.

A comprehensive map of Quaternary faults has been generated for offshore of California. The Quaternary fault map includes mapped geometries and attribute information for offshore fault systems located in California State and Federal waters. The polyline shapefile has been compiled from previously published mapping where relatively dense, high-resolution marine geophysical data exist. The data are also available in kml format and are accompanied by a pdf containing citations for the compiled source data. In the last decade, a number of new marine geophysical datasets collected by the U.S. Geological Survey (USGS), the Ocean Exploration Trust, and other organizations has led to substantially improved high-resolution mapping of the seafloor in areas including California's mainland State waters and the southern California continental borderland. Data include comprehensive multibeam bathymetry, seismic-reflection, and marine magnetic data in numerous offshore areas. Most of these data have been processed, merged, and released by the USGS in maps, data releases, and journal publications in support of the California Seafloor Mapping Program and the U.S. West Coast and Alaska Marine Geohazards Project. Improved data coverage has allowed researchers to better map offshore faults in areas previously unmapped or covered only by low-resolution data. Additionally, subsurface imaging and seafloor sampling has led to better understanding of fault kinematics and recency of deformation, which are critical for accurately assessing California's seismic and coastal hazards.

This data release presents nearshore bathymetry data collected at the mouth of the Unalakleet River in Alaska, near the city of Unalakleet. The data were collected in August 2019 by the U.S. Geological Survey, Pacific Coastal and Marine Science Center. Nearshore bathymetry was measured along survey lines from the shore to a depth of approximately -7.4 m NAVD88 and in a portion of the estuary closest to the mouth. Bathymetry data were collected using small boat equipped with a single-beam sonar system and global navigation satellite system (GNSS) receiver. The sonar system consisted of an Odom Echotrac CV-100 single-beam echosounder and 200 kHz transducer with a 9-degree beam. Depths from the seafloor to the echosounder were calculated using the digitized acoustic backscatter and sound velocity profiles, measured in multiple locations using a YSI CastAway CTD. The position of the boat and echosounder was recorded at 10 Hz using a Trimble R7 receiver and Zephyr 2 antenna. Survey-grade vertical and horizontal positions were achieved by applying differential corrections from a nearby GNSS base station installed for the purposes of this survey.

Structure-from-Motion (SfM) surface models were created using seafloor video collected over a visible fault scarp in the Channel Islands, California, during a 2016 U.S. Geological Survey (USGS) field activity. Four SfM surface models were created, each with a different combination of locating, scaling, and optimizing methods. Video imagery was collected using the USGS Pacific Coastal and Marine Science Center's BOBSled, equipped with high-definition (720p) video cameras (video published in Coastal and Marine Geology Program video and photo portal, Golden and others, 2015). The sled was towed behind the R/V Shearwater and shipboard GPS locations were recorded every 1 second in the video's audio channel. The models were geolocated and scaled using either shipboard GPS or georeferencing the imagery to existing sonar bathymetry at a lower resolution (Cochrane and others, 2018). The models were optimized using either a fixed lens model or automatic calibration in the SfM software, and the files presented here are named to reflect their processing method: "AutoCal" refers to automatic calibration by the SfM software; "Cal" refers to image calibration using a fixed lens model; "Georef" refers to locations derived from georeferencing the video imagery to the existing sonar data; and "ShipGPS" refers to locations derived from the shipboard GPS embedded in the video. Each file was created using one of each of the calibration and location methods, indicated in the filename as "SfM_CalibrationMethod_LocationMethod_UTM10N."

High-resolution chirp sub-bottom data were collected by the U.S. Geological Survey in April 2011 south of Bainbridge Island and west of Seattle in Puget Sound, Washington. Data were collected aboard the R/V Karluk during field activity K0211PS using an Edgetech SB-512i sub-bottom profiler. Sub-bottom acoustic penetration spans several tens of meters and is variable by location.

Chirp data were collected by the U.S. Geological Survey in September of 2013 in Port Valdez, Alaska. Data were collected aboard the USGS R/V Alaskan Gyre during field activity G-01-13-GA, using an EdgeTech SB-512i sub-bottom profiler. Sub-bottom acoustic penetration spans several tens of meters and is variable by location.

Multichannel minisparker and boomer seismic-reflection data were collected by the U.S. Geological Survey in September of 2013 in Port Valdez, Alaska. Data were collected aboard the USGS R/V Alaskan Gyre during field activity G-01-13-GA, using a 500-Joule SIG 2-mille minisparker or an Applied Acoustics triple plated S-Boomer sound source and a 24-channel Goemetrics hydrophone streamer. Sub-bottom acoustic penetration spans several hundreds of meters and is variable by location.

High-resolution multichannel minisparker seismic-reflection data were collected by the U.S. Geological Survey in July of 2018 between Point Conception and Coal Oil Point in the Santa Barbara Channel, California. Data were collected aboard the USGS R/V Parke Snavely during field activity 2018-645-FA, using SIG 2-mille minisparker and recorded using an 8-channel Geometrics digital hydrophone streamer. Sub-bottom acoustic penetration spans several hundreds of meters and is variable by location.

High-resolution chirp sub-bottom data were collected by the U.S. Geological Survey in July of 2018 between Point Conception and Coal Oil Point in the Santa Barbara Channel, California. Data were collected aboard the USGS R/V Parke Snavely during field activity 2018-645-FA, using an EdgeTech SB-512i sub-bottom profiler. Sub-bottom acoustic penetration spans several tens of meters and is variable by location.

Ferromanganese crust samples were collected via dredge during four oceanographic research cruises to the western equatorial Pacific Ocean. The location (latitude, longitude, depth) and concentrations of 27 major and trace elements in the most recent growth layers of ferromanganese crusts from 57 dredge sites are presented here, as well as select seawater chemistry at each location. These data were used in statistical analyses to determine how oceanographic conditions affect the chemical composition of ferromanganese crusts throughout the region. The changes in ferromanganese crust composition show that modern measurements of these primary oceanographic parameters, as well as paleoceanographic reconstructions, can be used to define regions of interest for FeMn crust exploration.

This data release presents reprocessed multichannel seismic-reflection (MCS) data that was originally collected in 1996 in partnership with the California Division of Mines and Geology and Caltrans as part of a seismic hazard assessment of the Coronado Bridge in San Diego Bay, California. The original survey collected 130 km of data with a 14-cubic inch sleeve-gun (airgun) source, a 24-channel streamer, and 3.125 m shot spacing. Reprocessed profiles show increased data resolution, with data recorded to 750 ms two-way-travel-time, and interpretable data down to about 400 m.

This portion of the data release presents digital surface models (DSMs) and hillshade images of the intertidal zone at Post Point, Bellingham Bay, WA. The DSMs were derived from structure-from-motion (SfM) processing of aerial imagery collected with an unmanned aerial system (UAS) on 2019-06-06. Unlike a digital elevation model (DEM), the DSMs represent the elevation of the highest object within the bounds of a cell. Vegetation, buildings and other objects have not been removed from the data. In addition, data artifacts resulting from noise in the original imagery have not been removed. The DSMs are presented with two resolutions: one DSM, covering the entire survey area, has a resolution of 4 centimeters per pixel; the other DSM which was derived from a lower-altitude flight, covers an inset area within the main survey area and has a resolution of 2 centimeters per-pixel. The raw imagery used to create these DSMs was acquired using a UAS fitted with a Ricoh GR II digital camera featuring a global shutter. The UAS was flown on pre-programmed autonomous flight lines spaced to provide approximately 70 percent overlap between images from adjacent lines. The camera was triggered at 1 Hz using a built-in intervalometer. For the main DSM, the UAS was flown at an approximate altitude of 70 meters above ground level (AGL), resulting in a nominal ground-sample-distance (GSD) of 1.8 centimeters per pixel. For the higher-resolution DSM, the UAS was flown at an approximate altitude of 35 meters (AGL), resulting in a nominal ground-sample-distance (GSD) of 0.9 centimeters per pixel. The raw imagery was geotagged using positions from the UAS onboard single-frequency autonomous GPS. Nineteen temporary ground control points (GCPs) were distributed throughout each survey area to establish survey control. The GCPs consisted of a combination of small square tarps with black-and-white cross patterns and "X" marks placed on the ground using temporary chalk. The GCP positions were measured using post-processed kinematic (PPK) GPS, using corrections from a GPS base station located approximately 5 kilometers from the study area. The DSMs and hillshade images have been formatted as cloud optimized GeoTIFFs with internal overviews and masks to facilitate cloud-based queries and display.

This portion of the data release presents the locations of the temporary ground control points (GCPs) used for the structure-from-motion (SfM) processing of the imagery collected during an unmanned aerial system (UAS) survey of the intertidal zone at Post Point, Bellingham Bay, WA on 2019-06-06. Nineteen temporary ground control points (GCPs) were distributed throughout each survey area to establish survey control. The GCPs consisted of a combination of small square tarps with black-and-white cross patterns and "X" marks placed on the ground using temporary chalk. The GCP positions were measured using post-processed kinematic (PPK) GPS, using corrections from a GPS base station located approximately 5 kilometers from the study area. The GCP positions are presented in a comma-delimited text file.

This portion of the data release presents a high-resolution orthomosaic images of the intertidal zone at Post Point, Bellingham Bay, WA. The orthomosaics were derived from structure-from-motion (SfM) processing of aerial imagery collected with an unmanned aerial system (UAS) on 2019-06-06. The orthomosaics are presented with two resolutions: one image, covering the entire survey area, has a resolution of 2 centimeters per pixel; the other image which was derived from a lower-altitude flight, covers an inset area within the main survey area and has a resolution of 1 centimeter per pixel. The raw imagery used to create the orthomosaics was acquired using a UAS fitted with a Ricoh GR II digital camera featuring a global shutter. The UAS was flown on pre-programmed autonomous flight lines spaced to provide approximately 70 percent overlap between images from adjacent lines. The camera was triggered at 1 Hz using a built-in intervalometer. For the main orthomosaic, the UAS was flown at an approximate altitude of 70 meters above ground level (AGL), resulting in a nominal ground-sample-distance (GSD) of 1.8 centimeters per pixel. For the higher-resolution orthomosaic, the UAS was flown at an approximate altitude of 35 meters (AGL), resulting in a nominal ground-sample-distance (GSD) of 0.9 centimeters per pixel. The raw imagery was geotagged using positions from the UAS onboard single-frequency autonomous GPS. Nineteen temporary ground control points (GCPs) were distributed throughout each survey area to establish survey control. The GCPs consisted of a combination of small square tarps with black-and-white cross patterns and "X" marks placed on the ground using temporary chalk. The GCP positions were measured using post-processed kinematic (PPK) GPS, using corrections from a GPS base station located approximately 5 kilometers from the study area. The orthomosaic images are provided in a three-band RGB format, with 8-bit unsigned integer values compressed using high-quality JPEG compression. The images have been formatted as cloud optimized GeoTIFFs with internal overviews and masks to facilitate cloud-based queries and display.

This portion of the data release presents topographic point clouds of the intertidal zone at Post Point, Bellingham Bay, WA. The point clouds were derived from structure-from-motion (SfM) processing of aerial imagery collected with an unmanned aerial system (UAS) on 2019-06-06. Two point clouds are presented with different resolutions: one point cloud (PostPoint_2019-06-06_pointcloud.zip) covers the entire survey area and has 145,653,2221 points with an average point density of 1,057 points per-square meter; the other point cloud (PostPointHighRes_2019-06-06_pointcloud.zip) has 139,427,055 points with an average point density of 3,487 points per-square meter and was derived from a lower-altitude flight covering an inset area within the main survey area. The point clouds are tiled to reduce individual files sizes and grouped within zip files for downloading. Each point in the point clouds contains an explicit horizontal and vertical coordinate, color, intensity, and classification. Water portions of the point cloud were classified using a polygon digitized from the orthomosaic imagery derived from these surveys (also available in this data release). No other classifications were performed. The raw imagery used to create these point clouds was acquired using a UAS fitted with a Ricoh GR II digital camera featuring a global shutter. The UAS was flown on pre-programmed autonomous flight lines spaced to provide approximately 70 percent overlap between images from adjacent lines. The camera was triggered at 1 Hz using a built-in intervalometer. For the main survey area point cloud, the UAS was flown at an approximate altitude of 70 meters above ground level (AGL), resulting in a nominal ground-sample-distance (GSD) of 1.8 centimeters per pixel. For the higher-resolution point cloud, the UAS was flown at an approximate altitude of 35 meters (AGL), resulting in a nominal ground-sample-distance (GSD) of 0.9 centimeters per pixel. The raw imagery was geotagged using positions from the UAS onboard single-frequency autonomous GPS. Nineteen temporary ground control points (GCPs) were distributed throughout each survey area to establish survey control. The GCPs consisted of a combination of small square tarps with black-and-white cross patterns and "X" marks placed on the ground using temporary chalk. The GCP positions were measured using post-processed kinematic (PPK) GPS, using corrections from a GPS base station located approximately 5 kilometers from the study area. The point clouds are formatted in LAZ format (LAS 1.2 specification).

These metadata describe high-resolution chirp sub-bottom data collected in May 2019 in Whiskeytown Lake, California. Data were collected and processed by the the U.S. Geological Survey (USGS), Pacific Coastal and Marine Science Center (PCMSC) with fieldwork activity number 2018-686-FA. The chirp sub-bottom data are provided in SEG-Y format.

This dataset contains information on the probabilities of storm-induced erosion (collision, inundation and overwash) for each 100-meter (m) section of the United States Pacific coast for return period storm scenarios. The analysis is based on a storm-impact scaling model that uses observations of beach morphology combined with sophisticated hydrodynamic models to predict how the coast will respond to the hydrodynamic forcing. Storm-induced water levels, due to both surge and waves, are compared to coastal elevations to determine the probabilities of three types of coastal change: collision (dune erosion), overwash, and inundation. Data on morphology (dune crest and toe elevation) and hydrodynamics (storm surge, wave setup and runup) are also included in this dataset. As new beach morphology observations and storm predictions become available, this analysis will be updated to describe how coastal vulnerability to storms will vary in the future. The data presented here include the dune morphology observations, as derived from lidar surveys.

Presented here is a point cloud produced by the U.S. Geological Survey (USGS) from historical U.S. Air Force vertical aerial imagery, covering the area of the Mud Creek landslide on California State Route 1 (SR1), Mud Creek, Big Sur, California. The point cloud is referenced to previously published lidar data and contains RGB information as well as XYZ. Point cloud coordinates are in NAD83 UTM Zone 10 meters. Imagery was downloaded from USGS Eros Data Center and processed using structure-from-motion photogrammetry with Agisoft PhotoScan version 1.2.8 through 1.3.2. Point clouds were clipped to an AOI using LASTools. The AOI was created from a KMZ in Google Earth and transformed to a shapefile using ArcMap 10.5.

Presented here is a point cloud collected by the U.S. Geological Survey (USGS) using an oblique plane-mounted camera system, covering the area of the Mud Creek landslide on California State Route 1 (SR1), Mud Creek, Big Sur, California. The point cloud is referenced to previously published lidar data and contains RGB information as well as XYZ. Point cloud coordinates are in NAD83 UTM Zone 10 meters. Imagery was collected with a Nikon D800 camera in RAW format and processed using structure-from-motion photogrammetry with Agisoft PhotoScan version 1.2.8 through 1.3.2. Point clouds were clipped to an AOI using LASTools. The AOI was created from a KMZ in Google Earth and transformed to a shapefile using ArcMap 10.5.

Presented here is a point cloud collected by the U.S. Geological Survey (USGS) using an oblique plane-mounted camera system, covering the area of the Mud Creek landslide on California State Route 1 (SR1), Mud Creek, Big Sur, California. The point cloud is referenced to previously published lidar data and contains RGB information as well as XYZ. Point Cloud Coordinates are in NAD83 UTM Zone 10 meters. Imagery was collected with a Nikon D800 camera in RAW format and processed using structure-from-motion photogrammetry with Agisoft PhotoScan version 1.2.8 through 1.3.2. Pointclouds were clipped to an AOI using LASTools. The AOI was created from a KMZ in Google Earth and transformed to a shapefile using ArcMap 10.5.

Presented here is a point cloud collected by the U.S. Geological Survey (USGS) using an oblique plane-mounted camera system, covering the area of the Mud Creek landslide on California State Route 1 (SR1), Mud Creek, Big Sur, California. The point cloud is referenced to previously published lidar data and contains RGB information as well as XYZ. Point cloud coordinates are in NAD83 UTM Zone 10 meters. Imagery was collected with a Nikon D800 camera in RAW format and processed using structure-from-motion photogrammetry with Agisoft PhotoScan version 1.2.8 through 1.3.2. Pointclouds were clipped to an AOI using LASTools. The AOI was created from a KMZ in Google Earth and transformed to a shapefile using ArcMap 10.5.

Presented here is a point cloud collected by the U.S. Geological Survey (USGS) using an oblique plane-mounted camera system, covering the area of the Mud Creek landslide on California State Route 1 (SR1), Mud Creek, Big Sur, California. The point cloud is referenced to previously published lidar data and contains RGB information as well as XYZ. Point cloud coordinates are in NAD83 UTM Zone 10 meters. Imagery was collected with a Nikon D800 camera in RAW format and processed using structure-from-motion photogrammetry with Agisoft PhotoScan version 1.2.8 through 1.3.2. Pointclouds were clipped to an AOI using LASTools. The AOI was created from a KMZ in Google Earth and transformed to a shapefile using ArcMap 10.5.

Presented here is a point cloud collected by the U.S. Geological Survey (USGS) using an oblique plane-mounted camera system, covering the area of the Mud Creek landslide on California State Route 1 (SR1), Mud Creek, Big Sur, California. The point cloud is referenced to previously published lidar data and contains RGB information as well as XYZ. Point cloud coordinates are in NAD83 UTM Zone 10 meters. Imagery was collected with a Nikon D800 camera in RAW format and processed using structure-from-motion photogrammetry with Agisoft PhotoScan version 1.2.8 through 1.3.2. Pointclouds were clipped to an AOI using LASTools. The AOI was created from a KMZ in Google Earth and transformed to a shapefile using ArcMap 10.5.

Presented here is a point cloud collected by the U.S. Geological Survey (USGS) using a UAS-mounted camera system, covering the area of the Mud Creek landslide on California State Route 1 (SR1), Mud Creek, Big Sur, California. The point cloud is referenced to previously published lidar data and contains RGB information as well as XYZ. Point cloud coordinates are in NAD83 UTM Zone 10 meters. Imagery was collected with a Ricoh GR camera in DNG format and processed using structure-from-motion photogrammetry with Agisoft PhotoScan version 1.2.8 through 1.3.2. Pointclouds were clipped to an AOI using LASTools. The AOI was created from a KMZ in Google Earth and transformed to a shapefile using ArcMap 10.5.

Presented here is a point cloud collected by the U.S. Geological Survey (USGS) using a UAS-mounted camera system, covering the area of the Mud Creek landslide on California State Route 1 (SR1), Mud Creek, Big Sur, California. Point cloud is referenced to previously published lidar data and contains RGB information as well as XYZ. Point cloud coordinates are in NAD83 UTM Zone 10 meters. Imagery was collected with a Ricoh GR camera in DNG format and processed using structure-from-motion photogrammetry with Agisoft PhotoScan version 1.2.8 through 1.3.2. Point clouds were clipped to an AOI using LASTools. The AOI was created from a KMZ in Google Earth and transformed to a shapefile using ArcMap 10.5.

Presented here is a point cloud collected by the U.S. Geological Survey (USGS) using an oblique plane-mounted camera system, covering the area of the Mud Creek landslide on California State Route 1 (SR1), Mud Creek, Big Sur, California. Point cloud is referenced to previously published lidar data and contains RGB information as well as XYZ. Point cloud coordinates are in NAD83 UTM Zone 10 meters. Imagery was collected with a Nikon D800 camera in RAW format and processed using structure-from-motion photogrammetry with Agisoft PhotoScan version 1.2.8 through 1.3.2. Point clouds were clipped to an AOI using LASTools. The AOI was created from a KMZ in Google Earth and transformed to a shapefile using ArcMap 10.5.

Presented here is a point cloud collected by the U.S. Geological Survey (USGS) using an oblique plane-mounted camera system, covering the area of the Mud Creek landslide on California State Route 1 (SR1), Mud Creek, Big Sur, California. Point cloud is referenced to previously published lidar data and contains RGB information as well as XYZ. Point cloud coordinates are in NAD83 UTM Zone 10 meters. Imagery was collected with a Nikon D800 camera in RAW format and processed using structure-from-motion photogrammetry with Agisoft PhotoScan version 1.2.8 through 1.3.2. Point clouds were clipped to an AOI using LASTools. The AOI was created from a KMZ in Google Earth and transformed to a shapefile using ArcMap 10.5.

Bathymetric change grids covering the periods of time from 1992 to 1998 and from 1994 to 2004 are presented. The grids cover a portion of the Sacramento River near Rio Vista, California, extending partially upstream on Cache and Steamboat sloughs by the Ryer Island Ferry, as well as continuing up the Sacramento River towards Isleton. Positive grid values indicate accretion, or a shallowing of the surface bathymetric surface, and negative grid values indicate erosion, or a deepening of the bathymetric surface. Bathymetry data sources include the U.S. Army Corps of Engineers, California Department of Water Resources, and NOAA’s National Ocean Service.

This dataset consists of physics-based XBeach Non-hydrostatic hydrodynamic models input files used to study how coral reef restoration affects waves and wave-driven water levels over coral reefs, and the resulting wave-driven runup on the adjacent shoreline. Coral reefs are effective natural coastal flood barriers that protect adjacent communities. Coral degradation compromises the coastal protection value of reefs while also reducing their other ecosystem services, making them a target for restoration. Here we provide a physics-based evaluation of how coral restoration can reduce coastal flooding for various types of reefs. These input files accompany the modeling conducted for the following publication: Roelvink, F.E., Storlazzi, C.D., van Dongeren, A.R., and Pearson, S.G., 2021, Coral reef restorations can be optimized to reduce coastal flooding hazards: Frontiers in Marine Science, https://doi.org/10.3389/fmars.2021.653945.

This portion of the USGS data release presents digital elevation models (DEMs) derived from bathymetric and topographic surveys conducted on the Elwha River delta in July 2017 (USGS Field Activity Number 2017-638-FA). Nearshore bathymetry data were collected using two personal watercraft (PWCs) and a kayak equipped with single-beam echosounders and survey-grade global navigation satellite systems (GNSS) receivers. Topographic data were collected on foot with survey-grade GNSS receivers mounted on backpacks. Positions of the survey platforms were referenced to a GNSS base station placed on a benchmark with known horizontal and vertical coordinates relative to the North American Datum of 1983 (CORS96 realization) and North American Vertical Datum of 1988. The final data were projected in Cartesian coordinates using the Washington State Plane North (meters) coordinate system. A total of 1,270,212 individual elevation points were collected within the survey area between July 20 and July 23, 2017. DEM surfaces were produced from all available elevation data using linear interpolation. Two separate DEMs were constructed. A DEM was produced that covered the entire survey area (approximately 511 ha) with 5-m horizontal resolution. A second DEM with 1-m resolution was produced that covered the river mouth and adjacent areas (approximately 131 ha). The DEMs were created by interpolating between measurements as much as 50 meters apart. For this reason, we cannot evaluate the accuracy of each point in the DEM, only the measurements it is based on. The estimated vertical uncertainties of the bathymetric and topographic measurements are 12 and 5 cm, respectively. Digital data files for each DEM are provided in ESRI ARC ASCII (*.asc) format.

This portion of the data release presents sediment grain-size data from samples collected on the Elwha River delta, Washington, in July 2017 (USGS Field Activity 2017-638-FA). Surface sediment was collected from 80 locations using a small ponar, or 'grab', sampler from the R/V Frontier in water depths between about 1 and 17 m around the delta. An additional 31 samples were collected by hand at low tide. A hand-held global satellite navigation system (GNSS) receiver was used to determine the locations of sediment samples. The grain size distributions of suitable samples were determined using standard techniques developed by the USGS Pacific Coastal and Marine Science Center sediment lab. Grab samples that yielded less than 50 g of sediment were omitted from analysis. The grain-size data are provided in a comma-delimited spreadsheet (.csv).

This part of the data release presents bathymetry data from the Elwha River delta collected in July 2017 using a kayak. The kayak was equipped with a single-beam echosounder and a survey-grade global navigation satellite system (GNSS) receiver.

This part of the data release presents bathymetry data from the Elwha River delta collected in July 2017 using two personal watercraft (PWCs). The PWCs were equipped with single beam echosounders and survey-grade global navigation satellite system (GNSS) receivers.

This part of the data release presents topography data from the Elwha River delta collected in July 2017. Topography data were collected on foot with global navigation satellite system (GNSS) receivers mounted on backpacks.

This data release contains 41 chirp sub-bottom profiles that were collected in February of 2016 from the Catalina Basin offshore southern California by the U.S. Geological Survey Pacific and Coastal Marine Science Center in cooperation with the University of Washington. Data were collected aboard the University of Washington’s R/V Thomas G. Thompson on USGS cruise 2016-616-FA. Chirp profiles were collected to image the Catalina and San Clemente fault systems as well as the San Gabriel Canyon system.

This data release contains 25 multichannel minisparker seismic reflection (MCS) profiles that were collected in February of 2016 from the Catalina Basin offshore southern California by the U.S. Geological Survey Pacific and Coastal Marine Science Center in cooperation with the University of Washington. Data were collected aboard the University of Washington’s R/V Thomas G. Thompson on USGS cruise 2016-616-FA. MCS profiles were collected to image the Catalina and San Clemente fault systems as well as the San Gabriel Canyon and Channel system.

This dataset includes a reference baseline used by the Digital Shoreline Analysis System (DSAS) to calculate rate-of-change statistics for the coastal bluffs at Barter Island, Alaska for the time period 1950 to 2020. This baseline layer serves as the starting point for all transects cast by the DSAS application and can be used to establish measurement points used to calculate bluff-change rates.

This dataset includes one vector shapefile delineating the position of the top edge of the coastal permafrost bluffs at Barter Island, Alaska spanning seven decades, between the years of 1950 and 2020. Bluff-edge positions delineated from a combination of aerial photography, declassified satellite photography, and very-high resolution satellite imagery can be used to quantify the movement of the bluff edge through time. These data were used to calculate rates of change every 10 meters alongshore using the Digital Shoreline Analysis System (DSAS) version 5.0. DSAS uses a measurement baseline method to calculate rate-of-change statistics. Transects are cast from the reference baseline to intersect each bluff edge vector, establishing measurement points used to calculate bluff change rates.

This dataset consists of rate-of-change statistics for the coastal bluffs at Barter Island, Alaska for the time period 1950 to 2020. Rate calculations were computed within a GIS using the Digital Shoreline Analysis System (DSAS) version 5.0, an ArcGIS extension developed by the U.S. Geological Survey. A reference baseline was used as the originating point for the orthogonal transects cast by the DSAS software. The transects intersect each bluff line establishing measurement points, which are then used to calculate bluff-change rates.

This dataset consists of rate-of-change statistics for the shorelines at Barter Island, Alaska for the time period 1947 to 2020. Rate calculations were computed within a GIS using the Digital Shoreline Analysis System (DSAS) version 5.0, an ArcGIS extension developed by the U.S. Geological Survey. A reference baseline was used as the originating point for the orthogonal transects cast by the DSAS software. The transects intersect each shoreline establishing measurement points, which are then used to calculate shoreline change rates.

This dataset includes a reference baseline used by the Digital Shoreline Analysis System (DSAS) to calculate rate-of-change statistics for the shorelines near Barter Island, Alaska for the time period 1947 to 2020. This baseline layer serves as the starting point for all transects cast by the DSAS application and can be used to establish measurement points used to calculate shoreline-change rates.

This dataset includes one vector shapefile delineating the position of the shorelines at Barter Island, Alaska spanning seven decades, between the years 1947 and 2020. Shoreline positions delineated from a combination of aerial photography, declassified satellite photography, and very-high resolution satellite imagery can be used to quantify the movement of the shoreline through time. These data were used to calculate rates of change every 10 meters alongshore using the Digital Shoreline Analysis System (DSAS) version 5.0. DSAS uses a measurement baseline method to calculate rate-of-change statistics. Transects are cast from the reference baseline to intersect each shoreline, establishing measurement points used to calculate shoreline change rates.

This data release presents projected flooding extent polygon (flood masks) shapefiles based on wave-driven total water levels for the State Florida (the Florida Peninsula and the Florida Keys). There are 12 associated flood mask shapefiles: one for each of four nearshore wave energy return periods (rp; 10-, 50-, 100-, and 500-years), the current scenario (base) and each of the degradation scenarios (Mean Elevation and Mean Erosion).

Geochemical analysis (including age-corrected radiocarbon stable isotopes, and elemental composition) and growth parameters (including calcification rate, density, and extension information) were measured from a coral core collected from a reef off the southern side of Tutuila, American Samoa. The core was collected near Matautuloa Point on 8 April 2012 in collaboration with the Ecosystem Sciences Division, Pacific Islands Fisheries Science Center, National Oceanic and Atmospheric Administration (NOAA), Honolulu, HI.

This part of the data release presents projected flooding extent polygon shapefiles based on wave-driven total water levels for the State Florida (the Florida Peninsula and the Florida Keys). There are eight associated flood mask and flood depth shapefiles: one for each of four nearshore wave energy return periods (rp; 10-, 50-, 100-, and 500-years), the pre-storm scenario (base) and the post-storm scenarios.

This part of the data release presents projected flooding extent polygon (flood masks) shapefiles based on wave-driven total water levels for Commonwealth of Puerto Rico. There are eight associated flood mask and flood depth shapefiles: one for each of four nearshore wave energy return periods (rp; 10-, 50-, 100-, and 500-years), the pre-storm scenario (base) and the post-storm scenarios.

A three-dimensional hydrodynamic model of the lower Columbia River (LCR) was constructed using the Delft3D Flexible Mesh (DFM) modeling suite to simulate water levels, flow, and seabed stresses for time period of January 1, 2017 to April 20, 2020. This data release describes the construction and validation of the model application and provides input files suitable to run the model on Delft3D Flexible Mesh software version 2021.01.

This data table includes in-situ near-shore seawater measurements of excess radon (Rn-222) and water levels collected in Faga'alu Bay, Tutuila, American Samoa.

This data release presents the concentration of zinc, oxygen, pH, temperature, and the time point at which measurements were taken for experimental oxidation work regarding zinc and copper sulfide minerals. These data accompany the following publication: Gartman, A., Whisman, S.P., and Hein, J.R., 2020, Interactive oxidation of sphalerite and covellite in seawater: implications for seafloor massive sulfide deposits and mine waste: ACS Earth and Space Chemistry, https://doi.org/10.1021/acsearthspacechem.0c00177, where they are also presented as Supplemental Table 1.

This portion of the data release presents a digital surface model (DSM) and hillshade of the Liberty Island Conservation Bank Wildlands restoration site in the Sacramento-San Joaquin Delta. The DSM has a resolution of 10 centimeters per-pixel and was derived from structure-from-motion (SfM) processing of aerial imagery collected with an Unmanned Aerial System (UAS) on 2018-10-23. Unlike a digital elevation model (DEM), the DSM represents the elevation of the highest object within the bounds of a cell. Vegetation, buildings and other objects have not been removed from the data. In addition, data artifacts resulting from noise in the original imagery have not been removed. The raw imagery used to create this DSM was acquired using two UAS fitted with Ricoh GR II digital cameras global shutters. The UAS were flown on pre-programmed autonomous flight lines at an approximate altitude of 120 meters above-ground-level. The flight lines were oriented roughly east-west and were spaced to provide approximately 66 percent overlap between images from adjacent lines. The cameras were triggered at 1 Hz using a built-in intervalometer. The imagery was geotagged using positions from the UAS onboard single-frequency autonomous GPS. Ground control was established using twenty-four ground control points (GCPs) consisting of small square tarps with black-and-white cross patterns distributed throughout the mapping area. The GCP positions were measured using RTK GPS, with real-time corrections from a GPS base station located approximately 3 kilometers south of the study area. The DSM and hillshade have been formatted as cloud optimized GeoTIFFs with internal overviews and masks to facilitate cloud-based queries and display.

This portion of the data release presents the locations of the temporary ground control points (GCPs) used for the structure-from-motion (SfM) processing of the imagery collected during the Unmanned Aerial System (UAS) survey on of the Liberty Island Conservation Bank Wildlands restoration site in the Sacramento-San Joaquin Delta on 2018-10-23. The GCPs were used to establish ground control for the survey and consisted of 24 small (80 x 80 centimeter) square tarps with black-and-white cross patterns placed on the ground surface throughout the mapping area during the survey. The GCP positions were measured using RTK GPS, with corrections from a GPS base station located approximately 3 kilometers south of the study area. The GCP positions are presented in a comma-delimited text file.

This portion of the data release presents the raw aerial imagery collected during the Unmanned Aerial System (UAS) survey of the Liberty Island Conservation Bank Wildlands restoration site in the Sacramento-San Joaquin Delta on 2018-10-23. The imagery was acquired using two Department of Interior owned 3DR Solo quadcopters fitted with Ricoh GR II digital cameras featuring global shutters. The cameras were mounted using a fixed mount on the bottom of the UAS and oriented in a roughly nadir orientation. The UAS were flown on pre-programmed autonomous flight lines at an approximate altitude of 120 meters above-ground-level, resulting in a nominal ground-sample-distance (GSD) of 3.2 centimeters per-pixel. The flight lines were oriented roughly east-west and were spaced to provide approximately 66 percent overlap between images from adjacent lines. The cameras were triggered at 1 Hz using a built in intervalometer. After acquisition, the images were renamed to include flight number and acquisition time in the file name. The coordinates of the approximate image acquisition location were added ('geotagged') to the image metadata (EXIF) using the telemetry log from the UAS onboard single-frequency autonomous GPS. The image EXIF were also updated to include additional information related to the acquisition. Although the images were recorded in both JPG and camera raw (Adobe DNG) formats, only the JPG images are provided in this data release. The data release includes a total of 3,567 JPG images. Images from takeoff and landing sequences were not used for processing, and have been omitted from the data release. The images from each flight are provided in a zip file named with the flight number.

This portion of the data release presents a high-resolution orthomosaic image of the Liberty Island Conservation Bank Wildlands restoration site in the Sacramento-San Joaquin Delta. The orthomosaic has a resolution of 3 centimeters per-pixel and was derived from structure-from-motion (SfM) processing of aerial imagery collected with an Unmanned Aerial System (UAS) on 2018-10-23. The raw imagery used to create the orthomosaic image was acquired using two UAS fitted with Ricoh GR II digital cameras with global shutters. The UAS were flown on pre-programmed autonomous flight lines at an approximate altitude of 120 meters above-ground-level. The flight lines were oriented roughly east-west and were spaced to provide approximately 66 percent overlap between images from adjacent lines. The cameras were triggered at 1 Hz using a built-in intervalometer. The imagery was geotagged using positions from the UAS onboard single-frequency autonomous GPS. Ground control was established using twenty-four ground control points (GCPs) consisting of small square tarps with black-and-white cross patterns distributed throughout the mapping area. The GCP positions were measured using RTK GPS, with real-time corrections from a GPS base station located approximately 3 kilometers south of the study area. The orthomosaic imagery is provided at a resolution of 3 centimeters per-pixel, in a three-band RGB cloud-optimized GeoTIFF format, with 8-bit unsigned integer values compressed using high-quality JPEG compression.

This portion of the data release presents a topographic point cloud of the Liberty Island Conservation Bank Wildlands restoration site in the Sacramento-San Joaquin Delta, derived from structure-from-motion (SfM) processing of aerial imagery collected with an Unmanned Aerial System (UAS) on 2018-10-23. The point cloud contains 380,296,568 points at an approximate point density of 323 point per square-meter. Each point contains an explicit horizontal and vertical coordinate, color, intensity, and classification. The point cloud is tiled into 500 x 500-meter tiles to reduce file size. The raw imagery used to create this point cloud was acquired using two UAS fitted with Ricoh GR II digital cameras global shutters. The UAS were flown on pre-programmed autonomous flight lines at an approximate altitude of 120 meters above-ground-level. The flight lines were oriented roughly east-west and were spaced to provide approximately 66 percent overlap between images from adjacent lines. The cameras were triggered at 1 Hz using a built-in intervalometer. The imagery was geotagged using positions from the UAS onboard single-frequency autonomous GPS. Ground control was established using twenty-four ground control points (GCPs) consisting of small square tarps with black-and-white cross patterns distributed throughout the mapping area. The GCP positions were measured using RTK GPS, with real-time corrections from a GPS base station located approximately 3 kilometers south of the study area.

High-resolution multichannel boomer seismic-reflection data were collected by the U.S. Geological Survey and the University of Washington in February of 2017 east of Seattle in Lake Washington, Washington. Data were collected aboard University of Washington’s R/V Clifford A. Barnes during USGS field activity 2017-612-FA using an Applied Acoustics triple plate S-Boom sound source and recorded on a 24 channel Geometrics digital hydrophone streamer. Sub-bottom acoustic penetration spans several hundreds of meters and is variable by location.

High-resolution chirp sub-bottom data were collected by the U.S. Geological Survey and the University of Washington in February of 2017 west of Seattle in Puget Sound and in Lake Washington, Washington. Data were collected aboard the University of Washington’s R/V Clifford A. Barnes during USGS field activity 2017-612-FA using an Edgetech SB-512i sub-bottom profiler. Sub-bottom acoustic penetration spans several tens of meters and is variable by location.

High-resolution multichannel minisparker seismic-reflection data were collected by the U.S. Geological Survey and the University of Washington in February of 2017 west of Seattle in Puget Sound and in Lake Washington, Washington. Data were collected aboard University of Washington’s R/V Clifford A. Barnes during USGS field activity 2017-612-FA using a 500 Joule SIG 2-mille minisparker sound source and recorded on a 48 channel Geometrics digital hydrophone streamer. Sub-bottom acoustic penetration spans several hundreds of meters and is variable by location.

This portion of the data release presents a digital surface model (DSM) and hillshade of Whiskeytown Lake and the surrounding area derived from Structure from Motion (SfM) processing of aerial imagery acquired on 2018-12-02. Unlike a digital elevation model (DEM), the DSM represents the elevation of the highest object within the bounds of a cell. Vegetation, buildings and other objects have not been removed from the data. In addition, data artifacts resulting from noise and vegetation in the original imagery have not been removed. However, in unvegetated areas such as reservoir shorelines and deltas, the DSM is equivalent to a DEM because it represents the ground surface elevation. The raw imagery used to create this DSM was acquired from a manned aircraft on 2018-12-02. The acquisition flight was conducted by The 111th Group Aerial Photography, using a Nikon D850 camera. The imagery was acquired from an approximate altitude of 610 meters (2,000 feet) above ground level, to produce a nominal ground sample distance (pixel size) of 5 centimeters (2 inches). An onboard single-frequency GPS receiver was used to record the precise time and position of each image. Coordinates for ground control points consisting of photo-identifiable objects were measured independently using survey-grade post-processed kinematic (PPK) GPS.

This portion of the data release presents an RGB orthomosaic image of Whiskeytown Lake and the surrounding area derived from Structure from Motion (SfM) processing of aerial imagery acquired on 2018-12-02. The orthomosaic is available in a high-resolution 6-centimeter (cm) version, as well as a medium-resolution 25 cm version. The high-resolution version is divided into two tiles (east and west) to reduce file download sizes. All imagery is provided in a three-band cloud optimized GeoTIFF format, with 8-bit unsigned integer values compressed using high-quality JPEG compression. The raw imagery used to create the orthomosaic image was acquired from a manned aircraft on 2018-12-02. The acquisition flight was conducted by The 111th Group Aerial Photography, using a Nikon D850 camera. The imagery was acquired from an approximate altitude of 610 meters (2,000 feet) above ground level, to produce a nominal ground-sample distance (pixel size) of 5 centimeters (2 inches). An onboard single-frequency GPS receiver was used to record the precise time and position of each image. Coordinates for ground control points consisting of photo-identifiable objects were measured independently using survey-grade post-processed kinematic (PPK) GPS.

These metadata describe bathymetric data collected during a December 2018 SWATHPlus survey of Whiskeytown Lake, California. Data were collected and processed by the U.S. Geological Survey (USGS), Pacific Coastal and Marine Science Center (PCMSC) during fieldwork activity number 2018-686-FA. The bathymetric data are provided as a GeoTIFF image.

This portion of the data release presents a digital surface model (DSM) and hillshade of Whiskeytown Lake and the surrounding area derived from Structure from Motion (SfM) processing of aerial imagery acquired on 2019-06-03. Unlike a digital elevation model (DEM), the DSM represents the elevation of the highest object within the bounds of a cell. Vegetation, buildings and other objects have not been removed from the data. In addition, data artifacts resulting from noise and vegetation in the original imagery have not been removed. However, in unvegetated areas such as reservoir shorelines and deltas, the DSM is equivalent to a DEM because it represents the ground surface elevation. The raw imagery used to create this DSM was acquired from a manned aircraft on 2019-06-03. The acquisition flight was conducted by The 111th Group Aerial Photography, using a Nikon D850 camera. The acquisition covered two areas-of-interest (AOI) at different scales. The AOI for this dataset (referred to as AOI-A) covered the area immediately surrounding Whiskeytown Lake, which was the same area imaged in the 2018-12-02 acquisition. The imagery was acquired from an approximate altitude of 610 meters (2,000 feet) above ground level, to produce a nominal ground sample distance (pixel size) of 5 centimeters (2 inches). An onboard dual-frequency GPS receiver was used to record the precise time and position of each image. Coordinates for ground control points consisting of photo-identifiable objects were measured independently using survey-grade post-processed kinematic (PPK) GPS.

This portion of the data release presents an RGB orthomosaic image of Whiskeytown Lake and the surrounding area derived from Structure from Motion (SfM) processing of aerial imagery acquired on 2019-06-03. The orthomosaic is available in a high-resolution 6-centimeter (cm) version, as well as a medium-resolution 25 cm version. The high-resolution version is divided into two tiles (east and west) to reduce file download sizes. All imagery is provided in a three-band cloud-optimized GeoTIFF format, with 8-bit unsigned integer values compressed using high-quality JPEG compression. The raw imagery used to create the orthomosaic was acquired from a manned aircraft on 2019-06-03. The acquisition flight was conducted by The 111th Group Aerial Photography, using a Nikon D850 camera. The acquisition covered two areas-of-interest (AOI) at different scales. The AOI for this dataset (referred to as AOI-A) covered the area immediately surrounding Whiskeytown Lake, which is the same area imaged in the 2018-12-02 acquisition (also available as a part of this data release). The imagery was acquired from an approximate altitude of 610 meters (2,000 feet) above ground level, to produce a nominal ground-sample distance (pixel size) of 5 centimeters (2 inches). An onboard dual-frequency GPS receiver was used to record the precise time and position of each image. Coordinates for ground control points consisting of photo-identifiable objects were measured independently using survey-grade post-processed kinematic (PPK) GPS.

This portion of the data release presents an RGB orthomosaic image of an expanded area surrounding Whiskeytown Lake derived from Structure from Motion (SfM) processing of aerial imagery acquired on 2019-06-03. The orthomosaic is available in a high-resolution 14-centimeter (cm) version, as well as a medium-resolution 25 cm version. The high-resolution version is divided into two tiles (east and west) to reduce file download sizes. All imagery is provided in a three-band cloud-optimized GeoTIFF format, with 8-bit unsigned integer values compressed using high-quality JPEG compression. The raw imagery used to create the orthomosaic image was acquired from a manned aircraft on 2019-06-03. The acquisition flight was conducted by The 111th Group Aerial Photography, using a Nikon D850 camera. The acquisition covered two areas-of-interest (AOI) at different scales. The expanded AOI for this dataset (referred to as AOI-B) covered an area around Whiskeytown Lake up to the adjacent ridgelines. The goal for expanding the AOI for this acquisition was to create an orthomosaic image to qualitatively assess the post-fire erosion patterns in upslope areas of the drainages. The imagery for this AOI was acquired from an approximate altitude of 1,160 meters (3,800 feet) above ground level, to produce a nominal ground sample distance (pixel size) of 10 centimeters (4 inches). An onboard dual-frequency GPS receiver was used to record the precise time and position of each image. Coordinates for ground control points consisting of photo-identifiable objects were measured independently using survey-grade post-processed kinematic (PPK) GPS.

These metadata describe bathymetric data collected during a May 2019 SWATHPlus survey of Whiskeytown Lake, California. Data were collected and processed by the U.S. Geological Survey (USGS), Pacific Coastal and Marine Science Center (PCMSC) during fieldwork activity number 2018-686-FA. The bathymetric data are provided as a GeoTIFF image.

This portion of the data release presents a digital surface model (DSM) and hillshade of Whiskeytown Lake and the surrounding area derived from Structure from Motion (SfM) processing of aerial imagery acquired on 2020-11-10. Unlike a digital elevation model (DEM), the DSM represents the elevation of the highest object within the bounds of a cell. Vegetation, buildings and other objects have not been removed from the data. In addition, data artifacts resulting from noise and vegetation in the original imagery have not been removed. However, in unvegetated areas such as reservoir shorelines and deltas, the DSM is equivalent to a DEM because it represents the ground surface elevation. The raw imagery used to create this DSM was acquired from a manned aircraft on 2020-11-10. The acquisition flight was conducted by The 111th Group Aerial Photography, using a Hasselblad A6D-100c camera. The imagery was acquired from an approximate altitude of 880 meters (2,900 feet) above ground level, to produce a nominal ground sample distance (pixel size) of 5 centimeters (2 inches). An onboard dual-frequency GPS receiver was used to record the precise time and position of each image. Coordinates for ground control points consisting of photo-identifiable objects were measured independently using survey-grade post-processed kinematic (PPK) GPS.

These metadata describe bathymetric data collected during a September 2021 SWATHPlus survey of Whiskeytown Lake, California. Data were collected and processed by the U.S. Geological Survey (USGS), Pacific Coastal and Marine Science Center (PCMSC) during fieldwork activity number 2018-686-FA. The bathymetric data are provided as a GeoTIFF image.

Time-series data of water surface elevation, waves, currents, temperature, and salinity collected between 17 May 2017 and 17 Jan 2018 off the southwest coast of Puerto Rico in support of a study on circulation and sediment transport dynamics over coral reefs. The data are available in NetCDF format, grouped together in zip files by instrument site location. A README.txt file details the files contained within each zip, including the file names, type of data collected, instrument that collected the data, depth, and start and end dates/times.

This data release indicates faunal presence or absence of shelly artifacts (invertebrate remains) from the Dominican University of California archaeological site MRN-CA-254, Marin County, California.

Results of lipid biomarker concentration and compound specific isotopes analyzed from authigenic carbonates and surrounding sediment collected from Baltimore and Norfolk seep fields along the United States Atlantic Margin are presented in csv format. Samples were collected by the U.S. Geological Survey and Duke University between 2012 and 2015 using remotely operated vehicles (ROVs). Geochemical analysis was performed using gas chromatography (GC) and GC-combustion isotope ratio mass spectrometry (GC-C-IRMS).

High-resolution acoustic backscatter data were collected by the U.S. Geological Survey (USGS) and the Alaska Department of Fish and Game in May of 2014 southwest of Chenega Island, Alaska. Data were collected aboard the Alaska Department of Fish and Game vessel, R/V Solstice, during USGS field activity 2014-622-FA, using a pole mounted 100-kHz Reson 7111 multibeam echosounder.

High-resolution multibeam data were collected by the U.S. Geological Survey (USGS) and the Alaska Department of Fish and Game in May of 2014 southwest of Chenega Island, Alaska. Data were collected aboard the Alaska Department of Fish and Game vessel, R/V Solstice, during USGS field activity 2014-622-FA, using a pole mounted 100-kHz Reson 7111 multibeam echosounder.

High-resolution acoustic backscatter data were collected by the U.S. Geological Survey (USGS) and the Alaska Department of Fish and Game in May of 2014 southwest of Montague Island, Alaska. Data were collected aboard the Alaska Department of Fish and Game vessel, R/V Solstice, during USGS field activity 2014-622-FA, using a pole mounted 100-kHz Reson 7111 multibeam echosounder.

High-resolution multibeam data were collected by the U.S. Geological Survey (USGS) and the Alaska Department of Fish and Game in May of 2014 southwest of Montague Island, Alaska. Data were collected aboard the Alaska Department of Fish and Game vessel, R/V Solstice, during USGS field activity 2014-622-FA, using a pole mounted 100-kHz Reson 7111 multibeam echosounder.

High-resolution single channel minisparker seismic-reflection data were collected by the U.S. Geological Survey and the Alaska Department of Fish and Game in May 2014 in southern Prince William Sound southwest of Chenega and from southwest of Montague Island, Alaska. Data were collected aboard the Alaska Department of Fish and Game vessel, R/V Solstice, during field activity 2014-622-FA, using a 500 Joule SIG 2-mille minisparker sound source and a single channel streamer and recorded with a Triton SB-Logger.

Bed sediment samples were collected in San Pablo Bay and Grizzly Bays on eight days from January through September 2020, to analyze for sediment properties including bulk density, particle size distribution, and percent organic carbon. Sediment samples were collected from a small vessel near pre-established USGS instrument moorings using a Gomex box corer that was subsampled with three push cores (37 mm in diameter) per Gomex core. Six subsamples were collected from the top 5 centimeters (cm) of each push core, a few push cores included the top 8 cm. The top two subsamples were each 0.5 cm thick, and all following subsamples were each 1 cm thick. Push core samples from the first, third, and fifth centimeter depth were analyzed for grain size and percent organic carbon, while all 6 sections were analyzed for bulk density. Data are provided in a comma-delimited values spreadsheet. These data were collected as part of a collaborative project with the USGS California Water Science Center and the USGS Water Mission Area on physical and biological controls on sediment erodibility, funded by the USGS Priority Ecosystems Program for San Francisco Bay and Delta and the USGS Coastal Marine Hazards and Resources Program.

These data present suspended particle size distributions collected by the U.S. Geological Survey (USGS) Pacific Coastal and Marine Science Center within two embayments of San Francisco Bay. Data were collected at one site in San Pablo Bay and one site in Grizzly Bay from January through February 2020 by deploying a Sequoia Scientific Laser In-situ Scattering and Transmissometry instrument (LISST 200x) from a small vessel near pre-established USGS instrument moorings. At both sites, data were collected on four dates at three depths, generally near the water surface, at mid depth, and near the sediment bed, for 1-3 minutes at each depth. LISST volume concentrations are most accurate when the optical percent transmission is above 30; when this is true, light passing through the sample volume is unlikely to be scattered by more than one particle. These files contain all samples collected; judgment should be applied when using them. Users are advised to check metadata and instrument information carefully for applicable time periods of specific data.

Water samples were collected in San Pablo Bay and Grizzly Bay on five days from January through June 2020. The water samples were collected from a small vessel near pre-established USGS instrument moorings using a peristaltic pump or a Niskin bottle. Data are provided in a comma-delimited values spreadsheet.

Hydrodynamic and sediment transport time-series data, including water depth, velocity, turbidity, conductivity, and temperature, were collected by the U.S. Geological Survey (USGS) Pacific Coastal and Marine Science Center within two embayments of San Francisco Bay. Data were collected in San Pablo Bay and Grizzly Bay from January to June 2020 at seven locations. Data files are grouped by area (shallows of San Pablo Bay, channel of San Pablo Bay, and shallows of Grizzly Bay). Each shallow site contained a variety of sensors located on two tripods, while the channel site consisted of one tripod. Users are advised to assess data quality carefully, and to check metadata for instrument information, as platform deployment times and data-processing methods varied.

This portion of the USGS data release presents eelgrass distribution and bathymetry data derived from acoustic surveys of the Nisqually River delta, Washington in 2012 (USGS Field Activity Number D-01-12-PS). Eelgrass and bathymetry data were collected from the R/V George Davidson equipped with a single-beam sonar system and global navigation satellite system (GNSS) receiver. The sonar system consisted of a Biosonics DT-X single-beam echosounder and 420 kHz transducer with a 6-degree beam angle. Depths from the echosounder were computed using sound velocity assuming a salinity of 30 psu and temperature of 10 degrees Celsius. Positioning of the survey vessel was determined at 5 to 10 Hz using a Trimble R7 GNSS receiver and Trimble Zephyr Model 2 antenna operating in real time kinematic (RTK) mode. Differential corrections were transmitted by a VHF radio to the GNSS receiver on the survey vessel at 1-Hz from a GNSS base station placed on a nearby benchmark with known horizontal and vertical coordinates relative to the North American Datum of 1983 (CORS96 realization). Output from the GNSS and sonar systems were combined in real time by the Biosonics DT-X deck unit and output to a computer running HYPACK hydrographic survey software. Navigation information was displayed on a video monitor, allowing the vessel operator to navigate along predefined survey lines spaced at 25-50 m intervals alongshore at speeds of 2 to 3 m/s. Acoustic backscatter data were analyzed using a custom graphical user interface that implements a signal processing algorithm applied to each sonar sounding that differentiates and extracts the location of the seafloor apart from the presence of vegetation (Stevens and others, 2008). Individual acoustic returns along a survey line were grouped into packets of ten, and eelgrass percent cover was calculated as the fractional percent of acoustic returns that were classified as vegetated within each group, resulting in an estimate of percent cover every 4 to 5 m (depending on the vessel speed). Orthometric elevations relative to the NAVD88 vertical datum were computed using National Geodetic Survey Geoid12a offsets. The average estimated vertical uncertainty of the bathymetric measurements is 12 cm. The point data are provided in a comma-separated text file and are projected in Cartesian coordinates using the Universal Transverse Mercator (UTM), Zone 10 north, meters coordinate system.

This portion of the USGS data release presents eelgrass distributions derived from towed underwater video surveys of the Nisqually River delta, Washington in 2012 (USGS Field Activity Number D-01-12-PS). Eelgrass data were collected from the R/V George Davidson equipped with a towed underwater video system and global navigation satellite system (GNSS) receiver. The underwater video system consisted of a Splashcam standard definition video camera connected to a Sony GV-D1000 video monitor and tape recorder. Positioning of the survey vessel was determined at 1 Hz intervals using a Trimble R7 GNSS receiver and Trimble Zephyr Model 2 antenna. The positioning data from the GNSS were encoded onto the audio track of the digital video recording using Red Hen Systems (RHS) VMS200 hardware. Underwater video data were recorded as the vessel navigated along a series of shore-perpendicular transects at speeds between 1 and 2 knots. The underwater video recording was later reviewed and the presence or absence of eelgrass was determined for each 1-s segment of video tape. These data were used to evaluate the classification of single-beam sonar data acquired during the same time period.

This portion of the USGS data release presents eelgrass distribution and bathymetry data derived from acoustic surveys of the Nisqually River delta, Washington in 2014 (USGS Field Activity Number D-01-14-PS). Eelgrass and bathymetry data were collected from the R/V George Davidson equipped with a single-beam sonar system and global navigation satellite system (GNSS) receiver. The sonar system consisted of a Biosonics DT-X single-beam echosounder and 420 kHz transducer with a 6-degree beam angle. Depths from the echosounder were computed using sound velocity data measured using a YSI CastAway CTD during the survey. Positioning of the survey vessel was determined at 5 to 10 Hz using a Trimble R7 GNSS receiver and Trimble Zephyr Model 2 antenna operating in real time kinematic (RTK) mode. Differential corrections were transmitted by a VHF radio to the GNSS receiver on the survey vessel at 1-Hz from a GNSS base station placed on a nearby benchmark with known horizontal and vertical coordinates relative to the North American Datum of 1983 (CORS96 realization). Output from the GNSS and sonar systems were combined in real time by the Biosonics DT-X deck unit and output to a computer running HYPACK hydrographic survey software. Navigation information was displayed on a video monitor, allowing the vessel operator to navigate along predefined survey lines spaced at 25-50 m intervals alongshore at speeds of 2 to 3 m/s. Acoustic backscatter data were analyzed using a custom graphical user interface that implements a signal processing algorithm applied to each sonar sounding that differentiates and extracts the location of the seafloor apart from the presence of vegetation (Stevens and others, 2008). Individual acoustic returns along a survey line were grouped into packets of ten, and eelgrass percent cover was calculated as the fractional percent of acoustic returns that were classified as vegetated within each group, resulting in an estimate of percent cover every 4 to 5 m (depending on the vessel speed). Orthometric elevations relative to the NAVD88 vertical datum were computed using National Geodetic Survey Geoid12a offsets. The average estimated vertical uncertainty of the bathymetric measurements is 5 cm. The point data are provided in a comma-separated text file and are projected in Cartesian coordinates using the Universal Transverse Mercator (UTM), Zone 10 north, meters coordinate system.

This portion of the USGS data release presents eelgrass distributions derived from towed underwater video surveys of the Nisqually River delta, Washington in 2014 (USGS Field Activity Number D-01-14-PS). Eelgrass data were collected from the R/V George Davidson equipped with a towed underwater video system and global navigation satellite system (GNSS) receiver. The underwater video system consisted of a Splashcam standard definition video camera connected to a Sony GV-D1000 video monitor and tape recorder. Positioning of the survey vessel was determined at 0.5 Hz intervals using a Garmin 76c GNSS receiver. The positioning data from the GNSS were encoded onto the audio track of the digital video recording using Red Hen Systems (RHS) VMS200 hardware. Underwater video data were recorded as the vessel navigated along a series of shore-perpendicular transects at speeds between 1 and 2 knots. The underwater video recording was later reviewed and the presence or absence of eelgrass was determined for each 2-s segment of video tape. These data were used to evaluate the classification of single-beam sonar data acquired during the same time period.

This portion of the USGS data release presents eelgrass distribution and bathymetry data derived from acoustic surveys of the Nisqually River delta, Washington in 2017 (USGS Field Activity Number 2017-614-FA). Eelgrass and bathymetry data were collected from the R/V George Davidson equipped with a single-beam sonar system and global navigation satellite system (GNSS) receiver. The sonar system consisted of a Biosonics DT-X single-beam echosounder and 420 kHz transducer with a 6-degree beam angle. Depths from the echosounder were computed using sound velocity data measured using a YSI CastAway CTD during the survey. Positioning of the survey vessel was determined at 5 to 10 Hz using a Trimble R7 GNSS receiver and Trimble Zephyr Model 2 antenna operating in real time kinematic (RTK) mode. Differential corrections were transmitted by a VHF radio to the GNSS receiver on the survey vessel at 1-Hz from a GNSS base station placed on a nearby benchmark with known horizontal and vertical coordinates relative to the North American Datum of 1983 (CORS96 realization). Output from the GNSS and sonar systems were combined in real time by the Biosonics DT-X deck unit and output to a computer running HYPACK hydrographic survey software. Navigation information was displayed on a video monitor, allowing the vessel operator to navigate along predefined survey lines spaced at 25-50 m intervals alongshore at speeds of 2-3 m/s. Acoustic backscatter data were analyzed using a custom graphical user interface that implements a signal processing algorithm applied to each sonar sounding that differentiates and extracts the location of the seafloor apart from the presence of vegetation (Stevens and others, 2008). Individual acoustic returns along a survey line were grouped into packets of ten, and eelgrass percent cover was calculated as the fractional percent of acoustic returns that were classified as vegetated within each group, resulting in an estimate of percent cover every 4 to 5 m (depending on the vessel speed). Orthometric elevations relative to the NAVD88 vertical datum were computed using National Geodetic Survey Geoid12a offsets. The average estimated vertical uncertainty of the bathymetric measurements is 5 cm. The point data are provided in a comma-separated text file and are projected in Cartesian coordinates using the Universal Transverse Mercator (UTM), Zone 10 north, meters coordinate system.

This portion of the USGS data release presents eelgrass distributions derived from towed underwater video surveys of the Nisqually River delta, Washington in 2017 (USGS Field Activity Number 2017-614-FA). Eelgrass data were collected from the R/V George Davidson equipped with a towed underwater video system and global navigation satellite system (GNSS) receiver. The underwater video system consisted of a Splashcam standard definition video camera connected to a Sony GV-D1000 video monitor and tape recorder. Positioning of the survey vessel was determined at 1 Hz intervals using a Trimble R7 GNSS receiver and Trimble Zephyr Model 2 antenna. The positioning data from the GNSS were encoded onto the audio track of the digital video recording using Red Hen Systems (RHS) VMS200 hardware. Underwater video data were recorded as the vessel navigated along a series of shore-perpendicular transects at speeds between 1 and 2 knots. The underwater video recording was later reviewed and the presence or absence of eelgrass was determined for each 1-s segment of video tape. These data were used to evaluate the classification of single-beam sonar data acquired during the same time period.

Bed sediment samples were collected in San Pablo Bay and Grizzly Bays on eight days from June through November 2019, to analyze for sediment properties including bulk density, particle size distribution, and percent organic carbon. Sediment samples were collected from a small vessel near pre-established USGS instrument moorings using a Gomex box corer that was subsampled with three push cores (37 mm in diameter) per Gomex core. Six subsamples were collected from the top 5 centimeters (cm) of each push core, a few push cores included the top 8 cm. The top two subsamples were each 0.5 cm thick, and all following subsamples were each 1 cm thick. Push core samples from the first, third, and fifth centimeter depth were analyzed for grain size and percent organic carbon, while all 6 sections were analyzed for bulk density. Data are provided in a comma-delimited values spreadsheet. These data were collected as part of a collaborative project with the USGS California Water Science Center and the USGS Water Mission Area on physical and biological controls on sediment erodibility, funded by the USGS Priority Ecosystems Program for San Francisco Bay and Delta and the USGS Coastal Marine Hazards and Resources Program.

Water samples were collected in San Pablo Bay and Grizzly Bay on five days from June through August 2019. The water samples were collected near pre-established USGS instrument moorings with a peristaltic pump or via a Niskin bottle, deployed off of a small vessel. Data are provided in a comma-delimited values spreadsheet.

These data present suspended particle size distributions collected by the U.S. Geological Survey (USGS) Pacific Coastal and Marine Science Center within two embayments of San Francisco Bay. Data were collected at one site in San Pablo Bay and one site in Grizzly Bay from June through August 2019, by deploying a Sequoia Scientific Laser In-situ Scattering and Transmissometry instrument (LISST 100x) from a small vessel near pre-established USGS instrument moorings. At both sites, data were collected on six dates at three depths, generally near the water surface, at mid depth, and near the sediment bed, for 1-3 minutes at each depth. LISST volume concentrations are most accurate when the optical percent transmission is above 30, as light passing through the sample volume is unlikely to be scattered by more than one particle. One file (ERO19PBP04) was removed due to poor data quality throughout the file. These files contain all samples collected; judgment should be applied when using them. Users are advised to check metadata and instrument information carefully for applicable time periods of specific data.

Hydrodynamic and sediment transport time-series data, including water depth, velocity, turbidity, conductivity, and temperature, were collected by the U.S. Geological Survey (USGS) Pacific Coastal and Marine Science Center within two embayments of San Francisco Bay. Data were collected in San Pablo Bay and Grizzly Bay from June to August 2019 at seven unique stations. Data files are grouped by area (shallows of San Pablo Bay, channel of San Pablo Bay, and shallows of Grizzly Bay). Each shallow site contained a variety of sensors located on two tripods and one surface mooring, while the channel site consisted of one tripod. Users are advised to assess data quality carefully, and to check metadata for instrument information, as platform deployment times and data-processing methods varied.

Time series data of water surface elevation, wave height, water column currents and temperature, and suspended sediment were acquired for 6 weeks on a coral reef off Jurabi Point, Ningaloo Coast UNESCO World Heritage site in Western Australia in support of a study on the circulation and sediment transport patterns of these reefs.

This part of DS 781 presents data for the depth-to-transition map of the Punta Gorda to Point Arena, California, region. The raster data file is included in the "DepthToTransition_PuntaGordaToPointArena.zip," which is accessible from https://doi.org/10.5066/P9PNNI9H. As part of the USGS's California State Waters Mapping Project, a 50-m grid of sediment thickness for the seafloor within the 3-nautical mile limit between Punta Gorda and Point Arena was generated from seismic-reflection data collected between 2010 and 2012, and supplemented with geologic structure (fault) information following the methodology of Wong (2012). Water depths determined from bathymetry data were added to the sediment thickness data to provide information on the depth to base of the post-LGM unit. Reference Cited: Wong, F. L., Phillips, E.L., Johnson, S.Y., and Sliter, R.W., 2012, Modeling of depth to base of Last Glacial Maximum and seafloor sediment thickness for the California State Waters Map Series, eastern Santa Barbara Channel, California: U.S. Geological Survey Open-File Report 2012-1161, 16 p. (available at https://pubs.usgs.gov/of/2012/1161/).

This part of DS 781 presents data for the faults of the Punta Gorda to Point Arena, California, region. The vector data file is included in the "Faults_PuntaGordaToPointArena.zip," which is accessible from https://doi.org/10.5066/P9PNNI9H. Faults in the Punta Gorda and Point Arena region are identified on seismic-reflection data based on abrupt truncation or warping of reflections and (or) juxtaposition of reflection panels with different seismic parameters such as reflection presence, amplitude, frequency, geometry, continuity, and vertical sequence. Faults were primarily mapped by interpretation of seismic reflection profile data collected by the U.S. Geological Survey between 2010 and 2012.

This part of DS 781 presents data for the isopachs of the Punta Gorda to Point Arena, California, region. The vector data file is included in the "Isopachs_PuntaGordaToPointArena.zip," which is accessible from https://doi.org/10.5066/P9PNNI9H. As part of the USGS's California State Waters Mapping Project, a 50-m grid of sediment thickness for the seafloor within the 3-nautical mile limit between Punta Gorda and Point Arena was generated from seismic-reflection data collected between 2010 and 2012, and supplemented with geologic structure (fault) information following the methodology of Wong (2012). Reference Cited: Wong, F. L., Phillips, E.L., Johnson, S.Y., and Sliter, R.W., 2012, Modeling of depth to base of Last Glacial Maximum and seafloor sediment thickness for the California State Waters Map Series, eastern Santa Barbara Channel, California: U.S. Geological Survey Open-File Report 2012-1161, 16 p. (available at https://pubs.usgs.gov/of/2012/1161/).

This part of DS 781 presents data for the sediment-thickness map of the Punta Gorda to Point Arena, California, region. The raster data file is included in the "SedimentThickness_PuntaGordaToPointArena.zip," which is accessible from https://doi.org/10.5066/P9PNNI9H. As part of the USGS's California State Waters Mapping Project, a 50-m grid of sediment thickness for the seafloor within the 3-nautical mile limit between Point Sur and Point Arguello was generated from seismic-reflection data collected between 2010 and 2012, and supplemented with geologic structure (fault) information following the methodology of Wong (2012). Reference Cited: Wong, F. L., Phillips, E.L., Johnson, S.Y., and Sliter, R.W., 2012, Modeling of depth to base of Last Glacial Maximum and seafloor sediment thickness for the California State Waters Map Series, eastern Santa Barbara Channel, California: U.S. Geological Survey Open-File Report 2012-1161, 16 p. (available at https://pubs.usgs.gov/of/2012/1161/).

This part of DS 781 presents data for the transgressive contours of the Punta Gorda to Point Arena, California, region. The vector data file is included in the "TransgressiveContours_PuntaGordaToPointArena.zip," which is accessible from https://doi.org/10.5066/P9PNNI9H. As part of the USGS's California State Waters Mapping Project, a 50-m grid of sediment thickness for the seafloor within the 3-nautical mile limit between Punta Gorda and Point Arena was generated from seismic-reflection data collected between 2010 and 2012, and supplemented with geologic structure (fault) information following the methodology of Wong (2012). Water depths determined from bathymetry data were added to the sediment thickness data to provide information on the depth to base of the post-LGM unit. Reference Cited: Wong, F. L., Phillips, E.L., Johnson, S.Y., and Sliter, R.W., 2012, Modeling of depth to base of Last Glacial Maximum and seafloor sediment thickness for the California State Waters Map Series, eastern Santa Barbara Channel, California: U.S. Geological Survey Open-File Report 2012-1161, 16 p. (available at https://pubs.usgs.gov/of/2012/1161/).

Results of radiocarbon dating of deep-sea (500 m to 700 m) black corals are presented. These corals were collected off the southeastern United States as part of the Southeastern United States Deep-Sea Corals (SEADESC) Initiative.

Tidal water discharge within two breaches constructed in a former flood-control levee of a restored agricultural area in Port Susan, Washington, was measured repeatedly during several tidal cycles. Measurements were made on March 27, 2014, April 16, 2014, May 18, 2014, and May 29, 2014 at breach PSB1, and on May 29, 2014 at breach PSB2. These data were collected using a boat-mounted Teledyne RDI RiverRay 600 kHz acoustic Doppler current profiler (ADCP) or a Teledyne RDI StreamPro 2000 kHz ADCP, depending on date. ADCP transect data were collected and initially reviewed using WinRiver II software and reprocessing and final review was completed with QRev software.

Water level, flow velocity, temperature, salinity, and turbidity were measured in a breach constructed in a flood-protection levee surrounding a restored former agricultural area in Port Susan, Washington, USA, near the mouth of the Stillaguamish River. Data were collected in a breach known as PSB1 at 15-minute intervals from March 21, 2014 to July 1, 2015 using a SonTek Argonaut-SW current meter, an In-Situ Aqua TROLL 200 pressure, conductivity, and temperature sensor, and an FTS DTS-12 turbidity sensor.

Geochemical analysis (including stable boron, boron:calcium ratio, and carbon and oxygen isotopes) were measured from coral cores collected in July 2013 from the shallow reef at Kahekili in Kaanapali, west Maui, Hawaii from scleractinian Porites lobata.

This portion of the data release presents the locations of the temporary ground control points (GCPs) used for the structure-from-motion (SfM) processing of the imagery collected during an unmanned aerial system (UAS) survey of the intertidal zones at Puget Creek and Dickman Mill Park, Tacoma, WA, on 2019-06-03. Twelve temporary ground control points (GCPs) were distributed throughout each survey area to establish survey control. The GCPs consisted of a combination of small square tarps with black-and-white cross patterns and "X" marks placed on the ground using temporary chalk. The GCP positions were measured using post-processed kinematic (PPK) GPS, using corrections from a GPS base station located approximately 5 kilometers from the study area. The GCP positions are presented in a comma-delimited text file.

This portion of the data release presents the raw aerial imagery collected during an Unmanned Aerial System (UAS) survey of the intertidal zone at Puget Creek and Dickman Mill Park, Tacoma, WA, on 2019-06-03. The imagery was acquired using a Department of Interior-owned 3DR Solo quadcopter fitted with a Ricoh GR II digital camera featuring a global shutter. The camera was mounted using a fixed mount on the bottom of the UAS and oriented in an approximately nadir orientation. The UAS was flown on pre-programmed autonomous flight lines at an approximate altitude of 50 meters above ground level (AGL), resulting in a nominal ground-sample-distance (GSD) of 1.3 centimeters per pixel. The flight lines were oriented roughly shore-parallel and were spaced to provide approximately 70 percent overlap between images from adjacent lines. The camera was triggered at 1 Hz using a built-in intervalometer. Flight F01 covered the Puget Creek area; flight F02 covered the Dickman Mill Park area. After acquisition, the images were renamed to include the flight number and acquisition time in the file name. The coordinates of the approximate image acquisition locations were added ('geotagged') to the image metadata (EXIF) using the telemetry log from the UAS onboard single-frequency autonomous GPS. The image EXIF were also updated to include additional information related to the acquisition. Although the images were recorded in both JPG and camera raw (Adobe DNG) formats, only the JPG images are provided in this data release. The data release includes a total of 1,171 JPG images. Images from takeoff and landing sequences were not used for processing and have been omitted from the data release. The images from each flight are provided in a zip file named with the flight number.

This portion of the data release presents topographic point clouds of the intertidal zone at Puget Creek and Dickman Mill Park, Tacoma, WA, derived from structure-from-motion (SfM) processing of aerial imagery collected with an unmanned aerial system (UAS) on 2019-06-03. The point clouds for Puget Creek and Dickman Mill Park contain 74,565,548 and 122,791,637 points, respectively, at an approximate point spacing of 1 point every 2 centimeters. Each point contains an explicit horizontal and vertical coordinate, color, intensity, and classification. Water portions of the point cloud were classified using a polygon digitized from the orthomosaic imagery derived from these surveys (also available in this data release). No other classifications were performed. The raw imagery used to create these point clouds was acquired using a UAS fitted with a Ricoh GR II digital camera featuring a global shutter. The UAS was flown on pre-programmed autonomous flight lines at an approximate altitude of 50 meters above ground level (AGL). The flight lines were oriented roughly shore-parallel and were spaced to provide approximately 70 percent overlap between images from adjacent lines. The camera was triggered at 1 Hz using a built-in intervalometer. The imagery was geotagged using positions from the UAS onboard single-frequency autonomous GPS. Twelve temporary ground control points (GCPs) were distributed throughout each survey area to establish survey control. The GCPs consisted of a combination of small square tarps with black-and-white cross patterns and "X" marks placed on the ground using temporary chalk. The GCP positions were measured using post-processed kinematic (PPK) GPS, using corrections from a GPS base station located approximately 5 kilometers from the study area.

Polygon shapefile showing the footprint boundaries, source agency origins, and resolutions of compiled bathymetric digital elevation models (DEMs) used to construct a continuous, high-resolution DEM of the central portion of San Francisco Bay.

A 1-m resolution, continuous surface, bathymetric digital elevation model (DEM) of the central portion of San Francisco Bay, was constructed from bathymetric surveys collected from 2005 to 2020. In 2014 and 2015 the California Ocean Protection Council (OPC) contracted the collection of bathymetric surveys of large portions of San Francisco Bay. A total of 93 surveys were collected using a combination of multibeam and interferometric side-scan sonar systems. Of those 93 surveys, 75 consist of swaths of data ranging from 18- to just over 100-meters wide. These swaths were separated by data gaps ranging from 10- to just over 300-meters wide. The no-data areas required interpolation to create a continuous surface. The OPC surveys were combined with additional datasets collected by the United States Geological Survey (USGS), National Oceanic and Atmospheric Administration (NOAA), NOAA’s National Ocean Service (NOS), and the United States Army Corps of Engineers (USACE) to create a continuous, high-resolution digital elevation model (DEM). The creation of this DEM refines techniques developed by the USGS to create DEMs from historic bathymetric data, and allow for the creation of a modern-day bathymetric surface that can be compared to earlier surveys to delineate regions of sediment erosion and deposition.

A 1-m resolution, continuous surface, bathymetric digital elevation model (DEM) of the central portion of San Francisco Bay, was constructed from bathymetric surveys collected from 2005 to 2020. In 2014 and 2015 the California Ocean Protection Council (OPC) contracted the collection of bathymetric surveys of large portions of San Francisco Bay. A total of 93 surveys were collected using a combination of multibeam and interferometric side-scan sonar systems. Of those 93 surveys, 75 consist of swaths of data ranging from 18- to just over 100-meters wide. These swaths were separated by data gaps ranging from 10- to just over 300-meters wide. The no-data areas required interpolation to create a continuous surface. The OPC surveys were combined with additional datasets collected by the United States Geological Survey (USGS), National Oceanic and Atmospheric Administration (NOAA), NOAA’s National Ocean Service (NOS), and the United States Army Corps of Engineers (USACE) to create a continuous, high-resolution digital elevation model (DEM). The creation of this DEM refines techniques developed by the USGS to create DEMs from historic bathymetric data, and allow for the creation of a modern-day bathymetric surface that can be compared to earlier surveys to delineate regions of sediment erosion and deposition.

Polygon shapefile showing the footprint boundaries, source agency origins, and resolutions of compiled bathymetric digital elevation models (DEMs) used to construct a continuous, high-resolution DEM of the northern portion of San Francisco Bay.

A 1-m resolution, continuous surface, bathymetric digital elevation model (DEM) of the northern portion of San Francisco Bay, which includes San Pablo Bay, Carquinez Strait, and portions of Suisun Bay, was constructed from bathymetric surveys collected from 1999 to 2016. In 2014 and 2015 the California Ocean Protection Council (OPC) contracted the collection of bathymetric surveys of large portions of San Francisco Bay. A total of 93 surveys were collected using a combination of multibeam and interferometric side-scan sonar systems. Of those 93 surveys, 75 consist of swaths of data ranging from 18- to just over 100-meters wide. These swaths were separated by data gaps ranging from 10- to just over 300-meters wide. The no-data areas required interpolation to create a continuous surface. The OPC surveys were combined with additional datasets collected by the United States Geological Survey (USGS), National Oceanic and Atmospheric Administration (NOAA), NOAA’s National Ocean Service (NOS), and the United States Army Corps of Engineers (USACE) to create a continuous, high-resolution digital elevation model (DEM). The creation of this DEM refines techniques developed by the USGS to create DEMs from historic bathymetric data, and allow for the creation of a modern-day bathymetric surface that can be compared to earlier surveys to delineate regions of sediment erosion and deposition.

A 1-m resolution, continuous surface, bathymetric digital elevation model (DEM) of the northern portion of San Francisco Bay, which includes San Pablo Bay, Carquinez Strait, and portions of Suisun Bay, was constructed from bathymetric surveys collected from 1999 to 2016. In 2014 and 2015 the California Ocean Protection Council (OPC) contracted the collection of bathymetric surveys of large portions of San Francisco Bay. A total of 93 surveys were collected using a combination of multibeam and interferometric side-scan sonar systems. Of those 93 surveys, 75 consist of swaths of data ranging from 18- to just over 100-meters wide. These swaths were separated by data gaps ranging from 10- to just over 300-meters wide. The no-data areas required interpolation to create a continuous surface. The OPC surveys were combined with additional datasets collected by the United States Geological Survey (USGS), National Oceanic and Atmospheric Administration (NOAA), NOAA’s National Ocean Service (NOS), and the United States Army Corps of Engineers (USACE) to create a continuous, high-resolution digital elevation model (DEM). The creation of this DEM refines techniques developed by the USGS to create DEMs from historic bathymetric data, and allow for the creation of a modern-day bathymetric surface that can be compared to earlier surveys to delineate regions of sediment erosion and deposition.

Polygon shapefile showing the footprint boundaries, source agency origins, and resolutions of compiled bathymetric digital elevation models (DEMs) used to construct a continuous, high-resolution DEM of the southern portion of San Francisco Bay.

A 1-m resolution, continuous surface, bathymetric digital elevation model (DEM) of the southern portion of San Francisco Bay, was constructed from bathymetric surveys collected from 2005 to 2020. In 2014 and 2015 the California Ocean Protection Council (OPC) contracted the collection of bathymetric surveys of large portions of San Francisco Bay. A total of 93 surveys were collected using a combination of multibeam and interferometric side-scan sonar systems. Of those 93 surveys, 75 consist of swaths of data ranging from 18- to just over 100-meters wide. These swaths were separated by data gaps ranging from 10- to just over 300-meters wide. The no-data areas required interpolation to create a continuous surface. The OPC surveys were combined with additional datasets collected by the United States Geological Survey (USGS), National Oceanic and Atmospheric Administration (NOAA), NOAA National Ocean Service (NOS), and the United States Army Corps of Engineers (USACE) to create a continuous, high-resolution digital elevation model (DEM). The creation of this DEM refines techniques developed by the USGS to create DEMs from historic bathymetric data, and allow for the creation of a modern-day bathymetric surface that can be compared to earlier surveys to delineate regions of sediment erosion and deposition.

A 1-m resolution, continuous surface, bathymetric digital elevation model (DEM) of the southern portion of San Francisco Bay, was constructed from bathymetric surveys collected from 2005 to 2020. In 2014 and 2015 the California Ocean Protection Council (OPC) contracted the collection of bathymetric surveys of large portions of San Francisco Bay. A total of 93 surveys were collected using a combination of multibeam and interferometric side-scan sonar systems. Of those 93 surveys, 75 consist of swaths of data ranging from 18- to just over 100-meters wide. These swaths were separated by data gaps ranging from 10- to just over 300-meters wide. The no-data areas required interpolation to create a continuous surface. The OPC surveys were combined with additional data sets collected by the United States Geological Survey (USGS), National Oceanic and Atmospheric Administration (NOAA), NOAA National Ocean Service (NOS), and the United States Army Corps of Engineers (USACE) to create a continuous, high-resolution digital elevation model (DEM). The creation of this DEM refines techniques developed by the USGS to create DEMs from historic bathymetric data, and allow for the creation of a modern-day bathymetric surface that can be compared to earlier surveys to delineate regions of sediment erosion and deposition.

This portion of the data release presents the locations of the temporary ground control points (GCPs) used for the structure-from-motion (SfM) processing of the imagery collected during an unmanned aerial system (UAS) survey of the intertidal zone at Lone Tree Point, Kiket Bay, WA on 2019-06-05. Eighteen temporary ground control points (GCPs) were distributed throughout the survey area to establish survey control. The GCPs consisted of a combination of small square tarps with black-and-white cross patterns and "X" marks placed on the ground using temporary chalk. The GCP positions were measured using post-processed kinematic (PPK) GPS, using corrections from a GPS base station located approximately 16 kilometers from the study area. The GCP positions are presented in a comma-delimited text file.

This portion of the data release presents the raw aerial imagery collected during the Unmanned Aerial System (UAS) survey of the intertidal zone at Lone Tree Point, Kiket Bay, WA, on 2019-06-05. The imagery was acquired using a Department of Interior-owned 3DR Solo quadcopter fitted with a Ricoh GR II digital camera featuring a global shutter. The camera was mounted using a fixed mount on the bottom of the UAS and oriented in an approximately nadir orientation. For flights F01, F02, F03, F04, and F05 the UAS was flown on pre-programmed autonomous flight lines at an approximate altitude of 70 meters above ground level (AGL), resulting in a nominal ground-sample-distance (GSD) of 1.8 centimeters per pixel. The flight lines were oriented roughly shore-parallel and were spaced to provide approximately 70 percent overlap between images from adjacent lines. For flight F05, the UAS was flown manually to acquire imagery over areas not mapped in the previous flights. Before each flight, the camera’s digital ISO, aperture, and shutter speed were adjusted for ambient light conditions. For all flights the camera was triggered at 1 Hz using a built-in intervalometer. After acquisition, the images were renamed to include flight number and acquisition time in the file name. The coordinates of the approximate image acquisition location were added ('geotagged') to the image metadata (EXIF) using the telemetry log from the UAS onboard single-frequency autonomous GPS. The image EXIF were also updated to include additional information related to the acquisition. Although the images were recorded in both JPG and camera raw (Adobe DNG) formats, only the JPG images are provided in this data release. The data release includes a total of 1,906 JPG images. Images from takeoff and landing sequences were not used for processing and have been omitted from the data release. The images from each flight are provided in a zip file named with the flight number.

This portion of the data release presents a topographic point cloud of the intertidal zone at Lone Tree Point, Kiket Bay, WA. The point cloud was derived from structure-from-motion (SfM) processing of aerial imagery collected with an unmanned aerial system (UAS) on 2019-06-05. The point cloud has 206,323,353 points with an average point density of 929 points per-square meter. The point cloud is tiled to reduce individual file sizes and is grouped within a zip file for downloading. Each point in the point cloud contains an explicit horizontal and vertical coordinate, color, intensity, and classification. Water portions of the point cloud were classified using a polygon digitized from the orthomosaic imagery derived from these surveys (also available in this data release). No other classifications were performed. The raw imagery used to create these point clouds was acquired using a UAS fitted with a Ricoh GR II digital camera featuring a global shutter. The UAS was flown on pre-programmed autonomous flight lines spaced to provide approximately 70 percent overlap between images from adjacent lines. The camera was triggered at 1 Hz using a built-in intervalometer. The UAS was flown at an approximate altitude of 70 meters above ground level (AGL), resulting in a nominal ground-sample-distance (GSD) of 1.8 centimeters per pixel. The raw imagery was geotagged using positions from the UAS onboard single-frequency autonomous GPS. Eighteen temporary ground control points (GCPs) were distributed throughout the survey area to establish survey control. The GCPs consisted of a combination of small square tarps with black-and-white cross patterns and "X" marks placed on the ground using temporary chalk. The GCP positions were measured using post-processed kinematic (PPK) GPS, using corrections from a GPS base station located approximately 16 kilometers from the study area. The point clouds are formatted in LAZ format (LAS 1.2 specification).

Fine-grained sediment was collected from the banks of Napa River, Sonoma Creek, and tributaries in March 2018 and from shallow nearshore areas of the northern reach of San Francisco Bay in April 2018. Bulk sediment was dated using activities of short-lived cosmogenic radionuclides (beryllium-7, cesium-137, and lead-210). Contents of potentially toxic metals and source-rock-indicative elements, including rare earth elements, were quantified in the fine fraction of sediment (particles less than 0.063 mm diameter). Ratios of stable carbon-13/carbon-12 isotopes and total carbon to total nitrogen were determined in sedimentary organic matter.

A one- and two-dimensional hydrodynamic model of the San Francisco Bay and Delta was constructed using the Delft3D Flexible Mesh Suite (Delft3D FM; Kernkamp and others, 2011; https://www.deltares.nl/en/software/delft3d-flexible-mesh-suite/) to simulate still water levels. Required model input files are provided to run the model for the time period from October 1, 2018, to April 30, 2019. This data release describes the construction and validation of the model application and provides input files suitable to run the model on Delft3D FM Suite 2020.04. Model Description: The San Francisco Bay and Delta Still Water Level Model (SFBD-SWL) utilizes the open-source Delft3D Flexible Mesh Suite (Delft3D FM; Kernkamp and others, 2011; https://www.deltares.nl/en/software/delft3d-flexible-mesh-suite/, 2020.04 release, SVN revision 601351) to compute Still Water Levels (SWLs) in San Francisco Bay and the Sacramento-San Joaquin Delta. SWL captures the effects of meteorological and fluvial forcing on the coastal water levels; however, it excludes the impacts of wave setup and runup on the water level. The model covers the Delta up to the approximate upstream limit of tidal influence and extends seaward to the Pacific Ocean. It must be noted that the main purpose of the model was to simulate SWL in open embayments of the San Francisco Bay. The model utilizes 1D elements used to represent tributaries and rivers flowing into the Bay and Delta. Model schematizations of the Delta (model grid and cross-section profiles) were derived from Delta Simulation Model II (DSM2, California Department of Water Resources, 2013). Topographic and bathymetric datasets from the USGS and California Department of Water Resources were applied across the San Francisco Bay and Delta Hydrodynamic model. In particular, the 2-meter resolution LEAN-corrected topography in the Bay (Buffington and others, 2016) and the seamless 10-meter resolution digital elevation model by Fregoso and others (2017) were applied. Data from the National Land Cover Database Land Cover (CONUS; Homer and others, 2020) were converted to roughness. The unstructured grid consists of more than 185,000 net nodes in the horizontal with a spatial resolution as fine as 100 meters. The 100-meter resolution model network is not fine enough to resolve smaller features such as narrow levees and dams. Therefore, an additional polyline has been included to account for constraining and rerouting effects of local levees and infrastructure. This file provides the location of each subgrid feature and, in combination with the latest topography, describes fine-scale elevations for the hydrodynamic simulations. The model is forced by astronomic tides and remote non-tidal residual (NTR) water levels at the offshore boundaries, fluvial discharges, and wind and atmospheric mean sea level pressure fields at the surface. Offshore Boundaries The model's offshore boundary conditions in the Pacific Ocean are based on 67 measured tidal constituents at San Francisco with spatial variability derived from TPXO 8.0 (Egbert and Erofeeva, 2002). Tidal constituents were calibrated based on the difference between modeled and observed tidal constituents at the NOAA tide stations located throughout the bay. Remote NTR derived from measurements at the San Francisco NOAA tide station (#9414290) are applied uniformly across the ocean boundary. The tidal forcing files are included in the model package, as well as the NTR offshore boundary forcing files for the time period from Oct-2018 to Apr-2019. Discharge Boundaries Fluvial discharges from 16 USGS gauged rivers that flow into the Bay are included in the model (https://waterdata.usgs.gov/nwis/dv/?referred_module=sw). Six fluvial inflows to the Delta are based on Dayflow model outputs (https://data.ca.gov/dataset/dayflow). The discharge forcing files for the time period from Oct-2018 to Apr-2019 are included in the model package. The discharge stations incorporated in the SFBD-SWL are as follows (Name, USGS Station Number): >Coyote Creek, 11172175 >Guadalupe River, 11169025 >Saratoga Creek, 11169500 >San Francisquito Creek, 11164500 >San Mateo Creek, 11162753 >Corte Madera Creek, 11460000 >San Rafael Creek, 11459800 >Novato Creek, 11459500 >Petaluma River, 11459150 >Sonoma Creek, 11458500 >Napa River, 11458000 >Wildcat Creek, 11181400 >San Lorenzo Creek #1, 11181000 >San Lorenzo Creek #2, 11181008 >Alameda Creek #1, 11180500 >Alameda Creek #2, 11179000 Wind and Atmospheric Pressure Meteorological forcing conditions (wind and pressure) provided in this example dataset are based on ERA5 (Hersbach and others, 2020). ERA5 provides hourly estimates of a large number of atmospheric, land, and oceanic climate variables at 30-kilometer resolution. The ERA5 wind and pressure data for the time period from Oct-2018 to Apr-2019 is provided in the model package. Model Validation Measured and simulated water levels for the time period of Oct-2018 to Apr-2019 are provided in SFBD_model_results.zip. Measured water levels (referenced to the vertical datum NAVD88) were obtained from the NOAA tide stations (https://tidesandcurrents.noaa.gov/) within San Francisco Bay. The following list contains the measurement stations and model error statistics during the simulation period of Oct-2018 to Apr-2019 (NOAA Station ID, Station Name, Root Mean Square Error in cm, Mean Absolute Error in cm, Bias in cm). Root-mean-square errors (RMSE) are less than 10 cm for water levels at all observation sites (tide gauges). >9414290, San Francisco, 5.2, 4.0, 0.7 >9414750, Alameda, 6.6, 5.0, 2.4 >9414863, Richmond, 5.4, 4.1, 0.0 >9414523, Redwood City, 7.4, 5.3, -2.0 >9415144, Port Chicago, 7.0, 5.2, -3.0 >9415102, Martinez, 6.5, 5.0, 1.2

This portion of the data release contains information on cores that were collected by the U.S. Geological Survey in Kahana Valley, O'ahu, Hawaii in 2015 and 2017. Sites were cored in order to describe wetland stratigraphy and to identify potential tsunami deposits. These cores contain mud, peat, fluvial sands, and marine carbonate sands, reflecting deposition in a variety of coastal environments. PDF files describe twenty-four (24) gouge and ‘Russian’ cores (hand held, side-filling peat augers) that were collected and described in the field. Cores collected in 2017 were described using the Troels-Smith sediment classification scheme (Troels-Smith, 1955; Nelson, 2015). Another pdf file (Kahana_cores_legend.pdf) contains a core-log legend. A comma-delimited text file (Kahana_sand_thickness.csv) includes tabulated information on the depth and thickness of sand bed K1. In addition, a shapefile (kahana_cores_locations.shp) provides sample locations of the vibracores.

This portion of the data release contains information on vibracores that were collected by the U.S. Geological Survey in Anahola Valley, Kaua'i, Hawai'i in 2015. Sites were cored in order to identify potential tsunami deposits and describe wetland stratigraphy. These vibracores contain mud, peat, volcanic sands, and carbonate sands, reflecting deposition in a variety of coastal environments. PDF files describe eight (8) vibracores that were split, imaged by a line-scanner camera, scanned to generate computed tomagraphic (CT) images, and visually described. Another pdf file (Anahola_cores_legend.pdf) contains a core-log legend. A comma-delimited text file (Anahola_sand_thickness.csv) includes tabulated information on the depth and thickness of sand beds A1, A2, and A3. In addition, a shapefile (anahola_vibracores_2015.shp) provides sample locations of vibracores.

This portion of the data release contains information on vibracores that were collected by the U.S. Geological Survey in Pololu Valley, Island of Hawai'i in 2014. Five sites were cored in order to describe wetland stratigraphy and to identify potential tsunami deposits. These vibracores contain mud, peat, fluvial sands, and marine volcanic sands, reflecting deposition in a variety of coastal environments. Two (2) pdf files (VC1.pdf, VC2.pdf) describe vibracores that were split, imaged by a line-scanner camera, scanned to generate computed tomagraphic (CT) images, and visually described. A detailed description of the upper 150 cm of VC1 using the Troels-Smith sediment classification scheme (Troels-Smith, 1955; Nelson, 2015) is included in VC1.pdf. Another pdf file (Pololu_cores_legend.pdf) contains a core-log legend. Cores VC3, VC4, and VC5 were collected using shorter sections (less than 200 cm) of extra pipe in order to capture the sand layer from the 1946 Aleutian tsunami that inundated the valley (Chague-Goff et al., 2012) and have not been photographed or CT-scanned. A comma-delimited text file (Pololu_sand_thickness.csv) includes tabulated information on the depth and thickness of sand beds P1, P2, and P3. In addition, a shapefile (pololu_vibracores_2014_locations.shp) provides sample locations of vibracores.

This portion of the data release presents radiocarbon age data from 66 samples collected from Anahola Valley (Kaua'i), Kahana Valley (O'ahu), and Pololu Valley (Hawai'i). Sample ages were determined by the National Ocean Sciences Accelerator Mass Spectrometry (NOSAMS) facility. The data are provided in a comma-delimited spreadsheet (.csv).

High-resolution Chirp seismic-reflection data were collected by the U.S. Geological Survey in September 2006 offshore San Francisco, California. Data were collected aboard the R/V Lakota, during field activity L-1-06-SF. Chirp data were collected using an EdgeTech 512 chirp subbottom profiler and recorded with a Triton SB-Logger.

High-resolution single-channel minisparker seismic-reflection data were collected by the U.S. Geological Survey in September and October 2006 offshore Bolinas to San Francisco, California. Data were collected aboard the R/V Lakota, during field activity L-1-06-SF. Minisparker data were collected using a SIG 2-mille minisparker sound source combined with a single-channel streamer, and recorded with a Triton SB-Logger.

This part of the data release presents projected flooding extent polygon (flood masks) shapefiles based on wave-driven total water levels for the State Florida (the Florida Peninsula and the Florida Keys). There are 16 associated flood mask and flood depth shapefiles: one for each of four nearshore wave energy return periods (rp; 10-, 50-, 100-, and 500-years), the current scenario (base) and each of the restoration scenarios (structural_25, structural_05, and ecological_25).

This part of the data release presents projected flooding extent polygon (flood masks) shapefiles based on wave-driven total water levels for the Commonwealth of Puerto Rico. There are 16 associated flood mask and flood depth shapefiles: one for each of four nearshore wave energy return periods (rp; 10-, 50-, 100-, and 500-years), the current scenario (base) and each of the restoration scenarios (structural_25, structural_05, and ecological_25).

This part of the data release presents projected flooding extent polygon (flood masks) shapefiles based on wave-driven total water levels for the Territory of the U.S. Virgin Islands. There are 16 associated flood mask and flood depth shapefiles: one for each of four nearshore wave energy return periods (rp; 10-, 50-, 100-, and 500-years), the current scenario (base) and each of the restoration scenarios (structural_25, structural_05, and ecological_25).

This data set consists of physics-based Delft3D-FLOW and SWAN hydrodynamic models input files used to study the wave-induced 3D flow over spur-and-groove (SAG) formations. SAG are a common and impressive characteristic of coral reefs. They are composed of a series of submerged shore-normal coral ridges (spurs) separated by shore-normal patches of sediment (grooves) on the fore reef of coral reef environments. Although their existence and geometrical properties are well documented, the literature concerning the hydrodynamics around them is sparse. Here, the three-dimensional flow patterns over SAG formations, and a sensitivity of those patterns to waves, currents, and SAG geometry were examined. Shore-normal shoaling waves over SAG formations were shown to drive two circulation cells: 1) a cell on the lower fore reef with offshore flow over the spur and onshore flow over the groove, except near the seabed where velocities were always onshore; and 2) a cell on the upper fore reef with offshore surface velocities and onshore bottom currents, which result in depth-averaged onshore and offshore flow over the spurs and grooves, respectively. These input files accompany the modeling conducted for the following publication: da Silva, R.F., Storlazzi, C.D., Rogers, J.S., Reyns, J., and McCall, R., 2020, Modeling three-dimensional flow over spur-and-groove morphology: Coral Reefs, https://doi.org/10.1007/s00338-020-02011-8.

This data release contains information on computed tomography (CT) images of a vibracore that was collected by the U.S. Geological Survey in 2019. A site next to the San Lorenzo River in Henry Cowell Redwoods State Park, California, was cored to understand the history of recent vertical incision and floodplain abandonment. The core was split into 3 segments after collection. Each segment was scanned using a Geotek Rotating X-ray CT (RXCT) System and CT reconstruction was performed using Geotek reconstruction software. Geotek CT_Quickview software was used to select a representative down-core orthogonal slice from each core segment and the Geotek AddRuler software was used to display core-lengths in each image. The downcore orthogonal slice CT images of each core segment are included as three separate TIFF files. Each TIFF file is accompanied by a software-proprietary XML file that contains the x-ray scanning configuration and settings in addition to dimensional data of the TIFF images.

This data layer (PAC_CLC.txt) is one of five point coverages of known sediment samples, inspections, and probes from the usSEABED data collection for the U.S Pacific continental margin integrated using the software system dbSEABED. This data layer represents the calculated (CLC) output of the dbSEABED mining software. Data in this file extend variables determined through the data extraction (EXT) and data parsing (PRS) processes of dbSEABED, calculated using empirical relations or known functions. The CLC data is the most derivative and least accurate of the usSEABED data files and should be used with caution; however, many users may appreciate that it extends the coverage of map areas with attributes, especially physical properties attributes. Please refer to the dbSEABED page (https://pubs.usgs.gov/ds/2006/182/dbseabed.html), and the Frequently Asked Questions (https://pubs.usgs.gov/ds/2006/182/faq.html) pages for more information on the calculation process. This file contains the same data fields as the extracted (PAC_EXT) and parsed (PAC_PRS) data files, and the three files may be combined.

This data layer (PAC_CMP.txt) is one of five point coverages of known sediment samples, inspections, and probes from the usSEABED data collection for the U.S. Pacific continental margin integrated using the software system dbSEABED. This data file gives numeric data about selected components (for example, minerals, rock type, microfossils, and benthic biota) and sea floor features (for example, bioturbation, structure, and ripples) at a given site. Values in the attribute fields represent the membership to that attribute's fuzzy set. For components such as minerals, rocks, micro-biota and plants, and (or) epifauna and infauna, corals and other geologic and biologic information, the value depends on sentence structure and other components in description. For features (denoted by an '_F') such as ripples, ophiuroids, sponges, shrimp, worm tubes, lamination, lumps, grading, and (or) bioturbation, the value of the fuzzy set depends on the development of the attribute. Only the relative fuzzy presence of components and features can be determined; the absence of information does not indicate a lack of the attribute, only lack of information about that attribute. Table 5 (https://pubs.usgs.gov/ds/2006/182/table5.html) in the Larger_Work_Citation gives more information about the words or phrases that trigger each component and feature.

The facies data layer (PAC_FAC.txt) is one of five point coverages of known sediment samples, inspections, and probes from the usSEABED data collection for the U.S. Pacific margin, integrated using the software system dbSEABED. The facies data layer (PAC_FAC.txt) represents concatenated information about components (minerals and rock type), genesis (igneous, metamorphic, carbonate, terrigenous), and other appropriate groupings of information about the sea floor. These data are parsed from written descriptions from cores, grabs, photographs, and videos, and may apply only to a subsample as denoted by the Top, Bottom, and SamplePhase fields. The value "0" in a defined facies field does not necessarily imply lack of the components defining that field, but may imply a lack of data for that field. Table 6 (https://pubs.usgs.gov/ds/2006/182/table6.html) in the Larger_Work_Citation gives for a list of the facies, the contributing components, and relative weights.

ArcInfo GRID format data generated from the 2000 multibeam sonar survey of Crater Lake, Oregon. The data include high-resolution bathymetry and calibrated acoustic backscatter. Data are also available as ASCII xyz format (see data download page of https://doi.org/10.3133/ds72)

This part of DS 781 presents data for the acoustic-backscatter map of Offshore of Aptos map area, California. Backscatter data are provided as two separate grids depending on mapping system and processing method. This metadata file refers to the data included in "BackscatterB_EM300_OffshoreAptos.zip," which is accessible from https://doi.org/10.5066/F7K35RQB. These data accompany the pamphlet and map sheets of Cochrane, G.R., Johnson, S.Y., Dartnell, P., Greene, H.G., Erdey, M.D, Dieter, B.E., Golden, N.E., Hartwell, S.R., Ritchie, A.C., Kvitek, r.G., Maier, K.L., Endris, C.A., Davenport, C.W., Watt, J.T., Sliter, R.W., Finlayson, D.P., and Krigsman, L.M., (G.R. Cochrane and S.A. Cochran, eds.), 2016, California State Waters Map Series—Offshore of Aptos, California: U.S. Geological Survey Open-File Report 2016–1025, 43 p., 10 sheets, scale 1:24,000, https://doi.org/10.3133/ofr20161025. The acoustic-backscatter map of Offshore of Aptos, California was generated from backscatter data collected by the U.S. Geological Survey (USGS) and by Monterey Bay Aquarium Research Institute (MBARI). Mapping was completed between 1998 and 2009, using a combination of a 234-kHz SWATHplus bathymetric sidescan-sonar system and a 30-kHz Simrad EM-300 multibeam echosounder. Within the final imagery, brighter tones indicate higher backscatter intensity, and darker tones indicate lower backscatter intensity. The intensity represents a complex interaction between the acoustic pulse and the seafloor, as well as characteristics within the shallow subsurface, providing a general indication of seafloor texture and composition. Backscatter intensity depends on the acoustic source level; the frequency used to image the seafloor; the grazing angle; the composition and character of the seafloor, including grain size, water content, bulk density, and seafloor roughness; and some biological cover. Harder and rougher bottom types such as rocky outcrops or coarse sediment typically return stronger intensities (high backscatter, lighter tones), whereas softer bottom types such as fine sediment return weaker intensities (low backscatter, darker tones).

This part of DS 781 presents data for the shaded-relief map of Offshore of Aptos map area, California. Shaded-relief data are provided as two separate grids depending on mapping agency and processing method. This metadata file refers to the data included in "BathymetryAHS_USGS_OffshoreAptos.zip," which is accessible from https://doi.org/10.5066/F7K35RQB. These data accompany the pamphlet and map sheets of Cochrane, G.R., Johnson, S.Y., Dartnell, P., Greene, H.G., Erdey, M.D, Dieter, B.E., Golden, N.E., Hartwell, S.R., Ritchie, A.C., Kvitek, r.G., Maier, K.L., Endris, C.A., Davenport, C.W., Watt, J.T., Sliter, R.W., Finlayson, D.P., and Krigsman, L.M., (G.R. Cochrane and S.A. Cochran, eds.), 2016, California State Waters Map Series—Offshore of Aptos, California: U.S. Geological Survey Open-File Report 2016–1025, 43 p., 10 sheets, scale 1:24,000, https://doi.org/10.3133/ofr20161025. The bathymetry and shaded-relief maps of Offshore of Aptos, California, were generated from bathymetry data collected by the U.S. Geological Survey (USGS) and by California State University, Monterey Bay (CSUMB). Mapping was completed between 2006 and 2009 using a combination of a 244-kHz Reson 8101 multibeam echosounder and a 234-kHz SEA SWATHplus bathymetric sidescan-sonar system. The mapping missions collected bathymetry data from about the 10-m isobath to beyond the 3-nautical-mile limit of California's State Waters.

This part of DS 781 presents data for the bathymetry map of Offshore of Aptos map area, California. Bathymetry data are provided as two separate grids depending on mapping agency and processing method. This metadata file refers to the data included in "BathymetryA_USGS_OffshoreAptos.zip" which are accessible from https://doi.org/10.5066/F7K35RQB. These data accompany the pamphlet and map sheets of Cochrane, G.R., Johnson, S.Y., Dartnell, P., Greene, H.G., Erdey, M.D, Dieter, B.E., Golden, N.E., Hartwell, S.R., Ritchie, A.C., Kvitek, r.G., Maier, K.L., Endris, C.A., Davenport, C.W., Watt, J.T., Sliter, R.W., Finlayson, D.P., and Krigsman, L.M., (G.R. Cochrane and S.A. Cochran, eds.), 2016, California State Waters Map Series—Offshore of Aptos, California: U.S. Geological Survey Open-File Report 2016–1025, 43 p., 10 sheets, scale 1:24,000, https://doi.org/10.3133/ofr20161025. The bathymetry and shaded-relief maps of Offshore of Aptos, California, were generated from bathymetry data collected by the U.S. Geological Survey (USGS) and by California State University, Monterey Bay (CSUMB). Mapping was completed between 2006 and 2009 using a combination of a 244-kHz Reson 8101 multibeam echosounder and a 234-kHz SEA SWATHplus bathymetric sidescan-sonar system. The mapping missions collected bathymetry data from about the 10-m isobath to beyond the 3-nautical-mile limit of California's State Waters. NOTE: The horizontal datum of this bathymetry data (NAD83) differs from the horizontal datum of other layers in this data release (WGS84).

This part of DS 781 presents data for the shaded-relief map of Offshore of Aptos map area, California. Shaded-relief data are provided as two separate grids depending on mapping agency and processing method. This metadata file refers to the data included in "BathymetryBHS_CSUMB_OffshoreAptos.zip," which is accessible from https://doi.org/10.5066/F7K35RQB. These data accompany the pamphlet and map sheets of Cochrane, G.R., Johnson, S.Y., Dartnell, P., Greene, H.G., Erdey, M.D, Dieter, B.E., Golden, N.E., Hartwell, S.R., Ritchie, A.C., Kvitek, r.G., Maier, K.L., Endris, C.A., Davenport, C.W., Watt, J.T., Sliter, R.W., Finlayson, D.P., and Krigsman, L.M., (G.R. Cochrane and S.A. Cochran, eds.), 2016, California State Waters Map Series—Offshore of Aptos, California: U.S. Geological Survey Open-File Report 2016–1025, 43 p., 10 sheets, scale 1:24,000, https://doi.org/10.3133/ofr20161025. The bathymetry and shaded-relief maps of Offshore Aptos, California, were generated from bathymetry data collected by the U.S. Geological Survey (USGS) and by California State University, Monterey Bay (CSUMB). Mapping was completed between 2006 and 2009 using a combination of a 244-kHz Reson 8101 multibeam echosounder and a 234-kHz SEA SWATHplus bathymetric sidescan-sonar system. The mapping missions collected bathymetry data from about the 10-m isobath to beyond the 3-nautical-mile limit of California's State Waters.

This part of DS 781 presents data for the bathymetry map of Offshore of Aptos map area, California. Bathymetry data are provided as two separate grids depending on mapping agency and processing method. This metadata file refers to the data included in "BathymetryB_CSUMB_OffshoreAptos.zip" which are accessible from https://doi.org/10.5066/F7K35RQB. These data accompany the pamphlet and map sheets of Cochrane, G.R., Johnson, S.Y., Dartnell, P., Greene, H.G., Erdey, M.D, Dieter, B.E., Golden, N.E., Hartwell, S.R., Ritchie, A.C., Kvitek, r.G., Maier, K.L., Endris, C.A., Davenport, C.W., Watt, J.T., Sliter, R.W., Finlayson, D.P., and Krigsman, L.M., (G.R. Cochrane and S.A. Cochran, eds.), 2016, California State Waters Map Series—Offshore of Aptos, California: U.S. Geological Survey Open-File Report 2016–1025, 43 p., 10 sheets, scale 1:24,000, https://doi.org/10.3133/ofr20161025. The bathymetry and shaded-relief maps of Offshore Aptos, California, were generated from bathymetry data collected by the U.S. Geological Survey (USGS) and by California State University, Monterey Bay (CSUMB). Mapping was completed between 2006 and 2009 using a combination of a 244-kHz Reson 8101 multibeam echosounder and a 234-kHz SEA SWATHplus bathymetric sidescan-sonar system. The mapping missions collected bathymetry data from about the 10-m isobath to beyond the 3-nautical-mile limit of California's State Waters. NOTE: The horizontal datum of this bathymetry data (NAD83) differs from the horizontal datum of other layers in this data release (WGS84).

This part of DS 781 presents data for the bathymetric contours for the Offshore of Aptos map area, California. The vector data file is included in "Contours_OffshoreAptos.zip," which is accessible from https://doi.org/10.5066/F7K35RQB. These data accompany the pamphlet and map sheets of Cochrane, G.R., Johnson, S.Y., Dartnell, P., Greene, H.G., Erdey, M.D, Dieter, B.E., Golden, N.E., Hartwell, S.R., Ritchie, A.C., Kvitek, r.G., Maier, K.L., Endris, C.A., Davenport, C.W., Watt, J.T., Sliter, R.W., Finlayson, D.P., and Krigsman, L.M., (G.R. Cochrane and S.A. Cochran, eds.), 2016, California State Waters Map Series—Offshore of Aptos, California: U.S. Geological Survey Open-File Report 2016–1025, 43 p., 10 sheets, scale 1:24,000, https://doi.org/10.3133/ofr20161025. 10-m interval contours of the Offshore Aptos map area, California, were generated from bathymetry data collected by the U.S. Geological Survey (USGS) and by California State University, Monterey Bay (CSUMB). Mapping was completed between 2006 and 2009 using a combination of a 244-kHz Reson 8101 multibeam echosounder and a 234-kHz SEA SWATHplus bathymetric sidescan-sonar system. The mapping missions collected bathymetry data from about the 10-m isobath to beyond the 3-nautical-mile limit of California's State Waters.

This part of DS 781 presents data for the folds for the geologic and geomorphic map of the Offshore Aptos map area, California. The vector data file is included in "Folds_OffshoreAptos.zip," which is accessible from https://doi.org/10.5066/F7K35RQB. These data accompany the pamphlet and map sheets of Cochrane, G.R., Johnson, S.Y., Dartnell, P., Greene, H.G., Erdey, M.D, Dieter, B.E., Golden, N.E., Hartwell, S.R., Ritchie, A.C., Kvitek, r.G., Maier, K.L., Endris, C.A., Davenport, C.W., Watt, J.T., Sliter, R.W., Finlayson, D.P., and Krigsman, L.M., (G.R. Cochrane and S.A. Cochran, eds.), 2016, California State Waters Map Series—Offshore of Aptos, California: U.S. Geological Survey Open-File Report 2016–1025, 43 p., 10 sheets, scale 1:24,000, https://doi.org/10.3133/ofr20161025. Folds in the Offshore of Aptos map area are identified on seismic-reflection data based on abrupt truncation or warping of reflections and (or) juxtaposition of reflection panels with different seismic parameters such as reflection presence, amplitude, frequency, geometry, continuity, and vertical sequence. Folds were primarily mapped by interpretation of seismic reflection profile data from USGS field activity S-N1-09-MB. The seismic reflection profiles were primarily collected in 2009.

This part of DS 781 presents data for the geologic and geomorphic map of the Offshore Aptos map area, California. The vector data file is included in "Geology_OffshoreAptos.zip," which is accessible from https://doi.org/10.5066/F7K35RQB. These data accompany the pamphlet and map sheets of Cochrane, G.R., Johnson, S.Y., Dartnell, P., Greene, H.G., Erdey, M.D, Dieter, B.E., Golden, N.E., Hartwell, S.R., Ritchie, A.C., Kvitek, r.G., Maier, K.L., Endris, C.A., Davenport, C.W., Watt, J.T., Sliter, R.W., Finlayson, D.P., and Krigsman, L.M., (G.R. Cochrane and S.A. Cochran, eds.), 2016, California State Waters Map Series—Offshore of Aptos, California: U.S. Geological Survey Open-File Report 2016–1025, 43 p., 10 sheets, scale 1:24,000, https://doi.org/10.3133/ofr20161025. Marine geology and geomorphology were mapped in the Offshore of Aptos map area, California, from approximate Mean High Water (MHW) to the 3-nautical-mile limit of California''s State Waters. Offshore geologic units were delineated on the basis of integrated analyses of adjacent onshore geology with multibeam bathymetry and backscatter imagery, seafloor-sediment and rock samples, digital camera and video imagery, and high-resolution seismic-reflection profiles.

This part of DS 781 presents data for the seafloor-character map Offshore of Aptos, California. Seafloor-character data are provided as two separate grids depending on resolution of the mapping system and processing method. This metadata file refers to the data included in "SeafloorCharacter_2m_OffshoreAptos.zip," which is accessible from https://doi.org/10.5066/F7K35RQB. These data accompany the pamphlet and map sheets of Cochrane, G.R., Johnson, S.Y., Dartnell, P., Greene, H.G., Erdey, M.D, Dieter, B.E., Golden, N.E., Hartwell, S.R., Ritchie, A.C., Kvitek, r.G., Maier, K.L., Endris, C.A., Davenport, C.W., Watt, J.T., Sliter, R.W., Finlayson, D.P., and Krigsman, L.M., (G.R. Cochrane and S.A. Cochran, eds.), 2016, California State Waters Map Series—Offshore of Aptos, California: U.S. Geological Survey Open-File Report 2016–1025, 43 p., 10 sheets, scale 1:24,000, https://doi.org/10.3133/ofr20161025. This raster-format seafloor-character map shows five substrate classes Offshore of Aptos, California. The substrate classes mapped in this area have been colored to indicate which of the following California Marine Life Protection Act depth zones and the Coastal and Marine Ecological Classification Standard (CMECS) slope classes they belong: Depth Zone 2 (intertidal to 30 m), Depth Zone 3 (30 to 100 m), Depth Zone 4 (100 to 200 m), Slope Class 1 (0 degrees - 5 degrees; flat), and Slope Class 2 (5 degrees - 30 degrees; sloping). Depth Zone 1 (intertidal), Depth Zone 5 (greater than 200 m), and Slopes Classes 3-4 (greater than 30 degrees) are not present in the region covered by this block. The map is created using a supervised classification method described by Cochrane (2008). References Cited: Cochrane, G.R., 2008, Video-supervised classification of sonar data for mapping seafloor habitat, in Reynolds, J.R., and Greene, H.G., eds., Marine habitat mapping technology for Alaska: Fairbanks, University of Alaska, Alaska Sea Grant College Program, p. 185-194, accessed April 5, 2011, at http://doc.nprb.org/web/research/research%20pubs/615_habitat_mapping_workshop/Individual%20Chapters%20High-Res/Ch13%20Cochrane.pdf.

This part of DS 781 presents data for the seafloor-character map Offshore of Aptos, California. Seafloor-character data are provided as two separate grids depending on resolution of the mapping system and processing method. This metadata file refers to the data included in "SeafloorCharacter_5m_OffshoreAptos.zip," which is accessible from https://doi.org/10.5066/F7K35RQB. These data accompany the pamphlet and map sheets of Cochrane, G.R., Johnson, S.Y., Dartnell, P., Greene, H.G., Erdey, M.D, Dieter, B.E., Golden, N.E., Hartwell, S.R., Ritchie, A.C., Kvitek, r.G., Maier, K.L., Endris, C.A., Davenport, C.W., Watt, J.T., Sliter, R.W., Finlayson, D.P., and Krigsman, L.M., (G.R. Cochrane and S.A. Cochran, eds.), 2016, California State Waters Map Series—Offshore of Aptos, California: U.S. Geological Survey Open-File Report 2016–1025, 43 p., 10 sheets, scale 1:24,000, https://doi.org/10.3133/ofr20161025. This raster-format seafloor character map shows three substrate classes Offshore of Aptos, California. The substrate classes mapped in this area have been further divided into the following California Marine Life Protection Act depth zones and slope classes: Depth Zone 3 (30 to 100 m), Depth Zone 4 (100 to 200 m), Depth Zone 5 (greater than 200 m), Slope Class 1 (0 degrees - 5 degrees), and Slope Class 2 (5 degrees - 30 degrees). Depth Zones 1-2 (intertidal to 30 m), and Slopes Classes 3-4 (greater than 30 degrees) are not present in the region covered by this dataset. The map is created using a supervised classification method described by Cochrane (2008). References Cited: Cochrane, G.R., 2008, Video-supervised classification of sonar data for mapping seafloor habitat, in Reynolds, J.R., and Greene, H.G., eds., Marine habitat mapping technology for Alaska: Fairbanks, University of Alaska, Alaska Sea Grant College Program, p. 185-194, accessed April 5, 2011, at http://doc.nprb.org/web/research/research%20pubs/615_habitat_mapping_workshop/Individual%20Chapters%20High-Res/Ch13%20Cochrane.pdf.

This part of DS 781 presents data for the depth-to-transition map of the Point Sur to Point Arguello, California, region. The raster data file is included in the “DepthToTransition_PointSurToPointArguello.zip,” which is accessible from https://doi.org/10.5066/P97CZ0T7. As part of the USGS's California State Waters Mapping Project, a 50-m grid of sediment thickness for the seafloor within the 3-nautical mile limit between Point Sur and Point Arguello was generated from seismic-reflection data collected between 2008 and 2014, and supplemented with geologic structure (fault and fold) information following the methodology of Wong (2012). Water depths determined from bathymetry data were added to the sediment thickness data to provide information on the depth to base of the post-LGM unit. Reference Cited: Wong, F. L., Phillips, E.L., Johnson, S.Y., and Sliter, R.W., 2012, Modeling of depth to base of Last Glacial Maximum and seafloor sediment thickness for the California State Waters Map Series, eastern Santa Barbara Channel, California: U.S. Geological Survey Open-File Report 2012-1161, 16 p. (available at https://pubs.usgs.gov/of/2012/1161/).

This part of DS 781 presents data for the faults of the Point Sur to Point Arguello, California, region. The vector data file is included in the “Faults_PointSurToPointArguello.zip,” which is accessible from https://doi.org/10.5066/P97CZ0T7. Faults in the Point Sur to Point Arguello region are identified on seismic-reflection data based on abrupt truncation or warping of reflections and (or) juxtaposition of reflection panels with different seismic parameters such as reflection presence, amplitude, frequency, geometry, continuity, and vertical sequence. Faults were primarily mapped by interpretation of seismic reflection profile data collected by the U.S. Geological Survey between 2008 and 2014.

This part of DS 781 presents data for the folds of the Point Sur to Point Arguello, California, region. The vector data file is included in the “Folds_PointSurToPointArguello.zip,” which is accessible from https://doi.org/10.5066/P97CZ0T7. Folds in the Point Sur to Point Arguello region are identified on seismic-reflection data based on warping and tilting of reflections. Folds were primarily mapped by interpretation of seismic reflection profile data collected by the U.S. Geological Survey between 2008 and 2014 and interpretation of high-resolution bathymetry data.

This part of DS 781 presents data for the isopachs of the Point Sur to Point Arguello, California, region. The vector data file is included in the “Isopachs_PointSurToPointArguello.zip,” which is accessible from https://doi.org/10.5066/P97CZ0T7. As part of the USGS's California State Waters Mapping Project, a 50-m grid of sediment thickness for the seafloor within the 3-nautical mile limit between Point Sur and Point Arguello was generated from seismic-reflection data collected between 2008 and 2014, and supplemented with geologic structure (fault and fold) information following the methodology of Wong (2012). Reference Cited: Wong, F. L., Phillips, E.L., Johnson, S.Y., and Sliter, R.W., 2012, Modeling of depth to base of Last Glacial Maximum and seafloor sediment thickness for the California State Waters Map Series, eastern Santa Barbara Channel, California: U.S. Geological Survey Open-File Report 2012-1161, 16 p. (available at https://pubs.usgs.gov/of/2012/1161/).

This part of DS 781 presents data for the sediment-thickness map of the Point Sur to Point Arguello, California, region. The raster data file is included in the “SedimentThickness_PointSurToPointArguello.zip,” which is accessible from https://doi.org/10.5066/P97CZ0T7. As part of the USGS's California State Waters Mapping Project, a 50-m grid of sediment thickness for the seafloor within the 3-nautical mile limit between Point Sur and Point Arguello was generated from seismic-reflection data collected between 2008 and 2014, and supplemented with geologic structure (fault and fold) information following the methodology of Wong (2012). Reference Cited: Wong, F. L., Phillips, E.L., Johnson, S.Y., and Sliter, R.W., 2012, Modeling of depth to base of Last Glacial Maximum and seafloor sediment thickness for the California State Waters Map Series, eastern Santa Barbara Channel, California: U.S. Geological Survey Open-File Report 2012-1161, 16 p. (available at https://pubs.usgs.gov/of/2012/1161/).

This part of DS 781 presents data for the transgressive contours of the Point Sur to Point Arguello, California, region. The vector data file is included in the “TransgressiveContours_PointSurToPointArguello.zip,” which is accessible from https://doi.org/10.5066/P97CZ0T7. As part of the USGS's California State Waters Mapping Project, a 50-m grid of sediment thickness for the seafloor within the 3-nautical mile limit between Point Sur and Point Arguello was generated from seismic-reflection data collected between 2008 and 2014, and supplemented with geologic structure (fault and fold) information following the methodology of Wong (2012). Water depths determined from bathymetry data were added to the sediment thickness data to provide information on the depth to base of the post-LGM unit. Reference Cited: Wong, F. L., Phillips, E.L., Johnson, S.Y., and Sliter, R.W., 2012, Modeling of depth to base of Last Glacial Maximum and seafloor sediment thickness for the California State Waters Map Series, eastern Santa Barbara Channel, California: U.S. Geological Survey Open-File Report 2012-1161, 16 p. (available at https://pubs.usgs.gov/of/2012/1161/).

The Cascadia Tsunami Deposit Database contains data on the location and sedimentological properties of tsunami deposits found along the Cascadia margin. Data have been compiled from 52 studies, documenting 59 sites from northern California to Vancouver Island, British Columbia that contain known or potential tsunami deposits. Bibliographical references are provided for all sites included in the database. Cascadia tsunami deposits are usually seen as anomalous sand layers in coastal marsh or lake sediments. The studies cited in the database use numerous criteria based on sedimentary characteristics to distinguish tsunami deposits from sand layers deposited by other processes, such as river flooding and storm surges. Several studies cited in the database contain evidence for more than one tsunami at a site. Data categories include age, thickness, layering, grainsize, and other sedimentological characteristics of Cascadia tsunami deposits. The database documents the variability observed in tsunami deposits found along the Cascadia margin.

Between 1993 and 1997, the U.S. Geological Survey acquired high-resolution, marine seismic-reflection profile data across submerged portions of known and inferred upper crustal fault zones throughout the greater San Francisco Bay area. This particular dataset was acquired in 1995 during USGS Field Activity G2-95-SF using the vessel Robert Gray. The dataset includes navigational data in ASCII format, gif images of the seismic-profile lines, and seismic data in industry-standard SEG-Y format. These data are also available via GeoMapApp (http://www.geomapapp.org/) and Virtual Ocean (http://www.virtualocean.org/) earth science exploration and visualization applications.

Between 1993 and 1997, the U.S. Geological Survey acquired high-resolution, marine seismic-reflection profile data across submerged portions of known and inferred upper crustal fault zones throughout the greater San Francisco Bay area. This particular dataset was acquired in 1995 during USGS Field Activity J2-94-SF using the vessel David Johnson. The dataset includes navigational data in ASCII format, gif images of the seismic-profile lines, and seismic data in industry-standard SEG-Y format. These data are also available via GeoMapApp (http://www.geomapapp.org/) and Virtual Ocean (http://www.virtualocean.org/) earth science exploration and visualization applications.

Between 1993 and 1997, the U.S. Geological Survey acquired high-resolution, marine seismic-reflection profile data across submerged portions of known and inferred upper crustal fault zones throughout the greater San Francisco Bay area. This particular dataset was acquired in 1997 during USGS Field Activity J4-97-SF using the vessel David Johnston. The dataset includes navigational data in ASCII format, gif images of the seismic-profile lines, and seismic data in industry-standard SEG-Y format. These data are also available via GeoMapApp (http://www.geomapapp.org/) and Virtual Ocean (http://www.virtualocean.org/) earth science exploration and visualization applications.

Between 1993 and 1997, the U.S. Geological Survey acquired high-resolution, marine seismic-reflection profile data across submerged portions of known and inferred upper crustal fault zones throughout the greater San Francisco Bay area. This particular dataset was acquired in 1993 during USGS Field Activity J8-93-SF using the vessel David Johnston. The dataset includes navigational data in ASCII format, gif images of the seismic-profile lines, and seismic data in industry-standard SEG-Y format. These data are also available via GeoMapApp (http://www.geomapapp.org/) and Virtual Ocean (http://www.virtualocean.org/) earth science exploration and visualization applications.

A benthic habitat polygon coverage has been created of the coral reef ecosystem on the south shore of Moloka'i. Polygons were hand-digitized from visual interpretation of aerial photography and SHOALS bathymetry data. We also utilized in situ knowledge from towed instruments, underwater photography and videography, and diver and snorkeler observations. The polygons have attributes for Main Structure/Substrate, Dominant Structure/Substrate, Major Biological Cover, Percent of Major Biological Cover, Reef Zone, and Unique ID, and measurements of acreage, area (m2) and perimeter (m) of each polygon.

Acoustic backscatter data were collected by the U.S. Geological Survey in July 2008 in the northern Santa Barbara Channel in southern California. Data were collected aboard the R/V Parke Snavely, during USGS Field Activity S-9-08-SC, using a bathymetric sidescan system.

Bathymetry data were collected by the U.S. Geological Survey in July 2008 in the northern Santa Barbara Channel in southern California. Data were collected aboard the R/V Parke Snavely, during USGS Field Activity S-9-08-SC, using a bathymetric sidescan system.

A benthic habitat polygon coverage has been created of the coral reef ecosystem within the U.S. Coral Reef Task Force Watershed Partnership Initiative Kaanapali priority study area and the State of Hawaii Kahekili Herbivore Fisheries Management Area, West-Central Maui, Hawaii. Polygons were hand-digitized from visual interpretation of QuickBird-2 satellite imagery (2005), and SHOALS bathymetry data. We also utilized in situ knowledge from underwater photography and videography (2002-2011), side-scan sonar data, and diver and snorkeler observations. The polygons have attributes for Main Structure/Substrate, Dominant Structure/Substrate, Major Biological Cover, Percent of Major Biological Cover, Reef Zone, Unique ID, and measurements of Area (in square meters) of each polygon.

The Arctic Coastal Plain of northern Alaska is an area of strategic economic importance to the United States, is home to remote Native American communities, and encompasses unique habitats of global significance. Coastal erosion along the north coast of Alaska is chronic, widespread, may be accelerating, and is threatening defense and energy-related infrastructure, natural shoreline habitats, and Native communities. There is an increased demand for accurate information regarding past and present shoreline changes across the United States. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline data along sandy shores of the conterminous United States and parts of Alaska and Hawaii under the National Assessment of Shoreline Change project. There is no widely accepted standard for analyzing shoreline change. Existing shoreline data measurements and rate calculation methods vary from study to study and prevent combining results into state-wide or regional assessments. The impetus behind the National Assessment project was to develop a standardized method of measuring changes in shoreline position that is consistent from coast to coast. The goal was to facilitate the process of periodically and systematically updating the results in an internally consistent manner.

The Arctic Coastal Plain of northern Alaska is an area of strategic economic importance to the United States, is home to remote Native American communities, and encompasses unique habitats of global significance. Coastal erosion along the north coast of Alaska is chronic, widespread, may be accelerating, and is threatening defense and energy-related infrastructure, natural shoreline habitats, and Native communities. There is an increased demand for accurate information regarding past and present shoreline changes across the United States. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline data along sandy shores of the conterminous United States and parts of Alaska and Hawaii under the National Assessment of Shoreline Change project. There is no widely accepted standard for analyzing shoreline change. Existing shoreline data measurements and rate calculation methods vary from study to study and prevent combining results into state-wide or regional assessments. The impetus behind the National Assessment project was to develop a standardized method of measuring changes in shoreline position that is consistent from coast to coast. The goal was to facilitate the process of periodically and systematically updating the results in an internally consistent manner.

The Arctic Coastal Plain of northern Alaska is an area of strategic economic importance to the United States, is home to remote Native American communities, and encompasses unique habitats of global significance. Coastal erosion along the north coast of Alaska is chronic, widespread, may be accelerating, and is threatening defense and energy-related infrastructure, natural shoreline habitats, and Native communities. There is an increased demand for accurate information regarding past and present shoreline changes across the United States. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline data along sandy shores of the conterminous United States and parts of Alaska and Hawaii under the National Assessment of Shoreline Change project. There is no widely accepted standard for analyzing shoreline change. Existing shoreline data measurements and rate calculation methods vary from study to study and prevent combining results into state-wide or regional assessments. The impetus behind the National Assessment project was to develop a standardized method of measuring changes in shoreline position that is consistent from coast to coast. The goal was to facilitate the process of periodically and systematically updating the results in an internally consistent manner.

The Arctic Coastal Plain of northern Alaska is an area of strategic economic importance to the United States, is home to remote Native American communities, and encompasses unique habitats of global significance. Coastal erosion along the north coast of Alaska is chronic, widespread, may be accelerating, and is threatening defense and energy-related infrastructure, natural shoreline habitats, and Native communities. There is an increased demand for accurate information regarding past and present shoreline changes across the United States. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline data along sandy shores of the conterminous United States and parts of Alaska and Hawaii under the National Assessment of Shoreline Change project. There is no widely accepted standard for analyzing shoreline change. Existing shoreline data measurements and rate calculation methods vary from study to study and prevent combining results into state-wide or regional assessments. The impetus behind the National Assessment project was to develop a standardized method of measuring changes in shoreline position that is consistent from coast to coast. The goal was to facilitate the process of periodically and systematically updating the results in an internally consistent manner.

The Arctic Coastal Plain of northern Alaska is an area of strategic economic importance to the United States, is home to remote Native American communities, and encompasses unique habitats of global significance. Coastal erosion along the north coast of Alaska is chronic, widespread, may be accelerating, and is threatening defense and energy-related infrastructure, natural shoreline habitats, and Native communities. There is an increased demand for accurate information regarding past and present shoreline changes across the United States. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline data along sandy shores of the conterminous United States and parts of Alaska and Hawaii under the National Assessment of Shoreline Change project. There is no widely accepted standard for analyzing shoreline change. Existing shoreline data measurements and rate calculation methods vary from study to study and prevent combining results into state-wide or regional assessments. The impetus behind the National Assessment project was to develop a standardized method of measuring changes in shoreline position that is consistent from coast to coast. The goal was to facilitate the process of periodically and systematically updating the results in an internally consistent manner.

The Arctic Coastal Plain of northern Alaska is an area of strategic economic importance to the United States, is home to remote Native American communities, and encompasses unique habitats of global significance. Coastal erosion along the north coast of Alaska is chronic, widespread, may be accelerating, and is threatening defense and energy-related infrastructure, natural shoreline habitats, and Native communities. There is an increased demand for accurate information regarding past and present shoreline changes across the United States. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline data along sandy shores of the conterminous United States and parts of Alaska and Hawaii under the National Assessment of Shoreline Change project. There is no widely accepted standard for analyzing shoreline change. Existing shoreline data measurements and rate calculation methods vary from study to study and prevent combining results into state-wide or regional assessments. The impetus behind the National Assessment project was to develop a standardized method of measuring changes in shoreline position that is consistent from coast to coast. The goal was to facilitate the process of periodically and systematically updating the results in an internally consistent manner.

The Arctic Coastal Plain of northern Alaska is an area of strategic economic importance to the United States, is home to remote Native American communities, and encompasses unique habitats of global significance. Coastal erosion along the north coast of Alaska is chronic, widespread, may be accelerating, and is threatening defense and energy-related infrastructure, natural shoreline habitats, and Native communities. There is an increased demand for accurate information regarding past and present shoreline changes across the United States. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline data along sandy shores of the conterminous United States and parts of Alaska and Hawaii under the National Assessment of Shoreline Change project. There is no widely accepted standard for analyzing shoreline change. Existing shoreline data measurements and rate calculation methods vary from study to study and prevent combining results into state-wide or regional assessments. The impetus behind the National Assessment project was to develop a standardized method of measuring changes in shoreline position that is consistent from coast to coast. The goal was to facilitate the process of periodically and systematically updating the results in an internally consistent manner.

The Arctic Coastal Plain of northern Alaska is an area of strategic economic importance to the United States, is home to remote Native American communities, and encompasses unique habitats of global significance. Coastal erosion along the north coast of Alaska is chronic, widespread, may be accelerating, and is threatening defense and energy-related infrastructure, natural shoreline habitats, and Native communities. There is an increased demand for accurate information regarding past and present shoreline changes across the United States. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline data along sandy shores of the conterminous United States and parts of Alaska and Hawaii under the National Assessment of Shoreline Change project. There is no widely accepted standard for analyzing shoreline change. Existing shoreline data measurements and rate calculation methods vary from study to study and prevent combining results into state-wide or regional assessments. The impetus behind the National Assessment project was to develop a standardized method of measuring changes in shoreline position that is consistent from coast to coast. The goal was to facilitate the process of periodically and systematically updating the results in an internally consistent manner.

The Arctic Coastal Plain of northern Alaska is an area of strategic economic importance to the United States, is home to remote Native American communities, and encompasses unique habitats of global significance. Coastal erosion along the north coast of Alaska is chronic, widespread, may be accelerating, and is threatening defense and energy-related infrastructure, natural shoreline habitats, and Native communities. There is an increased demand for accurate information regarding past and present shoreline changes across the United States. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline data along sandy shores of the conterminous United States and parts of Alaska and Hawaii under the National Assessment of Shoreline Change project. There is no widely accepted standard for analyzing shoreline change. Existing shoreline data measurements and rate calculation methods vary from study to study and prevent combining results into state-wide or regional assessments. The impetus behind the National Assessment project was to develop a standardized method of measuring changes in shoreline position that is consistent from coast to coast. The goal was to facilitate the process of periodically and systematically updating the results in an internally consistent manner.

The Arctic Coastal Plain of northern Alaska is an area of strategic economic importance to the United States, is home to remote Native American communities, and encompasses unique habitats of global significance. Coastal erosion along the north coast of Alaska is chronic, widespread, may be accelerating, and is threatening defense and energy-related infrastructure, natural shoreline habitats, and Native communities. There is an increased demand for accurate information regarding past and present shoreline changes across the United States. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline data along sandy shores of the conterminous United States and parts of Alaska and Hawaii under the National Assessment of Shoreline Change project. There is no widely accepted standard for analyzing shoreline change. Existing shoreline data measurements and rate calculation methods vary from study to study and prevent combining results into state-wide or regional assessments. The impetus behind the National Assessment project was to develop a standardized method of measuring changes in shoreline position that is consistent from coast to coast. The goal was to facilitate the process of periodically and systematically updating the results in an internally consistent manner.

The Arctic Coastal Plain of northern Alaska is an area of strategic economic importance to the United States, is home to remote Native American communities, and encompasses unique habitats of global significance. Coastal erosion along the north coast of Alaska is chronic, widespread, may be accelerating, and is threatening defense and energy-related infrastructure, natural shoreline habitats, and Native communities. There is an increased demand for accurate information regarding past and present shoreline changes across the United States. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline data along sandy shores of the conterminous United States and parts of Alaska and Hawaii under the National Assessment of Shoreline Change project. There is no widely accepted standard for analyzing shoreline change. Existing shoreline data measurements and rate calculation methods vary from study to study and prevent combining results into state-wide or regional assessments. The impetus behind the National Assessment project was to develop a standardized method of measuring changes in shoreline position that is consistent from coast to coast. The goal was to facilitate the process of periodically and systematically updating the results in an internally consistent manner.

The Arctic Coastal Plain of northern Alaska is an area of strategic economic importance to the United States, is home to remote Native American communities, and encompasses unique habitats of global significance. Coastal erosion along the north coast of Alaska is chronic, widespread, may be accelerating, and is threatening defense and energy-related infrastructure, natural shoreline habitats, and Native communities. There is an increased demand for accurate information regarding past and present shoreline changes across the United States. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline data along sandy shores of the conterminous United States and parts of Alaska and Hawaii under the National Assessment of Shoreline Change project. There is no widely accepted standard for analyzing shoreline change. Existing shoreline data measurements and rate calculation methods vary from study to study and prevent combining results into state-wide or regional assessments. The impetus behind the National Assessment project was to develop a standardized method of measuring changes in shoreline position that is consistent from coast to coast. The goal was to facilitate the process of periodically and systematically updating the results in an internally consistent manner.

The Arctic Coastal Plain of northern Alaska is an area of strategic economic importance to the United States, is home to remote Native American communities, and encompasses unique habitats of global significance. Coastal erosion along the north coast of Alaska is chronic, widespread, may be accelerating, and is threatening defense and energy-related infrastructure, natural shoreline habitats, and Native communities. There is an increased demand for accurate information regarding past and present shoreline changes across the United States. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline data along sandy shores of the conterminous United States and parts of Alaska and Hawaii under the National Assessment of Shoreline Change project. There is no widely accepted standard for analyzing shoreline change. Existing shoreline data measurements and rate calculation methods vary from study to study and prevent combining results into state-wide or regional assessments. The impetus behind the National Assessment project was to develop a standardized method of measuring changes in shoreline position that is consistent from coast to coast. The goal was to facilitate the process of periodically and systematically updating the results in an internally consistent manner.

The Arctic Coastal Plain of northern Alaska is an area of strategic economic importance to the United States, is home to remote Native American communities, and encompasses unique habitats of global significance. Coastal erosion along the north coast of Alaska is chronic, widespread, may be accelerating, and is threatening defense and energy-related infrastructure, natural shoreline habitats, and Native communities. There is an increased demand for accurate information regarding past and present shoreline changes across the United States. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline data along sandy shores of the conterminous United States and parts of Alaska and Hawaii under the National Assessment of Shoreline Change project. There is no widely accepted standard for analyzing shoreline change. Existing shoreline data measurements and rate calculation methods vary from study to study and prevent combining results into state-wide or regional assessments. The impetus behind the National Assessment project was to develop a standardized method of measuring changes in shoreline position that is consistent from coast to coast. The goal was to facilitate the process of periodically and systematically updating the results in an internally consistent manner.

The Arctic Coastal Plain of northern Alaska is an area of strategic economic importance to the United States, is home to remote Native American communities, and encompasses unique habitats of global significance. Coastal erosion along the north coast of Alaska is chronic, widespread, may be accelerating, and is threatening defense and energy-related infrastructure, natural shoreline habitats, and Native communities. There is an increased demand for accurate information regarding past and present shoreline changes across the United States. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline data along sandy shores of the conterminous United States and parts of Alaska and Hawaii under the National Assessment of Shoreline Change project. There is no widely accepted standard for analyzing shoreline change. Existing shoreline data measurements and rate calculation methods vary from study to study and prevent combining results into state-wide or regional assessments. The impetus behind the National Assessment project was to develop a standardized method of measuring changes in shoreline position that is consistent from coast to coast. The goal was to facilitate the process of periodically and systematically updating the results in an internally consistent manner.

The Arctic Coastal Plain of northern Alaska is an area of strategic economic importance to the United States, is home to remote Native American communities, and encompasses unique habitats of global significance. Coastal erosion along the north coast of Alaska is chronic, widespread, may be accelerating, and is threatening defense and energy-related infrastructure, natural shoreline habitats, and Native communities. There is an increased demand for accurate information regarding past and present shoreline changes across the United States. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline data along sandy shores of the conterminous United States and parts of Alaska and Hawaii under the National Assessment of Shoreline Change project. There is no widely accepted standard for analyzing shoreline change. Existing shoreline data measurements and rate calculation methods vary from study to study and prevent combining results into state-wide or regional assessments. The impetus behind the National Assessment project was to develop a standardized method of measuring changes in shoreline position that is consistent from coast to coast. The goal was to facilitate the process of periodically and systematically updating the results in an internally consistent manner.

The Arctic Coastal Plain of northern Alaska is an area of strategic economic importance to the United States, is home to remote Native American communities, and encompasses unique habitats of global significance. Coastal erosion along the north coast of Alaska is chronic, widespread, may be accelerating, and is threatening defense and energy-related infrastructure, natural shoreline habitats, and Native communities. There is an increased demand for accurate information regarding past and present shoreline changes across the United States. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline data along sandy shores of the conterminous United States and parts of Alaska and Hawaii under the National Assessment of Shoreline Change project. There is no widely accepted standard for analyzing shoreline change. Existing shoreline data measurements and rate calculation methods vary from study to study and prevent combining results into state-wide or regional assessments. The impetus behind the National Assessment project was to develop a standardized method of measuring changes in shoreline position that is consistent from coast to coast. The goal was to facilitate the process of periodically and systematically updating the results in an internally consistent manner.

The Arctic Coastal Plain of northern Alaska is an area of strategic economic importance to the United States, is home to remote Native American communities, and encompasses unique habitats of global significance. Coastal erosion along the north coast of Alaska is chronic, widespread, may be accelerating, and is threatening defense and energy-related infrastructure, natural shoreline habitats, and Native communities. There is an increased demand for accurate information regarding past and present shoreline changes across the United States. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline data along sandy shores of the conterminous United States and parts of Alaska and Hawaii under the National Assessment of Shoreline Change project. There is no widely accepted standard for analyzing shoreline change. Existing shoreline data measurements and rate calculation methods vary from study to study and prevent combining results into state-wide or regional assessments. The impetus behind the National Assessment project was to develop a standardized method of measuring changes in shoreline position that is consistent from coast to coast. The goal was to facilitate the process of periodically and systematically updating the results in an internally consistent manner.

The Arctic Coastal Plain of northern Alaska is an area of strategic economic importance to the United States, is home to remote Native American communities, and encompasses unique habitats of global significance. Coastal erosion along the north coast of Alaska is chronic, widespread, may be accelerating, and is threatening defense and energy-related infrastructure, natural shoreline habitats, and Native communities. There is an increased demand for accurate information regarding past and present shoreline changes across the United States. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline data along sandy shores of the conterminous United States and parts of Alaska and Hawaii under the National Assessment of Shoreline Change project. There is no widely accepted standard for analyzing shoreline change. Existing shoreline data measurements and rate calculation methods vary from study to study and prevent combining results into state-wide or regional assessments. The impetus behind the National Assessment project was to develop a standardized method of measuring changes in shoreline position that is consistent from coast to coast. The goal was to facilitate the process of periodically and systematically updating the results in an internally consistent manner.

The Arctic Coastal Plain of northern Alaska is an area of strategic economic importance to the United States, is home to remote Native American communities, and encompasses unique habitats of global significance. Coastal erosion along the north coast of Alaska is chronic, widespread, may be accelerating, and is threatening defense and energy-related infrastructure, natural shoreline habitats, and Native communities. There is an increased demand for accurate information regarding past and present shoreline changes across the United States. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline data along sandy shores of the conterminous United States and parts of Alaska and Hawaii under the National Assessment of Shoreline Change project. There is no widely accepted standard for analyzing shoreline change. Existing shoreline data measurements and rate calculation methods vary from study to study and prevent combining results into state-wide or regional assessments. The impetus behind the National Assessment project was to develop a standardized method of measuring changes in shoreline position that is consistent from coast to coast. The goal was to facilitate the process of periodically and systematically updating the results in an internally consistent manner.

Single-beam bathymetry data along with transit satellite navigation data was collected as part of field activity 69002 (K-0-69-GM) in Gulf of Mexico from 01/17/1969 to 01/29/1969, http://walrus.wr.usgs.gov/infobank/k/k069gm/html/k-0-69-gm.meta.html These data are reformatted from space-delimited ASCII text files located in the Coastal and Marine Geology Program (CMGP) InfoBank field activity catalog at http://walrus.wr.usgs.gov/infobank/k/k069gm/html/k-0-69-gm.bath.html into MGD77T format provided by the NOAA's National Geophysical Data Center(NGDC). The MGD77T format includes a header (documentation) file (.h77t) and a data file (.m77t). More information regarding this format can be found in the publication listed in the Cross_reference section of this metadata file.

Single-beam bathymetry, gravity, and magnetic data along with transit satellite navigation data was collected as part of field activity L-7-81-WG in Western Gulf of Alaska from 06/11/1981 to 06/30/1981,http://walrus.wr.usgs.gov/infobank/l/l781wg/html/l-7-81-wg.meta.html These data are reformatted from space-delimited ASCII text files located in the Coastal and Marine Geology Program (CMGP) InfoBank field activity catalog at http://walrus.wr.usgs.gov/infobank/l/l781wg/html/l-7-81-wg.bath.html http://walrus.wr.usgs.gov/infobank/l/l781wg/html/l-7-81-wg.grav.html and http://walrus.wr.usgs.gov/infobank/l/l781wg/html/l-7-81-wg.mag.html into MGD77T format provided by the NOAA's National Geophysical Data Center(NGDC). The MGD77T format includes a header (documentation) file (.h77t) and a data file (.m77t). More information regarding this format can be found in the publication listed in the Cross_reference section of this metadata file.

Single-beam bathymetry, gravity, and magnetic data along with transit satellite navigation data was collected as part of field activity L-7-82-SP in Solomon Islands from 05/19/1982 to 06/11/1982,http://walrus.wr.usgs.gov/infobank/l/l782sp/html/l-7-82-sp.meta.html These data are reformatted from space-delimited ASCII text files located in the Coastal and Marine Geology Program (CMGP) InfoBank field activity catalog at http://walrus.wr.usgs.gov/infobank/l/l782sp/html/l-7-82-sp.bath.html http://walrus.wr.usgs.gov/infobank/l/l782sp/html/l-7-82-sp.grav.html and http://walrus.wr.usgs.gov/infobank/l/l782sp/html/l-7-82-sp.mag.html into MGD77T format provided by the NOAA's National Geophysical Data Center(NGDC). The MGD77T format includes a header (documentation) file (.h77t) and a data file (.m77t). More information regarding this format can be found in the publication listed in the Cross_reference section of this metadata file.

Single-beam bathymetry, gravity, and magnetic data along with transit satellite navigation data was collected as part of field activity L-7-83-SP in Southern Pacific from 12/28/1983 to 01/03/1984, http://walrus.wr.usgs.gov/infobank/l/l783sp/html/l-7-83-sp.meta.html These data are reformatted from space-delimited ASCII text files located in the Coastal and Marine Geology Program (CMGP) InfoBank field activity catalog at http://walrus.wr.usgs.gov/infobank/l/l783sp/html/l-7-83-sp.bath.html http://walrus.wr.usgs.gov/infobank/l/l783sp/html/l-7-83-sp.grav.html and http://walrus.wr.usgs.gov/infobank/l/l783sp/html/l-7-83-sp.mag.html into MGD77T format provided by the NOAA's National Geophysical Data Center(NGDC). The MGD77T format includes a header (documentation) file (.h77t) and a data file (.m77t). More information regarding this format can be found in the publication listed in the Cross_reference section of this metadata file.

Single-beam bathymetry data along with transit satellite navigation data was collected as part of field activity L-7-85-NC in Northern California from 09/23/1985 to 10/04/1985, http://walrus.wr.usgs.gov/infobank/l/l785nc/html/l-7-85-nc.meta.html These data are reformatted from space-delimited ASCII text files located in the Coastal and Marine Geology Program (CMGP) InfoBank field activity catalog at http://walrus.wr.usgs.gov/infobank/l/l785nc/html/l-7-85-nc.bath.html into MGD77T format provided by the NOAA's National Geophysical Data Center(NGDC). The MGD77T format includes a header (documentation) file (.h77t) and a data file (.m77t). More information regarding this format can be found in the publication listed in the Cross_reference section of this metadata file.

Single-beam bathymetry, magnetics, and gravity data along with transit satellite navigation data was collected as part of field activity L-8-76-NP in Northern Pacific from 09/29/1976 to 10/21/1976, http://walrus.wr.usgs.gov/infobank/l/l876np/html/l-8-76-np.meta.html These data are reformatted from space-delimited ASCII text files located in the Coastal and Marine Geology Program (CMGP) InfoBank field activity catalog at http://walrus.wr.usgs.gov/infobank/l/l876np/html/l-8-76-np.bath.html http://walrus.wr.usgs.gov/infobank/l/l876np/html/l-8-76-np.grav.html http://walrus.wr.usgs.gov/infobank/l/l876np/html/l-8-76-np.mag.html into MGD77T format provided by the NOAA's National Geophysical Data Center(NGDC). The MGD77T format includes a header (documentation) file (.h77t) and a data file (.m77t). More information regarding this format can be found in the publication listed in the Cross_reference section of this metadata file.

Single-beam bathymetry, magnetics, and gravity data along with transit satellite navigation data was collected as part of field activity L-8-77-BS in Bering Sea, Alaska from 07/29/1977 to 08/21/1977, http://walrus.wr.usgs.gov/infobank/l/l877bs/html/l-8-77-bs.meta.html These data are reformatted from space-delimited ASCII text files located in the Coastal and Marine Geology Program (CMGP) InfoBank field activity catalog at http://walrus.wr.usgs.gov/infobank/l/l877bs/html/l-8-77-bs.bath.html http://walrus.wr.usgs.gov/infobank/l/l877bs/html/l-8-77-bs.grav.html http://walrus.wr.usgs.gov/infobank/l/l877bs/html/l-8-77-bs.mag.html into MGD77T format provided by the NOAA's National Geophysical Data Center(NGDC). The MGD77T format includes a header (documentation) file (.h77t) and a data file (.m77t). More information regarding this format can be found in the publication listed in the Cross_reference section of this metadata file.

Single-beam bathymetry, gravity, and magnetic data along with transit satellite navigation data was collected as part offield activity L-8-78-NP in Northern Pacific from 09/29/1978 to 10/07/1978, http://walrus.wr.usgs.gov/infobank/l/l878np/html/l-8-78-np.meta.html These data are reformatted from space-delimited ASCII text files located in the Coastal and Marine Geology Program (CMGP) InfoBank field activity catalog at http://walrus.wr.usgs.gov/infobank/l/l878np/html/l-8-78-np.bath.html http://walrus.wr.usgs.gov/infobank/l/l878np/html/l-8-78-np.grav.html and http://walrus.wr.usgs.gov/infobank/l/l878np/html/l-8-78-np.meta.html into MGD77T format provided by the NOAA's National Geophysical Data Center(NGDC). The MGD77T format includes a header (documentation) file (.h77t) and a data file (.m77t). More information regarding this format can be found in the publication listed in the Cross_reference section of this metadata file.

Single-beam bathymetry, magnetics, and gravity data along with transit satellite navigation data was collected as part of field activity L-8-81-WG in Western Gulf of Alaska from 07/04/1981 to 07/16/1981, http://walrus.wr.usgs.gov/infobank/l/l881wg/html/l-8-81-wg.meta.html These data are reformatted from space-delimited ASCII text files located in the Coastal and Marine Geology Program (CMGP) InfoBank field activity catalog at http://walrus.wr.usgs.gov/infobank/l/l881wg/html/l-8-81-wg.bath.html http://walrus.wr.usgs.gov/infobank/l/l881wg/html/l-8-81-wg.grav.html http://walrus.wr.usgs.gov/infobank/l/l881wg/html/l-8-81-wg.mag.html into MGD77T format provided by the NOAA's National Geophysical Data Center(NGDC). The MGD77T format includes a header (documentation) file (.h77t) and a data file (.m77t). More information regarding this format can be found in the publication listed in the Cross_reference section of this metadata file.

Single-beam bathymetry, magnetics, and gravity data along with transit satellite navigation data was collected as part of field activity L-8-82-NP in Northern Pacific from 06/18/1982 to 07/08/1982, http://walrus.wr.usgs.gov/infobank/l/l882np/html/L-8-82-NP.meta.html These data are reformatted from space-delimited ASCII text files located in the Coastal and Marine Geology Program (CMGP) InfoBank field activity catalog at http://walrus.wr.usgs.gov/infobank/l/l882np/html/L-8-82-NP.bath.html http://walrus.wr.usgs.gov/infobank/l/l882np/html/L-8-82-NP.grav.html http://walrus.wr.usgs.gov/infobank/l/l882np/html/L-8-82-NP.mag.html into MGD77T format provided by the NOAA's National Geophysical Data Center(NGDC). The MGD77T format includes a header (documentation) file (.h77t) and a data file (.m77t). More information regarding this format can be found in the publication listed in the Cross_reference section of this metadata file.

Single-beam bathymetry, magnetics, and gravity data along with transit satellite navigation data was collected as part of field activity L-8-84-SP in Southern Pacific from 07/19/1984 to 07/26/1984, http://walrus.wr.usgs.gov/infobank/l/l884sp/html/L-8-84-SP.meta.html These data are reformatted from space-delimited ASCII text files located in the Coastal and Marine Geology Program (CMGP) InfoBank field activity catalog at http://walrus.wr.usgs.gov/infobank/l/l884sp/html/L-8-84-SP.bath.html http://walrus.wr.usgs.gov/infobank/l/l884sp/html/L-8-84-SP.grav.html http://walrus.wr.usgs.gov/infobank/l/l884sp/html/L-8-84-SP.mag.html into MGD77T format provided by the NOAA's National Geophysical Data Center(NGDC). The MGD77T format includes a header (documentation) file (.h77t) and a data file (.m77t). More information regarding this format can be found in the publication listed in the Cross_reference section of this metadata file.

Single-beam bathymetry, magnetics, and gravity data along with transit satellite navigation data was collected as part of field activity L-9-77-AR in Arctic from 08/25/1977 to 10/08/1977, http://walrus.wr.usgs.gov/infobank/l/l977ar/html/l-9-77-ar.meta.html These data are reformatted from space-delimited ASCII text files located in the Coastal and Marine Geology Program (CMGP) InfoBank field activity catalog at http://walrus.wr.usgs.gov/infobank/l/l977ar/html/l-9-77-ar.bath.html http://walrus.wr.usgs.gov/infobank/l/l977ar/html/l-9-77-ar.grav.html http://walrus.wr.usgs.gov/infobank/l/l977ar/html/l-9-77-ar.mag.html into MGD77T format provided by the NOAA's National Geophysical Data Center(NGDC). The MGD77T format includes a header (documentation) file (.h77t) and a data file (.m77t). More information regarding this format can be found in the publication listed in the Cross_reference section of this metadata file.

Single-beam bathymetry, magnetics, and gravity data along with transit satellite navigation data was collected as part of field activity L-9-78-HW in Hawaii from 10/12/1978 to 10/19/1978, http://walrus.wr.usgs.gov/infobank/l/l978hw/html/l-9-78-hw.meta.html These data are reformatted from space-delimited ASCII text files located in the Coastal and Marine Geology Program (CMGP) InfoBank field activity catalog at http://walrus.wr.usgs.gov/infobank/l/l978hw/html/l-9-78-hw.bath.html http://walrus.wr.usgs.gov/infobank/l/l978hw/html/l-9-78-hw.grav.html http://walrus.wr.usgs.gov/infobank/l/l978hw/html/l-9-78-hw.mag.html into MGD77T format provided by the NOAA's National Geophysical Data Center(NGDC). The MGD77T format includes a header (documentation) file (.h77t) and a data file (.m77t). More information regarding this format can be found in the publication listed in the Cross_reference section of this metadata file.

Single-beam bathymetry, magnetics, and gravity data along with transit satellite navigation data was collected as part of field activity L-9-80-BS in Bering Sea, Alaska from 09/24/1980 to 10/06/1980, http://walrus.wr.usgs.gov/infobank/l/l980bs/html/l-9-80-bs.meta.html These data are reformatted from space-delimited ASCII text files located in the Coastal and Marine Geology Program (CMGP) InfoBank field activity catalog at http://walrus.wr.usgs.gov/infobank/l/l980bs/html/l-9-80-bs.bath.html http://walrus.wr.usgs.gov/infobank/l/l980bs/html/l-9-80-bs.grav.html http://walrus.wr.usgs.gov/infobank/l/l980bs/html/l-9-80-bs.mag.html into MGD77T format provided by the NOAA's National Geophysical Data Center(NGDC). The MGD77T format includes a header (documentation) file (.h77t) and a data file (.m77t). More information regarding this format can be found in the publication listed in the Cross_reference section of this metadata file.

Single-beam bathymetry, magnetics, and gravity data along with transit satellite navigation data was collected as part of field activity L-9-81-AA in Aleutian Arc, Alaska from 07/19/1981 to 08/13/1981, http://walrus.wr.usgs.gov/infobank/l/l981aa/html/l-9-81-aa.meta.html These data are reformatted from space-delimited ASCII text files located in the Coastal and Marine Geology Program (CMGP) InfoBank field activity catalog at http://walrus.wr.usgs.gov/infobank/l/l981aa/html/l-9-81-aa.bath.html http://walrus.wr.usgs.gov/infobank/l/l981aa/html/l-9-81-aa.grav.html http://walrus.wr.usgs.gov/infobank/l/l981aa/html/l-9-81-aa.mag.html into MGD77T format provided by the NOAA's National Geophysical Data Center(NGDC). The MGD77T format includes a header (documentation) file (.h77t) and a data file (.m77t). More information regarding this format can be found in the publication listed in the Cross_reference section of this metadata file.

Single-beam bathymetry, magnetics, and gravity data along with transit satellite navigation data was collected as part of field activity L-9-82-BS in Bering Sea, Alaska from 07/11/1982 to 08/03/1982, http://walrus.wr.usgs.gov/infobank/l/l982bs/html/l-9-82-bs.meta.html These data are reformatted from space-delimited ASCII text files located in the Coastal and Marine Geology Program (CMGP) InfoBank field activity catalog at http://walrus.wr.usgs.gov/infobank/l/l982bs/html/l-9-82-bs.bath.html http://walrus.wr.usgs.gov/infobank/l/l982bs/html/l-9-82-bs.grav.html http://walrus.wr.usgs.gov/infobank/l/l982bs/html/l-9-82-bs.mag.html into MGD77T format provided by the NOAA's National Geophysical Data Center(NGDC). The MGD77T format includes a header (documentation) file (.h77t) and a data file (.m77t). More information regarding this format can be found in the publication listed in the Cross_reference section of this metadata file.

Single-beam bathymetry, gravity, and magnetic data along with transit satellite navigation data was collected as part of field activity UGEOLEG_1 (U-1-71-GM) in Bay of Campeche, Gulf of Mexico from 05/27/1971 to 06/21/1971, http://walrus.wr.usgs.gov/infobank/u/u171gm/html/u-1-71-gm.meta.html These data are reformatted from space-delimited ASCII text files located in the Coastal and Marine Geology Program (CMGP) InfoBank field activity catalog at http://walrus.wr.usgs.gov/infobank/u/u171gm/html/u-1-71-gm.bath.html http://walrus.wr.usgs.gov/infobank/u/u171gm/html/u-1-71-gm.grav.html and http://walrus.wr.usgs.gov/infobank/u/u171gm/html/u-1-71-gm.mag.html into MGD77T format provided by the NOAA's National Geophysical Data Center(NGDC). The MGD77T format includes a header (documentation) file (.h77t) and a data file (.m77t). More information regarding this format can be found in the publication listed in the Cross_reference section of this metadata file.

Single-beam, bathymetry, gravity, and magnetic data along with transit satellite navigation data was collected as part of the U.S. Geological Survey cruise U271GM. in East Margin Yucatan Peninsula from 06/23/1971 to 07/08/1971, http://walrus.wr.usgs.gov/infobank/u/u271gm/html/u-2-71-gm.meta.html These data are reformatted from space-delimited ASCII text files located in the Coastal and Marine Geology Program (CMGP) InfoBank field activity catalog at http://walrus.wr.usgs.gov/infobank/u/u271gm/html/u-2-71-gm.bath.html http://walrus.wr.usgs.gov/infobank/u/u271gm/html/u-2-71-gm.grav.html and http://walrus.wr.usgs.gov/infobank/u/u271gm/html/u-2-71-gm.mag.html into MGD77T format provided by the NOAA's National Geophysical Data Center(NGDC). The MGD77T format includes a header (documentation) file (.h77t) and a data file (.m77t). More information regarding this format can be found in the publication listed in the Cross_reference section of this metadata file.

Single-beam bathymetry, gravity, and magnetic data along with transit satellite navigation data was collected as part of field activity UGEOLEG_3 (U-3-71-CB) in Eastern Greater Antilles, Caribbean from 07/17/1971 to 08/04/1971, http://walrus.wr.usgs.gov/infobank/u/u371cb/html/u-3-71-cb.meta.html The geophysical source was a Knudsen 12 kHz 320B/R echosounder. These data are reformatted from space-delimited ASCII text files located in the Coastal and Marine Geology Program (CMGP) InfoBank field activity catalog at http://walrus.wr.usgs.gov/infobank/u/u371cb/html/u-3-71-cb.bath.html http://walrus.wr.usgs.gov/infobank/u/u371cb/html/u-3-71-cb.grav.html and http://walrus.wr.usgs.gov/infobank/u/u371cb/html/u-3-71-cb.mag.html into MGD77T format provided by the NOAA's National Geophysical Data Center(NGDC). The MGD77T format includes a header (documentation) file (.h77t) and a data file (.m77t). More information regarding this format can be found in the publication listed in the Cross_reference section of this metadata file.

Single-beam bathymetry,gravity, and magnetic data along with transit satellite navigation data was collected as part of field activity UGEOLEG_4 (U-4-71-CB) in Venezuela, Caribbean Sea from 08/18/1971 to 10/01/1971, http://walrus.wr.usgs.gov/infobank/u/u471cb/html/u-4-71-cb.meta.html These data are reformatted from space-delimited ASCII text files located in the Coastal and Marine Geology Program (CMGP) InfoBank field activity catalog at http://walrus.wr.usgs.gov/infobank/u/u471cb/html/u-4-71-cb.bath.html http://walrus.wr.usgs.gov/infobank/u/u471cb/html/u-4-71-cb.grav.html and http://walrus.wr.usgs.gov/infobank/u/u471cb/html/u-4-71-cb.mag.html into MGD77T format provided by the NOAA's National Geophysical Data Center(NGDC). The MGD77T format includes a header (documentation) file (.h77t) and a data file (.m77t). More information regarding this format can be found in the publication listed in the Cross_reference section of this metadata file.

Single-beam bathymetry, gravity, and magnetic data along with transit satellite navigation data was collected as part of field activity 71005 (U-5-71-AF) in Continental Margin Liberia from 10/30/1971 to 11/20/1971, http://walrus.wr.usgs.gov/infobank/u/u571af/html/u-5-71-af.meta.html These data are reformatted from space-delimited ASCII text files located in the Coastal and Marine Geology Program (CMGP) InfoBank field activity catalog at http://walrus.wr.usgs.gov/infobank/u/u571af/html/u-5-71-af.bath.html http://walrus.wr.usgs.gov/infobank/u/u571af/html/u-5-71-af.grav.html http://walrus.wr.usgs.gov/infobank/u/u571af/html/u-5-71-af.mag.html into MGD77T format provided by the NOAA's National Geophysical Data Center(NGDC). The MGD77T format includes a header (documentation) file (.h77t) and a data file (.m77t). More information regarding this format can be found in the publication listed in the Cross_reference section of this metadata file.

Single-beam bathymetry, gravity, and magnetic data along with DGPS navigation data was collected as part of field activity 71006 (U-6-71-AT) in Liberia to Puerto Rico, Atlantic Ocean from 11/24/1971 to 12/09/1971, http://walrus.wr.usgs.gov/infobank/u/u671at/html/u-6-71-at.meta.html These data are reformatted from space-delimited ASCII text files located in the Coastal and Marine Geology Program (CMGP) InfoBank field activity catalog at http://walrus.wr.usgs.gov/infobank/u/u671at/html/u-6-71-at.bath.html http://walrus.wr.usgs.gov/infobank/u/u671at/html/u-6-71-at.grav.html and http://walrus.wr.usgs.gov/infobank/u/u671at/html/u-6-71-at.mag.html into MGD77T format provided by the NOAA's National Geophysical Data Center(NGDC). The MGD77T format includes a header (documentation) file (.h77t) and a data file (.m77t). More information regarding this format can be found in the publication listed in the Cross_reference section of this metadata file.

Single-beam bathymetry data along with transit satellite navigation data was collected as part of field activity 71007 (U-7-71-PR) in Puerto Rico from 12/11/1971 to 12/15/1971, http://walrus.wr.usgs.gov/infobank/u/u771pr/html/u-7-71-pr.meta.html These data are reformatted from space-delimited ASCII text files located in the Coastal and Marine Geology Program (CMGP) InfoBank field activity catalog at http://walrus.wr.usgs.gov/infobank/u/u771pr/html/u-7-71-pr.bath.html into MGD77T format provided by the NOAA's National Geophysical Data Center(NGDC). The MGD77T format includes a header (documentation) file (.h77t) and a data file (.m77t). More information regarding this format can be found in the publication listed in the Cross_reference section of this metadata file.

Single-beam bathymetry, gravity, and magnetics data along with transit satellite navigation data was collected as part of field activity B-1-72-SC in Central California from 11/11/1972 to 11/15/1972, http://walrus.wr.usgs.gov/infobank/b/b172sc/html/b-1-72-sc.meta.html These data are reformatted from space-delimited ASCII text files located in the Coastal and Marine Geology Program (CMGP) InfoBank field activity catalog at http://walrus.wr.usgs.gov/infobank/b/b172sc/html/b-1-72-sc.bath.html http://walrus.wr.usgs.gov/infobank/b/b172sc/html/b-1-72-sc.grav.html and http://walrus.wr.usgs.gov/infobank/b/b172sc/html/b-1-72-sc.mag.html into MGD77T format provided by the NOAA's National Geophysical Data Center(NGDC). The MGD77T format includes a header (documentation) file (.h77t) and a data file (.m77t). More information regarding this format can be found in the publication listed in the Cross_reference section of this metadata file.

Single-beam bathymetry, gravity, and magnetics data along with transit satellite navigation data was collected as part of field activity B-1-74-AR in Arctic from 07/13/1974 to 08/30/1974, http://walrus.wr.usgs.gov/infobank/b/b174ar/html/b-1-74-ar.meta.html These data are reformatted from space-delimited ASCII text files located in the Coastal and Marine Geology Program (CMGP) InfoBank field activity catalog at http://walrus.wr.usgs.gov/infobank/b/b174ar/html/b-1-74-ar.bath.html http://walrus.wr.usgs.gov/infobank/b/b174ar/html/b-1-74-ar.grav.html and http://walrus.wr.usgs.gov/infobank/b/b174ar/html/b-1-74-ar.mag.html into MGD77T format provided by the NOAA's National Geophysical Data Center(NGDC). The MGD77T format includes a header (documentation) file (.h77t) and a data file (.m77t). More information regarding this format can be found in the publication listed in the Cross_reference section of this metadata file.

Single-beam bathymetry data along with transit satellite navigation data was collected as part of field activity C-1-80-NC in Monterey Bay, Northern California from 05/21/1980 to 05/22/1980, http://walrus.wr.usgs.gov/infobank/c/c180nc/html/c-1-80-nc.meta.html The geophysical source was a Knudsen 12 kHz 320B/R echosounder. These data are reformatted from space-delimited ASCII text files located in the Coastal and Marine Geology Program (CMGP) InfoBank field activity catalog at http://walrus.wr.usgs.gov/infobank/c/c180nc/html/c-1-80-nc.bath.html into MGD77T format provided by the NOAA's National Geophysical Data Center(NGDC). The MGD77T format includes a header (documentation) file (.h77t) and a data file (.m77t). More information regarding this format can be found in the publication listed in the Cross_reference section of this metadata file.

Single-beam bathymetry and magnetic data along with DGPS navigation data was collected as part of field activity F-1-84-SC in Southern California from 04/26/1984 to 05/21/1984, http://walrus.wr.usgs.gov/infobank/f/f184sc/html/f-1-84-sc.meta.html The geophysical source was a Knudsen 12 kHz 320B/R echosounder. These data are reformatted from space-delimited ASCII text files located in the Coastal and Marine Geology Program (CMGP) InfoBank field activity catalog at field activity F-1-84-SC in Southern California from 04/26/1984 to 05/21/1984, http://walrus.wr.usgs.gov/infobank/f/f184sc/html/f-1-84-sc.bath.html into MGD77T format provided by the NOAA's National Geophysical Data Center(NGDC). The MGD77T format includes a header (documentation) file (.h77t) and a data file (.m77t). More information regarding this format can be found in the publication listed in the Cross_reference section of this metadata file.

Single-beam bathymetry and magnetics data along with DGPS navigation data was collected as part of field activity F-3-84-NC in Northern California from 06/15/1984 to 07/08/1984, http://walrus.wr.usgs.gov/infobank/f/f384nc/html/f-3-84-nc.meta.html. The geophysical source was a Knudsen 12 kHz 320B/R echosounder. These data are reformatted from space-delimited ASCII text files located in the Coastal and Marine Geology Program (CMGP) InfoBank field activity catalog at http://walrus.wr.usgs.gov/infobank/f/f384nc/html/f-3-84-nc.bath.html. into MGD77T format provided by the NOAA's National Geophysical Data Center(NGDC). The MGD77T format includes a header (documentation) file (.h77t) and a data file (.m77t). More information regarding this format can be found in the publication listed in the Cross_reference section of this metadata file.

Single-beam bathymetry and magnetics data along with DGPS navigation data was collected as part of field activity F-4-84-WO in Washington, Oregon from 07/11/1984 to 08/15/1984, http://walrus.wr.usgs.gov/infobank/f/f484wo/html/f-4-84-wo.meta.html The geophysical source was a Knudsen 12 kHz 320B/R echosounder. These data are reformatted from space-delimited ASCII text files located in the Coastal and Marine Geology Program (CMGP) InfoBank field activity catalog at http://walrus.wr.usgs.gov/infobank/f/f484wo/html/f-4-84-wo.bath.html into MGD77T format provided by the NOAA's National Geophysical Data Center(NGDC). The MGD77T format includes a header (documentation) file (.h77t) and a data file (.m77t). More information regarding this format can be found in the publication listed in the Cross_reference section of this metadata file.

Single-beam bathymetry, gravity, and magnetic data along with DGPS navigation data was collected as part of field activity G-1-75-EG in Eastern Gulf of Alaska, Continental Shelf from 06/22/1975 to 08/27/1975, http://walrus.wr.usgs.gov/infobank/g/g175eg/html/g-1-75-eg.meta.html The geophysical source was a Knudsen 12 kHz 320B/R echosounder. These data are reformatted from space-delimited ASCII text files located in the Coastal and Marine Geology Program (CMGP) InfoBank field activity catalog at http://walrus.wr.usgs.gov/infobank/g/g175eg/html/g-1-75-eg.bath.html into MGD77T format provided by the NOAA's National Geophysical Data Center(NGDC). The MGD77T format includes a header (documentation) file (.h77t) and a data file (.m77t). More information regarding this format can be found in the publication listed in the Cross_reference section of this metadata file.

Single-beam bathymetry data along with DGPS navigation data was collected as part of field activity G-1-76-AR in Arctic from 09/07/1976 to 10/02/1976. The geophysical source was a Knudsen 12 kHz 320B/R echosounder. These data are reformatted from space-delimited ASCII text files, located in the former Coastal and Marine Geology Program (CMGP) InfoBank field activity catalog, into MGD77T format provided by the NOAA's National Geophysical Data Center (NGDC). The MGD77T format includes a header (documentation) file (.h77t) and a data file (.m77t). More information regarding this format can be found in the publication listed in the Cross_reference section of this metadata file. For additional information about USGS Field Activity G-1-76-AR go to https://cmgds.marine.usgs.gov/fan_info.php?fan=G176AR.

Single-beam bathymetry data along with DGPS navigation data was collected as part of field activity K-1-76-AR in Arctic from 07/24/1976 to 09/26/1976, http://walrus.wr.usgs.gov/infobank/k/k176ar/html/k-1-76-ar.meta.html. The geophysical source was a Knudsen 12 kHz 320B/R echosounder. These data are reformatted from space-delimited ASCII text files located in the Coastal and Marine Geology Program (CMGP) InfoBank field activity catalog at http://walrus.wr.usgs.gov/infobank/k/k176ar/html/k-1-76-ar.bath.html into MGD77T format provided by the NOAA's National Geophysical Data Center(NGDC). The MGD77T format includes a header (documentation) file (.h77t) and a data file (.m77t). More information regarding this format can be found in the publication listed in the Cross_reference section of this metadata file.

Single-beam bathymetry data along with DGPS navigation data was collected as part of field activity K-1-77-AR in Beaufort Sea, Arctic from 07/15/1977 to 08/25/1977, http://walrus.wr.usgs.gov/infobank/k/k177ar/html/k-1-77-ar.meta.html The geophysical source was a Knudsen 12 kHz 320B/R echosounder. These data are reformatted from space-delimited ASCII text files located in the Coastal and Marine Geology Program (CMGP) InfoBank field activity catalog at http://walrus.wr.usgs.gov/infobank/k/k177ar/html/k-1-77-ar.bath.html into MGD77T format provided by the NOAA's National Geophysical Data Center(NGDC). The MGD77T format includes a header (documentation) file (.h77t) and a data file (.m77t). More information regarding this format can be found in the publication listed in the Cross_reference section of this metadata file.

Single-beam bathymetry data along with DGPS navigation data was collected as part of field activity K-1-78-AR in Barrows to Pt. Barrows, Arctic from 08/18/1978 to 09/18/1978, http://walrus.wr.usgs.gov/infobank/k/k178ar/html/k-1-78-ar.meta.html The geophysical source was a Knudsen 12 kHz 320B/R echosounder. These data are reformatted from space-delimited ASCII text files located in the Coastal and Marine Geology Program (CMGP) InfoBank field activity catalog at http://walrus.wr.usgs.gov/infobank/k/k178ar/html/k-1-78-ar.bath.html into MGD77T format provided by the NOAA's National Geophysical Data Center(NGDC). The MGD77T format includes a header (documentation) file (.h77t) and a data file (.m77t). More information regarding this format can be found in the publication listed in the Cross_reference section of this metadata file.

Single-beam bathymetry data along with DGPS navigation data was collected as part of field activity K-1-79-AR in Prudhoe Bay, Alaska, Arctic Ocean from 07/23/1979 to 08/20/1979, http://walrus.wr.usgs.gov/infobank/k/k179ar/html/k-1-79-ar.meta.html The geophysical source was a Knudsen 12 kHz 320B/R echosounder. These data are reformatted from space-delimited ASCII text files located in the Coastal and Marine Geology Program (CMGP) InfoBank field activity catalog at http://walrus.wr.usgs.gov/infobank/k/k179ar/html/k-1-79-ar.bath.html into MGD77T format provided by the NOAA's National Geophysical Data Center(NGDC). The MGD77T format includes a header (documentation) file (.h77t) and a data file (.m77t). More information regarding this format can be found in the publication listed in the Cross_reference section of this metadata file.

Single-beam bathymetry data along with DGPS navigation data was collected as part of field activity K-1-80-AR in Arctic from 07/18/1980 to 08/19/1980, http://walrus.wr.usgs.gov/infobank/k/k180ar/html/k-1-80-ar.meta.html The geophysical source was a Knudsen 12 kHz 320B/R echosounder. These data are reformatted from space-delimited ASCII text files located in the Coastal and Marine Geology Program (CMGP) InfoBank field activity catalog at http://walrus.wr.usgs.gov/infobank/k/k180ar/html/k-1-80-ar.bath.html into MGD77T format provided by the NOAA's National Geophysical Data Center(NGDC). The MGD77T format includes a header (documentation) file (.h77t) and a data file (.m77t). More information regarding this format can be found in the publication listed in the Cross_reference section of this metadata file.

Single-beam bathymetry data along with DGPS navigation data was collected as part of field activity K-1-81-AR in Arctic from 07/15/1981 to 08/02/1981, http://walrus.wr.usgs.gov/infobank/k/k181ar/html/k-1-81-ar.meta.html The geophysical source was a Knudsen 12 kHz 320B/R echosounder. These data are reformatted from space-delimited ASCII text files located in the Coastal and Marine Geology Program (CMGP) InfoBank field activity catalog at http://walrus.wr.usgs.gov/infobank/k/k181ar/html/k-1-81-ar.bath.html into MGD77T format provided by the NOAA's National Geophysical Data Center(NGDC). The MGD77T format includes a header (documentation) file (.h77t) and a data file (.m77t). More information regarding this format can be found in the publication listed in the Cross_reference section of this metadata file.

Single-beam bathymetry data along with DGPS navigation data was collected as part of field activity K-1-81-HW in Hawaii from 01/26/1981 to 02/05/1981, http://walrus.wr.usgs.gov/infobank/k/k181hw/html/k-1-81-hw.meta.html The geophysical source was a Knudsen 12 kHz 320B/R echosounder. These data are reformatted from space-delimited ASCII text files located in the Coastal and Marine Geology Program (CMGP) InfoBank field activity catalog at http://walrus.wr.usgs.gov/infobank/k/k181hw/html/k-1-81-hw.bath.html into MGD77T format provided by the NOAA's National Geophysical Data Center(NGDC). The MGD77T format includes a header (documentation) file (.h77t) and a data file (.m77t). More information regarding this format can be found in the publication listed in the Cross_reference section of this metadata file.

Single-beam bathymetry data along with DGPS navigation data was collected as part of field activity K-1-83-AR in Arctic from 07/22/1983 to 08/03/1983, http://walrus.wr.usgs.gov/infobank/k/k183ar/html/k-1-83-ar.meta.html The geophysical source was a Knudsen 12 kHz 320B/R echosounder. These data are reformatted from space-delimited ASCII text files located in the Coastal and Marine Geology Program (CMGP) InfoBank field activity catalog at http://walrus.wr.usgs.gov/infobank/k/k183ar/html/k-1-83-ar.bath.html into MGD77T format provided by the NOAA's National Geophysical Data Center(NGDC). The MGD77T format includes a header (documentation) file (.h77t) and a data file (.m77t). More information regarding this format can be found in the publication listed in the Cross_reference section of this metadata file.

Single-beam bathymetry data along with DGPS navigation data was collected as part of field activity K-2-79-AR in Prudhoe Bay, Alaska, Arctic from 08/25/1979 to 09/23/1979, http://walrus.wr.usgs.gov/infobank/k/k279ar/html/k-2-79-ar.meta.html The geophysical source was a Knudsen 12 kHz 320B/R echosounder. These data are reformatted from space-delimited ASCII text files located in the Coastal and Marine Geology Program (CMGP) InfoBank field activity catalog at http://walrus.wr.usgs.gov/infobank/k/k279ar/html/k-2-79-ar.bath.html into MGD77T format provided by the NOAA's National Geophysical Data Center(NGDC). The MGD77T format includes a header (documentation) file (.h77t) and a data file (.m77t). More information regarding this format can be found in the publication listed in the Cross_reference section of this metadata file.

Single-beam bathymetry data along with DGPS navigation data was collected as part of field activity K-2-81-AR in Arctic from 08/04/1981 to 08/07/1981, http://walrus.wr.usgs.gov/infobank/k/k281ar/html/k-2-81-ar.meta.html The geophysical source was a Knudsen 12 kHz 320B/R echosounder. These data are reformatted from space-delimited ASCII text files located in the Coastal and Marine Geology Program (CMGP) InfoBank field activity catalog at http://walrus.wr.usgs.gov/infobank/k/k281ar/html/k-2-81-ar.bath.html into MGD77T format provided by the NOAA's National Geophysical Data Center(NGDC). The MGD77T format includes a header (documentation) file (.h77t) and a data file (.m77t). More information regarding this format can be found in the publication listed in the Cross_reference section of this metadata file.

Single-beam bathymetry data along with DGPS navigation data was collected as part of field activity K-2-83-AR in Arctic and Beaufort Sea, Alaska from 08/05/1983 to 08/22/1983, http://walrus.wr.usgs.gov/infobank/k/k283ar/html/k-2-83-ar.meta.html The geophysical source was a Knudsen 12 kHz 320B/R echosounder. These data are reformatted from space-delimited ASCII text files located in the Coastal and Marine Geology Program (CMGP) InfoBank field activity catalog at http://walrus.wr.usgs.gov/infobank/k/k283ar/html/k-2-83-ar.bath.html into MGD77T format provided by the NOAA's National Geophysical Data Center(NGDC). The MGD77T format includes a header (documentation) file (.h77t) and a data file (.m77t). More information regarding this format can be found in the publication listed in the Cross_reference section of this metadata file.

Single-beam bathymetry data along with DGPS navigation data was collected as part of field activity K-2-83-NP in Gorda Ridge, North Pacific from 10/08/1983 to 10/13/1983, http://walrus.wr.usgs.gov/infobank/k/k283np/html/k-2-83-np.meta.html These data are reformatted from space-delimited ASCII text files located in the Coastal and Marine Geology Program (CMGP) InfoBank field activity catalog at http://walrus.wr.usgs.gov/infobank/k/k283np/html/k-2-83-np.bath.html into MGD77T format provided by the NOAA's National Geophysical Data Center(NGDC). The MGD77T format includes a header (documentation) file (.h77t) and a data file (.m77t). More information regarding this format can be found in the publication listed in the Cross_reference section of this metadata file.

Single-beam bathymetry data along with DGPS navigation data was collected as part of field activity K-3-82-AR in Arctic from 08/25/1982 to 09/08/1982, http://walrus.wr.usgs.gov/infobank/k/k382ar/html/k-3-82-ar.meta.html These data are reformatted from space-delimited ASCII text files located in the Coastal and Marine Geology Program (CMGP) InfoBank field activity catalog at http://walrus.wr.usgs.gov/infobank/k/k382ar/html/k-3-82-ar.bath.html into MGD77T format provided by the NOAA's National Geophysical Data Center(NGDC). The MGD77T format includes a header (documentation) file (.h77t) and a data file (.m77t). More information regarding this format can be found in the publication listed in the Cross_reference section of this metadata file.

Single-beam bathymetry data along with DGPS navigation data was collected as part of field activity K-4-82-AR in Arctic from 09/13/1982 to 10/10/1982, http://walrus.wr.usgs.gov/infobank/k/k482ar/html/k-4-82-ar.meta.html These data are reformatted from space-delimited ASCII text files located in the Coastal and Marine Geology Program (CMGP) InfoBank field activity catalog at http://walrus.wr.usgs.gov/infobank/k/k482ar/html/k-4-82-ar.bath.html into MGD77T format provided by the NOAA's National Geophysical Data Center(NGDC). The MGD77T format includes a header (documentation) file (.h77t) and a data file (.m77t). More information regarding this format can be found in the publication listed in the Cross_reference section of this metadata file.

Single-beam bathymetry data along with DGPS navigation data was collected as part of field activity L-10-80-NP in Northern Pacific from 10/10/1980 to 10/18/1980, http://walrus.wr.usgs.gov/infobank/l/l1080np/html/l-10-80-np.meta.html These data are reformatted from space-delimited ASCII text files located in the Coastal and Marine Geology Program (CMGP) InfoBank field activity catalog at http://walrus.wr.usgs.gov/infobank/l/l1080np/html/l-10-80-np.bath.html into MGD77T format provided by the NOAA's National Geophysical Data Center(NGDC). The MGD77T format includes a header (documentation) file (.h77t) and a data file (.m77t). More information regarding this format can be found in the publication listed in the Cross_reference section of this metadata file.

Single-beam bathymetry data along with DGPS navigation data was collected as part of field activity L-10-81-AA in North Aleutians, Alaska from 08/16/1981 to 08/23/1981, The geophysical source was a Knudsen 12 kHz 320B/R echosounder. These data are reformatted from space-delimited ASCII text files located in the Coastal and Marine Geology Program (CMGP) InfoBank field activity catalog at http://walrus.wr.usgs.gov/infobank/l/l1081aa/html/l-10-81-aa.bath.html into MGD77T format provided by the NOAA's National Geophysical Data Center(NGDC). The MGD77T format includes a header (documentation) file (.h77t) and a data file (.m77t). More information regarding this format can be found in the publication listed in the Cross_reference section of this metadata file.

Single-beam bathymetry, gravity, and magnetic data along with DGPS navigation data was collected as part of field activity L-10-82-BS in Bering Sea, Alaska from 08/06/1982 to 08/24/1982, http://walrus.wr.usgs.gov/infobank/l/l1082bs/html/l-10-82-bs.meta.html These data are reformatted from space-delimited ASCII text files located in the Coastal and Marine Geology Program (CMGP) InfoBank field activity catalog at http://walrus.wr.usgs.gov/infobank/l/l1082bs/html/l-10-82-bs.bath.html into MGD77T format provided by the NOAA's National Geophysical Data Center(NGDC). The MGD77T format includes a header (documentation) file (.h77t) and a data file (.m77t). More information regarding this format can be found in the publication listed in the Cross_reference section of this metadata file.

Single-beam bathymetry, gravity, and magnetic data along with DGPS navigation data was collected as part of field activity L-11-82-CS in Chukchi Sea, Alaska from 08/27/1982 to 09/16/1982, http://walrus.wr.usgs.gov/infobank/l/l1182cs/html/l-11-82-cs.meta.html These data are reformatted from space-delimited ASCII text files located in the Coastal and Marine Geology Program (CMGP) InfoBank field activity catalog at http://walrus.wr.usgs.gov/infobank/l/l1182cs/html/l-11-82-cs.bath.html, http://walrus.wr.usgs.gov/infobank/l/l1182cs/html/l-11-82-cs.grav.html, and http://walrus.wr.usgs.gov/infobank/l/l1182cs/html/l-11-82-cs.mag.html into MGD77T format provided by the NOAA's National Geophysical Data Center(NGDC). The MGD77T format includes a header (documentation) file (.h77t) and a data file (.m77t). More information regarding this format can be found in the publication listed in the Cross_reference section of this metadata file.

Single-beam bathymetry and magnetic data along with DGPS navigation data was collected as part of field activity L-12-80-WF in Juan de Fuca from 10/29/1980 to 11/13/1980, http://walrus.wr.usgs.gov/infobank/l/l1280wf/html/l-12-80-wf.meta.html These data are reformatted from space-delimited ASCII text files located in the Coastal and Marine Geology Program (CMGP) InfoBank field activity catalog at http://walrus.wr.usgs.gov/infobank/l/l1280wf/html/l-12-80-wf.bath.html and http://walrus.wr.usgs.gov/infobank/l/l1280wf/html/l-12-80-wf.mag.html into MGD77T format provided by the NOAA's National Geophysical Data Center(NGDC). The MGD77T format includes a header (documentation) file (.h77t) and a data file (.m77t). More information regarding this format can be found in the publication listed in the Cross_reference section of this metadata file.

Single-beam bathymetry, gravity, and magnetic data along with DGPS navigation data was collected as part of field activity L-12-81-NP in North Pacific Ocean from 09/19/1981 to 10/05/1981, http://walrus.wr.usgs.gov/infobank/l/l1281np/html/l-12-81-np.meta.html These data are reformatted from space-delimited ASCII text files located in the Coastal and Marine Geology Program (CMGP) InfoBank field activity catalog at http://walrus.wr.usgs.gov/infobank/l/l1281np/html/l-12-81-np.bath.html, http://walrus.wr.usgs.gov/infobank/l/l1281np/html/l-12-81-np.grav.html, and http://walrus.wr.usgs.gov/infobank/l/l1281np/html/l-12-81-np.mag.html into MGD77T format provided by the NOAA's National Geophysical Data Center(NGDC). The MGD77T format includes a header (documentation) file (.h77t) and a data file (.m77t). More information regarding this format can be found in the publication listed in the Cross_reference section of this metadata file.

Single-beam bathymetry, gravity, and magnetic data along with DGPS navigation data was collected as part of field activity L-12-82-WG in Western Gulf of Alaska from 09/22/1982 to 10/05/1982, http://walrus.wr.usgs.gov/infobank/l/l1282wg/html/l-12-82-wg.meta.html These data are reformatted from space-delimited ASCII text files located in the Coastal and Marine Geology Program (CMGP) InfoBank field activity catalog at http://walrus.wr.usgs.gov/infobank/l/l1282wg/html/l-12-82-wg.bath.html, http://walrus.wr.usgs.gov/infobank/l/l1282wg/html/l-12-82-wg.grav.html, and http://walrus.wr.usgs.gov/infobank/l/l1282wg/html/l-12-82-wg.mag.html into MGD77T format provided by the NOAA's National Geophysical Data Center(NGDC). The MGD77T format includes a header (documentation) file (.h77t) and a data file (.m77t). More information regarding this format can be found in the publication listed in the Cross_reference section of this metadata file.

Single-beam bathymetry, gravity, and magnetic data along with DGPS navigation data was collected as part of field activity L-13-80-NP in Northern Pacific from 11/18/1980 to 12/11/1980, http://walrus.wr.usgs.gov/infobank/l/l1380np/html/l-13-80-np.meta.html These data are reformatted from space-delimited ASCII text files located in the Coastal and Marine Geology Program (CMGP) InfoBank field activity catalog at http://walrus.wr.usgs.gov/infobank/l/l1380np/html/l-13-80-np.bath.html, http://walrus.wr.usgs.gov/infobank/l/l1380np/html/l-13-80-np.grav.html, and http://walrus.wr.usgs.gov/infobank/l/l1380np/html/l-13-80-np.mag.html into MGD77T format provided by the NOAA's National Geophysical Data Center(NGDC). The MGD77T format includes a header (documentation) file (.h77t) and a data file (.m77t). More information regarding this format can be found in the publication listed in the Cross_reference section of this metadata file.

Single-beam bathymetry data along with DGPS navigation data was collected as part of field activity L-13-81-NC in Northern California from 10/09/1981 to 10/23/1981, http://walrus.wr.usgs.gov/infobank/l/l1381nc/html/l-13-81-nc.meta.html These data are reformatted from space-delimited ASCII text files located in the Coastal and Marine Geology Program (CMGP) InfoBank field activity catalog at http://walrus.wr.usgs.gov/infobank/l/l1381nc/html/l-13-81-nc.bath.html into MGD77T format provided by the NOAA's National Geophysical Data Center(NGDC). The MGD77T format includes a header (documentation) file (.h77t) and a data file (.m77t). More information regarding this format can be found in the publication listed in the Cross_reference section of this metadata file.

Single-beam bathymetry and gravity data along with DGPS navigation data was collected as part of field activity L-13-82-WF in Juan de Fuca from 10/17/1982 to 10/29/1982, http://walrus.wr.usgs.gov/infobank/l/l1382wf/html/l-13-82-wf.meta.html These data are reformatted from space-delimited ASCII text files located in the Coastal and Marine Geology Program (CMGP) InfoBank field activity catalog at http://walrus.wr.usgs.gov/infobank/l/l1382wf/html/l-13-82-wf.bath.html and http://walrus.wr.usgs.gov/infobank/l/l1382wf/html/l-13-82-wf.grav.html into MGD77T format provided by the NOAA's National Geophysical Data Center(NGDC). The MGD77T format includes a header (documentation) file (.h77t) and a data file (.m77t). More information regarding this format can be found in the publication listed in the Cross_reference section of this metadata file.

Single-beam bathymetry data along with DGPS navigation data was collected as part of field activity L-1-76-MX in Baja California from 01/06/1976 to 02/17/1976, http://walrus.wr.usgs.gov/infobank/l/l176mx/html/l-1-76-mx.meta.html These data are reformatted from space-delimited ASCII text files located in the Coastal and Marine Geology Program (CMGP) InfoBank field activity catalog at http://walrus.wr.usgs.gov/infobank/l/l176mx/html/l-1-76-mx.bath.html into MGD77T format provided by the NOAA's National Geophysical Data Center(NGDC). The MGD77T format includes a header (documentation) file (.h77t) and a data file (.m77t). More information regarding this format can be found in the publication listed in the Cross_reference section of this metadata file.

Single-beam bathymetry and magnetic data along with DGPS navigation data was collected as part of field activity L-1-82-NC in Northern California from 02/02/1982 to 02/03/1982, http://walrus.wr.usgs.gov/infobank/l/l182nc/html/l-1-82-nc.meta.html These data are reformatted from space-delimited ASCII text files located in the Coastal and Marine Geology Program (CMGP) InfoBank field activity catalog at http://walrus.wr.usgs.gov/infobank/l/l182nc/html/l-1-82-nc.bath.html and http://walrus.wr.usgs.gov/infobank/l/l182nc/html/l-1-82-nc.mag.html into MGD77T format provided by the NOAA's National Geophysical Data Center(NGDC). The MGD77T format includes a header (documentation) file (.h77t) and a data file (.m77t). More information regarding this format can be found in the publication listed in the Cross_reference section of this metadata file.

Single-beam bathymetry, gravity, and magnetic data along with DGPS navigation data was collected as part of field activity L-1-84-AN in Antarctica from 01/04/1984 to 02/01/1984, http://walrus.wr.usgs.gov/infobank/l/l184an/html/l-1-84-an.meta.html These data are reformatted from space-delimited ASCII text files located in the Coastal and Marine Geology Program (CMGP) InfoBank field activity catalog at http://walrus.wr.usgs.gov/infobank/l/l184an/html/l-1-84-an.bath.html, http://walrus.wr.usgs.gov/infobank/l/l184an/html/l-1-84-an.grav.html, and http://walrus.wr.usgs.gov/infobank/l/l184an/html/l-1-84-an.mag.html into MGD77T format provided by the NOAA's National Geophysical Data Center(NGDC). The MGD77T format includes a header (documentation) file (.h77t) and a data file (.m77t). More information regarding this format can be found in the publication listed in the Cross_reference section of this metadata file.

Single-beam bathymetry and magnetics data along with DGPS navigation data was collected as part of field activity L-2-75-NP in Gulf of Alaska from 08/25/1975 to 09/04/1975, http://walrus.wr.usgs.gov/infobank/l/l275np/html/l-2-75-np.meta.html These data are reformatted from space-delimited ASCII text files located in the Coastal and Marine Geology Program (CMGP) InfoBank field activity catalog at http://walrus.wr.usgs.gov/infobank/l/l275np/html/l-2-75-np.bath.html and http://walrus.wr.usgs.gov/infobank/l/l275np/html/l-2-75-np.mag.html into MGD77T format provided by the NOAA's National Geophysical Data Center(NGDC). The MGD77T format includes a header (documentation) file (.h77t) and a data file (.m77t). More information regarding this format can be found in the publication listed in the Cross_reference section of this metadata file.

Single-beam bathymetry, gravity, and magnetic data along with DGPS navigation data was collected as part of field activity L-2-82-NC in Off San Mateo, Northern California from 02/07/1982 to 02/12/1982, http://walrus.wr.usgs.gov/infobank/l/l282nc/html/l-2-82-nc.meta.html These data are reformatted from space-delimited ASCII text files located in the Coastal and Marine Geology Program (CMGP) InfoBank field activity catalog at http://walrus.wr.usgs.gov/infobank/l/l282nc/html/l-2-82-nc.bath.html, http://walrus.wr.usgs.gov/infobank/l/l282nc/html/l-2-82-nc.grav.html, and http://walrus.wr.usgs.gov/infobank/l/l282nc/html/l-2-82-nc.mag.html into MGD77T format provided by the NOAA's National Geophysical Data Center(NGDC). The MGD77T format includes a header (documentation) file (.h77t) and a data file (.m77t). More information regarding this format can be found in the publication listed in the Cross_reference section of this metadata file.

Single-beam bathymetry, gravity, and magnetic data along with DGPS navigation data was collected as part of field activity L-2-84-AN in Antarctica from 02/03/1984 to 03/03/1984, http://walrus.wr.usgs.gov/infobank/l/l284an/html/l-2-84-an.meta.html These data are reformatted from space-delimited ASCII text files located in the Coastal and Marine Geology Program (CMGP) InfoBank field activity catalog at http://walrus.wr.usgs.gov/infobank/l/l284an/html/l-2-84-an.bath.html, http://walrus.wr.usgs.gov/infobank/l/l284an/html/l-2-84-an.grav.html, and http://walrus.wr.usgs.gov/infobank/l/l284an/html/l-2-84-an.mag.html into MGD77T format provided by the NOAA's National Geophysical Data Center(NGDC). The MGD77T format includes a header (documentation) file (.h77t) and a data file (.m77t). More information regarding this format can be found in the publication listed in the Cross_reference section of this metadata file.

Single-beam bathymetry data along with DGPS navigation data was collected as part of field activity L-2-85-NC in Northern California from 04/03/1985 to 04/04/1985, http://walrus.wr.usgs.gov/infobank/l/l285nc/html/l-2-85-nc.meta.html These data are reformatted from space-delimited ASCII text files located in the Coastal and Marine Geology Program (CMGP) InfoBank field activity catalog at http://walrus.wr.usgs.gov/infobank/l/l285nc/html/l-2-85-nc.bath.html into MGD77T format provided by the NOAA's National Geophysical Data Center(NGDC). The MGD77T format includes a header (documentation) file (.h77t) and a data file (.m77t). More information regarding this format can be found in the publication listed in the Cross_reference section of this metadata file.

Single-beam bathymetry and magnetics data along with DGPS navigation data was collected as part of field activity L-2B-78-SC in Southern California from 05/25/1978 to 05/29/1978, http://walrus.wr.usgs.gov/infobank/l/l2b78sc/html/l-2b-78-sc.meta.html These data are reformatted from space-delimited ASCII text files located in the Coastal and Marine Geology Program (CMGP) InfoBank field activity catalog at http://walrus.wr.usgs.gov/infobank/l/l2b78sc/html/l-2b-78-sc.bath.html and http://walrus.wr.usgs.gov/infobank/l/l2b78sc/html/l-2b-78-sc.mag.html into MGD77T format provided by the NOAA's National Geophysical Data Center(NGDC). The MGD77T format includes a header (documentation) file (.h77t) and a data file (.m77t). More information regarding this format can be found in the publication listed in the Cross_reference section of this metadata file.

Single-beam bathymetry, gravity, and magnetic data along with DGPS navigation data was collected as part of field activity L-3-76-WO in Washington to Vancouver Island, British Columbia from 06/11/1976 to 06/20/1976, http://walrus.wr.usgs.gov/infobank/l/l376wo/html/l-3-76-wo.meta.html These data are reformatted from space-delimited ASCII text files located in the Coastal and Marine Geology Program (CMGP) InfoBank field activity catalog at http://walrus.wr.usgs.gov/infobank/l/l376wo/html/l-3-76-wo.bath.html, http://walrus.wr.usgs.gov/infobank/l/l376wo/html/l-3-76-wo.grav.html, and http://walrus.wr.usgs.gov/infobank/l/l376wo/html/l-3-76-wo.mag.html into MGD77T format provided by the NOAA's National Geophysical Data Center(NGDC). The MGD77T format includes a header (documentation) file (.h77t) and a data file (.m77t). More information regarding this format can be found in the publication listed in the Cross_reference section of this metadata file.

Single-beam bathymetry, gravity, and magnetic data along with DGPS navigation data was collected as part of field activity L-3-78-EG in Eastern Gulf of Alaska from 06/22/1978 to 07/04/1978, http://walrus.wr.usgs.gov/infobank/l/l378eg/html/l-3-78-eg.meta.html These data are reformatted from space-delimited ASCII text files located in the Coastal and Marine Geology Program (CMGP) InfoBank field activity catalog at http://walrus.wr.usgs.gov/infobank/l/l378eg/html/l-3-78-eg.bath.html, http://walrus.wr.usgs.gov/infobank/l/l378eg/html/l-3-78-eg.grav.html, and http://walrus.wr.usgs.gov/infobank/l/l378eg/html/l-3-78-eg.mag.html into MGD77T format provided by the NOAA's National Geophysical Data Center(NGDC). The MGD77T format includes a header (documentation) file (.h77t) and a data file (.m77t). More information regarding this format can be found in the publication listed in the Cross_reference section of this metadata file.

Single-beam bathymetry data along with DGPS navigation data was collected as part of field activity G-1-78-EG in Eastern Gulf of Alaska, Glacier Bay from 09/12/1978 to 09/23/1978, http://walrus.wr.usgs.gov/infobank/g/g178eg/html/g-1-78-eg.meta.html These data are reformatted from space-delimited ASCII text files located in the Coastal and Marine Geology Program (CMGP) InfoBank field activity catalog at http://walrus.wr.usgs.gov/infobank/g/g178eg/html/g-1-78-eg.bath.html into MGD77T format provided by the NOAA's National Geophysical Data Center(NGDC). The MGD77T format includes a header (documentation) file (.h77t) and a data file (.m77t). More information regarding this format can be found in the publication listed in the Cross_reference section of this metadata file.

Single-beam bathymetry and magnetics data along with DGPS navigation data was collected as part of field activity L-3-80-NP in North Pacific from 05/22/1980 to 06/04/1980, http://walrus.wr.usgs.gov/infobank/l/l380np/html/l-3-80-np.meta.html These data are reformatted from space-delimited ASCII text files located in the Coastal and Marine Geology Program (CMGP) InfoBank field activity catalog at http://walrus.wr.usgs.gov/infobank/l/l380np/html/l-3-80-np.bath.html and http://walrus.wr.usgs.gov/infobank/l/l380np/html/l-3-80-np.grav.html into MGD77T format provided by the NOAA's National Geophysical Data Center(NGDC). The MGD77T format includes a header (documentation) file (.h77t) and a data file (.m77t). More information regarding this format can be found in the publication listed in the Cross_reference section of this metadata file.

Single-beam bathymetry, gravity, and magnetic data along with DGPS navigation data was collected as part of field activity L-3-82-NC in Off San Mateo County, Northern California from 02/27/1982 to 03/01/1982, http://walrus.wr.usgs.gov/infobank/l/l382nc/html/l-3-82-nc.meta.html These data are reformatted from space-delimited ASCII text files located in the Coastal and Marine Geology Program (CMGP) InfoBank field activity catalog at http://walrus.wr.usgs.gov/infobank/l/l382nc/html/l-3-82-nc.bath.html, http://walrus.wr.usgs.gov/infobank/l/l382nc/html/l-3-82-nc.grav.html, and http://walrus.wr.usgs.gov/infobank/l/l382nc/html/l-3-82-nc.mag.html into MGD77T format provided by the NOAA's National Geophysical Data Center(NGDC). The MGD77T format includes a header (documentation) file (.h77t) and a data file (.m77t). More information regarding this format can be found in the publication listed in the Cross_reference section of this metadata file.

Single-beam bathymetry and gravity data along with DGPS navigation data was collected as part of field activity L-3-83-WF in Juan de Fuca from 08/19/1983 to 09/01/1983, http://walrus.wr.usgs.gov/infobank/l/l383wf/html/l-3-83-wf.meta.html These data are reformatted from space-delimited ASCII text files located in the Coastal and Marine Geology Program (CMGP) InfoBank field activity catalog at http://walrus.wr.usgs.gov/infobank/l/l383wf/html/l-3-83-wf.bath.html and http://walrus.wr.usgs.gov/infobank/l/l383wf/html/l-3-83-wf.grav.html into MGD77T format provided by the NOAA's National Geophysical Data Center(NGDC). The MGD77T format includes a header (documentation) file (.h77t) and a data file (.m77t). More information regarding this format can be found in the publication listed in the Cross_reference section of this metadata file.

Single-beam bathymetry, gravity, and magnetic data along with DGPS navigation data was collected as part of field activity L-3-84-SP in Tonga, Southern Pacific from 04/02/1984 to 05/01/1984, http://walrus.wr.usgs.gov/infobank/l/l384sp/html/l-3-84-sp.meta.html These data are reformatted from space-delimited ASCII text files located in the Coastal and Marine Geology Program (CMGP) InfoBank field activity catalog at http://walrus.wr.usgs.gov/infobank/l/l384sp/html/l-3-84-sp.bath.html, http://walrus.wr.usgs.gov/infobank/l/l384sp/html/l-3-84-sp.grav.html, and http://walrus.wr.usgs.gov/infobank/l/l384sp/html/l-3-84-sp.mag.html into MGD77T format provided by the NOAA's National Geophysical Data Center(NGDC). The MGD77T format includes a header (documentation) file (.h77t) and a data file (.m77t). More information regarding this format can be found in the publication listed in the Cross_reference section of this metadata file.

Single-beam bathymetry data along with DGPS navigation data was collected as part of field activity L-3-85-NC in Northern California from 07/15/1985 to 07/17/1985, http://walrus.wr.usgs.gov/infobank/l/l385nc/html/l-3-85-nc.meta.html These data are reformatted from space-delimited ASCII text files located in the Coastal and Marine Geology Program (CMGP) InfoBank field activity catalog at http://walrus.wr.usgs.gov/infobank/l/l385nc/html/l-3-85-nc.bath.html into MGD77T format provided by the NOAA's National Geophysical Data Center(NGDC). The MGD77T format includes a header (documentation) file (.h77t) and a data file (.m77t). More information regarding this format can be found in the publication listed in the Cross_reference section of this metadata file.

Single-beam bathymetry, gravity, and magnetic data along with DGPS navigation data was collected as part of field activity L-3A-81-NC in Central Coast, Northern California from 04/16/1981 to 04/26/1981, http://walrus.wr.usgs.gov/infobank/l/l3a81nc/html/l-3a-81-nc.meta.html These data are reformatted from space-delimited ASCII text files located in the Coastal and Marine Geology Program (CMGP) InfoBank field activity catalog at http://walrus.wr.usgs.gov/infobank/l/l3a81nc/html/l-3a-81-nc.bath.html, http://walrus.wr.usgs.gov/infobank/l/l3a81nc/html/l-3a-81-nc.grav.html, and http://walrus.wr.usgs.gov/infobank/l/l3a81nc/html/l-3a-81-nc.mag.html into MGD77T format provided by the NOAA's National Geophysical Data Center(NGDC). The MGD77T format includes a header (documentation) file (.h77t) and a data file (.m77t). More information regarding this format can be found in the publication listed in the Cross_reference section of this metadata file.

Single-beam bathymetry, gravity, and magnetic data along with DGPS navigation data was collected as part of field activity L-4-75-BS in Bering Sea, Aleutian Basin, Alaska from 09/07/1975 to 09/18/1975, http://walrus.wr.usgs.gov/infobank/l/l475bs/html/l-4-75-bs.meta.html These data are reformatted from space-delimited ASCII text files located in the Coastal and Marine Geology Program (CMGP) InfoBank field activity catalog at http://walrus.wr.usgs.gov/infobank/l/l475bs/html/l-4-75-bs.bath.html, http://walrus.wr.usgs.gov/infobank/l/l475bs/html/l-4-75-bs.grav.html, and http://walrus.wr.usgs.gov/infobank/l/l475bs/html/l-4-75-bs.mag.html into MGD77T format provided by the NOAA's National Geophysical Data Center(NGDC). The MGD77T format includes a header (documentation) file (.h77t) and a data file (.m77t). More information regarding this format can be found in the publication listed in the Cross_reference section of this metadata file.

Single-beam bathymetry, magnetics, gravity data along with DGPS navigation data was collected as part of field activity L-4-76-WG in Western Gulf of Alaska from 06/26/1976 to 07/25/1976, http://walrus.wr.usgs.gov/infobank/l/l476wg/html/l-4-76-wg.meta.html These data are reformatted from space-delimited ASCII text files located in the Coastal and Marine Geology Program (CMGP) InfoBank field activity catalog at http://walrus.wr.usgs.gov/infobank/l/l476wg/html/l-4-76-wg.bath.html, http://walrus.wr.usgs.gov/infobank/l/l476wg/html/l-4-76-wg.grav.html, and http://walrus.wr.usgs.gov/infobank/l/l476wg/html/l-4-76-wg.mag.html into MGD77T format provided by the NOAA's National Geophysical Data Center(NGDC). The MGD77T format includes a header (documentation) file (.h77t) and a data file (.m77t). More information regarding this format can be found in the publication listed in the Cross_reference section of this metadata file.

Single-beam bathymetry, gravity, and magnetic data along with DGPS navigation data was collected as part of field activity L-4-77-NC in Northern California from 05/10/1977 to 05/21/1977, http://walrus.wr.usgs.gov/infobank/l/l477nc/html/l-4-77-nc.meta.html These data are reformatted from space-delimited ASCII text files located in the Coastal and Marine Geology Program (CMGP) InfoBank field activity catalog at http://walrus.wr.usgs.gov/infobank/l/l477nc/html/l-4-77-nc.bath.html, http://walrus.wr.usgs.gov/infobank/l/l477nc/html/l-4-77-nc.grav.html, and http://walrus.wr.usgs.gov/infobank/l/l477nc/html/l-4-77-nc.mag.html into MGD77T format provided by the NOAA's National Geophysical Data Center(NGDC). The MGD77T format includes a header (documentation) file (.h77t) and a data file (.m77t). More information regarding this format can be found in the publication listed in the Cross_reference section of this metadata file.

Single-beam bathymetry, gravity, and magnetic data along with DGPS navigation data was collected as part of field activity L-4-78-BS in Bering Sea, Alaska from 07/08/1978 to 08/01/1978, http://walrus.wr.usgs.gov/infobank/l/l478bs/html/l-4-78-bs.meta.html These data are reformatted from space-delimited ASCII text files located in the Coastal and Marine Geology Program (CMGP) InfoBank field activity catalog at http://walrus.wr.usgs.gov/infobank/l/l478bs/html/l-4-78-bs.bath.html into MGD77T format provided by the NOAA's National Geophysical Data Center(NGDC). The MGD77T format includes a header (documentation) file (.h77t) and a data file (.m77t). More information regarding this format can be found in the publication listed in the Cross_reference section of this metadata file.

Single-beam bathymetry, gravity, and magnetic data along with DGPS navigation data was collected as part of field activity L-4-82-NP in Low-energy abyssal hill areas midway between San Franciso and Hawaii from 03/01/1982 to 03/15/1982, http://walrus.wr.usgs.gov/infobank/l/l482np/html/l-4-82-np.meta.html These data are reformatted from space-delimited ASCII text files located in the Coastal and Marine Geology Program (CMGP) InfoBank field activity catalog at http://walrus.wr.usgs.gov/infobank/l/l482np/html/l-4-82-np.bath.html, http://walrus.wr.usgs.gov/infobank/l/l482np/html/l-4-82-np.grav.html, and http://walrus.wr.usgs.gov/infobank/l/l482np/html/l-4-82-np.mag.html into MGD77T format provided by the NOAA's National Geophysical Data Center(NGDC). The MGD77T format includes a header (documentation) file (.h77t) and a data file (.m77t). More information regarding this format can be found in the publication listed in the Cross_reference section of this metadata file.

Single-beam bathymetry, gravity, and magnetic data along with DGPS navigation data was collected as part of field activity L-4-83-BS in Bering Sea, Alaska from 09/16/1983 to 10/02/1983, http://walrus.wr.usgs.gov/infobank/l/l483bs/html/l-4-83-bs.meta.html These data are reformatted from space-delimited ASCII text files located in the Coastal and Marine Geology Program (CMGP) InfoBank field activity catalog at http://walrus.wr.usgs.gov/infobank/l/l483bs/html/l-4-83-bs.bath.html, http://walrus.wr.usgs.gov/infobank/l/l483bs/html/l-4-83-bs.grav.html, and http://walrus.wr.usgs.gov/infobank/l/l483bs/html/l-4-83-bs.mag.html into MGD77T format provided by the NOAA's National Geophysical Data Center(NGDC). The MGD77T format includes a header (documentation) file (.h77t) and a data file (.m77t). More information regarding this format can be found in the publication listed in the Cross_reference section of this metadata file.

Single-beam bathymetry and magnetics data along with DGPS navigation data was collected as part of field activity L-4-85-WF in Juan de Fuca from 07/28/1985 to 08/08/1985, http://walrus.wr.usgs.gov/infobank/l/l485wf/html/l-4-85-wf.meta.html These data are reformatted from space-delimited ASCII text files located in the Coastal and Marine Geology Program (CMGP) InfoBank field activity catalog at http://walrus.wr.usgs.gov/infobank/l/l485wf/html/l-4-85-wf.bath.html http://walrus.wr.usgs.gov/infobank/l/l485wf/html/l-4-85-wf.bath.html into MGD77T format provided by the NOAA's National Geophysical Data Center(NGDC). The MGD77T format includes a header (documentation) file (.h77t) and a data file (.m77t). More information regarding this format can be found in the publication listed in the Cross_reference section of this metadata file.

Single-beam bathymetry, gravity, and magnetic data along with DGPS navigation data was collected as part of field activity L-5-76-BS in Southern Bering Sea Shelf from 07/28/1976 to 08/25/1976, http://walrus.wr.usgs.gov/infobank/l/l576bs/html/l-5-76-bs.meta.html These data are reformatted from space-delimited ASCII text files located in the Coastal and Marine Geology Program (CMGP) InfoBank field activity catalog at http://walrus.wr.usgs.gov/infobank/l/l576bs/html/l-5-76-bs.bath.html, http://walrus.wr.usgs.gov/infobank/l/l576bs/html/l-5-76-bs.grav.html, and http://walrus.wr.usgs.gov/infobank/l/l576bs/html/l-5-76-bs.mag.html into MGD77T format provided by the NOAA's National Geophysical Data Center(NGDC). The MGD77T format includes a header (documentation) file (.h77t) and a data file (.m77t). More information regarding this format can be found in the publication listed in the Cross_reference section of this metadata file.

Single-beam bathymetry and gravity data along with DGPS navigation data was collected as part of field activity L-5-78-BS in Bering Sea, Alaska from 08/05/1978 to 08/09/1978, http://walrus.wr.usgs.gov/infobank/l/l578bs/html/l-5-78-bs.meta.html These data are reformatted from space-delimited ASCII text files located in the Coastal and Marine Geology Program (CMGP) InfoBank field activity catalog at http://walrus.wr.usgs.gov/infobank/l/l578bs/html/l-5-78-bs.bath.html and http://walrus.wr.usgs.gov/infobank/l/l578bs/html/l-5-78-bs.grav.html into MGD77T format provided by the NOAA's National Geophysical Data Center(NGDC). The MGD77T format includes a header (documentation) file (.h77t) and a data file (.m77t). More information regarding this format can be found in the publication listed in the Cross_reference section of this metadata file.

Single-beam bathymetry, gravity, and magnetic data along with DGPS navigation data was collected as part of field activity L-5-80-AA in Aleutian Arc, Alaska from 06/23/1980 to 07/05/1980, http://walrus.wr.usgs.gov/infobank/l/l580aa/html/l-5-80-aa.meta.html These data are reformatted from space-delimited ASCII text files located in the Coastal and Marine Geology Program (CMGP) InfoBank field activity catalog at http://walrus.wr.usgs.gov/infobank/l/l580aa/html/l-5-80-aa.bath.html, http://walrus.wr.usgs.gov/infobank/l/l580aa/html/l-5-80-aa.grav.html , and http://walrus.wr.usgs.gov/infobank/l/l580aa/html/l-5-80-aa.mag.html into MGD77T format provided by the NOAA's National Geophysical Data Center(NGDC). The MGD77T format includes a header (documentation) file (.h77t) and a data file (.m77t). More information regarding this format can be found in the publication listed in the Cross_reference section of this metadata file.

Single-beam bathymetry, gravity, and magnetic data along with DGPS navigation data was collected as part of field activity L-5-82-SP in Tonga Ridge, Southern Pacific from 03/28/1982 to 04/25/1982, http://walrus.wr.usgs.gov/infobank/l/l582sp/html/l-5-82-sp.meta.html These data are reformatted from space-delimited ASCII text files located in the Coastal and Marine Geology Program (CMGP) InfoBank field activity catalog at http://walrus.wr.usgs.gov/infobank/l/l582sp/html/l-5-82-sp.bath.html, http://walrus.wr.usgs.gov/infobank/l/l582sp/html/l-5-82-sp.grav.html, and http://walrus.wr.usgs.gov/infobank/l/l582sp/html/l-5-82-sp.mag.html into MGD77T format provided by the NOAA's National Geophysical Data Center(NGDC). The MGD77T format includes a header (documentation) file (.h77t) and a data file (.m77t). More information regarding this format can be found in the publication listed in the Cross_reference section of this metadata file.

Single-beam bathymetry data along with DGPS navigation data was collected as part of field activity L-5-83-HW in Horizon Guyot, Necker Ridge, Hawaii, Johnston, Palmyra, Kingman Island from 10/29/1983 to 11/26/1983, http://walrus.wr.usgs.gov/infobank/l/l583hw/html/l-5-83-hw.meta.html These data are reformatted from space-delimited ASCII text files located in the Coastal and Marine Geology Program (CMGP) InfoBank field activity catalog at http://walrus.wr.usgs.gov/infobank/l/l583hw/html/l-5-83-hw.bath.html into MGD77T format provided by the NOAA's National Geophysical Data Center(NGDC). The MGD77T format includes a header (documentation) file (.h77t) and a data file (.m77t). More information regarding this format can be found in the publication listed in the Cross_reference section of this metadata file.

Single-beam bathymetry and magnetics data along with DGPS navigation data was collected as part of field activity L-5-85-NC in Northern California from 08/10/1985 to 08/31/1985, http://walrus.wr.usgs.gov/infobank/l/l585nc/html/l-5-85-nc.meta.html These data are reformatted from space-delimited ASCII text files located in the Coastal and Marine Geology Program (CMGP) InfoBank field activity catalog at http://walrus.wr.usgs.gov/infobank/l/l585nc/html/l-5-85-nc.bath.html and http://walrus.wr.usgs.gov/infobank/l/l585nc/html/l-5-85-nc.mag.html into MGD77T format provided by the NOAA's National Geophysical Data Center(NGDC). The MGD77T format includes a header (documentation) file (.h77t) and a data file (.m77t). More information regarding this format can be found in the publication listed in the Cross_reference section of this metadata file.

Single-beam bathymetry, gravity, and magnetic data along with DGPS navigation data was collected as part of field activity L-6-76-AR in Arctic from 08/27/1976 to 09/03/1976, http://walrus.wr.usgs.gov/infobank/l/l676ar/html/l-6-76-ar.meta.html These data are reformatted from space-delimited ASCII text files located in the Coastal and Marine Geology Program (CMGP) InfoBank field activity catalog at http://walrus.wr.usgs.gov/infobank/l/l676ar/html/l-6-76-ar.bath.html, http://walrus.wr.usgs.gov/infobank/l/l676ar/html/l-6-76-ar.grav.html, and http://walrus.wr.usgs.gov/infobank/l/l676ar/html/l-6-76-ar.mag.html into MGD77T format provided by the NOAA's National Geophysical Data Center(NGDC). The MGD77T format includes a header (documentation) file (.h77t) and a data file (.m77t). More information regarding this format can be found in the publication listed in the Cross_reference section of this metadata file.

Single-beam bathymetry, gravity, and magnetic data along with DGPS navigation data was collected as part of field activity L-6-77-EG in Eastern Gulf of Alaska from 06/13/1977 to 06/30/1977, http://walrus.wr.usgs.gov/infobank/l/l677eg/html/l-6-77-eg.meta.html These data are reformatted from space-delimited ASCII text files located in the Coastal and Marine Geology Program (CMGP) InfoBank field activity catalog at http://walrus.wr.usgs.gov/infobank/l/l677eg/html/l-6-77-eg.bath.html, http://walrus.wr.usgs.gov/infobank/l/l677eg/html/l-6-77-eg.grav.html, and http://walrus.wr.usgs.gov/infobank/l/l677eg/html/l-6-77-eg.mag.html into MGD77T format provided by the NOAA's National Geophysical Data Center(NGDC). The MGD77T format includes a header (documentation) file (.h77t) and a data file (.m77t). More information regarding this format can be found in the publication listed in the Cross_reference section of this metadata file.

Single-beam bathymetry and gravity data along with DGPS navigation data was collected as part of field activity L-6-78-AR in Arctic from 08/26/1978 to 09/20/1978, http://walrus.wr.usgs.gov/infobank/l/l678ar/html/l-6-78-ar.meta.html These data are reformatted from space-delimited ASCII text files located in the Coastal and Marine Geology Program (CMGP) InfoBank field activity catalog at http://walrus.wr.usgs.gov/infobank/l/l678ar/html/l-6-78-ar.bath.html and http://walrus.wr.usgs.gov/infobank/l/l678ar/html/l-6-78-ar.grav.html into MGD77T format provided by the NOAA's National Geophysical Data Center(NGDC). The MGD77T format includes a header (documentation) file (.h77t) and a data file (.m77t). More information regarding this format can be found in the publication listed in the Cross_reference section of this metadata file.

Single-beam bathymetry, gravity, and magnetic data along with DGPS navigation data was collected as part of field activity L-6-80-BS in North Bering Sea, Alaska from 07/08/1980 to 07/28/1980, http://walrus.wr.usgs.gov/infobank/l/l680bs/html/l-6-80-bs.meta.html These data are reformatted from space-delimited ASCII text files located in the Coastal and Marine Geology Program (CMGP) InfoBank field activity catalog at http://walrus.wr.usgs.gov/infobank/l/l680bs/html/l-6-80-bs.bath.html, http://walrus.wr.usgs.gov/infobank/l/l680bs/html/l-6-80-bs.grav.html, and http://walrus.wr.usgs.gov/infobank/l/l680bs/html/l-6-80-bs.mag.html into MGD77T format provided by the NOAA's National Geophysical Data Center(NGDC). The MGD77T format includes a header (documentation) file (.h77t) and a data file (.m77t). More information regarding this format can be found in the publication listed in the Cross_reference section of this metadata file.

Single-beam bathymetry, gravity, and magnetic data along with DGPS navigation data was collected as part of field activity L-6-81-NP in Off British Columbia and Washington, Northern Pacific from 05/31/1981 to 06/07/1981, http://walrus.wr.usgs.gov/infobank/l/l681np/html/l-6-81-np.meta.html These data are reformatted from space-delimited ASCII text files located in the Coastal and Marine Geology Program (CMGP) InfoBank field activity catalog at http://walrus.wr.usgs.gov/infobank/l/l681np/html/l-6-81-np.bath.html, http://walrus.wr.usgs.gov/infobank/l/l681np/html/l-6-81-np.grav.html, and http://walrus.wr.usgs.gov/infobank/l/l681np/html/l-6-81-np.mag.html into MGD77T format provided by the NOAA's National Geophysical Data Center(NGDC). The MGD77T format includes a header (documentation) file (.h77t) and a data file (.m77t). More information regarding this format can be found in the publication listed in the Cross_reference section of this metadata file.

Single-beam bathymetry, gravity, and magnetic data along with DGPS navigation data was collected as part of field activity L-6-82-SP in Vanuatu from 04/27/1982 to 05/16/1982, http://walrus.wr.usgs.gov/infobank/l/l682sp/html/l-6-82-sp.meta.html These data are reformatted from space-delimited ASCII text files located in the Coastal and Marine Geology Program (CMGP) InfoBank field activity catalog at http://walrus.wr.usgs.gov/infobank/l/l682sp/html/l-6-82-sp.bath.html, http://walrus.wr.usgs.gov/infobank/l/l682sp/html/l-6-82-sp.grav.html, and http://walrus.wr.usgs.gov/infobank/l/l682sp/html/l-6-82-sp.mag.html into MGD77T format provided by the NOAA's National Geophysical Data Center(NGDC). The MGD77T format includes a header (documentation) file (.h77t) and a data file (.m77t). More information regarding this format can be found in the publication listed in the Cross_reference section of this metadata file.

Single-beam bathymetry and gravity data along with DGPS navigation data was collected as part of field activity L-6-83-SP in Southern Pacific from 12/05/1983 to 12/11/1983, http://walrus.wr.usgs.gov/infobank/l/l683sp/html/l-6-83-sp.meta.html These data are reformatted from space-delimited ASCII text files located in the Coastal and Marine Geology Program (CMGP) InfoBank field activity catalog at http://walrus.wr.usgs.gov/infobank/l/l683sp/html/l-6-83-sp.bath.html and http://walrus.wr.usgs.gov/infobank/l/l683sp/html/l-6-83-sp.grav.html into MGD77T format provided by the NOAA's National Geophysical Data Center(NGDC). The MGD77T format includes a header (documentation) file (.h77t) and a data file (.m77t). More information regarding this format can be found in the publication listed in the Cross_reference section of this metadata file.

Single-beam bathymetry and magnetics data along with DGPS navigation data was collected as part of field activity L-6-85-NC in Northern California from 09/03/1985 to 09/20/1985, http://walrus.wr.usgs.gov/infobank/l/l685nc/html/l-6-85-nc.meta.html These data are reformatted from space-delimited ASCII text files located in the Coastal and Marine Geology Program (CMGP) InfoBank field activity catalog at http://walrus.wr.usgs.gov/infobank/l/l685nc/html/l-6-85-nc.bath.html and http://walrus.wr.usgs.gov/infobank/l/l685nc/html/l-6-85-nc.mag.html into MGD77T format provided by the NOAA's National Geophysical Data Center(NGDC). The MGD77T format includes a header (documentation) file (.h77t) and a data file (.m77t). More information regarding this format can be found in the publication listed in the Cross_reference section of this metadata file.

Single-beam bathymetry, gravity, and magnetic data along with DGPS navigation data was collected as part of field activity L-7-76-BS in Bering Sea, Alaska from 09/03/1976 to 09/10/1976, http://walrus.wr.usgs.gov/infobank/l/l776bs/html/l-7-76-bs.meta.html These data are reformatted from space-delimited ASCII text files located in the Coastal and Marine Geology Program (CMGP) InfoBank field activity catalog at http://walrus.wr.usgs.gov/infobank/l/l776bs/html/l-7-76-bs.bath.html, http://walrus.wr.usgs.gov/infobank/l/l776bs/html/l-7-76-bs.grav.html, and http://walrus.wr.usgs.gov/infobank/l/l776bs/html/l-7-76-bs.mag.html into MGD77T format provided by the NOAA's National Geophysical Data Center(NGDC). The MGD77T format includes a header (documentation) file (.h77t) and a data file (.m77t). More information regarding this format can be found in the publication listed in the Cross_reference section of this metadata file.

Single-beam bathymetry, gravity, and magnetic data along with DGPS navigation data was collected as part of field activity L-7-77-WG in Western Gulf of Alaska from 07/03/1977 to 07/22/1977, http://walrus.wr.usgs.gov/infobank/l/l777wg/html/l-7-77-wg.meta.html These data are reformatted from space-delimited ASCII text files located in the Coastal and Marine Geology Program (CMGP) InfoBank field activity catalog at http://walrus.wr.usgs.gov/infobank/l/l777wg/html/l-7-77-wg.bath.html, http://walrus.wr.usgs.gov/infobank/l/l777wg/html/l-7-77-wg.grav.html, and http://walrus.wr.usgs.gov/infobank/l/l777wg/html/l-7-77-wg.mag.html into MGD77T format provided by the NOAA's National Geophysical Data Center(NGDC). The MGD77T format includes a header (documentation) file (.h77t) and a data file (.m77t). More information regarding this format can be found in the publication listed in the Cross_reference section of this metadata file.

Single-beam bathymetry and gravity data along with DGPS navigation data was collected as part of field activity L-7-80-BS in Bering Sea, Alaska from 08/01/1980 to 08/26/1980, http://walrus.wr.usgs.gov/infobank/l/l780bs/html/l-7-80-bs.meta.html These data are reformatted from space-delimited ASCII text files located in the Coastal and Marine Geology Program (CMGP) InfoBank field activity catalog at http://walrus.wr.usgs.gov/infobank/l/l780bs/html/l-7-80-bs.bath.html and http://walrus.wr.usgs.gov/infobank/l/l780bs/html/l-7-80-bs.grav.html into MGD77T format provided by the NOAA's National Geophysical Data Center(NGDC). The MGD77T format includes a header (documentation) file (.h77t) and a data file (.m77t). More information regarding this format can be found in the publication listed in the Cross_reference section of this metadata file.

A benthic habitat polygon coverage has been created of the coral reef ecosystem off the coast of Pu'ukohola Heiau (PUHE) National Historic Site on the Kona Coast of Hawai'i. Polygons were hand-digitized from visual interpretation of aerial photography and SHOALS bathymetry data. We also utilized in situ knowledge from towed instruments, underwater photography and videography, and diver and snorkeler observations. The polygons have attributes for Main Structure/Substrate, Dominant Structure/Substrate, Major Biological Cover, Percent of Major Biological Cover, Reef Zone, and Unique ID, and measurements of area (m2) of each polygon.

A benthic habitat polygon coverage has been created of the coral reef ecosystem within and adjacent to Kaloko-Honokohau (KAHO) National Historical Park on the Kona Coast of Hawai'i. Polygons were hand-digitized from visual interpretation of aerial photography and SHOALS bathymetry data. We also utilized in situ knowledge from towed instruments, underwater photography and videography, and diver and snorkeler observations. The polygons have attributes for Main Structure/Substrate, Dominant Structure/Substrate, Major Biological Cover, Percent of Major Biological Cover, Reef Zone, Unique ID, and measurements of Area (m2) of each polygon.

A benthic habitat polygon coverage has been created of the coral reef ecosystem off the coast of Pu'uhonua O Honaunau (PUHO) National Historical Park on the Kona Coast of Hawai'i. Polygons were hand-digitized from visual interpretation of aerial photography and SHOALS bathymetry data. We also utilized in situ knowledge from towed instruments, underwater photography and videography, and diver and snorkeler observations. The polygons have attributes for Main Structure/Substrate, Dominant Structure/Substrate, Major Biological Cover, Percent of Major Biological Cover, Reef Zone, Unique ID, and measurements of area (m2) of each polygon.

Erosion Hazard Intensity Level in the coastal zone of Hawaii, Hawaii

Overall Hazard Assessment in the coastal zone of Hawaii, Hawaii

Sea Level Hazard Intensity Level in the coastal zone of Hawaii, Hawaii

Stream Flooding Hazard Intensity Level in the coastal zone of Hawaii, Hawaii

Storm Hazard Intensity Level in the coastal zone of Hawaii, Hawaii

Tsunami Hazard Intensity Level in the coastal zone of Hawaii, Hawaii

Volcanic and Seismic Hazard Intensity Level in the coastal zone of Hawaii, Hawaii

High Wave Hazard Intensity Level in the coastal zone of Hawaii, Hawaii

Erosion Hazard Intensity Level in the coastal zone of Kauai, Hawaii

Overall Hazard Assessment in the coastal zone of Kauai, Hawaii

Sea Level Hazard Intensity Level in the coastal zone of Kauai, Hawaii

Stream Flooding Hazard Intensity Level in the coastal zone of Kauai, Hawaii

Storm Hazard Intensity Level in the coastal zone of Kauai, Hawaii

Tsunami Hazard Intensity Level in the coastal zone of Kauai, Hawaii

Volcanic and Seismic Hazard Intensity Level in the coastal zone of Kauai, Hawaii

High Wave Hazard Intensity Level in the coastal zone of Kauai, Hawaii

Erosion Hazard Intensity Level in the coastal zone of Lanai, Hawaii

Overall Hazard Assessment in the coastal zone of Lanai, Hawaii

Sea Level Hazard Intensity Level in the coastal zone of Lanai, Hawaii

Stream Flooding Hazard Intensity Level in the coastal zone of Lanai, Hawaii

Storm Hazard Intensity Level in the coastal zone of Lanai, Hawaii

Tsunami Hazard Intensity Level in the coastal zone of Lanai, Hawaii

Volcanic and Seismic Hazard Intensity Level in the coastal zone of Lanai, Hawaii

High Wave Hazard Intensity Level in the coastal zone of Lanai, Hawaii

Erosion Hazard Intensity Level in the coastal zone of Maui, Hawaii

Overall Hazard Assessment in the coastal zone of Maui, Hawaii

Sea Level Hazard Intensity Level in the coastal zone of Maui, Hawaii

Stream Flooding Hazard Intensity Level in the coastal zone of Maui, Hawaii

Storm Hazard Intensity Level in the coastal zone of Maui, Hawaii

Tsunami Hazard Intensity Level in the coastal zone of Maui, Hawaii

Volcanic and Seismic Hazard Intensity Level in the coastal zone of Maui, Hawaii

High Wave Hazard Intensity Level in the coastal zone of Maui, Hawaii

Erosion Hazard Intensity Level in the coastal zone of Molokai, Hawaii

Overall Hazard Assessment in the coastal zone of Molokai, Hawaii

Sea Level Hazard Intensity Level in the coastal zone of Molokai, Hawaii

Stream Flooding Hazard Intensity Level in the coastal zone of Molokai, Hawaii

Storm Hazard Intensity Level in the coastal zone of Molokai, Hawaii

Tsunami Hazard Intensity Level in the coastal zone of Molokai, Hawaii

Volcanic and Seismic Hazard Intensity Level in the coastal zone of Molokai, Hawaii

High Wave Hazard Intensity Level in the coastal zone of Molokai, Hawaii

Erosion Hazard Intensity Level in the coastal zone of Oahu, Hawaii

Overall Hazard Assessment in the coastal zone of Oahu, Hawaii

Sea Level Hazard Intensity Level in the coastal zone of Oahu, Hawaii

Stream Flooding Hazard Intensity Level in the coastal zone of Oahu, Hawaii

Storm Hazard Intensity Level in the coastal zone of Oahu, Hawaii

Tsunami Hazard Intensity Level in the coastal zone of Oahu, Hawaii

Volcanic and Seismic Hazard Intensity Level in the coastal zone of Oahu, Hawaii

High Wave Hazard Intensity Level in the coastal zone of Oahu, Hawaii

Erosion Hazard Intensity Level in the coastal zone of Sand Island (Oahu), Hawaii

Overall Hazard Assessment in the coastal zone of Sand Island (Oahu), Hawaii

Sea Level Hazard Intensity Level in the coastal zone of Sand Island (Oahu), Hawaii

Stream Flooding Hazard Intensity Level in the coastal zone of Sand Island (Oahu), Hawaii

Storm Hazard Intensity Level in the coastal zone of Sand Island (Oahu), Hawaii

Tsunami Hazard Intensity Level in the coastal zone of Sand Island (Oahu), Hawaii

Volcanic and Seismic Hazard Intensity Level in the coastal zone of Sand Island (Oahu), Hawaii

High Wave Hazard Intensity Level in the coastal zone of Sand Island (Oahu), Hawaii

This metadata record describes the microbial uptake of nutrients by native planktonic and biofilm-associated bacteria within the Upper Floridan Aquifer (approximately 1,000 feet below land surface) in the Okeechobee, Florida area. Groundwater samples were collected between 2018 and 2019 from a South Florida Water Management District (SFWMD) monitoring well installed at the Kissimmee River Aquifer Storage and Recovery (ASR) facility, which is located at the discharge point of the Kissimmee River into Lake Okeechobee.

The Massachusetts Office of Coastal Zone Management (CZM) launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the Massachusetts coast. Seventy-six maps were produced in 1997 depicting a statistical analysis of shoreline change on ocean-facing shorelines from the mid-1800s to 1978 using multiple data sources. In 2001, a 1994 shoreline was added. More recently, in cooperation with CZM, the U.S. Geological Survey (USGS) delineated a new shoreline for Massachusetts using color aerial ortho-imagery from 2008 to 2009 and topographic lidar data collected in 2007. This update included a marsh shoreline, which was defined to be the tonal difference between low- and high-marsh seen in ortho-photos. Further cooperation between CZM and the U.S. Geological Survey (USGS) has resulted in another update in 2018, which includes beach shorelines, marsh shorelines and dune parameters, all of which were calculated from 2013-14 topographic lidar data. This metadata file describes the marsh shoreline that is part of the 2018 update. The marsh shoreline was defined to be the steep slope found at the seaward edge of the marsh vegetation. This definition was used because the marsh edge is the preferred shoreline indicator for computing rates of change and making position forecasts.

A critical question for assessing global greenhouse gas budgets is how much of the methane that escapes from seafloor cold seep sites to the overlying water column eventually crosses the sea-air interface and reaches the atmosphere. The issue is particularly important in Arctic Ocean waters since rapid warming there increases the likelihood that gas hydrate--an ice-like form of methane and water stable at particular pressure and temperature conditions within marine sediments--will break down and release its methane to the overlying ocean. Some researchers have even proposed the possibility of an Arctic methane catastrophe characterized by wholesale breakdown of gas hydrates in marine sediments and release of the methane to the atmosphere as climate warms. This dataset collected on the West Spitsbergen margin during U.S. Geological Survey Coastal and Marine Geology Program Field Activity 2014-013-FA, which was carried out in conjunction with the University of Tromso and the GEOMAR Helmholtz Centre for Ocean Research Kiel on the R/V Helmer Hanssen, records 30-second-gridded methane and carbon dioxide concentrations in near-surface seawater and the atmospheric marine boundary layer, the carbon-13 isotopic composition of methane and carbon dioxide in the near-surface waters, and also environmental parameters (e.g., seawater salinity, wind speed, water and air temperatures). The results of calculations required to determine the sea-air flux of methane and carbon dioxide are also provided.

A discrete sample introduction module (DSIM) was developed and interfaced to a cavity ring-down spectrometer to enable measurements of methane and CO2 concentrations and 13C values with a commercially available cavity ring-down spectrometer (CRDS). The DSIM-CRDS system permits the analysis of limited volume (5 - 100-ml) samples ranging six orders-of-magnitude from 100% analyte to the lower limit of instrument detection (2 ppm). We demonstrate system performance for methane by comparing concentrations and 13C results obtained by the DSIM and traditional methods for a variety of sample types, including low concentration (nanomolar) seawater and high concentration (> 90%) natural gas. The expansive concentration range the CRDS is able to analyze while being packaged in a field-portable analytical system greatly enhances functionality for investigating methane and CO2 dynamics, as well as other gases measured by laser absorption spectroscopy.

These data are part of the effort to map geologic substrates of the Stellwagen Bank National Marine Sanctuary (SBNMS) region off Boston, Massachusetts. The overall goal is to develop high-resolution (1:25,000) interpretive maps, based on multibeam sonar data and seabed sampling, showing surficial geology and seabed sediment dynamics. The data were collected in collaboration with the Stellwagen Bank National Marine Sanctuary and will aid research on the ecology of fish and invertebrate species that inhabit the region. Sediment samples were collected aboard the Sanctuary's research vessel, R/V Auk at 679 locations on and near Stellwagen Bank using a customized Van Veen grab sampler integrated into the USGS SEABed Observation and Sampling System (SEABOSS). The SEABOSS, equipped with a video camera, was deployed from the Auk and allowed to drift for a shore period of time approximately 1 meter from the sea floor collecting video imagery of the sea floor. The SEABOSS drift typically culminated in the collection of a sediment sample used for grain-size analysis. Biological specimens were collected from the sediment, if present, and retained by scientists at teh SBNMS in Scituate, Massachusetts.

Two marine geological surveys were conducted in Long Island Sound, Connecticut and New York, in fall 2017 and spring 2018 by the U.S. Geological Survey (USGS), University of Connecticut, and University of New Haven through the Long Island Sound Mapping and Research Collaborative. Sea-floor images and videos were collected at 210 sampling sites within the survey area, and surficial sediment samples were collected at 179 of the sites. The sediment data and the observations from the images and videos are used to identify sediment texture and sea-floor habitats.

Two marine geological surveys were conducted in Long Island Sound, Connecticut and New York, in fall 2017 and spring 2018 by the U.S. Geological Survey (USGS), University of Connecticut, and University of New Haven through the Long Island Sound Mapping and Research Collaborative. Sea-floor images and videos were collected at 210 sampling sites within the survey area, and surficial sediment samples were collected at 179 of the sites. The sediment data and the observations from the images and videos are used to identify sediment texture and sea-floor habitats.

Two marine geological surveys were conducted in Long Island Sound, Connecticut and New York, in fall 2017 and spring 2018 by the U.S. Geological Survey (USGS), University of Connecticut, and University of New Haven through the Long Island Sound Mapping and Research Collaborative. Sea-floor images and videos were collected at 210 sampling sites within the survey area, and surficial sediment samples were collected at 179 of the sites. The sediment data and the observations from the images and videos are used to identify sediment texture and sea-floor habitats.

The Herring River estuary (Wellfleet, Cape Cod, Massachusetts) has been tidally restricted for over a century by a dike constructed near the mouth of the river. Behind the dike, the tidal restriction has caused the conversion of salt marsh wetlands to various other ecosystems including impounded freshwater marshes, flooded shrub land, drained forested upland, and wetlands dominated by Phragmites australis. This estuary is now managed by the National Park Service, which has plans to replace the dike and restore tidal flow to the estuary. To assist National Park Service land managers with restoration planning, study collaborators have been investigating differences in soil properties, carbon accumulation, and greenhouse gas fluxes across differing ecosystems within the Herring River Estuary. The U.S. Geological Survey collected continuous monitoring data (including water level, soil temperature, air temperature, and meteorological parameters). These datasets can help evaluate key ecosystem drivers to make predictions about potential changes as restoration commences.

Two marine geological surveys were conducted in Nantucket Sound, Massachusetts, in May 2016 and May 2017 by the U.S. Geological Survey (USGS) as part of an agreement with the Massachusetts Office of Coastal Zone Management to map the geology of the sea floor offshore of Massachusetts. Samples of surficial sediment and photographs of the sea floor were collected at 76 sampling sites within the survey area, and sea-floor videos were collected at 75 of the sites. The sediment data and the observations from the photos and videos are used to explore the nature of the sea floor; in conjunction with high-resolution geophysical data, the observations are used to make interpretive maps of sedimentary environments and validate acoustic remote sensing data.

Two marine geological surveys were conducted in Nantucket Sound, Massachusetts, in May 2016 and May 2017 by the U.S. Geological Survey (USGS) as part of an agreement with the Massachusetts Office of Coastal Zone Management to map the geology of the sea floor offshore of Massachusetts. Samples of surficial sediment and photographs of the sea floor were collected at 76 sampling sites within the survey area, and sea-floor videos were collected at 75 of the sites. The sediment data and the observations from the photos and videos are used to explore the nature of the sea floor; in conjunction with high-resolution geophysical data, the observations are used to make interpretive maps of sedimentary environments and validate acoustic remote sensing data.

Two marine geological surveys were conducted in Nantucket Sound, Massachusetts, in May 2016 and May 2017 by the U.S. Geological Survey (USGS) as part of an agreement with the Massachusetts Office of Coastal Zone Management to map the geology of the sea floor offshore of Massachusetts. Samples of surficial sediment and photographs of the sea floor were collected at 76 sampling sites within the survey area, and sea-floor videos were collected at 75 of the sites. The sediment data and the observations from the photos and videos are used to explore the nature of the sea floor; in conjunction with high-resolution geophysical data, the observations are used to make interpretive maps of sedimentary environments and validate acoustic remote sensing data.

Continuous monitoring data reported are a portion of data from a larger study investigating changes in soil properties, carbon accumulation, and greenhouse gas fluxes in four recently restored salt marsh sites and nearby natural salt marshes. For several decades, local towns, conservation groups, and government organizations have worked to identify, replace, repair, and enlarge culverts to restore tidal flow upstream from historical tidal restrictions in an effort to restore salt marsh ecosystems on Cape Cod, Massachusetts. Undersized or failed culverts restrict tidal exchange between the marsh and the bays and estuaries, which leads to alterations in plant community composition and in fundamental processes controlling soil carbon accumulation, soil carbon transformations, and greenhouse gas emissions. In this study, sites were selected to compare salt marshes restored over a range of years and to compare marshes upstream and downstream from a restored tidal restriction. Salt marshes downstream from tidal restrictions represent "natural" conditions because hydrology was not substantially altered, whereas marshes upstream from repaired culverts represent "restored" conditions. At each of the four salt marsh sites, study plots were established on the natural and restored sides of the former tidal restriction. Well water-level loggers, soil and air temperature loggers, and photosynthetically active radiation (PAR) sensors were deployed over the growing season to coincide with discrete measurements of greenhouse gas fluxes made by study collaborators. Water-level loggers were also deployed in creeks near the restored tidal restriction.

Multichannel seismic-reflection (MCS) data were collected in the California Continental Borderland as part of southern California Earthquake Hazards Task. Five data acquisition cruises conducted over a six-year span collected MCS data from offshore Santa Barbara, California south to the Exclusive Economic Zone boundary with Mexico. The primary mission was to map late Quaternary deformation as well as identify and characterize fault zones that have potential to impact high population areas of southern California. To meet its objectives, the project work focused on the distribution, character, and relative intensity of active (i.e., Holocene) deformation along the continental shelf and basins adjacent to the most highly populated areas. In addition, the project examined the Pliocene-Pleistocene record of how deformation shifted in space and time to help identify actively deforming structures that may constitute current significant seismic hazards. The MCS data accessible through this report cover the first four years of survey activity and include data from offshore Malibu coastal area west of Santa Monica, California to the southern survey limit offshore San Diego. The MCS data, which were collected with a 250-m-long, 24-channel streamer used a small gas-injector airgun source. This system provided optimum resolution of the upper 1 to 2 km of sediment for mapping active fault systems. The report includes trackline maps showing the location of the data, as well as both digital data files (SEG-Y) and images of all of the profiles. These data are also available via GeoMapApp (http://www.geomapapp.org/) and Virtual Ocean ( http://www.virtualocean.org/) earth science exploration and visualization applications.

CCALBATC consists of bathymetric contours at 10-m and 50-m intervals for the area offshore of central California between Point Arena to the north and Point Sur to the south. The lines were digitized from 1:250,000-scale NOAA charts. This is one of a collection of digital files of a geographic information system of spatially referenced data related to the USGS Coastal and Marine Geology Program Monterey Bay National Marine Sanctuary Project (see this and other older Monterey Bay USGS works archived at https://archive.usgs.gov/archive/sites/walrus.wr.usgs.gov/monterey/index.html.

This dataset contains bathymetric contours for the greater Monterey Bay area between Point Ano Nuevo to the north and Point Sur to the south. Contours are provided at 10-m intervals to a depth of 200 m and 100-m intervals to maximum depth. The data from which the contours were derived were hydrographic survey points published by NOAA NOS in 1998. This is one of a collection of digital files of a geographic information system of spatially referenced data related to the USGS Coastal and Marine Geology Program Monterey Bay National Marine Sanctuary Project (see this and other older Monterey Bay USGS works archived at https://archive.usgs.gov/archive/sites/walrus.wr.usgs.gov/monterey/index.html.

ArcInfo GRID format bathymetry data generated from the 2001 multibeam sonar survey the major deltas of southern Puget Sound, WA., including Nisqually, Puyallup, and Duwamish Deltas. This is metadata for the Duwamish Delta multibeam bathymetry data.

ArcInfo GRID format bathymetry data generated from the 2001 multibeam sonar survey the major deltas of southern Puget Sound, WA., including Nisqually, Puyallup, and Duwamish Deltas. This is meatadata for the Nisqually Delta multibeam bathymetry data.

ArcInfo GRID format bathymetry data generated from the 2001 multibeam sonar survey the major deltas of southern Puget Sound (Tacoma), WA., including Nisqually, Puyallup, and Duwamish Deltas. This is metadata for the Puyallup Delta multibeam bathymetry data.

1-m resolution bathymetry collected in Coyote Creek and Alviso Slough in April 2018. Projection = UTM, zone 10 in meters, Horizontal Datum = NAD83 (CORS96), Vertical Datum = MLLW, all units in meters. The surveys extend east from Calaveras Point along Coyote Creek to the railroad bridge, along Alviso Slough to the town of Alviso (just over 7 km), and along the 3.7 km of Guadalupe Slough closest to the San Francisco Bay, California.

1-m resolution bathymetry collected in Coyote Creek and Alviso Slough in April 2018. Projection = UTM, zone 10 in meters, Horizontal Datum = NAD83 (CORS96), Vertical Datum = NAVD88, all units in meters. The surveys extend east from Calaveras Point along Coyote Creek to the railroad bridge, along Alviso Slough to the town of Alviso (just over 7 km), and along the 3.7 km of Guadalupe Slough closest to the San Francisco Bay, California.

1-m resolution bathymetry collected in Coyote Creek and Alviso Slough in April 2018. Projection = UTM, zone 10 in meters, Horizontal Datum = WGS84(G1150), Elevations relative to the WGS84 Ellipsoid, all units in meters. The surveys extend east from Calaveras Point along Coyote Creek to the railroad bridge, along Alviso Slough to the town of Alviso (just over 7 km), and along the 3.7 km of Guadalupe Slough closest to the San Francisco Bay, California.

1-m resolution bathymetry collected in Coyote Creek and Alviso Slough in April 2019. Projection = UTM, zone 10 in meters, Horizontal Datum = NAD83 (CORS96), Vertical Datum = MLLW, all units in meters. The surveys extend east from Calaveras Point along Coyote Creek to the railroad bridge, along Alviso Slough to the town of Alviso (just over 7 km), and along the 9.8 km of Guadalupe Slough closest to the San Francisco Bay, California.

1-m resolution bathymetry collected in Coyote Creek and Alviso Slough in April 2019. Projection = UTM, zone 10 in meters, Horizontal Datum = NAD83 (CORS96), Vertical Datum = NAVD88, all units in meters. The surveys extend east from Calaveras Point along Coyote Creek to the railroad bridge, along Alviso Slough to the town of Alviso (just over 7 km), and along the 9.8 km of Guadalupe Slough closest to the San Francisco Bay, California.

1-m resolution bathymetry collected in Coyote Creek and Alviso Slough in April 2019. Projection = UTM, zone 10 in meters, Horizontal Datum = WGS84(G1150), Elevations relative to the WGS84 Ellipsoid, all units in meters. The surveys extend east from Calaveras Point along Coyote Creek to the railroad bridge, along Alviso Slough to the town of Alviso (just over 7 km), and along the 9.8 km of Guadalupe Slough closest to the San Francisco Bay, California.

Single-beam bathymetry, magnetics, and gravity data along with transit satellite navigation data was collected as part of field activity 84015 (L-9-84-CP) from Majuro to Honolulu, Central Pacific from 07/27/1984 to 08/16/1984. These data are reformatted from space-delimited ASCII text files, located in the former Coastal and Marine Geology Program (CMGP) InfoBank field activity catalog, into MGD77T format for NOAA's National Geophysical Data Center (NGDC). The MGD77T format includes a header (documentation) file (.h77t) and a data file (.m77t). More information regarding this format can be found in the publication listed in the Cross_Reference section of this metadata file.

Geologic attributes of the coastal zone of Hawaii, Hawaii

Coastal Slope along the coastal zone of Hawaii, Hawaii

Geologic attributes of the coastal zone of Kauai, Hawaii

Coastal Slope along the coastal zone of Kauai,Hawaii

Geologic attributes of the coastal zone of Lanai, Hawaii

Coastal Slope along the coastal zone of Lanai, Hawaii

Geologic attributes of the coastal zone of Maui, Hawaii

Coastal Slope along the coastal zone of Maui, Hawaii

Geologic attributes of the coastal zone of Molokai, Hawaii

Coastal Slope along the coastal zone of Molokai, Hawaii

Geologic attributes of the coastal zone of Oahu, Hawaii

Coastal Slope along the coastal zone of Oahu, Hawaii

Geologic attributes of the coastal zone of Sand Island (Oahu), Hawaii

This dataset consists of autonomous underwater vehicle (AUV) bathymetry data collected in April 2018 aboard the R/V Rachel Carson, which is owned and operated by the Monterey Bay Aquarium Research Institute (MBARI). During the cruise, bathymetry data were collected across six AUV dives, all six of which collected coincident bathymetry and Chirp seismic-reflection data. A seventh bathymetric survey, 201804_LuciaChica2m, consists of MBARI data from several AUV dives that were conducted pre-2018 but were compiled in April 2018; these data are not being released with coincident Chirp data. The collection of these data was funded entirely by MBARI, and the data have been donated to the U.S. Geological Survey (USGS). The data were collected in collaboration with the USGS and the Bureau of Ocean Energy Management (BOEM), and they are located in the same study area as the collaborative California Deepwater Investigations and Groundtruthing I (Cal DIG I) project. The purpose of the overall Cal DIG I study is to assess shallow geohazards, benthic habitats, and thereby the potential for alternative energy infrastructure (namely floating wind turbines) offshore south-central California due to the study area's proximity to power grid infrastructure associated with the Morro Bay power plant. The AUV data in this part of the data release provide ultra-high-resolution seafloor imaging of seven different patches of seafloor offshore of the south-central California coast. The AUV mapping navigation has not been accurately positioned and is considered as only partially processed.

This dataset consists of autonomous underwater vehicle (AUV) Chirp seismic-reflection data collected in April 2018 aboard the R/V Rachel Carson, which is owned and operated by the Monterey Bay Aquarium Research Institute (MBARI). During the cruise, data were collected across eight AUV dives, six of which collected coincident bathymetry and Chirp seismic-reflection data (two dives collected Chirp seismic-reflection data only). The collection of these data was funded entirely by MBARI, and the data have been donated to the U.S. Geological Survey (USGS). The data were collected in collaboration with the USGS and the Bureau of Ocean Energy Management (BOEM) and they are located in the same study area as the collaborative California Deepwater Investigations and Groundtruthing I (Cal DIG I) project. The purpose of the overall Cal DIG I study is to assess shallow geohazards, benthic habitats, and thereby the potential for alternative energy infrastructure (namely floating wind turbines) offshore south-central California due to the study area's proximity to power grid infrastructure associated with the Morro Bay power plant. The AUV data in this portion of the data release provide ultra-high-resolution subsurface imaging of eight different patches of seafloor offshore of the south-central California coast. The AUV mapping navigation has not been accurately positioned and is considered as only partially processed.

This dataset consists of autonomous underwater vehicle (AUV) bathymetry data collected in March 2019 aboard the R/V Rachel Carson, which is owned and operated by the Monterey Bay Aquarium Research Institute (MBARI). During the cruise, bathymetry data were collected across eight AUV dives, all eight of which collected coincident bathymetry and Chirp seismic-reflection data. The collection of these data was funded entirely by MBARI, and the data have been donated to the U.S. Geological Survey (USGS). The data were collected in collaboration with the USGS and the Bureau of Ocean Energy Management (BOEM), and they are located in the same study area as the collaborative California Deepwater Investigations and Groundtruthing I (Cal DIG I) project. The purpose of the overall Cal DIG I study is to assess shallow geohazards, benthic habitats, and thereby the potential for alternative energy infrastructure (namely floating wind turbines) offshore south-central California due to the study area's proximity to power grid infrastructure associated with the Morro Bay power plant. The AUV data in this part of the data release provide ultra-high-resolution seafloor imaging of eight different patches of seafloor offshore of the south-central California coast. The AUV mapping navigation has not been accurately positioned and is considered as only partially processed.

This dataset consists of autonomous underwater vehicle (AUV) Chirp seismic-reflection data collected in March 2019 aboard the R/V Rachel Carson, which is owned and operated by the Monterey Bay Aquarium Research Institute (MBARI). During the cruise, data were collected across eight AUV dives, all eight of which collected coincident bathymetry and Chirp seismic-reflection data. The collection of these data was funded entirely by MBARI, and the data have been donated to the U.S. Geological Survey (USGS). The data were collected in collaboration with the USGS and the Bureau of Ocean Energy Management (BOEM), and they are located in the same study area as the collaborative California Deepwater Investigations and Groundtruthing I (Cal DIG I) project. The purpose of the overall Cal DIG I study is to assess shallow geohazards, benthic habitats, and thereby the potential for alternative energy infrastructure (namely floating wind turbines) offshore south-central California due to the study area's proximity to power grid infrastructure associated with the Morro Bay power plant. The AUV data in this portion of the data release provide ultra-high-resolution subsurface imaging of eight different patches of seafloor offshore of the south-central California coast. The AUV mapping navigation has not been accurately positioned and is considered as only partially processed.

This dataset consists of autonomous underwater vehicle (AUV) bathymetry data collected in May 2019 aboard the R/V Rachel Carson, which is owned and operated by the Monterey Bay Aquarium Research Institute (MBARI). During the cruise, bathymetry data were collected across four AUV dives, all four of which collected coincident bathymetry and Chirp and seismic-reflection data. The collection of these data was funded entirely by MBARI, and the data have been donated to the U.S. Geological Survey (USGS). The data were collected in collaboration with the USGS and the Bureau of Ocean Energy Management (BOEM), and they are located in the same study area as the collaborative California Deepwater Investigations and Groundtruthing (Cal DIG I) project. The purpose of the overall Cal DIG I study is to assess shallow geohazards, benthic habitats, and thereby the potential for alternative energy infrastructure (namely floating wind turbines) offshore south-central California due to the study area's proximity to power grid infrastructure associated with the Morro Bay power plant. The AUV data in this part of the data release provide ultra-high-resolution seafloor imaging of four different patches of seafloor offshore of the south-central California coast. The AUV mapping navigation has not been accurately positioned and is considered as only partially processed.

This dataset consists of autonomous underwater vehicle (AUV) Chirp seismic-reflection data collected in May 2019 aboard the R/V Rachel Carson, which is owned and operated by the Monterey Bay Aquarium Research Institute (MBARI). During the cruise, data were collected across four AUV dives, all four of which collected coincident bathymetry and Chirp seismic-reflection data. The collection of these data was funded entirely by MBARI, and the data have been donated to the U.S. Geological Survey (USGS). The data were collected in collaboration with the USGS and the Bureau of Ocean Energy Management (BOEM), and they are located in the same study area as the collaborative California Deepwater Investigations and Groundtruthing I (Cal DIG I) project. The purpose of the overall Cal DIG I study is to assess shallow geohazards, benthic habitats, and thereby the potential for alternative energy infrastructure (namely floating wind turbines) offshore south-central California due to the study area's proximity to power grid infrastructure associated with the Morro Bay power plant. The AUV data in this portion of the data release provide ultra-high-resolution subsurface imaging of four different patches of seafloor offshore of the south-central California coast. The AUV mapping navigation has not been accurately positioned and is considered as only partially processed.

The required grid and bathymetry files to run a nested spectral wave model (Simulating Waves WAves Nearshore [SWAN]; Booij and others, 1999) for the central Beaufort Sea coast of Alaska are provided. A three-level SWAN nesting grid with grid resolutions of 5000 meters, 1000 meters, and 200 meters for the overall, intermediate and detail grids, respectively (see included Browse Graphic) has been developed. For this purpose, available local bathymetry (Coastal Frontiers Corporation, 2014; Kasper and others, 2019) was merged with a larger-scale product (IBCAO Version 4.0 Compilation Group, 2020). Further details about the development of this model, model forcings and model settings can be found in Nederhoff and others (2021).

A nested spectral wave model (Simulating Waves WAves Nearshore [SWAN]; Booij and others, 1999) was deployed for the central Beaufort Sea coast of Alaska to simulate waves for the period from 1979 to 2019. Results in the form of spatial summary statistics, describing wave parameters, wind speed and sea-ice area cover for the intermediate grid (see Overview Image on main page of data release), are provided. Further information can be found in Nederhoff and others (2021).

Time series output from a spectral wave model (Simulating Waves WAves Nearshore [SWAN]; Booij and others 1999), implemented for the central Beaufort Sea coast of Alaska from 1979 to 2019, are provided. The variables include significant wave heights, mean wave periods, mean wave directions, wave steepness, and orbital velocities. Additionally, water depths, x (east-west) and y (north-south) components of the wind, and sea ice concentrations are provided. Further information can be found in Nederhoff and others (2021).

High-resolution Chirp seismic-reflection data were collected offshore south-central California as part of a geophysical survey aboard the NOAA Ship Rainier during two legs at sea, the first from 8/28/2018 to 9/7/2018 and the second from 9/10/2018 to 9/21/2018. The data were collected using an Edgetech 512i towfish with a 1-6 kHz sweep. Consistently high winds and rough seas prevented additional Chirp data collection and caused noisy data in some cases, especially during the second leg of the survey, which largely took place in the southern part of the study area. The Chirp data were post-processed to include filtering and other noise removal corrections.

Multi-channel seismic (MCS) reflection data were collected as part of a geophysical survey aboard the NOAA Ship Rainier during two legs at sea, the first from 8/28/2018 to 9/7/2018 and the second from 9/10/2018 to 9/21/2018. The data were collected using a SIG 2-Mille minisparker and a 64-channel streamer, although the majority of the survey was conducted using a 56-channel setup due to technical issues with one 8-channel section early on in the survey. The MCS data were processed to post-stack time migration and include filtering and other noise removal corrections.

This part of the data release contains approximately 783 line-kilometers of processed, high-resolution, chirp seismic-reflection data that were collected aboard the R/V Bold Horizon in 2019 on U.S. Geological Survey cruise 2019-649-FA offshore San Francisco, California. The chirp profiles were acquired using an Edgetech 3200 5-16 chirp sub-bottom profiling system. These data are divided up and presented by navigation line, as reflected in the individual file names.

This section of the data release contains core logger tabular data of 34 vibracores that were collected aboard the R/V Bold Horizon in 2019 on U.S. Geological Survey Field Activity 2019-649-FA offshore San Francisco, California. The cores were analyzed for gamma ray density and magnetic susceptibility. The logging was performed at 1-cm intervals from the top of each core section. In addition to the core logger data, the locations of the cores are available as either a comma-delimited file or a shapefile.

This section of the data release contains photographs of 34 vibracores that were collected aboard the R/V Bold Horizon in 2019 on U.S. Geological Survey Field Activity 2019-649-FA offshore San Francisco, California. Continuous line-scan photographs were created in the lab to assess sand and gravel resources in Federal and State waters for potential use in future beach nourishment projects along stretches of the coast where critical erosion hotspots have been identified.

This part of the data release contains marine magnetic data that were collected aboard the R/V Bold Horizon in 2019 on U.S. Geological Survey Field Activity 2019-649-FA offshore San Francisco, California.

This text file (2021-607-FA_Image_Locations.txt) provides the GNSS antenna location for underwater images collected near Dollar Point, Lake Tahoe, CA, using a recently developed towed-surface vehicle with multiple downward-looking underwater cameras. The GNSS antenna location for the time of each image capture is presented with greater precision than is stored in the individual image’s EXIF header due to decimal place limitations of the EXIF format.

Underwater images were collected near Dollar Point, Lake Tahoe, CA, using a recently developed towed-surface vehicle with multiple downward-looking underwater cameras. The images are organized in zipped files grouped by survey line. The SQUID-5 system records images as TIFF (.tif) format to maintain the highest resolution and bit depth. Each image includes EXIF metadata, containing GNSS date, time, and latitude and longitude of the GNSS antenna mounted on the towed surface vehicle, copyright, keywords, and other fields.

These metadata describe acoustic-backscatter data collected during a 2016 SWATHPlus-M survey offshore the Elwha River mouth, Strait of Juan de Fuca, Washington. Data were collected and processed by the U.S. Geological Survey (USGS), Pacific Coastal and Marine Science Center (PCMSC) with fieldwork activity number 2016-605-FA. The acoustic-backscatter data are provided as a GeoTIFF image in UTM, zone 10, NAD83 coordinates.

These metadata describe bathymetry data collected during a 2016 SWATHPlus-M survey offshore the Elwha River mouth, Strait of Juan de Fuca, Washington. Data were collected and processed by the U.S. Geological Survey (USGS), Pacific Coastal and Marine Science Center (PCMSC) with fieldwork activity number 2016-605-FA. The bathymetry data are provided as a GeoTIFF image in UTM, zone 10, NAD83 coordinates, vertically referenced to NAVD88.

Conductivity-Temperature-Depth (CTD) profile data were collected along transects across study areas of west and east Hawaii Island between 2010 and 2014. Measurements were made over a range of tide and weather conditions and help characterize the spatial extent and variability in estuarine conditions across the reef when grouped by 1 to 2-hour survey period or by season. Sites of cold and warm groundwater discharge along east Hawaii were characterized for comparison.

Spatial measurements of water temperature, specific conductance, salinity, pH, dissolved oxygen, and turbidity between 0.25 and 0.50 m water depth were collected every 10-seconds along and across shore at 12 principal study areas along west and east Hawaii Island. Measurements were made between 2010 and 2013 during different seasons and tide states over the course of 1.0 to 2.5 hours to evaluate the spatial and temporal extent of water properties that influence coral reef health and coral reef habitat availability.

Time-series data of water level, water temperature, and salinity were collected at 10 locations along west Hawaii Island between 2010 and 2011 in nearshore coral reef settings. Conductivity-temperature-depth sensors were attached to fossil limestone, rock, or dead coral within otherwise healthy coral reef settings spanning water depths of 8 to 23 ft. Continuous measurements were made every 10 or 20 minutes.

Time series data of water surface elevation, wave height, currents, and turbidity were acquired during the winters of 2014-2015 and 2015-2016 in support of a study on the morphological change of rippled scour depressions off Santa Cruz, CA. One set of instruments (SCW) was mounted at the end of Santa Cruz Municipal Wharf during both winters. Another set of instruments (M1T) was deployed offshore in Monterey Bay each winter; the two offshore winter locations were different, but each were about 0.5 km offshore of Point Santa Cruz.

Between November 2014 and June 2016 the U.S. Geological Survey, Pacific Coastal and Marine Science Center (PCMSC) conducted eight repeat, high-resolution bathymetry and acoustic-backscatter surveys of a small patch of seafloor offshore Santa Cruz in northern Monterey Bay, California. PCMSC also collected oceanographic time-series data over the same two-year period. This metadata file describes the eight acoustic-backscatter datasets.

Between November 2014 and June 2016 the U.S. Geological Survey, Pacific Coastal and Marine Science Center (PCMSC) conducted eight repeat, high-resolution bathymetry and acoustic-backscatter surveys of a small patch of seafloor offshore Santa Cruz in northern Monterey Bay, California. PCMSC also collected oceanographic time-series data over the same two-year period. This metadata file describes the eight bathymetry datasets.

This part of the data release contains a grid of standard deviations of bathymetric soundings within each 0.5 m x 0.5 m grid cell. The bathymetry was collected on February 1, 2011, in the Sacramento River from the confluence of the Feather River to Knights Landing. The standard deviations represent one component of bathymetric uncertainty in the final digital elevation model (DEM), which is also available in this data release. The bathymetry data were collected by the USGS Pacific Coastal and Marine Science Center (PCMSC) team with collaboration and funding from the U.S. Army Corps of Engineers. This project used interferometric sidescan sonar to characterize the riverbed and channel banks along a 12 mile reach of the Sacramento River near the town of Knights Landing, California (River Mile 79 through River Mile 91) to aid in the understanding of fish response to the creation of safe habitat associated with levee restoration efforts in two 1.5 mile reaches of the Sacramento River between River Mile 80 and 86.

This part of the data release presents acoustic backscatter data collected on February 1, 2011, in the Sacramento River from the confluence of the Feather River to Knights Landing. The data were collected by the USGS Pacific Coastal and Marine Science Center (PCMSC) team with collaboration and funding from the U.S. Army Corp of Engineers. This project used interferometric sidescan sonar to characterize the riverbed and channel banks along a 12 mile reach of the Sacramento River, California (River Mile 79 through River Mile 91) to aid in the understanding of fish response to the creation of safe habitat associated with levee restoration efforts in two 1.5 mile reaches of the Sacramento River between River Mile 80 and 86.

This part of the data release presents a digital elevation model (DEM) created from bathymetry data collected on February 1, 2011, in the Sacramento River from the confluence of the Feather River to Knights Landing. The data were collected by the USGS Pacific Coastal and Marine Science Center (PCMSC) team with collaboration and funding from the U.S. Army Corps of Engineers. This project used interferometric sidescan sonar to characterize the riverbed and channel banks along a 12 mile reach of the Sacramento River, California (River Mile 79 through River Mile 91) to aid in the understanding of fish response to the creation of safe habitat associated with levee restoration efforts in two 1.5 mile reaches of the Sacramento River between River Mile 80 and 86.

This data release contains bathymetry and topography data from surveys performed on the Elwha River delta between 2010 and 2017. Sediment grain-size data are available for selected surveys performed after May 2012. This data release will be updated as additional bathymetry, topography, and surface-sediment grain-size data from future surveys become available.

Observations of bleached coral were documented by scuba divers along pre-determined transects and are presented here in comma-separated format. Included in the table are coral species observed, colony size, size of bleached area on colony, and seawater temperature.

Coral Point Count with Excel extensions (CPCe; Kohler and Gill, 2006) was used to help calculate percent of coral cover or other benthic substrates from a randomly selected subset of seafloor photographs collected on the west Hawaii Island coast.

Observations of coral disease and health indicators were documented by scuba divers along pre-determined transects and are presented here in comma-separated format. Included in the table are coral species observed, colony size, types and number of indicators observed, and a size range of indicators observed.

Observations of coral species and size were documented by scuba divers along pre-determined transects and are presented here in comma-separated format.

Seafloor photographs were collected by SCUBA divers along pre-determined transects using an underwater digital camera following benthic survey protocols developed by the National Park Service (NPS) at Kaloko-Honokohau National Historical Park (Marrack and others, 2014; Weijerman and others, 2014) and modeled after the U.S. Geological Survey (USGS) and NPS coral reef survey protocols (Rogers and others, 2001; Brown and others, 2011). This dataset includes seafloor photographs in jpg format, the locations of which are described in the accompanying comma-separated files.

Estuary geomorphic units delineated at a scale of 1:1500 using a combination of (a) 11 September 2009 1 meter resolution NAIP aerial imagery; and (b) elevation-colored and hillshaded digital elevation models from USGS backpack/jetski topobathy surveys (17 September 2009) for areas < MHHW and aerial lidar surveys (4-6 April 2009) for elevations > MHHW.

Estuary geomorphic units delineated at a scale of 1:1500 using a combination of (a) 03 September 2011* 0.3 meter resolution Microsoft/Digital Globe aerial imagery; and (b) elevation-colored and hillshaded digital elevation models from USGS backpack/jetski topobathy surveys (25 August 2011) for areas < MHHW and aerial lidar surveys (13-15 April 2012) for elevations > MHHW. *Image date of 3-Sep-11 corrected in metadata. During product generation the imagery date was believed to be 8-25-2011, as reported by DigitalGlobe reseller.

Estuary geomorphic units delineated at a scale of 1:1500 using a combination of (a) 26 August 2013 0.15 meter resolution NPS Elwha PlaneCam aerial imagery; and (b) elevation-colored and hillshaded digital elevation models from USGS backpack/jetski topobathy surveys (16 September 2013) for areas < MHHW and aerial lidar surveys (17 October 2012) supplemented with NPS Elwha PlaneCam SfM photogrammetry data (19 September 2013) for elevations > MHHW.

Estuary geomorphic units delineated at a scale of 1:1500 using a combination of (a) 28 August 2014 0.15 meter resolution NPS Elwha PlaneCam aerial imagery; and (b) elevation-colored and hillshaded digital elevation models from USGS backpack/jetski topobathy surveys (5-8 September 2014) for areas < MHHW and aerial lidar surveys (7 November 2014) supplemented with NPS Elwha PlaneCam SfM photogrammetry data (30 September 2014) for elevations > MHHW.

This portion of the data release presents fish abundance data from samples collected in the Elwha River estuary, Washington, in 2006, 2007, 2013, and 2014 (no associated USGS Field Activities numbers because data were collected predominantly by biologists from the Lower Elwha Klallam Tribe). We used the Puget Sound beach seining protocol (Simenstad and others, 1991) to sample fish populations in the Elwha River estuary complex. The beach seine was 38 m long x 2 m deep, with a 2 m x 2 m bag in the center of the net; mesh size was 3.18 mm, 6.35 mm, and 31.75 mm, for the bag, center panel, and wings, respectively. The seine net was deployed from bank to bank by a small skiff and then pulled on shore. The number of seines conducted each month varied based on estuary conditions and staff availability. Captured fish were quickly transferred to 20-liter plastic buckets filled with aerated estuary water, individually identified, counted, measured, and released at the point of capture. The locations of seines were determined with a hand-held global positioning system (GPS). Fish abundance is reported as catch per unit effort (CPUE), calculated as the total number of each fish species caught in all seines at each site and date, divided by the number of seines conducted. Fish abundance data are provided in a comma-delimited spreadsheet (.csv).

This portion of the data release presents fish diet data from Chinook and coho salmon collected in the Elwha River estuary, Washington, in 2006, 2007, 2013, and 2014 (there are no associated USGS Field Activities numbers because data were collected predominantly by biologists from the Lower Elwha Klallam Tribe). Fish were collected using a beach seine at six locations throughout the estuary. Fish were transferred to buckets containing aerated ambient water and kept cool until handling. We anesthetized fish in a diluted solution of tricaine methanesulfonate (MS-222) to count, identify, and measure fork length (FL) to the nearest mm and weight to the nearest 0.1 g. After recovery from anesthesia, all fish were released at the point of capture. Stomach contents from a sub-sample of Chinook and coho were extracted via non-lethal gastric lavage and preserved in ethanol. When possible, stomach contents from ten individuals of each species between 55–199 mm fork length were collected at each site during each sampling event. Fish with no regurgitated prey were recorded as empty stomachs. Fish diet samples were sent to a professional taxonomist for processing and identification to the lowest practical taxonomic level. Aquatic taxa were typically identified to the genus or species level and terrestrial taxa to family or genus. Some less-common taxa or partially-digested prey items were identified to Order or Class. The locations of samples were determined with a hand-held global positioning system. Fish diet data are provided in a comma-delimited spreadsheet.

This portion of the data release presents terrestrial invertebrate abundance data from samples collected in emergent and shrub vegetation along the edge of the Elwha River estuary, Washington, in 2007 and 2013 (no associated USGS Field Activities numbers because data were collected predominantly by biologists from the Lower Elwha Klallam Tribe). We deployed terrestrial insect fallout traps at ten locations in the east estuary, five replicates each in shrub and emergent (littoral) vegetation habitats. Clear, rectangular traps (2,400 cm2 in 2007 and 3,526 cm2 in 2013) were filled with 5 cm of filtered soapy water and deployed for 72 hours. Invertebrate counts from 2013 were standardized to the 2007 bin size to account for the different area of the fallout traps between years. Samples were filtered through a funnel sieve and stored in 70 percent ethanol until processing. Invertebrates were identified to genera when possible. However, taxonomic resolution was not consistent across species so we grouped data by Order for our analyses (unless otherwise noted in the attributes). The locations of samples were determined with a hand-held global positioning system (GPS). Terrestrial invertebrate abundance data are provided in a comma-delimited spreadsheet (.csv).

This portion of the data release presents water column dissolved nutrient concentration data and water quality parameters from samples collected in the Elwha River estuary, Washington, in 2006, 2007, 2013, and 2014 (USGS Field Activities L-15-13-PS, L-24-13-PS, T-R5-13-PS, T-R6-13-PS, T-RA-14-PS, 2014-614-FA, 2014-628-FA, 2014-633-FA, 2014-666-FA). Water column samples were collected by hand in acid-washed opaque bottles from multiple locations. Water quality was measured using a handheld Hydolab Data Sonde 4a. The locations of water sample collections were determined with a hand-held global positioning system (GPS). The water samples were filtered through GF/F filters immediately after collection. The filtrate was collected in scintillation vials and frozen until analysis at either the University of Washington marine chemistry lab (2006 and 2007 samples) or the University of California Santa Barbara Marine Science Institute analytical lab (2013 and 2014 samples). Nutrient concentration and water quality data are provided in a comma-delimited spreadsheet (.csv).

This portion of the data release presents riparian plant species abundance (percent cover) data from plots sampled in the Elwha River estuary, Washington, in 2007 and 2014. In August 2007, we established 21 vegetation plots within the study area in a stratified random fashion, with three to five plots in five of the vegetation cover types denoted in the habitat classification maps: mixed riparian forest, willow-alder forest, riparian shrub, emergent marsh/marsh-shrub transition, and dunegrass. Each plot was 100 square meter, usually 10 m x 10 m, but in areas where the vegetation patch was narrow, plots were either 4 m x 25 m or 5 m x 20 m. We visually estimated percent cover by species of all vascular plants within one of ten cover classes: trace; 0-1 percent; 1-2 percent; 2-5 percent; 5-10 percent; 10-25 percent; 25-50 percent; 50-75 percent; 75-95 percent; 95-100 percent and used the midpoints of the classes for analysis. We resampled all plots in August 2014. Plant abundance data are provided in a comma-delimited spreadsheet (.csv).

This portion of the data release presents riparian plant species richness (number of unique taxa) data from plots sampled in the Elwha River estuary, Washington, in 2007 and 2014. In August 2007, we established 21 vegetation plots within the study area in a stratified random fashion, with three to five plots in five of the vegetation cover types denoted in the habitat classification maps: mixed riparian forest, willow-alder forest, riparian shrub, emergent marsh/marsh-shrub transition, and dunegrass. Each plot was 100 square meters, usually 10 m x 10 m, but in areas where the vegetation patch was narrow, plots were either 4 m x 25 m or 5 m x 20 m. We visually estimated percent cover by species of all vascular plants within one of ten cover classes and tallied the number of unique taxa to estimate total species richness in each plot. We resampled all plots in August 2014. Plant species richness data are provided in a comma-delimited spreadsheet (.csv).

Estuary vegetation cover delineated from 11 September 2009 1-meter-resolution NAIP aerial imagery at a scale of 1:1500.

Estuary vegetation cover delineated from 3 September 2011* 0.3-meter-resolution aerial imagery (Microsoft/Digital Globe) at a scale of 1:1500. *Image date of 3-Sep corrected in metadata. During product generation the imagery date was believed to be 8-25-2011, as reported by DigitalGlobe reseller.

Estuary vegetation cover delineated from 26 August 2013 0.15-meter-resolution NPS Elwha PlaneCam aerial imagery at a scale of 1:1500.

Estuary vegetation cover delineated from 28 August 2014 0.15-meter-resolution NPS Elwha PlaneCam aerial imagery at a scale of 1:1500.

Isotopic analyses of authigenic carbonates and methanotrophic deep-sea mussels, Bathymodiolus sp., was performed on samples collected from seep fields in the Baltimore and Norfolk Canyons on the north Atlantic margin. Samples were collected using remotely operated underwater vehicles (ROVs) during three different research cruises in 2012, 2013, and 2015. Analyses were performed by several different laboratories, and the results are presented in spreadsheet format.

This portion of the data release presents acoustic backscatter data from the San Miguel Passage, in the Channel Islands, California. The data were collected in August 2007 by the U.S. Geological Survey, Pacific Coastal and Marine Science Center (USGS, PCMSC) using a 234.5 kHz SEA (AP) Ltd. SWATHplus-M phase-differencing sidescan sonar mounted on the NOAA, Channel Islands National Marine Sanctuary R/V Shearwater as part of the research cruise S-2-07-SC. Data were collected in water depths up to 89 meters. The San Miguel Passage is within the Channel Islands National Marine Sanctuary and is the body of water between the two western-most islands of the chain - Santa Rosa and San Miguel Islands. The data were processed at the USGS, PCMSC to create a 2-meter resolution TIFF raster, presented here.

This portion of the data release presents bathymetry data from the San Miguel Passage, in the Channel Islands, California. Bathymetry data were collected in the San Miguel Passage, Channel Islands, California in August 2007 by the U.S. Geological Survey, Pacific Coastal and Marine Science Center (USGS, PCMSC). Collection was accomplished using a 234.5 kHz SEA (AP) Ltd. SWATHplus-M phase-differencing sidescan sonar mounted on the NOAA, Channel Islands National Marine Sanctuary R/V Shearwater as part of the USGS research cruise S-2-07-SC. Data were collected in water depths up to 89 meters. The San Miguel Passage is within the Channel Islands National Marine Sanctuary and is the body of water between the two western-most islands of the chain - Santa Rosa and San Miguel Islands. The data were processed at the USGS, PCMSC to create a 2-meter resolution TIFF raster, presented here.

This data release provides seafloor-characteristics point data across the Gulf of Alaska, as digitized directly from National Oceanic and Atmospheric Administration (NOAA) National Ocean Service (NOS) smooth sheets published from 1892 to 2001, and archived at the National Geophysics Data Center (NGDC). Geo-rectification and digitization methods were adapted from Zimmermann and Benson (2013). Each location includes information for the smooth sheet number (H#####), a unique site number location, latitude, longitude, collection date, seafloor notation, and the translation of the notation. Unique site numbers were assigned randomly to each notation on a smooth sheet, starting at “_0”. Examples of seafloor notations include: rk (= rock); bu C (= blue clay); hrd (= hard); fne S (= fine sand); Co (= coral) or similar codes; the full code key is given in the Department of Commerce and Department of Defense Chart 1 (2013). In some cases, a diagrammatic indication of the seafloor character is used on the smooth sheet, such as a “*”. During digitization, the corresponding value given in “Chart 1” is assigned to the location; in this case, “*” denotes “rk” or “rock”. Distribution of NOS seafloor-characteristics data across the Gulf of Alaska varies widely: nearer the shoreline, data are more densely distributed; on the mid and outer continental shelf, data are more sparsely spaced. The cited locations of the points were adjusted as necessary in GIS to match the location on the geo-rectified smooth sheet in GIS as projected in North American Datum of 1983. NOAA has published the companion regional bathymetric data and its derivatives and sediment characteristics data for Cook Inlet and areas of the Aleutian Islands at http://www.afsc.noaa.gov/RACE/groundfish/bathymetry/. The project was funded through the USGS Coastal and Marine Geology Program, NOAA National Marine Fisheries Service, Alaska Fisheries Science Center, and Alaska Fisheries Science Center Interagency Agreement AKC-119 (May 2012). References: Zimmermann, M., and Benson, J., 2013, Smooth sheets: How to work with them in a GIS to derive bathymetry, features and substrates: U.S. Department of Commerce, NOAA Tech. Memo. NMFS-AFSC-249, 52 p., available at http://www.afsc.noaa.gov/Publications/AFSC-TM/NOAA-TM-AFSC-249.pdf. Department of Commerce, National Oceanic and Atmospheric Administration and Department of Defense, National Geospatial-Intelligence Agency, 2013, U.S. Chart No. 1: Symbols, abbreviations and terms used on paper and electronic navigational charts, 12th edition, 132 p., available at http://www.nauticalcharts.noaa.gov/mcd/chartno1.htm.

This table lists the NOS smooth sheets included in the associated shapefile (GulfofAlaskaDigitizationProject_NOSSeafloorCharacter.zip; N = 329, plus insets), the number of samples for each smooth sheet, the year of collection (1892 to 2001), and the smooth sheet scale (from 1:2,000 to 1:600,000). Smooth sheets are available through the National Geophysics Data Center’s online data portal (NDGC, http://www.ngdc.noaa.gov).

This data release contains 43 chirp sub-bottom profiles that were collected in November of 2014 from the Catalina and Santa Cruz Basins offshore southern California by the U.S. Geological Survey Pacific and Coastal Marine Science Center. Data were collected aboard the University of California’s R/V Robert Gordon Sproul on USGS cruise 2014-645-FA. Chirp profiles were collected to assess earthquake and submarine landslide hazards offshore southern California.

This data release contains 35 multichannel sparker and 24 multichannel minisparker seismic reflection (MCS) profiles that were collected in November of 2014 from the Catalina and Santa Cruz Basins offshore southern California by the U.S. Geological Survey Pacific and Coastal Marine Science Center. Data were collected aboard the University of California’s R/V Robert Gordon Sproul on USGS cruise 2014-645-FA. MCS profiles were collected to assess earthquake and submarine landslide hazards offshore southern California.

This part of the data release includes 10-m resolution multibeam acoustic-backscatter data collected in 2016 in Catalina Basin, southern California. The data are presented as a TIFF file. In February 2016 the University of Washington in cooperation with the U.S. Geological Survey, Pacific Coastal and Marine Science Center (USGS, PCMSC) collected multibeam bathymetry and acoustic backscatter data in Catalina Basin aboard the University of Washington's Research Vessel Thomas G. Thompson. Data were collected using a Kongsberg EM300 multibeam echosounder hull-mounted to the 274-foot R/V Thomas G. Thompson. The USGS, PCMSC processed these data and produced a series of bathymetric surfaces and acoustic-backscatter images for scientific research purposes.

This part of the data release includes 10-m resolution multibeam-bathymetry data collected in 2016 in Catalina Basin, southern California. The data are presented as a TIFF image. In February 2016 the University of Washington in cooperation with the U.S. Geological Survey, Pacific Coastal and Marine Science Center (USGS, PCMSC) collected multibeam bathymetry and acoustic backscatter data in Catalina Basin aboard the University of Washington's Research Vessel Thomas G. Thompson. Data were collected using a Kongsberg EM300 multibeam echosounder hull-mounted to the 274-foot R/V Thomas G. Thompson. The USGS, PCMSC processed these data and produced a series of bathymetric surfaces and acoustic-backscatter images for scientific research purposes.

This part of the data release includes 10-m resolution merged multibeam-bathymetry data of Catalina Basin and northern Gulf of Santa Catalina. The data are presented as a TIFF file. In February 2016 the University of Washington in cooperation with the U.S. Geological Survey, Pacific Coastal and Marine Science Center (USGS, PCMSC) collected multibeam bathymetry and acoustic backscatter data in Catalina Basin aboard the University of Washington's Research Vessel Thomas G. Thompson. Data were collected using a Kongsberg EM300 multibeam echosounder hull-mounted to the 274-foot R/V Thomas G. Thompson. The USGS, PCMSC processed these data and produced a series of bathymetric surfaces and acoustic backscatter images for scientific research purposes. A 10-m bathymetric surface produced from this work (available in this report) was merged with re-processed 10-m resolution multibeam bathymetry data collected in the Gulf of Santa Catalina in 2013 by Scripps Institution of Oceanography and processed by USGS, PCMSC (available at, https://pubs.usgs.gov/sim/3324/). These data can be used to assess the hazards posed by offshore faults, submarine landslides, and tsunamis as well as map sediment transport pathways and sedimentary sinks.

This part of the data release includes 25-m resolution merged multibeam-bathymetry data of the northern portion of the Southern California Continental Borderland. The data are presented as a TIFF file. In February 2016 the University of Washington in cooperation with the U.S. Geological Survey, Pacific Coastal and Marine Science Center (USGS, PCMSC) collected multibeam bathymetry and acoustic backscatter data in Catalina Basin aboard the University of Washington's Research Vessel Thomas G. Thompson. Data were collected using a Kongsberg EM300 multibeam echosounder hull-mounted to the 274-foot R/V Thomas G. Thompson. The USGS, PCMSC processed these data and produced a series of bathymetric surfaces and acoustic backscatter images for scientific research purposes. A 25-m bathymetric surface produced from this work was merged with publically available multibeam bathymetry data, as well as 2015, 2016, and 2017 multibeam bathymetry data collected in the continental borderland region by the Ocean Exploration Trust's Nautilus Exploration Program. The USGS, PCMSC processed the survey line files received from the Nautilus Exploration Program to include in the overall merged 25-m multibeam bathymetry surface of the northern portion of the Southern California Continental Borderland region that is available in this data release. These data can be used to assess the hazards posed by offshore faults, submarine landslides, and tsunamis as well as map sediment transport pathways and sedimentary sinks.

Projected wave climate trends from WAVEWATCH3 model output were used as input for nearshore wave models (for example, SWAN) for the main Hawaiian Islands to derive data and statistical measures (mean and top 5 percent values) of wave height, wave period, and wave direction for the recent past (1996-2005) and future projections (2026-2045 and 2085-2100). Three-hourly global climate model (GCM) wind speed and wind direction output from four different GCMs provided by the Coupled Model Inter-Comparison Project, phase 5 (CMIP5), were used as boundary conditions to the physics-based WAVEWATCH3 numerical wave model for the area encompassing the main Hawaiian islands. Two climate change scenarios for each of the four GCMs were run: the representative concentration pathway (RCP)-4.5 and RCP-8.5, representing a medium mitigation and a high emissions scenario, respectively. Simulation timeframes were limited to the years 2026-2045 and 2085-2100, as prescribed by the CMIP5 modeling framework. The WAVEWATCH3 modeled deep-water wave heights, wave periods, and wave directions, with current bathymetry were used as boundary conditions to drive simulations of mean and top 5 percent wave conditions at higher resolution over the insular shelves of the main Hawaiian islands using the 3rd-generation SWAN wave model. For each scenario, 12 simulations were made representing the month-averaged or top 5 percent conditions. The SWAN model is based on discrete spectral action balance equations, computing the evolution of random, short-crested waves. Physical processes such as bottom friction and depth induced breaking, and, non-linear quadruplet and triad wave-wave interactions are included. Wave propagation, growth, and decay are solved periodically throughout the model grid. The SWAN model has been shown to accurately model the propagation and breaking of waves over Pacific coral reefs.

This dataset includes swell-filtered, high-resolution seismic-reflection data jointly collected by the U.S. Geological Survey (USGS) and Oregon State University in 2012, between Punta Gorda and Fort Bragg in northern California.

This dataset includes Magnetic-field level data jointly collected by the U.S. Geological Survey (USGS) and Oregon State University in 2012, between Punta Gorda and Fort Bragg in northern California.

This dataset includes navigation data for marine geophysical data jointly collected by the U.S. Geological Survey (USGS) and Oregon State University in 2012, between Punta Gorda and Fort Bragg in northern California.

This dataset includes raw, high-resolution seismic-reflection data jointly collected by the U.S. Geological Survey (USGS) and Oregon State University in 2012, between Punta Gorda and Fort Bragg in northern California.

This dataset includes swell-filtered, high-resolution seismic-reflection data jointly collected by the U.S. Geological Survey (USGS) and Oregon State University in 2010, between Shelter Cove and Fort Bragg in northern California.

This dataset includes navigation data for marine geophysical data jointly collected by the U.S. Geological Survey (USGS) and Oregon State University in 2010, between Shelter Cove and Fort Bragg in northern California.

This dataset includes raw, high-resolution seismic-reflection data jointly collected by the U.S. Geological Survey (USGS) and Oregon State University in 2010, between Shelter Cove and Fort Bragg in northern California.

This dataset includes swell-filtered, high-resolution seismic-reflection data jointly collected by the U.S. Geological Survey (USGS) and Oregon State University in 2010, between Fort Bragg and Point Arena in northern California.

This dataset includes navigation data for marine geophysical data jointly collected by the U.S. Geological Survey (USGS) and Oregon State University in 2010, between Fort Bragg and Point Arena in northern California.

This dataset includes raw, high-resolution seismic-reflection data jointly collected by the U.S. Geological Survey (USGS) and Oregon State University in 2010, between Fort Bragg and Point Arena in northern California.

Files contain hydrodynamic and sediment transport data for the location and deployment indicated. Time-series data of water depth, velocity, turbidity, and temperature were collected in San Pablo Bay and China Camp Marsh as part of the San Francisco Bay Marsh Sediment Experiments. Several instruments were deployed in tidal creek, marsh, mudflat, and Bay locations, gathering data on water depth, velocity, salinity/temperature, and turbidity. Deployment data are grouped by region (Bay channel (main Bay), Bay shallows, tidal creek, or marsh/mudflat/upper tidal creek). Users are advised check metadata and instrument information carefully for applicable time periods of specific data, as individual instrument deployment times vary.

Files contain hydrodynamic and sediment transport data for the location and deployment indicated. Time-series data of water depth, velocity, turbidity, and temperature were collected in San Pablo Bay and China Camp Marsh as part of the San Francisco Bay Marsh Sediment Experiments. Several instruments were deployed in tidal creek, marsh, mudflat, and Bay locations, gathering data on water depth, velocity, salinity/temperature, and turbidity. Deployment data are grouped by region (Bay channel (main Bay), Bay shallows, tidal creek, or marsh/mudflat/upper tidal creek). Users are advised to check metadata and instrument information carefully for applicable time periods of specific data, as individual instrument deployment times vary.

Files contain hydrodynamic and sediment transport data for the location and deployment indicated. Time-series data of water depth, velocity, turbidity, and temperature were collected in San Pablo Bay and China Camp Marsh as part of the San Francisco Bay Marsh Sediment Experiments. Several instruments were deployed in tidal creek, marsh, mudflat, and Bay locations, gathering data on water depth, velocity, salinity/temperature, and turbidity. Deployment data are grouped by region (Bay channel (main Bay), Bay shallows, tidal creek, or marsh/mudflat/upper tidal creek). Users are advised check metadata and instrument information carefully for applicable time periods of specific data, as individual instrument deployment times vary.

Files contain hydrodynamic and sediment transport data for the location and deployment indicated. Time-series data of water depth, velocity, turbidity, and temperature were collected in San Pablo Bay and China Camp Marsh as part of the San Francisco Bay Marsh Sediment Experiments. Several instruments were deployed in tidal creek, marsh, mudflat, and Bay locations, gathering data on water depth, velocity, salinity/temperature, and turbidity. Deployment data are grouped by region (Bay channel (main Bay), Bay shallows, tidal creek, or marsh/mudflat/upper tidal creek). Users are advised check metadata and instrument information carefully for applicable time periods of specific data, as individual instrument deployment times vary.

Files contain hydrodynamic and sediment transport data for the location and deployment indicated. Time-series data of water depth, velocity, turbidity, and temperature were collected in San Pablo Bay and China Camp Marsh as part of the San Francisco Bay Marsh Sediment Experiments. Several instruments were deployed in tidal creek, marsh, mudflat, and Bay locations, gathering data on water depth, velocity, salinity/temperature, and turbidity. Deployment data are grouped by region (Bay channel (main Bay), Bay shallows, tidal creek, or marsh/mudflat/upper tidal creek). Users are advised check metadata and instrument information carefully for applicable time periods of specific data, as individual instrument deployment times vary.

Files contain hydrodynamic and sediment transport data for the location and deployment indicated. Time-series data of water depth, velocity, turbidity, and temperature were collected in San Pablo Bay and China Camp Marsh as part of the San Francisco Bay Marsh Sediment Experiments. Several instruments were deployed in tidal creek, marsh, mudflat, and Bay locations, gathering data on water depth, velocity, salinity/temperature, and turbidity. Deployment data are grouped by region (Bay channel (main Bay), Bay shallows, tidal creek, or marsh/mudflat/upper tidal creek). Users are advised check metadata and instrument information carefully for applicable time periods of specific data, as individual instrument deployment times vary.

Files contain hydrodynamic and sediment transport data for the location and deployment indicated. Time-series data of water depth, velocity, turbidity, and temperature were collected in San Pablo Bay and China Camp Marsh as part of the San Francisco Bay Marsh Sediment Experiments. Several instruments were deployed in tidal creek, marsh, mudflat, and Bay locations, gathering data on water depth, velocity, salinity/temperature, and turbidity. Deployment data are grouped by region (Bay channel (main Bay), Bay shallows, tidal creek, or marsh/mudflat/upper tidal creek). Users are advised check metadata and instrument information carefully for applicable time periods of specific data, as individual instrument deployment times vary.

Files contain hydrodynamic and sediment transport data for the location and deployment indicated. Time-series data of water depth, velocity, turbidity, and temperature were collected in San Pablo Bay and China Camp Marsh as part of the San Francisco Bay Marsh Sediment Experiments. Several instruments were deployed in tidal creek, marsh, mudflat, and Bay locations, gathering data on water depth, velocity, salinity/temperature, and turbidity. Deployment data are grouped by region (Bay channel (main Bay), Bay shallows, tidal creek, or marsh/mudflat/upper tidal creek). Users are advised check metadata and instrument information carefully for applicable time periods of specific data, as individual instrument deployment times vary.

Files contain hydrodynamic and sediment transport data for the location and deployment indicated. Time-series data of water depth, velocity, turbidity, and temperature were collected in San Pablo Bay and China Camp Marsh as part of the San Francisco Bay Marsh Sediment Experiments. Several instruments were deployed in tidal creek, marsh, mudflat, and Bay locations, gathering data on water depth, velocity, salinity/temperature, and turbidity. Deployment data are grouped by region (Bay channel (main Bay), Bay shallows, tidal creek, or marsh/mudflat/upper tidal creek). Users are advised check metadata and instrument information carefully for applicable time periods of specific data, as individual instrument deployment times vary.

Files contain hydrodynamic and sediment transport data for the location and deployment indicated. Time-series data of water depth, velocity, turbidity, and temperature were collected in San Pablo Bay and China Camp Marsh as part of the San Francisco Bay Marsh Sediment Experiments. Several instruments were deployed in tidal creek, marsh, mudflat, and Bay locations, gathering data on water depth, velocity, salinity/temperature, and turbidity. Deployment data are grouped by region (Bay channel (main Bay), Bay shallows, tidal creek, or marsh/mudflat/upper tidal creek). Users are advised check metadata and instrument information carefully for applicable time periods of specific data, as individual instrument deployment times vary.

Files contain hydrodynamic and sediment transport data for the location and deployment indicated. Time-series data of water depth, velocity, turbidity, and temperature were collected in San Pablo Bay and China Camp Marsh as part of the San Francisco Bay Marsh Sediment Experiments. Several instruments were deployed in tidal creek, marsh, mudflat, and Bay locations, gathering data on water depth, velocity, salinity/temperature, and turbidity. Deployment data are grouped by region (Bay channel (main Bay), Bay shallows, tidal creek, or marsh/mudflat/upper tidal creek). Users are advised to check metadata and instrument information carefully for applicable time periods of specific data, as individual instrument deployment times vary.

Files contain hydrodynamic and sediment transport data for the location and deployment indicated. Time-series data of water depth, velocity, turbidity, and temperature were collected in San Pablo Bay and China Camp Marsh as part of the San Francisco Bay Marsh Sediment Experiments. Several instruments were deployed in tidal creek, marsh, mudflat, and Bay locations, gathering data on water depth, velocity, salinity/temperature, and turbidity. Deployment data are grouped by region (Bay channel (main Bay), Bay shallows, tidal creek, or marsh/mudflat/upper tidal creek). Users are advised to check metadata and instrument information carefully for applicable time periods of specific data, as individual instrument deployment times vary.

Files contain hydrodynamic and sediment transport data for the location and deployment indicated. Time-series data of water depth, velocity, turbidity, and temperature were collected in San Pablo Bay and China Camp Marsh as part of the San Francisco Bay Marsh Sediment Experiments. Several instruments were deployed in tidal creek, marsh, mudflat, and Bay locations, gathering data on water depth, velocity, salinity/temperature, and turbidity. Deployment data are grouped by region (Bay channel (main Bay), Bay shallows, tidal creek, or marsh/mudflat/upper tidal creek). Users are advised check metadata and instrument information carefully for applicable time periods of specific data, as individual instrument deployment times vary.

Files contain hydrodynamic and sediment transport data for the location and deployment indicated. Time-series data of water depth, velocity, turbidity, and temperature were collected in San Pablo Bay and China Camp Marsh as part of the San Francisco Bay Marsh Sediment Experiments. Several instruments were deployed in tidal creek, marsh, mudflat, and Bay locations, gathering data on water depth, velocity, salinity/temperature, and turbidity. Deployment data are grouped by region (Bay channel (main Bay), Bay shallows, tidal creek, or marsh/mudflat/upper tidal creek). Users are advised check metadata and instrument information carefully for applicable time periods of specific data, as individual instrument deployment times vary.

Files contain hydrodynamic and sediment transport data for the location and deployment indicated. Time-series data of water depth, velocity, turbidity, and temperature were collected in San Pablo Bay and China Camp Marsh as part of the San Francisco Bay Marsh Sediment Experiments. Several instruments were deployed in tidal creek, marsh, mudflat, and Bay locations, gathering data on water depth, velocity, salinity/temperature, and turbidity. Deployment data are grouped by region (Bay channel (main Bay), Bay shallows, tidal creek, or marsh/mudflat/upper tidal creek). Users are advised check metadata and instrument information carefully for applicable time periods of specific data, as individual instrument deployment times vary.

Files contain hydrodynamic and sediment transport data for the location and deployment indicated. Time-series data of water depth, velocity, turbidity, and temperature were collected in San Pablo Bay and China Camp Marsh as part of the San Francisco Bay Marsh Sediment Experiments. Several instruments were deployed in tidal creek, marsh, mudflat, and Bay locations, gathering data on water depth, velocity, salinity/temperature, and turbidity. Deployment data are grouped by region (Bay channel (main Bay), Bay shallows, tidal creek, or marsh/mudflat/upper tidal creek). Users are advised check metadata and instrument information carefully for applicable time periods of specific data, as individual instrument deployment times vary.

As part of the San Francisco Bay Marsh Sediment Experiments and hydrodynamic investigations in San Pablo Bay and China Camp Marsh, California, vegetation sampling measurements were taken over several periods at numerous locations. This portion of the data release presents physical characteristics and percent cover data from vegetation plots sampled in China Camp State Park salt marsh (northern San Francisco Bay) in 2015, 2016, and 2017. One-meter quadrats (1m x 1m) were analyzed for percent cover of each vegetation species present, average canopy height, and maximum canopy height. The percent cover was done by visual inspection. A quarter-meter quadrat (0.25m x 0.25m) was then used for stem count and stem diameter measurements. Stem counts were only done for S. foliosa. These quadrats ran along the instruments used to collect sediment and hydrodynamic data, which is described elsewhere in this data release. This survey was conducted on January 23, 2015 (winter conditions) and again on June 6, 2016 (summer conditions). A separate survey was conducted on September 29, 2016 and May 3, 2017, to measure the length, width, and spacing of S. foliosa leaves. Data collected are provided as a comma-delimited spreadsheet (.csv).

This part of the data release includes graphical representation (figures) of data of sediment cores collected in 2014 in Monterey Canyon. It is one of five files included in this U.S. Geological Survey data release that include data from a set of sediment cores acquired from the continental slope, north of Monterey Canyon, offshore central California. Vibracores and push cores were collected with the Monterey Bay Aquarium Research Institute’s (MBARI’s) remotely operated vehicle (ROV) Doc Ricketts in 2014 (cruise ID 2014-615-FA). One spreadsheet (NorthernFlankMontereyCanyonCores_Info.xlsx) contains core name, location, and length. One spreadsheet (NorthernFlankMontereyCanyonCores_MSCLdata.xlsx) contains Multi-Sensor Core Logger P-wave velocity and gamma-ray density whole-core logs of vibracores. One zipped folder of .bmp files (NorthernFlankMontereyCanyonCores_Photos.zip) contains continuous core photographs of the archive half of each vibracore. One spreadsheet (NorthernFlankMontereyCanyonCores_Radiocarbon.xlsx) contains radiocarbon sample information, results, and calibrated ages. One .pdf file (NorthernFlankMontereyCanyonCores_Figures.pdf) contains combined displays of data for each vibracore, including graphic diagram descriptive logs. This particular metadata file describes the information contained in the file NorthernFlankMontereyCanyon_Figures.pdf. All vibracores are archived by the U.S. Geological Survey Pacific Coastal and Marine Science Center. Other remaining core material, if available, is archived at MBARI.

This part of the data release is a spreadsheet including the name, location, and length of sediment cores collected in 2014 in Monterey Canyon. It is one of five files in this U.S. Geological Survey data release that include data from a set of sediment cores acquired from the continental slope, north of Monterey Canyon, offshore central California. Vibracores and push cores were collected with the Monterey Bay Aquarium Research Institute’s (MBARI’s) remotely operated vehicle (ROV) Doc Ricketts in 2014 (USGS cruise ID 2014-615-FA). One spreadsheet (NorthernFlankMontereyCanyonCores_Info.xlsx) contains core name, location, and length. One spreadsheet (NorthernFlankMontereyCanyonCores_MSCLdata.xlsx) contains Multi-Sensor Core Logger P-wave velocity and gamma-ray density whole-core logs of vibracores. One zipped folder of .bmp files (NorthernFlankMontereyCanyonCores_Photos.zip) contains continuous core photographs of the archive half of each vibracore. One spreadsheet (NorthernFlankMontereyCanyonCores_Radiocarbon.xlsx) contains radiocarbon sample information, results, and calibrated ages. One .pdf file (NorthernFlankMontereyCanyonCores_Figures.pdf) contains combined displays of data for each vibracore, including graphic diagram descriptive logs. This particular metadata file describes the information contained in the file NorthernFlankMontereyCanyonCores_Info.xlsx. All vibracores are archived by the U.S. Geological Survey Pacific Coastal and Marine Science Center. Other remaining core material, if available, is archived at MBARI.

This part of the data release includes Multi-Sensor Core Logger (MSCL) P-wave velocity and gamma-ray density whole-core logs of sediment cores collected in 2014 from the northern flank of Monterey Canyon, offshore California. It is one of five files in this U.S. Geological Survey data release that include data from a set of sediment cores acquired from the continental slope, north of Monterey Canyon, offshore central California. Vibracores and push cores were collected with the Monterey Bay Aquarium Research Institute’s (MBARI’s) remotely operated vehicle (ROV) Doc Ricketts in 2014 (USGS cruise ID 2014-615-FA). One spreadsheet (NorthernFlankMontereyCanyonCores_Info.xlsx) contains core name, location, and length. One spreadsheet (NorthernFlankMontereyCanyonCores_MSCLdata.xlsx) contains Multi-Sensor Core Logger P-wave velocity and gamma-ray density whole-core logs of vibracores. One zipped folder of .bmp files (NorthernFlankMontereyCanyonCores_Photos.zip) contains continuous core photographs of the archive half of each vibracore. One spreadsheet (NorthernFlankMontereyCanyonCores_Radiocarbon.xlsx) contains radiocarbon sample information, results, and calibrated ages. One .pdf file (NorthernFlankMontereyCanyonCores_Figures.pdf) contains combined displays of data for each core, including graphic diagram descriptive logs. This particular metadata file describes the information contained in the file NorthernFlankMontereyCanyonCores_MSCL.xlsx. All vibracores are archived by the U.S. Geological Survey Pacific Coastal and Marine Science Center. Other remaining core material, if available, is archived at MBARI.

This part of the data release includes continuous core photographs in bmp format of sediment cores collected in 2014 from the northern flank of Monterey Canyon, offshore California. It is one of five files in this U.S. Geological Survey data release that include data from a set of sediment cores acquired from the continental slope, north of Monterey Canyon, offshore central California. Vibracores and push cores were collected with the Monterey Bay Aquarium Research Institute’s (MBARI’s) remotely operated vehicle (ROV) Doc Ricketts in 2014 (USGS cruise ID 2014-615-FA). One spreadsheet (NorthernFlankMontereyCanyonCores_Info.xlsx) contains core name, location, and length. One spreadsheet (NorthernFlankMontereyCanyonCores_MSCLdata.xlsx) contains Multi-Sensor Core Logger P-wave velocity and gamma-ray density whole-core logs of vibracores. One zipped folder of .bmp files (NorthernFlankMontereyCanyonCores_Photos.zip) contains continuous core photographs of the archive half of each vibracore. One spreadsheet (NorthernFlankMontereyCanyonCores_Radiocarbon.xlsx) contains radiocarbon sample information, results, and calibrated ages. One .pdf file (NorthernFlankMontereyCanyonCores_Figures.pdf) contains combined displays of data for each vibracore, including graphic diagram descriptive logs. This particular metadata file describes the information contained in the file NorthernFlankMontereyCanyonCores_Photos. All vibracores are archived by the U.S. Geological Survey Pacific Coastal and Marine Science Center. Other remaining core material, if available, is archived at MBARI.

This part of the data release is a spreadsheet including radiocarbon sample information and calibrated ages of sediment cores collected in 2014 from the northern flank of Monterey Canyon, offshore California. It is one of five files in this U.S. Geological Survey data release that include data from a set of sediment cores acquired from the continental slope, north of Monterey Canyon, offshore central California. Vibracores and push cores were collected with the Monterey Bay Aquarium Research Institute’s (MBARI’s) remotely operated vehicle (ROV) Doc Ricketts in 2014 (USGS cruise ID 2014-615-FA). One spreadsheet (NorthernFlankMontereyCanyonCores_Info.xlsx) contains core name, location, and length. One spreadsheet (NorthernFlankMontereyCanyonCores_MSCLdata.xlsx) contains Multi-Sensor Core Logger P-wave velocity and gamma-ray density whole-core logs of vibracores. One zipped folder of .bmp files (NorthernFlankMontereyCanyonCores_Photos.zip) contains continuous core photographs of the archive half of each vibracore. One spreadsheet (NorthernFlankMontereyCanyonCores_Radiocarbon.xlsx) contains radiocarbon sample information, results, and calibrated ages. One .pdf file (NorthernFlankMontereyCanyonCores_Figures.pdf) contains combined displays of data for each vibracore, including graphic diagram descriptive logs. This particular metadata file describes the information contained in the file NorthernFlankMontereyCanyonCores_Radiocarbon.xlsx. All vibracores are archived by the U.S. Geological Survey Pacific Coastal and Marine Science Center. Other remaining core material, if available, is archived at MBARI.

This data release contains high-resolution multichannel seismic (MCS) reflection data collected in August of 2016 along the southeast Alaska continental margin. Structure perpendicular MCS profiles were collected along the Queen Charlotte-Fairweather fault. The data were collected aboard the R/V Norseman using a Delta sparker sound source and recorded on a 64-channel digital streamer. Subbottom acoustic penetration spans up to several hundreds of meters, and is variable by location.

Multichannel sparker (MCS) seismic-reflection data were collected along the Queen Charlotte-Fairweather Fault between Cross Sound and Dixon Entrance, offshore southeastern Alaska from 2016-05-17 to 2016-06-12. Data were collected aboard the Alaska Department of Fish and Game R/V Medeia, and recorded using a 32 channel GeoEel digital streamer, an Applied Acoustics power supply, and a SIG SLP 790 Sparker Electrode. MCS profiles were collected coincident with multibeam data collected at higher survey speeds (5-6 knots), which reduced the MCS data quality.

This portion of the data release contains image mosaics generated using digitized 1:24K natural color photographs collected in June 2000 by the National Oceanic and Atmospheric Administration (NOAA) National Ocean Service (NOS). These four image mosaics have 1.0 meter-per-pixel resolution, and intermittently cover approximately 53 km (33 mi) of shallow, coastal waters along the west, Kona coast, of the island of Hawai'i, including (from north to south) the Kawaihae, Waikoloa, Kukio, and Kailua-Kona areas. Each digital image mosaic area is downloadable as a separate zip file (area_1m.zip) that contains two versions of the image mosaic--one with and one without a lidar bathymetry shaded-relief image digitally combined with the aerial photography mosaic results. The shaded-relief image was derived using airborne SHOALS (Scanning Hydrographic Operational Lidar Survey) lidar (LIght Detection And Ranging) data collected for the U.S. Geological Survey (USGS) by the U.S. Army Corp of Engineers (USACE) in April 1999. Also included in each zip file is a lower-resolution 'browse' graphic of each image mosaic and associated metadata.

This portion of the data release contains an image mosaic generated using digitized 1:35K natural color photographs collected in September 1993 by the National Oceanic and Atmospheric Administration (NOAA) National Ocean Service (NOS) of the Napili-Honokowai area along the northwest coast of Maui. The area is downloadable as a zip file (napili_honokowai_1m.zip) and includes a high-resolution (1.0 meter per pixel) digital image mosaic, as well as a lower-resolution 'browse' image and associated metadata.

This portion of the data release contains digital image mosaics along the southeast coast of O'ahu. Digital mosaics at 1-foot (0.3048-meter) resolution, including the areas of Waikiki, Diamond Head, Wai'alae, Maunalua Bay, and Portlock, were generated from 1:10K aerial photography and are presented in one zip file (oahu_1ft.zip) that also contains lower-resolution 'browse' graphics of each image mosaic area, as well as associated metadata. All of the digital image areas (from Waikiki to Portlock) were combined into one large digital mosaic at 1-meter resolution, which is presented in another zip file (oahu_1m.zip) that includes a 'browse' graphic of the image mosaic area and associated metadata. The 1-meter resolution digital image mosaic was also combined with lidar bathymetry data to create a shaded-relief image, which is presented in a third zip file (oahu_1m_shaded.zip), along with a 'browse' graphic of the image mosaic area and associated metadata.

This data release contains high-resolution seismic reflection data collected in August of 2015 to explore marine geologic hazards of inland waterways of southeastern Alaska. Sub-bottom profiles were acquired in the inland waters between Glacier Bay and Juneau, including Cross Sound and Chatham Strait. High-resolution seismic-reflection profiles were acquired to assess evidence for active seabed faulting and submarine landslide hazards. The data were collected aboard the US Geological Survey R/V Alaskan Gyre. The seismic-reflection data were acquired using a tow-fish Edgetech 512 chirp subbottom profiler. Subbottom acoustic penetration spans up to several tens of meters, and is variable by location. This data release contains processed digital SEG-Y. This data release will be updated as subsequent lines of data from this field activity are published.

This data release contains high-resolution multichannel seismic (MCS) reflection data collected in August of 2015 to explore marine geologic hazards of inland waterways of southeastern Alaska. Sub-bottom profiles were acquired in the inland waters between Glacier Bay and Juneau, including Cross Sound and Chatham Strait. High-resolution seismic-reflection profiles were acquired to assess evidence for active seabed faulting and submarine landslide hazards. The data were collected aboard the US Geological Survey R/V Alaskan Gyre. The seismic-reflection data were acquired using a 500-Joule minisparker source and a 48-channel Geometrics GeoEel digital streamer. Subbottom acoustic penetration spans up to several hundreds of meters, and is variable by location. This data release contains CMP sorted digital data in SEG-Y format. This data release will be updated as subsequent lines of data from this field activity are published.

This data release presents data for 5-m resolution acoustic-backscatter data of the northern Channel Islands region, southern California. In 2004 the U.S. Geological Survey, Pacific Coastal and Marine Science Center collected multibeam-bathymetry and acoustic-backscatter data in the northern Channel Islands region, southern California. The region was mapped aboard the R/V Ewing using a Kongsberg Simrad EM-1002 multibeam echosounder. These data were previously published on-line at http://pubs.usgs.gov/of/2005/1153/. In this data release the data have been reprocessed to a finer spatial resolution (5-m versus 15-m) using more modern processing techniques. Due to the large file sizes the entire survey area is provided as two ASCIIRaster files (one for the north portion of the study area and another for the south). A few survey line files in the northern region did not process and are missing from the ASCIIRaster file.

This data release presents data for 5-m resolution multibeam-bathymetry data of the northern Channel Islands region, southern California. In 2004 The U.S. Geological Survey, Pacific Coastal and Marine Science Center collected multibeam-bathymetry and acoustic-backscatter data in the northern Channel Islands region, southern California. The region was mapped aboard the R/V Ewing using a Kongsberg Simrad EM-1002 multibeam echosounder. These data were previously published on-line at http://pubs.usgs.gov/of/2005/1153/. In this data release the data have been reprocessed to a finer spatial resolution (5-m versus 15-m) using more modern processing techniques. Due to the large file sizes the entire survey area is provided as two ASCIIRaster files (one for the north portion of the study area and another for the south).

We developed the HyCReWW metamodel to predict wave run-up under a wide range of coral reef morphometric and offshore forcing characteristics. Due to the complexity and high dimensionality of the problem, we assumed an idealized one-dimensional reef profile, characterized by seven primary parameters. XBeach Non-Hydrostatic was chosen to create the synthetic dataset and Radial Basis Functions implemented in Matlab were chosen for interpolation. Results demonstrate the applicability of the metamodel to obtain fast and accurate results of wave run-up for a large range of intrinsic coral reef morphologic and extrinsic hydrodynamic forcing parameters, offering a useful tool for risk management and early warning systems. These data accompany the following publication: Rueda, A., Cagigal, L., Pearson, S., Antolinez J.A.A., Storlazzi, C., van Dongeren, A., Camus, P., Mendez, F.J., 2019, HyCReWW: A hybrid coral reef waves and water level metamodel: Computers & Geosciences, https://doi.org/10.1016/j.cageo.2019.03.004.

This part of the data release presents a digital elevation model (DEM) derived from bathymetry and topography data of the Elwha River delta collected in January 2015. Two dams on the Elwha River, Washington State, USA trapped over 20 million m3 of sediment, reducing downstream sediment fluxes and contributing to erosion of the river's coastal delta. The removal of the Elwha and Glines Canyon dams between 2011 and 2014 induced massive increases in river sediment supply and provided an unprecedented opportunity to examine the response of a delta system to changes in sediment supply. The U.S. Geological Survey developed an integrated research program aimed at understanding the ecosystem responses following dam removal that included regular monitoring of coastal and nearshore bathymetry and topography. As part of this monitoring program, the USGS conducted a bathymetric and topographic survey in the Strait of Juan de Fuca on the Elwha River delta, Washington (USGS Field Activity Number 2015-605-FA). Bathymetry data were collected using two personal watercraft (PWCs) and a kayak, each equipped with single beam echosounders and survey-grade global navigation satellite systems (GNSS). Topography data were collected on foot with GNSS receivers mounted on backpacks. DEM surfaces were produced from all available elevation data using linear interpolation.

This portion of the data release presents sediment grain-size data from samples collected on the Elwha River delta, Washington, in January 2015 (USGS Field Activity 2015-605-FA). Surface sediment was collected from 61 locations using a small ponar, or 'grab', sampler from the R/V Frontier in depths between about 1 and 17 m around the delta. A handheld global satellite navigation system (GNSS) receiver was used to determine the locations of sediment samples. The grain-size distributions of samples were determined using standard techniques developed by the USGS Pacific Coastal and Marine Science Center sediment lab. The grain-size data are provided in a comma-delimited spreadsheet (.csv).

This part of the data release presents bathymetry data from the Elwha River delta collected in January 2015 using a kayak. The kayak was equipped with a single-beam echosounder and a survey-grade global navigation satellite system (GNSS) receiver.

This part of the data release presents bathymetry data from the Elwha River delta collected in January 2015 using two personal watercraft (PWCs). The PWCs were equipped with single-beam echosounders and survey-grade global navigation satellite system (GNSS) receivers.

This part of the data release presents topography data from the Elwha River delta collected in January 2015. Topography data were collected on foot with global navigation satellite system (GNSS) receivers mounted on backpacks.

Grain size distribution and bulk density are reported for sediment push cores from two flooded agricultural tracts, Little Holland Tract and Liberty Island, in the Sacramento-San Joaquin Delta, California. Push core samples were collected from 14 sites by the U.S Geological Survey in August, 2014. Each core was analyzed at multiple depths to investigate variations in particle sizes with depth below the sediment surface. The same sites were sampled again in 2016 (https://www.sciencebase.gov/catalog/item/5a73aa70e4b0a9a2e9e172de). These data provide insight into the variation of particle size distributions in space, bed depth, and time.

Grain size distribution and bulk density are reported for sediment push cores from two flooded agricultural tracts, Little Holland Tract and Liberty Island, in the Sacramento-San Joaquin Delta, California. Push core samples were collected from 17 sites by the U.S. Geological Survey in June 2016. Each core was analyzed at multiple depths to investigate variations in particle sizes with depth below the sediment surface. The same sites were sampled previously in 2014 (https://www.sciencebase.gov/catalog/item/5a73a58fe4b0a9a2e9e172cf). These data provide insight into the variation of particle size distributions in space, bed depth, and time.

Sediment cores were collected from San Pablo Bay, in the Sacramento-San Joaquin Delta in California by the U.S. Geological Survey Pacific Coastal and Marine Science Center (PCMSC) during multiple surveys from 2011 to 2012. The cores were analyzed for grain-size distributions at the PCMSC sediment lab.

The U.S. Geological Survey Pacific Coastal and Marine Science Center collected data to investigate sediment dynamics in the shallows of San Pablo Bay in two deployments: February to March 2011 (ITX11) and May to June 2012 (ITX12). This data release includes time-series data and grain-size distributions from sediment grabs collected during the deployments. During each deployment, time series of current velocity, water depth, and turbidity were collected at several stations in the shallows, and one station in the channel. Velocity and depth (pressure) were collected at high frequency (10 Hz) to allow calculation of wave parameters and turbulence statistics.

This part of the data release contains high-resolution acoustic-backscatter data collected by the U.S. Geological Survey (USGS) Pacific Coastal and Marine Science Center at three study locations in the Sacramento-San Joaquin Delta, California. Data were collected in Lindsey Slough in April 2017, Middle River in March 2018, and Mokelumne River in March 2018, using an interferometric bathymetric sidescan sonar systems mounted to the USGS R/V Parke Snavely. Data are provided in 1-m resolution GeoTIFF formats. These data were collected as part of a study of the effects of invasive aquatic vegetation on sediment transport in the Sacramento-San Joaquin Delta.

Bed sediment samples were collected in Lindsey Slough in April 2017, and Middle River and the Mokelumne River in March 2018, to analyze for sediment properties, including bulk density, particle size distribution, and percent organic carbon. Sediment samples were collected within the vegetation with push corers deployed from a small vessel, and in the unvegetated channel with a Gomex box corer, which was subsampled with three push cores per Gomex core. Data are provided in a comma-delimited values spreadsheet. These data were collected as part of a cooperative project, with the USGS California Water Science Center and the California Department of Fish and Wildlife, on the effects of invasive aquatic vegetation on sediment transport in the Sacramento-San Joaquin Delta.

Hydrodynamic and sediment transport time-series data, including water depth, velocity, turbidity, conductivity, and temperature, were collected by the U.S. Geological Survey (USGS) Pacific Coastal and Marine Science Center at three locations in the Sacramento-San Joaquin Delta. Data were collected in Lindsey Slough in April 2017, and Middle River and the Mokelumne River in March 2018. Data files are grouped by location. At each of the three sites, data were collected at stations outside and within patches of vegetation, to determine how submerged invasive vegetation influences tidal currents and suspended-sediment concentration. The Table below shows the data types collected at each station, and classifies stations as Vegetated (V) or Unvegetated (U). These data were collected as part of a study of the effects of invasive aquatic vegetation on sediment transport in the Sacramento-San Joaquin Delta. At times, vegetation caught on instrument frames (both within and outside patches) compromised data quality. Users are advised to check data quality carefully, and to check metadata and instrument information, as individual instrument deployment times vary.

This part of the data release contains high-resolution swath bathymetry data collected by the U.S. Geological Survey (USGS) Pacific Coastal and Marine Science Center at three locations in the Sacramento-San Joaquin Delta. Data were collected in Lindsey Slough in April 2017, Middle River in March 2018, and Mokelumne River in March 2018 using an interferometric bathymetric sidescan sonar systems mounted to the USGS R/V Parke Snavely. Data are provided in 1-m resolution GeoTIFF formats. These data were collected as part of a study on the effects of invasive aquatic vegetation on sediment transport in the Sacramento-San Joaquin Delta.

This data release presents eelgrass distributions and bathymetry data derived from acoustic surveys of Bellingham Bay, Washington. Survey operations were conducted between February 16 and February 21, 2019 (USGS Field Activity Number 2019-606-FA) by a team of scientists from the U.S. Geological Survey Pacific Coastal and Marine Science Center and Washington State Department of Ecology. Eelgrass and bathymetry data were collected from the R/V George Davidson equipped with a single-beam sonar system and global navigation satellite system (GNSS) receiver. The sonar system consisted of a Biosonics DT-X single-beam echosounder and 420 kHz transducer with a 6-degree beam angle. Depths from the echosounder were computed using sound velocity data measured using a YSI CastAway CTD during the survey. Positioning of the vessel was determined at 5 Hz using a Trimble R9s GNSS receiver and Trimble Zephyr Model 2 antenna operating in real time kinematic (RTK) mode. Differential corrections were transmitted by a cellular modem to the GNSS receiver on the survey vessel at 1-Hz from a GNSS continuously operating reference station operated by the Washington State Reference Network (WSRN; http://www.wsrn3.org/) located in the city of Bellingham (station BELI). Output from the GNSS and sonar systems were combined in real time by the Biosonics DT-X deck unit and output to a computer running HYPACK hydrographic survey software. Navigation information was displayed on a video monitor, allowing the vessel operator to navigate along predefined survey lines spaced at 25- to 100-m intervals alongshore at speeds of approximately 2 m/s. Acoustic backscatter data were analyzed using a custom graphical user interface (GUI) that implements a signal processing algorithm applied to each sonar sounding to extract the location of the bottom and presence of vegetation (Stevens and others, 2008 ). Individual acoustic returns along a survey line were grouped into packets of ten, and eelgrass percent cover was calculated as the fractional percent of acoustic returns that were classified as vegetated within each group, resulting in a estimate of percent cover every 4 to 5 m (depending on vessel speed). The positioning data from the bathymetric survey were postprocessed using Waypoint Grafnav to apply differential corrections with data recorded at the GNSS base station BELI and archived by the WSRN; these data superseded the original positions recorded in real time. The GUI was used to combine filtered sonar data with postprocessed positioning data and orthometric elevations relative to the NAVD88 vertical datum were computed using National Geodetic Survey Geoid12a offsets. The estimated vertical uncertainty of the bathymetric measurements ranged from 2.0 cm to 18.3 cm with a mean of 6.7 cm. Uncertainty in the vertical positions associated with pitch and roll of the survey vessel is unknown. The final point data are provided in a comma-separated text file and are projected in Cartesian coordinates using the Universal Transverse Mercator (UTM), Zone 10 north, meters coordinate system.

Digital surface elevation models (DSMs) of the coastline of Barter Island, Alaska derived from aerial photographs collected on July 01 2014, September 07 2014, and July 05 2015. Aerial photographs and coincident elevation data were processed using Structure-from-Motion (SfM) photogrammetric techniques. These files are single-band, 32-bit floating point DSMs (digital surface models) that represent surface elevations of buildings, vegetation, and uncovered ground surfaces in meters with 23 cm ground sample distance (GSD). The No Data value is set to -32767. The file employs Lempel-Ziv-Welch (LZW) compression.

Ground control points and checkpoints were collected during Global Positioning System (GPS) surveys conducted between September 6, 2014 and September 18, 2016 along the northern coast of Barter Island, Alaska. Data were acquired and post-processed using precise positioning and used to co-register and assess accuracy of photogrammetric data sets.

Aerial photographs were collected from a small, fixed-wing aircraft over the coast of Barter Island, Alaska on July 01 2014, September 07 2014. Precise aircraft position information and structure-from-motion photogrammetric methods were combined to derive a high-resolution orthophotomosaic. This orthophotomosaic contain 3-band, 8-bit, unsigned raster data (red/green/blue; file format-GeoTIFF) with a ground sample distance (GSD) resolution of 19 cm. The file employs Lempel-Ziv-Welch (LZW) compression. This orthophotomosaic was shifted (registered) to coincide with surveyed ground control points relative to the WGS84 datum.

Aerial photographs were collected from a small, fixed-wing aircraft over the coast of Barter Island, Alaska on September 07 2014. Precise aircraft position information and structure-from-motion photogrammetric methods were combined to derive a high-resolution orthophotomosaic. This orthophotomosaic contain 3-band, 8-bit, unsigned raster data (red/green/blue; file format-GeoTIFF) with a ground sample distance (GSD) resolution of 11 cm. The file employs Lempel-Ziv-Welch (LZW) compression. This orthophotomosaic was shifted (registered) to coincide with surveyed ground control points relative to the WGS84 datum.

Aerial photographs were collected from a small, fixed-wing aircraft over the coast of Barter Island, Alaska on July 05 2015. Precise aircraft position information and structure-from-motion photogrammetric methods were combined to a derive high-resolution orthophotomosaic. This orthophotomosaic contain 3-band, 8-bit, unsigned raster data (red/green/blue; file format-GeoTIFF) with a ground sample distance (GSD) resolution of 8 cm. The file employs Lempel-Ziv-Welch (LZW) compression. This orthophotomosaic was shifted (registered) to coincide with surveyed ground control points relative to the WGS84 datum.

Acoustic-backscatter data were collected during a 2018 swath survey in the Cache Slough Complex and the Sacramento River Deep Water Ship Channel, California. Data were collected by the U.S. Geological Survey (USGS) during USGS field activity 2018-684-FA, using interferometric bathymetric sidescan sonar systems mounded to the USGS R/V San Lorenzo and the R/V Kelpfly. The backscatter data are provided as GeoTIFF images.

Bathymetry data were collected during a 2018 swath survey in the Cache Slough Complex and the Sacramento River Deep Water Ship Channel, California. Data were collected by the U.S. Geological Survey (USGS) during USGS field activity 2018-684-FA, using interferometric bathymetric sidescan sonar systems mounded to the USGS R/V San Lorenzo and the R/V Kelpfly. The bathymetry data and a shaded-relief version are provided as GeoTIFF images.

This metadata describes a digital elevation model (DEM) created from bathymetric and topographic data collected between 2004 and 2019 in the Cache Slough Complex (CSC), northern Sacramento-San Joaquin Delta, California. We merged the newly collected bathymetric and topographic data presented in this data release (DOI:10.5066/P9AQSRVH) with 2019 surveys by the California Department of Water Resources (DWR), 2017 USGS Sacramento Delta Lidar, and 2004 bathymetry data from the Army Corp of Engineers. Small gaps of missing data were filled with existing DWR/USGS Delta DEMs to produce a seamless DEM of the Cache Slough Complex with a grid resolution of 1 m. Remaining gaps in the DEM are areas where there is currently no available data.

This metadata describes a digital elevation model (DEM) created from bathymetric and topographic data collected between 2017 and 2019 in the Sacramento River Deep Water Ship Channel (DWSC), northern Sacramento-San Joaquin Delta, California. We merged the newly collected bathymetric and topographic data presented in this data release (DOI:10.5066/P9AQSRVH) with 2019 surveys by the California Department of Water Resources (DWR) and 2017 USGS Sacramento Delta Lidar, to produce a seamless digital elevation model of the DWSC at a grid resolution of 1 m.

This portion of the USGS data release presents single beam bathymetry data collected during surveys performed in the Cache Slough Complex, Sacramento-San Joaquin Delta, California in 2017 and 2018 (USGS Field Activity Numbers 2017-649-FA and 2018-684-FA). Bathymetry data were collected using personal watercraft (PWCs) equipped with single-beam sonar systems and global navigation satellite system (GNSS) receivers. The final point data from the PWCs are provided in a comma-separated text file and are projected in cartesian coordinates using the Universal Transverse Mercator (UTM) Zone 10 North, meters coordinate system.

This portion of the USGS data release presents topography data acquired in the Liberty Island Conservation Wildlands restoration site in 2017 (USGS Field Activity Number 2017-649-FA). Topographic data were collected on June 26 and 27, 2017 by walking with global navigation satellite system (GNSS) receivers mounted on backpacks. Hand-held data collectors were used to log raw data and display navigational information as the surveyors traversed the landscape. The final point data are provided in a comma-separated text file and are projected in cartesian coordinates using the Universal Transverse Mercator (UTM) Zone 10 North, meters coordinate system.

Sediment grain-size distributions, stable carbon isotope ratios (d13C), total carbon to total nitrogen ratios (C:N), short-lived radionuclides (Beryllium-7, Cesium-137, and Lead-210), concentrations of 76 parent and alkylated polycyclic aromatic hydrocarbons (PAHs) and concentrations of 33 per- and polyfluoroalkyl substances (PFAS) were measured in the northern reach of San Francisco Bay (San Pablo and Suisun Bays), and in stream beds of the lower reaches of Napa River and Sonoma Creek, 5 months and 20 months after the 2017 Atlas and Nuns wildfires. New sites for sediment geochemistry analyses added 20 months post-fire included the lower reaches of Petaluma Creek and Suisun Slough, and in marsh sediment on Napa River and Sonoma Creek.

This data release includes representative cluster profiles (RCPs) from a large (>24,000) selection of coral reef topobathymetric cross-shore profiles (Scott and others, 2020). We used statistics, machine learning, and numerical modelling to develop the set of RCPs, which can be used to accurately represent the shoreline hydrodynamics of a large variety of coral reef-lined coasts around the globe. In two stages, the data were reduced by clustering cross-shore profiles based on morphology and hydrodynamic response to typical wind and swell wave conditions. By representing a large variety of coral reef morphologies with a reduced number of RCPs, a computationally feasible number of numerical model simulations can be done to obtain wave-runup estimates. The RCPs identified here can be combined with probabilistic tools that can provide an enhanced prediction given a multivariate wave and water level climate and reef ecology state. These data accompany the following publication: Scott, F., Antolinez, J.A., McCall, R.T., Storlazzi, C.D., Reniers, A., and Pearson, S., 2020, Hydro-morphological characterization of coral reefs for wave runup prediction: Frontiers in Marine Science, https://doi.org/10.3389/fmars.2020.000361.

A three-dimensional hydrodynamic and sediment transport model of San Pablo and Suisun Bays was constructed using the Delft3D4 (D3D) modeling suite (Deltares, 2021a) to simulate water levels, flow, waves, and suspended sediment for time period of Nov 1 to Dec 31, 2014. This data release describes the construction and validation of the model application and provides input files suitable to run the model on D3D software version 4.04.01.

This dataset includes a reference baseline used by the Digital Shoreline Analysis System (DSAS) to calculate rate-of-change statistics for the exposed coast of Alaska from Icy Cape and Cape Prince Wales for the time period 1948 to 2016. This baseline layer serves as the starting point for all transects cast by the DSAS application and can be used to establish measurement points used to calculate shoreline-change rates.

This dataset consists of long-term (less than 68 years) shoreline change rates for the exposed coast of the north coast of Alaska from Icy Cape to Cape Prince of Wales. Rate calculations were computed within a GIS using the Digital Shoreline Analysis System (DSAS) version 4.4, an ArcGIS extension developed by the U.S. Geological Survey. Rates of shoreline change were calculated using a linear regression rate-of-change (lrr) method based on available shoreline data between 1948 and 2016. A reference baseline was used as the originating point for the orthogonal transects cast by the DSAS software. The transects intersect each shoreline establishing measurement points, which are then used to calculate shoreline change rates.

This dataset consists of short-term (less than 37 years) shoreline change rates for the exposed coast of the north coast of Alaska from Icy Cape to Cape Prince of Wales. Rate calculations were computed within a GIS using the Digital Shoreline Analysis System (DSAS) version 4.4, an ArcGIS extension developed by the U.S. Geological Survey. Rates of shoreline change were calculated using an end point rate-of-change (epr) method based on available shoreline data between 1980 and 2016. A reference baseline was used as the originating point for the orthogonal transects cast by the DSAS software. The transects intersect each shoreline establishing measurement points, which are then used to calculate shoreline change rates.

This dataset consists of short-term (less than 37 years) shoreline change rates for the north coast of Alaska from Icy Cape to Cape Prince of Wales. Rate calculations were computed within a GIS using the Digital Shoreline Analysis System (DSAS) version 4.4, an ArcGIS extension developed by the U.S. Geological Survey. Rates of shoreline change were calculated using a linear regression rate-of-change (lrr) method based on available shoreline data between 1980s and 2016. A reference baseline was used as the originating point for the orthogonal transects cast by the DSAS software. The transects intersect each shoreline establishing measurement points, which are then used to calculate rates of change.

This dataset includes shorelines that span 68 years, from 1948 to 2016, for the north coast of Alaska from Icy Cape to Cape Prince of Wales. Shorelines were compiled from topographic survey sheets (T-sheets; National Oceanic and Atmospheric Administration (NOAA)) and aerial orthophotographs (U.S. Geological Survey (USGS) and Alaska High Altitude Photography (AHAP)). Historical shoreline positions serve as easily understood features that can be used to describe the movement of beaches through time. These data are used to calculate rates of shoreline change for the U.S. Geological Survey's National Assessment of Shoreline Change Project. Rates of long-term and short-term shoreline change were generated in a GIS using the Digital Shoreline Analysis System (DSAS) version 4.4. DSAS uses a measurement baseline method to calculate rate-of-change statistics. Transects are cast from the reference baseline to intersect each shoreline, establishing measurement points used to calculate shoreline change rates.

Seamless unconfined groundwater heads for coastal California groundwater systems were modeled with homogeneous, steady-state MODFLOW simulations. The geographic extent examined was limited primarily to low-elevation (i.e. land surface less than approximately 10 m above mean sea level) areas. In areas where coastal elevations increase rapidly (e.g., bluff stretches), the model boundary was set approximately 1 kilometer inland of the present-day shoreline. Steady-state MODFLOW groundwater flow models were used to obtain detailed (10-meter-scale) predictions over large geographic scales (100s of kilometers) of groundwater heads for both current and future sea-level rise (SLR) scenarios (0 to 2 meters (m) in 0.25 m increments, 2.5 m, 3 m, and 5 m) using a range of horizontal hydraulic conductivity (Kh) scenarios (0.1, 1, and 10 m/day). For each SLR/Kh combination, results are provided for two marine boundary conditions, local mean sea level (LMSL) and mean higher-high water (MHHW), and two model versions. In the first model version, groundwater reaching the land surface is removed from the model, simulating loss via natural drainage. In the second model version, groundwater reaching the land surface is retained, simulating the worst-case "linear" response of groundwater head to sea-level rise. Modeled groundwater heads were then subtracted from high-resolution topographic digital elevation model (DEM) data to obtain the water table depths, which are represented as polygons for specific depth ranges in this dataset. Additional details about the groundwater model and data sources are outlined in Befus and others (2020) and in Groundwater_model_methods.pdf (available at https://www.sciencebase.gov/catalog/file/get/5b8ef008e4b0702d0e7ec72b?name=Groundwater_model_methods.pdf). Methods specific to groundwater head and water table depth products are outlined in Groundwater_head_and_water_table_depth_methods.pdf (available at https://www.sciencebase.gov/catalog/file/get/5bda1563e4b0b3fc5cec39b4?name=Groundwater_head _and_water_table_depth_methods.pdf). Methods specific to groundwater emergence and shoaling products are outlined in Groundwater_emergence_and_shoaling_methods.pdf (available at https://www.sciencebase.gov/catalog/file/get/5bd9f318e4b0b3fc5cec20ed?name=Groundwater_emergence_and_shoaling_methods.pdf). Please read the model details, data sources and methods summaries and inspect model output carefully. Data are complete for the information presented. Users should note that while the metadata Spatial Reference Information/UTM Zone Number in this document is 10, some files in southern California are in UTM Zone 11, as noted in the Format Specification for individual downloadable files. As a result users may need to modify the metadata for automated import and display of Zone 11 datafiles.

Seamless unconfined groundwater heads for coastal California groundwater systems were modeled with homogeneous, steady-state MODFLOW simulations. The geographic extent examined was limited primarily to low-elevation (i.e. land surface less than approximately 10 m above mean sea level) areas. In areas where coastal elevations increase rapidly (e.g., bluff stretches), the model boundary was set approximately 1 kilometer inland of the present-day shoreline. Steady-state MODFLOW groundwater flow models were used to obtain detailed (10-meter-scale) predictions over large geographic scales (100s of kilometers) of groundwater heads for both current and future sea-level rise (SLR) scenarios (0 to 2 meters (m) in 0.25 m increments, 2.5 m, 3 m, and 5 m) using a range of horizontal hydraulic conductivity (Kh) scenarios (0.1, 1, and 10 m/day). For each SLR/Kh combination, results are provided for two marine boundary conditions, local mean sea level (LMSL) and mean higher-high water (MHHW), and two model versions. In the first model version, groundwater reaching the land surface is removed from the model, simulating loss via natural drainage. In the second model version, groundwater reaching the land surface is retained, simulating the worst-case "linear" response of groundwater head to sea-level rise. Additional details about the groundwater model and data sources are outlined in Befus and others (2020) and in Groundwater_model_methods.pdf (available at https://www.sciencebase.gov/catalog/file/get/5b8ef008e4b0702d0e7ec72b?name=Groundwater_model_methods.pdf). Methods specific to groundwater head and water table depth products are outlined in Groundwater_head_and_water_table_depth_methods.pdf (available at https://www.sciencebase.gov/catalog/file/get/5bda1563e4b0b3fc5cec39b4?name=Groundwater_head _and_water_table_depth_methods.pdf). Please read the model details, data sources and methods summaries and inspect model output carefully. Data are complete for the information presented. Users should note that while the metadata Spatial Reference Information/UTM Zone Number in this document is 10, some files in southern California are in UTM Zone 11, as noted in the Format Specification for individual downloadable files. As a result users may need to modify the metadata for automated import and display of Zone 11 datafiles.

Seamless unconfined groundwater heads for coastal California groundwater systems were modeled with homogeneous, steady-state MODFLOW simulations. The geographic extent examined was limited primarily to low-elevation (i.e. land surface less than approximately 10 m above mean sea level) areas. In areas where coastal elevations increase rapidly (e.g., bluff stretches), the model boundary was set approximately 1 kilometer inland of the present-day shoreline. Steady-state MODFLOW groundwater flow models were used to obtain detailed (10-meter-scale) predictions over large geographic scales (100s of kilometers) of groundwater heads for both current and future sea-level rise (SLR) scenarios (0 to 2 meters (m) in 0.25 m increments, 2.5 m, 3 m, and 5 m) using a range of horizontal hydraulic conductivity (Kh) scenarios (0.1, 1, and 10 m/day). For each SLR/Kh combination, results are provided for two marine boundary conditions, local mean sea level (LMSL) and mean higher-high water (MHHW), and two model versions. In the first model version, groundwater reaching the land surface is removed from the model, simulating loss via natural drainage. In the second model version, groundwater reaching the land surface is retained, simulating the worst-case "linear" response of groundwater head to sea-level rise. Modeled groundwater heads were then subtracted from high-resolution topographic digital elevation model (DEM) data to obtain the water table depths. Additional details about the groundwater model and data sources are outlined in Befus and others (2020) and in Groundwater_model_methods.pdf (available at https://www.sciencebase.gov/catalog/file/get/5b8ef008e4b0702d0e7ec72b?name=Groundwater_model_methods.pdf). Methods specific to groundwater head and water table depth products are outlined in Groundwater_head_and_water_table_depth_methods.pdf (available at https://www.sciencebase.gov/catalog/file/get/5bda1563e4b0b3fc5cec39b4?name=Groundwater_head _and_water_table_depth_methods.pdf). Please read the model details, data sources and methods summaries, and inspect model output carefully. Data are complete for the information presented. Users should note that while the metadata Spatial Reference Information/UTM Zone Number in this document is 10, some files in southern California are in UTM Zone 11, as noted in the Format Specification for individual downloadable files. As a result users may need to modify the metadata for automated import and display of Zone 11 datafiles.

Nearshore surface sediment was collected with a petit ponar grab sampler between April 22 and September 17, 2015, at five sites in Puget Sound, Washington. Four sites were adjacent to the Burlington Northern Santa Fe rail line in urban and non-urban areas, and one site was in an urban area that was not adjacent to the rail line. Total and near-total major, minor, trace, and rare earth element contents of the <0.063 mm sediment fraction were determined by inductively coupled plasma atomic emission spectroscopy and mass spectroscopy. These data accompany Takesue, R.K., and Campbell, P.L., 2019, Contaminant baselines and sediment provenance along the Puget Sound Energy Transport Corridor, 2015: U.S. Geological Survey Open-File Report 2018-1196, https://doi.org/10.3133/ofr20181196.

RBRduo pressure and temperature sensors (early 2015 generation), mounted on aluminum frames, were moored in shallow (< 6 m) water depths in Bellingham Bay, Washington, to capture wave heights and periods. Continuous pressure fluctuations are transformed into surface-wave observations of wave heights, periods, and frequency spectra at 30-minute intervals.

RBRduo pressure and temperature sensors (early 2015 generation), mounted on aluminum frames, were moored in shallow (< 6 m) water depths in Skagit Bay to capture wave heights and periods. Continuous pressure fluctuations are transformed into surface-wave observations of wave heights, periods, and frequency spectra at 30-minute intervals.

This part of the data release presents projected flooding extent polygon (flood masks) and flooding depth points (flood points) shapefiles based on wave-driven total water levels for American Samoa (the islands of Tutuila, Ofu-Olosega, and Tau). For each island there are 8 associated flood mask and flood depth shapefiles: one for each of four nearshore wave energy return periods (rp; 10-, 50-, 100-, and 500-years) and both with (wrf) and without (worf) the presence of coral reefs. Flooding depth point data are also presented as a comma-separated value (.csv) text file.

This part of the data release presents projected flooding extent polygon (flood masks) and flooding depth points (flood points) shapefiles based on wave-driven total water levels for Commonwealth of the Northern Mariana Islands (the islands of Saipan and Tinian). For each island there are 8 associated flood mask and flood depth shapefiles: one for each of four nearshore wave energy return periods (rp; 10-, 50-, 100-, and 500-years) and both with (wrf) and without (worf) the presence of coral reefs.

This part of the data release presents projected flooding extent polygon (flood masks) and flooding depth points (flood points) shapefiles based on wave-driven total water levels for the State Florida (the Florida Peninsula and the Florida Keys). For each island there are 8 associated flood mask and flood depth shapefiles: one for each of four nearshore wave energy return periods (rp; 10-, 50-, 100-, and 500-years) and both with (wrf) and without (worf) the presence of coral reefs. Flooding depth point data are also presented as a comma-separated value (.csv) text file.

This part of the data release presents projected flooding extent polygon (flood masks) and flooding depth points (flood points) shapefiles based on wave-driven total water levels for the Territory of Guam. There are 8 associated flood mask and flood depth shapefiles: one for each of four nearshore wave energy return periods (rp; 10-, 50-, 100-, and 500-years) and both with (wrf) and without (worf) the presence of coral reefs. Flooding depth point data are also presented as a comma-separated value (.csv) text file.

This part of the data release presents projected flooding extent polygon (flood masks) and flooding depth points (flood points) shapefiles based on wave-driven total water levels for the State of Hawaii (the islands of Hawaii, Kahoolawe, Kauai, Lanai, Maui, Molokai, Niihau, and Oahu). For each island there are 8 associated flood mask and flood depth shapefiles: one for each of four nearshore wave energy return periods (rp; 10-, 50-, 100-, and 500-years) and both with (wrf) and without (worf) the presence of coral reefs. Flooding depth point data are also presented as a comma-separated value (.csv) text file.

This part of the data release presents projected flooding extent polygon (flood masks) and flooding depth points (flood points) shapefiles based on wave-driven total water levels for the Territory of Puerto Rico (the islands of Culebra, Puerto Rico, and Vieques). For each island there are 8 associated flood mask and flood depth shapefiles: one for each four nearshore wave energy return periods (rp; 10-, 50-, 100-, and 500-years) and both with (wrf) and without (worf) the presence of coral reefs. Flooding depth point data are also presented as a comma-separated value (.csv) text file.

This part of the data release presents projected flooding extent polygon (flood masks) and flooding depth points (flood points) shapefiles based on wave-driven total water levels for the Territory of the U.S. Virgin Islands (the islands of Saint Croix, Saint John, and Saint Thomas). For each island there are 8 associated flood mask and flood depth shapefiles: one for each four nearshore wave energy return periods (rp; 10-, 50-, 100-, and 500-years) and both with (wrf) and without (worf) the presence of coral reefs. Flooding depth point data are also presented as a comma-separated value (.csv) text file.

Single-beam bathymetry data were collected in 2010 and 2011 in the nearshore waters around Barter Island, Arey Island, and within Arey Lagoon, Alaska. Measurements were made from a small boat or dinghy using one of three systems: a Humminbird 898 SI Fish Finder with integrated GPS (2010 and 2011), an Ohmex Sonarmite BT integrated with a Trimble GeoHX series GPS (2011), or a Garmin Sounder with integrated GPS (2011). Each system collected single-beam water depth with accuracies better than 4 meters (m) horizontal and 25 centimeters (cm) vertical.

Beach elevation profiles were measured along 29 shore-normal transects on and around Arey and Barter Islands, Alaska in August 2010 and July 2011. Profile data are available in a single comma-delimited file and a zip file including multiple .jpg images that show a visual representation of the individual profiles.

Thermokarst lake water temperature and salinity data were collected in 2011 in the vicinity of Arey Lagoon and Barter Island, Alaska. Pond temperatures and salinity (conductivity) were measured along two transects traversing across a wet sedge area. A hand-held YSI 556 MPS (plus or minus 0.5 percent accuracy) with a cable-attached instrument probe was placed in 10-15 cm of water within 1 m of each of the pond edges and allowed to equilibrate, and readings were recorded manually. In all, 35 ponds were sampled over a distance of approximately 1.5 km.

Time-series measurements of waves, currents, water levels, sea surface temperatures, ocean salinity, and water, air, and ground temperatures were collected in July through September 2011 in and around Arey Lagoon, near Barter Island, Alaska. Directional wave spectra, currents, water levels, salinity, and bottom and surface water temperatures were measured with a bottom-mounted 1MHz Nortek AWAC, HOBO temperature loggers, and a Solinst Levelogger in ~5m water depth offshore of Arey Island. Within Arey Lagoon, a bottom-mounted frame equipped with a Nortek 1MHz Aquadopp, Solinst Levelogger, and HOBO temperature loggers measured currents, water levels, and water temperatures. Ground temperatures (maximum depth 3 meters below the surface), were measured with HOBO temperature loggers and EMS iButtons at incremental depths across a tundra bluff, within a wet sedge region, and on the Arey Island island surrounding Arey Lagoon. This metadata file describes the conductivity, temperature, and depth (CTD) measurements that were collected, and the salinity that was calculated from the conductivity. Data summaries and further details can be found in Erikson and others, 2020.

Time-series measurements of waves, currents, water levels, sea surface temperatures, ocean salinity, and water, air, and ground temperatures were collected in July through September 2011 in and around Arey Lagoon, near Barter Island, Alaska. Directional wave spectra, currents, water levels, salinity, and bottom and surface water temperatures were measured with a bottom-mounted 1MHz Nortek AWAC, HOBO temperature loggers, and a Solinst Levelogger in ~5m water depth offshore of Arey Island. Within Arey Lagoon, a bottom-mounted frame equipped with a Nortek 1MHz Aquadopp, Solinst Levelogger, and HOBO temperature loggers measured currents, water levels, and water temperatures. Ground temperatures (maximum depth 3 meters below the surface), were measured with HOBO temperature loggers and EMS iButtons at incremental depths across a tundra bluff, within a wet sedge region, and on the Arey Island island surrounding Arey Lagoon. This metadata describes the current-velocity data that were collected. Data summaries and further details can be found in Erikson and others, 2020.

Time-series measurements of waves, currents, water levels, sea surface temperatures, ocean salinity, and water, air, and ground temperatures were collected in July through September 2011 in and around Arey Lagoon, near Barter Island, Alaska. Directional wave spectra, currents, water levels, salinity, and bottom and surface water temperatures were measured with a bottom-mounted 1MHz Nortek AWAC, HOBO temperature loggers, and a Solinst Levelogger in ~5m water depth offshore of Arey Island. Within Arey Lagoon, a bottom-mounted frame equipped with a Nortek 1MHz Aquadopp, Solinst Levelogger, and HOBO temperature loggers measured currents, water levels, and water temperatures. Ground temperatures (maximum depth 3 meters below the surface), were measured with HOBO temperature loggers and EMS iButtons at incremental depths across a tundra bluff, within a wet sedge region, and on the Arey Island island surrounding Arey Lagoon. This metadata describes the ground temperature measurements that were collected. Data summaries and further details can be found in Erikson and others, 2020.

Time-series measurements of waves, currents, water levels, sea surface temperatures, ocean salinity, and water, air, and ground temperatures were collected in July through September 2011 in and around Arey Lagoon, near Barter Island, Alaska. Directional wave spectra, currents, water levels, salinity, and bottom and surface water temperatures were measured with a bottom-mounted 1MHz Nortek AWAC, HOBO temperature loggers, and a Solinst Levelogger in ~5m water depth offshore of Arey Island. Within Arey Lagoon, a bottom-mounted frame equipped with a Nortek 1MHz Aquadopp, Solinst Levelogger, and HOBO temperature loggers measured currents, water levels, and water temperatures. Ground temperatures (maximum depth 3 meters below the surface), were measured with HOBO temperature loggers and EMS iButtons at incremental depths across a tundra bluff, within a wet sedge region, and on the Arey Island island surrounding Arey Lagoon. This metadata describes the sea-surface water temperature data that were collected. Data summaries and further details can be found in Erikson and others, 2020.

Time-series measurements of waves, currents, water levels, sea surface temperatures, ocean salinity, and water, air, and ground temperatures were collected in July through September 2011 in and around Arey Lagoon, near Barter Island, Alaska. Directional wave spectra, currents, water levels, salinity, and bottom and surface water temperatures were measured with a bottom-mounted 1MHz Nortek AWAC, HOBO temperature loggers, and a Solinst Levelogger in ~5m water depth offshore of Arey Island. Within Arey Lagoon, a bottom-mounted frame equipped with a Nortek 1MHz Aquadopp, Solinst Levelogger, and HOBO temperature loggers measured currents, water levels, and water temperatures. Ground temperatures (maximum depth 3 meters below the surface), were measured with HOBO temperature loggers and EMS iButtons at incremental depths across a tundra bluff, within a wet sedge region, and on the Arey Island island surrounding Arey Lagoon. This metadata describes the wave time-series data that were collected. Data summaries and further details can be found in Erikson and others, 2020.

A seamless topographic-bathymetric digital elevation model for an area around Arey Lagoon, Alaska created from a combination of lidar elevation data collected in 2009, single-beam bathymetric data collected in 2011, and NOS sounding data collected in 1948.

Estimated start date, end date, and duration of open water at a location fronting Barter Island, Alaska derived from projected sea ice extents in 4 global climate models: MIROC5, BCC-CSM1.1, INM-CM4, and GFDL-ESM2M. Starting and ending dates are when sea ice retreated or is projected to retreat offshore by more than 80 kilometers fronting Barter Island. Projected coastal storm events were derived by downscaling atmospheric conditions of the RCP 4.5 climate scenario with the MIROC5 global climate model (GCM). Sea ice retreat distances were estimated from 4 separate GCMs: MIROC5, BCC-CSM1.1, INM-CM4, GFDL-CM3.

Numerically modeled ocean storm conditions of hindcast (1981-2010) and projected (2011-2100) storm events in the nearshore region of Arey Lagoon, Alaska. Storms were identified from time-series of dynamically downscaled deep-water wave conditions using WaveWatch3 (WW3) and nearshore storm surges using the Deltares Delft3D model. A storm was defined as having offshore water wave heights >= 2 meters (m) and storm surges >=0 m. The data in this file provide a listing of individual storm dates, storm duration, and the maximum offshore wave heights and resulting nearshore wave conditions (seas and swell) and storm surges associated with each storm. A series of hindcast and projected flooding extreme and decadal flood maps are also available.

In-situ near-shore seawater measurements of dissolved radon, conductivity, and water level were used to determine the advection rate of groundwater onto the fringing reef off Makua, HI, USA.

Transects of near-surface seawater properties were collected over the fringing reef off Makua, HI, on the north shore of Kauai using a Conductivity-Temperature-Depth (CTD) logger, either hand-carried or mounted to a kayak. The instrument returns temperature, salinity as a function of depth, and latitude/longitude.

Along-shore surface-based 2D electrical resistivity tomography (ERT) surveys were collected in the nearshore region of Makua, Kauai.

Satellite-tracked, DGPS-equipped Lagrangian surface-current drifter deployments were conducted over 6 days between 30 July and 4 August 2016 at various locations and stages of the tide over the coral reef off Makua, HI. The drifters internally logged their location every 1 minute, and they transmitted their positions to satellites every 5 minutes. A drogue was attached to the drifters at 1 m below sea level in order to track the currents at that depth.

Time-series data of water-surface elevation, wave height, water-column currents, temperature were acquired for 6 days off the north coast of the island of Kauai, Hawaii in support of a study on the coastal circulation patterns and groundwater input to the coral reefs of Makua.

This dataset contains spatial projections of coastal cliff retreat (and associated uncertainty) for future scenarios of sea-level rise (SLR) in Central California. Present-day cliff-edge positions used as the baseline for projections are also included. Projections were made using numerical models and field observations such as historical cliff retreat rate, nearshore slope, coastal cliff height, and mean annual wave power, as part of Coastal Storm Modeling System (CoSMoS). Read metadata and references carefully. Details: Cliff-retreat position projections and associated uncertainties are for scenarios of 0.25, 0.5, 0.75, 0.92, 1, 1.25, 1.5, 1.75, 2, 2.5, 3.0 and 5 meters of SLR. Projections were made at CoSMoS cross-shore transects (CST) spaced 100-200 m alongshore using a baseline sea-cliff edge from 2016 (included in the dataset). Within the zip file, there are two separate datasets available: 1) one that ignores coastal armoring, such as seawalls and revetments, and allows the cliff to retreat unimpeded (“Do Not Hold the Line”); and 2) another that assumes that current coastal armoring will be maintained and 100% effective at stopping future cliff erosion ("Hold the Line"). An ensemble of four numerical models synthesized from literature were used to make projections. All models relate breaking-wave height and period to cliff rock or unconsolidated sediment erosion. As sea level rises, waves break closer to the sea cliff, more wave energy impacts the cliffs, and cliff erosion rates accelerate. The final projections are a weighted average of all models (weighted by model performance), and the final uncertainties are proportional to 1) underlying uncertainties in the model input data, such as historical cliff retreat rates, and 2) the differences between individual model forecasts at each CST so that uncertainty is larger when the models do not agree. Uncertainty represents the 95% confidence level (two standard deviations about the mean projection). Model behavior also includes wave run-up and wave set-up that raises the water level during big-wave events. Please refer to Limber and others (2018) for more detailed information on the model and data sources.

This dataset contains projections of shoreline positions and uncertainty bands for future scenarios of sea-level rise. Projections were made using the Coastal Storm Modeling System - Coastal One-line Assimilated Simulation Tool (CoSMoS-COAST), a numerical model forced with global-to-local nested wave models and assimilated with lidar-derived shoreline vectors. Read metadata carefully. Details: Projections of shoreline position in the Central Coast of California are made for scenarios of 25, 50, 75, 92, 100, 125, 150, 175, 200, 250, 300 and 500 centimeters (cm) of SLR by the year 2100. SLR scenarios for 25, 50 and 75 cm are included in the National Research Council (NRC) excel and KMZ files. Four datasets are available for different management conditions: shorelines are allowed to retreat unimpeded past urban structures ("NO Hold the Line") or are limited to this urban boundary ("Hold the Line"), and shorelines are allowed to progress with projected increases in sediment ("Continued Nourishment") or with no projected increases ("No Nourishment"). Projections are made at CoSMoS Monitoring and Observation Points, which represent shore-normal transects spaced 100 m alongshore. The CoSMoS-COAST model solves a coupled set of partial differential equations that resembles conservation of sediment for the series of transects. The model is synthesized from several shoreline models in the scientific literature, which is described in more detail, along with the CoSMoS-Coast methodology, in Vitousek and others 2017. Significant uncertainty is associated with the process noise of the model and unresolved coastal processes. This makes estimation of uncertainty difficult. The uncertainty bands predicted here represent 95 percent confidence bands associated with the modeled shoreline fluctuations. Unresolved processes are not accounted for in the uncertainty bands and could lead to significantly more uncertainty than reported in these predictions.

This data contains maximum depth of flooding (cm) in the region landward of the present-day shoreline for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated 1-year/20-year/100-year return interval coastal storms. Methods and processes used in Central California are replicated from and described in O'Neill and others (2018). Please read metadata and inspect output carefully. Data are complete for the information presented. Due to file size constraints, data are available in two parts: part 1 includes SLR conditions 0 - 1.5 m, and part 2 includes SLR conditions 2.0 - 5.0 m.

This data contains maximum depth of flooding (cm) in the region landward of the present-day shoreline for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated 1-year/20-year/100-year return interval coastal storms. Methods and processes used in Central California are replicated from and described in O'Neill and others (2018). Please read metadata and inspect output carefully. Data are complete for the information presented. Due to file size constraints, data are available in two parts: part 1 includes SLR conditions 0 - 1.5 m, and part 2 includes SLR conditions 2.0 - 5.0 m.

This data contains maximum depth of flooding (cm) in the region landward of the present-day shoreline for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated 1-year/20-year/100-year return interval coastal storms. Methods and processes used in Central California are replicated from and described in O'Neill and others (2018). Please read metadata and inspect output carefully. Data are complete for the information presented. Due to file size constraints, data are available in two parts: part 1 includes SLR conditions 0 - 1.5 m, and part 2 includes SLR conditions 2.0 - 5.0 m.

This data contains maximum depth of flooding (cm) in the region landward of the present-day shoreline for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated 1-year/20-year/100-year return interval coastal storms. Methods and processes used in Central California are replicated from and described in O'Neill and others (2018). Please read metadata and inspect output carefully. Data are complete for the information presented. Due to file size constraints, data are available in two parts: part 1 includes SLR conditions 0 - 1.5 m, and part 2 includes SLR conditions 2.0 - 5.0 m.

This data contains geographic extents of projected coastal flooding, low-lying vulnerable areas, and maximum/minimum flood potential (flood uncertainty) associated with the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated 1-year/20-year/100-year return interval coastal storms. Methods and processes used in Central California are replicated from and described in O'Neill and others (2018). Please read metadata and inspect output carefully. Data are complete for the information presented.

This data contains geographic extents of projected coastal flooding, low-lying vulnerable areas, and maximum/minimum flood potential (flood uncertainty) associated with the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated 1-year/20-year/100-year return interval coastal storms. Methods and processes used in Central California are replicated from and described in O'Neill and others (2018). Please read metadata and inspect output carefully. Data are complete for the information presented.

This data contains geographic extents of projected coastal flooding, low-lying vulnerable areas, and maximum/minimum flood potential (flood uncertainty) associated with the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated 1-year/20-year/100-year return interval coastal storms. Methods and processes used in Central California are replicated from and described in O'Neill and others (2018). Please read metadata and inspect output carefully. Data are complete for the information presented.

This data contains geographic extents of projected coastal flooding, low-lying vulnerable areas, and maximum/minimum flood potential (flood uncertainty) associated with the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated 1-year/20-year/100-year return interval coastal storms. Methods and processes used in Central California are replicated from and described in O'Neill and others (2018). Please read metadata and inspect output carefully. Data are complete for the information presented.

This data contains maximum model-derived ocean currents (in meters per second) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated 1-year/20-year/100-year return interval coastal storms. Methods and processes used in Central California are replicated from and described in O'Neill and others (2018). Please read metadata and inspect output carefully. Data are complete for the information presented. Due to file size constraints, data are available in two parts: part 1 includes SLR conditions 0 - 1.5 m, and part 2 includes SLR conditions 2.0 - 5.0 m.

This data contains maximum model-derived ocean currents (in meters per second) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated 1-year/20-year/100-year return interval coastal storms. Methods and processes used in Central California are replicated from and described in O'Neill and others (2018). Please read metadata and inspect output carefully. Data are complete for the information presented. Due to file size constraints, data are available in two parts: part 1 includes SLR conditions 0 - 1.5 m, and part 2 includes SLR conditions 2.0 - 5.0 m.

This data contains maximum model-derived ocean currents (in meters per second) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated 1-year/20-year/100-year return interval coastal storms. Methods and processes used in Central California are replicated from and described in O'Neill and others (2018). Please read metadata and inspect output carefully. Data are complete for the information presented. Due to file size constraints, data are available in two parts: part 1 includes SLR conditions 0 - 1.5 m, and part 2 includes SLR conditions 2.0 - 5.0 m.

This data contains maximum model-derived ocean currents (in meters per second) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated 1-year/20-year/100-year return interval coastal storms. Methods and processes used in Central California are replicated from and described in O'Neill and others (2018). Please read metadata and inspect output carefully. Data are complete for the information presented. Due to file size constraints, data are available in two parts: part 1 includes SLR conditions 0 - 1.5 m, and part 2 includes SLR conditions 2.0 - 5.0 m.

This data contains model-derived total water elevation (in meters) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated 1-year/20-year/100-year return interval coastal storms. Methods and processes used in Central California are replicated from and described in O'Neill and others (2018). Please read metadata and inspect output carefully. Data are complete for the information presented. Due to file size constraints, data are available in two parts: part 1 includes SLR conditions 0 - 1.5 m, and part 2 includes SLR conditions 2.0 - 5.0 m.

This data contains model-derived total water elevation (in meters) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated 1-year/20-year/100-year return interval coastal storms. Methods and processes used in Central California are replicated from and described in O'Neill and others (2018). Please read metadata and inspect output carefully. Data are complete for the information presented. Due to file size constraints, data are available in two parts: part 1 includes SLR conditions 0 - 1.5 m, and part 2 includes SLR conditions 2.0 - 5.0 m.

This data contains model-derived total water elevation (in meters) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated 1-year/20-year/100-year return interval coastal storms. Methods and processes used in Central California are replicated from and described in O'Neill and others (2018). Please read metadata and inspect output carefully. Data are complete for the information presented. Due to file size constraints, data are available in two parts: part 1 includes SLR conditions 0 - 1.5 m, and part 2 includes SLR conditions 2.0 - 5.0 m.

This data contains model-derived total water elevation (in meters) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated 1-year/20-year/100-year return interval coastal storms. Methods and processes used in Central California are replicated from and described in O'Neill and others (2018). Please read metadata and inspect output carefully. Data are complete for the information presented. Due to file size constraints, data are available in two parts: part 1 includes SLR conditions 0 - 1.5 m, and part 2 includes SLR conditions 2.0 - 5.0 m.

This data contains maximum model-derived significant wave height (in meters) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated 1-year/20-year/100-year return interval coastal storms. Methods and processes used in Central California are replicated from and described in O'Neill and others (2018). Please read metadata and inspect output carefully. Data are complete for the information presented. Due to file size constraints, data are available in two parts: part 1 includes SLR conditions 0 - 1.5 m, and part 2 includes SLR conditions 2.0 - 5.0 m.

This data contains maximum model-derived significant wave height (in meters) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated 1-year/20-year/100-year return interval coastal storms. Methods and processes used in Central California are replicated from and described in O'Neill and others (2018). Please read metadata and inspect output carefully. Data are complete for the information presented. Due to file size constraints, data are available in two parts: part 1 includes SLR conditions 0 - 1.5 m, and part 2 includes SLR conditions 2.0 - 5.0 m.

This data contains maximum model-derived significant wave height (in meters) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated 1-year/20-year/100-year return interval coastal storms. Methods and processes used in Central California are replicated from and described in O'Neill and others (2018). Please read metadata and inspect output carefully. Data are complete for the information presented. Due to file size constraints, data are available in two parts: part 1 includes SLR conditions 0 - 1.5 m, and part 2 includes SLR conditions 2.0 - 5.0 m.

This data contains maximum model-derived significant wave height (in meters) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated 1-year/20-year/100-year return interval coastal storms. Methods and processes used in Central California are replicated from and described in O'Neill and others (2018). Please read metadata and inspect output carefully. Data are complete for the information presented. Due to file size constraints, data are available in two parts: part 1 includes SLR conditions 0 - 1.5 m, and part 2 includes SLR conditions 2.0 - 5.0 m.

This data contains maximum depth of flooding (cm) in the region landward of the present-day shoreline for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated 1-year/20-year/100-year return interval coastal storms. Methods and processes used in Central California are replicated from and described in O'Neill and others (2018). Please read metadata and inspect output carefully. Data are complete for the information presented.

This data contains maximum depth of flooding (cm) in the region landward of the present-day shoreline for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated 1-year/20-year/100-year return interval coastal storms. Methods and processes used in Central California are replicated from and described in O'Neill and others (2018). Please read metadata and inspect output carefully. Data are complete for the information presented.

This data contains maximum depth of flooding (cm) in the region landward of the present-day shoreline for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated 1-year/20-year/100-year return interval coastal storms. Methods and processes used in Central California are replicated from and described in O'Neill and others (2018). Please read metadata and inspect output carefully. Data are complete for the information presented.

This data contains maximum depth of flooding (cm) in the region landward of the present-day shoreline for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated 1-year/20-year/100-year return interval coastal storms. Methods and processes used in Central California are replicated from and described in O'Neill and others (2018). Please read metadata and inspect output carefully. Data are complete for the information presented.

This data contains geographic extents of projected coastal flooding, low-lying vulnerable areas, and maximum/minimum flood potential (flood uncertainty) associated with the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated 1-year/20-year/100-year return interval coastal storms. Methods and processes used in Central California are replicated from and described in O'Neill and others (2018). Please read metadata and inspect output carefully. Data are complete for the information presented.

This data contains geographic extents of projected coastal flooding, low-lying vulnerable areas, and maximum/minimum flood potential (flood uncertainty) associated with the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated 1-year/20-year/100-year return interval coastal storms. Methods and processes used in Central California are replicated from and described in O'Neill and others (2018). Please read metadata and inspect output carefully. Data are complete for the information presented.

This data contains geographic extents of projected coastal flooding, low-lying vulnerable areas, and maximum/minimum flood potential (flood uncertainty) associated with the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated 1-year/20-year/100-year return interval coastal storms. Methods and processes used in Central California are replicated from and described in O'Neill and others (2018). Please read metadata and inspect output carefully. Data are complete for the information presented.

This data contains geographic extents of projected coastal flooding, low-lying vulnerable areas, and maximum/minimum flood potential (flood uncertainty) associated with the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated 1-year/20-year/100-year return interval coastal storms. Methods and processes used in Central California are replicated from and described in O'Neill and others (2018). Please read metadata and inspect output carefully. Data are complete for the information presented.

This data contains maximum model-derived ocean currents (in meters per second) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated 1-year/20-year/100-year return interval coastal storms. Methods and processes used in Central California are replicated from and described in O'Neill and others (2018). Please read metadata and inspect output carefully. Data are complete for the information presented.

This data contains maximum model-derived ocean currents (in meters per second) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated 1-year/20-year/100-year return interval coastal storms. Methods and processes used in Central California are replicated from and described in O'Neill and others (2018). Please read metadata and inspect output carefully. Data are complete for the information presented.

This data contains maximum model-derived ocean currents (in meters per second) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated 1-year/20-year/100-year return interval coastal storms. Methods and processes used in Central California are replicated from and described in O'Neill and others (2018). Please read metadata and inspect output carefully. Data are complete for the information presented.

This data contains maximum model-derived ocean currents (in meters per second) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated 1-year/20-year/100-year return interval coastal storms. Methods and processes used in Central California are replicated from and described in O'Neill and others (2018). Please read metadata and inspect output carefully. Data are complete for the information presented.

This data contains model-derived total water levels (in meters) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated 1-year/20-year/100-year return interval coastal storms. Methods and processes used in Central California are replicated from and described in O'Neill and others (2018). Please read metadata and inspect output carefully. Data are complete for the information presented.

This data contains model-derived total water levels (in meters) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated 1-year/20-year/100-year return interval coastal storms. Methods and processes used in Central California are replicated from and described in O'Neill and others (2018). Please read metadata and inspect output carefully. Data are complete for the information presented.

This data contains model-derived total water levels (in meters) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated 1-year/20-year/100-year return interval coastal storms. Methods and processes used in Central California are replicated from and described in O'Neill and others (2018). Please read metadata and inspect output carefully. Data are complete for the information presented.

This data contains model-derived total water levels (in meters) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated 1-year/20-year/100-year return interval coastal storms. Methods and processes used in Central California are replicated from and described in O'Neill and others (2018). Please read metadata and inspect output carefully. Data are complete for the information presented.

This data contains maximum model-derived significant wave height (in meters) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated 1-year/20-year/100-year return interval coastal storms. Methods and processes used in Central California are replicated from and described in O'Neill and others (2018). Please read metadata and inspect output carefully. Data are complete for the information presented.

This data contains maximum model-derived significant wave height (in meters) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated 1-year/20-year/100-year return interval coastal storms. Methods and processes used in Central California are replicated from and described in O'Neill and others (2018). Please read metadata and inspect output carefully. Data are complete for the information presented.

This data contains maximum model-derived significant wave height (in meters) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated 1-year/20-year/100-year return interval coastal storms. Methods and processes used in Central California are replicated from and described in O'Neill and others (2018). Please read metadata and inspect output carefully. Data are complete for the information presented.

This data contains maximum model-derived significant wave height (in meters) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated 1-year/20-year/100-year return interval coastal storms. Methods and processes used in Central California are replicated from and described in O'Neill and others (2018). Please read metadata and inspect output carefully. Data are complete for the information presented.

This data contains maximum depth of flooding (cm) in the region landward of the present-day shoreline for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated 1-year/20-year/100-year return interval coastal storms. Methods and processes used in Central California are replicated from and described in O'Neill and others (2018).Please read metadata and inspect output carefully. Data are complete for the information presented.

This data contains maximum depth of flooding (cm) in the region landward of the present-day shoreline for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated 1-year/20-year/100-year return interval coastal storms. Methods and processes used in Central California are replicated from and described in O'Neill and others (2018).Please read metadata and inspect output carefully. Data are complete for the information presented.

This data contains maximum depth of flooding (cm) in the region landward of the present-day shoreline for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated 1-year/20-year/100-year return interval coastal storms. Methods and processes used in Central California are replicated from and described in O'Neill and others (2018).Please read metadata and inspect output carefully. Data are complete for the information presented.

This data contains maximum depth of flooding (cm) in the region landward of the present-day shoreline for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated 1-year/20-year/100-year return interval coastal storms. Methods and processes used in Central California are replicated from and described in O'Neill and others (2018).Please read metadata and inspect output carefully. Data are complete for the information presented.

This data contains geographic extents of projected coastal flooding, low-lying vulnerable areas, and maximum/minimum flood potential (flood uncertainty) associated with the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated 1-year/20-year/100-year return interval coastal storms. Methods and processes used in Central California are replicated from and described in O'Neill and others (2018).Please read metadata and inspect output carefully. Data are complete for the information presented.

This data contains geographic extents of projected coastal flooding, low-lying vulnerable areas, and maximum/minimum flood potential (flood uncertainty) associated with the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated 1-year/20-year/100-year return interval coastal storms. Methods and processes used in Central California are replicated from and described in O'Neill and others (2018).Please read metadata and inspect output carefully. Data are complete for the information presented.

This data contains geographic extents of projected coastal flooding, low-lying vulnerable areas, and maximum/minimum flood potential (flood uncertainty) associated with the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated 1-year/20-year/100-year return interval coastal storms. Methods and processes used in Central California are replicated from and described in O'Neill and others (2018).Please read metadata and inspect output carefully. Data are complete for the information presented.

This data contains geographic extents of projected coastal flooding, low-lying vulnerable areas, and maximum/minimum flood potential (flood uncertainty) associated with the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated 1-year/20-year/100-year return interval coastal storms. Methods and processes used in Central California are replicated from and described in O'Neill and others (2018).Please read metadata and inspect output carefully. Data are complete for the information presented.

This data contains maximum model-derived ocean currents (in meters per second) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated 1-year/20-year/100-year return interval coastal storms. Methods and processes used in Central California are replicated from and described in O'Neill and others (2018).Please read metadata and inspect output carefully. Data are complete for the information presented.

This data contains maximum model-derived ocean currents (in meters per second) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated 1-year/20-year/100-year return interval coastal storms. Methods and processes used in Central California are replicated from and described in O'Neill and others (2018).Please read metadata and inspect output carefully. Data are complete for the information presented.

This data contains maximum model-derived ocean currents (in meters per second) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated 1-year/20-year/100-year return interval coastal storms. Methods and processes used in Central California are replicated from and described in O'Neill and others (2018).Please read metadata and inspect output carefully. Data are complete for the information presented.

This data contains maximum model-derived ocean currents (in meters per second) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated 1-year/20-year/100-year return interval coastal storms. Methods and processes used in Central California are replicated from and described in O'Neill and others (2018).Please read metadata and inspect output carefully. Data are complete for the information presented.

This data contains model-derived total water levels (in meters) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated 1-year/20-year/100-year return interval coastal storms. Methods and processes used in Central California are replicated from and described in O'Neill and others (2018).Please read metadata and inspect output carefully. Data are complete for the information presented.

This data contains model-derived total water levels (in meters) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated 1-year/20-year/100-year return interval coastal storms. Methods and processes used in Central California are replicated from and described in O'Neill and others (2018).Please read metadata and inspect output carefully. Data are complete for the information presented.

This data contains model-derived total water levels (in meters) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated 1-year/20-year/100-year return interval coastal storms. Methods and processes used in Central California are replicated from and described in O'Neill and others (2018).Please read metadata and inspect output carefully. Data are complete for the information presented.

This data contains model-derived total water levels (in meters) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated 1-year/20-year/100-year return interval coastal storms. Methods and processes used in Central California are replicated from and described in O'Neill and others (2018).Please read metadata and inspect output carefully. Data are complete for the information presented.

This data contains maximum model-derived significant wave height (in meters) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated 1-year/20-year/100-year return interval coastal storms. Methods and processes used in Central California are replicated from and described in O'Neill and others (2018).Please read metadata and inspect output carefully. Data are complete for the information presented.

This data contains maximum model-derived significant wave height (in meters) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated 1-year/20-year/100-year return interval coastal storms. Methods and processes used in Central California are replicated from and described in O'Neill and others (2018).Please read metadata and inspect output carefully. Data are complete for the information presented.

This data contains maximum model-derived significant wave height (in meters) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated 1-year/20-year/100-year return interval coastal storms. Methods and processes used in Central California are replicated from and described in O'Neill and others (2018).Please read metadata and inspect output carefully. Data are complete for the information presented.

This data contains maximum model-derived significant wave height (in meters) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated 1-year/20-year/100-year return interval coastal storms. Methods and processes used in Central California are replicated from and described in O'Neill and others (2018).Please read metadata and inspect output carefully. Data are complete for the information presented.

This data contains maximum depth of flooding (cm) in the region landward of the present-day shoreline for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated 1-year/20-year/100-year return interval coastal storms. Methods and processes used in Central California are replicated from and described in O'Neill and others (2018).Please read metadata and inspect output carefully. Data are complete for the information presented.

This data contains maximum depth of flooding (cm) in the region landward of the present-day shoreline for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated 1-year/20-year/100-year return interval coastal storms. Methods and processes used in Central California are replicated from and described in O'Neill and others (2018).Please read metadata and inspect output carefully. Data are complete for the information presented.

This data contains maximum depth of flooding (cm) in the region landward of the present-day shoreline for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated 1-year/20-year/100-year return interval coastal storms. Methods and processes used in Central California are replicated from and described in O'Neill and others (2018).Please read metadata and inspect output carefully. Data are complete for the information presented.

This data contains maximum depth of flooding (cm) in the region landward of the present-day shoreline for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated 1-year/20-year/100-year return interval coastal storms. Methods and processes used in Central California are replicated from and described in O'Neill and others (2018).Please read metadata and inspect output carefully. Data are complete for the information presented.

This data contains geographic extents of projected coastal flooding, low-lying vulnerable areas, and maximum/minimum flood potential (flood uncertainty) associated with the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated 1-year/20-year/100-year return interval coastal storms. Methods and processes used in Central California are replicated from and described in O'Neill and others (2018).Please read metadata and inspect output carefully. Data are complete for the information presented.

This data contains geographic extents of projected coastal flooding, low-lying vulnerable areas, and maximum/minimum flood potential (flood uncertainty) associated with the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated 1-year/20-year/100-year return interval coastal storms. Methods and processes used in Central California are replicated from and described in O'Neill and others (2018).Please read metadata and inspect output carefully. Data are complete for the information presented.

This data contains geographic extents of projected coastal flooding, low-lying vulnerable areas, and maximum/minimum flood potential (flood uncertainty) associated with the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated 1-year/20-year/100-year return interval coastal storms. Methods and processes used in Central California are replicated from and described in O'Neill and others (2018).Please read metadata and inspect output carefully. Data are complete for the information presented.

This data contains geographic extents of projected coastal flooding, low-lying vulnerable areas, and maximum/minimum flood potential (flood uncertainty) associated with the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated 1-year/20-year/100-year return interval coastal storms. Methods and processes used in Central California are replicated from and described in O'Neill and others (2018).Please read metadata and inspect output carefully. Data are complete for the information presented.

This data contains maximum model-derived ocean currents (in meters per second) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated 1-year/20-year/100-year return interval coastal storms. Methods and processes used in Central California are replicated from and described in O'Neill and others (2018).Please read metadata and inspect output carefully. Data are complete for the information presented.

This data contains maximum model-derived ocean currents (in meters per second) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated 1-year/20-year/100-year return interval coastal storms. Methods and processes used in Central California are replicated from and described in O'Neill and others (2018).Please read metadata and inspect output carefully. Data are complete for the information presented.

This data contains maximum model-derived ocean currents (in meters per second) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated 1-year/20-year/100-year return interval coastal storms. Methods and processes used in Central California are replicated from and described in O'Neill and others (2018).Please read metadata and inspect output carefully. Data are complete for the information presented.

This data contains maximum model-derived ocean currents (in meters per second) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated 1-year/20-year/100-year return interval coastal storms. Methods and processes used in Central California are replicated from and described in O'Neill and others (2018).Please read metadata and inspect output carefully. Data are complete for the information presented.

This data contains model-derived total water levels (in meters) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated 1-year/20-year/100-year return interval coastal storms. Methods and processes used in Central California are replicated from and described in O'Neill and others (2018).Please read metadata and inspect output carefully. Data are complete for the information presented.

This data contains model-derived total water levels (in meters) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated 1-year/20-year/100-year return interval coastal storms. Methods and processes used in Central California are replicated from and described in O'Neill and others (2018).Please read metadata and inspect output carefully. Data are complete for the information presented.

This data contains model-derived total water levels (in meters) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated 1-year/20-year/100-year return interval coastal storms. Methods and processes used in Central California are replicated from and described in O'Neill and others (2018).Please read metadata and inspect output carefully. Data are complete for the information presented.

This data contains model-derived total water levels (in meters) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated 1-year/20-year/100-year return interval coastal storms. Methods and processes used in Central California are replicated from and described in O'Neill and others (2018).Please read metadata and inspect output carefully. Data are complete for the information presented.

This data contains maximum model-derived significant wave height (in meters) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated 1-year/20-year/100-year return interval coastal storms. Methods and processes used in Central California are replicated from and described in O'Neill and others (2018).Please read metadata and inspect output carefully. Data are complete for the information presented.

This data contains maximum model-derived significant wave height (in meters) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated 1-year/20-year/100-year return interval coastal storms. Methods and processes used in Central California are replicated from and described in O'Neill and others (2018).Please read metadata and inspect output carefully. Data are complete for the information presented.

This data contains maximum model-derived significant wave height (in meters) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated 1-year/20-year/100-year return interval coastal storms. Methods and processes used in Central California are replicated from and described in O'Neill and others (2018).Please read metadata and inspect output carefully. Data are complete for the information presented.

This data contains maximum model-derived significant wave height (in meters) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated 1-year/20-year/100-year return interval coastal storms. Methods and processes used in Central California are replicated from and described in O'Neill and others (2018).Please read metadata and inspect output carefully. Data are complete for the information presented.

This data contains maximum depth of flooding (cm) in the region landward of the present-day shoreline for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated 1-year/20-year/100-year return interval coastal storms. Methods and processes used in Central California are replicated from and described in O'Neill and others (2018).Please read metadata and inspect output carefully. Data are complete for the information presented.

This data contains maximum depth of flooding (cm) in the region landward of the present-day shoreline for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated 1-year/20-year/100-year return interval coastal storms. Methods and processes used in Central California are replicated from and described in O'Neill and others (2018).Please read metadata and inspect output carefully. Data are complete for the information presented.

This data contains maximum depth of flooding (cm) in the region landward of the present-day shoreline for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated 1-year/20-year/100-year return interval coastal storms. Methods and processes used in Central California are replicated from and described in O'Neill and others (2018).Please read metadata and inspect output carefully. Data are complete for the information presented.

This data contains maximum depth of flooding (cm) in the region landward of the present-day shoreline for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated 1-year/20-year/100-year return interval coastal storms. Methods and processes used in Central California are replicated from and described in O'Neill and others (2018).Please read metadata and inspect output carefully. Data are complete for the information presented.

This data contains geographic extents of projected coastal flooding, low-lying vulnerable areas, and maximum/minimum flood potential (flood uncertainty) associated with the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated 1-year/20-year/100-year return interval coastal storms. Methods and processes used in Central California are replicated from and described in O'Neill and others (2018).Please read metadata and inspect output carefully. Data are complete for the information presented.

This data contains geographic extents of projected coastal flooding, low-lying vulnerable areas, and maximum/minimum flood potential (flood uncertainty) associated with the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated 1-year/20-year/100-year return interval coastal storms. Methods and processes used in Central California are replicated from and described in O'Neill and others (2018).Please read metadata and inspect output carefully. Data are complete for the information presented.

This data contains geographic extents of projected coastal flooding, low-lying vulnerable areas, and maximum/minimum flood potential (flood uncertainty) associated with the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated 1-year/20-year/100-year return interval coastal storms. Methods and processes used in Central California are replicated from and described in O'Neill and others (2018).Please read metadata and inspect output carefully. Data are complete for the information presented.

This data contains geographic extents of projected coastal flooding, low-lying vulnerable areas, and maximum/minimum flood potential (flood uncertainty) associated with the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated 1-year/20-year/100-year return interval coastal storms. Methods and processes used in Central California are replicated from and described in O'Neill and others (2018).Please read metadata and inspect output carefully. Data are complete for the information presented.

This data contains maximum model-derived ocean currents (in meters per second) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated 1-year/20-year/100-year return interval coastal storms. Methods and processes used in Central California are replicated from and described in O'Neill and others (2018).Please read metadata and inspect output carefully. Data are complete for the information presented.

This data contains maximum model-derived ocean currents (in meters per second) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated 1-year/20-year/100-year return interval coastal storms. Methods and processes used in Central California are replicated from and described in O'Neill and others (2018).Please read metadata and inspect output carefully. Data are complete for the information presented.

This data contains maximum model-derived ocean currents (in meters per second) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated 1-year/20-year/100-year return interval coastal storms. Methods and processes used in Central California are replicated from and described in O'Neill and others (2018).Please read metadata and inspect output carefully. Data are complete for the information presented.

This data contains maximum model-derived ocean currents (in meters per second) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated 1-year/20-year/100-year return interval coastal storms. Methods and processes used in Central California are replicated from and described in O'Neill and others (2018).Please read metadata and inspect output carefully. Data are complete for the information presented.

This data contains model-derived total water levels (in meters) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated 1-year/20-year/100-year return interval coastal storms. Methods and processes used in Central California are replicated from and described in O'Neill and others (2018).Please read metadata and inspect output carefully. Data are complete for the information presented.

This data contains model-derived total water levels (in meters) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated 1-year/20-year/100-year return interval coastal storms. Methods and processes used in Central California are replicated from and described in O'Neill and others (2018).Please read metadata and inspect output carefully. Data are complete for the information presented.

This data contains model-derived total water levels (in meters) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated 1-year/20-year/100-year return interval coastal storms. Methods and processes used in Central California are replicated from and described in O'Neill and others (2018).Please read metadata and inspect output carefully. Data are complete for the information presented.

This data contains model-derived total water levels (in meters) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated 1-year/20-year/100-year return interval coastal storms. Methods and processes used in Central California are replicated from and described in O'Neill and others (2018).Please read metadata and inspect output carefully. Data are complete for the information presented.

This data contains maximum model-derived significant wave height (in meters) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated 1-year/20-year/100-year return interval coastal storms. Methods and processes used in Central California are replicated from and described in O'Neill and others (2018).Please read metadata and inspect output carefully. Data are complete for the information presented.

This data contains maximum model-derived significant wave height (in meters) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated 1-year/20-year/100-year return interval coastal storms. Methods and processes used in Central California are replicated from and described in O'Neill and others (2018).Please read metadata and inspect output carefully. Data are complete for the information presented.

This data contains maximum model-derived significant wave height (in meters) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated 1-year/20-year/100-year return interval coastal storms. Methods and processes used in Central California are replicated from and described in O'Neill and others (2018).Please read metadata and inspect output carefully. Data are complete for the information presented.

This data contains maximum model-derived significant wave height (in meters) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated 1-year/20-year/100-year return interval coastal storms. Methods and processes used in Central California are replicated from and described in O'Neill and others (2018).Please read metadata and inspect output carefully. Data are complete for the information presented.

This data contains maximum depth of flooding (cm) in the region landward of the present-day shoreline for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated 1-year/20-year/100-year return interval coastal storms. Methods and processes used in Central California are replicated from and described in O'Neill and others (2018). Please read metadata and inspect output carefully. Data are complete for the information presented.

This data contains maximum depth of flooding (cm) in the region landward of the present-day shoreline for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated 1-year/20-year/100-year return interval coastal storms. Methods and processes used in Central California are replicated from and described in O'Neill and others (2018). Please read metadata and inspect output carefully. Data are complete for the information presented.

This data contains maximum depth of flooding (cm) in the region landward of the present-day shoreline for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated 1-year/20-year/100-year return interval coastal storms. Methods and processes used in Central California are replicated from and described in O'Neill and others (2018). Please read metadata and inspect output carefully. Data are complete for the information presented.

This data contains maximum depth of flooding (cm) in the region landward of the present-day shoreline for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated 1-year/20-year/100-year return interval coastal storms. Methods and processes used in Central California are replicated from and described in O'Neill and others (2018). Please read metadata and inspect output carefully. Data are complete for the information presented.

This data contains geographic extents of projected coastal flooding, low-lying vulnerable areas, and maximum/minimum flood potential (flood uncertainty) associated with the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated 1-year/20-year/100-year return interval coastal storms. Methods and processes used in Central California are replicated from and described in O'Neill and others (2018). Please read metadata and inspect output carefully. Data are complete for the information presented.

This data contains geographic extents of projected coastal flooding, low-lying vulnerable areas, and maximum/minimum flood potential (flood uncertainty) associated with the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated 1-year/20-year/100-year return interval coastal storms. Methods and processes used in Central California are replicated from and described in O'Neill and others (2018). Please read metadata and inspect output carefully. Data are complete for the information presented.

This data contains geographic extents of projected coastal flooding, low-lying vulnerable areas, and maximum/minimum flood potential (flood uncertainty) associated with the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated 1-year/20-year/100-year return interval coastal storms. Methods and processes used in Central California are replicated from and described in O'Neill and others (2018). Please read metadata and inspect output carefully. Data are complete for the information presented.

This data contains geographic extents of projected coastal flooding, low-lying vulnerable areas, and maximum/minimum flood potential (flood uncertainty) associated with the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated 1-year/20-year/100-year return interval coastal storms. Methods and processes used in Central California are replicated from and described in O'Neill and others (2018). Please read metadata and inspect output carefully. Data are complete for the information presented.

This data contains maximum model-derived ocean currents (in meters per second) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated 1-year/20-year/100-year return interval coastal storms. Methods and processes used in Central California are replicated from and described in O'Neill and others (2018). Please read metadata and inspect output carefully. Data are complete for the information presented.

This data contains maximum model-derived ocean currents (in meters per second) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated 1-year/20-year/100-year return interval coastal storms. Methods and processes used in Central California are replicated from and described in O'Neill and others (2018). Please read metadata and inspect output carefully. Data are complete for the information presented.

This data contains maximum model-derived ocean currents (in meters per second) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated 1-year/20-year/100-year return interval coastal storms. Methods and processes used in Central California are replicated from and described in O'Neill and others (2018). Please read metadata and inspect output carefully. Data are complete for the information presented.

This data contains maximum model-derived ocean currents (in meters per second) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated 1-year/20-year/100-year return interval coastal storms. Methods and processes used in Central California are replicated from and described in O'Neill and others (2018). Please read metadata and inspect output carefully. Data are complete for the information presented.

This data contains model-derived total water levels (in meters) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated 1-year/20-year/100-year return interval coastal storms. Methods and processes used in Central California are replicated from and described in O'Neill and others (2018). Please read metadata and inspect output carefully. Data are complete for the information presented.

This data contains model-derived total water levels (in meters) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated 1-year/20-year/100-year return interval coastal storms. Methods and processes used in Central California are replicated from and described in O'Neill and others (2018). Please read metadata and inspect output carefully. Data are complete for the information presented.

This data contains model-derived total water levels (in meters) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated 1-year/20-year/100-year return interval coastal storms. Methods and processes used in Central California are replicated from and described in O'Neill and others (2018). Please read metadata and inspect output carefully. Data are complete for the information presented.

This data contains model-derived total water levels (in meters) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated 1-year/20-year/100-year return interval coastal storms. Methods and processes used in Central California are replicated from and described in O'Neill and others (2018). Please read metadata and inspect output carefully. Data are complete for the information presented.

This data contains maximum model-derived significant wave height (in meters) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated 1-year/20-year/100-year return interval coastal storms. Methods and processes used in Central California are replicated from and described in O'Neill and others (2018). Please read metadata and inspect output carefully. Data are complete for the information presented.

This data contains maximum model-derived significant wave height (in meters) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated 1-year/20-year/100-year return interval coastal storms. Methods and processes used in Central California are replicated from and described in O'Neill and others (2018). Please read metadata and inspect output carefully. Data are complete for the information presented.

This data contains maximum model-derived significant wave height (in meters) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated 1-year/20-year/100-year return interval coastal storms. Methods and processes used in Central California are replicated from and described in O'Neill and others (2018). Please read metadata and inspect output carefully. Data are complete for the information presented.

This data contains maximum model-derived significant wave height (in meters) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated 1-year/20-year/100-year return interval coastal storms. Methods and processes used in Central California are replicated from and described in O'Neill and others (2018). Please read metadata and inspect output carefully. Data are complete for the information presented.

Using global climate model projections of sea-surface temperature at coral reef sites, we modeled the effects of depth and exposure to semidiurnal temperature fluctuations to examine how these effects may alter the projected year of annual severe bleaching for coral reef sites globally. Here we present the first global maps of the effects these processes have on bleaching projections for three IPCC-AR5 emissions scenarios.

Geochemical and isotopic compositions were determined in stream sediment and parent rocks collected in April 2017 and June 2017 and in nearshore sediment collected bimonthly in sediment traps from May 2017 to June 2018 in the coastal zone and 12 drainages of southwest Puerto Rico: Rio Loco, Yauco, Guayanilla, Macana, Tallaboa, Matilde, Portugues, Bucana, Inabon, Jacaquas, Descalabrado, and Coamo. Geochemical compositional data include: a) total contents of major, minor, trace, and rare earth elements in the <0.063 mm-diameter fraction of terrestrial (n=53) and nearshore sediment (n=63) and powdered rocks (n=19) analyzed by inductively coupled plasma optical emission spectroscopy (ICP-OES) and inductively coupled plasma mass spectroscopy (ICP-MS); b) major oxide contents of stream sediment (n=46) and rocks (n=19) determined by wavelength dispersive x-ray fluorescence spectrometry (WD-XRF); and c) total organic carbon and carbonate contents of stream (n=48) and nearshore (n=64) sediment determined coulometrically. Isotopic compositional data include: 1) strontium isotope ratios (87Sr/86Sr) determined by thermal ionization mass spectrometry in the <0.063 mm-diameter fraction of select stream (n=50) and nearshore (n=40) sediment, and in all rocks; and 2) activities of the short-lived cosmogenic nuclides beryllium-7, cesium-137, and excess (unsupported) lead-210 determined by gamma spectrometry on bulk nearshore sediment (n=44). The percentage by weight of the <0.063 mm-diameter sediment fraction (percent fines), the median grain size, and the silt to clay ratio are reported for stream (n=48) and nearshore (n=64) sediments. These data accompany Takesue, R.K., Sherman, C., Ramirez, N.I., Reyes, A.O., Cheriton, O.M., Rios, R.V., and Storlazzi, C.D., 2021, Land-based sediment sources and transport to southwest Puerto Rico coral reefs after Hurricane Maria, May 2017 to June 2018: Estuarine, Coastal and Shelf Science, v. 59, p. 107476, https://doi.org/10.1016/j.ecss.2021.107476.

This portion of the data release presents a digital surface model (DSM) and hillshade image of the intertidal zone at West Whidbey Island, WA. The DSM has a resolution of 4 centimeters per pixel and was derived from structure-from-motion (SfM) processing of aerial imagery collected with an unmanned aerial system (UAS) on 2019-06-04. Unlike a digital elevation model (DEM), the DSM represents the elevation of the highest object within the bounds of a cell. Vegetation, buildings and other objects have not been removed from the data. In addition, data artifacts resulting from noise in the original imagery have not been removed. The raw imagery used to create the DSM was acquired using a UAS fitted with a Ricoh GR II digital camera featuring a global shutter. The UAS was flown on pre-programmed autonomous flight lines spaced to provide approximately 70 percent overlap between images from adjacent lines. The camera was triggered at 1 Hz using a built-in intervalometer. The UAS was flown at an approximate altitude of 70 meters above ground level (AGL), resulting in a nominal ground-sample-distance (GSD) of 1.8 centimeters per pixel. Additional imagery was collected with the camera in an oblique orientation toward the coastal bluff face to image vertical faces. The raw imagery was geotagged using positions from the UAS onboard single-frequency autonomous GPS. Twenty-five temporary ground control points (GCPs) were distributed throughout the survey area to establish survey control. The GCPs consisted of a combination of small square tarps with black-and-white cross patterns and "X" marks placed on the ground using temporary chalk. The GCP positions were measured using post-processed kinematic (PPK) GPS, using corrections from a GPS base station located approximately 7 kilometers from the study area. The DSM and hillshade images have been formatted as cloud optimized GeoTIFFs with internal overviews and masks to facilitate cloud-based queries and display.

This portion of the data release presents the locations of the temporary ground control points (GCPs) used for the structure-from-motion (SfM) processing of the imagery collected during an unmanned aerial system (UAS) survey of the intertidal zone at West Whidbey Island, WA on 2019-06-04. Twenty-five temporary ground control points (GCPs) were distributed throughout the survey area to establish survey control. The GCPs consisted of a combination of small square tarps with black-and-white cross patterns and "X" marks placed on the ground using temporary chalk. The GCP positions were measured using post-processed kinematic (PPK) GPS, using corrections from a GPS base station located approximately 7 kilometers from the study area. The GCP positions are presented in a comma-delimited text file.

This portion of the data release presents the raw aerial imagery collected during the unmanned aerial system (UAS) survey of the intertidal zone at West Whidbey Island, WA, on 2019-06-04. The imagery was acquired using a Department of Interior-owned 3DR Solo quadcopter fitted with a Ricoh GR II digital camera featuring a global shutter. Flights using both a nadir camera orientation and an oblique camera orientation were conducted. For the nadir flights (F04, F05, F06, F07, and F08), the camera was mounted using a fixed mount on the bottom of the UAS and oriented in an approximately nadir orientation. The UAS was flown on pre-programmed autonomous flight lines at an approximate altitude of 70 meters above ground level (AGL), resulting in a nominal ground-sample-distance (GSD) of 1.8 centimeters per pixel. The flight lines were oriented roughly shore-parallel and were spaced to provide approximately 70 percent overlap between images from adjacent lines. For the oblique orientation flights (F03, F09, F10, and F11), the camera was mounted using a fixed mount on the bottom of the UAS and oriented facing forward with a downward tilt. The UAS was flown manually in a sideways-facing orientation with the camera pointed toward the bluff. The camera was triggered at 1 Hz using a built-in intervalometer. After acquisition, the images were renamed to include flight number and acquisition time in the file name. The coordinates of the approximate image acquisition location were added ('geotagged') to the image metadata (EXIF) using the telemetry log from the UAS onboard single-frequency autonomous GPS. The image EXIF were also updated to include additional information related to the acquisition. Although the images were recorded in both JPG and camera raw (Adobe DNG) formats, only the JPG images are provided in this data release. The data release includes a total of 3,336 JPG images. Images from takeoff and landing sequences were not used for processing and have been omitted from the data release. The images from each flight are provided in a zip file named with the flight number.

This portion of the data release presents a high-resolution orthomosaic image of the intertidal zone at West Whidbey Island, WA. The orthomosaic has a resolution of 2 centimeters per pixel and was derived from structure-from-motion (SfM) processing of aerial imagery collected with an unmanned aerial system (UAS) on 2019-06-04. The raw imagery used to create the orthomosaic was acquired using a UAS fitted with a Ricoh GR II digital camera featuring a global shutter. The UAS was flown on pre-programmed autonomous flight lines spaced to provide approximately 70 percent overlap between images from adjacent lines. The camera was triggered at 1 Hz using a built-in intervalometer. The UAS was flown at an approximate altitude of 70 meters above ground level (AGL), resulting in a nominal ground-sample-distance (GSD) of 1.8 centimeters per pixel. Additional imagery was collected with the camera in an oblique orientation toward the coastal bluff face to image vertical faces. The raw imagery was geotagged using positions from the UAS onboard single-frequency autonomous GPS. Twenty-five temporary ground control points (GCPs) were distributed throughout the survey area to establish survey control. The GCPs consisted of a combination of small square tarps with black-and-white cross patterns and "X" marks placed on the ground using temporary chalk. The GCP positions were measured using post-processed kinematic (PPK) GPS, using corrections from a GPS base station located approximately 7 kilometers from the study area. The orthomosaic image is provided in a three-band RGB format, with 8-bit unsigned integer values compressed using high-quality JPEG compression. The image has been formatted as a cloud optimized GeoTIFF with internal overviews and masks to facilitate cloud-based queries and display.

This portion of the data release presents a topographic point cloud of the intertidal zone at West Whidbey Island, WA. The point cloud was derived from structure-from-motion (SfM) processing of aerial imagery collected with an unmanned aerial system (UAS) on 2019-06-04. The point cloud has 293,261,002 points with an average point density of 1,063 points per-square meter. The point cloud is tiled to reduce individual file sizes and is grouped within a zip file for downloading. Each point in the point cloud contains an explicit horizontal and vertical coordinate, color, intensity, and classification. Water portions of the point cloud were classified using a polygon digitized from the orthomosaic imagery derived from these surveys (also available in this data release). No other classifications were performed. The raw imagery used to create these point clouds was acquired using a UAS fitted with a Ricoh GR II digital camera featuring a global shutter. The UAS was flown on pre-programmed autonomous flight lines spaced to provide approximately 70 percent overlap between images from adjacent lines. The camera was triggered at 1 Hz using a built-in intervalometer. The UAS was flown at an approximate altitude of 70 meters above ground level (AGL), resulting in a nominal ground-sample-distance (GSD) of 1.8 centimeters per pixel. Additional imagery was collected with the camera in an oblique orientation toward the coastal bluff face to image vertical faces. The raw imagery was geotagged using positions from the UAS onboard single-frequency autonomous GPS. Twenty-five temporary ground control points (GCPs) were distributed throughout the survey area to establish survey control. The GCPs consisted of a combination of small square tarps with black-and-white cross patterns and "X" marks placed on the ground using temporary chalk. The GCP positions were measured using post-processed kinematic (PPK) GPS, using corrections from a GPS base station located approximately 7 kilometers from the study area. The point clouds are formatted in LAZ format (LAS 1.2 specification).

Geochemical analyses of authigenic carbonates, bivalves, and pore fluids were performed on samples collected from seep fields along the Queen Charlotte Fault, a right lateral transform boundary that separates the Pacific and North American tectonic plates. Samples were collected using grab samplers and piston cores, and were collected during three different research cruises in 2011, 2015, and 2017.

Time series data of wave height and water surface elevation were acquired for 147 days at four locations off of the north coast and four locations off the south coast of Buck Island, U.S. Virgin Islands, in support of a study on the coastal circulation patterns and the transformation of surface waves over the coral reefs. The relative placement of sensors on the reefs were as follows: BUI15S1T and BUI15N1T – fore reef BUI15S2T and BUI15N2T – outer reef flat BUI15S3T and BUI15N3T – middle reef flat BUI15S4T and BUI15N4T – inner reef flat

Time series data of wave height and water surface elevation were acquired for 109 days at four locations off of the north coast and four locations off the south coast of Buck Island, U.S. Virgin Islands, in support of a study on the coastal circulation patterns and the transformation of surface waves over the coral reefs. The relative placement of sensors on the reefs were as follows: BUI16S1T and BUI16N1T – fore reef BUI16S2T and BUI16N2T – outer reef flat BUI16S3T and BUI16N3T – middle reef flat BUI16S4T and BUI16N4T – inner reef flat

Time series data of water surface elevation and wave height were acquired at ten locations for 518 days (in three separate deployments) off the south coast of Kwajalein Island, Marshall Islands, in support of a study on the coastal circulation patterns and the transformation of surface waves over the coral reefs. The relative placement of sensors on the reefs were as follows: KWA13W1 and KWA13E1 – fore reef KWA13W2 and KWA13E2 – outer reef flat KWA13W1 and KWA13E1 – middle reef flat KWA13W1 and KWA13E1 – inner reef flat

Time series data of wave height and water surface elevation were acquired at ten locations for 75 days south of Lahaina, off of the west coast of the island of Maui, Hawaii, in support of a study on the coastal circulation patterns and the transformation of surface waves over the coral reefs. The relative placement of sensors on the reefs were as follows: MAU17TP1 and MAU17LA1 – middle fore reef MAU17TP2 and MAU17LA2 – upper fore reef MAU17TP3 and MAU17LA3 – outer reef flat MAU17TP4 and MAU17LA4 – middle reef flat MAU17TP5 and MAU17LA5 – inner reef flat

Time series data of water surface elevation and wave height were acquired at ten locations for 153 days off San Juan, on the north coast of Puerto Rico, in support of a study on the transformation of surface waves and resulting water levels over the coral reefs. The relative placement of sensors on the reefs were as follows: PRI18E01, PRI18W01 – fore reef PRI18E02, PRI18W02 – reef crest PRI18E03, PRI18W03 – outer reef flat PRI18E04, PRI18W04 – middle reef flat PRI18E05, PRI18W05 – inner reef flat PRI18E06 – lagoon PRI18E07 – near-shore

Time series data of wave height and water surface elevation were acquired for 147 days at eleven locations, in two cross-reef transects, off of the west coast of Rincon, Puerto Rico, in support of a study on the coastal circulation patterns and the transformation of surface waves over the coral reefs. The relative placement of sensors on the reef were as follows: PRI19N01 – offshore reef crest, north transect PRI19N02, PRI19N03 – offshore reef flat, north transect PRI19S03 – offshore reef flat, south transect PRI19N04, PRI19N05 and PRI19N06 – inner reef flat, north transect PRI19S04, PRI19S05, PRI19S06, PRI19S07 and PRI19S08 – inner reef flat, south transect

Time series data of wave height and water surface elevation were acquired for 135 days at six locations off of the west coast of Rincon, Puerto Rico, in support of a study on the coastal circulation patterns and the transformation of surface waves over the coral reefs. The relative placement of sensors on the reef were as follows: PRI20N01 – offshore PRI20N02 and PRI20N03 – fore reef PRI20N35, PRI20N04 and PRI20N45 – reef flat

Time series data of water surface elevation and wave height were acquired at ten locations for 517 days (in three separate deployments) off the north coast of Roi-Namur Island, Kwajalein Atoll, Marshall Islands, in support of a study on the coastal circulation patterns and the transformation of surface waves over the coral reefs. The relative placement of sensors on the reefs were as follows: ROI13W1 and ROI13E1 – fore reef ROI13W2 and ROI13E2 – outer reef flat ROI13W1 and ROI13E1 – middle reef flat ROI13W1 and ROI13E1 – inner reef flat

Time series data of wave height and water surface elevation were acquired for 100 days at three locations off of the island of Nanumanga, three locations off of the island of Nanumea, three locations off of the island of Nui, two locations off of the island of Nikulaelae, and two locations off of the island of Niulakita, in the island nation of Tuvalu, in support of a study on the coastal circulation patterns and the transformation of surface waves over the coral reefs. The relative placement of sensors on the reefs were as follows: TVL19NG3, TVL19NM1, TVL19NU1, TVL19NK1 and TVL19NL1 – offshore TVL19NG1, TVL19NM2 and TVL19NU2 – reef crest TVL19NG3, TVL19NM3, TVL19NU3, TVL19NK2 and TVL19NL2 – reef flat

On February 27, 2010, a tsunami originating near Chile arrived in Monterey Bay, California. This data release comprises two hours of pressure and near-bed velocity data spanning the largest tsunami waves. At the time, the U.S. Geological Survey Pacific Coastal and Marine Science Center had a remotely-controlled instrumented platform deployed adjacent to the Santa Cruz Municipal Wharf (mean depth 9 m) for collecting hydrodynamic and sediment transport data. In anticipation of the arrival of the tsunami, sampling was changed to better capture the event. Pressure and near-bed velocity profiles were measured at 1 Hz for 25 minutes every half hour. The velocities are influenced by surface waves, tsunami waves, and tidal currents. The velocity profiles capture the unsteady boundary layer that developed due to the tsunami-induced currents. They are useful for understanding the frictional interaction of the tsunami with the sea floor, as well as sediment transport produced by the tsunami.

Schematic atoll models with varying theoretical morphologies were used to evaluate the relative control of individual morphological parameters on alongshore transport gradients. Here we present physics-based numerical SWAN model results of incident wave transformations for a range of atoll and island morphologies and sea-level rise scenarios. Model results are presented in NetCDF format, accompanied by a README text file that lists the parameters used in each model run. These data accompany the following publication: Shope, J.B., and Storlazzi, C.D., 2019, Assessing morphologic controls on atoll island alongshore sediment transport gradients due to future sea-level rise: Frontiers in Marine Science, doi:10.3389/fmars.2019.00245.

This portion of the USGS data release presents bathymetry data collected during surveys performed in the Columbia River littoral cell, Washington and Oregon in 2014 (USGS Field Activity Number 2014-631-FA). Bathymetry data were collected using four personal watercraft (PWCs) equipped with single-beam sonar systems and global navigation satellite system (GNSS) receivers. The sonar systems consisted of an Odom Echotrac CV-100 single-beam echosounder and 200 kHz transducer with a 9° beam angle. Raw acoustic backscatter returns were digitized by the echosounder with a vertical resolution of 1.25 cm. Depths from the echosounders were computed using sound velocity profiles measured using a YSI CastAway CTD during the survey. Positioning of the survey vessels was determined at 5 to 10 Hz using Trimble R7 GNSS receivers. Output from the GNSS receivers and sonar systems were combined in real time on the PWC by a computer running HYPACK hydrographic survey software. Navigation information was displayed on a video monitor, allowing PWC operators to navigate along survey lines at speeds of 2–3 m/s. Survey-grade positions of the PWCs were achieved with a single-base station and differential post-processing. Positioning data from the GNSS receivers were post-processed using Waypoint Grafnav to apply differential corrections from a GNSS base station with known horizontal and vertical coordinates relative to the North American Datum of 1983. Orthometric elevations relative to the NAVD88 vertical datum were computed using National Geodetic Survey Geoid12a offsets. Bathymetric data were merged with post-processed positioning data and spurious soundings were removed using a custom Graphical User Interface (GUI) programmed with the computer program MATLAB. The average estimated vertical uncertainty of the bathymetric measurements is 10 cm. The final point data from the PWCs are provided in a comma-separated text file and are projected in cartesian coordinates using the Washington State Plane South, meters coordinate system.

This portion of the USGS data release presents topography data collected during surveys performed in the Columbia River littoral cell, Washington and Oregon, in 2014 (USGS Field Activity Number 2014-631-FA). Topographic profiles were collected by walking along survey lines with global navigation satellite system (GNSS) receivers mounted on backpacks. Prior to data collection, vertical distances between the GNSS antennas and the ground were measured using a tape measure. Hand-held data collectors were used to log raw data and display navigational information allowing surveyors to navigate survey lines spaced at 100- to 1,000-m intervals along the beach. Profiles were surveyed from the landward edge of the study area (either the base of a bluff, engineering structure, or just landward of the primary dune) over the beach foreshore, to wading depth on the same series of transects as nearshore bathymetric surveys that were conducted during the same time period. Additional topographic data were collected between survey lines in some areas with an all-terrain vehicle (ATV) equipped with a GNSS receiver to constrain the elevations and alongshore extent of major morphological features. During the 2014 survey, mechanical problems resulted in limited data collection with the ATV. Positioning data from the survey platforms were referenced to a GNSS base station with known horizontal and vertical coordinates relative to the North American Datum of 1983. Differential corrections from the GNSS base stations to the survey platforms were either applied in real-time with a VHF radio link, or post-processed using Trimble Business Center software. Orthometric elevations relative to the NAVD88 vertical datum were computed using National Geodetic Survey Geoid12a offsets. The average estimated vertical uncertainty of the topographic measurements is 4 cm. The final point data are provided in comma-separated text format and are projected in Cartesian coordinates using the Washington State Plane South, meters coordinate system.

This portion of the USGS data release presents bathymetry data collected during surveys performed in the Columbia River littoral cell, Washington and Oregon in 2015 (USGS Field Activity Number 2015-647-FA). Bathymetry data were collected using four personal watercraft (PWCs) equipped with single-beam sonar systems and global navigation satellite system (GNSS) receivers. The sonar systems consisted of an Odom Echotrac CV-100 single-beam echosounder and 200 kHz transducer with a 9 degree beam angle. Raw acoustic backscatter returns were digitized by the echosounder with a vertical resolution of 1.25 cm. Depths from the echosounders were computed using sound velocity profiles measured using a YSI CastAway CTD during the survey. Positioning of the survey vessels was determined at 5 to 10 Hz using Trimble R7 GNSS receivers. Output from the GNSS receivers and sonar systems were combined in real time on the PWC by a computer running HYPACK hydrographic survey software. Navigation information was displayed on a video monitor, allowing PWC operators to navigate along survey lines at speeds of 2 to 3 m/s. Survey-grade positions of the PWCs were achieved with a single-base station and differential post-processing. Positioning data from the GNSS receivers were post-processed using Waypoint Grafnav to apply differential corrections from a GNSS base station with known horizontal and vertical coordinates relative to the North American Datum of 1983. Orthometric elevations relative to the NAVD88 vertical datum were computed using National Geodetic Survey Geoid12a offsets. Bathymetric data were merged with post-processed positioning data and spurious soundings were removed using a custom Graphical User Interface (GUI) programmed with the computer program MATLAB. The average estimated vertical uncertainty of the bathymetric measurements is 10 cm. The final point data from the PWCs are provided in a comma-separated text file and are projected in cartesian coordinates using the Washington State Plane South, meters coordinate system.

This portion of the USGS data release presents topography data collected during surveys performed in the Columbia River littoral cell, Washington and Oregon, in 2015 (USGS Field Activity Number 2015-647-FA). Topographic profiles were collected by walking along survey lines with global navigation satellite system (GNSS) receivers mounted on backpacks. Prior to data collection, vertical distances between the GNSS antennas and the ground were measured using a tape measure. Hand-held data collectors were used to log raw data and display navigational information allowing surveyors to navigate survey lines spaced at 100- to 1000-m intervals along the beach. Profiles were surveyed from the landward edge of the study area (either the base of a bluff, engineering structure, or just landward of the primary dune) over the beach foreshore, to wading depth on the same series of transects as nearshore bathymetric surveys that were conducted during the same time period. Additional topographic data were collected between survey lines in some areas with an all-terrain vehicle (ATV) equipped with a GNSS receiver to constrain the elevations and alongshore extent of major morphological features. Positioning data from the survey platforms were referenced to a GNSS base station with known horizontal and vertical coordinates relative to the North American Datum of 1983. Differential corrections from the GNSS base stations to the survey platforms were either applied in real-time with a VHF radio link, or post-processed using Trimble Business Center software. Orthometric elevations relative to the NAVD88 vertical datum were computed using National Geodetic Survey Geoid12a offsets. The average estimated vertical uncertainty of the topographic measurements is 4 cm. The final point data are provided in comma-separated text format and are projected in Cartesian coordinates using the Washington State Plane South, meters coordinate system.

This portion of the USGS data release presents bathymetry data collected during surveys performed in the Columbia River littoral cell, Washington and Oregon in 2016 (USGS Field Activity Number 2016-663-FA). Bathymetry data were collected using four personal watercraft (PWCs) equipped with single-beam sonar systems and global navigation satellite system (GNSS) receivers. The sonar systems consisted of an Odom Echotrac CV-100 single-beam echosounder and 200 kHz transducer with a 9 degree beam angle. Raw acoustic backscatter returns were digitized by the echosounder with a vertical resolution of 1.25 cm. Depths from the echosounders were computed using sound velocity profiles measured using a YSI CastAway CTD during the survey. Positioning of the survey vessels was determined at 5 to 10 Hz using Trimble R7 GNSS receivers. Output from the GNSS receivers and sonar systems were combined in real time on the PWC by a computer running HYPACK hydrographic survey software. Navigation information was displayed on a video monitor, allowing PWC operators to navigate along survey lines at speeds of 2 to 3 m/s. Survey-grade positions of the PWCs were achieved with a single-base station and differential post-processing. Positioning data from the GNSS receivers were post-processed using Waypoint Grafnav to apply differential corrections from a GNSS base station with known horizontal and vertical coordinates relative to the North American Datum of 1983. Orthometric elevations relative to the NAVD88 vertical datum were computed using National Geodetic Survey Geoid12a offsets. Bathymetric data were merged with post-processed positioning data and spurious soundings were removed using a custom Graphical User Interface (GUI) programmed with the computer program MATLAB. The average estimated vertical uncertainty of the bathymetric measurements is 10 cm. The final point data from the PWCs are provided in a comma-separated text file and are projected in cartesian coordinates using the Washington State Plane South, meters coordinate system.

This portion of the USGS data release presents topography data collected during surveys performed in the Columbia River littoral cell, Washington and Oregon, in 2016 (USGS Field Activity Number 2016-663-FA). Topographic profiles were collected by walking along survey lines with global navigation satellite system (GNSS) receivers mounted on backpacks. Prior to data collection, vertical distances between the GNSS antennas and the ground were measured using a tape measure. Hand-held data collectors were used to log raw data and display navigational information allowing surveyors to navigate survey lines spaced at 100- to 1000-m intervals along the beach. Profiles were surveyed from the landward edge of the study area (either the base of a bluff, engineering structure, or just landward of the primary dune) over the beach foreshore, to wading depth on the same series of transects as nearshore bathymetric surveys that were conducted during the same time period. Additional topographic data were collected between survey lines in some areas with an all-terrain vehicle (ATV) equipped with a GNSS receiver to constrain the elevations and alongshore extent of major morphological features. Positioning data from the survey platforms were referenced to a GNSS base station with known horizontal and vertical coordinates relative to the North American Datum of 1983. Differential corrections from the GNSS base stations to the survey platforms were either applied in real-time with a VHF radio link, or post-processed using Trimble Business Center software. Orthometric elevations relative to the NAVD88 vertical datum were computed using National Geodetic Survey Geoid12a offsets. The average estimated vertical uncertainty of the topographic measurements is 4 cm. The final point data are provided in comma-separated text format and are projected in Cartesian coordinates using the Washington State Plane South, meters coordinate system.

This portion of the USGS data release presents bathymetry data collected during surveys performed in the Columbia River littoral cell, Washington and Oregon in 2017 (USGS Field Activity Number 2017-666-FA). Bathymetry data were collected using four personal watercraft (PWCs) equipped with single-beam sonar systems and global navigation satellite system (GNSS) receivers. The sonar systems consisted of an Odom Echotrac CV-100 single-beam echosounder and 200 kHz transducer with a 9 degree beam angle. Raw acoustic backscatter returns were digitized by the echosounder with a vertical resolution of 1.25 cm. Depths from the echosounders were computed using sound velocity profiles measured using a YSI CastAway CTD during the survey. Positioning of the survey vessels was determined at 5 to 10 Hz using Trimble R7 GNSS receivers. Output from the GNSS receivers and sonar systems were combined in real time on the PWC by a computer running HYPACK hydrographic survey software. Navigation information was displayed on a video monitor, allowing PWC operators to navigate along survey lines at speeds of 2 to 3 m/s. Survey-grade positions of the PWCs were achieved with a single-base station and differential post-processing. Positioning data from the GNSS receivers were post-processed using Waypoint Grafnav to apply differential corrections from a GNSS base station with known horizontal and vertical coordinates relative to the North American Datum of 1983. Orthometric elevations relative to the NAVD88 vertical datum were computed using National Geodetic Survey Geoid12a offsets. Bathymetric data were merged with post-processed positioning data and spurious soundings were removed using a custom Graphical User Interface (GUI) programmed with the computer program MATLAB. The average estimated vertical uncertainty of the bathymetric measurements is 10 cm. The final point data from the PWCs are provided in a comma-separated text file and are projected in cartesian coordinates using the Washington State Plane South, meters coordinate system.

This portion of the USGS data release presents topography data collected during surveys performed in the Columbia River littoral cell, Washington and Oregon, in 2017 (USGS Field Activity Number 2017-666-FA). Topographic profiles were collected by walking along survey lines with global navigation satellite system (GNSS) receivers mounted on backpacks. Prior to data collection, vertical distances between the GNSS antennas and the ground were measured using a tape measure. Hand-held data collectors were used to log raw data and display navigational information allowing surveyors to navigate survey lines spaced at 100- to 1000-m intervals along the beach. Profiles were surveyed from the landward edge of the study area (either the base of a bluff, engineering structure, or just landward of the primary dune) over the beach foreshore, to wading depth on the same series of transects as nearshore bathymetric surveys that were conducted during the same time period. Additional topographic data were collected between survey lines in some areas with an all-terrain vehicle (ATV) equipped with a GNSS receiver to constrain the elevations and alongshore extent of major morphological features. Positioning data from the survey platforms were referenced to a GNSS base station with known horizontal and vertical coordinates relative to the North American Datum of 1983. Differential corrections from the GNSS base stations to the survey platforms were either applied in real-time with a VHF radio link, or post-processed using Trimble Business Center software. Orthometric elevations relative to the NAVD88 vertical datum were computed using National Geodetic Survey Geoid12a offsets. The average estimated vertical uncertainty of the topographic measurements is 4 cm. The final point data are provided in comma-separated text format and are projected in Cartesian coordinates using the Washington State Plane South, meters coordinate system.

This portion of the USGS data release presents bathymetry data collected during surveys performed in the Columbia River littoral cell, Washington and Oregon in 2018 (USGS Field Activity Number 2018-652-FA). Bathymetry data were collected using four personal watercraft (PWCs) equipped with single-beam sonar systems and global navigation satellite system (GNSS) receivers. The sonar systems consisted of an Odom Echotrac CV-100 single-beam echosounder and 200 kHz transducer with a 9 degree beam angle. Raw acoustic backscatter returns were digitized by the echosounder with a vertical resolution of 1.25 cm. Depths from the echosounders were computed using sound velocity profiles measured using a YSI CastAway CTD during the survey. Positioning of the survey vessels was determined at 5 to 10 Hz using Trimble R7 GNSS receivers. Output from the GNSS receivers and sonar systems were combined in real time on the PWC by a computer running HYPACK hydrographic survey software. Navigation information was displayed on a video monitor, allowing PWC operators to navigate along survey lines at speeds of 2 to 3 m/s. Survey-grade positions of the PWCs were achieved with a single-base station and differential post-processing. Positioning data from the GNSS receivers were post-processed using Waypoint Grafnav to apply differential corrections from a GNSS base station with known horizontal and vertical coordinates relative to the North American Datum of 1983. Orthometric elevations relative to the NAVD88 vertical datum were computed using National Geodetic Survey Geoid12a offsets. Bathymetric data were merged with post-processed positioning data and spurious soundings were removed using a custom Graphical User Interface (GUI) programmed with the computer program MATLAB. The average estimated vertical uncertainty of the bathymetric measurements is 10 cm. The final point data from the PWCs are provided in a comma-separated text file and are projected in cartesian coordinates using the Washington State Plane South, meters coordinate system.

This portion of the USGS data release presents topography data collected during surveys performed in the Columbia River littoral cell, Washington and Oregon, in 2018 (USGS Field Activity Number 2018-652-FA). Topographic profiles were collected by walking along survey lines with global navigation satellite system (GNSS) receivers mounted on backpacks. Prior to data collection, vertical distances between the GNSS antennas and the ground were measured using a tape measure. Hand-held data collectors were used to log raw data and display navigational information allowing surveyors to navigate survey lines spaced at 100- to 1000-m intervals along the beach. Profiles were surveyed from the landward edge of the study area (either the base of a bluff, engineering structure, or just landward of the primary dune) over the beach foreshore, to wading depth on the same series of transects as nearshore bathymetric surveys that were conducted during the same time period. Additional topographic data were collected between survey lines in some areas with an all-terrain vehicle (ATV) equipped with a GNSS receiver to constrain the elevations and alongshore extent of major morphological features. Positioning data from the survey platforms were referenced to a GNSS base station with known horizontal and vertical coordinates relative to the North American Datum of 1983. Differential corrections from the GNSS base stations to the survey platforms were either applied in real-time with a VHF radio link, or post-processed using Trimble Business Center software. Orthometric elevations relative to the NAVD88 vertical datum were computed using National Geodetic Survey Geoid12a offsets. The average estimated vertical uncertainty of the topographic measurements is 4 cm. The final point data are provided in comma-separated text format and are projected in Cartesian coordinates using the Washington State Plane South, meters coordinate system.

This portion of the USGS data release presents bathymetry data collected during surveys performed in the Columbia River littoral cell, Washington and Oregon in 2019 (USGS Field Activity Number 2019-632-FA). Bathymetry data were collected using four personal watercraft (PWCs) equipped with single-beam sonar systems and global navigation satellite system (GNSS) receivers. The sonar systems consisted of an Odom Echotrac CV-100 single-beam echosounder and 200 kHz transducer with a 9-degree beam angle. Raw acoustic backscatter returns were digitized by the echosounder with a vertical resolution of 1.25 cm. Depths from the echosounders were computed using sound velocity profiles measured using a YSI CastAway CTD during the survey. Positioning of the survey vessels was determined at 5 to 10 Hz using Trimble R7 GNSS receivers. Output from the GNSS receivers and sonar systems were combined in real time on the PWC by a computer running HYPACK hydrographic survey software. Navigation information was displayed on a video monitor, allowing PWC operators to navigate along survey lines at speeds of 2 to 3 m/s. Survey-grade positions of the PWCs were achieved with a single-base station and differential post-processing. Positioning data from the GNSS receivers were post-processed using Waypoint Grafnav to apply differential corrections from a GNSS base station with known horizontal and vertical coordinates relative to the North American Datum of 1983. Orthometric elevations relative to the NAVD88 vertical datum were computed using National Geodetic Survey Geoid12a offsets. Bathymetric data were merged with post-processed positioning data and spurious soundings were removed using a custom Graphical User Interface (GUI) programmed with the computer program MATLAB. The average estimated vertical uncertainty of the bathymetric measurements is 10 cm. The final point data from the PWCs are provided in a comma-separated text file and are projected in cartesian coordinates using the Washington State Plane South, meters coordinate system.

This portion of the USGS data release presents topography data collected during surveys performed in the Columbia River littoral cell, Washington and Oregon, in 2019 (USGS Field Activity Number 2019-632-FA). Topographic profiles were collected by walking along survey lines with global navigation satellite system (GNSS) receivers mounted on backpacks. Prior to data collection, vertical distances between the GNSS antennas and the ground were measured using a tape measure. Hand-held data collectors were used to log raw data and display navigational information allowing surveyors to navigate survey lines spaced at 100- to 1000-m intervals along the beach. Profiles were surveyed from the landward edge of the study area (either the base of a bluff, engineering structure, or just landward of the primary dune) over the beach foreshore, to wading depth on the same series of transects as nearshore bathymetric surveys that were conducted during the same time period. Additional topographic data were collected between survey lines in some areas with an all-terrain vehicle (ATV) equipped with a GNSS receiver to constrain the elevations and alongshore extent of major morphological features. Positioning data from the survey platforms were referenced to a GNSS base station with known horizontal and vertical coordinates relative to the North American Datum of 1983. Differential corrections from the GNSS base stations to the survey platforms were either applied in real-time with a VHF radio link, or post-processed using Trimble Business Center software. Orthometric elevations relative to the NAVD88 vertical datum were computed using National Geodetic Survey Geoid12a offsets. The average estimated vertical uncertainty of the topographic measurements is 4 cm. The final point data are provided in comma-separated text format and are projected in Cartesian coordinates using the Washington State Plane South, meters coordinate system.

These metadata describe acoustic-backscatter data collected during an October 2016 multibeam-echosounder survey of the northern portion of the Santa Barbara Channel, California. Data were collected and processed by the U.S. Geological Survey (USGS), Pacific Coastal and Marine Science Center (PCMSC) with fieldwork activity number 2016-666-FA. The acoustic-backscatter data are provided as a GeoTIFF image.

These metadata describe bathymetry data collected during an October 2016 multibeam-echosounder survey of the northern portion of the Santa Barbara Channel, California. Data were collected and processed by the the U.S. Geological Survey (USGS), Pacific Coastal and Marine Science Center (PCMSC) with fieldwork activity number 2016-666-FA. The bathymetry data are provided as a GeoTIFF image.

High-resolution multichannel minkisparker seismic-reflection (MCS) profiles were collected by the U.S. Geological Survey in September and October of 2016 from the northern portion of the Santa Barbara Basin offshore southern California. Data were collected aboard the USGS R/V Parke Snavely and NOAA R/V Shearwater during field activity 2016-666-FA using a SIG 2-mille minisparker and recorded using 48- or 24-channel Geometrics digital hydrophone streamer. Sub-bottom acoustic penetration spans several hundreds of meters and is variable by location.

This data release provides census counts of benthic foraminifera (in percent for the total fauna and as raw counts for just the living specimens) as well as environmental parameters (temperature, salinity, and oxygen concentration) at the sampling sites, and radiocarbon measurements from selected push core samples obtained under and near a whale-fall off western Vancouver Island, British Columbia, Canada.

This data release includes the XBeach input data files used to evaluate the importance of explicitly modeling sea-swell waves for runup. This was examined using a 2D XBeach short wave-averaged (surfbeat, XB-SB) and a wave-resolving (non-hydrostatic, XB-NH) model of Roi-Namur Island on Kwajalein Atoll in the Republic of Marshall Islands. Results show that explicitly modelling the sea-swell component (using XB-NH) provides a better approximation of the observed runup than XB-SB (which only models the time-variation of the sea-swell wave height), despite good model performance of both models on reef flat water levels and wave heights. However, both models under-predict runup peaks. The difference between XB-SB and XB-NH increases for more extreme wave events and higher sea levels, as XB-NH resolves individual waves and therefore captures SS-wave motions in runup. However, for even larger forcing conditions with offshore wave heights of 6 m, the island is flooded in both XB-SB and XB-NH computations, regardless of the sea-swell wave energy contribution. In such cases, XB-SB would be adequate to model flooding depths and extents on the island while requiring 4-5 times less computational effort. These input files accompany the modeling for following publication: Quataert, E., Storlazzi, C., van Dongeren, A., and McCall, R., 2020, The importance of explicitly modeling sea-swell waves for runup on reef-lined coasts: Coastal Engineering, https://doi.org/10.1016/j.coastaleng.2020.103704

Projected coastal squeeze derived from CoSMoS Phase 2 shoreline change and cliff retreat projections. Projected coastal squeeze extents illustrate the available area between shoreline (mean high water; MHW) positions and man-made structures and barriers (referred to as non-erodible structures) or cliff-top retreat, as applicable, for a range of sea-level rise scenarios. The coastal squeeze polygons include results from the Coastal Storm Modeling System (CoSMoS) shoreline change (CoSMoS-COAST; Vitousek and others, 2017; available at https://www.sciencebase.gov/catalog/item/57f426b9e4b0bc0bec033fad) and cliff retreat models (Limber and others, 2015; available at https://www.sciencebase.gov/catalog/item/57f4234de4b0bc0bec033f90) using future wave-climate conditions derived from Global Climate Models (GCMs). Coastal squeeze areas are identified and defined from combined model projections, using model scenarios where erosion was limited by non-erodible structures (for shoreline change models) and armoring (for cliff retreat models; both cliff and shoreline cases referred to as 'hold the line') and where no beach-nourishment was included. Coastal squeeze projections are defined for each sea-level rise scenario. Shoreline change and cliff retreat model details and data sources are outlined in CoSMoS_3.0_Phase_2_Southern_California_Bight:_Summary_of_data_and_methods (available at https://www.sciencebase.gov/catalog/file/get/57f1d4f3e4b0bc0bebfee139?name=CoSMoS_SoCalv3_Phase2_summary_of_methods.pdf). Phase 2 data for Southern California include information for the coast from the border of Mexico to Pt. Conception. Please read the Summary of methods and inspect output carefully. Data are complete for the information presented.

Geographic extent of projected runup associated with the sea-level rise and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal planners with critical storm-hazards information that can be used to increase public safety, mitigate physical damages, and more effectively manage and allocate resources within complex coastal settings. Model details and data sources are outlined in CoSMoS_3.0_Phase_2_Southern_California_Bight:_Summary_of_data_and_methods (available at https://www.sciencebase.gov/catalog/file/get/57f1d4f3e4b0bc0bebfee139?name=CoSMoS_SoCalv3_Phase2_summary_of_methods.pdf). Phase 2 data for Southern California include flood-hazard information for the coast from the border of Mexico to Pt. Conception. Several changes from Phase 1 projections are reflected in many areas; please read the Summary of methods and inspect output carefully. Data are complete for the information presented.

This part of the data release presents a digital elevation model (DEM) derived from bathymetry and topography data of the Elwha River delta collected in May 2011. Two dams on the Elwha River, Washington State, USA trapped over 20 million m3 of sediment, reducing downstream sediment fluxes and contributing to erosion of the river's coastal delta. The removal of the Elwha and Glines Canyon dams between 2011 and 2014 induced massive increases in river sediment supply and provided an unprecedented opportunity to examine the response of a delta system to changes in sediment supply. The U.S. Geological Survey developed an integrated research program aimed at understanding the ecosystem responses following dam removal that included regular monitoring of coastal and nearshore bathymetry and topography. As part of this monitoring program, the USGS conducted a bathymetric and topographic survey in the Strait of Juan de Fuca on the Elwha River delta, Washington (USGS Field Activity Number W-04-11-PS). Bathymetry data were collected using two personal watercraft (PWCs), each equipped with single-beam echosounders and survey-grade global navigation satellite system (GNSS) receivers. Topography data were collected on foot with GNSS receivers mounted on backpacks. DEM surfaces were produced from all available elevation data using linear interpolation.

This part of the data release presents bathymetry data from the Elwha River delta collected in May 2011 using two personal watercraft (PWCs). The PWCs were equipped with single-beam echosounders and survey-grade global navigation satellite system (GNSS) receivers.

This part of the data release presents topography data from the Elwha River delta collected in May 2011. Topography data were collected on foot with global navigation satellite system (GNSS) receivers mounted on backpacks.

Water depth and turbidity time-series data were collected in Little Holland Tract (LHT) in 2015. Depth (from pressure) was measured in high-frequency (6 or 8 Hz) bursts. Burst means represent tidal stage, and burst data can be used to determine wave height and period. The turbidity sensors were calibrated to suspended-sediment concentration measured in water samples collected on site. The calibration and fit parameters for all of the turbidity sensors used in the study are tabulated and provided with the data. Data were sequentially added to this data release as they were collected and post-processed. Typically, each zip folder for a deployment period contains one file from an optical backscatter sensor and two files of data from a bursting pressure sensor.

This part of the data release presents a digital elevation model (DEM) derived from bathymetry and topography data of the Elwha River delta collected in August 2011. Two dams on the Elwha River, Washington State, USA trapped over 20 million m3 of sediment, reducing downstream sediment fluxes and contributing to erosion of the river's coastal delta. The removal of the Elwha and Glines Canyon dams between 2011 and 2014 induced massive increases in river sediment supply and provided an unprecedented opportunity to examine the response of a delta system to changes in sediment supply. The U.S. Geological Survey developed an integrated research program aimed at understanding the ecosystem responses following dam removal that included regular monitoring of coastal and nearshore bathymetry and topography. As part of this monitoring program, the USGS conducted a bathymetric and topographic survey in the Strait of Juan de Fuca on the Elwha River delta, Washington (USGS Field Activity Number W-06-11-PS). Bathymetry data were collected using two personal watercraft (PWCs), each equipped with single-beam echosounders and survey-grade global navigation satellite system (GNSS) receivers. Topography data were collected on foot with GNSS receivers mounted on backpacks. DEM surfaces were produced from all available elevation data using linear interpolation.

This part of the data release presents bathymetry data from the Elwha River delta collected in August 2011 using two personal watercraft (PWCs). The PWCs were equipped with single-beam echosounders and survey-grade global navigation satellite system (GNSS) receivers.

This part of the data release presents topography data from the Elwha River delta collected in August 2011. Topography data were collected on foot with global navigation satellite system (GNSS) receivers mounted on backpacks.

This part of the data release presents a digital elevation model (DEM) derived from bathymetry and topography data of the Elwha River delta collected in April 2014. Two dams on the Elwha River, Washington State, USA trapped over 20 million m3 of sediment, reducing downstream sediment fluxes and contributing to erosion of the river's coastal delta. The removal of the Elwha and Glines Canyon dams between 2011 and 2014 induced massive increases in river sediment supply and provided an unprecedented opportunity to examine the response of a delta system to changes in sediment supply. The U.S. Geological Survey developed an integrated research program aimed at understanding the ecosystem responses following dam removal that included regular monitoring of coastal and nearshore bathymetry and topography. As part of this monitoring program, the USGS conducted a bathymetric and topographic survey in the Strait of Juan de Fuca on the Elwha River delta, Washington (USGS Field Activity Number 2014-620-FA). Bathymetry data were collected using two personal watercraft (PWCs), each equipped with single-beam echosounders and survey-grade global navigation satellite system (GNSS) receivers. Topography data were collected on foot with GNSS receivers mounted on backpacks. DEM surfaces were produced from all available elevation data using linear interpolation.

This part of the data release presents bathymetry data from the Elwha River delta collected in April 2014 using two personal watercraft (PWCs). The PWCs were equipped with single-beam echosounders and survey-grade global navigation satellite system (GNSS) receivers.

This part of the data release presents topography data from the Elwha River delta collected in April 2014. Topography data were collected on foot with global navigation satellite system (GNSS) receivers mounted on backpacks.

This portion of the data release presents sediment grain-size data from samples collected on the Elwha River delta, Washington, in May 2014 (USGS Field Activity 2014-620-FA). Surface sediment was collected from 43 locations using a small ponar, or 'grab', sampler from a small boat on May 12, 2014 in depths between about 1 and 12 m around the delta. The locations of grab samples were determined with a hand-held global navigation satellite system (GNSS). The grain-size distributions of samples were determined using standard techniques developed by the USGS Pacific Coastal and Marine Science Center sediment lab. Grab samples that yielded less than 50 g of sediment were omitted from analysis and are classified as "no sample". The grain-size data are provided in a comma-delimited spreadsheet (.csv).

This part of the data release presents a digital elevation model (DEM) derived from bathymetry and topography data of the Elwha River delta collected in May 2012. Two dams on the Elwha River, Washington State, USA trapped over 20 million m3 of sediment, reducing downstream sediment fluxes and contributing to erosion of the river's coastal delta. The removal of the Elwha and Glines Canyon dams between 2011 and 2014 induced massive increases in river sediment supply and provided an unprecedented opportunity to examine the response of a delta system to changes in sediment supply. The U.S. Geological Survey developed an integrated research program aimed at understanding the ecosystem responses following dam removal that included regular monitoring of coastal and nearshore bathymetry and topography. As part of this monitoring program, the USGS conducted a bathymetric and topographic survey in the Strait of Juan de Fuca on the Elwha River delta, Washington (USGS Field Activity Number W-03-12-PS). Bathymetry data were collected using two personal watercraft (PWCs), each equipped with single-beam echosounders and survey-grade global navigation satellite system (GNSS) receivers. Topography data were collected on foot with GNSS receivers mounted on backpacks. DEM surfaces were produced from all available elevation data using linear interpolation.

This part of the data release presents bathymetry data from the Elwha River delta collected in May 2012 using two personal watercraft (PWCs). The PWCs were equipped with single-beam echosounders and survey-grade global navigation satellite system (GNSS) receivers.

This part of the data release presents topography data from the Elwha River delta collected in May 2012. Topography data were collected on foot with global navigation satellite system (GNSS) receivers mounted on backpacks.

This part of the data release presents bathymetry data from the Elwha River delta collected in September 2010 using a personal watercraft (PWC) and a small boat. Both survey vessels were equipped with single-beam echosounders and survey-grade global navigation satellite system (GNSS) receivers.

This part of the data release presents a digital elevation model (DEM) derived from bathymetry and topography data of the Elwha River delta collected in September 2010. Two dams on the Elwha River, Washington State, USA trapped over 20 million m3 of sediment, reducing downstream sediment fluxes and contributing to erosion of the river's coastal delta. The removal of the Elwha and Glines Canyon dams between 2011 and 2014 induced massive increases in river sediment supply and provided an unprecedented opportunity to examine the response of a delta system to changes in sediment supply. The U.S. Geological Survey developed an integrated research program aimed at understanding the ecosystem responses following dam removal that included regular monitoring of coastal and nearshore bathymetry and topography. As part of this monitoring program, the USGS conducted a bathymetric and topographic survey in the Strait of Juan de Fuca on the Elwha River delta, Washington (USGS Field Activity Number W-03-10-PS). Bathymetry data were collected using a personal watercraft (PWC) and a small boat, each equipped with single-beam echosounders and survey-grade global navigation satellite system (GNSS) receivers. Topography data were collected on foot with GNSS receivers mounted on backpacks. DEM surfaces were produced from all available elevation data using linear interpolation.

This part of the data release presents topography data from the Elwha River delta collected in September 2010. Topography data were collected on foot with global navigation satellite system (GNSS) receivers mounted on backpacks.

A high-resolution (10-meter per pixel) digital elevation model (DEM) was created for the Sacramento-San Joaquin Delta using both bathymetry and topography data. This DEM is the result of collaborative efforts of the U.S. Geological Survey (USGS) and the California Department of Water Resources (DWR). The base of the DEM is from a 10-m DEM released in 2004 and updated in 2005 (Foxgrover and others, 2005) that used Environmental Systems Research Institute(ESRI), ArcGIS Topo to Raster module to interpolate grids from single beam bathymetric surveys collected by DWR, the Army Corp of Engineers (COE), the National Oceanic and Atmospheric Administration (NOAA), and the USGS, into a continuous surface. The Topo to Raster interpolation method was specifically designed to create hydrologically correct DEMs from point, line, and polygon data (Environmental Systems Research Institute, Inc., 2015). Elevation contour lines were digitized based on the single beam point data for control of channel morphology during the interpolation process. Checks were performed to ensure that the interpolated surfaces honored the source bathymetry, and additional contours and(or) point data were added as needed to help constrain the data. The original data were collected in the tidal datum Mean Lower or Low Water (MLLW), or the National Geodetic Vertical Datum of 1929 (NGVD29). All data were converted to NGVD29. The 2005 USGS DEM was updated by DWR, first by converting the DEM to the current modern datum of National Geodetic Vertical Datum of 1988 (NGVD88) and then by following the methodology of the USGS DEM, established for the 2005 DEM (Foxgrover and others, 2005) for adding newly collected single and multibeam bathymetric data. They then included topographic data from lidar surveys, providing the first DEM that included the land/water interface (Wang and Ateljevich, 2012). The USGS further updated and expanded the DWR DEM with the inclusion of USGS interpolated sections of single beam bathymetry data collected by the COE and USGS scientists, expanding the DEM to include the northernmost areas of the Sacramento-San Joaquin Delta, and by making use of a two-meter seamless bathymetric/topographic DEM from the USGS EROS Data Center (2013) of the San Francisco Bay region. The resulting 10-meter USGS DEM encompasses the entirety of Suisun Bay, beginning with the Carquinez Strait in the west, east to California Interstate 5, north following the path of the Yolo Bypass and the Sacramento River up to Knights Landing, and the American River northeast to the Nimbus Dam, and south to areas around Tracy. The DEM incorporates the newest available bathymetry data at the time of release, as well as including, at minimum, a 100-meter band of available topography data adjacent to most shorelines. No data areas within the DEM are areas where no elevation data exists, either due to a gap in the land/water interface, or because lidar was collected over standing water that was then cut out of the DEM.

This part of the data release presents a digital elevation model (DEM) derived from bathymetry and topography data of the Elwha River delta collected in July 2016. Two dams on the Elwha River, Washington State, USA trapped over 20 million m3 of sediment, reducing downstream sediment fluxes and contributing to erosion of the river's coastal delta. The removal of the Elwha and Glines Canyon dams between 2011 and 2014 induced massive increases in river sediment supply and provided an unprecedented opportunity to examine the response of a delta system to changes in sediment supply. The U.S. Geological Survey developed an integrated research program aimed at understanding the ecosystem responses following dam removal that included regular monitoring of coastal and nearshore bathymetry and topography. As part of this monitoring program, the USGS conducted a bathymetric and topographic survey in the Strait of Juan de Fuca on the Elwha River delta, Washington (USGS Field Activity Number 2016-653-FA). Bathymetry data were collected using two personal watercraft (PWCs) and a kayak, each equipped with single beam echosounders and survey-grade global navigation satellite systems (GNSS). Topography data were collected on foot with GNSS receivers mounted on backpacks. DEM surfaces were produced from all available elevation data using linear interpolation.

This portion of the data release presents sediment grain-size data from samples collected on the Elwha River delta, Washington, in July 2016 (USGS Field Activity 2016-653-FA). Surface sediment was collected from 67 locations using a small ponar, or 'grab', sampler from the R/V Frontier in water depths between about 1 and 17 m around the delta. An additional 38 samples were collected by hand at low tide. A hand-held global satellite navigation system (GNSS) receiver was used to determine the locations of sediment samples. The grain size distributions of suitable samples were determined using standard techniques developed by the USGS Pacific Coastal and Marine Science Center sediment lab. Grab samples that yielded less than 50 g of sediment were omitted from analysis and are classified as "no sample". The grain-size data are provided in a comma-delimited spreadsheet (.csv).

This part of the data release presents bathymetry data from the Elwha River delta collected in July 2016 using a kayak. The kayak was equipped with a single-beam echosounder and a survey-grade global navigation satellite system (GNSS) receiver.

This part of the data release presents bathymetry data from the Elwha River delta collected in July 2016 using two personal watercraft (PWCs). The PWCs were equipped with single beam echosounders and survey-grade global navigation satellite system (GNSS) receivers.

This part of the data release presents topography data from the Elwha River delta collected in July 2016. Topography data were collected on foot with global navigation satellite system (GNSS) receivers mounted on backpacks.

This portion of the data release contains a digital image mosaic with 1.0 foot-per-pixel (0.3048 meter-per-pixel) resolution of the Diamond Head area on the southeast coast of O'ahu. This image mosaic was generated using digitized 1:10K natural color photographs collected by the National Oceanic and Atmospheric Administration (NOAA) National Ocean Service. Also available is a lower-resolution 'browse' graphic of the image mosaic area and associated metadata.

This portion of the data release contains a digital image mosaic with 1 meter-per-pixel resolution of the Haleolono Point area on the south coast of Moloka'i. This image mosaic was generated using digitized 1:35K natural color photographs collected in September 1993 by the National Oceanic and Atmospheric Administration (NOAA) National Ocean Service (NOS). Also available is a lower-resolution 'browse' graphic of the image mosaic and associated metadata.

This portion of the data release contains an image mosaic with 1.0 meter-per-pixel resolution of the Kailua-Kona area on the west 'Kona' coast of the island of Hawai'i. This image mosaic was generated using digitized 1:24K natural color photographs collected in June 2000 by the National Oceanic and Atmospheric Administration (NOAA) National Ocean Service (NOS). Two versions of the image mosaic are available--one with and one without a lidar bathymetry shaded-relief image digitally combined with the aerial photography mosaic results. The shaded-relief image was derived using airborne SHOALS (Scanning Hydrographic Operational Lidar Survey) lidar (LIght Detection And Ranging) data collected for the U.S. Geological Survey (USGS) by the U.S. Army Corp of Engineers (USACE) in April 1999. Also available is a lower-resolution 'browse' graphic of the image mosaic and associated metadata.

This portion of the data release contains a digital image mosaic with 1.0 foot-per-pixel resolution of the Kalaeloa area on the south coast of Moloka'i. This image mosaic was generated using digitized 1:10K natural color photographs collected in January 2000 by Air Survey Hawai'i, Inc. for the U.S. Geological Survey. Also available is a lower-resolution 'browse' graphic of the image mosaic area and associated metadata.

This portion of the data release contains a digital image mosaic with 1.0 foot-per-pixel resolution of the Kamalo area on the south coast of Moloka'i. This image mosaic was generated using digitized 1:10K natural color photographs collected in January 2000 by Air Survey Hawai'i, Inc. for the U.S. Geological Survey. Also available is a lower-resolution 'browse' graphic of the image mosaic area and associated metadata.

This portion of the data release contains a digital image mosaic with 1 meter-per-pixel resolution of the Kamalo area on the south coast of Moloka'i. This image mosaic was generated using digitized 1:35K natural color photographs collected in September 1993 by the National Oceanic and Atmospheric Administration (NOAA) National Ocean Service (NOS). Also available is a lower-resolution 'browse' graphic of the image mosaic and associated metadata.

This portion of the data release contains a digital image mosaic with 1.0 foot-per-pixel resolution of the Kamiloloa area on the south coast of Moloka'i. This image mosaic was generated using digitized 1:10K natural color photographs collected in January 2000 by Air Survey Hawai'i, Inc. for the U.S. Geological Survey. Also available is a lower-resolution 'browse' graphic of the image mosaic area and associated metadata.

This portion of the data release contains a digital image mosaic with 1 meter-per-pixel resolution of the Kamiloloa area on the south coast of Moloka'i. This image mosaic was generated using digitized 1:35K natural color photographs collected in September 1993 by the National Oceanic and Atmospheric Administration (NOAA) National Ocean Service (NOS). Also available is a lower-resolution 'browse' graphic of the image mosaic and associated metadata.

This portion of the data release contains a digital image mosaic with 1.0 foot-per-pixel resolution of the Kaunakakai area on the south coast of Moloka'i. This image mosaic was generated using digitized 1:10K natural color photographs collected in January 2000 by Air Survey Hawai'i, Inc. for the U.S. Geological Survey. Also available is a lower-resolution 'browse' graphic of the image mosaic area and associated metadata.

This portion of the data release contains a digital image mosaic with 1 meter-per-pixel resolution of the Kaunakakai area on the south coast of Moloka'i. This image mosaic was generated using digitized 1:35K natural color photographs collected in September 1993 by the National Oceanic and Atmospheric Administration (NOAA) National Ocean Service (NOS). Also available is a lower-resolution 'browse' graphic of the image mosaic and associated metadata.

This portion of the data release contains an image mosaic of the Kawaihae area on the west 'Kona' coast of the island of Hawai'i. This image mosaic was generated using digitized 1:24K natural color photographs collected in June 2000 by the National Oceanic and Atmospheric Administration (NOAA) National Ocean Service (NOS). Two versions of the image mosaic are available--one with and one without a lidar bathymetry shaded-relief image digitally combined with the aerial photography mosaic results. The shaded-relief image was derived using airborne SHOALS (Scanning Hydrographic Operational Lidar Survey) lidar (LIght Detection And Ranging) data collected for the U.S. Geological Survey (USGS) by the U.S. Army Corp of Engineers (USACE) in April 1999. Also available is a lower-resolution 'browse' graphic of the image mosaic and associated metadata.

This portion of the data release contains a digital image mosaic with 1.0 foot-per-pixel resolution of the Kawela area on the south coast of Moloka'i. This image mosaic was generated using digitized 1:10K natural color photographs collected in January 2000 by Air Survey Hawai'i, Inc. for the U.S. Geological Survey. Also available is a lower-resolution 'browse' graphic of the image mosaic area and associated metadata.

This portion of the data release contains a digital image mosaic with 1 meter-per-pixel resolution of the Kawela area on the south coast of Moloka'i. This image mosaic was generated using digitized 1:35K natural color photographs collected in September 1993 by the National Oceanic and Atmospheric Administration (NOAA) National Ocean Service (NOS). Also available is a lower-resolution 'browse' graphic of the image mosaic and associated metadata.

This portion of the data release contains an image mosaic of the Kukio area on the west 'Kona' coast of the island of Hawai'i. This image mosaic was generated using digitized 1:24K natural color photographs collected in June 2000 by the National Oceanic and Atmospheric Administration (NOAA) National Ocean Service (NOS). Two versions of the image mosaic are available--one with and one without a lidar bathymetry shaded-relief image digitally combined with the aerial photography mosaic results. The shaded-relief image was derived using airborne SHOALS (Scanning Hydrographic Operational Lidar Survey) lidar (LIght Detection And Ranging) data collected for the U.S. Geological Survey (USGS) by the U.S. Army Corp of Engineers (USACE) in April 1999. Also available is a lower-resolution 'browse' graphic of the image mosaic and associated metadata.

This portion of the data release contains a digital image mosaic with 1 meter-per-pixel resolution of the La'au Point area on the south coast of Moloka'i. This image mosaic was generated using digitized 1:35K natural color photographs collected in September 1993 by the National Oceanic and Atmospheric Administration (NOAA) National Ocean Service (NOS). Also available is a lower-resolution 'browse' graphic of the image mosaic and associated metadata.

This portion of the data release contains a digital image mosaic with 1.0 foot-per-pixel (0.3048 meter-per-pixel) resolution of the Maunalua Bay area on the southeast coast of O'ahu. This image mosaic was generated using digitized 1:10K natural color photographs collected by the National Oceanic and Atmospheric Administration (NOAA) National Ocean Service. Also available is a lower-resolution 'browse' graphic of the image mosaic area and associated metadata.

This portion of the data release contains an image mosaic of the Napili-Honokowai area on the northwest coast of Maui. This image mosaic was generated using digitized 1:35K natural color photographs collected in September 1993 by the National Oceanic and Atmospheric Administration (NOAA) National Ocean Service (NOS). The image mosaic has been geometrically corrected using lidar data. Also available is a lower-resolution 'browse' image, and associated metadata.

This portion of the data release contains a digital image mosaic with 1.0 foot-per-pixel resolution of the Pala'au area on the south coast of Moloka'i. This image mosaic was generated using digitized 1:10K natural color photographs collected in January 2000 by Air Survey Hawai'i, Inc. for the U.S. Geological Survey. Also available is a lower-resolution 'browse' graphic of the image mosaic area and associated metadata.

This portion of the data release contains a digital image mosaic with 1 meter-per-pixel resolution of the Pala'au area on the south coast of Moloka'i. This image mosaic was generated using digitized 1:35K natural color photographs collected in September 1993 by the National Oceanic and Atmospheric Administration (NOAA) National Ocean Service (NOS). Also available is a lower-resolution 'browse' graphic of the image mosaic and associated metadata.

This portion of the data release contains a digital image mosaic with 1.0 foot-per-pixel (0.3048 meter-per-pixel) resolution of the Portlock area on the southeast coast of O'ahu. This image mosaic was generated using digitized 1:10K natural color photographs collected by the National Oceanic and Atmospheric Administration (NOAA) National Ocean Service. Also available is a lower-resolution 'browse' graphic of the image mosaic area and associated metadata.

This portion of the data release contains a digital image mosaic with 1 meter-per-pixel resolution of the Puko'o area on the south coast of Moloka'i. This image mosaic was generated using digitized 1:35K natural color photographs collected in September 1993 by the National Oceanic and Atmospheric Administration (NOAA) National Ocean Service (NOS). Also available is a lower-resolution 'browse' graphic of the image mosaic and associated metadata.

This portion of the data release contains a shaded-relief image mosaic of the nearshore coastal waters along southcentral Moloka'i. This image mosaic was generated using digitized 1:35K natural color photographs collected in September 1993 by the National Oceanic and Atmospheric Administration (NOAA) National Ocean Service (NOS) and scanned in at 1-meter resolution. Several of the 1-meter-resolution images have been merged together and combined with lidar bathymetry data to create a large shaded-relief image. Also available is a lower-resolution 'browse' graphic and associated metadata.

This portion of the data release contains a shaded-relief image mosaic of the nearshore coastal waters along southeast Moloka'i. This image mosaic was generated using digitized 1:35K natural color photographs collected in September 1993 by the National Oceanic and Atmospheric Administration (NOAA) National Ocean Service (NOS) and scanned in at 1-meter resolution. Several of the 1-meter-resolution images have been merged together and combined with lidar bathymetry data to create a large shaded-relief image. Also available is a lower-resolution 'browse' graphic and associated metadata.

This portion of the data release contains a shaded-relief image mosaic of the nearshore coastal waters along southwest Moloka'i. This image mosaic was generated using digitized 1:35K natural color photographs collected in September 1993 by the National Oceanic and Atmospheric Administration (NOAA) National Ocean Service (NOS) and scanned in at 1-meter resolution. Several of the 1-meter-resolution images have been merged together and combined with lidar bathymetry data to create a large shaded-relief image. Also available is a lower-resolution 'browse' graphic and associated metadata.

This portion of the data release contains a digital image mosaic with 1.0 foot-per-pixel resolution of the Umipa'a area on the south coast of Moloka'i. This image mosaic was generated using digitized 1:10K natural color photographs collected in January 2000 by Air Survey Hawai'i, Inc. for the U.S. Geological Survey. Also available is a lower-resolution 'browse' graphic of the image mosaic area and associated metadata.

This portion of the data release contains a digital image mosaic with 1 meter-per-pixel resolution of the Waiakane area on the south coast of Moloka'i. This image mosaic was generated using digitized 1:35K natural color photographs collected in September 1993 by the National Oceanic and Atmospheric Administration (NOAA) National Ocean Service (NOS). Also available is a lower-resolution 'browse' graphic of the image mosaic and associated metadata.

This portion of the data release contains a digital image mosaic with 1.0 foot-per-pixel (0.3048 meter-per-pixel) resolution of the Wai'alae area on the southeast coast of O'ahu. This image mosaic was generated using digitized 1:10K natural color photographs collected by the National Oceanic and Atmospheric Administration (NOAA) National Ocean Service. Also available is a lower-resolution 'browse' graphic of the image mosaic area and associated metadata.

This portion of the data release contains a digital image mosaic with 1.0 foot-per-pixel (0.3048 meter-per-pixel) resolution of the Waikiki area on the southeast coast of O'ahu. This image mosaic was generated using digitized 1:10K natural color photographs collected by the National Oceanic and Atmospheric Administration (NOAA) National Ocean Service. Also available is a lower-resolution 'browse' graphic of the image mosaic area and associated metadata.

This portion of the data release contains a digital image mosaic from Waikiki to Portlock along the southeast coast of O'ahu. Digital mosaics at 1-foot (0.3048-meter) resolution, including the areas of Waikiki, Diamond Head, Wai'alae, Maunalua Bay, and Portlock, were generated from 1:10K aerial photography. These five image mosaics were then combined into one larger mosaic and resampled to 1-meter resolution.

This portion of the data release contains a digital image mosaic from Waikiki to Portlock along the southeast coast of O'ahu. Digital mosaics at 1-foot (0.3048-meter) resolution, including the areas of Waikiki, Diamond Head, Wai'alae, Maunalua Bay, and Portlock, were generated from 1:10K aerial photography. These five image mosaics were then combined into one larger mosaic, resampled to 1-meter resolution, and merged with lidar bathymetry data to produce the shaded-relief image.

This portion of the data release contains an image mosaic of the Waikoloa area on the west 'Kona' coast of the island of Hawai'i. This image mosaic was generated using digitized 1:24K natural color photographs collected in June 2000 by the National Oceanic and Atmospheric Administration (NOAA) National Ocean Service (NOS). Two versions of the image mosaic are available--one with and one without a lidar bathymetry shaded-relief image digitally combined with the aerial photography mosaic results. The shaded-relief image was derived using airborne SHOALS (Scanning Hydrographic Operational Lidar Survey) lidar (LIght Detection And Ranging) data collected for the U.S. Geological Survey (USGS) by the U.S. Army Corp of Engineers (USACE) in April 1999. Also available is a lower-resolution 'browse' graphic of the image mosaic and associated metadata.

This part of the data release presents a digital elevation model (DEM) derived from bathymetry and topography data of the Elwha River delta collected in September 2014. Two dams on the Elwha River, Washington State, USA trapped over 20 million m3 of sediment, reducing downstream sediment fluxes and contributing to erosion of the river's coastal delta. The removal of the Elwha and Glines Canyon dams between 2011 and 2014 induced massive increases in river sediment supply and provided an unprecedented opportunity to examine the response of a delta system to changes in sediment supply. The U.S. Geological Survey developed an integrated research program aimed at understanding the ecosystem responses following dam removal that included regular monitoring of coastal and nearshore bathymetry and topography. As part of this monitoring program, the USGS conducted a bathymetric and topographic survey in the Strait of Juan de Fuca on the Elwha River delta, Washington (USGS Field Activity Number 2014-649-FA). Bathymetry data were collected using two personal watercraft (PWCs), each equipped with single-beam echosounders and survey-grade global navigation satellite systems (GNSS). Topography data were collected on foot with GNSS receivers mounted on backpacks. DEM surfaces were produced from all available elevation data using linear interpolation.

This portion of the data release presents sediment grain-size data from samples collected on the Elwha River delta, Washington, in September 2014 (USGS Field Activity 2014-649-FA). Surface sediment was collected from 63 locations using a small ponar, or 'grab', sampler from the R/V Frontier on September 5, 2014 in depths between about 1 and 17 m around the delta. The locations of grab samples were determined with a hand-held global navigation satellite system (GNSS). The grain-size distributions of samples were determined using standard techniques developed by the USGS Pacific Coastal and Marine Science Center sediment lab. The grain-size data are provided in a comma-delimited spreadsheet (.csv).

This part of the data release presents bathymetry data from the Elwha River delta collected in September 2014 using two personal watercraft (PWCs). The PWCs were equipped with single-beam echosounders and survey-grade global navigation satellite system (GNSS) receivers.

This part of the data release presents topography data from the Elwha River delta collected in September 2014. Topography data were collected on foot with global navigation satellite system (GNSS) receivers mounted on backpacks.

This part of the data release presents bathymetry data from the Elwha River delta collected in August 2012 using a personal watercraft (PWC) and the R/V Frontier. Both survey vessels were equipped with single-beam echosounders and survey-grade global navigation satellite system (GNSS) receivers.

This part of the data release presents a digital elevation model (DEM) derived from bathymetry and topography data of the Elwha River delta collected in August 2012. Two dams on the Elwha River, Washington State, USA trapped over 20 million m3 of sediment, reducing downstream sediment fluxes and contributing to erosion of the river's coastal delta. The removal of the Elwha and Glines Canyon dams between 2011 and 2014 induced massive increases in river sediment supply and provided an unprecedented opportunity to examine the response of a delta system to changes in sediment supply. The U.S. Geological Survey developed an integrated research program aimed at understanding the ecosystem responses following dam removal that included regular monitoring of coastal and nearshore bathymetry and topography. As part of this monitoring program, the USGS conducted a bathymetric and topographic survey in the Strait of Juan de Fuca on the Elwha River delta, Washington (USGS Field Activity Number W-05-12-PS). Bathymetry data were collected using two survey vessels, each equipped with single-beam echosounders and survey-grade global navigation satellite system (GNSS) receivers. Topography data were collected on foot with GNSS receivers mounted on backpacks. DEM surfaces were produced from all available elevation data using linear interpolation.

This portion of the data release presents sediment grain-size data from samples collected on the Elwha River delta, Washington, in August 2012 (USGS Field Activity W-05-12-PS). Surface sediment was sampled using a small ponar, or 'grab', sampler between August 28 and August 30, 2012 from the R/V Frontier at a total of 57 locations in water depths between about 1 and 9 m around the delta. The locations of grab samples were determined with a hand-held global navigation satellite system (GNSS). The grain-size distributions of samples were determined using standard techniques developed by the USGS Pacific Coastal and Marine Science Center sediment lab. Grab samples that yielded less than 50 g of sediment were omitted from analysis and are classified as "no sample". The grain-size data are provided in a comma-delimited spreadsheet (.csv).

This part of the data release presents topography data from the Elwha River delta collected in August 2012. Topography data were collected on foot with global navigation satellite system (GNSS) receivers mounted on backpacks.

This part of the data release presents a digital elevation model (DEM) derived from bathymetry and topography data of the Elwha River delta collected in March 2013. Two dams on the Elwha River, Washington State, USA trapped over 20 million m3 of sediment, reducing downstream sediment fluxes and contributing to erosion of the river's coastal delta. The removal of the Elwha and Glines Canyon dams between 2011 and 2014 induced massive increases in river sediment supply and provided an unprecedented opportunity to examine the response of a delta system to changes in sediment supply. The U.S. Geological Survey developed an integrated research program aimed at understanding the ecosystem responses following dam removal that included regular monitoring of coastal and nearshore bathymetry and topography. As part of this monitoring program, the USGS conducted a bathymetric and topographic survey in the Strait of Juan de Fuca on the Elwha River delta, Washington (USGS Field Activity Number W-01-13-PS). Bathymetry data were collected using two personal watercraft (PWCs), each equipped with single-beam echosounders and survey-grade global navigation satellite system (GNSS) receivers. Topography data were collected on foot with GNSS receivers mounted on backpacks. DEM surfaces were produced from all available elevation data using linear interpolation.

This portion of the data release presents sediment grain-size data from samples collected on the Elwha River delta, Washington, in March 2013 (USGS Field Activity W-01-13-PS). Surface sediment was sampled using a small ponar, or 'grab', sampler on March 4, 2013 from the R/V Frontier at a total of 48 locations in water depths between about 1 and 12 m around the delta. An additional 7 sediment samples were collected between March 6 and March 7, 2013 at low tide from intertidal locations on the delta. The locations of grab samples were determined with a hand-held global navigation satellite system (GNSS). The grain-size distributions of samples were determined using standard techniques developed by the USGS Pacific Coastal and Marine Science Center sediment lab. Grab samples that yielded less than 50 g of sediment were omitted from analysis and are classified as "no sample". The grain-size data are provided in a comma-delimited spreadsheet (.csv).

This part of the data release presents bathymetry data from the Elwha River delta collected in March 2013 using two personal watercraft (PWCs). The PWCs were equipped with single-beam echosounders and survey-grade global navigation satellite system (GNSS) receivers.

This part of the data release presents topography data from the Elwha River delta collected in March 2013. Topography data were collected on foot with global navigation satellite system (GNSS) receivers mounted on backpacks.

This part of the data release presents a digital elevation model (DEM) derived from bathymetry and topography data of the Elwha River delta collected in July 2015. Two dams on the Elwha River, Washington State, USA trapped over 20 million m3 of sediment, reducing downstream sediment fluxes and contributing to erosion of the river's coastal delta. The removal of the Elwha and Glines Canyon dams between 2011 and 2014 induced massive increases in river sediment supply and provided an unprecedented opportunity to examine the response of a delta system to changes in sediment supply. The U.S. Geological Survey developed an integrated research program aimed at understanding the ecosystem responses following dam removal that included regular monitoring of coastal and nearshore bathymetry and topography. As part of this monitoring program, the USGS conducted a bathymetric and topographic survey in the Strait of Juan de Fuca on the Elwha River delta, Washington (USGS Field Activity Number 2015-648-FA). Bathymetry data were collected using two personal watercraft (PWCs), each equipped with single-beam echosounders and survey-grade global navigation satellite systems (GNSS). Topography data were collected on foot with GNSS receivers mounted on backpacks. DEM surfaces were produced from all available elevation data using linear interpolation.

This portion of the data release presents sediment grain-size data from samples collected on the Elwha River delta, Washington, between July and August 2015 (USGS Field Activities 2015-648-FA and 2015-652-FA). Surface sediment was collected from 70 locations using a small ponar, or 'grab', sampler from the R/V Frontier on July 28, 2015. An additional 17 sediment samples were collected between July 22 and August 23, 2015 by scuba divers. Forty-eight sediment samples were collected at low tide using a push corer at intertidal locations on the delta. The locations of grab samples and intertidal samples were determined with a hand-held global navigation satellite system (GNSS). Samples obtained by divers were collected adjacent to fixed monuments on the seabed with previously determined coordinates. The grain-size distributions of samples were determined using standard techniques developed by the USGS Pacific Coastal and Marine Science Center sediment lab. The grain-size data are provided in a comma-delimited spreadsheet (.csv).

This part of the data release presents bathymetry data from the Elwha River delta collected in July 2015 using two personal watercraft (PWCs). The PWCs were equipped with single-beam echosounders and survey-grade global navigation satellite systems (GNSS) receivers.

This part of the data release presents topography data from the Elwha River delta collected in July 2015. Topography data were collected on foot with global navigation satellite system (GNSS) receivers mounted on backpacks.

This part of the data release presents a digital elevation model (DEM) derived from bathymetry and topography data of the Elwha River delta collected in September 2013. Two dams on the Elwha River, Washington State, USA trapped over 20 million m3 of sediment, reducing downstream sediment fluxes and contributing to erosion of the river's coastal delta. The removal of the Elwha and Glines Canyon dams between 2011 and 2014 induced massive increases in river sediment supply and provided an unprecedented opportunity to examine the response of a delta system to changes in sediment supply. The U.S. Geological Survey developed an integrated research program aimed at understanding the ecosystem responses following dam removal that included regular monitoring of coastal and nearshore bathymetry and topography. As part of this monitoring program, the USGS conducted a bathymetric and topographic survey in the Strait of Juan de Fuca on the Elwha River delta, Washington (USGS Field Activity Number W-07-13-PS). Bathymetry data were collected using two personal watercraft (PWCs), each equipped with single-beam echosounders and survey-grade global navigation satellite system (GNSS) receivers. Topography data were collected on foot with GNSS receivers mounted on backpacks. DEM surfaces were produced from all available elevation data using linear interpolation.

This portion of the data release presents sediment grain-size data from samples collected on the Elwha River delta, Washington, in September 2013 (USGS Field Activity W-07-13-PS). Surface sediment was collected from 62 locations using a small ponar, or 'grab', sampler from the R/V Frontier on September 19, 2013 in depths between about 1 and 12 m around the delta. An additional 21 sediment samples were collected between September 16 and September 19, 2013 at low tide from intertidal locations on the delta. The locations of grab samples were determined with a hand-held global navigation satellite system (GNSS). The grain-size distributions of samples were determined using standard techniques developed by the USGS Pacific Coastal and Marine Science Center sediment lab. The grain-size data are provided in a comma-delimited spreadsheet (.csv).

This part of the data release presents bathymetry data from the Elwha River delta collected in September 2013 using two personal watercraft (PWCs). The PWCs were equipped with single-beam echosounders and survey-grade global navigation satellite system (GNSS) receivers.

This part of the data release presents topography data from the Elwha River delta collected in September 2013. Topography data were collected on foot with global navigation satellite system (GNSS) receivers mounted on backpacks.

This dataset contains projections of coastal cliff-retreat rates and positions for future scenarios of sea-level rise (SLR). Present-day cliff-edge positions used as the baseline for projections are also included. Projections were made using numerical and statistical models based on field observations such as historical cliff retreat rate, nearshore slope, coastal cliff height, and mean annual wave power, as part of Coastal Storm Modeling System (CoSMoS) v.3.0 Phase 2 in Southern California. Details: Cliff-retreat position projections and associated uncertainties are for scenarios of 0.25, 0.5, 0.75, 1, 1.25, 1.5, 1.75, 2, and 5 meters of SLR. Projections were made at CoSMoS cross-shore transects (CST) spaced 100 m alongshore using a baseline sea-cliff edge from 2010 (included in the dataset). Within each zip file, there are two separate datasets available: one that ignores coastal armoring, such as seawalls and revetments, and allows the cliff to retreat unimpeded (“Do Not Hold the Line”); and another that assumes that current coastal armoring will be maintained and 100% effective at stopping future cliff erosion ("Hold the Line"). Eight numerical models synthesized from literature (Trenhaile, 2000; Walkden and Hall, 2005; Trenhaile, 2009; Trenhaile, 2011; Ruggiero and others, 2011; Hackney and others, 2013) were used to make projections. All models relate breaking-wave height and period to cliff rock or unconsolidated sediment erosion. Models range in complexity from 2-D models in which the entire profile evolves, from below water to the cliff edge, to simple 1-D empirical or statistical models in which only the cliff edge evolves as a function of wave impact intensity and frequency. The projections are a robust average of all models, and the uncertainties are proportional to 1) underlying uncertainties in the model input data, such as historical cliff retreat rates, and 2) the differences between individual model forecasts at each CST so that uncertainty is larger when the models do not agree. As sea level rises, waves break closer to the sea cliff, more wave energy impacts the cliffs, cliff erosion rates accelerate. Model behavior also includes wave run-up (Stockdon and others, 2006), wave set-up that raises the water level during big-wave events, and tidal levels. The more complex 2-D models were run on idealized cliff profiles extending from about 10 m water depth to 1 kilometer inland from the cliff edge. Profiles were extracted by overlaying the cross-shore transects on a high-resolution digital elevation model (DEM) covering the Southern California study area. For all models, the presence of a beach was recorded (yes or no) for all transects using aerial photography, and the cliff toe elevation (or beach/cliff junction) was digitized from the DEM profiles. Using historic cliff edge retreat rates by Hapke and Reid (2007), unknown coefficients within the cliff-profile models were calibrated using a Monte Carlo simulation (in other words, coefficients were tuned until the modeled mean retreat rate equaled the observed mean retreat rate for a given transect). Uncertainty was tallied using a root mean squared error (RMSE) approach. The RMSE represents cumulative uncertainty from multiple sources and assumes that different sources of error will, at times, cancel each other out. It is therefore not a 'worst-case uncertainty' (in other words, a straight sum of errors) but instead an average uncertainty. Total RMSE increased with SLR rate and varied between +/- 2-3 m to a maximum of +/- 50 m for the extreme 5 m SLR scenario. For more information on model details, data sources, and integration with other parts of the CoSMoS framework, see CoSMoS_3.0_Phase_2_Southern_California_Bight:_Summary_of_data_and_methods (available at https://www.sciencebase.gov/catalog/file/get/57f1d4f3e4b0bc0bebfee139?name=CoSMoS_SoCalv3_Phase2_summary_of_methods.pdf).

This dataset contains projections of shoreline positions and uncertainty bands for future scenarios of sea-level rise. Projections were made using CoSMoS-COAST, a numerical model forced with global-to-local nested wave models and assimilated with lidar-derived shoreline vectors. Details: Projections of shoreline position in Southern California are made for scenarios of 0.25, 0.5, 0.75, 1.0, 1.25, 1.5, 1.75, 2.0, and 5.0 meters of sea-level rise by the year 2100. Four datasets are available for different management conditions: shorelines are allowed to retreat unimpeded past urban tructures ("NO Hold the Line") or are limited to this urban boundary ("Hold the Line"), and shorelines are allowed to progress with projected increases in sediment ("Continued Nourishment") or with no projected increases ("No Nourishment"). Projections are made at CoSMoS Monitoring and Observation Points, which represent shore-normal transects spaced 100 m alongshore. The newly developed CoSMoS-COAST model solves a coupled set of partial differential equations that resembles conservation of sediment for the series of transects. The model is synthesized from several shoreline models in the scientific literature: One-line model formulations (Pelnard-Considere, 1956; Larson and others, 1997; Vitousek and Barnard, 2015) account for longshore transport, equilibrium shoreline-model formulations (Yates and others, 2009) account for wave-driven cross-shore transport, and equilibrium beach-profile formulations (Bruun, 1954; Davidson-Arnot, 2005; Anderson and others, 2015) account for long-term beach-profile adjustments due to sea-level rise. The model uses an extended Kalman filter data-assimilation method to improve the fit of the model to lidar-derived observed shoreline positions. As with previous studies (Hapke and others, 2006), the available shoreline data are spatially and temporally sparse. The data-assimilation method automatically adjusts model parameters and estimates the effects of unresolved processes such as natural and anthropogenic sediment supply. The data-assimilation method used in CoSMoS-COAST has been improved over the original method of Long and Plant (2012). The new method ensures that the coefficients of the equilibrium shoreline-change model retain their preferred sign. Without this improvement, the data-assimilation method was subject to instability. Data assimilation is performed only on days of the simulations where shoreline data are observed. For the shoreline projection period (2015–2100), no such data are available and thus no data-assimilation can be performed. Some of the model components are ignored for certain transects and geographic locations. For example, on small pocket beaches longshore transport is assumed negligible and, therefore, is not computed via the model. Generally, projections were not made at transects where the shoreline is armored and sandy beaches are not present. The formulations that comprise the shoreline model are only valid for sandy beaches. Furthermore, they become invalid as the beach becomes fully eroded and possibly undermines coastal infrastructure. Hence, we have specified a maximally eroded shoreline state that represents the interface of sandy beaches and coastal infrastructure (for example, roads, homes, buildings, sea-walls). If the beach erodes to this line, then it is not permitted to erode further. However, we note that the model can be run without specifying this unerodible line. The shoreline model uses a series of global-to-local nested wave models (such as WaveWatch III and SWAN) forced with Global Climate Model (GCM)-derived wind fields. Historical and projected time series of daily maximum wave height and corresponding wave period and direction from 1990 to 2100 force the shoreline model. The modeled wave predictions are a key input to the CoSMoS-COAST shoreline model because the calculation of both the longshore sediment-transport rate (obtained via the "CERC" equation developed by the Army Corp of Engineers; Shore Protection Manual, 1984) and equilibrium shoreline change (Yates and others, 2009) critically depends on the wave conditions. Notably, variations in nearshore wave angle can significantly affect the calculation of longshore transport. Thus, high-resolution modeling efforts to predict nearshore wave conditions are integral components of the shoreline modeling. Sea level vs. time curves are modeled as a quadratic function. Coefficients of the quadratic curves are obtained via three equations: (1) present sea level is assumed to be at zero elevation, (2) the present rate of sea-level rise is assumed to be 3 mm/yr, which is consistent with values observed at local tide gages, (3) future sea-level elevation at 2100 is either 0.93, 1.25, 1.5, 1.75, 2.0 or 5.0 m based on the scenarios considered. We note that sea level only affects the equilibrium-profile changes derived via the Anderson and others (2015) model. The model uses a forward Euler time-stepping method with a daily time step. The longshore sediment-transport term has the option of using a second-order, implicit time-stepping method (Vitousek and Barnard 2015). However, for these modeling efforts, the forward Euler time-stepping method is sufficient and does not violate numerical stability determined by the Courant-Friedrichs-Lewy CFL condition when using a daily time step on 100 m-spaced transects. The model is composed of numerous scripts and functions implemented in Matlab. The main modeling routines have approximately 1,000-plus lines of code. However, many other functions exist that are necessary to initialize and operate the model. Overall the entire shoreline-modeling system is estimated to have approximately 10,000 lines of code. The modeling system is computationally efficient in comparison to traditional coupled hydrodynamic-wave-morphology models like Delft3D. Century-scale simulations for the entire 400 km coast of Southern California take approximately 20–30 minutes of wall-clock time. This limited computational cost allows the possibility of applying ensemble prediction. Significant uncertainty is associated with the process noise of the model and unresolved coastal processes. This makes estimation of uncertainty difficult. The uncertainty bands predicted here represent 95 percent confidence bands associated with the modeled shoreline fluctuations. Unresolved processes are not accounted for in the uncertainty bands and could lead to significantly more uncertainty than reported in these predictions. These results should be considered preliminary. Although some QA/QC has been completed, the results will improve through time as 1) more shoreline data become available to the data-assimilation method, 2) the models are improved, and 3) ensemble wave-forcing is applied to the model. For more information on model details, data sources, and integration with other parts of the CoSMoS framework, see CoSMoS_3.0_Phase_2_Southern_California_Bight:_Summary_of_data_and_methods (available at https://www.sciencebase.gov/catalog/file/get/57f1d4f3e4b0bc0bebfee139?name=CoSMoS_SoCalv3_Phase2_summary_of_methods.pdf).

This part of the data release presents a digital elevation model (DEM) derived from bathymetry and topography data of the Elwha River delta collected in February 2016. Two dams on the Elwha River, Washington State, USA trapped over 20 million m3 of sediment, reducing downstream sediment fluxes and contributing to erosion of the river's coastal delta. The removal of the Elwha and Glines Canyon dams between 2011 and 2014 induced massive increases in river sediment supply and provided an unprecedented opportunity to examine the response of a delta system to changes in sediment supply. The U.S. Geological Survey developed an integrated research program aimed at understanding the ecosystem responses following dam removal that included regular monitoring of coastal and nearshore bathymetry and topography. As part of this monitoring program, the USGS conducted a bathymetric and topographic survey in the Strait of Juan de Fuca on the Elwha River delta, Washington (USGS Field Activity Number 2016-608-FA). Bathymetry data were collected using two personal watercraft (PWCs) and a kayak, each equipped with single beam echosounders and survey-grade global navigation satellite systems (GNSS). Topography data were collected on foot with GNSS receivers mounted on backpacks. DEM surfaces were produced from all available elevation data using linear interpolation.

This portion of the data release presents sediment grain-size data from samples collected on the Elwha River delta, Washington, in February 2016. Surface sediment was collected from 83 locations using a small ponar, or 'grab' sampler from the R/V Frontier in water depths between 17 and 1 m around the delta. An additional 18 samples were collected by hand at low tide. A handheld global satellite navigation system (GNSS) receiver was used to determine the locations of sediment samples. The grain size distributions of suitable samples were determined using standard techniques developed by the USGS Pacific Coastal and Marine Science Center sediment lab. Grab samples that yielded less than 50 g of sediment were omitted from analysis and are classified as "no sample". The grain-size data are provided in a comma-delimited spreadsheet (.csv).

This part of the data release presents topography data from the Elwha River delta collected in February 2016. Topography data were collected on foot with global navigation satellite system (GNSS) receivers mounted on backpacks.

This portion of the data release presents the raw aerial imagery collected during an Unmanned Aerial System (UAS) survey of the intertidal zone at Post Point, Bellingham Bay, WA, on 2019-06-06. The imagery was acquired using a Department of Interior-owned 3DR Solo quadcopter fitted with a Ricoh GR II digital camera featuring a global shutter. The camera was mounted using a fixed mount on the bottom of the UAS and oriented in an approximately nadir orientation. The UAS was flown on pre-programmed autonomous flight lines which were oriented roughly shore-parallel and were spaced to provide approximately 70 percent overlap between images from adjacent lines. Three flights (F01, F02, F03) covering the survey area were conducted at an approximate altitude of 70 meters above ground level (AGL), resulting in a nominal ground-sample-distance (GSD) of 1.8 centimeters per pixel. Two additional flights (F04, which was aborted early and not included in this data release, and F05) were conducted over a smaller area within the main survey area at an approximate altitude of 35 meters AGL, resulting in a nominal GSD of 0.9 centimeters per pixel. The camera was triggered at 1 Hz using a built-in intervalometer. After acquisition, the images were renamed to include the flight number and acquisition time in the file name. The coordinates of the approximate image acquisition locations were added ('geotagged') to the image metadata (EXIF) using the telemetry log from the UAS onboard single-frequency autonomous GPS. The image EXIF were also updated to include additional information related to the acquisition. Although the images were recorded in both JPG and camera raw (Adobe DNG) formats, only the JPG images are provided in this data release. The data release includes a total of 1,662 JPG images. Images from takeoff and landing sequences were not used for processing and have been omitted from the data release. The images from each flight are provided in a zip file named with the flight number.

Bed sediment samples were collected in south San Francisco Bay on two days in July 2020 to analyze for sediment grain size distributions. Sediment samples were collected from the R/V Snavely near pre-established U.S. Geological Survey instrument moorings using a Gomex or Ponar box corer that was subsampled by scraping the top 0.5 cm of the core. Data are provided in a comma-delimited values spreadsheet.

Water samples were collected in south San Francisco Bay on four days in July 2020. The water samples were collected near pre-established USGS instrument moorings with a Niskin bottle, lowered from the R/V Snavely. Data are provided in comma-delimited values spreadsheets.

Profiles of salinity, temperature, turbidity, and particle size distribution were collected by the U.S. Geological Survey (USGS) Pacific Coastal and Marine Science Center at two locations in south San Francisco Bay. Data were collected at depth intervals ranging between 0.5 and 2 m (depending on total water depth); sensors remained at each depth for 1 minute. Each profile was collected from surface to bed, and the near-surface region was sampled again at the end of the profile to check steady-state conditions. Profiles were collected for 4 days, for about 7.75 hours each day: Jul 21, 22, 24, and 28, 2020. Data files are grouped by site (channel or shallows) and by instrument (CTD or LISST). Users are advised to assess data quality carefully, and to check metadata for instrument information.

Hydrodynamic and sediment transport time-series data, including water depth, velocity, turbidity, suspended particle size, conductivity, and temperature, were collected by the U.S. Geological Survey (USGS) Pacific Coastal and Marine Science Center at two locations in south San Francisco Bay. Data were collected in the channel (one platform) and in the shallows (three co-located platforms) for 2 weeks in July 2020. Data files are grouped by site (channel or shallows). Each site contained instrumentation to collect the data listed, with slight instrument and setup variations between the two sites due to logistics. Users are advised to assess data quality carefully, and to check metadata for instrument information, as platform deployment times and data-processing methods varied.

This dataset consists of short-term (less than 37 years) shoreline change rates for the sheltered north coast of Alaska from Icy Cape to Cape Prince of Wales. Rate calculations were computed within a GIS using the Digital Shoreline Analysis System (DSAS) version 4.4, an ArcGIS extension developed by the U.S. Geological Survey. Rates of shoreline change were calculated using an end point rate-of-change (epr) method based on available shoreline data between 1980 and 2016. A reference baseline was used as the originating point for the orthogonal transects cast by the DSAS software. The transects intersect each shoreline establishing measurement points, which are then used to calculate rates of change.

Sediment grab samples were collected on the barriers and nearby seabed on and around Arey and Barter Islands, Alaska in July 2011 and analyzed for mean grain size. 43 terrestrial grab samples were collected along 14 shore-normal beach transects (12 on Arey Island and 2 on the western spit of Barter Island) at the seaward water line, the berm crest or top of the island, and at the lagoon water line. 11 seabed samples were collected using a small pipe dredge deployed from a small boat; 2 in the vicinity of deployed oceanographic instruments, 8 on the ocean side of Arey Island, and 1 in Arey Lagoon. Two of the grab samples were sieved and analyzed for grain size distributions. Mean grain size of remaining samples was determined from referenced photographs of collected samples taken in the lab (Barnard and others, 2007) using two-dimensional spectral decomposition of sediment images (Buscombe and others, 2010). Results of sieved samples were used for verification of mean grain size values obtained with the image processing algorithm.

This portion of the data release contains image mosaics generated using digitized 1:24K natural color photographs collected in June 2000 by the National Oceanic and Atmospheric Administration (NOAA) National Ocean Service (NOS). These four image mosaics have 1.0 meter-per-pixel resolution, and intermittently cover approximately 53 km (33 mi) of shallow, coastal waters along the west, Kona coast, of the island of Hawai'i, including (from north to south) the Kawaihae, Waikoloa, Kukio, and Kailua-Kona areas. Each digital image mosaic area is downloadable as a separate zip file (area_1m.zip) that contains two versions of the image mosaic--one with and one without a lidar bathymetry shaded-relief image digitally combined with the aerial photography mosaic results. The shaded-relief image was derived using airborne SHOALS (Scanning Hydrographic Operational Lidar Survey) lidar (LIght Detection And Ranging) data collected for the U.S. Geological Survey (USGS) by the U.S. Army Corp of Engineers (USACE) in April 1999. Also included in each zip file is a lower-resolution 'browse' graphic of each image mosaic and associated metadata.

This portion of the data release contains an image mosaic generated using digitized 1:35K natural color photographs collected in September 1993 by the National Oceanic and Atmospheric Administration (NOAA) National Ocean Service (NOS) of the Napili-Honokowai area along the northwest coast of Maui. The area is downloadable as a zip file (napili_honokowai_1m.zip) and includes a high-resolution (1.0 meter per pixel) digital image mosaic, as well as a lower-resolution 'browse' image and associated metadata.

This portion of the data release contains digital image mosaics along the southeast coast of O'ahu. Digital mosaics at 1-foot (0.3048-meter) resolution, including the areas of Waikiki, Diamond Head, Wai'alae, Maunalua Bay, and Portlock, were generated from 1:10K aerial photography and are presented in one zip file (oahu_1ft.zip) that also contains lower-resolution 'browse' graphics of each image mosaic area, as well as associated metadata. All of the digital image areas (from Waikiki to Portlock) were combined into one large digital mosaic at 1-meter resolution, which is presented in another zip file (oahu_1m.zip) that includes a 'browse' graphic of the image mosaic area and associated metadata. The 1-meter resolution digital image mosaic was also combined with lidar bathymetry data to create a shaded-relief image, which is presented in a third zip file (oahu_1m_shaded.zip), along with a 'browse' graphic of the image mosaic area and associated metadata.

This dataset consists of long-term (less than 68 years) shoreline change rates for the sheltered north coast of Alaska from Icy Cape to Cape Prince of Wales. Rate calculations were computed within a GIS using the Digital Shoreline Analysis System (DSAS) version 4.4, an ArcGIS extension developed by the U.S. Geological Survey. Rates of shoreline change were calculated using a linear regression rate-of-change (lrr) method based on available shoreline data between 1948 and 2016. A reference baseline was used as the originating point for the orthogonal transects cast by the DSAS software. The transects intersect each shoreline establishing measurement points, which are then used to calculate rates of change.

This dataset includes multi-sensor core logger (MSCL) data for 39 piston and gravity cores that were collected as part of a groundtruthing survey in September 2019 aboard the R/V Bold Horizon. This dataset is one of several collected as part of the Bureau of Ocean Energy Management (BOEM)-funded California Deepwater Investigations and Groundtruthing (Cal DIG I) project. The purpose of the study is to assess shallow geohazards, benthic habitats, and thereby the potential for alternative energy infrastructure (namely floating wind turbines) offshore south-central California due to its proximity to power grid infrastructure associated with the Morro Bay power plant. These core data provide information about the geology of the seafloor and shallow subsurface offshore of the south-central California coast.

This dataset includes the location and depth information for 39 piston and gravity cores that were collected as part of a groundtruthing survey in September 2019 aboard the R/V Bold Horizon. This dataset is one of several collected as part of the Bureau of Ocean Energy Management (BOEM)-funded California Deepwater Investigations and Groundtruthing (Cal DIG I) project. The purpose of the study is to assess shallow geohazards, benthic habitats, and thereby the potential for alternative energy infrastructure (namely floating wind turbines) offshore south-central California due to its proximity to power grid infrastructure associated with the Morro Bay power plant. These core data provide information about the geology of the seafloor and shallow subsurface offshore of the south-central California coast.

This dataset includes photographs of 39 piston and gravity cores that were collected as part of a groundtruthing survey in September 2019 aboard the R/V Bold Horizon. This dataset is one of several collected as part of the Bureau of Ocean Energy Management (BOEM)-funded California Deepwater Investigations and Groundtruthing (Cal DIG I) project. The purpose of the study is to assess shallow geohazards, benthic habitats, and thereby the potential for alternative energy infrastructure (namely floating wind turbines) offshore south-central California due to its proximity to power grid infrastructure associated with the Morro Bay power plant. These core data provide information about the geology of the seafloor and shallow subsurface offshore of the south-central California coast.

This dataset includes concentrations chloride and sulfate in porewater from piston and gravity cores collected in September 2019 offshore of south-central California aboard the R/V Bold Horizon. This dataset is one of several collected as part of the Bureau of Ocean Energy Management (BOEM)-funded California Deepwater Investigations and Groundtruthing (Cal DIG I) project. The purpose of the study is to assess shallow geohazards, benthic habitats, and thereby the potential for alternative energy infrastructure (namely floating wind turbines) offshore south-central California due to its proximity to power grid infrastructure associated with the Morro Bay power plant. These core data provide information about the geology of the seafloor and shallow subsurface offshore of the south-central California coast.

This dataset includes the location and depth information for 49 vibracores that were collected by the Monterey Bay Aquarium Research Institute (MBARI) in February 2019 aboard the R/V Western Flyer using the remotely operated vehicle (ROV) Doc Ricketts. The collection of these cores was funded entirely by MBARI, and the cores have been donated to the U.S. Geological Survey (USGS). The cores were collected in collaboration with the USGS and the Bureau of Ocean Energy Management (BOEM) and are located in the same study area as the collaborative California Deepwater Investigations and Groundtruthing (Cal DIG I) project. The purpose of the overall Cal DIG I study is to assess shallow geohazards, benthic habitats, and thereby the potential for alternative energy infrastructure (namely floating wind turbines) offshore south-central California due to the study area's proximity to power grid infrastructure associated with the Morro Bay power plant. These data provide information about the geology of the seafloor and shallow subsurface offshore of the south-central California coast.

This dataset includes photographs of 49 vibracores that were collected by the Monterey Bay Aquarium Research Institute (MBARI) in February 2019 aboard the R/V Western Flyer using the remotely operated vehicle (ROV) Doc Ricketts. The collection of these cores was funded entirely by MBARI, and the cores have been donated to the U.S. Geological Survey (USGS). The cores were collected in collaboration with the USGS and the Bureau of Ocean Energy Management (BOEM) and are located in the same study area as the collaborative California Deepwater Investigations and Groundtruthing (Cal DIG I) project. The purpose of the overall Cal DIG I study is to assess shallow geohazards, benthic habitats, and thereby the potential for alternative energy infrastructure (namely floating wind turbines) offshore south-central California due to the study area's proximity to power grid infrastructure associated with the Morro Bay power plant. These data provide information about the geology of the seafloor and shallow subsurface offshore of the south-central California coast.

This dataset includes the location information for 49 vibracores that were collected by the Monterey Bay Aquarium Research Institute (MBARI) in November 2019 aboard the R/V Western Flyer using the remotely operated vehicle (ROV) Doc Ricketts. The collection of these cores was funded entirely by MBARI, and the cores have been donated to the U.S. Geological Survey (USGS). The cores were collected in collaboration with the USGS and the Bureau of Ocean Energy Management (BOEM) and are located in the same study area as the collaborative California Deepwater Investigations and Groundtruthing (Cal DIG I) project. The purpose of the overall Cal DIG I study is to assess shallow geohazards, benthic habitats, and thereby the potential for alternative energy infrastructure (namely floating wind turbines) offshore south-central California due to the study area's proximity to power grid infrastructure associated with the Morro Bay power plant. These data provide information about the geology of the seafloor and shallow subsurface offshore of the south-central California coast.

This portion of the data release contains digital image mosaics along the south coast of Moloka'i. Digital mosaics at 1-foot (0.3048-meter) resolution, including the areas of Pala'au, Umipa'a, Kaunakakai, Kamiloloa, Kawela, Kamalo, and Kalaeloa, were generated from 1:10K aerial photography, and are presented in one zip file (molokai_1ft.zip) that also contains lower-resolution 'browse' graphics of each image-mosaic area, as well as associated metadata. Digital mosaics at 1-meter resolution, including the areas of La'au Point, Hale O Lono, Waiakane, Pala'au, Kaunakakai, Kamiloloa, Kawela, Kamalo, and Puko‘o, were generated from 1:35K aerial photography, and are presented in a second zip file (molokai_1m.zip) that also contains 'browse' graphics of each image mosaic area, as well as associated metadata. Several of the 1-meter-resolution images have been merged together and combined with lidar bathymetry data to create three large shaded-relief images along the southwest (Waiakane to Pala'au), southcentral (Pala'au to Kawela), and southeast (Kawela to Puko'o) coasts of Moloka'i. These shaded-relief images are presented in a third zip file (molokai_1m_shaded.zip), along with 'browse' graphics of each image mosaic area and associated metadata.

These data present suspended particle size distributions collected by the U.S. Geological Survey (USGS) Pacific Coastal and Marine Science Center at three locations in the Sacramento-San Joaquin Delta. Data were collected in Lindsey Slough on April 4 and April 18, 2017, and near the mouth of the Mokelumne River and in Middle River on March 14, 2018 by deploying a Sequoia Scientific Laser In-situ Scattering and Transmissometry instrument (LISST 100x) from a small vessel during the deployment of the hydrographic time series data instruments. At each site, data were collected 1 to 2 times, generally near the water surface, at mid depth, and near the sediment bed. These data were collected as part of a study on the effects of invasive aquatic vegetation on sediment transport in the Sacramento-San Joaquin Delta. Users are advised to check metadata and instrument information carefully for applicable time periods of specific data.

Bathymetric change grids covering the periods of time from 1934 to 2011, from 2011 to 2018, and from 1934 to 2018 are presented. The grids cover a portion of the Mokelumne River, California, starting at its terminus at the San Joaquin River and moving upriver to the confluences of the north and south branches of the Mokelumne. Positive grid values indicate accretion, or a shallowing of the surface bathymetric surface, and negative grid values indicate erosion, or a deepening of the bathymetric surface. Bathymetry data sources include the U.S. Geological Survey, California Department of Water Resources, and NOAA’s National Ocean Service.

Six elevation point cloud files in LAZ format (compressed LAS binary data) are included in this data release: 3 raw point clouds of unclassified and unedited points and 3 modified point clouds that were spatially shifted and edited to remove outliers and spurious elevation values associated with moving water surfaces. An XYZ coordinate shift was applied to each data set in order to register the data sets to an earth-based datum established from surveyed ground control points. Points are unclassified and ground-reflected color values in the red-green-blue (RGB) schema are included. The horizontal coordinate system is WGS84, UTM Zone 7 North meters; vertical coordinates are relative to the WGS84 ellipsoid. Aerial photographs were collected from a small, fixed-wing aircraft over the coast of Barter Island Alaska on three separate dates: July 01 2014, September 07 2014, and July 05 2015. Precise aircraft position information and structure-from-motion photogrammetric methods were combined to derive high-resolution elevation point clouds.

Time series data of water surface elevation, wave height, and water column currents and temperature were acquired at seven locations for 86 days off of Waiakane on the south coast of the island of Molokai, Hawaii, in support of a study on the coastal circulation patterns and the transformation of surface waves over the coral reefs.

This dataset includes photographs of 49 vibracores that were collected by the Monterey Bay Aquarium Research Institute (MBARI) in November 2019 aboard the R/V Western Flyer using the remotely operated vehicle (ROV) Doc Ricketts. The collection of these cores was funded entirely by MBARI, and the cores have been donated to the U.S. Geological Survey (USGS). The cores were collected in collaboration with the USGS and the Bureau of Ocean Energy Management (BOEM) and are located in the same study area as the collaborative California Deepwater Investigations and Groundtruthing (Cal DIG I) project. The purpose of the overall Cal DIG I study is to assess shallow geohazards, benthic habitats, and thereby the potential for alternative energy infrastructure (namely floating wind turbines) offshore south-central California due to the study area's proximity to power grid infrastructure associated with the Morro Bay power plant. These data provide information about the geology of the seafloor and shallow subsurface offshore of the south-central California coast.

This dataset includes raw and processed, high-resolution seismic-reflection data collected in 2009 to explore a possible connection between the San Diego Trough Fault and the San Pedro Basin Fault. The survey is in the San Pedro Basin between Santa Catalina Island and San Pedro, California. The data were collected aboard the U.S. Geological Survey R/V Parke Snavely. The seismic-reflection data were acquired using an EdgeTech 512 chirp subbottom profiler. Subbottom acoustic penetration spanned tens to about 50 meters, variable by location.

This dataset includes reprocessed boomer 3D seismic data collected by the Fugro Consultants Inc. in 2012, offshore Point Sal, central California.

This dataset includes raw, and swell-filtered, high-resolution seismic-reflection data, collected by the U.S. Geological Survey (USGS) in 2011, between Point Sur and Morro Bay in central California.

This dataset includes reprocessed boomer 3D seismic data collected by the Fugro Consultants Inc. in 2012, in San Luis Obispo Bay, offshore of Pismo Beach, central California.

This dataset includes raw and processed, high-resolution seismic-reflection data collected in 2008 to collect information on active offshore faults. The survey area is offshore southern California between Huntington Beach and San Diego. The data were collected aboard the R/V Bold. The seismic-reflection data were acquired using a SIG 2mille minisparker. Subbottom acoustic penetration spanned tens to several hundreds of meters, variable by location.

This dataset includes raw and processed, high-resolution seismic-reflection data collected in 2010 to collect information on active offshore faults. The survey area is offshore southern California between Oceanside and La Jolla. The data were collected aboard the U.S. Geological Survey R/V Parke Snavely. The seismic-reflection data were acquired using a SIG 2mille minisparker. Subbottom acoustic penetration spanned tens to several hundreds of meters, variable by location.

This dataset includes reprocessed boomer 3D seismic data collected by the Fugro Consultants Inc. in 2012, in Estero Bay, offshore of Morro Bay, central California.

This dataset includes raw and processed, high-resolution seismic-reflection data collected in 2011 to collect information on active offshore faults. The survey area is offshore southern California between Long Beach and San Diego. The data were collected aboard the U.S. Geological Survey R/V Parke Snavely. The seismic-reflection data were acquired using a SIG 2mille minisparker system. Subbottom acoustic penetration spanned tens to several hundreds of meters, variable by location and equipment type.

This dataset includes raw and processed, high-resolution seismic-reflection data collected in 2010 to collect information on active offshore faults. The survey is area is offshore southern California between Oceanside and La Jolla. The data were collected aboard the U.S. Geological Survey R/V Parke Snavely. The seismic-reflection data were acquired using an EdgeTech 512 chirp subbottom profiler. Subbottom acoustic penetration spanned tens to about 50 meters, variable by location.

This dataset includes raw and processed, high-resolution seismic-reflection data collected in 2011 to collect information on active offshore faults. The survey area is offshore southern California between Long Beach and San Diego. The data were collected aboard the U.S. Geological Survey R/V Parke Snavely. The seismic-reflection data were acquired using an EdgeTech 512 subbottom profiler. Subbottom acoustic penetration spanned tens to about 50 meters, variable by location.

Structure-from-Motion (SfM) bathymetric maps were created using seafloor images collected using the new 5-camera system SfM Quantitative Underwater Imaging Device with Five Cameras (SQUID-5). Images were collected during July 2019 by towing the SQUID-5 in 3 to 4 meters of water off of Islamorada in the Florida Keys during 3 days. The five cameras were synchronized together and with a survey-grade Global Positioning System (GPS). Images were collected over diverse benthic settings, including living and senile reefs, rubble, and sand. Bathymetric maps were created from the photos using SfM photogrammetric software.

Georeferenced orthophotos were created from structure-from-motion (SfM) data using seafloor images collected using the new 5-camera system SfM Quantitative Underwater Imaging Device with Five Cameras (SQUID-5). Images were collected in July 2019 by towing the SQUID-5 in 3 to 4 meters of water off of Islamorada in the Florida Keys during 3 days. The five cameras were synchronized together and with a survey-grade Global Positioning System (GPS). Images were collected over diverse benthic settings, including living and senile reefs, rubble, and sand. Orthomosaics were created from the photos using SfM photogrammetric software.

Structure-from-Motion (SfM) point clouds were created from seafloor images collected using the new 5-camera system SfM Quantitative Underwater Imaging Device with Five Cameras (SQUID-5). Images were collected in July 2019 by towing the SQUID-5 in 3 to 4 meters of water off of Islamorada in the Florida Keys during 3 days. The five cameras were synchronized together and with a survey-grade Global Positioning System (GPS). Images were collected over diverse benthic settings, including living and senile reefs, rubble, and sand. Point clouds were created from the photos using SfM photogrammetric software.

Underwater photos were collected using a new 5-camera system, the Structure-from-Motion (SfM) Quantitative Underwater Imaging Device with Five Cameras (SQUID-5). Images were collected in July 2019 by towing the SQUID-5 in 3 to 4 meters of water off of Islamorada in the Florida Keys. The five cameras were synchronized together and with a survey-grade Global Positioning System (GPS). Images were collected over diverse benthic settings, including living and senile reefs, rubble, and sand. The images are presented here in zipped files grouped by Julian day. The SQUID-5 system records images in bitmap (.bmp) format to maintain the highest resolution and bit depth, and these were the files used in SfM processing. The zip files also contain portable network graphics (.png) files, an open-source format, and which include Exif metadata, including GPS date, time, and latitude and longitude, copyright, keywords, and other fields.

These metadata describe acoustic backscatter data collected during a 2008 SWATHPlus-M survey offshore Tijuana River Estuary, California. Data were collected and processed by the U.S. Geological Survey (USGS), Pacific Coastal and Marine Science Center (PCMSC) with fieldwork activity number S-5-08-SC. The acoustic backscatter data are provided as a GeoTIFF image.

These metadata describe bathymetry data collected during a 2008 SWATHPlus-M survey offshore Tijuana River Estuary, California. Data were collected and processed by the U.S. Geological Survey (USGS), Pacific Coastal and Marine Science Center (PCMSC) with fieldwork activity number S-5-08-SC. The bathymetry data are provided as GeoTIFF images in UTM, zone 11, NAD83 coordinates, vertically referenced to both NAVD88 and WGS84. A standard deviation grid is also provided.

Time-series data of water temperature were collected at 33 locations along the west coast of the Island of Hawaii, including within Kaloko-Honokohau National Historical Park (KAHO), and Puu o Honaunau National Historical Park (PUHO) between 2010 and 2013 in nearshore coral reef and anchialine pool settings. Temperature sensors were attached to fossil limestone, rock or dead coral within otherwise healthy coral reef settings spanning water depths of 0.1 to 8.84 m (0.3 to 29.0 ft). Continuous measurements were made every 10 or 20 minutes. Due to the large amount of data, the dataset has been split into three files. WaterTempTimeSeries_KAHO-KC.csv includes data from nearshore coral reef locations within Kaloko Bay, which lies within the KAHO boundaries; WaterTempTimeSeries_KAHO.csv includes data from nearshore reef locations and two anchialine pools within the remainder of KAHO; WaterTempTimeSeries_westHawaii.csv includes data from nearshore coral reef locations along west Hawaii coastline, outside of the KAHO boundaries.

Chronology and time-series geochemistry data of a coral core collected from Olowalu, West Maui, Hawaii. The chronology is based on density banding, radiocarbon bomb-curve, and uranium thorium dating techniques. The geochemistry time-series data contains major and minor elements over the length of the coral life span, as measured from laser ablation inductively coupled mass spectrometry (LA-ICP-MS).

Multibeam bathymetry data were collected along the Queen Charlotte-Fairweather Fault between Icy Point and Dixon Entrance, offshore southeastern Alaska from 2016-05-17 to 2016-06-12. Data were collected aboard the Alaska Department of Fish and Game R/V Medeia using a Reson SeaBat 7160 multibeam echosounder, Reson 7k Control Center, and HYPACK. This data release contains approximately 4,600 square kilometers of multibeam bathymetry and backscatter data, organized into zip files for each Julian Day of the survey.

Geophysical properties (P-wave velocity, gamma ray density, and magnetic susceptibility), geochronologic (radiocarbon, excess Lead-210, and Cesium-137), and geochemical data (organic carbon content and 60 element contents) are reported for select vibracores collected aboard the S/V Retriever October 17-20, 2016, in San Pablo Bay, California. Geophysical properties were measured with a Geotek Multi-Sensor Core Logger (MSCL). Radiocarbon was measured by accelerator mass spectrometry (AMS). Excess Lead-210 and Cesium-137 activities were measured by gamma-ray counting in a high purity, low background germanium well detector (HPGe). Total organic carbon was measured in bulk sediment. Element contents were determined on the less than 0.063 mm (fine) size fraction of sediment by inductively coupled plasma mass spectrometry (ICP-MS).

This data release provides flooding extent polygons (flood masks) and depth values (flood points) based on wave-driven total water levels for 22 locations within the States of Hawaii and Florida, the Territories of Guam, American Samoa, Puerto Rico, and the U.S. Virgin Islands, and the Commonwealth of the Northern Mariana Islands. For each of the 22 locations there are eight associated flood mask polygons and flood depth point files: one for each four nearshore wave energy return periods (rp; 10-, 50-, 100-, and 500-years) and both with (wrf) and without (worf) the presence of coral reefs. These flood masks can be combined with economic, ecological, and engineering tools to provide a rigorous financial valuation of the coastal protection benefits of coral reefs of the United States, Territories, and Affiliated Islands. The degradation of coastal habitats, particularly coral reefs, raises risks by exposing communities to flooding hazards. The protective services of these natural defenses are not assessed in the same rigorous, economic terms as artificial defenses such as seawalls, and therefore often not considered in decision-making. Engineering, ecologic, social, and economic tools were combined to provide a quantitative valuation of the coastal protection benefits of the coral reefs of the United States. The goal of this effort was to identify how, where, and when coral reefs provide the most significant coastal flood reduction benefits socially and economically under current and future climate change scenarios. A risk-based valuation framework to estimate the risk reduction benefits from coral reefs and provide annual expected benefits in social and economic terms was followed. The methods follow a sequence of steps integrating physics-based hydrodynamic modeling, quantitative geospatial modeling, and economic and social analyses to quantify the hazard, the role of coral reefs in reducing the hazard, and the resulting consequences (described in Storlazzi and others, 2019).

Meteorological data, including wind speed, wind direction, air temperature, relative humidity, and air pressure, were collected by the U.S. Geological Survey (USGS) Pacific Coastal and Marine Science Center at a site located in Grizzly Bay, California. A Vaisala WXT530 meteorological station was mounted atop of a dolphin-type mooring structure, from January to June 2020. The data were truncated based on deployment and recovery times of hydrodynamic time-series data, spurious data points from the wind sensor were removed, and the file was written to netCDF. Spurious points were identified based on a recorded wind speed of 0. These points were set to NaN (Not a Number). Users are advised to assess data quality carefully, and to check metadata for additional instrument information.

This data layer (PAC_PRS.txt) is one of five point coverages of known sediment samples, inspections, and probes from the usSEABED data collection for the U.S. Pacific continental margin integrated using the dbSEABED software system. This data layer represents the parsed (PRS) output of the dbSEABED mining software. It contains the numeric results parsed from text-based descriptions held in the data resource files (DRF). Because it relies on descriptions, the PRS data are less precise than the extracted data (PAC_EXT), but may include information on outsized elements and consolidation that are often not in lab-analyzed data. This file contains the same data fields as the extracted (PAC_EXT) and calculated (PAC_CLC) data files, and the three files may be combined.

There are critical needs for a nationwide compilation of reliable shoreline data. To meet these needs, the USGS has produced a comprehensive database of digital vector shorelines by compiling shoreline positions from pre-existing historical shoreline databases and by generating historical and modern shoreline data. Shorelines are compiled by state and generally correspond to one of four time periods: 1800s, 1920s-1930s, 1970s, and 1998-2002. Each shoreline may represent a compilation of data from one or more sources for one or more dates provided by one or more agencies. Details regarding source are provided in the 'Data Quality Information' section of this metadata report. Shoreline vectors derived from historic sources (first three time periods) represent the high water line at the time of the survey, whereas modern shorelines (final time period) represent the mean high water line.

There are critical needs for a nationwide compilation of reliable shoreline data. To meet these needs, the U.S. Geological Survey (USGS) has produced a comprehensive database of digital vector shorelines by compiling shoreline positions from pre-existing historical shoreline databases and by generating historical and modern shoreline data. Shorelines are compiled by state and generally correspond to one of four time periods: 1800s, 1920s-1930s, 1970s, and 1998-2002. Each shoreline may represent a compilation of data from one or more sources for one or more dates provided by one or more agencies. Details regarding source are provided in the 'Data Quality Information' section of this metadata report. Shoreline vectors derived from historic sources (first three time periods) represent the high water line at the time of the survey, whereas modern shorelines (final time period) represent the mean high water line.

Rates of long-term and short-term shoreline change were generated in a GIS using the Digital Shoreline Analysis System (DSAS) version 3.0; An ArcGIS extension for calculating shoreline change: U.S. Geological Survey Open-File Report 2005-1304, Thieler, E.R., Himmelstoss, E.A., Zichichi, J.L., and Miller, T.M. The extension is designed to efficiently lead a user through the major steps of shoreline change analysis. This extension to ArcGIS contains three main components that define a baseline, generate orthogonal transects at a user-defined separation along the coast, and calculate rates of change (linear regression, endpoint rate, average of rates, average of endpoints, jackknife).

There are critical needs for a nationwide compilation of reliable shoreline data. To meet these needs, the U.S. Geological Survey (USGS) has produced a comprehensive database of digital vector shorelines by compiling shoreline positions from pre-existing historical shoreline databases and by generating historical and modern shoreline data. Shorelines are compiled by state and generally correspond to one of four time periods: 1800s, 1920s-1930s, 1970s, and 1998-2002. Each shoreline may represent a compilation of data from one or more sources for one or more dates provided by one or more agencies. Details regarding source are provided in the 'Data Quality Information' section of this metadata report. Shoreline vectors derived from historic sources (first three time periods) represent the high water line at the time of the survey, whereas modern shorelines (final time period) represent the mean high water line.

There are critical needs for a nationwide compilation of reliable shoreline data. To meet these needs, the U.S. Geological Survey (USGS) has produced a comprehensive database of digital vector shorelines by compiling shoreline positions from pre-existing historical shoreline databases and by generating historical and modern shoreline data. Shorelines are compiled by state and generally correspond to one of four time periods: 1800s, 1920s-1930s, 1970s, and 1998-2002. Each shoreline may represent a compilation of data from one or more sources for one or more dates provided by one or more agencies. Details regarding source are provided in the 'Data Quality Information' section of this metadata report. Shoreline vectors derived from historic sources (first three time periods) represent the high water line at the time of the survey, whereas modern shorelines (final time period) represent the mean high water line.

There are critical needs for a nationwide compilation of reliable shoreline data. To meet these needs, the U.S. Geological Survey (USGS) has produced a comprehensive database of digital vector shorelines by compiling shoreline positions from pre-existing historical shoreline databases and by generating historical and modern shoreline data. Shorelines are compiled by state and generally correspond to one of four time periods: 1800s, 1920s-1930s, 1970s, and 1998-2002. Each shoreline may represent a compilation of data from one or more sources for one or more dates provided by one or more agencies. Details regarding source are provided in the 'Data Quality Information' section of this metadata report. Shoreline vectors derived from historic sources (first three time periods) represent the high water line at the time of the survey, whereas modern shorelines (final time period) represent the mean high water line.

There are critical needs for a nationwide compilation of reliable shoreline data. To meet these needs, the U.S. Geological Survey (USGS) has produced a comprehensive database of digital vector shorelines by compiling shoreline positions from pre-existing historical shoreline databases and by generating historical and modern shoreline data. Shorelines are compiled by state and generally correspond to one of four time periods: 1800s, 1920s-1930s, 1970s, and 1998-2002. Each shoreline may represent a compilation of data from one or more sources for one or more dates provided by one or more agencies. Details regarding source are provided in the 'Data Quality Information' section of this metadata report. Shoreline vectors derived from historic sources (first three time periods) represent the high water line at the time of the survey, whereas modern shorelines (final time period) represent the mean high water line.

Rates of long-term and short-term shoreline change were generated in a GIS using the Digital Shoreline Analysis System (DSAS) version 3.0; An ArcGIS extension for calculating shoreline change: U.S. Geological Survey Open-File Report 2005-1304, Thieler, E.R., Himmelstoss, E.A., Zichichi, J.L., and Miller, T.M. The extension is designed to efficiently lead a user through the major steps of shoreline change analysis. This extension to ArcGIS contains three main components that define a baseline, generate orthogonal transects at a user-defined separation along the coast, and calculate rates of change (linear regression, endpoint rate, average of rates, average of endpoints, jackknife).

The USGS has produced a comprehensive database of digital vector shorelines by compiling shoreline positions from pre-existing historical shoreline databases and by generating historical and modern shoreline data. Shorelines are compiled by state and generally correspond to one of four time periods: 1800s, 1920s-1930s, 1970s, and 1998-2002. These shorelines were used to calculate long-term and short-term change rates in a GIS using the Digital Shoreline Analysis System (DSAS) version 3.0; An ArcGIS extension for calculating shoreline change: U.S. Geological Survey Open-File Report 2005-1304, Thieler, E.R., Himmelstoss, E.A., Zichichi, J.L., and Miller, T.M. Shoreline vectors derived from historic sources (first three time periods) represent the high water line (HWL) at the time of the survey, whereas modern shorelines (final time period) represent the mean high water line (MHW). Changing the shoreline definition from a proxy-based physical feature that is uncontrolled in terms of an elevation datum (HWL) to a datum-based shoreline defined by an elevation contour (MHW) has important implications with regard to inferred changes in shoreline position and calculated rates of change. This proxy-datum offset is particularly important when averaging shoreline change rates alongshore. Since the proxy-datum offset is a bias, virtually always acting in the same direction, the error associated with the apparent shoreline change rate shift does not cancel during averaging and it is important to quantify the bias in order to account for the rate shift. The shoreline change rates presented in this report have been calculated by accounting for the proxy-datum bias.

Rates of long-term and short-term shoreline change were generated in a GIS using the Digital Shoreline Analysis System (DSAS) version 3.0; An ArcGIS extension for calculating shoreline change: U.S. Geological Survey Open-File Report 2005-1304, Thieler, E.R., Himmelstoss, E.A., Zichichi, J.L., and Miller, T.M. The extension is designed to efficiently lead a user through the major steps of shoreline change analysis. This extension to ArcGIS contains three main components that define a baseline, generate orthogonal transects at a user-defined separation along the coast, and calculate rates of change (linear regression, endpoint rate, average of rates, average of endpoints, jackknife).

Rates of long-term and short-term shoreline change were generated in a GIS using the Digital Shoreline Analysis System (DSAS) version 3.0; An ArcGIS extension for calculating shoreline change: U.S. Geological Survey Open-File Report 2005-1304, Thieler, E.R., Himmelstoss, E.A., Zichichi, J.L., and Miller, T.M. The extension is designed to efficiently lead a user through the major steps of shoreline change analysis. This extension to ArcGIS contains three main components that define a baseline, generate orthogonal transects at a user-defined separation along the coast, and calculate rates of change (linear regression, endpoint rate, average of rates, average of endpoints, jackknife).

Rates of long-term and short-term shoreline change were generated in a GIS using the Digital Shoreline Analysis System (DSAS) version 3.0; An ArcGIS extension for calculating shoreline change: U.S. Geological Survey Open-File Report 2005-1304, Thieler, E.R., Himmelstoss, E.A., Zichichi, J.L., and Miller, T.M. The extension is designed to efficiently lead a user through the major steps of shoreline change analysis. This extension to ArcGIS contains three main components that define a baseline, generate orthogonal transects at a user-defined separation along the coast, and calculate rates of change (linear regression, endpoint rate, average of rates, average of endpoints, jackknife).

Rates of long-term and short-term shoreline change were generated in a GIS using the Digital Shoreline Analysis System (DSAS) version 3.0; An ArcGIS extension for calculating shoreline change: U.S. Geological Survey Open-File Report 2005-1304, Thieler, E.R., Himmelstoss, E.A., Zichichi, J.L., and Miller, T.M. The extension is designed to efficiently lead a user through the major steps of shoreline change analysis. This extension to ArcGIS contains three main components that define a baseline, generate orthogonal transects at a user-defined separation along the coast, and calculate rates of change (linear regression, endpoint rate, average of rates, average of endpoints, jackknife).

There are critical needs for a nationwide compilation of reliable shoreline data. To meet these needs, the U.S. Geological Survey (USGS) has produced a comprehensive database of digital vector shorelines by compiling shoreline positions from pre-existing historical shoreline databases and by generating historical and modern shoreline data. Shorelines are compiled by state and generally correspond to one of four time periods: 1800s, 1920s-1930s, 1970s, and 1998-2002. Each shoreline may represent a compilation of data from one or more sources for one or more dates provided by one or more agencies. Details regarding source are provided in the 'Data Quality Information' section of this metadata report. Shoreline vectors derived from historic sources (first three time periods) represent the high water line at the time of the survey, whereas modern shorelines (final time period) represent the mean high water line.

There are critical needs for a nationwide compilation of reliable shoreline data. To meet these needs, the U.S. Geological Survey (USGS) has produced a comprehensive database of digital vector shorelines by compiling shoreline positions from pre-existing historical shoreline databases and by generating historical and modern shoreline data. Shorelines are compiled by state and generally correspond to one of four time periods: 1800s, 1920s-1930s, 1970s, and 1998-2002. Each shoreline may represent a compilation of data from one or more sources for one or more dates provided by one or more agencies. Details regarding source are provided in the 'Data Quality Information' section of this metadata report. Shoreline vectors derived from historic sources (first three time periods) represent the high water line at the time of the survey, whereas modern shorelines (final time period) represent the mean high water line.

Rates of long-term and short-term shoreline change were generated in a GIS using the Digital Shoreline Analysis System (DSAS) version 3.0; An ArcGIS extension for calculating shoreline change: U.S. Geological Survey Open-File Report 2005-1304, Thieler, E.R., Himmelstoss, E.A., Zichichi, J.L., and Miller, T.M. The extension is designed to efficiently lead a user through the major steps of shoreline change analysis. This extension to ArcGIS contains three main components that define a baseline, generate orthogonal transects at a user-defined separation along the coast, and calculate rates of change (linear regression, endpoint rate, average of rates, average of endpoints, jackknife).

The USGS has produced a comprehensive database of digital vector shorelines by compiling shoreline positions from pre-existing historical shoreline databases and by generating historical and modern shoreline data. Shorelines are compiled by state and generally correspond to one of four time periods: 1800s, 1920s-1930s, 1970s, and 1998-2002. These shorelines were used to calculate long-term and short-term change rates in a GIS using the Digital Shoreline Analysis System (DSAS) version 3.0; An ArcGIS extension for calculating shoreline change: U.S. Geological Survey Open-File Report 2005-1304, Thieler, E.R., Himmelstoss, E.A., Zichichi, J.L., and Miller, T.M. Shoreline vectors derived from historic sources (first three time periods) represent the high water line (HWL) at the time of the survey, whereas modern shorelines (final time period) represent the mean high water line (MHW). Changing the shoreline definition from a proxy-based physical feature that is uncontrolled in terms of an elevation datum (HWL) to a datum-based shoreline defined by an elevation contour (MHW) has important implications with regard to inferred changes in shoreline position and calculated rates of change. This proxy-datum offset is particularly important when averaging shoreline change rates alongshore. Since the proxy-datum offset is a bias, virtually always acting in the same direction, the error associated with the apparent shoreline change rate shift does not cancel during averaging and it is important to quantify the bias in order to account for the rate shift. The shoreline change rates presented in this report have been calculated by accounting for the proxy-datum bias.

Rates of long-term and short-term shoreline change were generated in a GIS using the Digital Shoreline Analysis System (DSAS) version 3.0; An ArcGIS extension for calculating shoreline change: U.S. Geological Survey Open-File Report 2005-1304, Thieler, E.R., Himmelstoss, E.A., Zichichi, J.L., and Miller, T.M. The extension is designed to efficiently lead a user through the major steps of shoreline change analysis. This extension to ArcGIS contains three main components that define a baseline, generate orthogonal transects at a user-defined separation along the coast, and calculate rates of change (linear regression, endpoint rate, average of rates, average of endpoints, jackknife).

Rates of long-term and short-term shoreline change were generated in a GIS using the Digital Shoreline Analysis System (DSAS) version 3.0; An ArcGIS extension for calculating shoreline change: U.S. Geological Survey Open-File Report 2005-1304, Thieler, E.R., Himmelstoss, E.A., Zichichi, J.L., and Miller, T.M. The extension is designed to efficiently lead a user through the major steps of shoreline change analysis. This extension to ArcGIS contains three main components that define a baseline, generate orthogonal transects at a user-defined separation along the coast, and calculate rates of change (linear regression, endpoint rate, average of rates, average of endpoints, jackknife).

Rates of long-term and short-term shoreline change were generated in a GIS using the Digital Shoreline Analysis System (DSAS) version 3.0; An ArcGIS extension for calculating shoreline change: U.S. Geological Survey Open-File Report 2005-1304, Thieler, E.R., Himmelstoss, E.A., Zichichi, J.L., and Miller, T.M. The extension is designed to efficiently lead a user through the major steps of shoreline change analysis. This extension to ArcGIS contains three main components that define a baseline, generate orthogonal transects at a user-defined separation along the coast, and calculate rates of change (linear regression, endpoint rate, average of rates, average of endpoints, jackknife).

Rates of long-term and short-term shoreline change were generated in a GIS using the Digital Shoreline Analysis System (DSAS) version 3.0; An ArcGIS extension for calculating shoreline change: U.S. Geological Survey Open-File Report 2005-1304, Thieler, E.R., Himmelstoss, E.A., Zichichi, J.L., and Miller, T.M. The extension is designed to efficiently lead a user through the major steps of shoreline change analysis. This extension to ArcGIS contains three main components that define a baseline, generate orthogonal transects at a user-defined separation along the coast, and calculate rates of change (linear regression, endpoint rate, average of rates, average of endpoints, jackknife).

There are critical needs for a nationwide compilation of reliable shoreline data. To meet these needs, the U.S. Geological Survey (USGS) has produced a comprehensive database of digital vector shorelines by compiling shoreline positions from pre-existing historical shoreline databases and by generating historical and modern shoreline data. Shorelines are compiled by state and generally correspond to one of four time periods: 1800s, 1920s-1930s, 1970s, and 1998-2002. Each shoreline may represent a compilation of data from one or more sources for one or more dates provided by one or more agencies. Details regarding source are provided in the 'Data Quality Information' section of this metadata report. Shoreline vectors derived from historic sources (first three time periods) represent the high water line at the time of the survey, whereas modern shorelines (final time period) represent the mean high water line.

There are critical needs for a nationwide compilation of reliable shoreline data. To meet these needs, the U.S. Geological Survey (USGS) has produced a comprehensive database of digital vector shorelines by compiling shoreline positions from pre-existing historical shoreline databases and by generating historical and modern shoreline data. Shorelines are compiled by state and generally correspond to one of four time periods: 1800s, 1920s-1930s, 1970s, and 1998-2002. Each shoreline may represent a compilation of data from one or more sources for one or more dates provided by one or more agencies. Details regarding source are provided in the 'Data Quality Information' section of this metadata report. Shoreline vectors derived from historic sources (first three time periods) represent the high water line at the time of the survey, whereas modern shorelines (final time period) represent the mean high water line.

There are critical needs for a nationwide compilation of reliable shoreline data. To meet these needs, the U.S. Geological Survey (USGS) has produced a comprehensive database of digital vector shorelines by compiling shoreline positions from pre-existing historical shoreline databases and by generating historical and modern shoreline data. Shorelines are compiled by state and generally correspond to one of four time periods: 1800s, 1920s-1930s, 1970s, and 1998-2002. Each shoreline may represent a compilation of data from one or more sources for one or more dates provided by one or more agencies. Details regarding source are provided in the 'Data Quality Information' section of this metadata report. Shoreline vectors derived from historic sources (first three time periods) represent the high water line at the time of the survey, whereas modern shorelines (final time period) represent the mean high water line.

There are critical needs for a nationwide compilation of reliable shoreline data. To meet these needs, the U.S. Geological Survey (USGS) has produced a comprehensive database of digital vector shorelines by compiling shoreline positions from pre-existing historical shoreline databases and by generating historical and modern shoreline data. Shorelines are compiled by state and generally correspond to one of four time periods: 1800s, 1920s-1930s, 1970s, and 1998-2002. Each shoreline may represent a compilation of data from one or more sources for one or more dates provided by one or more agencies. Details regarding source are provided in the 'Data Quality Information' section of this metadata report. Shoreline vectors derived from historic sources (first three time periods) represent the high water line at the time of the survey, whereas modern shorelines (final time period) represent the mean high water line.

Rates of long-term and short-term shoreline change were generated in a GIS using the Digital Shoreline Analysis System (DSAS) version 3.0; An ArcGIS extension for calculating shoreline change: U.S. Geological Survey Open-File Report 2005-1304, Thieler, E.R., Himmelstoss, E.A., Zichichi, J.L., and Miller, T.M. The extension is designed to efficiently lead a user through the major steps of shoreline change analysis. This extension to ArcGIS contains three main components that define a baseline, generate orthogonal transects at a user-defined separation along the coast, and calculate rates of change (linear regression, endpoint rate, average of rates, average of endpoints, jackknife).

The USGS has produced a comprehensive database of digital vector shorelines by compiling shoreline positions from pre-existing historical shoreline databases and by generating historical and modern shoreline data. Shorelines are compiled by state and generally correspond to one of four time periods: 1800s, 1920s-1930s, 1970s, and 1998-2002. These shorelines were used to calculate long-term and short-term change rates in a GIS using the Digital Shoreline Analysis System (DSAS) version 3.0; An ArcGIS extension for calculating shoreline change: U.S. Geological Survey Open-File Report 2005-1304, Thieler, E.R., Himmelstoss, E.A., Zichichi, J.L., and Miller, T.M. Shoreline vectors derived from historic sources (first three time periods) represent the high water line (HWL) at the time of the survey, whereas modern shorelines (final time period) represent the mean high water line (MHW). Changing the shoreline definition from a proxy-based physical feature that is uncontrolled in terms of an elevation datum (HWL) to a datum-based shoreline defined by an elevation contour (MHW) has important implications with regard to inferred changes in shoreline position and calculated rates of change. This proxy-datum offset is particularly important when averaging shoreline change rates alongshore. Since the proxy-datum offset is a bias, virtually always acting in the same direction, the error associated with the apparent shoreline change rate shift does not cancel during averaging and it is important to quantify the bias in order to account for the rate shift. The shoreline change rates presented in this report have been calculated by accounting for the proxy-datum bias.

Rates of long-term and short-term shoreline change were generated in a GIS using the Digital Shoreline Analysis System (DSAS) version 3.0; An ArcGIS extension for calculating shoreline change: U.S. Geological Survey Open-File Report 2005-1304, Thieler, E.R., Himmelstoss, E.A., Zichichi, J.L., and Miller, T.M. The extension is designed to efficiently lead a user through the major steps of shoreline change analysis. This extension to ArcGIS contains three main components that define a baseline, generate orthogonal transects at a user-defined separation along the coast, and calculate rates of change (linear regression, endpoint rate, average of rates, average of endpoints, jackknife).

Rates of long-term and short-term shoreline change were generated in a GIS using the Digital Shoreline Analysis System (DSAS) version 3.0; An ArcGIS extension for calculating shoreline change: U.S. Geological Survey Open-File Report 2005-1304, Thieler, E.R., Himmelstoss, E.A., Zichichi, J.L., and Miller, T.M. The extension is designed to efficiently lead a user through the major steps of shoreline change analysis. This extension to ArcGIS contains three main components that define a baseline, generate orthogonal transects at a user-defined separation along the coast, and calculate rates of change (linear regression, endpoint rate, average of rates, average of endpoints, jackknife).

Rates of long-term and short-term shoreline change were generated in a GIS using the Digital Shoreline Analysis System (DSAS) version 3.0; An ArcGIS extension for calculating shoreline change: U.S. Geological Survey Open-File Report 2005-1304, Thieler, E.R., Himmelstoss, E.A., Zichichi, J.L., and Miller, T.M. The extension is designed to efficiently lead a user through the major steps of shoreline change analysis. This extension to ArcGIS contains three main components that define a baseline, generate orthogonal transects at a user-defined separation along the coast, and calculate rates of change (linear regression, endpoint rate, average of rates, average of endpoints, jackknife).

Coastal Slope along the coastal zone of Sand Island (Oahu), Hawaii

This portion of the data release presents sediment deposition in the estuary as measured using rod surface elevation tables (RSETs) at fifteen locations throughout the Elwha River estuary, Washington, from August 2011 to June 2014 (no associated USGS Field Activities numbers because data were collected predominantly by biologists from the Lower Elwha Klallam Tribe). The locations of the RSETs were determined with a hand-held global positioning system (GPS). We measured sediment deposition from 2011 to 2013 using the RSET table and pins at 36 points around each RSET. Because of extensive sediment deposition in the estuary, we needed to modify our methodology in 2014. We fabricated a new attachment for the RSET base that consisted of a 30 cm horizontal measuring rod and level, which replaced the RSET table. We leveled the rod with respect to the RSET receiver and measured the distance from the end of the rod to the sediment surface at six locations around the center point using a weighted line. The two methods were calibrated so that measurements were comparable. Measurements were taken approximately every two months in 2011 and 2012 and before and after the rainy and freshet (snowmelt) seasons in 2013 and 2014. The sediment deposition data are provided in a comma-delimited spreadsheet (.csv).

This portion of the data release presents sediment grain-size data from samples collected in the Elwha River estuary, Washington, in July 2013 and June 2014 (USGS Field Activities L-15-13-PS and 2014-628-FA). Surface sediment was collected from one location in 2013 and five locations in 2014 using a using a push core. The locations of grab samples were determined with a hand-held global positioning system (GPS). The cores were split into one- to three-centimeter sections. The grain-size distributions of samples were determined using standard techniques developed by the USGS Pacific Coastal and Marine Science Center sediment lab. Size fractions are defined as gravel (greater than 2 mm), sand (63 micron to 2 mm), silt (4 micron to 63 micron), clay (less than 4 micron), and mud (less than 63 micron). The grain-size data are provided in a comma-delimited spreadsheet (.csv).

Estuary vegetation cover delineated from 30 August 2012 0.15-meter-resolution NPS Elwha PlaneCam aerial imagery at a scale of 1:1500.

Maximum depth of flooding surface (in cm) in the region landward of the present day shoreline that is inundated for the storm condition and sea-level rise (SLR) scenario indicated. Note: Duration datasets may have occasional gaps in open-coast sections. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal planners with critical storm-hazards information that can be used to increase public safety, mitigate physical damages, and more effectively manage and allocate resources within complex coastal settings. Model details and data sources are outlined in CoSMoS_3.0_Phase_2_Southern_California_Bight:_Summary_of_data_and_methods (available at https://www.sciencebase.gov/catalog/file/get/57f1d4f3e4b0bc0bebfee139?name=CoSMoS_SoCalv3_Phase2_summary_of_methods.pdf). Phase 2 data for Southern California include flood-hazard information for the coast from the border of Mexico to Pt. Conception to include the Channel Islands. Please read the Summary of Methods and inspect output carefully. Data are complete for the information presented.

Maximum depth of flooding surface (in cm) in the region landward of the present day shoreline that is inundated for the storm condition and sea-level rise (SLR) scenario indicated. Note: Duration datasets may have occasional gaps in open-coast sections. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal planners with critical storm-hazards information that can be used to increase public safety, mitigate physical damages, and more effectively manage and allocate resources within complex coastal settings. Model details and data sources are outlined in CoSMoS_3.0_Phase_2_Southern_California_Bight:_Summary_of_data_and_methods (available at https://www.sciencebase.gov/catalog/file/get/57f1d4f3e4b0bc0bebfee139?name=CoSMoS_SoCalv3_Phase2_summary_of_methods.pdf). Phase 2 data for Southern California include flood-hazard information for the coast from the border of Mexico to Pt. Conception to include the Channel Islands. Please read the Summary of Methods and inspect output carefully. Data are complete for the information presented.

Maximum depth of flooding surface (in cm) in the region landward of the present day shoreline that is inundated for the storm condition and sea-level rise (SLR) scenario indicated. Note: Duration datasets may have occasional gaps in open-coast sections. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal planners with critical storm-hazards information that can be used to increase public safety, mitigate physical damages, and more effectively manage and allocate resources within complex coastal settings. Model details and data sources are outlined in CoSMoS_3.0_Phase_2_Southern_California_Bight:_Summary_of_data_and_methods (available at https://www.sciencebase.gov/catalog/file/get/57f1d4f3e4b0bc0bebfee139?name=CoSMoS_SoCalv3_Phase2_summary_of_methods.pdf). Phase 2 data for Southern California include flood-hazard information for the coast from the border of Mexico to Pt. Conception to include the Channel Islands. Please read the Summary of Methods and inspect output carefully. Data are complete for the information presented.

Maximum depth of flooding surface (in cm) in the region landward of the present day shoreline that is inundated for the storm condition and sea-level rise (SLR) scenario indicated. Note: Duration datasets may have occasional gaps in open-coast sections. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal planners with critical storm-hazards information that can be used to increase public safety, mitigate physical damages, and more effectively manage and allocate resources within complex coastal settings. Model details and data sources are outlined in CoSMoS_3.0_Phase_2_Southern_California_Bight:_Summary_of_data_and_methods (available at https://www.sciencebase.gov/catalog/file/get/57f1d4f3e4b0bc0bebfee139?name=CoSMoS_SoCalv3_Phase2_summary_of_methods.pdf). Phase 2 data for Southern California include flood-hazard information for the coast from the border of Mexico to Pt. Conception to include the Channel Islands. Please read the Summary of Methods and inspect output carefully. Data are complete for the information presented.

Geographic extent of projected coastal flooding, low-lying vulnerable areas, and maxium/minimum flood potential (flood uncertainty) associated with the sea-level rise and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal planners with critical storm-hazards information that can be used to increase public safety, mitigate physical damages, and more effectively manage and allocate resources within complex coastal settings. Model details and data sources are outlined in CoSMoS_3.0_Phase_2_Southern_California_Bight:_Summary_of_data_and_methods (available at https://www.sciencebase.gov/catalog/file/get/57f1d4f3e4b0bc0bebfee139?name=CoSMoS_SoCalv3_Phase2_summary_of_methods.pdf). Phase 2 data for Southern California include flood-hazard information for the coast from the border of Mexico to Pt. Conception to include the Channel Islands. Please read the Summary of Methods and inspect output carefully. Data are complete for the information presented.

Geographic extent of projected coastal flooding, low-lying vulnerable areas, and maxium/minimum flood potential (flood uncertainty) associated with the sea-level rise and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal planners with critical storm-hazards information that can be used to increase public safety, mitigate physical damages, and more effectively manage and allocate resources within complex coastal settings. Model details and data sources are outlined in CoSMoS_3.0_Phase_2_Southern_California_Bight:_Summary_of_data_and_methods (available at https://www.sciencebase.gov/catalog/file/get/57f1d4f3e4b0bc0bebfee139?name=CoSMoS_SoCalv3_Phase2_summary_of_methods.pdf). Phase 2 data for Southern California include flood-hazard information for the coast from the border of Mexico to Pt. Conception to include the Channel Islands. Please read the Summary of Methods and inspect output carefully. Data are complete for the information presented.

Geographic extent of projected coastal flooding, low-lying vulnerable areas, and maxium/minimum flood potential (flood uncertainty) associated with the sea-level rise and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal planners with critical storm-hazards information that can be used to increase public safety, mitigate physical damages, and more effectively manage and allocate resources within complex coastal settings. Model details and data sources are outlined in CoSMoS_3.0_Phase_2_Southern_California_Bight:_Summary_of_data_and_methods (available at https://www.sciencebase.gov/catalog/file/get/57f1d4f3e4b0bc0bebfee139?name=CoSMoS_SoCalv3_Phase2_summary_of_methods.pdf). Phase 2 data for Southern California include flood-hazard information for the coast from the border of Mexico to Pt. Conception to include the Channel Islands. Please read the Summary of Methods and inspect output carefully. Data are complete for the information presented.

Geographic extent of projected coastal flooding, low-lying vulnerable areas, and maxium/minimum flood potential (flood uncertainty) associated with the sea-level rise and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal planners with critical storm-hazards information that can be used to increase public safety, mitigate physical damages, and more effectively manage and allocate resources within complex coastal settings. Model details and data sources are outlined in CoSMoS_3.0_Phase_2_Southern_California_Bight:_Summary_of_data_and_methods (available at https://www.sciencebase.gov/catalog/file/get/57f1d4f3e4b0bc0bebfee139?name=CoSMoS_SoCalv3_Phase2_summary_of_methods.pdf). Phase 2 data for Southern California include flood-hazard information for the coast from the border of Mexico to Pt. Conception to include the Channel Islands. Please read the Summary of Methods and inspect output carefully. Data are complete for the information presented.

Model-derived ocean current velocities (in meters per second) for the given storm condition and sea-level rise (SLR) scenario. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal planners with critical storm-hazards information that can be used to increase public safety, mitigate physical damages, and more effectively manage and allocate resources within complex coastal settings. Model details and data sources are outlined in CoSMoS_3.0_Phase_2_Southern_California_Bight:_Summary_of_data_and_methods (available at https://www.sciencebase.gov/catalog/file/get/57f1d4f3e4b0bc0bebfee139?name=CoSMoS_SoCalv3_Phase2_summary_of_methods.pdf). Phase 2 data for Southern California include flood-hazard information for the coast from the border of Mexico to Pt. Conception to include the Channel Islands. Please read the Summary of Methods and inspect output carefully. Data are complete for the information presented.

Model-derived ocean current velocities (in meters per second) for the given storm condition and sea-level rise (SLR) scenario. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal planners with critical storm-hazards information that can be used to increase public safety, mitigate physical damages, and more effectively manage and allocate resources within complex coastal settings. Model details and data sources are outlined in CoSMoS_3.0_Phase_2_Southern_California_Bight:_Summary_of_data_and_methods (available at https://www.sciencebase.gov/catalog/file/get/57f1d4f3e4b0bc0bebfee139?name=CoSMoS_SoCalv3_Phase2_summary_of_methods.pdf). Phase 2 data for Southern California include flood-hazard information for the coast from the border of Mexico to Pt. Conception to include the Channel Islands. Please read the Summary of Methods and inspect output carefully. Data are complete for the information presented.

Model-derived ocean current velocities (in meters per second) for the given storm condition and sea-level rise (SLR) scenario. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal planners with critical storm-hazards information that can be used to increase public safety, mitigate physical damages, and more effectively manage and allocate resources within complex coastal settings. Model details and data sources are outlined in CoSMoS_3.0_Phase_2_Southern_California_Bight:_Summary_of_data_and_methods (available at https://www.sciencebase.gov/catalog/file/get/57f1d4f3e4b0bc0bebfee139?name=CoSMoS_SoCalv3_Phase2_summary_of_methods.pdf). Phase 2 data for Southern California include flood-hazard information for the coast from the border of Mexico to Pt. Conception to include the Channel Islands. Please read the Summary of Methods and inspect output carefully. Data are complete for the information presented.

Model-derived ocean current velocities (in meters per second) for the given storm condition and sea-level rise (SLR) scenario. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal planners with critical storm-hazards information that can be used to increase public safety, mitigate physical damages, and more effectively manage and allocate resources within complex coastal settings. Model details and data sources are outlined in CoSMoS_3.0_Phase_2_Southern_California_Bight:_Summary_of_data_and_methods (available at https://www.sciencebase.gov/catalog/file/get/57f1d4f3e4b0bc0bebfee139?name=CoSMoS_SoCalv3_Phase2_summary_of_methods.pdf). Phase 2 data for Southern California include flood-hazard information for the coast from the border of Mexico to Pt. Conception to include the Channel Islands. Please read the Summary of Methods and inspect output carefully. Data are complete for the information presented.

Model-derived total water levels (in meters) for the given storm condition and sea-level rise (SLR) scenario. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal planners with critical storm-hazards information that can be used to increase public safety, mitigate physical damages, and more effectively manage and allocate resources within complex coastal settings. Model details and data sources are outlined in CoSMoS_3.0_Phase_2_Southern_California_Bight:_Summary_of_data_and_methods (available at https://www.sciencebase.gov/catalog/file/get/57f1d4f3e4b0bc0bebfee139?name=CoSMoS_SoCalv3_Phase2_summary_of_methods.pdf). Phase 2 data for Southern California include flood-hazard information for the coast from the border of Mexico to Pt. Conception to include the Channel Islands. Please read the Summary of Methods and inspect output carefully. Data are complete for the information presented.

Model-derived total water levels (in meters) for the given storm condition and sea-level rise (SLR) scenario. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal planners with critical storm-hazards information that can be used to increase public safety, mitigate physical damages, and more effectively manage and allocate resources within complex coastal settings. Model details and data sources are outlined in CoSMoS_3.0_Phase_2_Southern_California_Bight:_Summary_of_data_and_methods (available at https://www.sciencebase.gov/catalog/file/get/57f1d4f3e4b0bc0bebfee139?name=CoSMoS_SoCalv3_Phase2_summary_of_methods.pdf). Phase 2 data for Southern California include flood-hazard information for the coast from the border of Mexico to Pt. Conception to include the Channel Islands. Please read the Summary of Methods and inspect output carefully. Data are complete for the information presented.

Model-derived total water levels (in meters) for the given storm condition and sea-level rise (SLR) scenario. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal planners with critical storm-hazards information that can be used to increase public safety, mitigate physical damages, and more effectively manage and allocate resources within complex coastal settings. Model details and data sources are outlined in CoSMoS_3.0_Phase_2_Southern_California_Bight:_Summary_of_data_and_methods (available at https://www.sciencebase.gov/catalog/file/get/57f1d4f3e4b0bc0bebfee139?name=CoSMoS_SoCalv3_Phase2_summary_of_methods.pdf). Phase 2 data for Southern California include flood-hazard information for the coast from the border of Mexico to Pt. Conception to include the Channel Islands. Please read the Summary of Methods and inspect output carefully. Data are complete for the information presented.

Model-derived total water levels (in meters) for the given storm condition and sea-level rise (SLR) scenario. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal planners with critical storm-hazards information that can be used to increase public safety, mitigate physical damages, and more effectively manage and allocate resources within complex coastal settings. Model details and data sources are outlined in CoSMoS_3.0_Phase_2_Southern_California_Bight:_Summary_of_data_and_methods (available at https://www.sciencebase.gov/catalog/file/get/57f1d4f3e4b0bc0bebfee139?name=CoSMoS_SoCalv3_Phase2_summary_of_methods.pdf). Phase 2 data for Southern California include flood-hazard information for the coast from the border of Mexico to Pt. Conception to include the Channel Islands. Please read the Summary of Methods and inspect output carefully. Data are complete for the information presented.

Model-derived significant wave height (in meters) for the given storm condition and sea-level rise (SLR) scenario. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal planners with critical storm-hazards information that can be used to increase public safety, mitigate physical damages, and more effectively manage and allocate resources within complex coastal settings. Model details and data sources are outlined in CoSMoS_3.0_Phase_2_Southern_California_Bight:_Summary_of_data_and_methods (available at https://www.sciencebase.gov/catalog/file/get/57f1d4f3e4b0bc0bebfee139?name=CoSMoS_SoCalv3_Phase2_summary_of_methods.pdf). Phase 2 data for Southern California include flood-hazard information for the coast from the border of Mexico to Pt. Conception to include the Channel Islands. Please read the Summary of Methods and inspect output carefully. Data are complete for the information presented.

Model-derived significant wave height (in meters) for the given storm condition and sea-level rise (SLR) scenario. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal planners with critical storm-hazards information that can be used to increase public safety, mitigate physical damages, and more effectively manage and allocate resources within complex coastal settings. Model details and data sources are outlined in CoSMoS_3.0_Phase_2_Southern_California_Bight:_Summary_of_data_and_methods (available at https://www.sciencebase.gov/catalog/file/get/57f1d4f3e4b0bc0bebfee139?name=CoSMoS_SoCalv3_Phase2_summary_of_methods.pdf). Phase 2 data for Southern California include flood-hazard information for the coast from the border of Mexico to Pt. Conception to include the Channel Islands. Please read the Summary of Methods and inspect output carefully. Data are complete for the information presented.

Model-derived significant wave height (in meters) for the given storm condition and sea-level rise (SLR) scenario. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal planners with critical storm-hazards information that can be used to increase public safety, mitigate physical damages, and more effectively manage and allocate resources within complex coastal settings. Model details and data sources are outlined in CoSMoS_3.0_Phase_2_Southern_California_Bight:_Summary_of_data_and_methods (available at https://www.sciencebase.gov/catalog/file/get/57f1d4f3e4b0bc0bebfee139?name=CoSMoS_SoCalv3_Phase2_summary_of_methods.pdf). Phase 2 data for Southern California include flood-hazard information for the coast from the border of Mexico to Pt. Conception to include the Channel Islands. Please read the Summary of Methods and inspect output carefully. Data are complete for the information presented.

Model-derived significant wave height (in meters) for the given storm condition and sea-level rise (SLR) scenario. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal planners with critical storm-hazards information that can be used to increase public safety, mitigate physical damages, and more effectively manage and allocate resources within complex coastal settings. Model details and data sources are outlined in CoSMoS_3.0_Phase_2_Southern_California_Bight:_Summary_of_data_and_methods (available at https://www.sciencebase.gov/catalog/file/get/57f1d4f3e4b0bc0bebfee139?name=CoSMoS_SoCalv3_Phase2_summary_of_methods.pdf). Phase 2 data for Southern California include flood-hazard information for the coast from the border of Mexico to Pt. Conception to include the Channel Islands. Please read the Summary of Methods and inspect output carefully. Data are complete for the information presented.

Projected Hazard: Maximum depth of flooding surface (in cm) in the region landward of the present day shoreline that is inundated for the storm condition and sea-level rise (SLR) scenario indicated. Note: Duration datasets may have occasional gaps in open-coast sections. Model Summary: The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal planners with critical storm-hazards information that can be used to increase public safety, mitigate physical damages, and more effectively manage and allocate resources within complex coastal settings. Phase 2 data for Southern California include flood-hazard information for the coast from the border of Mexico to Pt. Conception. Several changes from Phase 1 projections are reflected in many areas; please read the model summary and inspect output carefully. Data are complete for the information presented. Details: Model background: The CoSMoS model comprises three tiers. Tier I consists of one Delft3D hydrodynamics FLOW grid for computation of tides, water level variations, flows, and currents and one SWAN grid for computation of wave generation and propagation across the continental shelf. The FLOW and SWAN models are two-way coupled so that tidal currents are accounted for in wave propagation and growth and conversely, so that orbital velocities generated by waves impart changes on tidal currents. The Tier I SWAN and FLOW models consist of identical structured curvilinear grids that extend from far offshore to the shore and range in resolution from 0.5 km in the offshore to 0.2 km in the nearshore. Spatially varying astronomic tidal amplitudes and phases and steric rises in water levels due to large-scale effects (for example, a prolonged rise in sea level) are applied along all open boundaries of the Tier I FLOW grid. Winds (split into eastward and northward components) and sea-level pressure (SLP) fields from CaRD10 (Dr. Dan Cayan, Scripps Institute of Oceanography, Los Angeles, California, written commun., 2014) that vary in both space and time are applied to all grid cells at each model time-step. Deep-water wave conditions, applied at the open boundaries of the Tier I SWAN model runs, were projected for the 21st century Representative Concentration Pathway (RCP) 4.5 climate scenario (2011-2100) using the WaveWatch III numerical wave model (Tolman and others, 2002) and 3-hourly winds from the GFDL-ESM2M Global Climate Model (GCM). Tier II provides higher resolution near the shore and in areas that require greater resolution of physical processes (such as bays, harbors, and estuaries). A single nested outer grid and multiple two-way coupled domain decomposition (DD) structured grids allow for local grid refinement and higher resolution where needed. Tier II was segmented into 11 sections along the Southern California Bight, to reduce computation time and complete runs within computational limitations. Water-level and Neumann time-series, extracted from Tier I simulations, are applied to the shore-parallel and lateral open boundaries of each Tier II sub-model outer grid respectively. Several of the sub-models proved to be unstable with lateral Neumann boundaries; for those cases one or both of the lateral boundaries were converted to water-level time-series or left unassigned. The open-boundary time-series are extracted from completed Tier I simulations so that there is no communication from Tier II to Tier I. Because this one-way nesting could produce erroneous results near the boundaries of Tier II and because data near any model boundary are always suspect, Tier II sub-model extents were designed to overlap in the along-coast direction. In the landward direction, Tier II DD grids extend to the 10-m topographic contour; exceptions exist where channels (such as the Los Angeles River) or other low-lying regions extend very far inland. Space- and time-varying wind and SLP fields, identical to those used in Tier I simulations, are applied to all Tier II DD grids to allow for wind-setup and local inverse barometer effects (IBE, rise or depression of water levels in response to atmospheric pressure gradients). A total of 42 time-series fluvial discharges are included in the Tier II FLOW domains in an effort to simulate exacerbated flooding caused by backflow at the confluence of high river seaward flows and elevated coastal surge levels migrating inland. Time-varying fluvial discharges are applied either at the closed boundaries or distributed as point sources within the relevant model domains. Wave computations are accomplished with the SWAN model using two grids for each Tier II sub-model: one larger grid covering the same area as the outer FLOW grid and a second finer resolution two-way coupled nearshore nested grid. The nearshore grid extends from approximately 800-1,000 m water depth up to 8-10 m elevations onshore. The landward extension is included to allow for wave computations of the higher SLR scenarios. Time- and space-varying 2D wave spectra extracted from previously completed Tier I simulations are applied approximately every kilometer along the open boundaries of the outer Tier II sub-model SWAN grids. The same space- and time-varying wind fields used in Tier I simulations are also applied to both Tier II SWAN grids to allow for computation of local wave generation. Tier III for the entire Southern California Bight consists of 4,802 cross-shore transects (CST) spaced approximately 100 m apart in the along-shore direction. The profiles extend from the -15 m isobath to at least 10 m above NAVD88. The CSTs are truncated for cases where a lagoon or other waterway exists on the landward end of the profile. Time-varying water levels and wave parameters (significant wave heights, Hs; peak periods, Tp; and peak incident wave directions, Dp), extracted from Tier II grid cells that coincide with the seaward end of the CSTs, are applied at the open boundary of each CST. The XBeach model is run in a hydrostatic (no vertical pressure gradients) mode including event-based morphodynamic change. Wave propagation, two-way wave-current interaction, water-level variations, and wave runup are computed at each transect. XBeach simulations are included in the CoSMoS model to account for infragravity waves that can significantly extend the reach of wave runup (Roelvink and others, 2009) compared to short-wave incident waves. The U.S. west coast is particularly susceptible to infragravity waves at the shore due to breaking of long-period swell waves (Tp > 15). Resulting water levels (WLs) from both Delft3D (high interest bays and marshes) and open-coast XBeach (CSTs) were spatially combined and interpolated to a 10 m grid. These WL elevations are differenced from the originating 2 m digital elevation model (DEM) to determine final flooding extent and depth of flooding. Events: The model system is run for pre-determined scenarios of interest such as the 1-yr or 100-yr storm event in combination with sea-level rise. Storms are first identified from time-series of total water level proxies (TWLpx) at the shore. TWLpx are computed for the majority of the 21st century (2010-2100), assuming a linear super-position of the major processes that contribute to the overall total water level. TWLpx time-series are then evaluated for extreme events, which define the boundary conditions for subsequent modeling with CoSMoS. Multiple 100-yr events are determined (varying Hs, Tp, Dp) and used for multiple model runs to better account for regional and directional flooding affects. Model results are combined and compiled into scenario-specific composites of flood projection. Digital Elevation Model (DEM): Our seamless, topobathymetric digital elevation model (DEM) was based largely upon the Coastal California TopoBathy Merge Project DEM, with some modifications performed by the USGS Earth Resources Observation and Science (EROS) Center to incorporate the most recent, high-resolution topographic and bathymetric datasets available. Topography is derived from bare-earth light detection and ranging (lidar) data collected in 2009-2011 for the CA Coastal Conservancy Lidar Project and bathymetry from 2009-2010 bathymetric lidar as well as acoustic multi- and single-beam data collected primarily between 2001 and 2013. The DEM was constructed to define the shape of nearshore, beach, and cliff surfaces as accurately as possible, utilizing dozens of bathymetric and topographic data sets. These data were used to populate the majority of the Tier I and II grids. To describe and include impacts from long-term shoreline evolution, including cumulative storm activity, seasonal trends, ENSO, and SLR, the DEM was modified for each SLR scenario. Long-term shoreline (Vitousek and Barnard, 2015) and cliff (Limber and others, 2015) erosion projections were efficiently combined along the cross-shore transects to evolve the shore-normal profiles. Elevation changes from the profiles were spatially-merged for a cohesive, 3D depiction of coastal evolution used to modify the DEM. These data are used to generate initial profiles of the 4,802 CSTs used for Phase 2 Tier III XBeach modeling and determining final projected flood depths in each SLR scenario. All data are referenced to NAD83 horizontal datum and NAVD88 vertical datum. Data for Tiers II and III are projected in UTM, zone 11. Outputs include: Projected flood-hazard depth and duration for the storm and sea-level rise indicated. Data correspond to the areas vulnerable to coastal flooding due to storm surge, sea-level anomalies, tide elevation, and wave run-up during the same storm and sea-level rise simulation. References Cited: Howell, S., Smith-Konter, B., Frazer, N., Tong, X., and Sandwell, D., 2016, The vertical fingerprint of earthquake cycle loading in southern California: Nature Geoscience, v. 9, p. 611-614, doi:10.1038/ngeo2741. Limber, P., Barnard, P.L. and Hapke., C., 2015, Towards projecting the retreat of California’s coastal cliffs during the 21st Century: in, Wang, P., Rosati, J.D., and Cheng, J., (eds.), The Proceedings of the Coastal Sediments: 2015, World Scientific, 14 p., doi:10.1142/9789814689977_0245 Roelvink, J.A., Reniers, A., van Dongeren, A.R., van Thiel de Vries, J., McCall, R., and Lescinski, J., 2009, Modeling storm impacts on beaches, dunes and barrier islands: Coastal Engineering, v. 56, p. 1,133–1,152, doi:10.1016/j.coastaleng.2009.08.006. Tolman, H.L., Balasubramaniyan, B., Burroughs, L.D., Chalikov, D.V., Chao, Y.Y., Chen H.S., Gerald, V.M., 2002, Development and implementation of wind generated ocean surface wave models at NCEP: Weather and Forecasting, v. 17, p. 311-333. Vitousek, S. and Barnard, P.L., 2015, A non-linear, implicit one-line model to predict long-term shoreline change: in, Wang, P., Rosati, J.D., and Cheng, J., (eds.), The Proceedings of the Coastal Sediments: 2015, World Scientific, 14 p., doi:10.1142/9789814689977_0215.

Projected Hazard: Maximum depth of flooding surface (in cm) in the region landward of the present day shoreline that is inundated for the storm condition and sea-level rise (SLR) scenario indicated. Note: Duration datasets may have occasional gaps in open-coast sections. Model Summary: The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal planners with critical storm-hazards information that can be used to increase public safety, mitigate physical damages, and more effectively manage and allocate resources within complex coastal settings. Phase 2 data for Southern California include flood-hazard information for the coast from the border of Mexico to Pt. Conception. Several changes from Phase 1 projections are reflected in many areas; please read the model summary and inspect output carefully. Data are complete for the information presented. Details: Model background: The CoSMoS model comprises three tiers. Tier I consists of one Delft3D hydrodynamics FLOW grid for computation of tides, water level variations, flows, and currents and one SWAN grid for computation of wave generation and propagation across the continental shelf. The FLOW and SWAN models are two-way coupled so that tidal currents are accounted for in wave propagation and growth and conversely, so that orbital velocities generated by waves impart changes on tidal currents. The Tier I SWAN and FLOW models consist of identical structured curvilinear grids that extend from far offshore to the shore and range in resolution from 0.5 km in the offshore to 0.2 km in the nearshore. Spatially varying astronomic tidal amplitudes and phases and steric rises in water levels due to large-scale effects (for example, a prolonged rise in sea level) are applied along all open boundaries of the Tier I FLOW grid. Winds (split into eastward and northward components) and sea-level pressure (SLP) fields from CaRD10 (Dr. Dan Cayan, Scripps Institute of Oceanography, Los Angeles, California, written commun., 2014) that vary in both space and time are applied to all grid cells at each model time-step. Deep-water wave conditions, applied at the open boundaries of the Tier I SWAN model runs, were projected for the 21st century Representative Concentration Pathway (RCP) 4.5 climate scenario (2011-2100) using the WaveWatch III numerical wave model (Tolman and others, 2002) and 3-hourly winds from the GFDL-ESM2M Global Climate Model (GCM). Tier II provides higher resolution near the shore and in areas that require greater resolution of physical processes (such as bays, harbors, and estuaries). A single nested outer grid and multiple two-way coupled domain decomposition (DD) structured grids allow for local grid refinement and higher resolution where needed. Tier II was segmented into 11 sections along the Southern California Bight, to reduce computation time and complete runs within computational limitations. Water-level and Neumann time-series, extracted from Tier I simulations, are applied to the shore-parallel and lateral open boundaries of each Tier II sub-model outer grid respectively. Several of the sub-models proved to be unstable with lateral Neumann boundaries; for those cases one or both of the lateral boundaries were converted to water-level time-series or left unassigned. The open-boundary time-series are extracted from completed Tier I simulations so that there is no communication from Tier II to Tier I. Because this one-way nesting could produce erroneous results near the boundaries of Tier II and because data near any model boundary are always suspect, Tier II sub-model extents were designed to overlap in the along-coast direction. In the landward direction, Tier II DD grids extend to the 10-m topographic contour; exceptions exist where channels (such as the Los Angeles River) or other low-lying regions extend very far inland. Space- and time-varying wind and SLP fields, identical to those used in Tier I simulations, are applied to all Tier II DD grids to allow for wind-setup and local inverse barometer effects (IBE, rise or depression of water levels in response to atmospheric pressure gradients). A total of 42 time-series fluvial discharges are included in the Tier II FLOW domains in an effort to simulate exacerbated flooding caused by backflow at the confluence of high river seaward flows and elevated coastal surge levels migrating inland. Time-varying fluvial discharges are applied either at the closed boundaries or distributed as point sources within the relevant model domains. Wave computations are accomplished with the SWAN model using two grids for each Tier II sub-model: one larger grid covering the same area as the outer FLOW grid and a second finer resolution two-way coupled nearshore nested grid. The nearshore grid extends from approximately 800-1,000 m water depth up to 8-10 m elevations onshore. The landward extension is included to allow for wave computations of the higher SLR scenarios. Time- and space-varying 2D wave spectra extracted from previously completed Tier I simulations are applied approximately every kilometer along the open boundaries of the outer Tier II sub-model SWAN grids. The same space- and time-varying wind fields used in Tier I simulations are also applied to both Tier II SWAN grids to allow for computation of local wave generation. Tier III for the entire Southern California Bight consists of 4,802 cross-shore transects (CST) spaced approximately 100 m apart in the along-shore direction. The profiles extend from the -15 m isobath to at least 10 m above NAVD88. The CSTs are truncated for cases where a lagoon or other waterway exists on the landward end of the profile. Time-varying water levels and wave parameters (significant wave heights, Hs; peak periods, Tp; and peak incident wave directions, Dp), extracted from Tier II grid cells that coincide with the seaward end of the CSTs, are applied at the open boundary of each CST. The XBeach model is run in a hydrostatic (no vertical pressure gradients) mode including event-based morphodynamic change. Wave propagation, two-way wave-current interaction, water-level variations, and wave runup are computed at each transect. XBeach simulations are included in the CoSMoS model to account for infragravity waves that can significantly extend the reach of wave runup (Roelvink and others, 2009) compared to short-wave incident waves. The U.S. west coast is particularly susceptible to infragravity waves at the shore due to breaking of long-period swell waves (Tp > 15). Resulting water levels (WLs) from both Delft3D (high interest bays and marshes) and open-coast XBeach (CSTs) were spatially combined and interpolated to a 10 m grid. These WL elevations are differenced from the originating 2 m digital elevation model (DEM) to determine final flooding extent and depth of flooding. Events: The model system is run for pre-determined scenarios of interest such as the 1-yr or 100-yr storm event in combination with sea-level rise. Storms are first identified from time-series of total water level proxies (TWLpx) at the shore. TWLpx are computed for the majority of the 21st century (2010-2100), assuming a linear super-position of the major processes that contribute to the overall total water level. TWLpx time-series are then evaluated for extreme events, which define the boundary conditions for subsequent modeling with CoSMoS. Multiple 100-yr events are determined (varying Hs, Tp, Dp) and used for multiple model runs to better account for regional and directional flooding affects. Model results are combined and compiled into scenario-specific composites of flood projection. Digital Elevation Model (DEM): Our seamless, topobathymetric digital elevation model (DEM) was based largely upon the Coastal California TopoBathy Merge Project DEM, with some modifications performed by the USGS Earth Resources Observation and Science (EROS) Center to incorporate the most recent, high-resolution topographic and bathymetric datasets available. Topography is derived from bare-earth light detection and ranging (lidar) data collected in 2009-2011 for the CA Coastal Conservancy Lidar Project and bathymetry from 2009-2010 bathymetric lidar as well as acoustic multi- and single-beam data collected primarily between 2001 and 2013. The DEM was constructed to define the shape of nearshore, beach, and cliff surfaces as accurately as possible, utilizing dozens of bathymetric and topographic data sets. These data were used to populate the majority of the Tier I and II grids. To describe and include impacts from long-term shoreline evolution, including cumulative storm activity, seasonal trends, ENSO, and SLR, the DEM was modified for each SLR scenario. Long-term shoreline (Vitousek and Barnard, 2015) and cliff (Limber and others, 2015) erosion projections were efficiently combined along the cross-shore transects to evolve the shore-normal profiles. Elevation changes from the profiles were spatially-merged for a cohesive, 3D depiction of coastal evolution used to modify the DEM. These data are used to generate initial profiles of the 4,802 CSTs used for Phase 2 Tier III XBeach modeling and determining final projected flood depths in each SLR scenario. All data are referenced to NAD83 horizontal datum and NAVD88 vertical datum. Data for Tiers II and III are projected in UTM, zone 11. Outputs include: Projected flood-hazard depth and duration for the storm and sea-level rise indicated. Data correspond to the areas vulnerable to coastal flooding due to storm surge, sea-level anomalies, tide elevation, and wave run-up during the same storm and sea-level rise simulation. References Cited: Howell, S., Smith-Konter, B., Frazer, N., Tong, X., and Sandwell, D., 2016, The vertical fingerprint of earthquake cycle loading in southern California: Nature Geoscience, v. 9, p. 611-614, doi:10.1038/ngeo2741. Limber, P., Barnard, P.L. and Hapke., C., 2015, Towards projecting the retreat of California’s coastal cliffs during the 21st Century: in, Wang, P., Rosati, J.D., and Cheng, J., (eds.), The Proceedings of the Coastal Sediments: 2015, World Scientific, 14 p., doi:10.1142/9789814689977_0245 Roelvink, J.A., Reniers, A., van Dongeren, A.R., van Thiel de Vries, J., McCall, R., and Lescinski, J., 2009, Modeling storm impacts on beaches, dunes and barrier islands: Coastal Engineering, v. 56, p. 1,133–1,152, doi:10.1016/j.coastaleng.2009.08.006. Tolman, H.L., Balasubramaniyan, B., Burroughs, L.D., Chalikov, D.V., Chao, Y.Y., Chen H.S., Gerald, V.M., 2002, Development and implementation of wind generated ocean surface wave models at NCEP: Weather and Forecasting, v. 17, p. 311-333. Vitousek, S. and Barnard, P.L., 2015, A non-linear, implicit one-line model to predict long-term shoreline change: in, Wang, P., Rosati, J.D., and Cheng, J., (eds.), The Proceedings of the Coastal Sediments: 2015, World Scientific, 14 p., doi:10.1142/9789814689977_0215.

Projected Hazard: Maximum depth of flooding surface (in cm) in the region landward of the present day shoreline that is inundated for the storm condition and sea-level rise (SLR) scenario indicated. Note: Duration datasets may have occasional gaps in open-coast sections. Model Summary: The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal planners with critical storm-hazards information that can be used to increase public safety, mitigate physical damages, and more effectively manage and allocate resources within complex coastal settings. Phase 2 data for Southern California include flood-hazard information for the coast from the border of Mexico to Pt. Conception. Several changes from Phase 1 projections are reflected in many areas; please read the model summary and inspect output carefully. Data are complete for the information presented. Details: Model background: The CoSMoS model comprises three tiers. Tier I consists of one Delft3D hydrodynamics FLOW grid for computation of tides, water level variations, flows, and currents and one SWAN grid for computation of wave generation and propagation across the continental shelf. The FLOW and SWAN models are two-way coupled so that tidal currents are accounted for in wave propagation and growth and conversely, so that orbital velocities generated by waves impart changes on tidal currents. The Tier I SWAN and FLOW models consist of identical structured curvilinear grids that extend from far offshore to the shore and range in resolution from 0.5 km in the offshore to 0.2 km in the nearshore. Spatially varying astronomic tidal amplitudes and phases and steric rises in water levels due to large-scale effects (for example, a prolonged rise in sea level) are applied along all open boundaries of the Tier I FLOW grid. Winds (split into eastward and northward components) and sea-level pressure (SLP) fields from CaRD10 (Dr. Dan Cayan, Scripps Institute of Oceanography, Los Angeles, California, written commun., 2014) that vary in both space and time are applied to all grid cells at each model time-step. Deep-water wave conditions, applied at the open boundaries of the Tier I SWAN model runs, were projected for the 21st century Representative Concentration Pathway (RCP) 4.5 climate scenario (2011-2100) using the WaveWatch III numerical wave model (Tolman and others, 2002) and 3-hourly winds from the GFDL-ESM2M Global Climate Model (GCM). Tier II provides higher resolution near the shore and in areas that require greater resolution of physical processes (such as bays, harbors, and estuaries). A single nested outer grid and multiple two-way coupled domain decomposition (DD) structured grids allow for local grid refinement and higher resolution where needed. Tier II was segmented into 11 sections along the Southern California Bight, to reduce computation time and complete runs within computational limitations. Water-level and Neumann time-series, extracted from Tier I simulations, are applied to the shore-parallel and lateral open boundaries of each Tier II sub-model outer grid respectively. Several of the sub-models proved to be unstable with lateral Neumann boundaries; for those cases one or both of the lateral boundaries were converted to water-level time-series or left unassigned. The open-boundary time-series are extracted from completed Tier I simulations so that there is no communication from Tier II to Tier I. Because this one-way nesting could produce erroneous results near the boundaries of Tier II and because data near any model boundary are always suspect, Tier II sub-model extents were designed to overlap in the along-coast direction. In the landward direction, Tier II DD grids extend to the 10-m topographic contour; exceptions exist where channels (such as the Los Angeles River) or other low-lying regions extend very far inland. Space- and time-varying wind and SLP fields, identical to those used in Tier I simulations, are applied to all Tier II DD grids to allow for wind-setup and local inverse barometer effects (IBE, rise or depression of water levels in response to atmospheric pressure gradients). A total of 42 time-series fluvial discharges are included in the Tier II FLOW domains in an effort to simulate exacerbated flooding caused by backflow at the confluence of high river seaward flows and elevated coastal surge levels migrating inland. Time-varying fluvial discharges are applied either at the closed boundaries or distributed as point sources within the relevant model domains. Wave computations are accomplished with the SWAN model using two grids for each Tier II sub-model: one larger grid covering the same area as the outer FLOW grid and a second finer resolution two-way coupled nearshore nested grid. The nearshore grid extends from approximately 800-1,000 m water depth up to 8-10 m elevations onshore. The landward extension is included to allow for wave computations of the higher SLR scenarios. Time- and space-varying 2D wave spectra extracted from previously completed Tier I simulations are applied approximately every kilometer along the open boundaries of the outer Tier II sub-model SWAN grids. The same space- and time-varying wind fields used in Tier I simulations are also applied to both Tier II SWAN grids to allow for computation of local wave generation. Tier III for the entire Southern California Bight consists of 4,802 cross-shore transects (CST) spaced approximately 100 m apart in the along-shore direction. The profiles extend from the -15 m isobath to at least 10 m above NAVD88. The CSTs are truncated for cases where a lagoon or other waterway exists on the landward end of the profile. Time-varying water levels and wave parameters (significant wave heights, Hs; peak periods, Tp; and peak incident wave directions, Dp), extracted from Tier II grid cells that coincide with the seaward end of the CSTs, are applied at the open boundary of each CST. The XBeach model is run in a hydrostatic (no vertical pressure gradients) mode including event-based morphodynamic change. Wave propagation, two-way wave-current interaction, water-level variations, and wave runup are computed at each transect. XBeach simulations are included in the CoSMoS model to account for infragravity waves that can significantly extend the reach of wave runup (Roelvink and others, 2009) compared to short-wave incident waves. The U.S. west coast is particularly susceptible to infragravity waves at the shore due to breaking of long-period swell waves (Tp > 15). Resulting water levels (WLs) from both Delft3D (high interest bays and marshes) and open-coast XBeach (CSTs) were spatially combined and interpolated to a 10 m grid. These WL elevations are differenced from the originating 2 m digital elevation model (DEM) to determine final flooding extent and depth of flooding. Events: The model system is run for pre-determined scenarios of interest such as the 1-yr or 100-yr storm event in combination with sea-level rise. Storms are first identified from time-series of total water level proxies (TWLpx) at the shore. TWLpx are computed for the majority of the 21st century (2010-2100), assuming a linear super-position of the major processes that contribute to the overall total water level. TWLpx time-series are then evaluated for extreme events, which define the boundary conditions for subsequent modeling with CoSMoS. Multiple 100-yr events are determined (varying Hs, Tp, Dp) and used for multiple model runs to better account for regional and directional flooding affects. Model results are combined and compiled into scenario-specific composites of flood projection. Digital Elevation Model (DEM): Our seamless, topobathymetric digital elevation model (DEM) was based largely upon the Coastal California TopoBathy Merge Project DEM, with some modifications performed by the USGS Earth Resources Observation and Science (EROS) Center to incorporate the most recent, high-resolution topographic and bathymetric datasets available. Topography is derived from bare-earth light detection and ranging (lidar) data collected in 2009-2011 for the CA Coastal Conservancy Lidar Project and bathymetry from 2009-2010 bathymetric lidar as well as acoustic multi- and single-beam data collected primarily between 2001 and 2013. The DEM was constructed to define the shape of nearshore, beach, and cliff surfaces as accurately as possible, utilizing dozens of bathymetric and topographic data sets. These data were used to populate the majority of the Tier I and II grids. To describe and include impacts from long-term shoreline evolution, including cumulative storm activity, seasonal trends, ENSO, and SLR, the DEM was modified for each SLR scenario. Long-term shoreline (Vitousek and Barnard, 2015) and cliff (Limber and others, 2015) erosion projections were efficiently combined along the cross-shore transects to evolve the shore-normal profiles. Elevation changes from the profiles were spatially-merged for a cohesive, 3D depiction of coastal evolution used to modify the DEM. These data are used to generate initial profiles of the 4,802 CSTs used for Phase 2 Tier III XBeach modeling and determining final projected flood depths in each SLR scenario. All data are referenced to NAD83 horizontal datum and NAVD88 vertical datum. Data for Tiers II and III are projected in UTM, zone 11. Outputs include: Projected flood-hazard depth and duration for the storm and sea-level rise indicated. Data correspond to the areas vulnerable to coastal flooding due to storm surge, sea-level anomalies, tide elevation, and wave run-up during the same storm and sea-level rise simulation. References Cited: Howell, S., Smith-Konter, B., Frazer, N., Tong, X., and Sandwell, D., 2016, The vertical fingerprint of earthquake cycle loading in southern California: Nature Geoscience, v. 9, p. 611-614, doi:10.1038/ngeo2741. Limber, P., Barnard, P.L. and Hapke., C., 2015, Towards projecting the retreat of California’s coastal cliffs during the 21st Century: in, Wang, P., Rosati, J.D., and Cheng, J., (eds.), The Proceedings of the Coastal Sediments: 2015, World Scientific, 14 p., doi:10.1142/9789814689977_0245 Roelvink, J.A., Reniers, A., van Dongeren, A.R., van Thiel de Vries, J., McCall, R., and Lescinski, J., 2009, Modeling storm impacts on beaches, dunes and barrier islands: Coastal Engineering, v. 56, p. 1,133–1,152, doi:10.1016/j.coastaleng.2009.08.006. Tolman, H.L., Balasubramaniyan, B., Burroughs, L.D., Chalikov, D.V., Chao, Y.Y., Chen H.S., Gerald, V.M., 2002, Development and implementation of wind generated ocean surface wave models at NCEP: Weather and Forecasting, v. 17, p. 311-333. Vitousek, S. and Barnard, P.L., 2015, A non-linear, implicit one-line model to predict long-term shoreline change: in, Wang, P., Rosati, J.D., and Cheng, J., (eds.), The Proceedings of the Coastal Sediments: 2015, World Scientific, 14 p., doi:10.1142/9789814689977_0215.

Projected Hazard: Maximum depth of flooding surface (in cm) in the region landward of the present day shoreline that is inundated for the storm condition and sea-level rise (SLR) scenario indicated. Note: Duration datasets may have occasional gaps in open-coast sections. Model Summary: The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal planners with critical storm-hazards information that can be used to increase public safety, mitigate physical damages, and more effectively manage and allocate resources within complex coastal settings. Phase 2 data for Southern California include flood-hazard information for the coast from the border of Mexico to Pt. Conception. Several changes from Phase 1 projections are reflected in many areas; please read the model summary and inspect output carefully. Data are complete for the information presented. Details: Model background: The CoSMoS model comprises three tiers. Tier I consists of one Delft3D hydrodynamics FLOW grid for computation of tides, water level variations, flows, and currents and one SWAN grid for computation of wave generation and propagation across the continental shelf. The FLOW and SWAN models are two-way coupled so that tidal currents are accounted for in wave propagation and growth and conversely, so that orbital velocities generated by waves impart changes on tidal currents. The Tier I SWAN and FLOW models consist of identical structured curvilinear grids that extend from far offshore to the shore and range in resolution from 0.5 km in the offshore to 0.2 km in the nearshore. Spatially varying astronomic tidal amplitudes and phases and steric rises in water levels due to large-scale effects (for example, a prolonged rise in sea level) are applied along all open boundaries of the Tier I FLOW grid. Winds (split into eastward and northward components) and sea-level pressure (SLP) fields from CaRD10 (Dr. Dan Cayan, Scripps Institute of Oceanography, Los Angeles, California, written commun., 2014) that vary in both space and time are applied to all grid cells at each model time-step. Deep-water wave conditions, applied at the open boundaries of the Tier I SWAN model runs, were projected for the 21st century Representative Concentration Pathway (RCP) 4.5 climate scenario (2011-2100) using the WaveWatch III numerical wave model (Tolman and others, 2002) and 3-hourly winds from the GFDL-ESM2M Global Climate Model (GCM). Tier II provides higher resolution near the shore and in areas that require greater resolution of physical processes (such as bays, harbors, and estuaries). A single nested outer grid and multiple two-way coupled domain decomposition (DD) structured grids allow for local grid refinement and higher resolution where needed. Tier II was segmented into 11 sections along the Southern California Bight, to reduce computation time and complete runs within computational limitations. Water-level and Neumann time-series, extracted from Tier I simulations, are applied to the shore-parallel and lateral open boundaries of each Tier II sub-model outer grid respectively. Several of the sub-models proved to be unstable with lateral Neumann boundaries; for those cases one or both of the lateral boundaries were converted to water-level time-series or left unassigned. The open-boundary time-series are extracted from completed Tier I simulations so that there is no communication from Tier II to Tier I. Because this one-way nesting could produce erroneous results near the boundaries of Tier II and because data near any model boundary are always suspect, Tier II sub-model extents were designed to overlap in the along-coast direction. In the landward direction, Tier II DD grids extend to the 10-m topographic contour; exceptions exist where channels (such as the Los Angeles River) or other low-lying regions extend very far inland. Space- and time-varying wind and SLP fields, identical to those used in Tier I simulations, are applied to all Tier II DD grids to allow for wind-setup and local inverse barometer effects (IBE, rise or depression of water levels in response to atmospheric pressure gradients). A total of 42 time-series fluvial discharges are included in the Tier II FLOW domains in an effort to simulate exacerbated flooding caused by backflow at the confluence of high river seaward flows and elevated coastal surge levels migrating inland. Time-varying fluvial discharges are applied either at the closed boundaries or distributed as point sources within the relevant model domains. Wave computations are accomplished with the SWAN model using two grids for each Tier II sub-model: one larger grid covering the same area as the outer FLOW grid and a second finer resolution two-way coupled nearshore nested grid. The nearshore grid extends from approximately 800-1,000 m water depth up to 8-10 m elevations onshore. The landward extension is included to allow for wave computations of the higher SLR scenarios. Time- and space-varying 2D wave spectra extracted from previously completed Tier I simulations are applied approximately every kilometer along the open boundaries of the outer Tier II sub-model SWAN grids. The same space- and time-varying wind fields used in Tier I simulations are also applied to both Tier II SWAN grids to allow for computation of local wave generation. Tier III for the entire Southern California Bight consists of 4,802 cross-shore transects (CST) spaced approximately 100 m apart in the along-shore direction. The profiles extend from the -15 m isobath to at least 10 m above NAVD88. The CSTs are truncated for cases where a lagoon or other waterway exists on the landward end of the profile. Time-varying water levels and wave parameters (significant wave heights, Hs; peak periods, Tp; and peak incident wave directions, Dp), extracted from Tier II grid cells that coincide with the seaward end of the CSTs, are applied at the open boundary of each CST. The XBeach model is run in a hydrostatic (no vertical pressure gradients) mode including event-based morphodynamic change. Wave propagation, two-way wave-current interaction, water-level variations, and wave runup are computed at each transect. XBeach simulations are included in the CoSMoS model to account for infragravity waves that can significantly extend the reach of wave runup (Roelvink and others, 2009) compared to short-wave incident waves. The U.S. west coast is particularly susceptible to infragravity waves at the shore due to breaking of long-period swell waves (Tp > 15). Resulting water levels (WLs) from both Delft3D (high interest bays and marshes) and open-coast XBeach (CSTs) were spatially combined and interpolated to a 10 m grid. These WL elevations are differenced from the originating 2 m digital elevation model (DEM) to determine final flooding extent and depth of flooding. Events: The model system is run for pre-determined scenarios of interest such as the 1-yr or 100-yr storm event in combination with sea-level rise. Storms are first identified from time-series of total water level proxies (TWLpx) at the shore. TWLpx are computed for the majority of the 21st century (2010-2100), assuming a linear super-position of the major processes that contribute to the overall total water level. TWLpx time-series are then evaluated for extreme events, which define the boundary conditions for subsequent modeling with CoSMoS. Multiple 100-yr events are determined (varying Hs, Tp, Dp) and used for multiple model runs to better account for regional and directional flooding affects. Model results are combined and compiled into scenario-specific composites of flood projection. Digital Elevation Model (DEM): Our seamless, topobathymetric digital elevation model (DEM) was based largely upon the Coastal California TopoBathy Merge Project DEM, with some modifications performed by the USGS Earth Resources Observation and Science (EROS) Center to incorporate the most recent, high-resolution topographic and bathymetric datasets available. Topography is derived from bare-earth light detection and ranging (lidar) data collected in 2009-2011 for the CA Coastal Conservancy Lidar Project and bathymetry from 2009-2010 bathymetric lidar as well as acoustic multi- and single-beam data collected primarily between 2001 and 2013. The DEM was constructed to define the shape of nearshore, beach, and cliff surfaces as accurately as possible, utilizing dozens of bathymetric and topographic data sets. These data were used to populate the majority of the Tier I and II grids. To describe and include impacts from long-term shoreline evolution, including cumulative storm activity, seasonal trends, ENSO, and SLR, the DEM was modified for each SLR scenario. Long-term shoreline (Vitousek and Barnard, 2015) and cliff (Limber and others, 2015) erosion projections were efficiently combined along the cross-shore transects to evolve the shore-normal profiles. Elevation changes from the profiles were spatially-merged for a cohesive, 3D depiction of coastal evolution used to modify the DEM. These data are used to generate initial profiles of the 4,802 CSTs used for Phase 2 Tier III XBeach modeling and determining final projected flood depths in each SLR scenario. All data are referenced to NAD83 horizontal datum and NAVD88 vertical datum. Data for Tiers II and III are projected in UTM, zone 11. Outputs include: Projected flood-hazard depth and duration for the storm and sea-level rise indicated. Data correspond to the areas vulnerable to coastal flooding due to storm surge, sea-level anomalies, tide elevation, and wave run-up during the same storm and sea-level rise simulation. References Cited: Howell, S., Smith-Konter, B., Frazer, N., Tong, X., and Sandwell, D., 2016, The vertical fingerprint of earthquake cycle loading in southern California: Nature Geoscience, v. 9, p. 611-614, doi:10.1038/ngeo2741. Limber, P., Barnard, P.L. and Hapke., C., 2015, Towards projecting the retreat of California’s coastal cliffs during the 21st Century: in, Wang, P., Rosati, J.D., and Cheng, J., (eds.), The Proceedings of the Coastal Sediments: 2015, World Scientific, 14 p., doi:10.1142/9789814689977_0245 Roelvink, J.A., Reniers, A., van Dongeren, A.R., van Thiel de Vries, J., McCall, R., and Lescinski, J., 2009, Modeling storm impacts on beaches, dunes and barrier islands: Coastal Engineering, v. 56, p. 1,133–1,152, doi:10.1016/j.coastaleng.2009.08.006. Tolman, H.L., Balasubramaniyan, B., Burroughs, L.D., Chalikov, D.V., Chao, Y.Y., Chen H.S., Gerald, V.M., 2002, Development and implementation of wind generated ocean surface wave models at NCEP: Weather and Forecasting, v. 17, p. 311-333. Vitousek, S. and Barnard, P.L., 2015, A non-linear, implicit one-line model to predict long-term shoreline change: in, Wang, P., Rosati, J.D., and Cheng, J., (eds.), The Proceedings of the Coastal Sediments: 2015, World Scientific, 14 p., doi:10.1142/9789814689977_0215.

Projected Hazard: Geographic extent of projected coastal flooding, low-lying vulnerable areas, and maxium/minimum flood potential (flood uncertainty) associated with the sea-level rise and storm condition indicated. Model Summary: The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal planners with critical storm-hazards information that can be used to increase public safety, mitigate physical damages, and more effectively manage and allocate resources within complex coastal settings. Phase 2 data for Southern California include flood-hazard information for the coast from the border of Mexico to Pt. Conception. Several changes from Phase 1 projections are reflected in many areas; please read the model summary and inspect output carefully. Data are complete for the information presented. Details: Model background: The CoSMoS model comprises three tiers. Tier I consists of one Delft3D hydrodynamics FLOW grid for computation of tides, water level variations, flows, and currents and one SWAN grid for computation of wave generation and propagation across the continental shelf. The FLOW and SWAN models are two-way coupled so that tidal currents are accounted for in wave propagation and growth and conversely, so that orbital velocities generated by waves impart changes on tidal currents. The Tier I SWAN and FLOW models consist of identical structured curvilinear grids that extend from far offshore to the shore and range in resolution from 0.5 km in the offshore to 0.2 km in the nearshore. Spatially varying astronomic tidal amplitudes and phases and steric rises in water levels due to large-scale effects (for example, a prolonged rise in sea level) are applied along all open boundaries of the Tier I FLOW grid. Winds (split into eastward and northward components) and sea-level pressure (SLP) fields from CaRD10 (Dr. Dan Cayan, Scripps Institute of Oceanography, Los Angeles, California, written commun., 2014) that vary in both space and time are applied to all grid cells at each model time-step. Deep-water wave conditions, applied at the open boundaries of the Tier I SWAN model runs, were projected for the 21st century Representative Concentration Pathway (RCP) 4.5 climate scenario (2011-2100) using the WaveWatch III numerical wave model (Tolman and others, 2002) and 3-hourly winds from the GFDL-ESM2M Global Climate Model (GCM). Tier II provides higher resolution near the shore and in areas that require greater resolution of physical processes (such as bays, harbors, and estuaries). A single nested outer grid and multiple two-way coupled domain decomposition (DD) structured grids allow for local grid refinement and higher resolution where needed. Tier II was segmented into 11 sections along the Southern California Bight, to reduce computation time and complete runs within computational limitations. Water-level and Neumann time-series, extracted from Tier I simulations, are applied to the shore-parallel and lateral open boundaries of each Tier II sub-model outer grid respectively. Several of the sub-models proved to be unstable with lateral Neumann boundaries; for those cases one or both of the lateral boundaries were converted to water-level time-series or left unassigned. The open-boundary time-series are extracted from completed Tier I simulations so that there is no communication from Tier II to Tier I. Because this one-way nesting could produce erroneous results near the boundaries of Tier II and because data near any model boundary are always suspect, Tier II sub-model extents were designed to overlap in the along-coast direction. In the landward direction, Tier II DD grids extend to the 10-m topographic contour; exceptions exist where channels (such as the Los Angeles River) or other low-lying regions extend very far inland. Space- and time-varying wind and SLP fields, identical to those used in Tier I simulations, are applied to all Tier II DD grids to allow for wind-setup and local inverse barometer effects (IBE, rise or depression of water levels in response to atmospheric pressure gradients). A total of 42 time-series fluvial discharges are included in the Tier II FLOW domains in an effort to simulate exacerbated flooding caused by backflow at the confluence of high river seaward flows and elevated coastal surge levels migrating inland. Time-varying fluvial discharges are applied either at the closed boundaries or distributed as point sources within the relevant model domains. Wave computations are accomplished with the SWAN model using two grids for each Tier II sub-model: one larger grid covering the same area as the outer FLOW grid and a second finer resolution two-way coupled nearshore nested grid. The nearshore grid extends from approximately 800-1,000 m water depth up to 8-10 m elevations onshore. The landward extension is included to allow for wave computations of the higher SLR scenarios. Time- and space-varying 2D wave spectra extracted from previously completed Tier I simulations are applied approximately every kilometer along the open boundaries of the outer Tier II sub-model SWAN grids. The same space- and time-varying wind fields used in Tier I simulations are also applied to both Tier II SWAN grids to allow for computation of local wave generation. Tier III for the entire Southern California Bight consists of 4,802 cross-shore transects (CST) spaced approximately 100 m apart in the along-shore direction. The profiles extend from the -15 m isobath to at least 10 m above NAVD88. The CSTs are truncated for cases where a lagoon or other waterway exists on the landward end of the profile. Time-varying water levels and wave parameters (significant wave heights, Hs; peak periods, Tp; and peak incident wave directions, Dp), extracted from Tier II grid cells that coincide with the seaward end of the CSTs, are applied at the open boundary of each CST. The XBeach model is run in a hydrostatic (no vertical pressure gradients) mode including event-based morphodynamic change. Wave propagation, two-way wave-current interaction, water-level variations, and wave runup are computed at each transect. XBeach simulations are included in the CoSMoS model to account for infragravity waves that can significantly extend the reach of wave runup (Roelvink and others, 2009) compared to short-wave incident waves. The U.S. west coast is particularly susceptible to infragravity waves at the shore due to breaking of long-period swell waves (Tp > 15). Resulting water levels (WLs) from both Delft3D (high interest bays and marshes) and open-coast XBeach (CSTs) were spatially combined and interpolated to a 10 m grid. These WL elevations are differenced from the originating 2 m digital elevation model (DEM) to determine final flooding extent and depth of flooding. Events: The model system is run for pre-determined scenarios of interest such as the 1-yr or 100-yr storm event in combination with sea-level rise. Storms are first identified from time-series of total water level proxies (TWLpx) at the shore. TWLpx are computed for the majority of the 21st century (2010-2100), assuming a linear super-position of the major processes that contribute to the overall total water level. TWLpx time-series are then evaluated for extreme events, which define the boundary conditions for subsequent modeling with CoSMoS. Multiple 100-yr events are determined (varying Hs, Tp, Dp) and used for multiple model runs to better account for regional and directional flooding affects. Model results are combined and compiled into scenario-specific composites of flood projection. Digital Elevation Model (DEM): Our seamless, topobathymetric digital elevation model (DEM) was based largely upon the Coastal California TopoBathy Merge Project DEM, with some modifications performed by the USGS Earth Resources Observation and Science (EROS) Center to incorporate the most recent, high-resolution topographic and bathymetric datasets available. Topography is derived from bare-earth light detection and ranging (lidar) data collected in 2009-2011 for the CA Coastal Conservancy Lidar Project and bathymetry from 2009-2010 bathymetric lidar as well as acoustic multi- and single-beam data collected primarily between 2001 and 2013. The DEM was constructed to define the shape of nearshore, beach, and cliff surfaces as accurately as possible, utilizing dozens of bathymetric and topographic data sets. These data were used to populate the majority of the tier I and II grids. To describe and include impacts from long-term shoreline evolution, including cumulative storm activity, seasonal trends, ENSO, and SLR, the DEM was modified for each SLR scenario. Long-term shoreline (Vitousek and Barnard, 2015) and cliff (Limber et al., 2015) erosion projections were efficiently combined along the cross-shore transects to evolve the shore-normal profiles. Elevation changes from the profiles were spatially-merged for a cohesive, 3D depiction of coastal evolution used to modify the DEM. These data are used to generate initial profiles of the 4,802 CSTs used for Phase 2 tier III XBeach modeling and determining final projected flood depths in each SLR scenario. All data are referenced to NAD83 horizontal datum and NAVD88 vertical datum. Data for Tiers II and III are projected in UTM, zone 11. Outputs include: Areas of projected flood hazards: The area vulnerable to coastal flooding due to storm surge, sea-level anomalies, tide elevation, and wave run-up during the storm simulation, based on the maximum elevation of still-water level (inundation for several minutes) at each CST profile. Enclosed areas illustrate the projected water surface and is shown extending from offshore to the extent of coastal flooding for different SLR scenarios between 0 - 2.0 m (0.25 m increments), and at 5.0 m. Low-lying vulnerable areas depict locations where projections indicate flood potential but are not connected to the primary flood surface. Flood potential indicates the maximum and minimum areas of flooding extent considering accuracy of the DEM, hydrodynamic model accuracy, and vertical land motion (Howell et al., 2016). References Cited: Howell, S., Smith-Konter, B., Frazer, N., Tong, X., and Sandwell, D., 2016, The vertical fingerprint of earthquake cycle loading in southern California: Nature Geoscience, v. 9, p. 611-614, doi:10.1038/ngeo2741. Limber, P., Barnard, P.L. and Hapke., C., 2015, Towards projecting the retreat of California’s coastal cliffs during the 21st Century: in, Wang, P., Rosati, J.D., and Cheng, J., (eds.), The Proceedings of the Coastal Sediments: 2015, World Scientific, 14 p., doi:10.1142/9789814689977_0245 Roelvink, J.A., Reniers, A., van Dongeren, A.R., van Thiel de Vries, J., McCall, R., and Lescinski, J., 2009, Modeling storm impacts on beaches, dunes and barrier islands: Coastal Engineering, v. 56, p. 1,133–1,152, doi:10.1016/j.coastaleng.2009.08.006. Tolman, H.L., Balasubramaniyan, B., Burroughs, L.D., Chalikov, D.V., Chao, Y.Y., Chen H.S., Gerald, V.M., 2002, Development and implementation of wind generated ocean surface wave models at NCEP: Weather and Forecasting, v. 17, p. 311-333. Vitousek, S. and Barnard, P.L., 2015, A non-linear, implicit one-line model to predict long-term shoreline change: in, Wang, P., Rosati, J.D., and Cheng, J., (eds.), The Proceedings of the Coastal Sediments: 2015, World Scientific, 14 p., doi:10.1142/9789814689977_0215.

Projected Hazard: Geographic extent of projected coastal flooding, low-lying vulnerable areas, and maxium/minimum flood potential (flood uncertainty) associated with the sea-level rise and storm condition indicated. Model Summary: The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal planners with critical storm-hazards information that can be used to increase public safety, mitigate physical damages, and more effectively manage and allocate resources within complex coastal settings. Phase 2 data for Southern California include flood-hazard information for the coast from the border of Mexico to Pt. Conception. Several changes from Phase 1 projections are reflected in many areas; please read the model summary and inspect output carefully. Data are complete for the information presented. Details: Model background: The CoSMoS model comprises three tiers. Tier I consists of one Delft3D hydrodynamics FLOW grid for computation of tides, water level variations, flows, and currents and one SWAN grid for computation of wave generation and propagation across the continental shelf. The FLOW and SWAN models are two-way coupled so that tidal currents are accounted for in wave propagation and growth and conversely, so that orbital velocities generated by waves impart changes on tidal currents. The Tier I SWAN and FLOW models consist of identical structured curvilinear grids that extend from far offshore to the shore and range in resolution from 0.5 km in the offshore to 0.2 km in the nearshore. Spatially varying astronomic tidal amplitudes and phases and steric rises in water levels due to large-scale effects (for example, a prolonged rise in sea level) are applied along all open boundaries of the Tier I FLOW grid. Winds (split into eastward and northward components) and sea-level pressure (SLP) fields from CaRD10 (Dr. Dan Cayan, Scripps Institute of Oceanography, Los Angeles, California, written commun., 2014) that vary in both space and time are applied to all grid cells at each model time-step. Deep-water wave conditions, applied at the open boundaries of the Tier I SWAN model runs, were projected for the 21st century Representative Concentration Pathway (RCP) 4.5 climate scenario (2011-2100) using the WaveWatch III numerical wave model (Tolman and others, 2002) and 3-hourly winds from the GFDL-ESM2M Global Climate Model (GCM). Tier II provides higher resolution near the shore and in areas that require greater resolution of physical processes (such as bays, harbors, and estuaries). A single nested outer grid and multiple two-way coupled domain decomposition (DD) structured grids allow for local grid refinement and higher resolution where needed. Tier II was segmented into 11 sections along the Southern California Bight, to reduce computation time and complete runs within computational limitations. Water-level and Neumann time-series, extracted from Tier I simulations, are applied to the shore-parallel and lateral open boundaries of each Tier II sub-model outer grid respectively. Several of the sub-models proved to be unstable with lateral Neumann boundaries; for those cases one or both of the lateral boundaries were converted to water-level time-series or left unassigned. The open-boundary time-series are extracted from completed Tier I simulations so that there is no communication from Tier II to Tier I. Because this one-way nesting could produce erroneous results near the boundaries of Tier II and because data near any model boundary are always suspect, Tier II sub-model extents were designed to overlap in the along-coast direction. In the landward direction, Tier II DD grids extend to the 10-m topographic contour; exceptions exist where channels (such as the Los Angeles River) or other low-lying regions extend very far inland. Space- and time-varying wind and SLP fields, identical to those used in Tier I simulations, are applied to all Tier II DD grids to allow for wind-setup and local inverse barometer effects (IBE, rise or depression of water levels in response to atmospheric pressure gradients). A total of 42 time-series fluvial discharges are included in the Tier II FLOW domains in an effort to simulate exacerbated flooding caused by backflow at the confluence of high river seaward flows and elevated coastal surge levels migrating inland. Time-varying fluvial discharges are applied either at the closed boundaries or distributed as point sources within the relevant model domains. Wave computations are accomplished with the SWAN model using two grids for each Tier II sub-model: one larger grid covering the same area as the outer FLOW grid and a second finer resolution two-way coupled nearshore nested grid. The nearshore grid extends from approximately 800-1,000 m water depth up to 8-10 m elevations onshore. The landward extension is included to allow for wave computations of the higher SLR scenarios. Time- and space-varying 2D wave spectra extracted from previously completed Tier I simulations are applied approximately every kilometer along the open boundaries of the outer Tier II sub-model SWAN grids. The same space- and time-varying wind fields used in Tier I simulations are also applied to both Tier II SWAN grids to allow for computation of local wave generation. Tier III for the entire Southern California Bight consists of 4,802 cross-shore transects (CST) spaced approximately 100 m apart in the along-shore direction. The profiles extend from the -15 m isobath to at least 10 m above NAVD88. The CSTs are truncated for cases where a lagoon or other waterway exists on the landward end of the profile. Time-varying water levels and wave parameters (significant wave heights, Hs; peak periods, Tp; and peak incident wave directions, Dp), extracted from Tier II grid cells that coincide with the seaward end of the CSTs, are applied at the open boundary of each CST. The XBeach model is run in a hydrostatic (no vertical pressure gradients) mode including event-based morphodynamic change. Wave propagation, two-way wave-current interaction, water-level variations, and wave runup are computed at each transect. XBeach simulations are included in the CoSMoS model to account for infragravity waves that can significantly extend the reach of wave runup (Roelvink and others, 2009) compared to short-wave incident waves. The U.S. west coast is particularly susceptible to infragravity waves at the shore due to breaking of long-period swell waves (Tp > 15). Resulting water levels (WLs) from both Delft3D (high interest bays and marshes) and open-coast XBeach (CSTs) were spatially combined and interpolated to a 10 m grid. These WL elevations are differenced from the originating 2 m digital elevation model (DEM) to determine final flooding extent and depth of flooding. Events: The model system is run for pre-determined scenarios of interest such as the 1-yr or 100-yr storm event in combination with sea-level rise. Storms are first identified from time-series of total water level proxies (TWLpx) at the shore. TWLpx are computed for the majority of the 21st century (2010-2100), assuming a linear super-position of the major processes that contribute to the overall total water level. TWLpx time-series are then evaluated for extreme events, which define the boundary conditions for subsequent modeling with CoSMoS. Multiple 100-yr events are determined (varying Hs, Tp, Dp) and used for multiple model runs to better account for regional and directional flooding affects. Model results are combined and compiled into scenario-specific composites of flood projection. Digital Elevation Model (DEM): Our seamless, topobathymetric digital elevation model (DEM) was based largely upon the Coastal California TopoBathy Merge Project DEM, with some modifications performed by the USGS Earth Resources Observation and Science (EROS) Center to incorporate the most recent, high-resolution topographic and bathymetric datasets available. Topography is derived from bare-earth light detection and ranging (lidar) data collected in 2009-2011 for the CA Coastal Conservancy Lidar Project and bathymetry from 2009-2010 bathymetric lidar as well as acoustic multi- and single-beam data collected primarily between 2001 and 2013. The DEM was constructed to define the shape of nearshore, beach, and cliff surfaces as accurately as possible, utilizing dozens of bathymetric and topographic data sets. These data were used to populate the majority of the tier I and II grids. To describe and include impacts from long-term shoreline evolution, including cumulative storm activity, seasonal trends, ENSO, and SLR, the DEM was modified for each SLR scenario. Long-term shoreline (Vitousek and Barnard, 2015) and cliff (Limber et al., 2015) erosion projections were efficiently combined along the cross-shore transects to evolve the shore-normal profiles. Elevation changes from the profiles were spatially-merged for a cohesive, 3D depiction of coastal evolution used to modify the DEM. These data are used to generate initial profiles of the 4,802 CSTs used for Phase 2 tier III XBeach modeling and determining final projected flood depths in each SLR scenario. All data are referenced to NAD83 horizontal datum and NAVD88 vertical datum. Data for Tiers II and III are projected in UTM, zone 11. Outputs include: Areas of projected flood hazards: The area vulnerable to coastal flooding due to storm surge, sea-level anomalies, tide elevation, and wave run-up during the storm simulation, based on the maximum elevation of still-water level (inundation for several minutes) at each CST profile. Enclosed areas illustrate the projected water surface and is shown extending from offshore to the extent of coastal flooding for different SLR scenarios between 0 - 2.0 m (0.25 m increments), and at 5.0 m. Low-lying vulnerable areas depict locations where projections indicate flood potential but are not connected to the primary flood surface. Flood potential indicates the maximum and minimum areas of flooding extent considering accuracy of the DEM, hydrodynamic model accuracy, and vertical land motion (Howell et al., 2016). References Cited: Howell, S., Smith-Konter, B., Frazer, N., Tong, X., and Sandwell, D., 2016, The vertical fingerprint of earthquake cycle loading in southern California: Nature Geoscience, v. 9, p. 611-614, doi:10.1038/ngeo2741. Limber, P., Barnard, P.L. and Hapke., C., 2015, Towards projecting the retreat of California’s coastal cliffs during the 21st Century: in, Wang, P., Rosati, J.D., and Cheng, J., (eds.), The Proceedings of the Coastal Sediments: 2015, World Scientific, 14 p., doi:10.1142/9789814689977_0245 Roelvink, J.A., Reniers, A., van Dongeren, A.R., van Thiel de Vries, J., McCall, R., and Lescinski, J., 2009, Modeling storm impacts on beaches, dunes and barrier islands: Coastal Engineering, v. 56, p. 1,133–1,152, doi:10.1016/j.coastaleng.2009.08.006. Tolman, H.L., Balasubramaniyan, B., Burroughs, L.D., Chalikov, D.V., Chao, Y.Y., Chen H.S., Gerald, V.M., 2002, Development and implementation of wind generated ocean surface wave models at NCEP: Weather and Forecasting, v. 17, p. 311-333. Vitousek, S. and Barnard, P.L., 2015, A non-linear, implicit one-line model to predict long-term shoreline change: in, Wang, P., Rosati, J.D., and Cheng, J., (eds.), The Proceedings of the Coastal Sediments: 2015, World Scientific, 14 p., doi:10.1142/9789814689977_0215.

Projected Hazard: Geographic extent of projected coastal flooding, low-lying vulnerable areas, and maxium/minimum flood potential (flood uncertainty) associated with the sea-level rise and storm condition indicated. Model Summary: The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal planners with critical storm-hazards information that can be used to increase public safety, mitigate physical damages, and more effectively manage and allocate resources within complex coastal settings. Phase 2 data for Southern California include flood-hazard information for the coast from the border of Mexico to Pt. Conception. Several changes from Phase 1 projections are reflected in many areas; please read the model summary and inspect output carefully. Data are complete for the information presented. Details: Model background: The CoSMoS model comprises three tiers. Tier I consists of one Delft3D hydrodynamics FLOW grid for computation of tides, water level variations, flows, and currents and one SWAN grid for computation of wave generation and propagation across the continental shelf. The FLOW and SWAN models are two-way coupled so that tidal currents are accounted for in wave propagation and growth and conversely, so that orbital velocities generated by waves impart changes on tidal currents. The Tier I SWAN and FLOW models consist of identical structured curvilinear grids that extend from far offshore to the shore and range in resolution from 0.5 km in the offshore to 0.2 km in the nearshore. Spatially varying astronomic tidal amplitudes and phases and steric rises in water levels due to large-scale effects (for example, a prolonged rise in sea level) are applied along all open boundaries of the Tier I FLOW grid. Winds (split into eastward and northward components) and sea-level pressure (SLP) fields from CaRD10 (Dr. Dan Cayan, Scripps Institute of Oceanography, Los Angeles, California, written commun., 2014) that vary in both space and time are applied to all grid cells at each model time-step. Deep-water wave conditions, applied at the open boundaries of the Tier I SWAN model runs, were projected for the 21st century Representative Concentration Pathway (RCP) 4.5 climate scenario (2011-2100) using the WaveWatch III numerical wave model (Tolman and others, 2002) and 3-hourly winds from the GFDL-ESM2M Global Climate Model (GCM). Tier II provides higher resolution near the shore and in areas that require greater resolution of physical processes (such as bays, harbors, and estuaries). A single nested outer grid and multiple two-way coupled domain decomposition (DD) structured grids allow for local grid refinement and higher resolution where needed. Tier II was segmented into 11 sections along the Southern California Bight, to reduce computation time and complete runs within computational limitations. Water-level and Neumann time-series, extracted from Tier I simulations, are applied to the shore-parallel and lateral open boundaries of each Tier II sub-model outer grid respectively. Several of the sub-models proved to be unstable with lateral Neumann boundaries; for those cases one or both of the lateral boundaries were converted to water-level time-series or left unassigned. The open-boundary time-series are extracted from completed Tier I simulations so that there is no communication from Tier II to Tier I. Because this one-way nesting could produce erroneous results near the boundaries of Tier II and because data near any model boundary are always suspect, Tier II sub-model extents were designed to overlap in the along-coast direction. In the landward direction, Tier II DD grids extend to the 10-m topographic contour; exceptions exist where channels (such as the Los Angeles River) or other low-lying regions extend very far inland. Space- and time-varying wind and SLP fields, identical to those used in Tier I simulations, are applied to all Tier II DD grids to allow for wind-setup and local inverse barometer effects (IBE, rise or depression of water levels in response to atmospheric pressure gradients). A total of 42 time-series fluvial discharges are included in the Tier II FLOW domains in an effort to simulate exacerbated flooding caused by backflow at the confluence of high river seaward flows and elevated coastal surge levels migrating inland. Time-varying fluvial discharges are applied either at the closed boundaries or distributed as point sources within the relevant model domains. Wave computations are accomplished with the SWAN model using two grids for each Tier II sub-model: one larger grid covering the same area as the outer FLOW grid and a second finer resolution two-way coupled nearshore nested grid. The nearshore grid extends from approximately 800-1,000 m water depth up to 8-10 m elevations onshore. The landward extension is included to allow for wave computations of the higher SLR scenarios. Time- and space-varying 2D wave spectra extracted from previously completed Tier I simulations are applied approximately every kilometer along the open boundaries of the outer Tier II sub-model SWAN grids. The same space- and time-varying wind fields used in Tier I simulations are also applied to both Tier II SWAN grids to allow for computation of local wave generation. Tier III for the entire Southern California Bight consists of 4,802 cross-shore transects (CST) spaced approximately 100 m apart in the along-shore direction. The profiles extend from the -15 m isobath to at least 10 m above NAVD88. The CSTs are truncated for cases where a lagoon or other waterway exists on the landward end of the profile. Time-varying water levels and wave parameters (significant wave heights, Hs; peak periods, Tp; and peak incident wave directions, Dp), extracted from Tier II grid cells that coincide with the seaward end of the CSTs, are applied at the open boundary of each CST. The XBeach model is run in a hydrostatic (no vertical pressure gradients) mode including event-based morphodynamic change. Wave propagation, two-way wave-current interaction, water-level variations, and wave runup are computed at each transect. XBeach simulations are included in the CoSMoS model to account for infragravity waves that can significantly extend the reach of wave runup (Roelvink and others, 2009) compared to short-wave incident waves. The U.S. west coast is particularly susceptible to infragravity waves at the shore due to breaking of long-period swell waves (Tp > 15). Resulting water levels (WLs) from both Delft3D (high interest bays and marshes) and open-coast XBeach (CSTs) were spatially combined and interpolated to a 10 m grid. These WL elevations are differenced from the originating 2 m digital elevation model (DEM) to determine final flooding extent and depth of flooding. Events: The model system is run for pre-determined scenarios of interest such as the 1-yr or 100-yr storm event in combination with sea-level rise. Storms are first identified from time-series of total water level proxies (TWLpx) at the shore. TWLpx are computed for the majority of the 21st century (2010-2100), assuming a linear super-position of the major processes that contribute to the overall total water level. TWLpx time-series are then evaluated for extreme events, which define the boundary conditions for subsequent modeling with CoSMoS. Multiple 100-yr events are determined (varying Hs, Tp, Dp) and used for multiple model runs to better account for regional and directional flooding affects. Model results are combined and compiled into scenario-specific composites of flood projection. Digital Elevation Model (DEM): Our seamless, topobathymetric digital elevation model (DEM) was based largely upon the Coastal California TopoBathy Merge Project DEM, with some modifications performed by the USGS Earth Resources Observation and Science (EROS) Center to incorporate the most recent, high-resolution topographic and bathymetric datasets available. Topography is derived from bare-earth light detection and ranging (lidar) data collected in 2009-2011 for the CA Coastal Conservancy Lidar Project and bathymetry from 2009-2010 bathymetric lidar as well as acoustic multi- and single-beam data collected primarily between 2001 and 2013. The DEM was constructed to define the shape of nearshore, beach, and cliff surfaces as accurately as possible, utilizing dozens of bathymetric and topographic data sets. These data were used to populate the majority of the tier I and II grids. To describe and include impacts from long-term shoreline evolution, including cumulative storm activity, seasonal trends, ENSO, and SLR, the DEM was modified for each SLR scenario. Long-term shoreline (Vitousek and Barnard, 2015) and cliff (Limber et al., 2015) erosion projections were efficiently combined along the cross-shore transects to evolve the shore-normal profiles. Elevation changes from the profiles were spatially-merged for a cohesive, 3D depiction of coastal evolution used to modify the DEM. These data are used to generate initial profiles of the 4,802 CSTs used for Phase 2 tier III XBeach modeling and determining final projected flood depths in each SLR scenario. All data are referenced to NAD83 horizontal datum and NAVD88 vertical datum. Data for Tiers II and III are projected in UTM, zone 11. Outputs include: Areas of projected flood hazards: The area vulnerable to coastal flooding due to storm surge, sea-level anomalies, tide elevation, and wave run-up during the storm simulation, based on the maximum elevation of still-water level (inundation for several minutes) at each CST profile. Enclosed areas illustrate the projected water surface and is shown extending from offshore to the extent of coastal flooding for different SLR scenarios between 0 - 2.0 m (0.25 m increments), and at 5.0 m. Low-lying vulnerable areas depict locations where projections indicate flood potential but are not connected to the primary flood surface. Flood potential indicates the maximum and minimum areas of flooding extent considering accuracy of the DEM, hydrodynamic model accuracy, and vertical land motion (Howell et al., 2016). References Cited: Howell, S., Smith-Konter, B., Frazer, N., Tong, X., and Sandwell, D., 2016, The vertical fingerprint of earthquake cycle loading in southern California: Nature Geoscience, v. 9, p. 611-614, doi:10.1038/ngeo2741. Limber, P., Barnard, P.L. and Hapke., C., 2015, Towards projecting the retreat of California’s coastal cliffs during the 21st Century: in, Wang, P., Rosati, J.D., and Cheng, J., (eds.), The Proceedings of the Coastal Sediments: 2015, World Scientific, 14 p., doi:10.1142/9789814689977_0245 Roelvink, J.A., Reniers, A., van Dongeren, A.R., van Thiel de Vries, J., McCall, R., and Lescinski, J., 2009, Modeling storm impacts on beaches, dunes and barrier islands: Coastal Engineering, v. 56, p. 1,133–1,152, doi:10.1016/j.coastaleng.2009.08.006. Tolman, H.L., Balasubramaniyan, B., Burroughs, L.D., Chalikov, D.V., Chao, Y.Y., Chen H.S., Gerald, V.M., 2002, Development and implementation of wind generated ocean surface wave models at NCEP: Weather and Forecasting, v. 17, p. 311-333. Vitousek, S. and Barnard, P.L., 2015, A non-linear, implicit one-line model to predict long-term shoreline change: in, Wang, P., Rosati, J.D., and Cheng, J., (eds.), The Proceedings of the Coastal Sediments: 2015, World Scientific, 14 p., doi:10.1142/9789814689977_0215.

Projected Hazard: Geographic extent of projected coastal flooding, low-lying vulnerable areas, and maxium/minimum flood potential (flood uncertainty) associated with the sea-level rise and storm condition indicated. Model Summary: The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal planners with critical storm-hazards information that can be used to increase public safety, mitigate physical damages, and more effectively manage and allocate resources within complex coastal settings. Phase 2 data for Southern California include flood-hazard information for the coast from the border of Mexico to Pt. Conception. Several changes from Phase 1 projections are reflected in many areas; please read the model summary and inspect output carefully. Data are complete for the information presented. Details: Model background: The CoSMoS model comprises three tiers. Tier I consists of one Delft3D hydrodynamics FLOW grid for computation of tides, water level variations, flows, and currents and one SWAN grid for computation of wave generation and propagation across the continental shelf. The FLOW and SWAN models are two-way coupled so that tidal currents are accounted for in wave propagation and growth and conversely, so that orbital velocities generated by waves impart changes on tidal currents. The Tier I SWAN and FLOW models consist of identical structured curvilinear grids that extend from far offshore to the shore and range in resolution from 0.5 km in the offshore to 0.2 km in the nearshore. Spatially varying astronomic tidal amplitudes and phases and steric rises in water levels due to large-scale effects (for example, a prolonged rise in sea level) are applied along all open boundaries of the Tier I FLOW grid. Winds (split into eastward and northward components) and sea-level pressure (SLP) fields from CaRD10 (Dr. Dan Cayan, Scripps Institute of Oceanography, Los Angeles, California, written commun., 2014) that vary in both space and time are applied to all grid cells at each model time-step. Deep-water wave conditions, applied at the open boundaries of the Tier I SWAN model runs, were projected for the 21st century Representative Concentration Pathway (RCP) 4.5 climate scenario (2011-2100) using the WaveWatch III numerical wave model (Tolman and others, 2002) and 3-hourly winds from the GFDL-ESM2M Global Climate Model (GCM). Tier II provides higher resolution near the shore and in areas that require greater resolution of physical processes (such as bays, harbors, and estuaries). A single nested outer grid and multiple two-way coupled domain decomposition (DD) structured grids allow for local grid refinement and higher resolution where needed. Tier II was segmented into 11 sections along the Southern California Bight, to reduce computation time and complete runs within computational limitations. Water-level and Neumann time-series, extracted from Tier I simulations, are applied to the shore-parallel and lateral open boundaries of each Tier II sub-model outer grid respectively. Several of the sub-models proved to be unstable with lateral Neumann boundaries; for those cases one or both of the lateral boundaries were converted to water-level time-series or left unassigned. The open-boundary time-series are extracted from completed Tier I simulations so that there is no communication from Tier II to Tier I. Because this one-way nesting could produce erroneous results near the boundaries of Tier II and because data near any model boundary are always suspect, Tier II sub-model extents were designed to overlap in the along-coast direction. In the landward direction, Tier II DD grids extend to the 10-m topographic contour; exceptions exist where channels (such as the Los Angeles River) or other low-lying regions extend very far inland. Space- and time-varying wind and SLP fields, identical to those used in Tier I simulations, are applied to all Tier II DD grids to allow for wind-setup and local inverse barometer effects (IBE, rise or depression of water levels in response to atmospheric pressure gradients). A total of 42 time-series fluvial discharges are included in the Tier II FLOW domains in an effort to simulate exacerbated flooding caused by backflow at the confluence of high river seaward flows and elevated coastal surge levels migrating inland. Time-varying fluvial discharges are applied either at the closed boundaries or distributed as point sources within the relevant model domains. Wave computations are accomplished with the SWAN model using two grids for each Tier II sub-model: one larger grid covering the same area as the outer FLOW grid and a second finer resolution two-way coupled nearshore nested grid. The nearshore grid extends from approximately 800-1,000 m water depth up to 8-10 m elevations onshore. The landward extension is included to allow for wave computations of the higher SLR scenarios. Time- and space-varying 2D wave spectra extracted from previously completed Tier I simulations are applied approximately every kilometer along the open boundaries of the outer Tier II sub-model SWAN grids. The same space- and time-varying wind fields used in Tier I simulations are also applied to both Tier II SWAN grids to allow for computation of local wave generation. Tier III for the entire Southern California Bight consists of 4,802 cross-shore transects (CST) spaced approximately 100 m apart in the along-shore direction. The profiles extend from the -15 m isobath to at least 10 m above NAVD88. The CSTs are truncated for cases where a lagoon or other waterway exists on the landward end of the profile. Time-varying water levels and wave parameters (significant wave heights, Hs; peak periods, Tp; and peak incident wave directions, Dp), extracted from Tier II grid cells that coincide with the seaward end of the CSTs, are applied at the open boundary of each CST. The XBeach model is run in a hydrostatic (no vertical pressure gradients) mode including event-based morphodynamic change. Wave propagation, two-way wave-current interaction, water-level variations, and wave runup are computed at each transect. XBeach simulations are included in the CoSMoS model to account for infragravity waves that can significantly extend the reach of wave runup (Roelvink and others, 2009) compared to short-wave incident waves. The U.S. west coast is particularly susceptible to infragravity waves at the shore due to breaking of long-period swell waves (Tp > 15). Resulting water levels (WLs) from both Delft3D (high interest bays and marshes) and open-coast XBeach (CSTs) were spatially combined and interpolated to a 10 m grid. These WL elevations are differenced from the originating 2 m digital elevation model (DEM) to determine final flooding extent and depth of flooding. Events: The model system is run for pre-determined scenarios of interest such as the 1-yr or 100-yr storm event in combination with sea-level rise. Storms are first identified from time-series of total water level proxies (TWLpx) at the shore. TWLpx are computed for the majority of the 21st century (2010-2100), assuming a linear super-position of the major processes that contribute to the overall total water level. TWLpx time-series are then evaluated for extreme events, which define the boundary conditions for subsequent modeling with CoSMoS. Multiple 100-yr events are determined (varying Hs, Tp, Dp) and used for multiple model runs to better account for regional and directional flooding affects. Model results are combined and compiled into scenario-specific composites of flood projection. Digital Elevation Model (DEM): Our seamless, topobathymetric digital elevation model (DEM) was based largely upon the Coastal California TopoBathy Merge Project DEM, with some modifications performed by the USGS Earth Resources Observation and Science (EROS) Center to incorporate the most recent, high-resolution topographic and bathymetric datasets available. Topography is derived from bare-earth light detection and ranging (lidar) data collected in 2009-2011 for the CA Coastal Conservancy Lidar Project and bathymetry from 2009-2010 bathymetric lidar as well as acoustic multi- and single-beam data collected primarily between 2001 and 2013. The DEM was constructed to define the shape of nearshore, beach, and cliff surfaces as accurately as possible, utilizing dozens of bathymetric and topographic data sets. These data were used to populate the majority of the tier I and II grids. To describe and include impacts from long-term shoreline evolution, including cumulative storm activity, seasonal trends, ENSO, and SLR, the DEM was modified for each SLR scenario. Long-term shoreline (Vitousek and Barnard, 2015) and cliff (Limber et al., 2015) erosion projections were efficiently combined along the cross-shore transects to evolve the shore-normal profiles. Elevation changes from the profiles were spatially-merged for a cohesive, 3D depiction of coastal evolution used to modify the DEM. These data are used to generate initial profiles of the 4,802 CSTs used for Phase 2 tier III XBeach modeling and determining final projected flood depths in each SLR scenario. All data are referenced to NAD83 horizontal datum and NAVD88 vertical datum. Data for Tiers II and III are projected in UTM, zone 11. Outputs include: Areas of projected flood hazards: The area vulnerable to coastal flooding due to storm surge, sea-level anomalies, tide elevation, and wave run-up during the storm simulation, based on the maximum elevation of still-water level (inundation for several minutes) at each CST profile. Enclosed areas illustrate the projected water surface and is shown extending from offshore to the extent of coastal flooding for different SLR scenarios between 0 - 2.0 m (0.25 m increments), and at 5.0 m. Low-lying vulnerable areas depict locations where projections indicate flood potential but are not connected to the primary flood surface. Flood potential indicates the maximum and minimum areas of flooding extent considering accuracy of the DEM, hydrodynamic model accuracy, and vertical land motion (Howell et al., 2016). References Cited: Howell, S., Smith-Konter, B., Frazer, N., Tong, X., and Sandwell, D., 2016, The vertical fingerprint of earthquake cycle loading in southern California: Nature Geoscience, v. 9, p. 611-614, doi:10.1038/ngeo2741. Limber, P., Barnard, P.L. and Hapke., C., 2015, Towards projecting the retreat of California’s coastal cliffs during the 21st Century: in, Wang, P., Rosati, J.D., and Cheng, J., (eds.), The Proceedings of the Coastal Sediments: 2015, World Scientific, 14 p., doi:10.1142/9789814689977_0245 Roelvink, J.A., Reniers, A., van Dongeren, A.R., van Thiel de Vries, J., McCall, R., and Lescinski, J., 2009, Modeling storm impacts on beaches, dunes and barrier islands: Coastal Engineering, v. 56, p. 1,133–1,152, doi:10.1016/j.coastaleng.2009.08.006. Tolman, H.L., Balasubramaniyan, B., Burroughs, L.D., Chalikov, D.V., Chao, Y.Y., Chen H.S., Gerald, V.M., 2002, Development and implementation of wind generated ocean surface wave models at NCEP: Weather and Forecasting, v. 17, p. 311-333. Vitousek, S. and Barnard, P.L., 2015, A non-linear, implicit one-line model to predict long-term shoreline change: in, Wang, P., Rosati, J.D., and Cheng, J., (eds.), The Proceedings of the Coastal Sediments: 2015, World Scientific, 14 p., doi:10.1142/9789814689977_0215.

Projected Hazard: Model-derived ocean current velocities (in meters per second) for the given storm condition and sea-level rise (SLR) scenario. Model Summary: The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal planners with critical storm-hazards information that can be used to increase public safety, mitigate physical damages, and more effectively manage and allocate resources within complex coastal settings. Phase 2 data for Southern California include flood-hazard information for the coast from the border of Mexico to Pt. Conception. Several changes from Phase 1 projections are reflected in many areas; please read the model summary and inspect output carefully. Data are complete for the information presented. Details: Model background: The CoSMoS model comprises three tiers. Tier I consists of one Delft3D hydrodynamics FLOW grid for computation of tides, water level variations, flows, and currents and one SWAN grid for computation of wave generation and propagation across the continental shelf. The FLOW and SWAN models are two-way coupled so that tidal currents are accounted for in wave propagation and growth and conversely, so that orbital velocities generated by waves impart changes on tidal currents. The Tier I SWAN and FLOW models consist of identical structured curvilinear grids that extend from far offshore to the shore and range in resolution from 0.5 km in the offshore to 0.2 km in the nearshore. Spatially varying astronomic tidal amplitudes and phases and steric rises in water levels due to large-scale effects (for example, a prolonged rise in sea level) are applied along all open boundaries of the Tier I FLOW grid. Winds (split into eastward and northward components) and sea-level pressure (SLP) fields from CaRD10 (Dr. Dan Cayan, Scripps Institute of Oceanography, Los Angeles, California, written commun., 2014) that vary in both space and time are applied to all grid cells at each model time-step. Deep-water wave conditions, applied at the open boundaries of the Tier I SWAN model runs, were projected for the 21st century Representative Concentration Pathway (RCP) 4.5 climate scenario (2011-2100) using the WaveWatch III numerical wave model (Tolman and others, 2002) and 3-hourly winds from the GFDL-ESM2M Global Climate Model (GCM). Tier II provides higher resolution near the shore and in areas that require greater resolution of physical processes (such as bays, harbors, and estuaries). A single nested outer grid and multiple two-way coupled domain decomposition (DD) structured grids allow for local grid refinement and higher resolution where needed. Tier II was segmented into 11 sections along the Southern California Bight, to reduce computation time and complete runs within computational limitations. Water-level and Neumann time-series, extracted from Tier I simulations, are applied to the shore-parallel and lateral open boundaries of each Tier II sub-model outer grid respectively. Several of the sub-models proved to be unstable with lateral Neumann boundaries; for those cases one or both of the lateral boundaries were converted to water-level time-series or left unassigned. The open-boundary time-series are extracted from completed Tier I simulations so that there is no communication from Tier II to Tier I. Because this one-way nesting could produce erroneous results near the boundaries of Tier II and because data near any model boundary are always suspect, Tier II sub-model extents were designed to overlap in the along-coast direction. In the landward direction, Tier II DD grids extend to the 10-m topographic contour; exceptions exist where channels (such as the Los Angeles River) or other low-lying regions extend very far inland. Space- and time-varying wind and SLP fields, identical to those used in Tier I simulations, are applied to all Tier II DD grids to allow for wind-setup and local inverse barometer effects (IBE, rise or depression of water levels in response to atmospheric pressure gradients). A total of 42 time-series fluvial discharges are included in the Tier II FLOW domains in an effort to simulate exacerbated flooding caused by backflow at the confluence of high river seaward flows and elevated coastal surge levels migrating inland. Time-varying fluvial discharges are applied either at the closed boundaries or distributed as point sources within the relevant model domains. Wave computations are accomplished with the SWAN model using two grids for each Tier II sub-model: one larger grid covering the same area as the outer FLOW grid and a second finer resolution two-way coupled nearshore nested grid. The nearshore grid extends from approximately 800-1,000 m water depth up to 8-10 m elevations onshore. The landward extension is included to allow for wave computations of the higher SLR scenarios. Time- and space-varying 2D wave spectra extracted from previously completed Tier I simulations are applied approximately every kilometer along the open boundaries of the outer Tier II sub-model SWAN grids. The same space- and time-varying wind fields used in Tier I simulations are also applied to both Tier II SWAN grids to allow for computation of local wave generation. Tier III for the entire Southern California Bight consists of 4,802 cross-shore transects (CST) spaced approximately 100 m apart in the along-shore direction. The profiles extend from the -15 m isobath to at least 10 m above NAVD88. The CSTs are truncated for cases where a lagoon or other waterway exists on the landward end of the profile. Time-varying water levels and wave parameters (significant wave heights, Hs; peak periods, Tp; and peak incident wave directions, Dp), extracted from Tier II grid cells that coincide with the seaward end of the CSTs, are applied at the open boundary of each CST. The XBeach model is run in a hydrostatic (no vertical pressure gradients) mode including event-based morphodynamic change. Wave propagation, two-way wave-current interaction, water-level variations, and wave runup are computed at each transect. XBeach simulations are included in the CoSMoS model to account for infragravity waves that can significantly extend the reach of wave runup (Roelvink and others, 2009) compared to short-wave incident waves. The U.S. west coast is particularly susceptible to infragravity waves at the shore due to breaking of long-period swell waves (Tp > 15). Resulting water levels (WLs) from both Delft3D (high interest bays and marshes) and open-coast XBeach (CSTs) were spatially combined and interpolated to a 10 m grid. These WL elevations are differenced from the originating 2 m digital elevation model (DEM) to determine final flooding extent and depth of flooding. Events: The model system is run for pre-determined scenarios of interest such as the 1-yr or 100-yr storm event in combination with sea-level rise. Storms are first identified from time-series of total water level proxies (TWLpx) at the shore. TWLpx are computed for the majority of the 21st century (2010-2100), assuming a linear super-position of the major processes that contribute to the overall total water level. TWLpx time-series are then evaluated for extreme events, which define the boundary conditions for subsequent modeling with CoSMoS. Multiple 100-yr events are determined (varying Hs, Tp, Dp) and used for multiple model runs to better account for regional and directional flooding affects. Model results are combined and compiled into scenario-specific composites of flood projection. Digital Elevation Model (DEM): Our seamless, topobathymetric digital elevation model (DEM) was based largely upon the Coastal California TopoBathy Merge Project DEM, with some modifications performed by the USGS Earth Resources Observation and Science (EROS) Center to incorporate the most recent, high-resolution topographic and bathymetric datasets available. Topography is derived from bare-earth light detection and ranging (lidar) data collected in 2009-2011 for the CA Coastal Conservancy Lidar Project and bathymetry from 2009-2010 bathymetric lidar as well as acoustic multi- and single-beam data collected primarily between 2001 and 2013. The DEM was constructed to define the shape of nearshore, beach, and cliff surfaces as accurately as possible, utilizing dozens of bathymetric and topographic data sets. These data were used to populate the majority of the Tier I and II grids. To describe and include impacts from long-term shoreline evolution, including cumulative storm activity, seasonal trends, ENSO, and SLR, the DEM was modified for each SLR scenario. Long-term shoreline (Vitousek and Barnard, 2015) and cliff (Limber and others, 2015) erosion projections were efficiently combined along the cross-shore transects to evolve the shore-normal profiles. Elevation changes from the profiles were spatially-merged for a cohesive, 3D depiction of coastal evolution used to modify the DEM. These data are used to generate initial profiles of the 4,802 CSTs used for Phase 2 Tier III XBeach modeling and determining final projected flood depths in each SLR scenario. All data are referenced to NAD83 horizontal datum and NAVD88 vertical datum. Data for Tiers II and III are projected in UTM, zone 11. Outputs include: Projected ocean current velocities for the 100-year storm and 0.0 m sea-level rise scenario. Data correspond to the near-shore region including areas vulnerable to coastal flooding due to storm surge, sea-level anomalies, tide elevation, and wave run-up during the same storm and sea-level rise simulation. References Cited: Howell, S., Smith-Konter, B., Frazer, N., Tong, X., and Sandwell, D., 2016, The vertical fingerprint of earthquake cycle loading in southern California: Nature Geoscience, v. 9, p. 611-614, doi:10.1038/ngeo2741. Limber, P., Barnard, P.L. and Hapke., C., 2015, Towards projecting the retreat of California’s coastal cliffs during the 21st Century: in, Wang, P., Rosati, J.D., and Cheng, J., (eds.), The Proceedings of the Coastal Sediments: 2015, World Scientific, 14 p., doi:10.1142/9789814689977_0245 Roelvink, J.A., Reniers, A., van Dongeren, A.R., van Thiel de Vries, J., McCall, R., and Lescinski, J., 2009, Modeling storm impacts on beaches, dunes and barrier islands: Coastal Engineering, v. 56, p. 1,133–1,152, doi:10.1016/j.coastaleng.2009.08.006. Tolman, H.L., Balasubramaniyan, B., Burroughs, L.D., Chalikov, D.V., Chao, Y.Y., Chen H.S., Gerald, V.M., 2002, Development and implementation of wind generated ocean surface wave models at NCEP: Weather and Forecasting, v. 17, p. 311-333. Vitousek, S. and Barnard, P.L., 2015, A non-linear, implicit one-line model to predict long-term shoreline change: in, Wang, P., Rosati, J.D., and Cheng, J., (eds.), The Proceedings of the Coastal Sediments: 2015, World Scientific, 14 p., doi:10.1142/9789814689977_0215.

Projected Hazard: Model-derived ocean current velocities (in meters per second) for the given storm condition and sea-level rise (SLR) scenario. Model Summary: The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal planners with critical storm-hazards information that can be used to increase public safety, mitigate physical damages, and more effectively manage and allocate resources within complex coastal settings. Phase 2 data for Southern California include flood-hazard information for the coast from the border of Mexico to Pt. Conception. Several changes from Phase 1 projections are reflected in many areas; please read the model summary and inspect output carefully. Data are complete for the information presented. Details: Model background: The CoSMoS model comprises three tiers. Tier I consists of one Delft3D hydrodynamics FLOW grid for computation of tides, water level variations, flows, and currents and one SWAN grid for computation of wave generation and propagation across the continental shelf. The FLOW and SWAN models are two-way coupled so that tidal currents are accounted for in wave propagation and growth and conversely, so that orbital velocities generated by waves impart changes on tidal currents. The Tier I SWAN and FLOW models consist of identical structured curvilinear grids that extend from far offshore to the shore and range in resolution from 0.5 km in the offshore to 0.2 km in the nearshore. Spatially varying astronomic tidal amplitudes and phases and steric rises in water levels due to large-scale effects (for example, a prolonged rise in sea level) are applied along all open boundaries of the Tier I FLOW grid. Winds (split into eastward and northward components) and sea-level pressure (SLP) fields from CaRD10 (Dr. Dan Cayan, Scripps Institute of Oceanography, Los Angeles, California, written commun., 2014) that vary in both space and time are applied to all grid cells at each model time-step. Deep-water wave conditions, applied at the open boundaries of the Tier I SWAN model runs, were projected for the 21st century Representative Concentration Pathway (RCP) 4.5 climate scenario (2011-2100) using the WaveWatch III numerical wave model (Tolman and others, 2002) and 3-hourly winds from the GFDL-ESM2M Global Climate Model (GCM). Tier II provides higher resolution near the shore and in areas that require greater resolution of physical processes (such as bays, harbors, and estuaries). A single nested outer grid and multiple two-way coupled domain decomposition (DD) structured grids allow for local grid refinement and higher resolution where needed. Tier II was segmented into 11 sections along the Southern California Bight, to reduce computation time and complete runs within computational limitations. Water-level and Neumann time-series, extracted from Tier I simulations, are applied to the shore-parallel and lateral open boundaries of each Tier II sub-model outer grid respectively. Several of the sub-models proved to be unstable with lateral Neumann boundaries; for those cases one or both of the lateral boundaries were converted to water-level time-series or left unassigned. The open-boundary time-series are extracted from completed Tier I simulations so that there is no communication from Tier II to Tier I. Because this one-way nesting could produce erroneous results near the boundaries of Tier II and because data near any model boundary are always suspect, Tier II sub-model extents were designed to overlap in the along-coast direction. In the landward direction, Tier II DD grids extend to the 10-m topographic contour; exceptions exist where channels (such as the Los Angeles River) or other low-lying regions extend very far inland. Space- and time-varying wind and SLP fields, identical to those used in Tier I simulations, are applied to all Tier II DD grids to allow for wind-setup and local inverse barometer effects (IBE, rise or depression of water levels in response to atmospheric pressure gradients). A total of 42 time-series fluvial discharges are included in the Tier II FLOW domains in an effort to simulate exacerbated flooding caused by backflow at the confluence of high river seaward flows and elevated coastal surge levels migrating inland. Time-varying fluvial discharges are applied either at the closed boundaries or distributed as point sources within the relevant model domains. Wave computations are accomplished with the SWAN model using two grids for each Tier II sub-model: one larger grid covering the same area as the outer FLOW grid and a second finer resolution two-way coupled nearshore nested grid. The nearshore grid extends from approximately 800-1,000 m water depth up to 8-10 m elevations onshore. The landward extension is included to allow for wave computations of the higher SLR scenarios. Time- and space-varying 2D wave spectra extracted from previously completed Tier I simulations are applied approximately every kilometer along the open boundaries of the outer Tier II sub-model SWAN grids. The same space- and time-varying wind fields used in Tier I simulations are also applied to both Tier II SWAN grids to allow for computation of local wave generation. Tier III for the entire Southern California Bight consists of 4,802 cross-shore transects (CST) spaced approximately 100 m apart in the along-shore direction. The profiles extend from the -15 m isobath to at least 10 m above NAVD88. The CSTs are truncated for cases where a lagoon or other waterway exists on the landward end of the profile. Time-varying water levels and wave parameters (significant wave heights, Hs; peak periods, Tp; and peak incident wave directions, Dp), extracted from Tier II grid cells that coincide with the seaward end of the CSTs, are applied at the open boundary of each CST. The XBeach model is run in a hydrostatic (no vertical pressure gradients) mode including event-based morphodynamic change. Wave propagation, two-way wave-current interaction, water-level variations, and wave runup are computed at each transect. XBeach simulations are included in the CoSMoS model to account for infragravity waves that can significantly extend the reach of wave runup (Roelvink and others, 2009) compared to short-wave incident waves. The U.S. west coast is particularly susceptible to infragravity waves at the shore due to breaking of long-period swell waves (Tp > 15). Resulting water levels (WLs) from both Delft3D (high interest bays and marshes) and open-coast XBeach (CSTs) were spatially combined and interpolated to a 10 m grid. These WL elevations are differenced from the originating 2 m digital elevation model (DEM) to determine final flooding extent and depth of flooding. Events: The model system is run for pre-determined scenarios of interest such as the 1-yr or 100-yr storm event in combination with sea-level rise. Storms are first identified from time-series of total water level proxies (TWLpx) at the shore. TWLpx are computed for the majority of the 21st century (2010-2100), assuming a linear super-position of the major processes that contribute to the overall total water level. TWLpx time-series are then evaluated for extreme events, which define the boundary conditions for subsequent modeling with CoSMoS. Multiple 100-yr events are determined (varying Hs, Tp, Dp) and used for multiple model runs to better account for regional and directional flooding affects. Model results are combined and compiled into scenario-specific composites of flood projection. Digital Elevation Model (DEM): Our seamless, topobathymetric digital elevation model (DEM) was based largely upon the Coastal California TopoBathy Merge Project DEM, with some modifications performed by the USGS Earth Resources Observation and Science (EROS) Center to incorporate the most recent, high-resolution topographic and bathymetric datasets available. Topography is derived from bare-earth light detection and ranging (lidar) data collected in 2009-2011 for the CA Coastal Conservancy Lidar Project and bathymetry from 2009-2010 bathymetric lidar as well as acoustic multi- and single-beam data collected primarily between 2001 and 2013. The DEM was constructed to define the shape of nearshore, beach, and cliff surfaces as accurately as possible, utilizing dozens of bathymetric and topographic data sets. These data were used to populate the majority of the Tier I and II grids. To describe and include impacts from long-term shoreline evolution, including cumulative storm activity, seasonal trends, ENSO, and SLR, the DEM was modified for each SLR scenario. Long-term shoreline (Vitousek and Barnard, 2015) and cliff (Limber and others, 2015) erosion projections were efficiently combined along the cross-shore transects to evolve the shore-normal profiles. Elevation changes from the profiles were spatially-merged for a cohesive, 3D depiction of coastal evolution used to modify the DEM. These data are used to generate initial profiles of the 4,802 CSTs used for Phase 2 Tier III XBeach modeling and determining final projected flood depths in each SLR scenario. All data are referenced to NAD83 horizontal datum and NAVD88 vertical datum. Data for Tiers II and III are projected in UTM, zone 11. Outputs include: Projected ocean current velocities for the 100-year storm and 0.0 m sea-level rise scenario. Data correspond to the near-shore region including areas vulnerable to coastal flooding due to storm surge, sea-level anomalies, tide elevation, and wave run-up during the same storm and sea-level rise simulation. References Cited: Howell, S., Smith-Konter, B., Frazer, N., Tong, X., and Sandwell, D., 2016, The vertical fingerprint of earthquake cycle loading in southern California: Nature Geoscience, v. 9, p. 611-614, doi:10.1038/ngeo2741. Limber, P., Barnard, P.L. and Hapke., C., 2015, Towards projecting the retreat of California’s coastal cliffs during the 21st Century: in, Wang, P., Rosati, J.D., and Cheng, J., (eds.), The Proceedings of the Coastal Sediments: 2015, World Scientific, 14 p., doi:10.1142/9789814689977_0245 Roelvink, J.A., Reniers, A., van Dongeren, A.R., van Thiel de Vries, J., McCall, R., and Lescinski, J., 2009, Modeling storm impacts on beaches, dunes and barrier islands: Coastal Engineering, v. 56, p. 1,133–1,152, doi:10.1016/j.coastaleng.2009.08.006. Tolman, H.L., Balasubramaniyan, B., Burroughs, L.D., Chalikov, D.V., Chao, Y.Y., Chen H.S., Gerald, V.M., 2002, Development and implementation of wind generated ocean surface wave models at NCEP: Weather and Forecasting, v. 17, p. 311-333. Vitousek, S. and Barnard, P.L., 2015, A non-linear, implicit one-line model to predict long-term shoreline change: in, Wang, P., Rosati, J.D., and Cheng, J., (eds.), The Proceedings of the Coastal Sediments: 2015, World Scientific, 14 p., doi:10.1142/9789814689977_0215.

Projected Hazard: Model-derived ocean current velocities (in meters per second) for the given storm condition and sea-level rise (SLR) scenario. Model Summary: The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal planners with critical storm-hazards information that can be used to increase public safety, mitigate physical damages, and more effectively manage and allocate resources within complex coastal settings. Phase 2 data for Southern California include flood-hazard information for the coast from the border of Mexico to Pt. Conception. Several changes from Phase 1 projections are reflected in many areas; please read the model summary and inspect output carefully. Data are complete for the information presented. Details: Model background: The CoSMoS model comprises three tiers. Tier I consists of one Delft3D hydrodynamics FLOW grid for computation of tides, water level variations, flows, and currents and one SWAN grid for computation of wave generation and propagation across the continental shelf. The FLOW and SWAN models are two-way coupled so that tidal currents are accounted for in wave propagation and growth and conversely, so that orbital velocities generated by waves impart changes on tidal currents. The Tier I SWAN and FLOW models consist of identical structured curvilinear grids that extend from far offshore to the shore and range in resolution from 0.5 km in the offshore to 0.2 km in the nearshore. Spatially varying astronomic tidal amplitudes and phases and steric rises in water levels due to large-scale effects (for example, a prolonged rise in sea level) are applied along all open boundaries of the Tier I FLOW grid. Winds (split into eastward and northward components) and sea-level pressure (SLP) fields from CaRD10 (Dr. Dan Cayan, Scripps Institute of Oceanography, Los Angeles, California, written commun., 2014) that vary in both space and time are applied to all grid cells at each model time-step. Deep-water wave conditions, applied at the open boundaries of the Tier I SWAN model runs, were projected for the 21st century Representative Concentration Pathway (RCP) 4.5 climate scenario (2011-2100) using the WaveWatch III numerical wave model (Tolman and others, 2002) and 3-hourly winds from the GFDL-ESM2M Global Climate Model (GCM). Tier II provides higher resolution near the shore and in areas that require greater resolution of physical processes (such as bays, harbors, and estuaries). A single nested outer grid and multiple two-way coupled domain decomposition (DD) structured grids allow for local grid refinement and higher resolution where needed. Tier II was segmented into 11 sections along the Southern California Bight, to reduce computation time and complete runs within computational limitations. Water-level and Neumann time-series, extracted from Tier I simulations, are applied to the shore-parallel and lateral open boundaries of each Tier II sub-model outer grid respectively. Several of the sub-models proved to be unstable with lateral Neumann boundaries; for those cases one or both of the lateral boundaries were converted to water-level time-series or left unassigned. The open-boundary time-series are extracted from completed Tier I simulations so that there is no communication from Tier II to Tier I. Because this one-way nesting could produce erroneous results near the boundaries of Tier II and because data near any model boundary are always suspect, Tier II sub-model extents were designed to overlap in the along-coast direction. In the landward direction, Tier II DD grids extend to the 10-m topographic contour; exceptions exist where channels (such as the Los Angeles River) or other low-lying regions extend very far inland. Space- and time-varying wind and SLP fields, identical to those used in Tier I simulations, are applied to all Tier II DD grids to allow for wind-setup and local inverse barometer effects (IBE, rise or depression of water levels in response to atmospheric pressure gradients). A total of 42 time-series fluvial discharges are included in the Tier II FLOW domains in an effort to simulate exacerbated flooding caused by backflow at the confluence of high river seaward flows and elevated coastal surge levels migrating inland. Time-varying fluvial discharges are applied either at the closed boundaries or distributed as point sources within the relevant model domains. Wave computations are accomplished with the SWAN model using two grids for each Tier II sub-model: one larger grid covering the same area as the outer FLOW grid and a second finer resolution two-way coupled nearshore nested grid. The nearshore grid extends from approximately 800-1,000 m water depth up to 8-10 m elevations onshore. The landward extension is included to allow for wave computations of the higher SLR scenarios. Time- and space-varying 2D wave spectra extracted from previously completed Tier I simulations are applied approximately every kilometer along the open boundaries of the outer Tier II sub-model SWAN grids. The same space- and time-varying wind fields used in Tier I simulations are also applied to both Tier II SWAN grids to allow for computation of local wave generation. Tier III for the entire Southern California Bight consists of 4,802 cross-shore transects (CST) spaced approximately 100 m apart in the along-shore direction. The profiles extend from the -15 m isobath to at least 10 m above NAVD88. The CSTs are truncated for cases where a lagoon or other waterway exists on the landward end of the profile. Time-varying water levels and wave parameters (significant wave heights, Hs; peak periods, Tp; and peak incident wave directions, Dp), extracted from Tier II grid cells that coincide with the seaward end of the CSTs, are applied at the open boundary of each CST. The XBeach model is run in a hydrostatic (no vertical pressure gradients) mode including event-based morphodynamic change. Wave propagation, two-way wave-current interaction, water-level variations, and wave runup are computed at each transect. XBeach simulations are included in the CoSMoS model to account for infragravity waves that can significantly extend the reach of wave runup (Roelvink and others, 2009) compared to short-wave incident waves. The U.S. west coast is particularly susceptible to infragravity waves at the shore due to breaking of long-period swell waves (Tp > 15). Resulting water levels (WLs) from both Delft3D (high interest bays and marshes) and open-coast XBeach (CSTs) were spatially combined and interpolated to a 10 m grid. These WL elevations are differenced from the originating 2 m digital elevation model (DEM) to determine final flooding extent and depth of flooding. Events: The model system is run for pre-determined scenarios of interest such as the 1-yr or 100-yr storm event in combination with sea-level rise. Storms are first identified from time-series of total water level proxies (TWLpx) at the shore. TWLpx are computed for the majority of the 21st century (2010-2100), assuming a linear super-position of the major processes that contribute to the overall total water level. TWLpx time-series are then evaluated for extreme events, which define the boundary conditions for subsequent modeling with CoSMoS. Multiple 100-yr events are determined (varying Hs, Tp, Dp) and used for multiple model runs to better account for regional and directional flooding affects. Model results are combined and compiled into scenario-specific composites of flood projection. Digital Elevation Model (DEM): Our seamless, topobathymetric digital elevation model (DEM) was based largely upon the Coastal California TopoBathy Merge Project DEM, with some modifications performed by the USGS Earth Resources Observation and Science (EROS) Center to incorporate the most recent, high-resolution topographic and bathymetric datasets available. Topography is derived from bare-earth light detection and ranging (lidar) data collected in 2009-2011 for the CA Coastal Conservancy Lidar Project and bathymetry from 2009-2010 bathymetric lidar as well as acoustic multi- and single-beam data collected primarily between 2001 and 2013. The DEM was constructed to define the shape of nearshore, beach, and cliff surfaces as accurately as possible, utilizing dozens of bathymetric and topographic data sets. These data were used to populate the majority of the Tier I and II grids. To describe and include impacts from long-term shoreline evolution, including cumulative storm activity, seasonal trends, ENSO, and SLR, the DEM was modified for each SLR scenario. Long-term shoreline (Vitousek and Barnard, 2015) and cliff (Limber and others, 2015) erosion projections were efficiently combined along the cross-shore transects to evolve the shore-normal profiles. Elevation changes from the profiles were spatially-merged for a cohesive, 3D depiction of coastal evolution used to modify the DEM. These data are used to generate initial profiles of the 4,802 CSTs used for Phase 2 Tier III XBeach modeling and determining final projected flood depths in each SLR scenario. All data are referenced to NAD83 horizontal datum and NAVD88 vertical datum. Data for Tiers II and III are projected in UTM, zone 11. Outputs include: Projected ocean current velocities for the 100-year storm and 0.0 m sea-level rise scenario. Data correspond to the near-shore region including areas vulnerable to coastal flooding due to storm surge, sea-level anomalies, tide elevation, and wave run-up during the same storm and sea-level rise simulation. References Cited: Howell, S., Smith-Konter, B., Frazer, N., Tong, X., and Sandwell, D., 2016, The vertical fingerprint of earthquake cycle loading in southern California: Nature Geoscience, v. 9, p. 611-614, doi:10.1038/ngeo2741. Limber, P., Barnard, P.L. and Hapke., C., 2015, Towards projecting the retreat of California’s coastal cliffs during the 21st Century: in, Wang, P., Rosati, J.D., and Cheng, J., (eds.), The Proceedings of the Coastal Sediments: 2015, World Scientific, 14 p., doi:10.1142/9789814689977_0245 Roelvink, J.A., Reniers, A., van Dongeren, A.R., van Thiel de Vries, J., McCall, R., and Lescinski, J., 2009, Modeling storm impacts on beaches, dunes and barrier islands: Coastal Engineering, v. 56, p. 1,133–1,152, doi:10.1016/j.coastaleng.2009.08.006. Tolman, H.L., Balasubramaniyan, B., Burroughs, L.D., Chalikov, D.V., Chao, Y.Y., Chen H.S., Gerald, V.M., 2002, Development and implementation of wind generated ocean surface wave models at NCEP: Weather and Forecasting, v. 17, p. 311-333. Vitousek, S. and Barnard, P.L., 2015, A non-linear, implicit one-line model to predict long-term shoreline change: in, Wang, P., Rosati, J.D., and Cheng, J., (eds.), The Proceedings of the Coastal Sediments: 2015, World Scientific, 14 p., doi:10.1142/9789814689977_0215.

Projected Hazard: Model-derived ocean current velocities (in meters per second) for the given storm condition and sea-level rise (SLR) scenario. Model Summary: The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal planners with critical storm-hazards information that can be used to increase public safety, mitigate physical damages, and more effectively manage and allocate resources within complex coastal settings. Phase 2 data for Southern California include flood-hazard information for the coast from the border of Mexico to Pt. Conception. Several changes from Phase 1 projections are reflected in many areas; please read the model summary and inspect output carefully. Data are complete for the information presented. Details: Model background: The CoSMoS model comprises three tiers. Tier I consists of one Delft3D hydrodynamics FLOW grid for computation of tides, water level variations, flows, and currents and one SWAN grid for computation of wave generation and propagation across the continental shelf. The FLOW and SWAN models are two-way coupled so that tidal currents are accounted for in wave propagation and growth and conversely, so that orbital velocities generated by waves impart changes on tidal currents. The Tier I SWAN and FLOW models consist of identical structured curvilinear grids that extend from far offshore to the shore and range in resolution from 0.5 km in the offshore to 0.2 km in the nearshore. Spatially varying astronomic tidal amplitudes and phases and steric rises in water levels due to large-scale effects (for example, a prolonged rise in sea level) are applied along all open boundaries of the Tier I FLOW grid. Winds (split into eastward and northward components) and sea-level pressure (SLP) fields from CaRD10 (Dr. Dan Cayan, Scripps Institute of Oceanography, Los Angeles, California, written commun., 2014) that vary in both space and time are applied to all grid cells at each model time-step. Deep-water wave conditions, applied at the open boundaries of the Tier I SWAN model runs, were projected for the 21st century Representative Concentration Pathway (RCP) 4.5 climate scenario (2011-2100) using the WaveWatch III numerical wave model (Tolman and others, 2002) and 3-hourly winds from the GFDL-ESM2M Global Climate Model (GCM). Tier II provides higher resolution near the shore and in areas that require greater resolution of physical processes (such as bays, harbors, and estuaries). A single nested outer grid and multiple two-way coupled domain decomposition (DD) structured grids allow for local grid refinement and higher resolution where needed. Tier II was segmented into 11 sections along the Southern California Bight, to reduce computation time and complete runs within computational limitations. Water-level and Neumann time-series, extracted from Tier I simulations, are applied to the shore-parallel and lateral open boundaries of each Tier II sub-model outer grid respectively. Several of the sub-models proved to be unstable with lateral Neumann boundaries; for those cases one or both of the lateral boundaries were converted to water-level time-series or left unassigned. The open-boundary time-series are extracted from completed Tier I simulations so that there is no communication from Tier II to Tier I. Because this one-way nesting could produce erroneous results near the boundaries of Tier II and because data near any model boundary are always suspect, Tier II sub-model extents were designed to overlap in the along-coast direction. In the landward direction, Tier II DD grids extend to the 10-m topographic contour; exceptions exist where channels (such as the Los Angeles River) or other low-lying regions extend very far inland. Space- and time-varying wind and SLP fields, identical to those used in Tier I simulations, are applied to all Tier II DD grids to allow for wind-setup and local inverse barometer effects (IBE, rise or depression of water levels in response to atmospheric pressure gradients). A total of 42 time-series fluvial discharges are included in the Tier II FLOW domains in an effort to simulate exacerbated flooding caused by backflow at the confluence of high river seaward flows and elevated coastal surge levels migrating inland. Time-varying fluvial discharges are applied either at the closed boundaries or distributed as point sources within the relevant model domains. Wave computations are accomplished with the SWAN model using two grids for each Tier II sub-model: one larger grid covering the same area as the outer FLOW grid and a second finer resolution two-way coupled nearshore nested grid. The nearshore grid extends from approximately 800-1,000 m water depth up to 8-10 m elevations onshore. The landward extension is included to allow for wave computations of the higher SLR scenarios. Time- and space-varying 2D wave spectra extracted from previously completed Tier I simulations are applied approximately every kilometer along the open boundaries of the outer Tier II sub-model SWAN grids. The same space- and time-varying wind fields used in Tier I simulations are also applied to both Tier II SWAN grids to allow for computation of local wave generation. Tier III for the entire Southern California Bight consists of 4,802 cross-shore transects (CST) spaced approximately 100 m apart in the along-shore direction. The profiles extend from the -15 m isobath to at least 10 m above NAVD88. The CSTs are truncated for cases where a lagoon or other waterway exists on the landward end of the profile. Time-varying water levels and wave parameters (significant wave heights, Hs; peak periods, Tp; and peak incident wave directions, Dp), extracted from Tier II grid cells that coincide with the seaward end of the CSTs, are applied at the open boundary of each CST. The XBeach model is run in a hydrostatic (no vertical pressure gradients) mode including event-based morphodynamic change. Wave propagation, two-way wave-current interaction, water-level variations, and wave runup are computed at each transect. XBeach simulations are included in the CoSMoS model to account for infragravity waves that can significantly extend the reach of wave runup (Roelvink and others, 2009) compared to short-wave incident waves. The U.S. west coast is particularly susceptible to infragravity waves at the shore due to breaking of long-period swell waves (Tp > 15). Resulting water levels (WLs) from both Delft3D (high interest bays and marshes) and open-coast XBeach (CSTs) were spatially combined and interpolated to a 10 m grid. These WL elevations are differenced from the originating 2 m digital elevation model (DEM) to determine final flooding extent and depth of flooding. Events: The model system is run for pre-determined scenarios of interest such as the 1-yr or 100-yr storm event in combination with sea-level rise. Storms are first identified from time-series of total water level proxies (TWLpx) at the shore. TWLpx are computed for the majority of the 21st century (2010-2100), assuming a linear super-position of the major processes that contribute to the overall total water level. TWLpx time-series are then evaluated for extreme events, which define the boundary conditions for subsequent modeling with CoSMoS. Multiple 100-yr events are determined (varying Hs, Tp, Dp) and used for multiple model runs to better account for regional and directional flooding affects. Model results are combined and compiled into scenario-specific composites of flood projection. Digital Elevation Model (DEM): Our seamless, topobathymetric digital elevation model (DEM) was based largely upon the Coastal California TopoBathy Merge Project DEM, with some modifications performed by the USGS Earth Resources Observation and Science (EROS) Center to incorporate the most recent, high-resolution topographic and bathymetric datasets available. Topography is derived from bare-earth light detection and ranging (lidar) data collected in 2009-2011 for the CA Coastal Conservancy Lidar Project and bathymetry from 2009-2010 bathymetric lidar as well as acoustic multi- and single-beam data collected primarily between 2001 and 2013. The DEM was constructed to define the shape of nearshore, beach, and cliff surfaces as accurately as possible, utilizing dozens of bathymetric and topographic data sets. These data were used to populate the majority of the Tier I and II grids. To describe and include impacts from long-term shoreline evolution, including cumulative storm activity, seasonal trends, ENSO, and SLR, the DEM was modified for each SLR scenario. Long-term shoreline (Vitousek and Barnard, 2015) and cliff (Limber and others, 2015) erosion projections were efficiently combined along the cross-shore transects to evolve the shore-normal profiles. Elevation changes from the profiles were spatially-merged for a cohesive, 3D depiction of coastal evolution used to modify the DEM. These data are used to generate initial profiles of the 4,802 CSTs used for Phase 2 Tier III XBeach modeling and determining final projected flood depths in each SLR scenario. All data are referenced to NAD83 horizontal datum and NAVD88 vertical datum. Data for Tiers II and III are projected in UTM, zone 11. Outputs include: Projected ocean current velocities for the 100-year storm and 0.0 m sea-level rise scenario. Data correspond to the near-shore region including areas vulnerable to coastal flooding due to storm surge, sea-level anomalies, tide elevation, and wave run-up during the same storm and sea-level rise simulation. References Cited: Howell, S., Smith-Konter, B., Frazer, N., Tong, X., and Sandwell, D., 2016, The vertical fingerprint of earthquake cycle loading in southern California: Nature Geoscience, v. 9, p. 611-614, doi:10.1038/ngeo2741. Limber, P., Barnard, P.L. and Hapke., C., 2015, Towards projecting the retreat of California’s coastal cliffs during the 21st Century: in, Wang, P., Rosati, J.D., and Cheng, J., (eds.), The Proceedings of the Coastal Sediments: 2015, World Scientific, 14 p., doi:10.1142/9789814689977_0245 Roelvink, J.A., Reniers, A., van Dongeren, A.R., van Thiel de Vries, J., McCall, R., and Lescinski, J., 2009, Modeling storm impacts on beaches, dunes and barrier islands: Coastal Engineering, v. 56, p. 1,133–1,152, doi:10.1016/j.coastaleng.2009.08.006. Tolman, H.L., Balasubramaniyan, B., Burroughs, L.D., Chalikov, D.V., Chao, Y.Y., Chen H.S., Gerald, V.M., 2002, Development and implementation of wind generated ocean surface wave models at NCEP: Weather and Forecasting, v. 17, p. 311-333. Vitousek, S. and Barnard, P.L., 2015, A non-linear, implicit one-line model to predict long-term shoreline change: in, Wang, P., Rosati, J.D., and Cheng, J., (eds.), The Proceedings of the Coastal Sediments: 2015, World Scientific, 14 p., doi:10.1142/9789814689977_0215.

Projected Hazard: Model-derived total water levels (in meters) for the given storm condition and sea-level rise (SLR) scenario. Model Summary: The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal planners with critical storm-hazards information that can be used to increase public safety, mitigate physical damages, and more effectively manage and allocate resources within complex coastal settings. Phase 2 data for Southern California include flood-hazard information for the coast from the border of Mexico to Pt. Conception. Several changes from Phase 1 projections are reflected in many areas; please read the model summary and inspect output carefully. Data are complete for the information presented. Details: Model background: The CoSMoS model comprises three tiers. Tier I consists of one Delft3D hydrodynamics FLOW grid for computation of tides, water level variations, flows, and currents and one SWAN grid for computation of wave generation and propagation across the continental shelf. The FLOW and SWAN models are two-way coupled so that tidal currents are accounted for in wave propagation and growth and conversely, so that orbital velocities generated by waves impart changes on tidal currents. The Tier I SWAN and FLOW models consist of identical structured curvilinear grids that extend from far offshore to the shore and range in resolution from 0.5 km in the offshore to 0.2 km in the nearshore. Spatially varying astronomic tidal amplitudes and phases and steric rises in water levels due to large-scale effects (for example, a prolonged rise in sea level) are applied along all open boundaries of the Tier I FLOW grid. Winds (split into eastward and northward components) and sea-level pressure (SLP) fields from CaRD10 (Dr. Dan Cayan, Scripps Institute of Oceanography, Los Angeles, California, written commun., 2014) that vary in both space and time are applied to all grid cells at each model time-step. Deep-water wave conditions, applied at the open boundaries of the Tier I SWAN model runs, were projected for the 21st century Representative Concentration Pathway (RCP) 4.5 climate scenario (2011-2100) using the WaveWatch III numerical wave model (Tolman and others, 2002) and 3-hourly winds from the GFDL-ESM2M Global Climate Model (GCM). Tier II provides higher resolution near the shore and in areas that require greater resolution of physical processes (such as bays, harbors, and estuaries). A single nested outer grid and multiple two-way coupled domain decomposition (DD) structured grids allow for local grid refinement and higher resolution where needed. Tier II was segmented into 11 sections along the Southern California Bight, to reduce computation time and complete runs within computational limitations. Water-level and Neumann time-series, extracted from Tier I simulations, are applied to the shore-parallel and lateral open boundaries of each Tier II sub-model outer grid respectively. Several of the sub-models proved to be unstable with lateral Neumann boundaries; for those cases one or both of the lateral boundaries were converted to water-level time-series or left unassigned. The open-boundary time-series are extracted from completed Tier I simulations so that there is no communication from Tier II to Tier I. Because this one-way nesting could produce erroneous results near the boundaries of Tier II and because data near any model boundary are always suspect, Tier II sub-model extents were designed to overlap in the along-coast direction. In the landward direction, Tier II DD grids extend to the 10-m topographic contour; exceptions exist where channels (such as the Los Angeles River) or other low-lying regions extend very far inland. Space- and time-varying wind and SLP fields, identical to those used in Tier I simulations, are applied to all Tier II DD grids to allow for wind-setup and local inverse barometer effects (IBE, rise or depression of water levels in response to atmospheric pressure gradients). A total of 42 time-series fluvial discharges are included in the Tier II FLOW domains in an effort to simulate exacerbated flooding caused by backflow at the confluence of high river seaward flows and elevated coastal surge levels migrating inland. Time-varying fluvial discharges are applied either at the closed boundaries or distributed as point sources within the relevant model domains. Wave computations are accomplished with the SWAN model using two grids for each Tier II sub-model: one larger grid covering the same area as the outer FLOW grid and a second finer resolution two-way coupled nearshore nested grid. The nearshore grid extends from approximately 800-1,000 m water depth up to 8-10 m elevations onshore. The landward extension is included to allow for wave computations of the higher SLR scenarios. Time- and space-varying 2D wave spectra extracted from previously completed Tier I simulations are applied approximately every kilometer along the open boundaries of the outer Tier II sub-model SWAN grids. The same space- and time-varying wind fields used in Tier I simulations are also applied to both Tier II SWAN grids to allow for computation of local wave generation. Tier III for the entire Southern California Bight consists of 4,802 cross-shore transects (CST) spaced approximately 100 m apart in the along-shore direction. The profiles extend from the -15 m isobath to at least 10 m above NAVD88. The CSTs are truncated for cases where a lagoon or other waterway exists on the landward end of the profile. Time-varying water levels and wave parameters (significant wave heights, Hs; peak periods, Tp; and peak incident wave directions, Dp), extracted from Tier II grid cells that coincide with the seaward end of the CSTs, are applied at the open boundary of each CST. The XBeach model is run in a hydrostatic (no vertical pressure gradients) mode including event-based morphodynamic change. Wave propagation, two-way wave-current interaction, water-level variations, and wave runup are computed at each transect. XBeach simulations are included in the CoSMoS model to account for infragravity waves that can significantly extend the reach of wave runup (Roelvink and others, 2009) compared to short-wave incident waves. The U.S. west coast is particularly susceptible to infragravity waves at the shore due to breaking of long-period swell waves (Tp > 15). Resulting water levels (WLs) from both Delft3D (high interest bays and marshes) and open-coast XBeach (CSTs) were spatially combined and interpolated to a 10 m grid. These WL elevations are differenced from the originating 2 m digital elevation model (DEM) to determine final flooding extent and depth of flooding. Events: The model system is run for pre-determined scenarios of interest such as the 1-yr or 100-yr storm event in combination with sea-level rise. Storms are first identified from time-series of total water level proxies (TWLpx) at the shore. TWLpx are computed for the majority of the 21st century (2010-2100), assuming a linear super-position of the major processes that contribute to the overall total water level. TWLpx time-series are then evaluated for extreme events, which define the boundary conditions for subsequent modeling with CoSMoS. Multiple 100-yr events are determined (varying Hs, Tp, Dp) and used for multiple model runs to better account for regional and directional flooding affects. Model results are combined and compiled into scenario-specific composites of flood projection. Digital Elevation Model (DEM): Our seamless, topobathymetric digital elevation model (DEM) was based largely upon the Coastal California TopoBathy Merge Project DEM, with some modifications performed by the USGS Earth Resources Observation and Science (EROS) Center to incorporate the most recent, high-resolution topographic and bathymetric datasets available. Topography is derived from bare-earth light detection and ranging (lidar) data collected in 2009-2011 for the CA Coastal Conservancy Lidar Project and bathymetry from 2009-2010 bathymetric lidar as well as acoustic multi- and single-beam data collected primarily between 2001 and 2013. The DEM was constructed to define the shape of nearshore, beach, and cliff surfaces as accurately as possible, utilizing dozens of bathymetric and topographic data sets. These data were used to populate the majority of the Tier I and II grids. To describe and include impacts from long-term shoreline evolution, including cumulative storm activity, seasonal trends, ENSO, and SLR, the DEM was modified for each SLR scenario. Long-term shoreline (Vitousek and Barnard, 2015) and cliff (Limber and others, 2015) erosion projections were efficiently combined along the cross-shore transects to evolve the shore-normal profiles. Elevation changes from the profiles were spatially-merged for a cohesive, 3D depiction of coastal evolution used to modify the DEM. These data are used to generate initial profiles of the 4,802 CSTs used for Phase 2 Tier III XBeach modeling and determining final projected flood depths in each SLR scenario. All data are referenced to NAD83 horizontal datum and NAVD88 vertical datum. Data for Tiers II and III are projected in UTM, zone 11. Outputs include: Projected water levels for the storm and sea-level rise scenario indicated. Data correspond to the near-shore region including areas vulnerable to coastal flooding due to storm surge, sea-level anomalies, tide elevation, and wave run-up during the same storm and sea-level rise simulation. References Cited: Howell, S., Smith-Konter, B., Frazer, N., Tong, X., and Sandwell, D., 2016, The vertical fingerprint of earthquake cycle loading in southern California: Nature Geoscience, v. 9, p. 611-614, doi:10.1038/ngeo2741. Limber, P., Barnard, P.L. and Hapke., C., 2015, Towards projecting the retreat of California’s coastal cliffs during the 21st Century: in, Wang, P., Rosati, J.D., and Cheng, J., (eds.), The Proceedings of the Coastal Sediments: 2015, World Scientific, 14 p., doi:10.1142/9789814689977_0245 Roelvink, J.A., Reniers, A., van Dongeren, A.R., van Thiel de Vries, J., McCall, R., and Lescinski, J., 2009, Modeling storm impacts on beaches, dunes and barrier islands: Coastal Engineering, v. 56, p. 1,133–1,152, doi:10.1016/j.coastaleng.2009.08.006. Tolman, H.L., Balasubramaniyan, B., Burroughs, L.D., Chalikov, D.V., Chao, Y.Y., Chen H.S., Gerald, V.M., 2002, Development and implementation of wind generated ocean surface wave models at NCEP: Weather and Forecasting, v. 17, p. 311-333. Vitousek, S. and Barnard, P.L., 2015, A non-linear, implicit one-line model to predict long-term shoreline change: in, Wang, P., Rosati, J.D., and Cheng, J., (eds.), The Proceedings of the Coastal Sediments: 2015, World Scientific, 14 p., doi:10.1142/9789814689977_0215.

Projected Hazard: Model-derived total water levels (in meters) for the given storm condition and sea-level rise (SLR) scenario. Model Summary: The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal planners with critical storm-hazards information that can be used to increase public safety, mitigate physical damages, and more effectively manage and allocate resources within complex coastal settings. Phase 2 data for Southern California include flood-hazard information for the coast from the border of Mexico to Pt. Conception. Several changes from Phase 1 projections are reflected in many areas; please read the model summary and inspect output carefully. Data are complete for the information presented. Details: Model background: The CoSMoS model comprises three tiers. Tier I consists of one Delft3D hydrodynamics FLOW grid for computation of tides, water level variations, flows, and currents and one SWAN grid for computation of wave generation and propagation across the continental shelf. The FLOW and SWAN models are two-way coupled so that tidal currents are accounted for in wave propagation and growth and conversely, so that orbital velocities generated by waves impart changes on tidal currents. The Tier I SWAN and FLOW models consist of identical structured curvilinear grids that extend from far offshore to the shore and range in resolution from 0.5 km in the offshore to 0.2 km in the nearshore. Spatially varying astronomic tidal amplitudes and phases and steric rises in water levels due to large-scale effects (for example, a prolonged rise in sea level) are applied along all open boundaries of the Tier I FLOW grid. Winds (split into eastward and northward components) and sea-level pressure (SLP) fields from CaRD10 (Dr. Dan Cayan, Scripps Institute of Oceanography, Los Angeles, California, written commun., 2014) that vary in both space and time are applied to all grid cells at each model time-step. Deep-water wave conditions, applied at the open boundaries of the Tier I SWAN model runs, were projected for the 21st century Representative Concentration Pathway (RCP) 4.5 climate scenario (2011-2100) using the WaveWatch III numerical wave model (Tolman and others, 2002) and 3-hourly winds from the GFDL-ESM2M Global Climate Model (GCM). Tier II provides higher resolution near the shore and in areas that require greater resolution of physical processes (such as bays, harbors, and estuaries). A single nested outer grid and multiple two-way coupled domain decomposition (DD) structured grids allow for local grid refinement and higher resolution where needed. Tier II was segmented into 11 sections along the Southern California Bight, to reduce computation time and complete runs within computational limitations. Water-level and Neumann time-series, extracted from Tier I simulations, are applied to the shore-parallel and lateral open boundaries of each Tier II sub-model outer grid respectively. Several of the sub-models proved to be unstable with lateral Neumann boundaries; for those cases one or both of the lateral boundaries were converted to water-level time-series or left unassigned. The open-boundary time-series are extracted from completed Tier I simulations so that there is no communication from Tier II to Tier I. Because this one-way nesting could produce erroneous results near the boundaries of Tier II and because data near any model boundary are always suspect, Tier II sub-model extents were designed to overlap in the along-coast direction. In the landward direction, Tier II DD grids extend to the 10-m topographic contour; exceptions exist where channels (such as the Los Angeles River) or other low-lying regions extend very far inland. Space- and time-varying wind and SLP fields, identical to those used in Tier I simulations, are applied to all Tier II DD grids to allow for wind-setup and local inverse barometer effects (IBE, rise or depression of water levels in response to atmospheric pressure gradients). A total of 42 time-series fluvial discharges are included in the Tier II FLOW domains in an effort to simulate exacerbated flooding caused by backflow at the confluence of high river seaward flows and elevated coastal surge levels migrating inland. Time-varying fluvial discharges are applied either at the closed boundaries or distributed as point sources within the relevant model domains. Wave computations are accomplished with the SWAN model using two grids for each Tier II sub-model: one larger grid covering the same area as the outer FLOW grid and a second finer resolution two-way coupled nearshore nested grid. The nearshore grid extends from approximately 800-1,000 m water depth up to 8-10 m elevations onshore. The landward extension is included to allow for wave computations of the higher SLR scenarios. Time- and space-varying 2D wave spectra extracted from previously completed Tier I simulations are applied approximately every kilometer along the open boundaries of the outer Tier II sub-model SWAN grids. The same space- and time-varying wind fields used in Tier I simulations are also applied to both Tier II SWAN grids to allow for computation of local wave generation. Tier III for the entire Southern California Bight consists of 4,802 cross-shore transects (CST) spaced approximately 100 m apart in the along-shore direction. The profiles extend from the -15 m isobath to at least 10 m above NAVD88. The CSTs are truncated for cases where a lagoon or other waterway exists on the landward end of the profile. Time-varying water levels and wave parameters (significant wave heights, Hs; peak periods, Tp; and peak incident wave directions, Dp), extracted from Tier II grid cells that coincide with the seaward end of the CSTs, are applied at the open boundary of each CST. The XBeach model is run in a hydrostatic (no vertical pressure gradients) mode including event-based morphodynamic change. Wave propagation, two-way wave-current interaction, water-level variations, and wave runup are computed at each transect. XBeach simulations are included in the CoSMoS model to account for infragravity waves that can significantly extend the reach of wave runup (Roelvink and others, 2009) compared to short-wave incident waves. The U.S. west coast is particularly susceptible to infragravity waves at the shore due to breaking of long-period swell waves (Tp > 15). Resulting water levels (WLs) from both Delft3D (high interest bays and marshes) and open-coast XBeach (CSTs) were spatially combined and interpolated to a 10 m grid. These WL elevations are differenced from the originating 2 m digital elevation model (DEM) to determine final flooding extent and depth of flooding. Events: The model system is run for pre-determined scenarios of interest such as the 1-yr or 100-yr storm event in combination with sea-level rise. Storms are first identified from time-series of total water level proxies (TWLpx) at the shore. TWLpx are computed for the majority of the 21st century (2010-2100), assuming a linear super-position of the major processes that contribute to the overall total water level. TWLpx time-series are then evaluated for extreme events, which define the boundary conditions for subsequent modeling with CoSMoS. Multiple 100-yr events are determined (varying Hs, Tp, Dp) and used for multiple model runs to better account for regional and directional flooding affects. Model results are combined and compiled into scenario-specific composites of flood projection. Digital Elevation Model (DEM): Our seamless, topobathymetric digital elevation model (DEM) was based largely upon the Coastal California TopoBathy Merge Project DEM, with some modifications performed by the USGS Earth Resources Observation and Science (EROS) Center to incorporate the most recent, high-resolution topographic and bathymetric datasets available. Topography is derived from bare-earth light detection and ranging (lidar) data collected in 2009-2011 for the CA Coastal Conservancy Lidar Project and bathymetry from 2009-2010 bathymetric lidar as well as acoustic multi- and single-beam data collected primarily between 2001 and 2013. The DEM was constructed to define the shape of nearshore, beach, and cliff surfaces as accurately as possible, utilizing dozens of bathymetric and topographic data sets. These data were used to populate the majority of the Tier I and II grids. To describe and include impacts from long-term shoreline evolution, including cumulative storm activity, seasonal trends, ENSO, and SLR, the DEM was modified for each SLR scenario. Long-term shoreline (Vitousek and Barnard, 2015) and cliff (Limber and others, 2015) erosion projections were efficiently combined along the cross-shore transects to evolve the shore-normal profiles. Elevation changes from the profiles were spatially-merged for a cohesive, 3D depiction of coastal evolution used to modify the DEM. These data are used to generate initial profiles of the 4,802 CSTs used for Phase 2 Tier III XBeach modeling and determining final projected flood depths in each SLR scenario. All data are referenced to NAD83 horizontal datum and NAVD88 vertical datum. Data for Tiers II and III are projected in UTM, zone 11. Outputs include: Projected water levels for the storm and sea-level rise scenario indicated. Data correspond to the near-shore region including areas vulnerable to coastal flooding due to storm surge, sea-level anomalies, tide elevation, and wave run-up during the same storm and sea-level rise simulation. References Cited: Howell, S., Smith-Konter, B., Frazer, N., Tong, X., and Sandwell, D., 2016, The vertical fingerprint of earthquake cycle loading in southern California: Nature Geoscience, v. 9, p. 611-614, doi:10.1038/ngeo2741. Limber, P., Barnard, P.L. and Hapke., C., 2015, Towards projecting the retreat of California’s coastal cliffs during the 21st Century: in, Wang, P., Rosati, J.D., and Cheng, J., (eds.), The Proceedings of the Coastal Sediments: 2015, World Scientific, 14 p., doi:10.1142/9789814689977_0245 Roelvink, J.A., Reniers, A., van Dongeren, A.R., van Thiel de Vries, J., McCall, R., and Lescinski, J., 2009, Modeling storm impacts on beaches, dunes and barrier islands: Coastal Engineering, v. 56, p. 1,133–1,152, doi:10.1016/j.coastaleng.2009.08.006. Tolman, H.L., Balasubramaniyan, B., Burroughs, L.D., Chalikov, D.V., Chao, Y.Y., Chen H.S., Gerald, V.M., 2002, Development and implementation of wind generated ocean surface wave models at NCEP: Weather and Forecasting, v. 17, p. 311-333. Vitousek, S. and Barnard, P.L., 2015, A non-linear, implicit one-line model to predict long-term shoreline change: in, Wang, P., Rosati, J.D., and Cheng, J., (eds.), The Proceedings of the Coastal Sediments: 2015, World Scientific, 14 p., doi:10.1142/9789814689977_0215.

Projected Hazard: Model-derived total water levels (in meters) for the given storm condition and sea-level rise (SLR) scenario. Model Summary: The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal planners with critical storm-hazards information that can be used to increase public safety, mitigate physical damages, and more effectively manage and allocate resources within complex coastal settings. Phase 2 data for Southern California include flood-hazard information for the coast from the border of Mexico to Pt. Conception. Several changes from Phase 1 projections are reflected in many areas; please read the model summary and inspect output carefully. Data are complete for the information presented. Details: Model background: The CoSMoS model comprises three tiers. Tier I consists of one Delft3D hydrodynamics FLOW grid for computation of tides, water level variations, flows, and currents and one SWAN grid for computation of wave generation and propagation across the continental shelf. The FLOW and SWAN models are two-way coupled so that tidal currents are accounted for in wave propagation and growth and conversely, so that orbital velocities generated by waves impart changes on tidal currents. The Tier I SWAN and FLOW models consist of identical structured curvilinear grids that extend from far offshore to the shore and range in resolution from 0.5 km in the offshore to 0.2 km in the nearshore. Spatially varying astronomic tidal amplitudes and phases and steric rises in water levels due to large-scale effects (for example, a prolonged rise in sea level) are applied along all open boundaries of the Tier I FLOW grid. Winds (split into eastward and northward components) and sea-level pressure (SLP) fields from CaRD10 (Dr. Dan Cayan, Scripps Institute of Oceanography, Los Angeles, California, written commun., 2014) that vary in both space and time are applied to all grid cells at each model time-step. Deep-water wave conditions, applied at the open boundaries of the Tier I SWAN model runs, were projected for the 21st century Representative Concentration Pathway (RCP) 4.5 climate scenario (2011-2100) using the WaveWatch III numerical wave model (Tolman and others, 2002) and 3-hourly winds from the GFDL-ESM2M Global Climate Model (GCM). Tier II provides higher resolution near the shore and in areas that require greater resolution of physical processes (such as bays, harbors, and estuaries). A single nested outer grid and multiple two-way coupled domain decomposition (DD) structured grids allow for local grid refinement and higher resolution where needed. Tier II was segmented into 11 sections along the Southern California Bight, to reduce computation time and complete runs within computational limitations. Water-level and Neumann time-series, extracted from Tier I simulations, are applied to the shore-parallel and lateral open boundaries of each Tier II sub-model outer grid respectively. Several of the sub-models proved to be unstable with lateral Neumann boundaries; for those cases one or both of the lateral boundaries were converted to water-level time-series or left unassigned. The open-boundary time-series are extracted from completed Tier I simulations so that there is no communication from Tier II to Tier I. Because this one-way nesting could produce erroneous results near the boundaries of Tier II and because data near any model boundary are always suspect, Tier II sub-model extents were designed to overlap in the along-coast direction. In the landward direction, Tier II DD grids extend to the 10-m topographic contour; exceptions exist where channels (such as the Los Angeles River) or other low-lying regions extend very far inland. Space- and time-varying wind and SLP fields, identical to those used in Tier I simulations, are applied to all Tier II DD grids to allow for wind-setup and local inverse barometer effects (IBE, rise or depression of water levels in response to atmospheric pressure gradients). A total of 42 time-series fluvial discharges are included in the Tier II FLOW domains in an effort to simulate exacerbated flooding caused by backflow at the confluence of high river seaward flows and elevated coastal surge levels migrating inland. Time-varying fluvial discharges are applied either at the closed boundaries or distributed as point sources within the relevant model domains. Wave computations are accomplished with the SWAN model using two grids for each Tier II sub-model: one larger grid covering the same area as the outer FLOW grid and a second finer resolution two-way coupled nearshore nested grid. The nearshore grid extends from approximately 800-1,000 m water depth up to 8-10 m elevations onshore. The landward extension is included to allow for wave computations of the higher SLR scenarios. Time- and space-varying 2D wave spectra extracted from previously completed Tier I simulations are applied approximately every kilometer along the open boundaries of the outer Tier II sub-model SWAN grids. The same space- and time-varying wind fields used in Tier I simulations are also applied to both Tier II SWAN grids to allow for computation of local wave generation. Tier III for the entire Southern California Bight consists of 4,802 cross-shore transects (CST) spaced approximately 100 m apart in the along-shore direction. The profiles extend from the -15 m isobath to at least 10 m above NAVD88. The CSTs are truncated for cases where a lagoon or other waterway exists on the landward end of the profile. Time-varying water levels and wave parameters (significant wave heights, Hs; peak periods, Tp; and peak incident wave directions, Dp), extracted from Tier II grid cells that coincide with the seaward end of the CSTs, are applied at the open boundary of each CST. The XBeach model is run in a hydrostatic (no vertical pressure gradients) mode including event-based morphodynamic change. Wave propagation, two-way wave-current interaction, water-level variations, and wave runup are computed at each transect. XBeach simulations are included in the CoSMoS model to account for infragravity waves that can significantly extend the reach of wave runup (Roelvink and others, 2009) compared to short-wave incident waves. The U.S. west coast is particularly susceptible to infragravity waves at the shore due to breaking of long-period swell waves (Tp > 15). Resulting water levels (WLs) from both Delft3D (high interest bays and marshes) and open-coast XBeach (CSTs) were spatially combined and interpolated to a 10 m grid. These WL elevations are differenced from the originating 2 m digital elevation model (DEM) to determine final flooding extent and depth of flooding. Events: The model system is run for pre-determined scenarios of interest such as the 1-yr or 100-yr storm event in combination with sea-level rise. Storms are first identified from time-series of total water level proxies (TWLpx) at the shore. TWLpx are computed for the majority of the 21st century (2010-2100), assuming a linear super-position of the major processes that contribute to the overall total water level. TWLpx time-series are then evaluated for extreme events, which define the boundary conditions for subsequent modeling with CoSMoS. Multiple 100-yr events are determined (varying Hs, Tp, Dp) and used for multiple model runs to better account for regional and directional flooding affects. Model results are combined and compiled into scenario-specific composites of flood projection. Digital Elevation Model (DEM): Our seamless, topobathymetric digital elevation model (DEM) was based largely upon the Coastal California TopoBathy Merge Project DEM, with some modifications performed by the USGS Earth Resources Observation and Science (EROS) Center to incorporate the most recent, high-resolution topographic and bathymetric datasets available. Topography is derived from bare-earth light detection and ranging (lidar) data collected in 2009-2011 for the CA Coastal Conservancy Lidar Project and bathymetry from 2009-2010 bathymetric lidar as well as acoustic multi- and single-beam data collected primarily between 2001 and 2013. The DEM was constructed to define the shape of nearshore, beach, and cliff surfaces as accurately as possible, utilizing dozens of bathymetric and topographic data sets. These data were used to populate the majority of the Tier I and II grids. To describe and include impacts from long-term shoreline evolution, including cumulative storm activity, seasonal trends, ENSO, and SLR, the DEM was modified for each SLR scenario. Long-term shoreline (Vitousek and Barnard, 2015) and cliff (Limber and others, 2015) erosion projections were efficiently combined along the cross-shore transects to evolve the shore-normal profiles. Elevation changes from the profiles were spatially-merged for a cohesive, 3D depiction of coastal evolution used to modify the DEM. These data are used to generate initial profiles of the 4,802 CSTs used for Phase 2 Tier III XBeach modeling and determining final projected flood depths in each SLR scenario. All data are referenced to NAD83 horizontal datum and NAVD88 vertical datum. Data for Tiers II and III are projected in UTM, zone 11. Outputs include: Projected water levels for the storm and sea-level rise scenario indicated. Data correspond to the near-shore region including areas vulnerable to coastal flooding due to storm surge, sea-level anomalies, tide elevation, and wave run-up during the same storm and sea-level rise simulation. References Cited: Howell, S., Smith-Konter, B., Frazer, N., Tong, X., and Sandwell, D., 2016, The vertical fingerprint of earthquake cycle loading in southern California: Nature Geoscience, v. 9, p. 611-614, doi:10.1038/ngeo2741. Limber, P., Barnard, P.L. and Hapke., C., 2015, Towards projecting the retreat of California’s coastal cliffs during the 21st Century: in, Wang, P., Rosati, J.D., and Cheng, J., (eds.), The Proceedings of the Coastal Sediments: 2015, World Scientific, 14 p., doi:10.1142/9789814689977_0245 Roelvink, J.A., Reniers, A., van Dongeren, A.R., van Thiel de Vries, J., McCall, R., and Lescinski, J., 2009, Modeling storm impacts on beaches, dunes and barrier islands: Coastal Engineering, v. 56, p. 1,133–1,152, doi:10.1016/j.coastaleng.2009.08.006. Tolman, H.L., Balasubramaniyan, B., Burroughs, L.D., Chalikov, D.V., Chao, Y.Y., Chen H.S., Gerald, V.M., 2002, Development and implementation of wind generated ocean surface wave models at NCEP: Weather and Forecasting, v. 17, p. 311-333. Vitousek, S. and Barnard, P.L., 2015, A non-linear, implicit one-line model to predict long-term shoreline change: in, Wang, P., Rosati, J.D., and Cheng, J., (eds.), The Proceedings of the Coastal Sediments: 2015, World Scientific, 14 p., doi:10.1142/9789814689977_0215.

Projected Hazard: Model-derived total water levels (in meters) for the given storm condition and sea-level rise (SLR) scenario. Model Summary: The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal planners with critical storm-hazards information that can be used to increase public safety, mitigate physical damages, and more effectively manage and allocate resources within complex coastal settings. Phase 2 data for Southern California include flood-hazard information for the coast from the border of Mexico to Pt. Conception. Several changes from Phase 1 projections are reflected in many areas; please read the model summary and inspect output carefully. Data are complete for the information presented. Details: Model background: The CoSMoS model comprises three tiers. Tier I consists of one Delft3D hydrodynamics FLOW grid for computation of tides, water level variations, flows, and currents and one SWAN grid for computation of wave generation and propagation across the continental shelf. The FLOW and SWAN models are two-way coupled so that tidal currents are accounted for in wave propagation and growth and conversely, so that orbital velocities generated by waves impart changes on tidal currents. The Tier I SWAN and FLOW models consist of identical structured curvilinear grids that extend from far offshore to the shore and range in resolution from 0.5 km in the offshore to 0.2 km in the nearshore. Spatially varying astronomic tidal amplitudes and phases and steric rises in water levels due to large-scale effects (for example, a prolonged rise in sea level) are applied along all open boundaries of the Tier I FLOW grid. Winds (split into eastward and northward components) and sea-level pressure (SLP) fields from CaRD10 (Dr. Dan Cayan, Scripps Institute of Oceanography, Los Angeles, California, written commun., 2014) that vary in both space and time are applied to all grid cells at each model time-step. Deep-water wave conditions, applied at the open boundaries of the Tier I SWAN model runs, were projected for the 21st century Representative Concentration Pathway (RCP) 4.5 climate scenario (2011-2100) using the WaveWatch III numerical wave model (Tolman and others, 2002) and 3-hourly winds from the GFDL-ESM2M Global Climate Model (GCM). Tier II provides higher resolution near the shore and in areas that require greater resolution of physical processes (such as bays, harbors, and estuaries). A single nested outer grid and multiple two-way coupled domain decomposition (DD) structured grids allow for local grid refinement and higher resolution where needed. Tier II was segmented into 11 sections along the Southern California Bight, to reduce computation time and complete runs within computational limitations. Water-level and Neumann time-series, extracted from Tier I simulations, are applied to the shore-parallel and lateral open boundaries of each Tier II sub-model outer grid respectively. Several of the sub-models proved to be unstable with lateral Neumann boundaries; for those cases one or both of the lateral boundaries were converted to water-level time-series or left unassigned. The open-boundary time-series are extracted from completed Tier I simulations so that there is no communication from Tier II to Tier I. Because this one-way nesting could produce erroneous results near the boundaries of Tier II and because data near any model boundary are always suspect, Tier II sub-model extents were designed to overlap in the along-coast direction. In the landward direction, Tier II DD grids extend to the 10-m topographic contour; exceptions exist where channels (such as the Los Angeles River) or other low-lying regions extend very far inland. Space- and time-varying wind and SLP fields, identical to those used in Tier I simulations, are applied to all Tier II DD grids to allow for wind-setup and local inverse barometer effects (IBE, rise or depression of water levels in response to atmospheric pressure gradients). A total of 42 time-series fluvial discharges are included in the Tier II FLOW domains in an effort to simulate exacerbated flooding caused by backflow at the confluence of high river seaward flows and elevated coastal surge levels migrating inland. Time-varying fluvial discharges are applied either at the closed boundaries or distributed as point sources within the relevant model domains. Wave computations are accomplished with the SWAN model using two grids for each Tier II sub-model: one larger grid covering the same area as the outer FLOW grid and a second finer resolution two-way coupled nearshore nested grid. The nearshore grid extends from approximately 800-1,000 m water depth up to 8-10 m elevations onshore. The landward extension is included to allow for wave computations of the higher SLR scenarios. Time- and space-varying 2D wave spectra extracted from previously completed Tier I simulations are applied approximately every kilometer along the open boundaries of the outer Tier II sub-model SWAN grids. The same space- and time-varying wind fields used in Tier I simulations are also applied to both Tier II SWAN grids to allow for computation of local wave generation. Tier III for the entire Southern California Bight consists of 4,802 cross-shore transects (CST) spaced approximately 100 m apart in the along-shore direction. The profiles extend from the -15 m isobath to at least 10 m above NAVD88. The CSTs are truncated for cases where a lagoon or other waterway exists on the landward end of the profile. Time-varying water levels and wave parameters (significant wave heights, Hs; peak periods, Tp; and peak incident wave directions, Dp), extracted from Tier II grid cells that coincide with the seaward end of the CSTs, are applied at the open boundary of each CST. The XBeach model is run in a hydrostatic (no vertical pressure gradients) mode including event-based morphodynamic change. Wave propagation, two-way wave-current interaction, water-level variations, and wave runup are computed at each transect. XBeach simulations are included in the CoSMoS model to account for infragravity waves that can significantly extend the reach of wave runup (Roelvink and others, 2009) compared to short-wave incident waves. The U.S. west coast is particularly susceptible to infragravity waves at the shore due to breaking of long-period swell waves (Tp > 15). Resulting water levels (WLs) from both Delft3D (high interest bays and marshes) and open-coast XBeach (CSTs) were spatially combined and interpolated to a 10 m grid. These WL elevations are differenced from the originating 2 m digital elevation model (DEM) to determine final flooding extent and depth of flooding. Events: The model system is run for pre-determined scenarios of interest such as the 1-yr or 100-yr storm event in combination with sea-level rise. Storms are first identified from time-series of total water level proxies (TWLpx) at the shore. TWLpx are computed for the majority of the 21st century (2010-2100), assuming a linear super-position of the major processes that contribute to the overall total water level. TWLpx time-series are then evaluated for extreme events, which define the boundary conditions for subsequent modeling with CoSMoS. Multiple 100-yr events are determined (varying Hs, Tp, Dp) and used for multiple model runs to better account for regional and directional flooding affects. Model results are combined and compiled into scenario-specific composites of flood projection. Digital Elevation Model (DEM): Our seamless, topobathymetric digital elevation model (DEM) was based largely upon the Coastal California TopoBathy Merge Project DEM, with some modifications performed by the USGS Earth Resources Observation and Science (EROS) Center to incorporate the most recent, high-resolution topographic and bathymetric datasets available. Topography is derived from bare-earth light detection and ranging (lidar) data collected in 2009-2011 for the CA Coastal Conservancy Lidar Project and bathymetry from 2009-2010 bathymetric lidar as well as acoustic multi- and single-beam data collected primarily between 2001 and 2013. The DEM was constructed to define the shape of nearshore, beach, and cliff surfaces as accurately as possible, utilizing dozens of bathymetric and topographic data sets. These data were used to populate the majority of the Tier I and II grids. To describe and include impacts from long-term shoreline evolution, including cumulative storm activity, seasonal trends, ENSO, and SLR, the DEM was modified for each SLR scenario. Long-term shoreline (Vitousek and Barnard, 2015) and cliff (Limber and others, 2015) erosion projections were efficiently combined along the cross-shore transects to evolve the shore-normal profiles. Elevation changes from the profiles were spatially-merged for a cohesive, 3D depiction of coastal evolution used to modify the DEM. These data are used to generate initial profiles of the 4,802 CSTs used for Phase 2 Tier III XBeach modeling and determining final projected flood depths in each SLR scenario. All data are referenced to NAD83 horizontal datum and NAVD88 vertical datum. Data for Tiers II and III are projected in UTM, zone 11. Outputs include: Projected water levels for the storm and sea-level rise scenario indicated. Data correspond to the near-shore region including areas vulnerable to coastal flooding due to storm surge, sea-level anomalies, tide elevation, and wave run-up during the same storm and sea-level rise simulation. References Cited: Howell, S., Smith-Konter, B., Frazer, N., Tong, X., and Sandwell, D., 2016, The vertical fingerprint of earthquake cycle loading in southern California: Nature Geoscience, v. 9, p. 611-614, doi:10.1038/ngeo2741. Limber, P., Barnard, P.L. and Hapke., C., 2015, Towards projecting the retreat of California’s coastal cliffs during the 21st Century: in, Wang, P., Rosati, J.D., and Cheng, J., (eds.), The Proceedings of the Coastal Sediments: 2015, World Scientific, 14 p., doi:10.1142/9789814689977_0245 Roelvink, J.A., Reniers, A., van Dongeren, A.R., van Thiel de Vries, J., McCall, R., and Lescinski, J., 2009, Modeling storm impacts on beaches, dunes and barrier islands: Coastal Engineering, v. 56, p. 1,133–1,152, doi:10.1016/j.coastaleng.2009.08.006. Tolman, H.L., Balasubramaniyan, B., Burroughs, L.D., Chalikov, D.V., Chao, Y.Y., Chen H.S., Gerald, V.M., 2002, Development and implementation of wind generated ocean surface wave models at NCEP: Weather and Forecasting, v. 17, p. 311-333. Vitousek, S. and Barnard, P.L., 2015, A non-linear, implicit one-line model to predict long-term shoreline change: in, Wang, P., Rosati, J.D., and Cheng, J., (eds.), The Proceedings of the Coastal Sediments: 2015, World Scientific, 14 p., doi:10.1142/9789814689977_0215.

Projected Hazard: Model-derived significant wave height (in meters) for the given storm condition and sea-level rise (SLR) scenario. Model Summary: The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal planners with critical storm-hazards information that can be used to increase public safety, mitigate physical damages, and more effectively manage and allocate resources within complex coastal settings. Phase 2 data for Southern California include flood-hazard information for the coast from the border of Mexico to Pt. Conception. Several changes from Phase 1 projections are reflected in many areas; please read the model summary and inspect output carefully. Data are complete for the information presented. Details: Model background: The CoSMoS model comprises three tiers. Tier I consists of one Delft3D hydrodynamics FLOW grid for computation of tides, water level variations, flows, and currents and one SWAN grid for computation of wave generation and propagation across the continental shelf. The FLOW and SWAN models are two-way coupled so that tidal currents are accounted for in wave propagation and growth and conversely, so that orbital velocities generated by waves impart changes on tidal currents. The Tier I SWAN and FLOW models consist of identical structured curvilinear grids that extend from far offshore to the shore and range in resolution from 0.5 km in the offshore to 0.2 km in the nearshore. Spatially varying astronomic tidal amplitudes and phases and steric rises in water levels due to large-scale effects (for example, a prolonged rise in sea level) are applied along all open boundaries of the Tier I FLOW grid. Winds (split into eastward and northward components) and sea-level pressure (SLP) fields from CaRD10 (Dr. Dan Cayan, Scripps Institute of Oceanography, Los Angeles, California, written commun., 2014) that vary in both space and time are applied to all grid cells at each model time-step. Deep-water wave conditions, applied at the open boundaries of the Tier I SWAN model runs, were projected for the 21st century Representative Concentration Pathway (RCP) 4.5 climate scenario (2011-2100) using the WaveWatch III numerical wave model (Tolman and others, 2002) and 3-hourly winds from the GFDL-ESM2M Global Climate Model (GCM). Tier II provides higher resolution near the shore and in areas that require greater resolution of physical processes (such as bays, harbors, and estuaries). A single nested outer grid and multiple two-way coupled domain decomposition (DD) structured grids allow for local grid refinement and higher resolution where needed. Tier II was segmented into 11 sections along the Southern California Bight, to reduce computation time and complete runs within computational limitations. Water-level and Neumann time-series, extracted from Tier I simulations, are applied to the shore-parallel and lateral open boundaries of each Tier II sub-model outer grid respectively. Several of the sub-models proved to be unstable with lateral Neumann boundaries; for those cases one or both of the lateral boundaries were converted to water-level time-series or left unassigned. The open-boundary time-series are extracted from completed Tier I simulations so that there is no communication from Tier II to Tier I. Because this one-way nesting could produce erroneous results near the boundaries of Tier II and because data near any model boundary are always suspect, Tier II sub-model extents were designed to overlap in the along-coast direction. In the landward direction, Tier II DD grids extend to the 10-m topographic contour; exceptions exist where channels (such as the Los Angeles River) or other low-lying regions extend very far inland. Space- and time-varying wind and SLP fields, identical to those used in Tier I simulations, are applied to all Tier II DD grids to allow for wind-setup and local inverse barometer effects (IBE, rise or depression of water levels in response to atmospheric pressure gradients). A total of 42 time-series fluvial discharges are included in the Tier II FLOW domains in an effort to simulate exacerbated flooding caused by backflow at the confluence of high river seaward flows and elevated coastal surge levels migrating inland. Time-varying fluvial discharges are applied either at the closed boundaries or distributed as point sources within the relevant model domains. Wave computations are accomplished with the SWAN model using two grids for each Tier II sub-model: one larger grid covering the same area as the outer FLOW grid and a second finer resolution two-way coupled nearshore nested grid. The nearshore grid extends from approximately 800-1,000 m water depth up to 8-10 m elevations onshore. The landward extension is included to allow for wave computations of the higher SLR scenarios. Time- and space-varying 2D wave spectra extracted from previously completed Tier I simulations are applied approximately every kilometer along the open boundaries of the outer Tier II sub-model SWAN grids. The same space- and time-varying wind fields used in Tier I simulations are also applied to both Tier II SWAN grids to allow for computation of local wave generation. Tier III for the entire Southern California Bight consists of 4,802 cross-shore transects (CST) spaced approximately 100 m apart in the along-shore direction. The profiles extend from the -15 m isobath to at least 10 m above NAVD88. The CSTs are truncated for cases where a lagoon or other waterway exists on the landward end of the profile. Time-varying water levels and wave parameters (significant wave heights, Hs; peak periods, Tp; and peak incident wave directions, Dp), extracted from Tier II grid cells that coincide with the seaward end of the CSTs, are applied at the open boundary of each CST. The XBeach model is run in a hydrostatic (no vertical pressure gradients) mode including event-based morphodynamic change. Wave propagation, two-way wave-current interaction, water-level variations, and wave runup are computed at each transect. XBeach simulations are included in the CoSMoS model to account for infragravity waves that can significantly extend the reach of wave runup (Roelvink and others, 2009) compared to short-wave incident waves. The U.S. west coast is particularly susceptible to infragravity waves at the shore due to breaking of long-period swell waves (Tp > 15). Resulting water levels (WLs) from both Delft3D (high interest bays and marshes) and open-coast XBeach (CSTs) were spatially combined and interpolated to a 10 m grid. These WL elevations are differenced from the originating 2 m digital elevation model (DEM) to determine final flooding extent and depth of flooding. Events: The model system is run for pre-determined scenarios of interest such as the 1-yr or 100-yr storm event in combination with sea-level rise. Storms are first identified from time-series of total water level proxies (TWLpx) at the shore. TWLpx are computed for the majority of the 21st century (2010-2100), assuming a linear super-position of the major processes that contribute to the overall total water level. TWLpx time-series are then evaluated for extreme events, which define the boundary conditions for subsequent modeling with CoSMoS. Multiple 100-yr events are determined (varying Hs, Tp, Dp) and used for multiple model runs to better account for regional and directional flooding affects. Model results are combined and compiled into scenario-specific composites of flood projection. Digital Elevation Model (DEM): Our seamless, topobathymetric digital elevation model (DEM) was based largely upon the Coastal California TopoBathy Merge Project DEM, with some modifications performed by the USGS Earth Resources Observation and Science (EROS) Center to incorporate the most recent, high-resolution topographic and bathymetric datasets available. Topography is derived from bare-earth light detection and ranging (lidar) data collected in 2009-2011 for the CA Coastal Conservancy Lidar Project and bathymetry from 2009-2010 bathymetric lidar as well as acoustic multi- and single-beam data collected primarily between 2001 and 2013. The DEM was constructed to define the shape of nearshore, beach, and cliff surfaces as accurately as possible, utilizing dozens of bathymetric and topographic data sets. These data were used to populate the majority of the Tier I and II grids. To describe and include impacts from long-term shoreline evolution, including cumulative storm activity, seasonal trends, ENSO, and SLR, the DEM was modified for each SLR scenario. Long-term shoreline (Vitousek and Barnard, 2015) and cliff (Limber and others, 2015) erosion projections were efficiently combined along the cross-shore transects to evolve the shore-normal profiles. Elevation changes from the profiles were spatially-merged for a cohesive, 3D depiction of coastal evolution used to modify the DEM. These data are used to generate initial profiles of the 4,802 CSTs used for Phase 2 Tier III XBeach modeling and determining final projected flood depths in each SLR scenario. All data are referenced to NAD83 horizontal datum and NAVD88 vertical datum. Data for Tiers II and III are projected in UTM, zone 11. Outputs include: Projected wave height for the storm and sea-level rise scenario indicated. Data correspond to the near-shore region including areas vulnerable to coastal flooding due to storm surge, sea-level anomalies, tide elevation, and wave run-up during the same storm and sea-level rise simulation. References Cited: Howell, S., Smith-Konter, B., Frazer, N., Tong, X., and Sandwell, D., 2016, The vertical fingerprint of earthquake cycle loading in southern California: Nature Geoscience, v. 9, p. 611-614, doi:10.1038/ngeo2741. Limber, P., Barnard, P.L. and Hapke., C., 2015, Towards projecting the retreat of California’s coastal cliffs during the 21st Century: in, Wang, P., Rosati, J.D., and Cheng, J., (eds.), The Proceedings of the Coastal Sediments: 2015, World Scientific, 14 p., doi:10.1142/9789814689977_0245 Roelvink, J.A., Reniers, A., van Dongeren, A.R., van Thiel de Vries, J., McCall, R., and Lescinski, J., 2009, Modeling storm impacts on beaches, dunes and barrier islands: Coastal Engineering, v. 56, p. 1,133–1,152, doi:10.1016/j.coastaleng.2009.08.006. Tolman, H.L., Balasubramaniyan, B., Burroughs, L.D., Chalikov, D.V., Chao, Y.Y., Chen H.S., Gerald, V.M., 2002, Development and implementation of wind generated ocean surface wave models at NCEP: Weather and Forecasting, v. 17, p. 311-333. Vitousek, S. and Barnard, P.L., 2015, A non-linear, implicit one-line model to predict long-term shoreline change: in, Wang, P., Rosati, J.D., and Cheng, J., (eds.), The Proceedings of the Coastal Sediments: 2015, World Scientific, 14 p., doi:10.1142/9789814689977_0215.

Projected Hazard: Model-derived significant wave height (in meters) for the given storm condition and sea-level rise (SLR) scenario. Model Summary: The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal planners with critical storm-hazards information that can be used to increase public safety, mitigate physical damages, and more effectively manage and allocate resources within complex coastal settings. Phase 2 data for Southern California include flood-hazard information for the coast from the border of Mexico to Pt. Conception. Several changes from Phase 1 projections are reflected in many areas; please read the model summary and inspect output carefully. Data are complete for the information presented. Details: Model background: The CoSMoS model comprises three tiers. Tier I consists of one Delft3D hydrodynamics FLOW grid for computation of tides, water level variations, flows, and currents and one SWAN grid for computation of wave generation and propagation across the continental shelf. The FLOW and SWAN models are two-way coupled so that tidal currents are accounted for in wave propagation and growth and conversely, so that orbital velocities generated by waves impart changes on tidal currents. The Tier I SWAN and FLOW models consist of identical structured curvilinear grids that extend from far offshore to the shore and range in resolution from 0.5 km in the offshore to 0.2 km in the nearshore. Spatially varying astronomic tidal amplitudes and phases and steric rises in water levels due to large-scale effects (for example, a prolonged rise in sea level) are applied along all open boundaries of the Tier I FLOW grid. Winds (split into eastward and northward components) and sea-level pressure (SLP) fields from CaRD10 (Dr. Dan Cayan, Scripps Institute of Oceanography, Los Angeles, California, written commun., 2014) that vary in both space and time are applied to all grid cells at each model time-step. Deep-water wave conditions, applied at the open boundaries of the Tier I SWAN model runs, were projected for the 21st century Representative Concentration Pathway (RCP) 4.5 climate scenario (2011-2100) using the WaveWatch III numerical wave model (Tolman and others, 2002) and 3-hourly winds from the GFDL-ESM2M Global Climate Model (GCM). Tier II provides higher resolution near the shore and in areas that require greater resolution of physical processes (such as bays, harbors, and estuaries). A single nested outer grid and multiple two-way coupled domain decomposition (DD) structured grids allow for local grid refinement and higher resolution where needed. Tier II was segmented into 11 sections along the Southern California Bight, to reduce computation time and complete runs within computational limitations. Water-level and Neumann time-series, extracted from Tier I simulations, are applied to the shore-parallel and lateral open boundaries of each Tier II sub-model outer grid respectively. Several of the sub-models proved to be unstable with lateral Neumann boundaries; for those cases one or both of the lateral boundaries were converted to water-level time-series or left unassigned. The open-boundary time-series are extracted from completed Tier I simulations so that there is no communication from Tier II to Tier I. Because this one-way nesting could produce erroneous results near the boundaries of Tier II and because data near any model boundary are always suspect, Tier II sub-model extents were designed to overlap in the along-coast direction. In the landward direction, Tier II DD grids extend to the 10-m topographic contour; exceptions exist where channels (such as the Los Angeles River) or other low-lying regions extend very far inland. Space- and time-varying wind and SLP fields, identical to those used in Tier I simulations, are applied to all Tier II DD grids to allow for wind-setup and local inverse barometer effects (IBE, rise or depression of water levels in response to atmospheric pressure gradients). A total of 42 time-series fluvial discharges are included in the Tier II FLOW domains in an effort to simulate exacerbated flooding caused by backflow at the confluence of high river seaward flows and elevated coastal surge levels migrating inland. Time-varying fluvial discharges are applied either at the closed boundaries or distributed as point sources within the relevant model domains. Wave computations are accomplished with the SWAN model using two grids for each Tier II sub-model: one larger grid covering the same area as the outer FLOW grid and a second finer resolution two-way coupled nearshore nested grid. The nearshore grid extends from approximately 800-1,000 m water depth up to 8-10 m elevations onshore. The landward extension is included to allow for wave computations of the higher SLR scenarios. Time- and space-varying 2D wave spectra extracted from previously completed Tier I simulations are applied approximately every kilometer along the open boundaries of the outer Tier II sub-model SWAN grids. The same space- and time-varying wind fields used in Tier I simulations are also applied to both Tier II SWAN grids to allow for computation of local wave generation. Tier III for the entire Southern California Bight consists of 4,802 cross-shore transects (CST) spaced approximately 100 m apart in the along-shore direction. The profiles extend from the -15 m isobath to at least 10 m above NAVD88. The CSTs are truncated for cases where a lagoon or other waterway exists on the landward end of the profile. Time-varying water levels and wave parameters (significant wave heights, Hs; peak periods, Tp; and peak incident wave directions, Dp), extracted from Tier II grid cells that coincide with the seaward end of the CSTs, are applied at the open boundary of each CST. The XBeach model is run in a hydrostatic (no vertical pressure gradients) mode including event-based morphodynamic change. Wave propagation, two-way wave-current interaction, water-level variations, and wave runup are computed at each transect. XBeach simulations are included in the CoSMoS model to account for infragravity waves that can significantly extend the reach of wave runup (Roelvink and others, 2009) compared to short-wave incident waves. The U.S. west coast is particularly susceptible to infragravity waves at the shore due to breaking of long-period swell waves (Tp > 15). Resulting water levels (WLs) from both Delft3D (high interest bays and marshes) and open-coast XBeach (CSTs) were spatially combined and interpolated to a 10 m grid. These WL elevations are differenced from the originating 2 m digital elevation model (DEM) to determine final flooding extent and depth of flooding. Events: The model system is run for pre-determined scenarios of interest such as the 1-yr or 100-yr storm event in combination with sea-level rise. Storms are first identified from time-series of total water level proxies (TWLpx) at the shore. TWLpx are computed for the majority of the 21st century (2010-2100), assuming a linear super-position of the major processes that contribute to the overall total water level. TWLpx time-series are then evaluated for extreme events, which define the boundary conditions for subsequent modeling with CoSMoS. Multiple 100-yr events are determined (varying Hs, Tp, Dp) and used for multiple model runs to better account for regional and directional flooding affects. Model results are combined and compiled into scenario-specific composites of flood projection. Digital Elevation Model (DEM): Our seamless, topobathymetric digital elevation model (DEM) was based largely upon the Coastal California TopoBathy Merge Project DEM, with some modifications performed by the USGS Earth Resources Observation and Science (EROS) Center to incorporate the most recent, high-resolution topographic and bathymetric datasets available. Topography is derived from bare-earth light detection and ranging (lidar) data collected in 2009-2011 for the CA Coastal Conservancy Lidar Project and bathymetry from 2009-2010 bathymetric lidar as well as acoustic multi- and single-beam data collected primarily between 2001 and 2013. The DEM was constructed to define the shape of nearshore, beach, and cliff surfaces as accurately as possible, utilizing dozens of bathymetric and topographic data sets. These data were used to populate the majority of the Tier I and II grids. To describe and include impacts from long-term shoreline evolution, including cumulative storm activity, seasonal trends, ENSO, and SLR, the DEM was modified for each SLR scenario. Long-term shoreline (Vitousek and Barnard, 2015) and cliff (Limber and others, 2015) erosion projections were efficiently combined along the cross-shore transects to evolve the shore-normal profiles. Elevation changes from the profiles were spatially-merged for a cohesive, 3D depiction of coastal evolution used to modify the DEM. These data are used to generate initial profiles of the 4,802 CSTs used for Phase 2 Tier III XBeach modeling and determining final projected flood depths in each SLR scenario. All data are referenced to NAD83 horizontal datum and NAVD88 vertical datum. Data for Tiers II and III are projected in UTM, zone 11. Outputs include: Projected wave height for the storm and sea-level rise scenario indicated. Data correspond to the near-shore region including areas vulnerable to coastal flooding due to storm surge, sea-level anomalies, tide elevation, and wave run-up during the same storm and sea-level rise simulation. References Cited: Howell, S., Smith-Konter, B., Frazer, N., Tong, X., and Sandwell, D., 2016, The vertical fingerprint of earthquake cycle loading in southern California: Nature Geoscience, v. 9, p. 611-614, doi:10.1038/ngeo2741. Limber, P., Barnard, P.L. and Hapke., C., 2015, Towards projecting the retreat of California’s coastal cliffs during the 21st Century: in, Wang, P., Rosati, J.D., and Cheng, J., (eds.), The Proceedings of the Coastal Sediments: 2015, World Scientific, 14 p., doi:10.1142/9789814689977_0245 Roelvink, J.A., Reniers, A., van Dongeren, A.R., van Thiel de Vries, J., McCall, R., and Lescinski, J., 2009, Modeling storm impacts on beaches, dunes and barrier islands: Coastal Engineering, v. 56, p. 1,133–1,152, doi:10.1016/j.coastaleng.2009.08.006. Tolman, H.L., Balasubramaniyan, B., Burroughs, L.D., Chalikov, D.V., Chao, Y.Y., Chen H.S., Gerald, V.M., 2002, Development and implementation of wind generated ocean surface wave models at NCEP: Weather and Forecasting, v. 17, p. 311-333. Vitousek, S. and Barnard, P.L., 2015, A non-linear, implicit one-line model to predict long-term shoreline change: in, Wang, P., Rosati, J.D., and Cheng, J., (eds.), The Proceedings of the Coastal Sediments: 2015, World Scientific, 14 p., doi:10.1142/9789814689977_0215.

Projected Hazard: Model-derived significant wave height (in meters) for the given storm condition and sea-level rise (SLR) scenario. Model Summary: The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal planners with critical storm-hazards information that can be used to increase public safety, mitigate physical damages, and more effectively manage and allocate resources within complex coastal settings. Phase 2 data for Southern California include flood-hazard information for the coast from the border of Mexico to Pt. Conception. Several changes from Phase 1 projections are reflected in many areas; please read the model summary and inspect output carefully. Data are complete for the information presented. Details: Model background: The CoSMoS model comprises three tiers. Tier I consists of one Delft3D hydrodynamics FLOW grid for computation of tides, water level variations, flows, and currents and one SWAN grid for computation of wave generation and propagation across the continental shelf. The FLOW and SWAN models are two-way coupled so that tidal currents are accounted for in wave propagation and growth and conversely, so that orbital velocities generated by waves impart changes on tidal currents. The Tier I SWAN and FLOW models consist of identical structured curvilinear grids that extend from far offshore to the shore and range in resolution from 0.5 km in the offshore to 0.2 km in the nearshore. Spatially varying astronomic tidal amplitudes and phases and steric rises in water levels due to large-scale effects (for example, a prolonged rise in sea level) are applied along all open boundaries of the Tier I FLOW grid. Winds (split into eastward and northward components) and sea-level pressure (SLP) fields from CaRD10 (Dr. Dan Cayan, Scripps Institute of Oceanography, Los Angeles, California, written commun., 2014) that vary in both space and time are applied to all grid cells at each model time-step. Deep-water wave conditions, applied at the open boundaries of the Tier I SWAN model runs, were projected for the 21st century Representative Concentration Pathway (RCP) 4.5 climate scenario (2011-2100) using the WaveWatch III numerical wave model (Tolman and others, 2002) and 3-hourly winds from the GFDL-ESM2M Global Climate Model (GCM). Tier II provides higher resolution near the shore and in areas that require greater resolution of physical processes (such as bays, harbors, and estuaries). A single nested outer grid and multiple two-way coupled domain decomposition (DD) structured grids allow for local grid refinement and higher resolution where needed. Tier II was segmented into 11 sections along the Southern California Bight, to reduce computation time and complete runs within computational limitations. Water-level and Neumann time-series, extracted from Tier I simulations, are applied to the shore-parallel and lateral open boundaries of each Tier II sub-model outer grid respectively. Several of the sub-models proved to be unstable with lateral Neumann boundaries; for those cases one or both of the lateral boundaries were converted to water-level time-series or left unassigned. The open-boundary time-series are extracted from completed Tier I simulations so that there is no communication from Tier II to Tier I. Because this one-way nesting could produce erroneous results near the boundaries of Tier II and because data near any model boundary are always suspect, Tier II sub-model extents were designed to overlap in the along-coast direction. In the landward direction, Tier II DD grids extend to the 10-m topographic contour; exceptions exist where channels (such as the Los Angeles River) or other low-lying regions extend very far inland. Space- and time-varying wind and SLP fields, identical to those used in Tier I simulations, are applied to all Tier II DD grids to allow for wind-setup and local inverse barometer effects (IBE, rise or depression of water levels in response to atmospheric pressure gradients). A total of 42 time-series fluvial discharges are included in the Tier II FLOW domains in an effort to simulate exacerbated flooding caused by backflow at the confluence of high river seaward flows and elevated coastal surge levels migrating inland. Time-varying fluvial discharges are applied either at the closed boundaries or distributed as point sources within the relevant model domains. Wave computations are accomplished with the SWAN model using two grids for each Tier II sub-model: one larger grid covering the same area as the outer FLOW grid and a second finer resolution two-way coupled nearshore nested grid. The nearshore grid extends from approximately 800-1,000 m water depth up to 8-10 m elevations onshore. The landward extension is included to allow for wave computations of the higher SLR scenarios. Time- and space-varying 2D wave spectra extracted from previously completed Tier I simulations are applied approximately every kilometer along the open boundaries of the outer Tier II sub-model SWAN grids. The same space- and time-varying wind fields used in Tier I simulations are also applied to both Tier II SWAN grids to allow for computation of local wave generation. Tier III for the entire Southern California Bight consists of 4,802 cross-shore transects (CST) spaced approximately 100 m apart in the along-shore direction. The profiles extend from the -15 m isobath to at least 10 m above NAVD88. The CSTs are truncated for cases where a lagoon or other waterway exists on the landward end of the profile. Time-varying water levels and wave parameters (significant wave heights, Hs; peak periods, Tp; and peak incident wave directions, Dp), extracted from Tier II grid cells that coincide with the seaward end of the CSTs, are applied at the open boundary of each CST. The XBeach model is run in a hydrostatic (no vertical pressure gradients) mode including event-based morphodynamic change. Wave propagation, two-way wave-current interaction, water-level variations, and wave runup are computed at each transect. XBeach simulations are included in the CoSMoS model to account for infragravity waves that can significantly extend the reach of wave runup (Roelvink and others, 2009) compared to short-wave incident waves. The U.S. west coast is particularly susceptible to infragravity waves at the shore due to breaking of long-period swell waves (Tp > 15). Resulting water levels (WLs) from both Delft3D (high interest bays and marshes) and open-coast XBeach (CSTs) were spatially combined and interpolated to a 10 m grid. These WL elevations are differenced from the originating 2 m digital elevation model (DEM) to determine final flooding extent and depth of flooding. Events: The model system is run for pre-determined scenarios of interest such as the 1-yr or 100-yr storm event in combination with sea-level rise. Storms are first identified from time-series of total water level proxies (TWLpx) at the shore. TWLpx are computed for the majority of the 21st century (2010-2100), assuming a linear super-position of the major processes that contribute to the overall total water level. TWLpx time-series are then evaluated for extreme events, which define the boundary conditions for subsequent modeling with CoSMoS. Multiple 100-yr events are determined (varying Hs, Tp, Dp) and used for multiple model runs to better account for regional and directional flooding affects. Model results are combined and compiled into scenario-specific composites of flood projection. Digital Elevation Model (DEM): Our seamless, topobathymetric digital elevation model (DEM) was based largely upon the Coastal California TopoBathy Merge Project DEM, with some modifications performed by the USGS Earth Resources Observation and Science (EROS) Center to incorporate the most recent, high-resolution topographic and bathymetric datasets available. Topography is derived from bare-earth light detection and ranging (lidar) data collected in 2009-2011 for the CA Coastal Conservancy Lidar Project and bathymetry from 2009-2010 bathymetric lidar as well as acoustic multi- and single-beam data collected primarily between 2001 and 2013. The DEM was constructed to define the shape of nearshore, beach, and cliff surfaces as accurately as possible, utilizing dozens of bathymetric and topographic data sets. These data were used to populate the majority of the Tier I and II grids. To describe and include impacts from long-term shoreline evolution, including cumulative storm activity, seasonal trends, ENSO, and SLR, the DEM was modified for each SLR scenario. Long-term shoreline (Vitousek and Barnard, 2015) and cliff (Limber and others, 2015) erosion projections were efficiently combined along the cross-shore transects to evolve the shore-normal profiles. Elevation changes from the profiles were spatially-merged for a cohesive, 3D depiction of coastal evolution used to modify the DEM. These data are used to generate initial profiles of the 4,802 CSTs used for Phase 2 Tier III XBeach modeling and determining final projected flood depths in each SLR scenario. All data are referenced to NAD83 horizontal datum and NAVD88 vertical datum. Data for Tiers II and III are projected in UTM, zone 11. Outputs include: Projected wave height for the storm and sea-level rise scenario indicated. Data correspond to the near-shore region including areas vulnerable to coastal flooding due to storm surge, sea-level anomalies, tide elevation, and wave run-up during the same storm and sea-level rise simulation. References Cited: Howell, S., Smith-Konter, B., Frazer, N., Tong, X., and Sandwell, D., 2016, The vertical fingerprint of earthquake cycle loading in southern California: Nature Geoscience, v. 9, p. 611-614, doi:10.1038/ngeo2741. Limber, P., Barnard, P.L. and Hapke., C., 2015, Towards projecting the retreat of California’s coastal cliffs during the 21st Century: in, Wang, P., Rosati, J.D., and Cheng, J., (eds.), The Proceedings of the Coastal Sediments: 2015, World Scientific, 14 p., doi:10.1142/9789814689977_0245 Roelvink, J.A., Reniers, A., van Dongeren, A.R., van Thiel de Vries, J., McCall, R., and Lescinski, J., 2009, Modeling storm impacts on beaches, dunes and barrier islands: Coastal Engineering, v. 56, p. 1,133–1,152, doi:10.1016/j.coastaleng.2009.08.006. Tolman, H.L., Balasubramaniyan, B., Burroughs, L.D., Chalikov, D.V., Chao, Y.Y., Chen H.S., Gerald, V.M., 2002, Development and implementation of wind generated ocean surface wave models at NCEP: Weather and Forecasting, v. 17, p. 311-333. Vitousek, S. and Barnard, P.L., 2015, A non-linear, implicit one-line model to predict long-term shoreline change: in, Wang, P., Rosati, J.D., and Cheng, J., (eds.), The Proceedings of the Coastal Sediments: 2015, World Scientific, 14 p., doi:10.1142/9789814689977_0215.

Projected Hazard: Model-derived significant wave height (in meters) for the given storm condition and sea-level rise (SLR) scenario. Model Summary: The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal planners with critical storm-hazards information that can be used to increase public safety, mitigate physical damages, and more effectively manage and allocate resources within complex coastal settings. Phase 2 data for Southern California include flood-hazard information for the coast from the border of Mexico to Pt. Conception. Several changes from Phase 1 projections are reflected in many areas; please read the model summary and inspect output carefully. Data are complete for the information presented. Details: Model background: The CoSMoS model comprises three tiers. Tier I consists of one Delft3D hydrodynamics FLOW grid for computation of tides, water level variations, flows, and currents and one SWAN grid for computation of wave generation and propagation across the continental shelf. The FLOW and SWAN models are two-way coupled so that tidal currents are accounted for in wave propagation and growth and conversely, so that orbital velocities generated by waves impart changes on tidal currents. The Tier I SWAN and FLOW models consist of identical structured curvilinear grids that extend from far offshore to the shore and range in resolution from 0.5 km in the offshore to 0.2 km in the nearshore. Spatially varying astronomic tidal amplitudes and phases and steric rises in water levels due to large-scale effects (for example, a prolonged rise in sea level) are applied along all open boundaries of the Tier I FLOW grid. Winds (split into eastward and northward components) and sea-level pressure (SLP) fields from CaRD10 (Dr. Dan Cayan, Scripps Institute of Oceanography, Los Angeles, California, written commun., 2014) that vary in both space and time are applied to all grid cells at each model time-step. Deep-water wave conditions, applied at the open boundaries of the Tier I SWAN model runs, were projected for the 21st century Representative Concentration Pathway (RCP) 4.5 climate scenario (2011-2100) using the WaveWatch III numerical wave model (Tolman and others, 2002) and 3-hourly winds from the GFDL-ESM2M Global Climate Model (GCM). Tier II provides higher resolution near the shore and in areas that require greater resolution of physical processes (such as bays, harbors, and estuaries). A single nested outer grid and multiple two-way coupled domain decomposition (DD) structured grids allow for local grid refinement and higher resolution where needed. Tier II was segmented into 11 sections along the Southern California Bight, to reduce computation time and complete runs within computational limitations. Water-level and Neumann time-series, extracted from Tier I simulations, are applied to the shore-parallel and lateral open boundaries of each Tier II sub-model outer grid respectively. Several of the sub-models proved to be unstable with lateral Neumann boundaries; for those cases one or both of the lateral boundaries were converted to water-level time-series or left unassigned. The open-boundary time-series are extracted from completed Tier I simulations so that there is no communication from Tier II to Tier I. Because this one-way nesting could produce erroneous results near the boundaries of Tier II and because data near any model boundary are always suspect, Tier II sub-model extents were designed to overlap in the along-coast direction. In the landward direction, Tier II DD grids extend to the 10-m topographic contour; exceptions exist where channels (such as the Los Angeles River) or other low-lying regions extend very far inland. Space- and time-varying wind and SLP fields, identical to those used in Tier I simulations, are applied to all Tier II DD grids to allow for wind-setup and local inverse barometer effects (IBE, rise or depression of water levels in response to atmospheric pressure gradients). A total of 42 time-series fluvial discharges are included in the Tier II FLOW domains in an effort to simulate exacerbated flooding caused by backflow at the confluence of high river seaward flows and elevated coastal surge levels migrating inland. Time-varying fluvial discharges are applied either at the closed boundaries or distributed as point sources within the relevant model domains. Wave computations are accomplished with the SWAN model using two grids for each Tier II sub-model: one larger grid covering the same area as the outer FLOW grid and a second finer resolution two-way coupled nearshore nested grid. The nearshore grid extends from approximately 800-1,000 m water depth up to 8-10 m elevations onshore. The landward extension is included to allow for wave computations of the higher SLR scenarios. Time- and space-varying 2D wave spectra extracted from previously completed Tier I simulations are applied approximately every kilometer along the open boundaries of the outer Tier II sub-model SWAN grids. The same space- and time-varying wind fields used in Tier I simulations are also applied to both Tier II SWAN grids to allow for computation of local wave generation. Tier III for the entire Southern California Bight consists of 4,802 cross-shore transects (CST) spaced approximately 100 m apart in the along-shore direction. The profiles extend from the -15 m isobath to at least 10 m above NAVD88. The CSTs are truncated for cases where a lagoon or other waterway exists on the landward end of the profile. Time-varying water levels and wave parameters (significant wave heights, Hs; peak periods, Tp; and peak incident wave directions, Dp), extracted from Tier II grid cells that coincide with the seaward end of the CSTs, are applied at the open boundary of each CST. The XBeach model is run in a hydrostatic (no vertical pressure gradients) mode including event-based morphodynamic change. Wave propagation, two-way wave-current interaction, water-level variations, and wave runup are computed at each transect. XBeach simulations are included in the CoSMoS model to account for infragravity waves that can significantly extend the reach of wave runup (Roelvink and others, 2009) compared to short-wave incident waves. The U.S. west coast is particularly susceptible to infragravity waves at the shore due to breaking of long-period swell waves (Tp > 15). Resulting water levels (WLs) from both Delft3D (high interest bays and marshes) and open-coast XBeach (CSTs) were spatially combined and interpolated to a 10 m grid. These WL elevations are differenced from the originating 2 m digital elevation model (DEM) to determine final flooding extent and depth of flooding. Events: The model system is run for pre-determined scenarios of interest such as the 1-yr or 100-yr storm event in combination with sea-level rise. Storms are first identified from time-series of total water level proxies (TWLpx) at the shore. TWLpx are computed for the majority of the 21st century (2010-2100), assuming a linear super-position of the major processes that contribute to the overall total water level. TWLpx time-series are then evaluated for extreme events, which define the boundary conditions for subsequent modeling with CoSMoS. Multiple 100-yr events are determined (varying Hs, Tp, Dp) and used for multiple model runs to better account for regional and directional flooding affects. Model results are combined and compiled into scenario-specific composites of flood projection. Digital Elevation Model (DEM): Our seamless, topobathymetric digital elevation model (DEM) was based largely upon the Coastal California TopoBathy Merge Project DEM, with some modifications performed by the USGS Earth Resources Observation and Science (EROS) Center to incorporate the most recent, high-resolution topographic and bathymetric datasets available. Topography is derived from bare-earth light detection and ranging (lidar) data collected in 2009-2011 for the CA Coastal Conservancy Lidar Project and bathymetry from 2009-2010 bathymetric lidar as well as acoustic multi- and single-beam data collected primarily between 2001 and 2013. The DEM was constructed to define the shape of nearshore, beach, and cliff surfaces as accurately as possible, utilizing dozens of bathymetric and topographic data sets. These data were used to populate the majority of the Tier I and II grids. To describe and include impacts from long-term shoreline evolution, including cumulative storm activity, seasonal trends, ENSO, and SLR, the DEM was modified for each SLR scenario. Long-term shoreline (Vitousek and Barnard, 2015) and cliff (Limber and others, 2015) erosion projections were efficiently combined along the cross-shore transects to evolve the shore-normal profiles. Elevation changes from the profiles were spatially-merged for a cohesive, 3D depiction of coastal evolution used to modify the DEM. These data are used to generate initial profiles of the 4,802 CSTs used for Phase 2 Tier III XBeach modeling and determining final projected flood depths in each SLR scenario. All data are referenced to NAD83 horizontal datum and NAVD88 vertical datum. Data for Tiers II and III are projected in UTM, zone 11. Outputs include: Projected wave height for the storm and sea-level rise scenario indicated. Data correspond to the near-shore region including areas vulnerable to coastal flooding due to storm surge, sea-level anomalies, tide elevation, and wave run-up during the same storm and sea-level rise simulation. References Cited: Howell, S., Smith-Konter, B., Frazer, N., Tong, X., and Sandwell, D., 2016, The vertical fingerprint of earthquake cycle loading in southern California: Nature Geoscience, v. 9, p. 611-614, doi:10.1038/ngeo2741. Limber, P., Barnard, P.L. and Hapke., C., 2015, Towards projecting the retreat of California’s coastal cliffs during the 21st Century: in, Wang, P., Rosati, J.D., and Cheng, J., (eds.), The Proceedings of the Coastal Sediments: 2015, World Scientific, 14 p., doi:10.1142/9789814689977_0245 Roelvink, J.A., Reniers, A., van Dongeren, A.R., van Thiel de Vries, J., McCall, R., and Lescinski, J., 2009, Modeling storm impacts on beaches, dunes and barrier islands: Coastal Engineering, v. 56, p. 1,133–1,152, doi:10.1016/j.coastaleng.2009.08.006. Tolman, H.L., Balasubramaniyan, B., Burroughs, L.D., Chalikov, D.V., Chao, Y.Y., Chen H.S., Gerald, V.M., 2002, Development and implementation of wind generated ocean surface wave models at NCEP: Weather and Forecasting, v. 17, p. 311-333. Vitousek, S. and Barnard, P.L., 2015, A non-linear, implicit one-line model to predict long-term shoreline change: in, Wang, P., Rosati, J.D., and Cheng, J., (eds.), The Proceedings of the Coastal Sediments: 2015, World Scientific, 14 p., doi:10.1142/9789814689977_0215.

Maximum depth of flooding surface (in cm) in the region landward of the present day shoreline that is inundated for the storm condition and sea-level rise (SLR) scenario indicated. Note: Duration datasets may have occasional gaps in open-coast sections. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal planners with critical storm-hazards information that can be used to increase public safety, mitigate physical damages, and more effectively manage and allocate resources within complex coastal settings. Model details and data sources are outlined in CoSMoS_3.0_Phase_2_Southern_California_Bight:_Summary_of_data_and_methods (available at https://www.sciencebase.gov/catalog/file/get/57f1d4f3e4b0bc0bebfee139?name=CoSMoS_SoCalv3_Phase2_summary_of_methods.pdf). Phase 2 data for Southern California include flood-hazard information for the coast from the border of Mexico to Pt. Conception. Several changes from Phase 1 projections are reflected in many areas; please read the Summary of methods and inspect output carefully. Data are complete for the information presented.

Maximum depth of flooding surface (in cm) in the region landward of the present day shoreline that is inundated for the storm condition and sea-level rise (SLR) scenario indicated. Note: Duration datasets may have occasional gaps in open-coast sections. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal planners with critical storm-hazards information that can be used to increase public safety, mitigate physical damages, and more effectively manage and allocate resources within complex coastal settings. Model details and data sources are outlined in CoSMoS_3.0_Phase_2_Southern_California_Bight:_Summary_of_data_and_methods (available at https://www.sciencebase.gov/catalog/file/get/57f1d4f3e4b0bc0bebfee139?name=CoSMoS_SoCalv3_Phase2_summary_of_methods.pdf). Phase 2 data for Southern California include flood-hazard information for the coast from the border of Mexico to Pt. Conception. Several changes from Phase 1 projections are reflected in many areas; please read the Summary of methods and inspect output carefully. Data are complete for the information presented.

Maximum depth of flooding surface (in cm) in the region landward of the present day shoreline that is inundated for the storm condition and sea-level rise (SLR) scenario indicated. Note: Duration datasets may have occasional gaps in open-coast sections. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal planners with critical storm-hazards information that can be used to increase public safety, mitigate physical damages, and more effectively manage and allocate resources within complex coastal settings. Model details and data sources are outlined in CoSMoS_3.0_Phase_2_Southern_California_Bight:_Summary_of_data_and_methods (available at https://www.sciencebase.gov/catalog/file/get/57f1d4f3e4b0bc0bebfee139?name=CoSMoS_SoCalv3_Phase2_summary_of_methods.pdf). Phase 2 data for Southern California include flood-hazard information for the coast from the border of Mexico to Pt. Conception. Several changes from Phase 1 projections are reflected in many areas; please read the Summary of methods and inspect output carefully. Data are complete for the information presented.

Maximum depth of flooding surface (in cm) in the region landward of the present day shoreline that is inundated for the storm condition and sea-level rise (SLR) scenario indicated. Note: Duration datasets may have occasional gaps in open-coast sections. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal planners with critical storm-hazards information that can be used to increase public safety, mitigate physical damages, and more effectively manage and allocate resources within complex coastal settings. Model details and data sources are outlined in CoSMoS_3.0_Phase_2_Southern_California_Bight:_Summary_of_data_and_methods (available at https://www.sciencebase.gov/catalog/file/get/57f1d4f3e4b0bc0bebfee139?name=CoSMoS_SoCalv3_Phase2_summary_of_methods.pdf). Phase 2 data for Southern California include flood-hazard information for the coast from the border of Mexico to Pt. Conception. Several changes from Phase 1 projections are reflected in many areas; please read the Summary of methods and inspect output carefully. Data are complete for the information presented.

Geographic extent of projected coastal flooding, low-lying vulnerable areas, and maxium/minimum flood potential (flood uncertainty) associated with the sea-level rise and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal planners with critical storm-hazards information that can be used to increase public safety, mitigate physical damages, and more effectively manage and allocate resources within complex coastal settings. Model details and data sources are outlined in CoSMoS_3.0_Phase_2_Southern_California_Bight:_Summary_of_data_and_methods (available at https://www.sciencebase.gov/catalog/file/get/57f1d4f3e4b0bc0bebfee139?name=CoSMoS_SoCalv3_Phase2_summary_of_methods.pdf). Phase 2 data for Southern California include flood-hazard information for the coast from the border of Mexico to Pt. Conception. Several changes from Phase 1 projections are reflected in many areas; please read the Summary of methods and inspect output carefully. Data are complete for the information presented.

Geographic extent of projected coastal flooding, low-lying vulnerable areas, and maxium/minimum flood potential (flood uncertainty) associated with the sea-level rise and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal planners with critical storm-hazards information that can be used to increase public safety, mitigate physical damages, and more effectively manage and allocate resources within complex coastal settings. Model details and data sources are outlined in CoSMoS_3.0_Phase_2_Southern_California_Bight:_Summary_of_data_and_methods (available at https://www.sciencebase.gov/catalog/file/get/57f1d4f3e4b0bc0bebfee139?name=CoSMoS_SoCalv3_Phase2_summary_of_methods.pdf). Phase 2 data for Southern California include flood-hazard information for the coast from the border of Mexico to Pt. Conception. Several changes from Phase 1 projections are reflected in many areas; please read the Summary of methods and inspect output carefully. Data are complete for the information presented.

Geographic extent of projected coastal flooding, low-lying vulnerable areas, and maxium/minimum flood potential (flood uncertainty) associated with the sea-level rise and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal planners with critical storm-hazards information that can be used to increase public safety, mitigate physical damages, and more effectively manage and allocate resources within complex coastal settings. Model details and data sources are outlined in CoSMoS_3.0_Phase_2_Southern_California_Bight:_Summary_of_data_and_methods (available at https://www.sciencebase.gov/catalog/file/get/57f1d4f3e4b0bc0bebfee139?name=CoSMoS_SoCalv3_Phase2_summary_of_methods.pdf). Phase 2 data for Southern California include flood-hazard information for the coast from the border of Mexico to Pt. Conception. Several changes from Phase 1 projections are reflected in many areas; please read the Summary of methods and inspect output carefully. Data are complete for the information presented.

Geographic extent of projected coastal flooding, low-lying vulnerable areas, and maxium/minimum flood potential (flood uncertainty) associated with the sea-level rise and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal planners with critical storm-hazards information that can be used to increase public safety, mitigate physical damages, and more effectively manage and allocate resources within complex coastal settings. Model details and data sources are outlined in CoSMoS_3.0_Phase_2_Southern_California_Bight:_Summary_of_data_and_methods (available at https://www.sciencebase.gov/catalog/file/get/57f1d4f3e4b0bc0bebfee139?name=CoSMoS_SoCalv3_Phase2_summary_of_methods.pdf). Phase 2 data for Southern California include flood-hazard information for the coast from the border of Mexico to Pt. Conception. Several changes from Phase 1 projections are reflected in many areas; please read the Summary of methods and inspect output carefully. Data are complete for the information presented.

Model-derived ocean current velocities (in meters per second) for the given storm condition and sea-level rise (SLR) scenario. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal planners with critical storm-hazards information that can be used to increase public safety, mitigate physical damages, and more effectively manage and allocate resources within complex coastal settings. Model details and data sources are outlined in CoSMoS_3.0_Phase_2_Southern_California_Bight:_Summary_of_data_and_methods (available at https://www.sciencebase.gov/catalog/file/get/57f1d4f3e4b0bc0bebfee139?name=CoSMoS_SoCalv3_Phase2_summary_of_methods.pdf). Phase 2 data for Southern California include flood-hazard information for the coast from the border of Mexico to Pt. Conception. Several changes from Phase 1 projections are reflected in many areas; please read the Summary of methods and inspect output carefully. Data are complete for the information presented.

Model-derived ocean current velocities (in meters per second) for the given storm condition and sea-level rise (SLR) scenario. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal planners with critical storm-hazards information that can be used to increase public safety, mitigate physical damages, and more effectively manage and allocate resources within complex coastal settings. Model details and data sources are outlined in CoSMoS_3.0_Phase_2_Southern_California_Bight:_Summary_of_data_and_methods (available at https://www.sciencebase.gov/catalog/file/get/57f1d4f3e4b0bc0bebfee139?name=CoSMoS_SoCalv3_Phase2_summary_of_methods.pdf). Phase 2 data for Southern California include flood-hazard information for the coast from the border of Mexico to Pt. Conception. Several changes from Phase 1 projections are reflected in many areas; please read the Summary of methods and inspect output carefully. Data are complete for the information presented.

Model-derived ocean current velocities (in meters per second) for the given storm condition and sea-level rise (SLR) scenario. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal planners with critical storm-hazards information that can be used to increase public safety, mitigate physical damages, and more effectively manage and allocate resources within complex coastal settings. Model details and data sources are outlined in CoSMoS_3.0_Phase_2_Southern_California_Bight:_Summary_of_data_and_methods (available at https://www.sciencebase.gov/catalog/file/get/57f1d4f3e4b0bc0bebfee139?name=CoSMoS_SoCalv3_Phase2_summary_of_methods.pdf). Phase 2 data for Southern California include flood-hazard information for the coast from the border of Mexico to Pt. Conception. Several changes from Phase 1 projections are reflected in many areas; please read the Summary of methods and inspect output carefully. Data are complete for the information presented.

Model-derived ocean current velocities (in meters per second) for the given storm condition and sea-level rise (SLR) scenario. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal planners with critical storm-hazards information that can be used to increase public safety, mitigate physical damages, and more effectively manage and allocate resources within complex coastal settings. Model details and data sources are outlined in CoSMoS_3.0_Phase_2_Southern_California_Bight:_Summary_of_data_and_methods (available at https://www.sciencebase.gov/catalog/file/get/57f1d4f3e4b0bc0bebfee139?name=CoSMoS_SoCalv3_Phase2_summary_of_methods.pdf). Phase 2 data for Southern California include flood-hazard information for the coast from the border of Mexico to Pt. Conception. Several changes from Phase 1 projections are reflected in many areas; please read the Summary of methods and inspect output carefully. Data are complete for the information presented.

Model-derived total water levels (in meters) for the given storm condition and sea-level rise (SLR) scenario. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal planners with critical storm-hazards information that can be used to increase public safety, mitigate physical damages, and more effectively manage and allocate resources within complex coastal settings. Model details and data sources are outlined in CoSMoS_3.0_Phase_2_Southern_California_Bight:_Summary_of_data_and_methods (available at https://www.sciencebase.gov/catalog/file/get/57f1d4f3e4b0bc0bebfee139?name=CoSMoS_SoCalv3_Phase2_summary_of_methods.pdf). Phase 2 data for Southern California include flood-hazard information for the coast from the border of Mexico to Pt. Conception. Several changes from Phase 1 projections are reflected in many areas; please read the Summary of methods and inspect output carefully. Data are complete for the information presented.

Model-derived total water levels (in meters) for the given storm condition and sea-level rise (SLR) scenario. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal planners with critical storm-hazards information that can be used to increase public safety, mitigate physical damages, and more effectively manage and allocate resources within complex coastal settings. Model details and data sources are outlined in CoSMoS_3.0_Phase_2_Southern_California_Bight:_Summary_of_data_and_methods (available at https://www.sciencebase.gov/catalog/file/get/57f1d4f3e4b0bc0bebfee139?name=CoSMoS_SoCalv3_Phase2_summary_of_methods.pdf). Phase 2 data for Southern California include flood-hazard information for the coast from the border of Mexico to Pt. Conception. Several changes from Phase 1 projections are reflected in many areas; please read the Summary of methods and inspect output carefully. Data are complete for the information presented.

Model-derived total water levels (in meters) for the given storm condition and sea-level rise (SLR) scenario. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal planners with critical storm-hazards information that can be used to increase public safety, mitigate physical damages, and more effectively manage and allocate resources within complex coastal settings. Model details and data sources are outlined in CoSMoS_3.0_Phase_2_Southern_California_Bight:_Summary_of_data_and_methods (available at https://www.sciencebase.gov/catalog/file/get/57f1d4f3e4b0bc0bebfee139?name=CoSMoS_SoCalv3_Phase2_summary_of_methods.pdf). Phase 2 data for Southern California include flood-hazard information for the coast from the border of Mexico to Pt. Conception. Several changes from Phase 1 projections are reflected in many areas; please read the Summary of methods and inspect output carefully. Data are complete for the information presented.

Model-derived total water levels (in meters) for the given storm condition and sea-level rise (SLR) scenario. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal planners with critical storm-hazards information that can be used to increase public safety, mitigate physical damages, and more effectively manage and allocate resources within complex coastal settings. Model details and data sources are outlined in CoSMoS_3.0_Phase_2_Southern_California_Bight:_Summary_of_data_and_methods (available at https://www.sciencebase.gov/catalog/file/get/57f1d4f3e4b0bc0bebfee139?name=CoSMoS_SoCalv3_Phase2_summary_of_methods.pdf). Phase 2 data for Southern California include flood-hazard information for the coast from the border of Mexico to Pt. Conception. Several changes from Phase 1 projections are reflected in many areas; please read the Summary of methods and inspect output carefully. Data are complete for the information presented.

Model-derived significant wave height (in meters) for the given storm condition and sea-level rise (SLR) scenario. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal planners with critical storm-hazards information that can be used to increase public safety, mitigate physical damages, and more effectively manage and allocate resources within complex coastal settings. Model details and data sources are outlined in CoSMoS_3.0_Phase_2_Southern_California_Bight:_Summary_of_data_and_methods (available at https://www.sciencebase.gov/catalog/file/get/57f1d4f3e4b0bc0bebfee139?name=CoSMoS_SoCalv3_Phase2_summary_of_methods.pdf). Phase 2 data for Southern California include flood-hazard information for the coast from the border of Mexico to Pt. Conception. Several changes from Phase 1 projections are reflected in many areas; please read the Summary of methods and inspect output carefully. Data are complete for the information presented.

Model-derived significant wave height (in meters) for the given storm condition and sea-level rise (SLR) scenario. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal planners with critical storm-hazards information that can be used to increase public safety, mitigate physical damages, and more effectively manage and allocate resources within complex coastal settings. Model details and data sources are outlined in CoSMoS_3.0_Phase_2_Southern_California_Bight:_Summary_of_data_and_methods (available at https://www.sciencebase.gov/catalog/file/get/57f1d4f3e4b0bc0bebfee139?name=CoSMoS_SoCalv3_Phase2_summary_of_methods.pdf). Phase 2 data for Southern California include flood-hazard information for the coast from the border of Mexico to Pt. Conception. Several changes from Phase 1 projections are reflected in many areas; please read the Summary of methods and inspect output carefully. Data are complete for the information presented.

Model-derived significant wave height (in meters) for the given storm condition and sea-level rise (SLR) scenario. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal planners with critical storm-hazards information that can be used to increase public safety, mitigate physical damages, and more effectively manage and allocate resources within complex coastal settings. Model details and data sources are outlined in CoSMoS_3.0_Phase_2_Southern_California_Bight:_Summary_of_data_and_methods (available at https://www.sciencebase.gov/catalog/file/get/57f1d4f3e4b0bc0bebfee139?name=CoSMoS_SoCalv3_Phase2_summary_of_methods.pdf). Phase 2 data for Southern California include flood-hazard information for the coast from the border of Mexico to Pt. Conception. Several changes from Phase 1 projections are reflected in many areas; please read the Summary of methods and inspect output carefully. Data are complete for the information presented.

Model-derived significant wave height (in meters) for the given storm condition and sea-level rise (SLR) scenario. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal planners with critical storm-hazards information that can be used to increase public safety, mitigate physical damages, and more effectively manage and allocate resources within complex coastal settings. Model details and data sources are outlined in CoSMoS_3.0_Phase_2_Southern_California_Bight:_Summary_of_data_and_methods (available at https://www.sciencebase.gov/catalog/file/get/57f1d4f3e4b0bc0bebfee139?name=CoSMoS_SoCalv3_Phase2_summary_of_methods.pdf). Phase 2 data for Southern California include flood-hazard information for the coast from the border of Mexico to Pt. Conception. Several changes from Phase 1 projections are reflected in many areas; please read the Summary of methods and inspect output carefully. Data are complete for the information presented.

Maximum depth of flooding surface (in cm) in the region landward of the present day shoreline that is inundated for the storm condition and sea-level rise (SLR) scenario indicated. Note: Duration datasets may have occasional gaps in open-coast sections. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal planners with critical storm-hazards information that can be used to increase public safety, mitigate physical damages, and more effectively manage and allocate resources within complex coastal settings. Model details and data sources are outlined in CoSMoS_3.0_Phase_2_Southern_California_Bight:_Summary_of_data_and_methods (available at https://www.sciencebase.gov/catalog/file/get/57f1d4f3e4b0bc0bebfee139?name=CoSMoS_SoCalv3_Phase2_summary_of_methods.pdf). Phase 2 data for Southern California include flood-hazard information for the coast from the border of Mexico to Pt. Conception. Several changes from Phase 1 projections are reflected in many areas; please read the Summary of methods and inspect output carefully. Data are complete for the information presented.

Maximum depth of flooding surface (in cm) in the region landward of the present day shoreline that is inundated for the storm condition and sea-level rise (SLR) scenario indicated. Note: Duration datasets may have occasional gaps in open-coast sections. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal planners with critical storm-hazards information that can be used to increase public safety, mitigate physical damages, and more effectively manage and allocate resources within complex coastal settings. Model details and data sources are outlined in CoSMoS_3.0_Phase_2_Southern_California_Bight:_Summary_of_data_and_methods (available at https://www.sciencebase.gov/catalog/file/get/57f1d4f3e4b0bc0bebfee139?name=CoSMoS_SoCalv3_Phase2_summary_of_methods.pdf). Phase 2 data for Southern California include flood-hazard information for the coast from the border of Mexico to Pt. Conception. Several changes from Phase 1 projections are reflected in many areas; please read the Summary of methods and inspect output carefully. Data are complete for the information presented.

Maximum depth of flooding surface (in cm) in the region landward of the present day shoreline that is inundated for the storm condition and sea-level rise (SLR) scenario indicated. Note: Duration datasets may have occasional gaps in open-coast sections. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal planners with critical storm-hazards information that can be used to increase public safety, mitigate physical damages, and more effectively manage and allocate resources within complex coastal settings. Model details and data sources are outlined in CoSMoS_3.0_Phase_2_Southern_California_Bight:_Summary_of_data_and_methods (available at https://www.sciencebase.gov/catalog/file/get/57f1d4f3e4b0bc0bebfee139?name=CoSMoS_SoCalv3_Phase2_summary_of_methods.pdf). Phase 2 data for Southern California include flood-hazard information for the coast from the border of Mexico to Pt. Conception. Several changes from Phase 1 projections are reflected in many areas; please read the Summary of methods and inspect output carefully. Data are complete for the information presented.

Maximum depth of flooding surface (in cm) in the region landward of the present day shoreline that is inundated for the storm condition and sea-level rise (SLR) scenario indicated. Note: Duration datasets may have occasional gaps in open-coast sections. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal planners with critical storm-hazards information that can be used to increase public safety, mitigate physical damages, and more effectively manage and allocate resources within complex coastal settings. Model details and data sources are outlined in CoSMoS_3.0_Phase_2_Southern_California_Bight:_Summary_of_data_and_methods (available at https://www.sciencebase.gov/catalog/file/get/57f1d4f3e4b0bc0bebfee139?name=CoSMoS_SoCalv3_Phase2_summary_of_methods.pdf). Phase 2 data for Southern California include flood-hazard information for the coast from the border of Mexico to Pt. Conception. Several changes from Phase 1 projections are reflected in many areas; please read the Summary of methods and inspect output carefully. Data are complete for the information presented.

Geographic extent of projected coastal flooding, low-lying vulnerable areas, and maxium/minimum flood potential (flood uncertainty) associated with the sea-level rise and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal planners with critical storm-hazards information that can be used to increase public safety, mitigate physical damages, and more effectively manage and allocate resources within complex coastal settings. Model details and data sources are outlined in CoSMoS_3.0_Phase_2_Southern_California_Bight:_Summary_of_data_and_methods (available at https://www.sciencebase.gov/catalog/file/get/57f1d4f3e4b0bc0bebfee139?name=CoSMoS_SoCalv3_Phase2_summary_of_methods.pdf). Phase 2 data for Southern California include flood-hazard information for the coast from the border of Mexico to Pt. Conception. Several changes from Phase 1 projections are reflected in many areas; please read the Summary of methods and inspect output carefully. Data are complete for the information presented.

Geographic extent of projected coastal flooding, low-lying vulnerable areas, and maxium/minimum flood potential (flood uncertainty) associated with the sea-level rise and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal planners with critical storm-hazards information that can be used to increase public safety, mitigate physical damages, and more effectively manage and allocate resources within complex coastal settings. Model details and data sources are outlined in CoSMoS_3.0_Phase_2_Southern_California_Bight:_Summary_of_data_and_methods (available at https://www.sciencebase.gov/catalog/file/get/57f1d4f3e4b0bc0bebfee139?name=CoSMoS_SoCalv3_Phase2_summary_of_methods.pdf). Phase 2 data for Southern California include flood-hazard information for the coast from the border of Mexico to Pt. Conception. Several changes from Phase 1 projections are reflected in many areas; please read the Summary of methods and inspect output carefully. Data are complete for the information presented.

Geographic extent of projected coastal flooding, low-lying vulnerable areas, and maxium/minimum flood potential (flood uncertainty) associated with the sea-level rise and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal planners with critical storm-hazards information that can be used to increase public safety, mitigate physical damages, and more effectively manage and allocate resources within complex coastal settings. Model details and data sources are outlined in CoSMoS_3.0_Phase_2_Southern_California_Bight:_Summary_of_data_and_methods (available at https://www.sciencebase.gov/catalog/file/get/57f1d4f3e4b0bc0bebfee139?name=CoSMoS_SoCalv3_Phase2_summary_of_methods.pdf). Phase 2 data for Southern California include flood-hazard information for the coast from the border of Mexico to Pt. Conception. Several changes from Phase 1 projections are reflected in many areas; please read the Summary of methods and inspect output carefully. Data are complete for the information presented.

Geographic extent of projected coastal flooding, low-lying vulnerable areas, and maxium/minimum flood potential (flood uncertainty) associated with the sea-level rise and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal planners with critical storm-hazards information that can be used to increase public safety, mitigate physical damages, and more effectively manage and allocate resources within complex coastal settings. Model details and data sources are outlined in CoSMoS_3.0_Phase_2_Southern_California_Bight:_Summary_of_data_and_methods (available at https://www.sciencebase.gov/catalog/file/get/57f1d4f3e4b0bc0bebfee139?name=CoSMoS_SoCalv3_Phase2_summary_of_methods.pdf). Phase 2 data for Southern California include flood-hazard information for the coast from the border of Mexico to Pt. Conception. Several changes from Phase 1 projections are reflected in many areas; please read the Summary of methods and inspect output carefully. Data are complete for the information presented.

Model-derived ocean current velocities (in meters per second) for the given storm condition and sea-level rise (SLR) scenario. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal planners with critical storm-hazards information that can be used to increase public safety, mitigate physical damages, and more effectively manage and allocate resources within complex coastal settings. Model details and data sources are outlined in CoSMoS_3.0_Phase_2_Southern_California_Bight:_Summary_of_data_and_methods (available at https://www.sciencebase.gov/catalog/file/get/57f1d4f3e4b0bc0bebfee139?name=CoSMoS_SoCalv3_Phase2_summary_of_methods.pdf). Phase 2 data for Southern California include flood-hazard information for the coast from the border of Mexico to Pt. Conception. Several changes from Phase 1 projections are reflected in many areas; please read the Summary of methods and inspect output carefully. Data are complete for the information presented.

Model-derived ocean current velocities (in meters per second) for the given storm condition and sea-level rise (SLR) scenario. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal planners with critical storm-hazards information that can be used to increase public safety, mitigate physical damages, and more effectively manage and allocate resources within complex coastal settings. Model details and data sources are outlined in CoSMoS_3.0_Phase_2_Southern_California_Bight:_Summary_of_data_and_methods (available at https://www.sciencebase.gov/catalog/file/get/57f1d4f3e4b0bc0bebfee139?name=CoSMoS_SoCalv3_Phase2_summary_of_methods.pdf). Phase 2 data for Southern California include flood-hazard information for the coast from the border of Mexico to Pt. Conception. Several changes from Phase 1 projections are reflected in many areas; please read the Summary of methods and inspect output carefully. Data are complete for the information presented.

Model-derived ocean current velocities (in meters per second) for the given storm condition and sea-level rise (SLR) scenario. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal planners with critical storm-hazards information that can be used to increase public safety, mitigate physical damages, and more effectively manage and allocate resources within complex coastal settings. Model details and data sources are outlined in CoSMoS_3.0_Phase_2_Southern_California_Bight:_Summary_of_data_and_methods (available at https://www.sciencebase.gov/catalog/file/get/57f1d4f3e4b0bc0bebfee139?name=CoSMoS_SoCalv3_Phase2_summary_of_methods.pdf). Phase 2 data for Southern California include flood-hazard information for the coast from the border of Mexico to Pt. Conception. Several changes from Phase 1 projections are reflected in many areas; please read the Summary of methods and inspect output carefully. Data are complete for the information presented.

Model-derived ocean current velocities (in meters per second) for the given storm condition and sea-level rise (SLR) scenario. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal planners with critical storm-hazards information that can be used to increase public safety, mitigate physical damages, and more effectively manage and allocate resources within complex coastal settings. Model details and data sources are outlined in CoSMoS_3.0_Phase_2_Southern_California_Bight:_Summary_of_data_and_methods (available at https://www.sciencebase.gov/catalog/file/get/57f1d4f3e4b0bc0bebfee139?name=CoSMoS_SoCalv3_Phase2_summary_of_methods.pdf). Phase 2 data for Southern California include flood-hazard information for the coast from the border of Mexico to Pt. Conception. Several changes from Phase 1 projections are reflected in many areas; please read the Summary of methods and inspect output carefully. Data are complete for the information presented.

Model-derived total water levels (in meters) for the given storm condition and sea-level rise (SLR) scenario. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal planners with critical storm-hazards information that can be used to increase public safety, mitigate physical damages, and more effectively manage and allocate resources within complex coastal settings. Model details and data sources are outlined in CoSMoS_3.0_Phase_2_Southern_California_Bight:_Summary_of_data_and_methods (available at https://www.sciencebase.gov/catalog/file/get/57f1d4f3e4b0bc0bebfee139?name=CoSMoS_SoCalv3_Phase2_summary_of_methods.pdf). Phase 2 data for Southern California include flood-hazard information for the coast from the border of Mexico to Pt. Conception. Several changes from Phase 1 projections are reflected in many areas; please read the Summary of methods and inspect output carefully. Data are complete for the information presented.

Model-derived total water levels (in meters) for the given storm condition and sea-level rise (SLR) scenario. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal planners with critical storm-hazards information that can be used to increase public safety, mitigate physical damages, and more effectively manage and allocate resources within complex coastal settings. Model details and data sources are outlined in CoSMoS_3.0_Phase_2_Southern_California_Bight:_Summary_of_data_and_methods (available at https://www.sciencebase.gov/catalog/file/get/57f1d4f3e4b0bc0bebfee139?name=CoSMoS_SoCalv3_Phase2_summary_of_methods.pdf). Phase 2 data for Southern California include flood-hazard information for the coast from the border of Mexico to Pt. Conception. Several changes from Phase 1 projections are reflected in many areas; please read the Summary of methods and inspect output carefully. Data are complete for the information presented.

Model-derived total water levels (in meters) for the given storm condition and sea-level rise (SLR) scenario. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal planners with critical storm-hazards information that can be used to increase public safety, mitigate physical damages, and more effectively manage and allocate resources within complex coastal settings. Model details and data sources are outlined in CoSMoS_3.0_Phase_2_Southern_California_Bight:_Summary_of_data_and_methods (available at https://www.sciencebase.gov/catalog/file/get/57f1d4f3e4b0bc0bebfee139?name=CoSMoS_SoCalv3_Phase2_summary_of_methods.pdf). Phase 2 data for Southern California include flood-hazard information for the coast from the border of Mexico to Pt. Conception. Several changes from Phase 1 projections are reflected in many areas; please read the Summary of methods and inspect output carefully. Data are complete for the information presented.

Model-derived total water levels (in meters) for the given storm condition and sea-level rise (SLR) scenario. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal planners with critical storm-hazards information that can be used to increase public safety, mitigate physical damages, and more effectively manage and allocate resources within complex coastal settings. Model details and data sources are outlined in CoSMoS_3.0_Phase_2_Southern_California_Bight:_Summary_of_data_and_methods (available at https://www.sciencebase.gov/catalog/file/get/57f1d4f3e4b0bc0bebfee139?name=CoSMoS_SoCalv3_Phase2_summary_of_methods.pdf). Phase 2 data for Southern California include flood-hazard information for the coast from the border of Mexico to Pt. Conception. Several changes from Phase 1 projections are reflected in many areas; please read the Summary of methods and inspect output carefully. Data are complete for the information presented.

Model-derived significant wave height (in meters) for the given storm condition and sea-level rise (SLR) scenario. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal planners with critical storm-hazards information that can be used to increase public safety, mitigate physical damages, and more effectively manage and allocate resources within complex coastal settings. Model details and data sources are outlined in CoSMoS_3.0_Phase_2_Southern_California_Bight:_Summary_of_data_and_methods (available at https://www.sciencebase.gov/catalog/file/get/57f1d4f3e4b0bc0bebfee139?name=CoSMoS_SoCalv3_Phase2_summary_of_methods.pdf). Phase 2 data for Southern California include flood-hazard information for the coast from the border of Mexico to Pt. Conception. Several changes from Phase 1 projections are reflected in many areas; please read the Summary of methods and inspect output carefully. Data are complete for the information presented.

Model-derived significant wave height (in meters) for the given storm condition and sea-level rise (SLR) scenario. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal planners with critical storm-hazards information that can be used to increase public safety, mitigate physical damages, and more effectively manage and allocate resources within complex coastal settings. Model details and data sources are outlined in CoSMoS_3.0_Phase_2_Southern_California_Bight:_Summary_of_data_and_methods (available at https://www.sciencebase.gov/catalog/file/get/57f1d4f3e4b0bc0bebfee139?name=CoSMoS_SoCalv3_Phase2_summary_of_methods.pdf). Phase 2 data for Southern California include flood-hazard information for the coast from the border of Mexico to Pt. Conception. Several changes from Phase 1 projections are reflected in many areas; please read the Summary of methods and inspect output carefully. Data are complete for the information presented.

Model-derived significant wave height (in meters) for the given storm condition and sea-level rise (SLR) scenario. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal planners with critical storm-hazards information that can be used to increase public safety, mitigate physical damages, and more effectively manage and allocate resources within complex coastal settings. Model details and data sources are outlined in CoSMoS_3.0_Phase_2_Southern_California_Bight:_Summary_of_data_and_methods (available at https://www.sciencebase.gov/catalog/file/get/57f1d4f3e4b0bc0bebfee139?name=CoSMoS_SoCalv3_Phase2_summary_of_methods.pdf). Phase 2 data for Southern California include flood-hazard information for the coast from the border of Mexico to Pt. Conception. Several changes from Phase 1 projections are reflected in many areas; please read the Summary of methods and inspect output carefully. Data are complete for the information presented.

Model-derived significant wave height (in meters) for the given storm condition and sea-level rise (SLR) scenario. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal planners with critical storm-hazards information that can be used to increase public safety, mitigate physical damages, and more effectively manage and allocate resources within complex coastal settings. Model details and data sources are outlined in CoSMoS_3.0_Phase_2_Southern_California_Bight:_Summary_of_data_and_methods (available at https://www.sciencebase.gov/catalog/file/get/57f1d4f3e4b0bc0bebfee139?name=CoSMoS_SoCalv3_Phase2_summary_of_methods.pdf). Phase 2 data for Southern California include flood-hazard information for the coast from the border of Mexico to Pt. Conception. Several changes from Phase 1 projections are reflected in many areas; please read the Summary of methods and inspect output carefully. Data are complete for the information presented.

Maximum depth of flooding surface (in cm) in the region landward of the present day shoreline that is inundated for the storm condition and sea-level rise (SLR) scenario indicated. Note: Duration datasets may have occasional gaps in open-coast sections. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal planners with critical storm-hazards information that can be used to increase public safety, mitigate physical damages, and more effectively manage and allocate resources within complex coastal settings. Model details and data sources are outlined in CoSMoS_3.0_Phase_2_Southern_California_Bight:_Summary_of_data_and_methods (available at https://www.sciencebase.gov/catalog/file/get/57f1d4f3e4b0bc0bebfee139?name=CoSMoS_SoCalv3_Phase2_summary_of_methods.pdf). Phase 2 data for Southern California include flood-hazard information for the coast from the border of Mexico to Pt. Conception. Several changes from Phase 1 projections are reflected in many areas; please read the Summary of methods and inspect output carefully. Data are complete for the information presented.

Maximum depth of flooding surface (in cm) in the region landward of the present day shoreline that is inundated for the storm condition and sea-level rise (SLR) scenario indicated. Note: Duration datasets may have occasional gaps in open-coast sections. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal planners with critical storm-hazards information that can be used to increase public safety, mitigate physical damages, and more effectively manage and allocate resources within complex coastal settings. Model details and data sources are outlined in CoSMoS_3.0_Phase_2_Southern_California_Bight:_Summary_of_data_and_methods (available at https://www.sciencebase.gov/catalog/file/get/57f1d4f3e4b0bc0bebfee139?name=CoSMoS_SoCalv3_Phase2_summary_of_methods.pdf). Phase 2 data for Southern California include flood-hazard information for the coast from the border of Mexico to Pt. Conception. Several changes from Phase 1 projections are reflected in many areas; please read the Summary of methods and inspect output carefully. Data are complete for the information presented.

Maximum depth of flooding surface (in cm) in the region landward of the present day shoreline that is inundated for the storm condition and sea-level rise (SLR) scenario indicated. Note: Duration datasets may have occasional gaps in open-coast sections. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal planners with critical storm-hazards information that can be used to increase public safety, mitigate physical damages, and more effectively manage and allocate resources within complex coastal settings. Model details and data sources are outlined in CoSMoS_3.0_Phase_2_Southern_California_Bight:_Summary_of_data_and_methods (available at https://www.sciencebase.gov/catalog/file/get/57f1d4f3e4b0bc0bebfee139?name=CoSMoS_SoCalv3_Phase2_summary_of_methods.pdf). Phase 2 data for Southern California include flood-hazard information for the coast from the border of Mexico to Pt. Conception. Several changes from Phase 1 projections are reflected in many areas; please read the Summary of methods and inspect output carefully. Data are complete for the information presented.

Maximum depth of flooding surface (in cm) in the region landward of the present day shoreline that is inundated for the storm condition and sea-level rise (SLR) scenario indicated. Note: Duration datasets may have occasional gaps in open-coast sections. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal planners with critical storm-hazards information that can be used to increase public safety, mitigate physical damages, and more effectively manage and allocate resources within complex coastal settings. Model details and data sources are outlined in CoSMoS_3.0_Phase_2_Southern_California_Bight:_Summary_of_data_and_methods (available at https://www.sciencebase.gov/catalog/file/get/57f1d4f3e4b0bc0bebfee139?name=CoSMoS_SoCalv3_Phase2_summary_of_methods.pdf). Phase 2 data for Southern California include flood-hazard information for the coast from the border of Mexico to Pt. Conception. Several changes from Phase 1 projections are reflected in many areas; please read the Summary of methods and inspect output carefully. Data are complete for the information presented.

Geographic extent of projected coastal flooding, low-lying vulnerable areas, and maxium/minimum flood potential (flood uncertainty) associated with the sea-level rise and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal planners with critical storm-hazards information that can be used to increase public safety, mitigate physical damages, and more effectively manage and allocate resources within complex coastal settings. Model details and data sources are outlined in CoSMoS_3.0_Phase_2_Southern_California_Bight:_Summary_of_data_and_methods (available at https://www.sciencebase.gov/catalog/file/get/57f1d4f3e4b0bc0bebfee139?name=CoSMoS_SoCalv3_Phase2_summary_of_methods.pdf). Phase 2 data for Southern California include flood-hazard information for the coast from the border of Mexico to Pt. Conception. Several changes from Phase 1 projections are reflected in many areas; please read the Summary of methods and inspect output carefully. Data are complete for the information presented.

Geographic extent of projected coastal flooding, low-lying vulnerable areas, and maxium/minimum flood potential (flood uncertainty) associated with the sea-level rise and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal planners with critical storm-hazards information that can be used to increase public safety, mitigate physical damages, and more effectively manage and allocate resources within complex coastal settings. Model details and data sources are outlined in CoSMoS_3.0_Phase_2_Southern_California_Bight:_Summary_of_data_and_methods (available at https://www.sciencebase.gov/catalog/file/get/57f1d4f3e4b0bc0bebfee139?name=CoSMoS_SoCalv3_Phase2_summary_of_methods.pdf). Phase 2 data for Southern California include flood-hazard information for the coast from the border of Mexico to Pt. Conception. Several changes from Phase 1 projections are reflected in many areas; please read the Summary of methods and inspect output carefully. Data are complete for the information presented.

Geographic extent of projected coastal flooding, low-lying vulnerable areas, and maxium/minimum flood potential (flood uncertainty) associated with the sea-level rise and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal planners with critical storm-hazards information that can be used to increase public safety, mitigate physical damages, and more effectively manage and allocate resources within complex coastal settings. Model details and data sources are outlined in CoSMoS_3.0_Phase_2_Southern_California_Bight:_Summary_of_data_and_methods (available at https://www.sciencebase.gov/catalog/file/get/57f1d4f3e4b0bc0bebfee139?name=CoSMoS_SoCalv3_Phase2_summary_of_methods.pdf). Phase 2 data for Southern California include flood-hazard information for the coast from the border of Mexico to Pt. Conception. Several changes from Phase 1 projections are reflected in many areas; please read the Summary of methods and inspect output carefully. Data are complete for the information presented.

Geographic extent of projected coastal flooding, low-lying vulnerable areas, and maxium/minimum flood potential (flood uncertainty) associated with the sea-level rise and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal planners with critical storm-hazards information that can be used to increase public safety, mitigate physical damages, and more effectively manage and allocate resources within complex coastal settings. Model details and data sources are outlined in CoSMoS_3.0_Phase_2_Southern_California_Bight:_Summary_of_data_and_methods (available at https://www.sciencebase.gov/catalog/file/get/57f1d4f3e4b0bc0bebfee139?name=CoSMoS_SoCalv3_Phase2_summary_of_methods.pdf). Phase 2 data for Southern California include flood-hazard information for the coast from the border of Mexico to Pt. Conception. Several changes from Phase 1 projections are reflected in many areas; please read the Summary of methods and inspect output carefully. Data are complete for the information presented.

Model-derived ocean current velocities (in meters per second) for the given storm condition and sea-level rise (SLR) scenario. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal planners with critical storm-hazards information that can be used to increase public safety, mitigate physical damages, and more effectively manage and allocate resources within complex coastal settings. Model details and data sources are outlined in CoSMoS_3.0_Phase_2_Southern_California_Bight:_Summary_of_data_and_methods (available at https://www.sciencebase.gov/catalog/file/get/57f1d4f3e4b0bc0bebfee139?name=CoSMoS_SoCalv3_Phase2_summary_of_methods.pdf). Phase 2 data for Southern California include flood-hazard information for the coast from the border of Mexico to Pt. Conception. Several changes from Phase 1 projections are reflected in many areas; please read the Summary of methods and inspect output carefully. Data are complete for the information presented.

Model-derived ocean current velocities (in meters per second) for the given storm condition and sea-level rise (SLR) scenario. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal planners with critical storm-hazards information that can be used to increase public safety, mitigate physical damages, and more effectively manage and allocate resources within complex coastal settings. Model details and data sources are outlined in CoSMoS_3.0_Phase_2_Southern_California_Bight:_Summary_of_data_and_methods (available at https://www.sciencebase.gov/catalog/file/get/57f1d4f3e4b0bc0bebfee139?name=CoSMoS_SoCalv3_Phase2_summary_of_methods.pdf). Phase 2 data for Southern California include flood-hazard information for the coast from the border of Mexico to Pt. Conception. Several changes from Phase 1 projections are reflected in many areas; please read the Summary of methods and inspect output carefully. Data are complete for the information presented.

Model-derived ocean current velocities (in meters per second) for the given storm condition and sea-level rise (SLR) scenario. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal planners with critical storm-hazards information that can be used to increase public safety, mitigate physical damages, and more effectively manage and allocate resources within complex coastal settings. Model details and data sources are outlined in CoSMoS_3.0_Phase_2_Southern_California_Bight:_Summary_of_data_and_methods (available at https://www.sciencebase.gov/catalog/file/get/57f1d4f3e4b0bc0bebfee139?name=CoSMoS_SoCalv3_Phase2_summary_of_methods.pdf). Phase 2 data for Southern California include flood-hazard information for the coast from the border of Mexico to Pt. Conception. Several changes from Phase 1 projections are reflected in many areas; please read the Summary of methods and inspect output carefully. Data are complete for the information presented.

Model-derived ocean current velocities (in meters per second) for the given storm condition and sea-level rise (SLR) scenario. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal planners with critical storm-hazards information that can be used to increase public safety, mitigate physical damages, and more effectively manage and allocate resources within complex coastal settings. Model details and data sources are outlined in CoSMoS_3.0_Phase_2_Southern_California_Bight:_Summary_of_data_and_methods (available at https://www.sciencebase.gov/catalog/file/get/57f1d4f3e4b0bc0bebfee139?name=CoSMoS_SoCalv3_Phase2_summary_of_methods.pdf). Phase 2 data for Southern California include flood-hazard information for the coast from the border of Mexico to Pt. Conception. Several changes from Phase 1 projections are reflected in many areas; please read the Summary of methods and inspect output carefully. Data are complete for the information presented.

Model-derived total water levels (in meters) for the given storm condition and sea-level rise (SLR) scenario. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal planners with critical storm-hazards information that can be used to increase public safety, mitigate physical damages, and more effectively manage and allocate resources within complex coastal settings. Model details and data sources are outlined in CoSMoS_3.0_Phase_2_Southern_California_Bight:_Summary_of_data_and_methods (available at https://www.sciencebase.gov/catalog/file/get/57f1d4f3e4b0bc0bebfee139?name=CoSMoS_SoCalv3_Phase2_summary_of_methods.pdf). Phase 2 data for Southern California include flood-hazard information for the coast from the border of Mexico to Pt. Conception. Several changes from Phase 1 projections are reflected in many areas; please read the Summary of methods and inspect output carefully. Data are complete for the information presented.

Model-derived total water levels (in meters) for the given storm condition and sea-level rise (SLR) scenario. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal planners with critical storm-hazards information that can be used to increase public safety, mitigate physical damages, and more effectively manage and allocate resources within complex coastal settings. Model details and data sources are outlined in CoSMoS_3.0_Phase_2_Southern_California_Bight:_Summary_of_data_and_methods (available at https://www.sciencebase.gov/catalog/file/get/57f1d4f3e4b0bc0bebfee139?name=CoSMoS_SoCalv3_Phase2_summary_of_methods.pdf). Phase 2 data for Southern California include flood-hazard information for the coast from the border of Mexico to Pt. Conception. Several changes from Phase 1 projections are reflected in many areas; please read the Summary of methods and inspect output carefully. Data are complete for the information presented.

Model-derived total water levels (in meters) for the given storm condition and sea-level rise (SLR) scenario. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal planners with critical storm-hazards information that can be used to increase public safety, mitigate physical damages, and more effectively manage and allocate resources within complex coastal settings. Model details and data sources are outlined in CoSMoS_3.0_Phase_2_Southern_California_Bight:_Summary_of_data_and_methods (available at https://www.sciencebase.gov/catalog/file/get/57f1d4f3e4b0bc0bebfee139?name=CoSMoS_SoCalv3_Phase2_summary_of_methods.pdf). Phase 2 data for Southern California include flood-hazard information for the coast from the border of Mexico to Pt. Conception. Several changes from Phase 1 projections are reflected in many areas; please read the Summary of methods and inspect output carefully. Data are complete for the information presented.

Model-derived total water levels (in meters) for the given storm condition and sea-level rise (SLR) scenario. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal planners with critical storm-hazards information that can be used to increase public safety, mitigate physical damages, and more effectively manage and allocate resources within complex coastal settings. Model details and data sources are outlined in CoSMoS_3.0_Phase_2_Southern_California_Bight:_Summary_of_data_and_methods (available at https://www.sciencebase.gov/catalog/file/get/57f1d4f3e4b0bc0bebfee139?name=CoSMoS_SoCalv3_Phase2_summary_of_methods.pdf). Phase 2 data for Southern California include flood-hazard information for the coast from the border of Mexico to Pt. Conception. Several changes from Phase 1 projections are reflected in many areas; please read the Summary of methods and inspect output carefully. Data are complete for the information presented.

Model-derived significant wave height (in meters) for the given storm condition and sea-level rise (SLR) scenario. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal planners with critical storm-hazards information that can be used to increase public safety, mitigate physical damages, and more effectively manage and allocate resources within complex coastal settings. Model details and data sources are outlined in CoSMoS_3.0_Phase_2_Southern_California_Bight:_Summary_of_data_and_methods (available at https://www.sciencebase.gov/catalog/file/get/57f1d4f3e4b0bc0bebfee139?name=CoSMoS_SoCalv3_Phase2_summary_of_methods.pdf). Phase 2 data for Southern California include flood-hazard information for the coast from the border of Mexico to Pt. Conception. Several changes from Phase 1 projections are reflected in many areas; please read the Summary of methods and inspect output carefully. Data are complete for the information presented.

Model-derived significant wave height (in meters) for the given storm condition and sea-level rise (SLR) scenario. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal planners with critical storm-hazards information that can be used to increase public safety, mitigate physical damages, and more effectively manage and allocate resources within complex coastal settings. Model details and data sources are outlined in CoSMoS_3.0_Phase_2_Southern_California_Bight:_Summary_of_data_and_methods (available at https://www.sciencebase.gov/catalog/file/get/57f1d4f3e4b0bc0bebfee139?name=CoSMoS_SoCalv3_Phase2_summary_of_methods.pdf). Phase 2 data for Southern California include flood-hazard information for the coast from the border of Mexico to Pt. Conception. Several changes from Phase 1 projections are reflected in many areas; please read the Summary of methods and inspect output carefully. Data are complete for the information presented.

Model-derived significant wave height (in meters) for the given storm condition and sea-level rise (SLR) scenario. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal planners with critical storm-hazards information that can be used to increase public safety, mitigate physical damages, and more effectively manage and allocate resources within complex coastal settings. Model details and data sources are outlined in CoSMoS_3.0_Phase_2_Southern_California_Bight:_Summary_of_data_and_methods (available at https://www.sciencebase.gov/catalog/file/get/57f1d4f3e4b0bc0bebfee139?name=CoSMoS_SoCalv3_Phase2_summary_of_methods.pdf). Phase 2 data for Southern California include flood-hazard information for the coast from the border of Mexico to Pt. Conception. Several changes from Phase 1 projections are reflected in many areas; please read the Summary of methods and inspect output carefully. Data are complete for the information presented.

Model-derived significant wave height (in meters) for the given storm condition and sea-level rise (SLR) scenario. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. CoSMoS v3.0 for Southern California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal planners with critical storm-hazards information that can be used to increase public safety, mitigate physical damages, and more effectively manage and allocate resources within complex coastal settings. Model details and data sources are outlined in CoSMoS_3.0_Phase_2_Southern_California_Bight:_Summary_of_data_and_methods (available at https://www.sciencebase.gov/catalog/file/get/57f1d4f3e4b0bc0bebfee139?name=CoSMoS_SoCalv3_Phase2_summary_of_methods.pdf). Phase 2 data for Southern California include flood-hazard information for the coast from the border of Mexico to Pt. Conception. Several changes from Phase 1 projections are reflected in many areas; please read the Summary of methods and inspect output carefully. Data are complete for the information presented.

High-resolution single-channel Chirp and minisparker seismic-reflection data were collected by the U.S. Geological Survey in September and October 2006, offshore Bolinas to San Francisco, California. Data were collected aboard the R/V Lakota, during field activity L-1-06-SF. Chirp data were collected using an EdgeTech 512 chirp subbottom system and were recorded with a Triton SB-Logger. Minisparker data were collected using a SIG 2-mille minisparker sound source combined with a single-channel streamer, and both were recorded with a Triton SB-Logger.

Estuary geomorphic units delineated at a scale of 1:1500 using a combination of (a) 30 August 2012 0.15 meter resolution NPS Elwha PlaneCam aerial imagery; and (b) elevation-colored and hillshaded digital elevation models from USGS backpack/jetski topobathy surveys (28 August 2012) for areas < MHHW and aerial lidar surveys (17 October 2012) for elevations > MHHW.

This portion of the data release presents aquatic invertebrate abundance data from samples collected in the Elwha River estuary, Washington, in 2007 and 2013 (no associated USGS Field Activities numbers because data were collected predominantly by biologists from the Lower Elwha Klallam Tribe). Replicate benthic samples were collected at 18 locations throughout the estuary complex using a petite Ponar grab sampler (appx. 2400 mL sample) and sorted through a 500-micron sieve. Samples were fixed in 10 percent formalin for 3 to 5 days before being transferred to 70 percent ethanol until processing. Individuals were identified to the lowest possible taxonomic resolution, but are grouped according to insect Orders in the data for consistency (unless otherwise noted in the attributes). The locations of samples were determined with a hand-held global positioning system (GPS). Aquatic invertebrate abundance data (including fractions of individuals) are provided in a comma-delimited spreadsheet (.csv).

Time-series data of velocity, pressure, turbidity, conductivity, and temperature were collected near the mouth of the Elwha River, Washington, USA, from December 2010 through October 2014, for the Department of Interior’s Elwha River Restoration project. As part of this project, the U.S. Geological Survey studied the effects of renewed sediment supplies on the coastal ecosystems before, during, and following the removal of two dams, Elwha and Glines Canyon, from the Elwha River. Removal of the dams reintroduced sediment stored in the reservoirs to the river, and the river moved much of this sediment to the coast. Several benthic tripods were instrumented with oceanographic sensors to collect the time-series data. Initial deployment in December 2010 consisted of one tripod about 1 km east of the Elwha River mouth (Tripod A). In March of 2011, an identical tripod (Tripod B) was placed about 1 km west of the river mouth. A mooring was added to the western site in July 2012 to measure turbidity and conductivity near the surface.  A third tripod was placed in deeper water (50 m) directly offshore of the river mouth in an attempt to characterize sediment gravity flows near the seafloor if they occurred (Tripod C). Exceptional sedimentation was observed near the original tripod site A during the winter of 2013-2014. As a result, the tripod was relocated further east in April 2013 and renamed Tripod D. Please check metadata and instrument information carefully for applicable time periods of specific data, as individual instrument deployment times and duration of the time series vary. The naming convention for the NetCDF files included in this release is a 12-character alphanumeric code (ELWYYJKLNNXX.nc) where: ELW is a 3-digit alphabetic-code for this experiment located at the mouth of the Elwha River YY is the 2-digit year at the time of deployment J is the location with respect to the river mouth [A, East (December 2010 to April 2013); B, West; C, Offshore; D, East (April 2013 to March 2014)] K is the deployment number (1-9; beginning and ending dates of each deployment are given below) L is the instrument package type (T, tripod; M, surface mooring) NN indicates the position of instrument on the surface mooring (01, nearest the surface; NN increases with depth) XX denotes the instrument or data type (wh, RDInstruments ADCP current data; wv, RDInstruments ADCP derived wave parameters; nx, Falmouth Scientific NXIC CTD; aq, Aquatec Aqualogger OBS; bl, RBR, Ltd CTD; sc, SeaBird Electonics SBE16+ CT) Some derived parameters are included in these data. Deployment dates: 1. Dec 2010 to Mar 2011 2. Mar 2011 to Sep 2011 3. Sep 2011 to Mar 2012 4. Mar 2012 to Aug 2012 5. Aug 2012 to Jan 2013 6. Jan 2013 to Jun 2013 7. Jun 2013 to Dec 2013 8. Dec 2013 to Mar 2014 9. Mar 2014 to Oct 2014

This part of DS 781 presents data for the depth-to-transition map of the Bolinas to Pescadero, California, region. The raster data file is included in "DepthToTransition_BolinastoPescadero.zip," which is accessible from https://pubs.usgs.gov/ds/781/BolinasToPescadero/data_catalog_BolinasToPescadero.html. As part of the USGS's California State Waters Mapping Project, a 50-m grid of sediment thickness for the seafloor within the 3-nautical mile limit between Bolinas and Pescadero was generated from seismic-reflection data collected between 2006 and 2010, supplemented with outcrop and geologic structure (fault) information following the methodology of Wong (2012). Water depths determined from bathymetry data were added to the sediment thickness data to provide information on the depth to base of the post-LGM unit. Reference Cited: Wong, F. L., Phillips, E.L., Johnson, S.Y., and Sliter, R.W., 2012, Modeling of depth to base of Last Glacial Maximum and seafloor sediment thickness for the California State Waters Map Series, eastern Santa Barbara Channel, California: U.S. Geological Survey Open-File Report 2012-1161, 16 p. (available at https://pubs.usgs.gov/of/2012/1161/)

This part of DS 781 presents data for the isopachs for the Bolinas to Pescadero, California, region. The vector data file is included in "Isopachs_BolinastoPescadero.zip," which is accessible from https://pubs.usgs.gov/ds/781/BolinasToPescadero/data_catalog_BolinasToPescadero.html. As part of the USGS's California Seafloor Mapping Program, a 50-m grid of sediment thickness for the seafloor within the 3-nautical-mile limit of California's State Waters between offshore Offshore Bolinas and offshore Pescadero was generated from seismic-reflection data collected between 2006 and 2010, supplemented with outcrop and geologic structure (fault) information following the methodology of Wong (2012). Reference Cited: Wong, F. L., Phillips, E.L., Johnson, S.Y., and Sliter, R.W., 2012, Modeling of depth to base of Last Glacial Maximum and seafloor sediment thickness for the California State Waters Map Series, eastern Santa Barbara Channel, California: U.S. Geological Survey Open-File Report 2012-1161, 16 p. (available at https://pubs.usgs.gov/of/2012/1161/)

This part of DS 781 presents data for the sediment-thickness map of the Bolinas to Pescadero, California, region. The raster data file is included in "SedimentThickness_BolinastoPescadero.zip," which is accessible from http://pubs.usgs.gov/ds/781/BolinastoPescadero/data_catalog_BolinastoPescadero.html. As part of the USGS's California State Waters Mapping Project, a 50-m grid of sediment thickness for the seafloor within the 3-nautical mile limit between Bolinas and Pescadero was generated from seismic-reflection data collected between 2006 and 2010, supplemented with outcrop and geologic structure (fault) information following the methodology of Wong (2012). Reference Cited: Wong, F. L., Phillips, E.L., Johnson, S.Y., and Sliter, R.W., 2012, Modeling of depth to base of Last Glacial Maximum and seafloor sediment thickness for the California State Waters Map Series, eastern Santa Barbara Channel, California: U.S. Geological Survey Open-File Report 2012-1161, 16 p. (available at https://pubs.usgs.gov/of/2012/1161/)

This part of DS 781 presents data for the depth-to-transition map of the Bolinas to Pescadero, California, region. The raster data file is included in "TransgressiveContours_BolinasToPescadero.zip," which is accessible from https://pubs.usgs.gov/ds/781/BolinasToPescadero/data_catalog_BolinasToPescadero.html. As part of the USGS's California State Waters Mapping Project, a 50-m grid of sediment thickness for the seafloor within the 3-nautical mile limit between Bolinas and Pescadero was generated from seismic-reflection data collected between 2006 and 2010, supplemented with outcrop and geologic structure (fault) information following the methodology of Wong (2012). Water depths determined from bathymetry data were added to the sediment thickness data to provide information on the depth to base of the post-LGM unit. This polyline shapefile provides the depth-to-transition contours to accompany the grid layer. Reference Cited: Wong, F. L., Phillips, E.L., Johnson, S.Y., and Sliter, R.W., 2012, Modeling of depth to base of Last Glacial Maximum and seafloor sediment thickness for the California State Waters Map Series, eastern Santa Barbara Channel, California: U.S. Geological Survey Open-File Report 2012-1161, 16 p. (available at https://pubs.usgs.gov/of/2012/1161/)

This part of DS 781 presents data for the acoustic-backscatter map of Drakes Bay and Vicinity, California. Backscatter data are provided as separate grids depending on mapping system or processing method. The raster data file is included in "BackscatterA_8101_DrakesBay.zip", which is accessible from https://pubs.usgs.gov/ds/781/DrakesBay/data_catalog_DrakesBay.html. These data accompany the pamphlet and map sheets of Watt, J.T., Dartnell, P., Golden, N.E., Greene, H.G., Erdey, M.D., Cochrane, G.R., Johnson, S.Y., Hartwell, S.R., Kvitek, R.G., Manson, M.W., Endris, C.A., Dieter, B.E., Sliter, R.W., Krigsman, L.M., Lowe, E.N., and Chin, J.L. (J.T. Watt and S.A. Cochran, eds.), 2015, California State Waters Map Series—Drakes Bay and Vicinity, California: U.S. Geological Survey Open-File Report 2015–1041, pamphlet 36 p., 10 sheets, scale 1:24,000, https://doi.org/10.3133/ofr20151041. The acoustic-backscatter map of the Drakes Bay and Vicinity map area, California, was generated from backscatter collected by California State University, Monterey Bay (CSUMB), and by Fugro Pelagos. Mapping was completed between 2007 and 2010, using a combination of 200-kHz and 400-kHz Reson 7125, and 244-kHz Reson 8101 multibeam echosounders, as well as 468-kHz SEA SWATHPlus interferometric system. These mapping missions combined to collect backscatter data from about the 10-m isobath to beyond the 3-nautical-mile limit of California's State Waters. Within the acoustic-backscatter imagery, brighter tones indicate higher backscatter intensity, and darker tones indicate lower backscatter intensity. The intensity represents a complex interaction between the acoustic pulse and the seafloor, as well as characteristics within the shallow subsurface, providing a general indication of seafloor texture and composition. Backscatter intensity depends on the acoustic source level; the frequency used to image the seafloor; the grazing angle; the composition and character of the seafloor, including grain size, water content, bulk density, and seafloor roughness; and some biological cover. Harder and rougher bottom types such as rocky outcrops or coarse sediment typically return stronger intensities (high backscatter, lighter tones), whereas softer bottom types such as fine sediment return weaker intensities (low backscatter, darker tones). These data are not intended for navigational purposes.

This part of DS 781 presents data for the acoustic-backscatter map of Drakes Bay and Vicinity, California. Backscatter data are provided as separate grids depending on mapping system or processing method. The raster data file is included in "BackscatterB_Swath_DrakesBay.zip", which is accessible from https://pubs.usgs.gov/ds/781/DrakesBay/data_catalog_DrakesBay.html. These data accompany the pamphlet and map sheets of Watt, J.T., Dartnell, P., Golden, N.E., Greene, H.G., Erdey, M.D., Cochrane, G.R., Johnson, S.Y., Hartwell, S.R., Kvitek, R.G., Manson, M.W., Endris, C.A., Dieter, B.E., Sliter, R.W., Krigsman, L.M., Lowe, E.N., and Chin, J.L. (J.T. Watt and S.A. Cochran, eds.), 2015, California State Waters Map Series—Drakes Bay and Vicinity, California: U.S. Geological Survey Open-File Report 2015–1041, pamphlet 36 p., 10 sheets, scale 1:24,000, https://doi.org/10.3133/ofr20151041. The acoustic-backscatter map of Drakes Bay and Vicinity map area, California, was generated from backscatter collected by California State University, Monterey Bay (CSUMB), and by Fugro Pelagos. Mapping was completed between 2007 and 2010, using a combination of 200-kHz and 400-kHz Reson 7125, and 244-kHz Reson 8101 multibeam echosounders, as well as 468-kHz SEA SWATHPlus interferometric system. These mapping missions combined to collect backscatter data from about the 10-m isobath to beyond the 3-nautical-mile limit of California's State Waters. Within the acoustic-backscatter imagery, brighter tones indicate higher backscatter intensity, and darker tones indicate lower backscatter intensity. The intensity represents a complex interaction between the acoustic pulse and the seafloor, as well as characteristics within the shallow subsurface, providing a general indication of seafloor texture and composition. Backscatter intensity depends on the acoustic source level; the frequency used to image the seafloor; the grazing angle; the composition and character of the seafloor, including grain size, water content, bulk density, and seafloor roughness; and some biological cover. Harder and rougher bottom types such as rocky outcrops or coarse sediment typically return stronger intensities (high backscatter, lighter tones), whereas softer bottom types such as fine sediment return weaker intensities (low backscatter, darker tones). These data are not intended for navigational purposes.

This part of DS 781 presents data for the acoustic-backscatter map of Drakes bay and Vicinity map area, California. Backscatter data are provided as separate grids depending on mapping system or processing method. The raster data file is included in "BackscatterC_7125_DrakesBay.zip", which is accessible from https://pubs.usgs.gov/ds/781/DrakesBay/data_catalog_DrakesBay.html. The acoustic-backscatter map of Drakes Bay and Vicinity map area, California, was generated from backscatter collected by California State University, Monterey Bay (CSUMB), and by Fugro Pelagos. Mapping was completed between 2007 and 2010, using a combination of 200-kHz and 400-kHz Reson 7125, and 244-kHz Reson 8101 multibeam echosounders, as well as 468-kHz SEA SWATHPlus interferometric system. These mapping missions combined to collect backscatter data from about the 10-m isobath to beyond the 3-nautical-mile limit of California's State Waters. Within the acoustic-backscatter imagery, brighter tones indicate higher backscatter intensity, and darker tones indicate lower backscatter intensity. The intensity represents a complex interaction between the acoustic pulse and the seafloor, as well as characteristics within the shallow subsurface, providing a general indication of seafloor texture and composition. Backscatter intensity depends on the acoustic source level; the frequency used to image the seafloor; the grazing angle; the composition and character of the seafloor, including grain size, water content, bulk density, and seafloor roughness; and some biological cover. Harder and rougher bottom types such as rocky outcrops or coarse sediment typically return stronger intensities (high backscatter, lighter tones), whereas softer bottom types such as fine sediment return weaker intensities (low backscatter, darker tones). These data are not intended for navigational purposes.

This part of DS 781 presents data for the shaded-relief bathymetry map of Drakes Bay and Vicinity, California (raster data file is included in "BathymetryHS_DrakesBay.zip," which is accessible from https://pubs.usgs.gov/ds/781/DrakesBay/data_catalog_DrakesBay.html. These data accompany the pamphlet and map sheets of Watt, J.T., Dartnell, P., Golden, N.E., Greene, H.G., Erdey, M.D., Cochrane, G.R., Johnson, S.Y., Hartwell, S.R., Kvitek, R.G., Manson, M.W., Endris, C.A., Dieter, B.E., Sliter, R.W., Krigsman, L.M., Lowe, E.N., and Chin, J.L. (J.T. Watt and S.A. Cochran, eds.), 2015, California State Waters Map Series—Drakes Bay and Vicinity, California: U.S. Geological Survey Open-File Report 2015–1041, pamphlet 36 p., 10 sheets, scale 1:24,000, https://doi.org/10.3133/ofr20151041. The shaded-relief bathymetry map of Drakes Bay and Vicinity, California, was generated from bathymetry data collected by California State University, Monterey Bay (CSUMB), and by Fugro Pelagos. Mapping was completed between 2007 and 2010, using a combination of 200-kHz and 400-kHz Reson 7125, and 244-kHz Reson 8101 multibeam echosounders, as well as 468-kHz SEA SWATHPlus interferometric system. These mapping missions combined to collect bathymetry from about the 10-m isobath to beyond the 3-nautical-mile limit of California's State Waters.

This part of DS 781 presents data for the bathymetric contours for several seafloor maps of the Drakes Bay and Vicinity map area, California. The vector data file is included in "Contours_DrakesBay.zip," which is accessible from https://pubs.usgs.gov/ds/781/DrakesBay/data_catalog_DrakesBay.html. These data accompany the pamphlet and map sheets of Watt, J.T., Dartnell, P., Golden, N.E., Greene, H.G., Erdey, M.D., Cochrane, G.R., Johnson, S.Y., Hartwell, S.R., Kvitek, R.G., Manson, M.W., Endris, C.A., Dieter, B.E., Sliter, R.W., Krigsman, L.M., Lowe, E.N., and Chin, J.L. (J.T. Watt and S.A. Cochran, eds.), 2015, California State Waters Map Series—Drakes Bay and Vicinity, California: U.S. Geological Survey Open-File Report 2015–1041, pamphlet 36 p., 10 sheets, scale 1:24,000, https://doi.org/10.3133/ofr20151041. 10-m interval contours of the Drakes Bay and Vicinity map area, California, were generated from bathymetry data collected by California State University, Monterey Bay (CSUMB) and by Fugro Pelagos. Mapping was completed between 2007 and 2010, using a combination of 200-kHz and 400-kHz Reson 7125, and 244-kHz Reson 8101 multibeam echosounders, as well as 468-kHz SEA SWATHPlus interferometric system. These mapping missions combined to collect bathymetry from about the 10-m isobath to beyond the 3-nautical-mile limit of California's State Waters. Bathymetric contours at 10-m intervals were generated from a bathymetric surface model. The most continuous contour segments were preserved while smaller segments and isolated island polygons were excluded from the final output. Contours were smoothed via a polynomial approximation with exponential kernel (PAEK) algorithm using a tolerance value of 60 m. The contours were then clipped to the boundary of the map area. These data are not intended for navigational purposes.

This part of DS 781 presents data of faults for the geologic and geomorphologic map of the Drakes Bay and Vicinity map area, California. The vector data file is included in "Faults_DrakesBay.zip," which is accessible from https://pubs.usgs.gov/ds/781/DrakesBay/data_catalog_DrakesBay.html. These data accompany the pamphlet and map sheets of Watt, J.T., Dartnell, P., Golden, N.E., Greene, H.G., Erdey, M.D., Cochrane, G.R., Johnson, S.Y., Hartwell, S.R., Kvitek, R.G., Manson, M.W., Endris, C.A., Dieter, B.E., Sliter, R.W., Krigsman, L.M., Lowe, E.N., and Chin, J.L. (J.T. Watt and S.A. Cochran, eds.), 2015, California State Waters Map Series—Drakes Bay and Vicinity, California: U.S. Geological Survey Open-File Report 2015–1041, pamphlet 36 p., 10 sheets, scale 1:24,000, https://doi.org/10.3133/ofr20151041. Faults in the Drakes Bay and Vicinity map area are identified on seismic-reflection data based on abrupt truncation or warping of reflections and (or) juxtaposition of reflection panels with different seismic parameters such as reflection presence, amplitude, frequency, geometry, continuity, and vertical sequence. Faults were primarily mapped by interpretation of seismic reflection profile data from USGS field activity S-8-09-NC. The seismic reflection profiles were collected between 2006 and 2009.

This part of DS 781 presents data of folds for the geologic and geomorphologic map of the Drakes Bay and Vicinity map area, California. The vector data file is included in "Folds_DrakesBay.zip," which is accessible from https://pubs.usgs.gov/ds/781/DrakesBay/data_catalog_DrakesBay.html. These data accompany the pamphlet and map sheets of Watt, J.T., Dartnell, P., Golden, N.E., Greene, H.G., Erdey, M.D., Cochrane, G.R., Johnson, S.Y., Hartwell, S.R., Kvitek, R.G., Manson, M.W., Endris, C.A., Dieter, B.E., Sliter, R.W., Krigsman, L.M., Lowe, E.N., and Chin, J.L. (J.T. Watt and S.A. Cochran, eds.), 2015, California State Waters Map Series—Drakes Bay and Vicinity, California: U.S. Geological Survey Open-File Report 2015–1041, pamphlet 36 p., 10 sheets, scale 1:24,000, https://doi.org/10.3133/ofr20151041. Folds in the Drakes Bay and Vicinity map area are identified on seismic-reflection data based on abrupt truncation or warping of reflections and (or) juxtaposition of reflection panels with different seismic parameters such as reflection presence, amplitude, frequency, geometry, continuity, and vertical sequence. Folds were primarily mapped by interpretation of seismic reflection profile data from USGS field activity S-8-09-NC. The seismic reflection profiles were collected between 2006 in 2009.

This part of DS 781 presents data for the geologic and geomorphic map of the Drakes Bay and Vicinity, California. The polygon shapefile is included in "Geology_DrakesBay.zip," which is accessible from https://pubs.usgs.gov/ds/781/DrakesBay/data_catalog_DrakesBay.html. These data accompany the pamphlet and map sheets of Watt, J.T., Dartnell, P., Golden, N.E., Greene, H.G., Erdey, M.D., Cochrane, G.R., Johnson, S.Y., Hartwell, S.R., Kvitek, R.G., Manson, M.W., Endris, C.A., Dieter, B.E., Sliter, R.W., Krigsman, L.M., Lowe, E.N., and Chin, J.L. (J.T. Watt and S.A. Cochran, eds.), 2015, California State Waters Map Series—Drakes Bay and Vicinity, California: U.S. Geological Survey Open-File Report 2015–1041, pamphlet 36 p., 10 sheets, scale 1:24,000, https://doi.org/10.3133/ofr20151041. Marine geology and geomorphology was mapped in the Drakes Bay and Vicinity map area, California, from approximate Mean High Water (MHW) to the 3-nautical-mile limit of California's State Waters. Offshore geologic units were delineated on the basis of integrated analyses of adjacent onshore geology with multibeam bathymetry and backscatter imagery, seafloor-sediment and rock samples, digital camera and video imagery, and high-resolution seismic-reflection profiles.

This part of DS 781 presents data for the habitat map of the seafloor of the Drakes Bay and Vicinity map area, California. The vector data file is included in "Habitat_DrakesBay.zip," which is accessible from https://pubs.usgs.gov/ds/781/DrakesBay/data_catalog_DrakesBay.html. These data accompany the pamphlet and map sheets of Watt, J.T., Dartnell, P., Golden, N.E., Greene, H.G., Erdey, M.D., Cochrane, G.R., Johnson, S.Y., Hartwell, S.R., Kvitek, R.G., Manson, M.W., Endris, C.A., Dieter, B.E., Sliter, R.W., Krigsman, L.M., Lowe, E.N., and Chin, J.L. (J.T. Watt and S.A. Cochran, eds.), 2015, California State Waters Map Series—Drakes Bay and Vicinity, California: U.S. Geological Survey Open-File Report 2015–1041, pamphlet 36 p., 10 sheets, scale 1:24,000, https://doi.org/10.3133/ofr20151041. Potential marine benthic habitat maps were constructed using multibeam echosounder (MBES) bathymetry and backscatter data. The habitats were based on substrate types and documented or "ground truthed" using underwater video images and seafloor samples obtained by the USGS. These maps display various habitat types that range from flat, soft, unconsolidated sediment-covered seafloor to hard, deformed (folded), or highly rugose and differentially eroded bedrock exposures.

This part of DS 781 presents data for part of the acoustic-backscatter map of the Hueneme Canyon and Vicinity map area, California. Backscatter data are provided as separate grids depending on mapping system or processing method. The raster data file is included in "BackscatterA_CSUMB_HuenemeCanyon.zip," which is accessible from https://pubs.usgs.gov/ds/781/HuenemeCanyon/data_catalog_HuenemeCanyon.html. These data accompany the pamphlet and map sheets of Johnson, S.Y., Dartnell, P., Cochrane, G.R., Golden, N.E., Phillips, E.L., Ritchie, A.C., Kvitek, R.G., Greene, H.G., Krigsman, L.M., Endris, C.A., Clahan, K.B., Sliter, R.W., Wong, F.L., Yoklavich, M.M., and Normark, W.R. (S.Y. Johnson, ed.), 2012, California State Waters Map Series—-Hueneme Canyon and Vicinity, California: U.S. Geological Survey Scientific Investigations Map 3225, 41 p., 12 sheets, scale 1:24,000, https://pubs.usgs.gov/sim/3225/. The acoustic-backscatter map of Hueneme Canyon and Vicinity map area, California, was generated from backscatter data collected by California State University, Monterey Bay, Seafloor Mapping Lab (CSUMB) and by the U.S. Geological Survey (USGS). This metadata file describes the acoustic-backscatter data collected by CSUMB. See https://pubs.usgs.gov/ds/781/HuenemeCanyon/metadata/BackscatterB_USGS_HuenemeCanyon_metadata.txt for a description of the acoustic-backscatter data collected by the USGS. The majority of the acoustic-backscatter data within the Hueneme Canyon and vicinity, California, map area was collected by CSUMB in the summers of 2006 and 2007, using a 244-kHz Reson 8101 multibeam echosounder. Within the acoustic-backscatter imagery, brighter tones indicate higher backscatter intensity, and darker tones indicate lower backscatter intensity. The intensity represents a complex interaction between the acoustic pulse and the seafloor, as well as characteristics within the shallow subsurface, providing a general indication of seafloor texture and composition. Backscatter intensity depends on the acoustic source level; the frequency used to image the seafloor; the grazing angle; the composition and character of the seafloor, including grain size, water content, bulk density, and seafloor roughness; and some biological cover. Harder and rougher bottom types such as rocky outcrops or coarse sediment typically return stronger intensities (high backscatter, lighter tones), whereas softer bottom types such as fine sediment return weaker intensities (low backscatter, darker tones).

This part of DS 781 presents data for part of the acoustic-backscatter map of the Hueneme Canyon and Vicinity map area, California. Backscatter data are provided as separate grids depending on mapping system or processing method. The raster data file is included in "BackscatterB_USGS_HuenemeCanyon.zip," which is accessible from https://pubs.usgs.gov/ds/781/HuenemeCanyon/data_catalog_HuenemeCanyon.html. These data accompany the pamphlet and map sheets of Johnson, S.Y., Dartnell, P., Cochrane, G.R., Golden, N.E., Phillips, E.L., Ritchie, A.C., Kvitek, R.G., Greene, H.G., Krigsman, L.M., Endris, C.A., Clahan, K.B., Sliter, R.W., Wong, F.L., Yoklavich, M.M., and Normark, W.R. (S.Y. Johnson, ed.), 2012, California State Waters Map Series--Hueneme Canyon and Vicinity, California: U.S. Geological Survey Scientific Investigations Map 3225, 41 p., 12 sheets, scale 1:24,000, https://pubs.usgs.gov/sim/3225/. The acoustic-backscatter map of Hueneme Canyon and Vicinity map area, California, was generated from backscatter data collected by California State University, Monterey Bay, Seafloor Mapping Lab (CSUMB) and by the U.S. Geological Survey (USGS). This metadata file describes the acoustic-backscatter data collected by the USGS. See https://pubs.usgs.gov/ds/781/HuenemeCanyon/metadata/BackscatterA_CSUMB_HuenemeCanyon_metadata.txt for a description of the acoustic-backscatter data collected by CSUMB. The far northern part of the Hueneme Canyon and Vicinity, California map area was mapped by the USGS in 2006, using a 117-kHz SEA (AP) Ltd. SWATHplus-M phase-differencing sidescan sonar. This mapping mission collected acoustic-backscatter data from about the 10-m isobath to almost the 3-nautical-mile limit of California's State Waters. Within the acoustic-backscater imagery, brighter tones indicate higher backscatter intensity, and darker tones indicate lower backscatter intensity. The intensity represents a complex interaction between the acoustic pulse and the seafloor, as well as characteristics within the shallow subsurface, providing a general indication of seafloor texture and sediment type. Backscatter intensity depends on the acoustic source level; the frequency used to image the seafloor; the grazing angle; the composition and character of the seafloor, including grain size, water content, bulk density, and seafloor roughness; and some biological cover. Harder and rougher bottom types such as rocky outcrops or coarse sediment typically return stronger intensities (high backscatter, lighter tones), whereas softer bottom types such as fine sediment return weaker intensities (low backscatter, darker tones).

This part of DS 781 present the shaded-relief bathymetry map of the Hueneme Canyon and Vicinity map area, California. The raster data file for the shaded-relief map is included in "BathymetryHS_HuenemeCanyon.zip," which is accessible from https://pubs.usgs.gov/ds/781/HuenemeCanyon/data_catalog_HuenemeCanyon.html. These data accompany the pamphlet and map sheets of Johnson, S.Y., Dartnell, P., Cochrane, G.R., Golden, N.E., Phillips, E.L., Ritchie, A.C., Kvitek, R.G., Greene, H.G., Krigsman, L.M., Endris, C.A., Clahan, K.B., Sliter, R.W., Wong, F.L., Yoklavich, M.M., and Normark, W.R. (S.Y. Johnson, ed.), 2012, California State Waters Map Series-—Hueneme Canyon and Vicinity, California: U.S. Geological Survey Scientific Investigations Map 3225, 41 p., 12 sheets, scale 1:24,000, https://pubs.usgs.gov/sim/3225/. The shaded-relief bathymetry map of the Hueneme Canyon and Vicinity map area, California, was generated from bathymetry data collected by California State University, Monterey Bay, Seafloor Mapping Lab (CSUMB), by the U.S. Geological Survey (USGS), and by Fugro Pelagos for the U.S. Army Corps of Engineers (USACE) Joint Lidar Bathymetry Technical Center of Expertise. Most of the offshore area was mapped by CSUMB in the summers of 2006 and 2007, using a 244-kHz Reson 8101 multibeam echosounder. The far northern part of the offshore area was mapped by the USGS in 2006, using a 117-kHz SEA (AP) Ltd. SWATHplus-M phase-differencing sidescan sonar. The nearshore bathymetry and coastal topography were mapped for USACE by Fugro Pelagos in 2009, using the SHOALS-1000T bathymetric-lidar and Leica ALS60 topographic-lidar systems. All these mapping missions combined to collect bathymetry from the 0-m isobath to beyond the 3-nautical-mile limit of California's State Waters.

This part of DS 781 presents data for the bathymetry map of the Hueneme Canyon and Vicinity map area, California. The raster data file for the bathymetry map is included in "Bathymetry_HuenemeCanyon.zip," which is accessible from https://pubs.usgs.gov/ds/781/HuenemeCanyon/data_catalog_HuenemeCanyon.html. These data accompany the pamphlet and map sheets of Johnson, S.Y., Dartnell, P., Cochrane, G.R., Golden, N.E., Phillips, E.L., Ritchie, A.C., Kvitek, R.G., Greene, H.G., Krigsman, L.M., Endris, C.A., Clahan, K.B., Sliter, R.W., Wong, F.L., Yoklavich, M.M., and Normark, W.R. (S.Y. Johnson, ed.), 2012, California State Waters Map Series—-Hueneme Canyon and Vicinity, California: U.S. Geological Survey Scientific Investigations Map 3225, 41 p., 12 sheets, scale 1:24,000, https://pubs.usgs.gov/sim/3225/. The bathymetry map of the Hueneme Canyon and Vicinity map area, California, was generated from bathymetry data collected by California State University, Monterey Bay (CSUMB), by the U.S. Geological Survey (USGS), and by Fugro Pelagos for the U.S. Army Corps of Engineers (USACE) Joint Lidar Bathymetry Technical Center of Expertise. Most of the offshore area was mapped by CSUMB in the summers of 2006 and 2007, using a 244-kHz Reson 8101 multibeam echosounder. The far northern part of the offshore area was mapped by the USGS in 2006, using a 117-kHz SEA (AP) Ltd. SWATHplus-M phase-differencing sidescan sonar. The nearshore bathymetry and coastal topography were mapped for USACE by Fugro Pelagos in 2009, using the SHOALS-1000T bathymetric-lidar and Leica ALS60 topographic-lidar systems. These mapping missions combined to collect bathymetry from the 0-m isobath to beyond the 3-nautical-mile limit of California's State Waters.

This part of DS 781 presents data for the bathymetric contours of the Hueneme Canyon and Vicinity map area, California. The vector data file is included in "Contours_HuenemeCanyon.zip," which is accessible from https://pubs.usgs.gov/ds/781/HuenemeCanyon/data_catalog_HuenemeCanyon.html. These data accompany the pamphlet and map sheets of Johnson, S.Y., Dartnell, P., Cochrane, G.R., Golden, N.E., Phillips, E.L., Ritchie, A.C., Kvitek, R.G., Greene, H.G., Krigsman, L.M., Endris, C.A., Clahan, K.B., Sliter, R.W., Wong, F.L., Yoklavich, M.M., and Normark, W.R. (S.Y. Johnson, ed.), 2012, California State Waters Map Series—-Hueneme Canyon and Vicinity, California: U.S. Geological Survey Scientific Investigations Map 3225, 41 p., 12 sheets, scale 1:24,000, https://pubs.usgs.gov/sim/3225/. The bathymetry map of Hueneme Canyon and Vicinity map area in southern California was generated from bathymetry data collected by California State University, Monterey Bay (CSUMB), by the U.S. Geological Survey (USGS), and by Fugro Pelagos for the U.S. Army Corps of Engineers (USACE) Joint Lidar Bathymetry Technical Center of Expertise. Most of the offshore area was mapped by CSUMB in the summers of 2006 and 2007, using a 244-kHz Reson 8101 multibeam echosounder. The far-northern part of the offshore area was mapped by the USGS in 2006, using a 117-kHz SEA (AP) Ltd. SWATHplus-M phase-differencing sidescan sonar. The nearshore bathymetry and coastal topography were mapped for USACE by Fugro Pelagos in 2009, using the SHOALS-1000T bathymetric-lidar and Leica ALS60 topographic-lidar systems. All these mapping missions combined to collect bathymetry from the 0-m isobath to beyond the 3-nautical-mile limit of California's State Waters. To generate contours, a smooth arithmetic mean convolution function was applied to the bathymetry. Following smoothing, contour lines were generated at 10-meter intervals from -10 m to -100 m and at 50-meter intervals from -100 m to -400 m.

This part of DS 781 presents data for the curvature map of the Hueneme Canyon and vicinity map area, California. The raster data file is included in "Curvature_HuenemeCanyon.zip," which is accessible from https://pubs.usgs.gov/ds/781/HuenemeCanyon/data_catalog_HuenemeCanyon.html. These data accompany the pamphlet and map sheets of Johnson, S.Y., Dartnell, P., Cochrane, G.R., Golden, N.E., Phillips, E.L., Ritchie, A.C., Kvitek, R.G., Greene, H.G., Krigsman, L.M., Endris, C.A., Clahan, K.B., Sliter, R.W., Wong, F.L., Yoklavich, M.M., and Normark, W.R. (S.Y. Johnson, ed.), 2012, California State Waters Map Series-—Hueneme Canyon and Vicinity, California: U.S. Geological Survey Scientific Investigations Map 3225, 41 p., 12 sheets, scale 1:24,000, https://pubs.usgs.gov/sim/3225/. This metadata describes a raster data set of smoothed curvature used as an interpretation aid for mapping geomorphology of Hueneme Canyon. The curvature raster, in conjunction with bathymetry data, amplitude data, and seismic reflection profiles, was used to interpret geology and geomorphology of Hueneme Canyon.

This part of DS 781 presents data for faults for the geologic and geomorphic map of the Hueneme Canyon and Vicinity map area, California. The vector data file is included in "Faults_HuenemeCanyon.zip," which is accessible from http://pubs.usgs.gov/ds/781/HuenemeCanyon/data_catalog_HuenemeCanyon.html. These data accompany the pamphlet and map sheets of Johnson, S.Y., Dartnell, P., Cochrane, G.R., Golden, N.E., Phillips, E.L., Ritchie, A.C., Kvitek, R.G., Greene, H.G., Krigsman, L.M., Endris, C.A., Clahan, K.B., Sliter, R.W., Wong, F.L., Yoklavich, M.M., and Normark, W.R. (S.Y. Johnson, ed.), 2012, California State Waters Map Series-—Hueneme Canyon and Vicinity, California: U.S. Geological Survey Scientific Investigations Map 3225, 41 p., 12 sheets, scale 1:24,000, https://pubs.usgs.gov/sim/3225/. Faults in the Hueneme Canyon and Vicinity map area are identified on seismic-reflection data based on abrupt truncation or warping of reflections and (or) juxtaposition of reflection panels with different seismic parameters such as reflection presence, amplitude, frequency, geometry, continuity, and vertical sequence. Faults were primarily mapped by interpretation of seismic reflection profile data from USGS field activities Z–3–07–SC and S-7-08-SC. The seismic reflection profiles were collected in 2007 and 2008.

This part of DS 781 presents data for folds for the geologic and geomorphic map of the Hueneme Canyon and Vicinity map area, California. The vector data file is included in "Folds_HuenemeCanyon.zip," which is accessible from http://pubs.usgs.gov/ds/781/HuenemeCanyon/data_catalog_HuenemeCanyon.html. These data accompany the pamphlet and map sheets of Johnson, S.Y., Dartnell, P., Cochrane, G.R., Golden, N.E., Phillips, E.L., Ritchie, A.C., Kvitek, R.G., Greene, H.G., Krigsman, L.M., Endris, C.A., Clahan, K.B., Sliter, R.W., Wong, F.L., Yoklavich, M.M., and Normark, W.R. (S.Y. Johnson, ed.), 2012, California State Waters Map Series—Hueneme Canyon and Vicinity, California: U.S. Geological Survey Scientific Investigations Map 3225, 41 p., 12 sheets, scale 1:24,000, https://pubs.usgs.gov/sim/3225/. Folds in the Hueneme Canyon and Vicinity map area are identified on seismic-reflection data based on abrupt truncation or warping of reflections and (or) juxtaposition of reflection panels with different seismic parameters such as reflection presence, amplitude, frequency, geometry, continuity, and vertical sequence. Faults were primarily mapped by interpretation of seismic reflection profile data from USGS field activities Z–3–07–SC and S-7-08-SC. The seismic reflection profiles were collected in 2007 and 2008.

This part of DS 781 presents data for the geologic and geomorphic map of the Hueneme Canyon and Vicinity map area, California. The vector data file is included in "Geology_HuenemeCanyon.zip," which is accessible from http://pubs.usgs.gov/ds/781/HuenemeCanyon/data_catalog_HuenemeCanyon.html. These data accompany the pamphlet and map sheets of Johnson, S.Y., Dartnell, P., Cochrane, G.R., Golden, N.E., Phillips, E.L., Ritchie, A.C., Kvitek, R.G., Greene, H.G., Krigsman, L.M., Endris, C.A., Clahan, K.B., Sliter, R.W., Wong, F.L., Yoklavich, M.M., and Normark, W.R. (S.Y. Johnson, ed.), 2012, California State Waters Map Series—-Hueneme Canyon and Vicinity, California: U.S. Geological Survey Scientific Investigations Map 3225, 41 p., 12 sheets, scale 1:24,000, https://pubs.usgs.gov/sim/3225/. Marine geology and geomorphology was mapped in the Hueneme Canyon and Vicinity map area, California, from approximate Mean High Water (MHW) to the 3-nautical-mile limit of California's State Waters, and even farther offshore on the east and west flanks of Hueneme Canyon. Offshore geologic units were delineated on the basis of integrated analyses of adjacent onshore geology with multibeam bathymetry and backscatter imagery, seafloor-sediment and rock samples, digital camera and video imagery, and high-resolution seismic-reflection profiles.

This part of DS 781 presents data for the habitat map of the seafloor of the Hueneme Canyon and Vicinity map area, California. The vector data file is included in "Habitat_HuenemeCanyon.zip," which is accessible from http://pubs.usgs.gov/ds/781/HuenemeCanyon/data_catalog_HuenemeCanyon.html. These data accompany the pamphlet and map sheets of Johnson, S.Y., Dartnell, P., Cochrane, G.R., Golden, N.E., Phillips, E.L., Ritchie, A.C., Kvitek, R.G., Greene, H.G., Krigsman, L.M., Endris, C.A., Clahan, K.B., Sliter, R.W., Wong, F.L., Yoklavich, M.M., and Normark, W.R. (S.Y. Johnson, ed.), 2012, California State Waters Map Series-—Hueneme Canyon and Vicinity, California: U.S. Geological Survey Scientific Investigations Map 3225, 41 p., 12 sheets, scale 1:24,000, https://pubs.usgs.gov/sim/3225/. Using multibeam echosounder (MBES) bathymetry and backscatter data, potential marine benthic habitat maps were constructed. The habitats were based on substrate types and documented or "ground truthed" using underwater video images and seafloor samples obtained by the USGS. These maps display various habitat types that range from flat, soft, unconsolidated sediment-covered seafloor to hard, deformed (folded), or highly rugose and differentially eroded bedrock exposures. Rugged, high-relief, rocky outcrops that have been eroded to form ledges and small caves are ideal habitat for rockfish (Sebastes spp.) and other bottom fish such as lingcod (Ophiodon elongatus).

This part of DS 781 presents data for the paleoshorelines for the geologic and geomorphic map of the Hueneme Canyon and Vicinity map area, California. The vector data file is included in "Paleoshorelines_HuenemeCanyon.zip," which is accessible from http://pubs.usgs.gov/ds/781/HuenemeCanyon/data_catalog_HuenemeCanyon.html. These data accompany the pamphlet and map sheets of Johnson, S.Y., Dartnell, P., Cochrane, G.R., Golden, N.E., Phillips, E.L., Ritchie, A.C., Kvitek, R.G., Greene, H.G., Krigsman, L.M., Endris, C.A., Clahan, K.B., Sliter, R.W., Wong, F.L., Yoklavich, M.M., and Normark, W.R. (S.Y. Johnson, ed.), 2012, California State Waters Map Series-—Hueneme Canyon and Vicinity, California: U.S. Geological Survey Scientific Investigations Map 3225, 41 p., 12 sheets, scale 1:24,000, https://pubs.usgs.gov/sim/3225/. The offshore map area is characterized by two major physiographic features: (1) the nearshore continental shelf and upper slope; and (2) Hueneme Canyon and parts of three smaller, unnamed submarine canyons incised into the shelf southeast of Hueneme Canyon. The nearshore, shelf, and slope are underlain by recent sediments and characterized by active sediment transport. Shelf and slope morphology and evolution result from drainage incision into deltaic sediments of the Oxnard plain during sea-level lowstand, and subsequent sedimentation as sea level rose about 125 to 130 m over the last ~18,000 to 20,000 years (Lambeck and Chappell, 2001). Sea-level rise (controlled by both eustasy and tectonic land-level change) was apparently not steady during this period, leading to development of shorelines during periods of relative sea-level stability. These paleoshorelines, characterized by shoreline angles and adjacent submerged wave-cut platforms and risers (Kern, 1977), are commonly buried by shelf sediment. However, their original morphology is at least partly reserved on the outer shelf and upper slope on the east flank of Hueneme Canyon. The geologic map includes four wave-cut platforms and risers separated by shoreline angles at depths of approximately 65 m, 75 to 85 m, 95 to 100 m, and 120 to 125 m. References Cited: Kern, J.P., 1977. J.P., Origin and history of upper Pleistocene marine terraces, San Diego, California: Geological Society of America Bulletin, v. 88, p. 1553-1566. Lambeck, K., and Chappell, J., 2001, Sea level change through the last glacial cycle: Science, v. 292, p. 679-686.

This part of DS 781 presents data for the seafloor-character map of the Hueneme Canyon and Vicinity map area, California. The raster data file is included in "SeafloorCharacter_HuenemeCanyon.zip," which is accessible from http://pubs.usgs.gov/ds/781/HuenemeCanyon/data_catalog_HuenemeCanyon.html. These data accompany the pamphlet and map sheets of Johnson, S.Y., Dartnell, P., Cochrane, G.R., Golden, N.E., Phillips, E.L., Ritchie, A.C., Kvitek, R.G., Greene, H.G., Krigsman, L.M., Endris, C.A., Clahan, K.B., Sliter, R.W., Wong, F.L., Yoklavich, M.M., and Normark, W.R. (S.Y. Johnson, ed.), 2012, California State Waters Map Series-—Hueneme Canyon and Vicinity, California: U.S. Geological Survey Scientific Investigations Map 3225, 41 p., 12 sheets, scale 1:24,000, https://pubs.usgs.gov/sim/3225/. This raster-format seafloor-character map shows four substrate classes of the Hueneme Canyon and Vicinity map area. The substrate classes mapped in this area have been further divided into the following California Marine Life Protection Act depth zones and slope classes: Depth Zone 2 (intertidal to 30 m), Depth Zone 3 (30 to 100 m), Depth Zone 4 (100 to 200 m), Depth Zone 5 (greater than 200 m), Slope Class 1 (0 degrees-5 degrees), Slope Class 2 (5 degrees-30 degrees), Slope Class 3 (30 degrees-60 degrees), and Slope Class 4 (60 degrees-90 degrees). Depth Zone 1 (intertidal) is not present in this map area. The map is created using a supervised classification method described by Cochrane (2008). References Cited: California Department of Fish and Game, 2008, California Marine Life Protection Act master plan for marine protected areas; Revised draft: California Department of Fish and Game, accessed April 5 2011, at http://www.dfg.ca.gov/mlpa/masterplan.asp. Cochrane, G.R., 2008, Video-supervised classification of sonar data for mapping seafloor habitat, in Reynolds, J.R., and Greene, H.G., eds., Marine habitat mapping technology for Alaska: Fairbanks, University of Alaska, Alaska Sea Grant College Program, p. 185-194, accessed April 5, 2011, at http://doc.nprb.org/web/research/research%20pubs/615_habitat_mapping_workshop/Individual%20Chapters%20High-Res/Ch13%20Cochrane.pdf. Sappington, J.M., Longshore, K.M., and Thompson, D.B., 2007, Quantifying landscape ruggedness for animal habitat analysis--A case study using bighorn sheep in the Mojave Desert: Journal of Wildlife Management, v. 71, p. 1419-1426.

This part of DS 781 presents data for the curvature map of the Hueneme Canyon and vicinity map area, California. The raster data file is included in "Curvature_HuenemeCanyon.zip," which is accessible from https://pubs.usgs.gov/ds/781/HuenemeCanyon/data_catalog_HuenemeCanyon.html. These data accompany the pamphlet and map sheets of Johnson, S.Y., Dartnell, P., Cochrane, G.R., Golden, N.E., Phillips, E.L., Ritchie, A.C., Kvitek, R.G., Greene, H.G., Krigsman, L.M., Endris, C.A., Clahan, K.B., Sliter, R.W., Wong, F.L., Yoklavich, M.M., and Normark, W.R. (S.Y. Johnson, ed.), 2012, California State Waters Map Series—-Hueneme Canyon and Vicinity, California: U.S. Geological Survey Scientific Investigations Map 3225, 41 p., 12 sheets, scale 1:24,000, https://pubs.usgs.gov/sim/3225/. This metadata describes a raster data set of smoothed curvature used as an interpretation aid for mapping geomorphology of Hueneme Canyon. The curvature raster, in conjunction with bathymetry data, amplitude data, and seismic reflection profiles, was used to interpret geology and geomorphology of Hueneme Canyon.

This part of DS 781 presents data for the submarine-landslide scarps for the geologic and geomorphic map of the Hueneme Canyon and Vicinity map area, California. The vector data file is included in "SubmarineLandslideScarps_HuenemeCanyon.zip," which is accessible from http://pubs.usgs.gov/ds/781/HuenemeCanyon/data_catalog_HuenemeCanyon.html. These data accompany the pamphlet and map sheets of Johnson, S.Y., Dartnell, P., Cochrane, G.R., Golden, N.E., Phillips, E.L., Ritchie, A.C., Kvitek, R.G., Greene, H.G., Krigsman, L.M., Endris, C.A., Clahan, K.B., Sliter, R.W., Wong, F.L., Yoklavich, M.M., and Normark, W.R. (S.Y. Johnson, ed.), 2012, California State Waters Map Series-—Hueneme Canyon and Vicinity, California: U.S. Geological Survey Scientific Investigations Map 3225, 41 p., 12 sheets, scale 1:24,000, https://pubs.usgs.gov/sim/3225/. Three different landslide units are mapped in Hueneme Canyon based on their morphology and relative age inferred from crosscutting and (or) draping relationships. Landslide units are undifferentiated where these morphology and relative age indicators are not distinct. The landslide units commonly include both steep erosional scarps and paired hummocky landslide deposits, and it is this genetic pairing (scarps with landslides) that distinguishes the scarps within landslide units from the scarps within the canyon-wall units. Lower-relief, sediment-draped, deep-seated slumps are mapped as separate landslide units.

This part of DS 781 presents data for the acoustic-backscatter map of Offshore of Pigeon Point map area, California. Backscatter data are provided as three separate grids depending on mapping system. This metadata file refers to the data included in "BackscatterA_8101_OffshorePigeonPoint.zip," which is accessible from https://doi.org/10.5066/F7513W80. These data accompany the pamphlet and map sheets of Cochrane, G.R., Watt, J.T., Dartnell, P., Greene, H.G., Erdey, M.D., Dieter, B.E., Golden, N.E., Johnson, S.Y., Endris, C.A., Hartwell, S.R., Kvitek, R.G., Davenport, C.W., Krigsman, L.M., Ritchie, A.C., Sliter, R.W., Finlayson, D.P., and Maier, K.L. (G.R. Cochrane and S.A. Cochran, eds.), 2015, California State Waters Map Series—Offshore of Pigeon Point, California: U.S. Geological Survey Open-File Report 2015–1232, pamphlet 40 p., 10 sheets, scale 1:24,000, https://doi.org/10.3133/ofr20151232. The acoustic-backscatter map of the Offshore of Pigeon Point map area, California, was generated from backscatter data collected by California State University, Monterey Bay (CSUMB), by Fugro Pelagos, and by the U.S. Geological Survey (USGS). Mapping was completed between 2006 and 2009, using a combination of 400-kHz Reson 7125 (CSUMB) and 244-kHz Reson 8101 (FUGRO) multibeam echosounders, as well as a 234-kHz SWATHplus bathymetric sidescan-sonar system (USGS). These mapping missions combined to collect backscatter data from about the 10-m isobath to beyond the 3-nautical-mile limit of California's State Waters. Within the final imagery, brighter tones indicate higher backscatter intensity, and darker tones indicate lower backscatter intensity. The intensity represents a complex interaction between the acoustic pulse and the seafloor, as well as characteristics within the shallow subsurface, providing a general indication of seafloor texture and composition. Backscatter intensity depends on the acoustic source level; the frequency used to image the seafloor; the grazing angle; the composition and character of the seafloor, including grain size, water content, bulk density, and seafloor roughness; and some biological cover. Harder and rougher bottom types such as rocky outcrops or coarse sediment typically return stronger intensities (high backscatter, lighter tones), whereas softer bottom types such as fine sediment return weaker intensities (low backscatter, darker tones).

This part of DS 781 presents data for the acoustic-backscatter map of Offshore of Pigeon Point map area, California. Backscatter data are provided as three separate grids depending on mapping system. This metadata file refers to the data included in "BackscatterB_7125_OffshorePigeonPoint.zip," which is accessible from https://doi.org/10.5066/F7513W80. These data accompany the pamphlet and map sheets of Cochrane, G.R., Watt, J.T., Dartnell, P., Greene, H.G., Erdey, M.D., Dieter, B.E., Golden, N.E., Johnson, S.Y., Endris, C.A., Hartwell, S.R., Kvitek, R.G., Davenport, C.W., Krigsman, L.M., Ritchie, A.C., Sliter, R.W., Finlayson, D.P., and Maier, K.L. (G.R. Cochrane and S.A. Cochran, eds.), 2015, California State Waters Map Series—Offshore of Pigeon Point, California: U.S. Geological Survey Open-File Report 2015–1232, pamphlet 40 p., 10 sheets, scale 1:24,000, https://doi.org/10.3133/ofr20151232. The acoustic-backscatter map of the Offshore of Pigeon Point, California was generated from backscatter data collected by California State University, Monterey Bay (CSUMB), by Fugro Pelagos, and by the U.S. Geological Survey (USGS). Mapping was completed between 2006 and 2009, using a combination of 400-kHz Reson 7125 and 244-kHz Reson 8101 multibeam echosounders, as well as a 234-kHz SWATHplus bathymetric sidescan-sonar system. Within the final imagery, brighter tones indicate higher backscatter intensity, and darker tones indicate lower backscatter intensity. The intensity represents a complex interaction between the acoustic pulse and the seafloor, as well as characteristics within the shallow subsurface, providing a general indication of seafloor texture and composition. Backscatter intensity depends on the acoustic source level; the frequency used to image the seafloor; the grazing angle; the composition and character of the seafloor, including grain size, water content, bulk density, and seafloor roughness; and some biological cover. Harder and rougher bottom types such as rocky outcrops or coarse sediment typically return stronger intensities (high backscatter, lighter tones), whereas softer bottom types such as fine sediment return weaker intensities (low backscatter, darker tones).

This part of DS 781 presents data for the acoustic-backscatter map of Offshore of Pigeon Point map area, California. Backscatter data are provided as three separate grids depending on mapping system. This metadata file refers to the data included in "BackscatterC_SWATH_OffshorePigeonPoint.zip," which is accessible from https://doi.org/10.5066/F7513W80. These data accompany the pamphlet and map sheets of Cochrane, G.R., Watt, J.T., Dartnell, P., Greene, H.G., Erdey, M.D., Dieter, B.E., Golden, N.E., Johnson, S.Y., Endris, C.A., Hartwell, S.R., Kvitek, R.G., Davenport, C.W., Krigsman, L.M., Ritchie, A.C., Sliter, R.W., Finlayson, D.P., and Maier, K.L. (G.R. Cochrane and S.A. Cochran, eds.), 2015, California State Waters Map Series—Offshore of Pigeon Point, California: U.S. Geological Survey Open-File Report 2015–1232, pamphlet 40 p., 10 sheets, scale 1:24,000, https://doi.org/10.3133/ofr20151232. The acoustic-backscatter map of the Offshore of Pigeon Point, California was generated from backscatter data collected by California State University, Monterey Bay (CSUMB), by Fugro Pelagos, and by the U.S. Geological Survey (USGS). Mapping was completed between 2006 and 2009, using a combination of 400-kHz Reson 7125 and 244-kHz Reson 8101 multibeam echosounders, as well as a 234-kHz SWATHplus bathymetric sidescan-sonar system. Within the final imagery, brighter tones indicate higher backscatter intensity, and darker tones indicate lower backscatter intensity. The intensity represents a complex interaction between the acoustic pulse and the seafloor, as well as characteristics within the shallow subsurface, providing a general indication of seafloor texture and composition. Backscatter intensity depends on the acoustic source level; the frequency used to image the seafloor; the grazing angle; the composition and character of the seafloor, including grain size, water content, bulk density, and seafloor roughness; and some biological cover. Harder and rougher bottom types such as rocky outcrops or coarse sediment typically return stronger intensities (high backscatter, lighter tones), whereas softer bottom types such as fine sediment return weaker intensities (low backscatter, darker tones).

This part of DS 781 presents data for the shaded-relief bathymetry map of Offshore Pigeon Point, California. The raster data file is included in "BathymetryHS_OffshorePigeonPoint.zip", which is accessible from https://doi.org/10.5066/F7513W80. These data accompany the pamphlet and map sheets of Cochrane, G.R., Watt, J.T., Dartnell, P., Greene, H.G., Erdey, M.D., Dieter, B.E., Golden, N.E., Johnson, S.Y., Endris, C.A., Hartwell, S.R., Kvitek, R.G., Davenport, C.W., Krigsman, L.M., Ritchie, A.C., Sliter, R.W., Finlayson, D.P., and Maier, K.L. (G.R. Cochrane and S.A. Cochran, eds.), 2015, California State Waters Map Series—Offshore of Pigeon Point, California: U.S. Geological Survey Open-File Report 2015–1232, pamphlet 40 p., 10 sheets, scale 1:24,000, https://doi.org/10.3133/ofr20151232. The shaded-relief bathymetry map of Offshore Pigeon Point, California, was generated from bathymetry data collected by California State University, Monterey Bay (CSUMB), by Fugro Pelagos, and by the U.S. Geological Survey (USGS). Mapping was completed between 2006 and 2009, using a combination of 400-kHz Reson 7125 (CSUMB) and 244-kHz Reson 8101 (Fugros) multibeam echosounders, as well as a 234-kHz SWATHplus bathymetric sidescan-sonar system (USGS). These mapping missions combined to collect bathymetry from about the 10-m isobath to beyond the 3-nautical-mile limit of California's State Waters.

This part of DS 781 presents data for the bathymetry map of Offshore Pigeon Point, California. The raster data file is included in "Bathymetry_OffshorePigeonPoint.zip", which is accessible from https://doi.org/10.5066/F7513W80. These data accompany the pamphlet and map sheets of Cochrane, G.R., Watt, J.T., Dartnell, P., Greene, H.G., Erdey, M.D., Dieter, B.E., Golden, N.E., Johnson, S.Y., Endris, C.A., Hartwell, S.R., Kvitek, R.G., Davenport, C.W., Krigsman, L.M., Ritchie, A.C., Sliter, R.W., Finlayson, D.P., and Maier, K.L. (G.R. Cochrane and S.A. Cochran, eds.), 2015, California State Waters Map Series—Offshore of Pigeon Point, California: U.S. Geological Survey Open-File Report 2015–1232, pamphlet 40 p., 10 sheets, scale 1:24,000, https://doi.org/10.3133/ofr20151232. The bathymetry map of Offshore Pigeon Point, California, was generated from bathymetry data collected by California State University, Monterey Bay (CSUMB), by Fugro Pelagos, and by the U.S. Geological Survey (USGS). Mapping was completed between 2006 and 2009, using a combination of 400-kHz Reson 7125 (CSUMB) and 244-kHz Reson 8101 (Fugros) multibeam echosounders, as well as a 234-kHz SWATHplus bathymetric sidescan-sonar system (USGS). These mapping missions combined to collect bathymetry from about the 10-m isobath to beyond the 3-nautical-mile limit of California's State Waters. NOTE: The horizontal datum of this bathymetry data (NAD83) differs from the horizontal datum of other layers in this SIM (WGS84). Some bathymetry grids within this map area were projected horizontally from WGS84 to NAD83 using ESRI tools to be more consistent with the vertical reference of the North American Vertical Datum of 1988 (NAVD88). These data are not intended for navigational purposes.

This part of DS 781 presents data for the bathymetric contours for several seafloor maps of the Offshore Pigeon Point map area, California. The vector data file is included in "Contours_OffshorePigeonPoint.zip", which is accessible from https://doi.org/10.5066/F7513W80. These data accompany the pamphlet and map sheets of Cochrane, G.R., Watt, J.T., Dartnell, P., Greene, H.G., Erdey, M.D., Dieter, B.E., Golden, N.E., Johnson, S.Y., Endris, C.A., Hartwell, S.R., Kvitek, R.G., Davenport, C.W., Krigsman, L.M., Ritchie, A.C., Sliter, R.W., Finlayson, D.P., and Maier, K.L. (G.R. Cochrane and S.A. Cochran, eds.), 2015, California State Waters Map Series—Offshore of Pigeon Point, California: U.S. Geological Survey Open-File Report 2015–1232, pamphlet 40 p., 10 sheets, scale 1:24,000, https://doi.org/10.3133/ofr20151232. 10-m interval contours of the Offshore Pigeon Point map area, California, were generated from bathymetry data collected by the U.S. Geological Survey (USGS) and by California State University, Monterey Bay (CSUMB). Mapping was completed between 2006 and 2009 using a combination of a 244-kHz Reson 8101 multibeam echosounder and a 234-kHz SEA SWATHplus bathymetric sidescan-sonar system. The mapping missions collected bathymetry data from about the 10-m isobath to beyond the 3-nautical-mile limit of California's State Waters. Bathymetric contours at 10-m intervals were generated from a modified 2-m bathymetric surface. The original surface was smoothed using the Focal Mean tool in ArcGIS and a circular neighborhood with a radius of 20 to 30 meters (depending on the area). The contours were generated from this smoothed surface using the ArcGIS Spatial Analyst Contour tool. The most continuous contour segments were preserved while smaller segments and isolated island polygons were excluded from the final output. The contours were then clipped to the boundary of the map area. These data are not intended for navigational purposes.

This part of DS 781 presents data for the folds for the geologic and geomorphic map of the Offshore Pigeon Point map area, California. The vector data file is included in "Folds_OffshorePigeonPoint.zip," which is accessible from https://doi.org/10.5066/F7513W80. These data accompany the pamphlet and map sheets of Cochrane, G.R., Watt, J.T., Dartnell, P., Greene, H.G., Erdey, M.D., Dieter, B.E., Golden, N.E., Johnson, S.Y., Endris, C.A., Hartwell, S.R., Kvitek, R.G., Davenport, C.W., Krigsman, L.M., Ritchie, A.C., Sliter, R.W., Finlayson, D.P., and Maier, K.L. (G.R. Cochrane and S.A. Cochran, eds.), 2015, California State Waters Map Series—Offshore of Pigeon Point, California: U.S. Geological Survey Open-File Report 2015–1232, pamphlet 40 p., 10 sheets, scale 1:24,000, https://doi.org/10.3133/ofr20151232.

This part of DS 781 presents the seafloor-character map Offshore of Pigeon Point, California. The raster data file is included in "SeafloorCharacter_OffshorePigeonPoint.zip," which is accessible from https://doi.org/10.5066/F7513W80. These data accompany the pamphlet and map sheets of Cochrane, G.R., Watt, J.T., Dartnell, P., Greene, H.G., Erdey, M.D., Dieter, B.E., Golden, N.E., Johnson, S.Y., Endris, C.A., Hartwell, S.R., Kvitek, R.G., Davenport, C.W., Krigsman, L.M., Ritchie, A.C., Sliter, R.W., Finlayson, D.P., and Maier, K.L. (G.R. Cochrane and S.A. Cochran, eds.), 2015, California State Waters Map Series—Offshore of Pigeon Point, California: U.S. Geological Survey Open-File Report 2015–1232, pamphlet 40 p., 10 sheets, scale 1:24,000, https://doi.org/10.3133/ofr20151232. This raster-format seafloor character map shows four substrate classes Offshore of Pigeon Point, California. The substrate classes mapped in this area have been further divided into the following California Marine Life Protection Act depth zones and slope classes: Depth Zone 2 (intertidal to 30 m), Depth Zone 3 (30 to 100 m), Slope Class 1 (0 degrees - 5 degrees), and Slope Class 2 (5 degrees - 30 degrees). Depth Zone 1 (intertidal), Depth Zones 4-5 (greater than 100 m), and Slopes Classes 3-4 (greater than 30 degrees) are not present in the region covered by this block. The map is created using a supervised classification method described by Cochrane (2008). Reference Cited: Cochrane, G.R., 2008, Video-supervised classification of sonar data for mapping seafloor habitat, in Reynolds, J.R., and Greene, H.G., eds., Marine habitat mapping technology for Alaska: Fairbanks, University of Alaska, Alaska Sea Grant College Program, p. 185-194, accessed April 5, 2011, at http://doc.nprb.org/web/research/research%20pubs/615_habitat_mapping_workshop/Individual%20Chapters%20High-Res/Ch13%20Cochrane.pdf.

This part of DS 781 presents data for the depth-to-transition map of the Pigeon Point to Monterey, California, map region. The raster data file is included in "DepthToTransition_PigeonPointToMonterey.zip," which is accessible from https://doi.org/10.5066/F7N29V0Z. As part of the USGS's California State Waters Mapping Project, a 50-m-resolution grid of sediment thickness for the seafloor within the limit of California’s State Waters between Pigeon Point and southern Monterey Bay was generated from seismic-reflection data collected in 2009, 2010, and 2011 (USGS activities (S-15-10-NC, S-N1-09-MB, and S-06-11-MB) supplemented with outcrop and geologic structure from DS 781. The resulting grid was subtracted from regional bathymetry to determine the depth to the transitional surface. Contours at 5-meter intervals were derived from this depth-to-transition grid and are also available in this data release.

This part of DS 781 presents data for the sediment-thickness isopachs for the Pigeon Point to Monterey Bay, California, map region. The vector data file is included in "Isopachs_PigeonPointToMonterey.zip," which is accessible from https://doi.org/10.5066/F7N29V0Z. As part of the USGS's California State Waters Mapping Project, a 50-m-resolution grid of sediment thickness for the seafloor within the limit of California’s State Waters between Pigeon Point and southern Monterey Bay was generated from seismic-reflection data collected in 2009, 2010, and 2011 (USGS activities (S-15-10-NC, S-N1-09-MB, and S-06-11-MB) supplemented with outcrop and geologic structure from DS 781. Isopach contours at 2.5-meter intervals were derived from this sediment-thickness grid.

This part of DS 781 presents data for the sediment-thickness map of the Pigeon Point to Monterey, California, map region. The raster data file is included in "SedimentThickness_PigeonPointToMontereyBay.zip," which is accessible from https://doi.org/10.5066/F7N29V0Z. As part of the USGS's California State Waters Mapping Project, a 50-m-resolution grid of sediment thickness for the seafloor within the limit of California’s State Waters between Pigeon Point and southern Monterey Bay was generated from seismic-reflection data collected in 2009, 2010, and 2011 (USGS activities (S-15-10-NC, S-N1-09-MB, and S-06-11-MB) supplemented with outcrop and geologic structure from DS 781. Isopach contours at 2.5-meter intervals were derived from this sediment-thickness grid and are also available in this data release.

This part of DS 781 presents data for the transgressive contours for the depth-to-transition map of the Pigeon Point to Monterey, California, map region. The vector file is included in T "TransgressiveContours_PigeonPointToMonterey.zip," which is accessible from https://doi.org/10.5066/F7N29V0Z. As part of the USGS's California State Waters Mapping Project, a 50-m grid of sediment thickness for the seafloor within the limit of California’s State Waters between Pigeon Point and southern Monterey Bay was generated from seismic-reflection data collected in 2009 and 2010 (USGS activities (S-15-10-NC, S-N1-09-MB, and S-06-11-MB) supplemented with outcrop and geologic structure from DS 781. The resulting grid covers was subtracted from regional bathymetry to determine the depth to the transitional surface. Contours at 5-meter intervals were derived from this grid.

This U.S. Geological Survey data release provides data on spatial variations in tidal datums, tidal range, and nuisance flooding in Chesapeake Bay and Delaware Bay. Tidal datums are standard elevations that are defined based on average tidal water levels. Datums are used as references to measure local water levels and to delineate regions in coastal environments. Nuisance flooding refers to the sporadic inundation of low-lying coastal areas by the maximum tidal water levels during spring tides, especially perigean spring tides (also known as king tides). Nuisance flooding is independent of storm event flooding, and it represents a cumulative or chronic hazard. The data were obtained by following a consistent methodology and at sufficient spatial resolution to resolve the distinct and complex features of each bay system. Tidal water levels were simulated by using the ADCIRC model system for the entire 2016 year. The year 2016 was chosen because it corresponded with the maximum magnitude of the combined 18.6-year and 4.4-year tidal modulations. The estuarine and bay areas were resolved with horizontal resolutions on the order of tens to hundreds of meters. The ADCIRC simulations provide time series of water levels at each computational point of an unstructured grid that covers the entire area of interest—from the open ocean to overland areas up to approximately 15 meters above the North American Vertical Datum of 1988. The water-level time series were analyzed to provide estimates of tidal range (great diurnal range and mean range of tide), tidal datums (mean high water, mean higher high water, mean low water, and mean lower low water), and nuisance flooding (highest astronomical tide and monthly mean high water). The resulting data are provided at all points of the computational grid for Chesapeake and Delaware Bays and the surrounding coastal area.

This dataset includes raw and processed, high-resolution seismic-reflection data collected in 2009 to explore a possible connection between the San Diego Trough Fault and the San Pedro Basin Fault. The survey is in the San Pedro Basin between Santa Catalina Island and San Pedro, California. The data were collected aboard the U.S. Geological Survey R/V Parke Snavely. The seismic-reflection data were acquired using a SIG 2mille minisparker. Subbottom acoustic penetration spanned tens to several hundreds of meters, variable by location.

This part of DS 781 presents data for the habitat map of the seafloor of the Offshore of Pigeon Point map area, California. The vector data file is included in "Habitat_OffshorePigeonPoint.zip," which is accessible from https://doi.org/10.5066/F7513W80. These data accompany the pamphlet and map sheets of Cochrane, G.R., Watt, J.T., Dartnell, P., Greene, H.G., Erdey, M.D., Dieter, B.E., Golden, N.E., Johnson, S.Y., Endris, C.A., Hartwell, S.R., Kvitek, R.G., Davenport, C.W., Krigsman, L.M., Ritchie, A.C., Sliter, R.W., Finlayson, D.P., and Maier, K.L. (G.R. Cochrane and S.A. Cochran, eds.), 2015, California State Waters Map Series—Offshore of Pigeon Point, California: U.S. Geological Survey Open-File Report 2015–1232, pamphlet 40 p., 10 sheets, scale 1:24,000, https://doi.org/10.3133/ofr20151232. Potential marine benthic habitat maps were constructed using multibeam echosounder (MBES) bathymetry and backscatter data. The habitats were based on substrate types and documented or "ground truthed" using underwater video images and seafloor samples obtained by the USGS. These maps display various habitat types that range from flat, soft, unconsolidated sediment-covered seafloor to hard, deformed (folded), or highly rugose and differentially eroded bedrock exposures.

This part of DS 781 presents data for the acoustic-backscatter map of Offshore of Scott Creek map area, California. Backscatter data are provided as three separate grids depending on mapping system. The raster data files are included in "BackscatterA_8101_OffshoreScottCreek.zip," which is accessible from https://doi.org/10.5066/F7CJ8BJW. These data accompany the pamphlet and map sheets of Cochrane, G.R., Dartnell, P., Johnson, S.Y., Greene, H.G., Erdey, M.D., Dieter, B.E., Golden, N.E., Endris, C.A., Hartwell, S.R., Kvitek, R.G., Davenport, C.W., Watt, J.T., Krigsman, L.M., Ritchie, A.C., Sliter, R.W., Finlayson, D.P., and Maier, K.L. (G.R. Cochrane and S.A. Cochran, eds.), 2015, California State Waters Map Series--Offshore of Scott Creek, California: U.S. Geological Survey Open-File Report 2015-1191, pamphlet 40 p., 10 sheets, scale 1:24,000, http://doi.org/10.3133/ofr20151191. The acoustic-backscatter map of the Offshore of Pigeon Point map area, California, was generated from backscatter data collected by California State University, Monterey Bay (CSUMB), by Fugro Pelagos, and by the U.S. Geological Survey (USGS). Mapping was completed between 2006 and 2009, using a combination of 400-kHz Reson 7125 (CSUMB) and 244-kHz Reson 8101 (FUGRO) multibeam echosounders, as well as a 234-kHz SWATHplus bathymetric sidescan-sonar system (USGS). These mapping missions combined to collect backscatter data from about the 10-m isobath to beyond the 3-nautical-mile limit of California's State Waters. Within the final imagery, brighter tones indicate higher backscatter intensity, and darker tones indicate lower backscatter intensity. The intensity represents a complex interaction between the acoustic pulse and the seafloor, as well as characteristics within the shallow subsurface, providing a general indication of seafloor texture and composition. Backscatter intensity depends on the acoustic source level; the frequency used to image the seafloor; the grazing angle; the composition and character of the seafloor, including grain size, water content, bulk density, and seafloor roughness; and some biological cover. Harder and rougher bottom types such as rocky outcrops or coarse sediment typically return stronger intensities (high backscatter, lighter tones), whereas softer bottom types such as fine sediment return weaker intensities (low backscatter, darker tones).

This part of DS 781 presents data for the acoustic-backscatter map of Offshore of Scott Creek map area, California. Backscatter data are provided as three separate grids depending on mapping system. The raster data files are included in "BackscatterB_7125_OffshoreScottCreek.zip," which is accessible from https://doi.org/10.5066/F7CJ8BJW. These data accompany the pamphlet and map sheets of Cochrane, G.R., Dartnell, P., Johnson, S.Y., Greene, H.G., Erdey, M.D., Dieter, B.E., Golden, N.E., Endris, C.A., Hartwell, S.R., Kvitek, R.G., Davenport, C.W., Watt, J.T., Krigsman, L.M., Ritchie, A.C., Sliter, R.W., Finlayson, D.P., and Maier, K.L. (G.R. Cochrane and S.A. Cochran, eds.), 2015, California State Waters Map Series--Offshore of Scott Creek, California: U.S. Geological Survey Open-File Report 2015-1191, pamphlet 40 p., 10 sheets, scale 1:24,000, http://doi.org/10.3133/ofr20151191. The acoustic-backscatter map of the Offshore of Scott Creek map area, California, was generated from backscatter data collected by California State University, Monterey Bay (CSUMB), by Fugro Pelagos, and by the U.S. Geological Survey (USGS). Mapping was completed between 2006 and 2009, using a combination of 400-kHz Reson 7125 (CSUMB) and 244-kHz Reson 8101 (FUGRO) multibeam echosounders, as well as a 234-kHz SWATHplus bathymetric sidescan-sonar system (USGS). These mapping missions combined to collect backscatter data from about the 10-m isobath to beyond the 3-nautical-mile limit of California's State Waters. Within the final imagery, brighter tones indicate higher backscatter intensity, and darker tones indicate lower backscatter intensity. The intensity represents a complex interaction between the acoustic pulse and the seafloor, as well as characteristics within the shallow subsurface, providing a general indication of seafloor texture and composition. Backscatter intensity depends on the acoustic source level; the frequency used to image the seafloor; the grazing angle; the composition and character of the seafloor, including grain size, water content, bulk density, and seafloor roughness; and some biological cover. Harder and rougher bottom types such as rocky outcrops or coarse sediment typically return stronger intensities (high backscatter, lighter tones), whereas softer bottom types such as fine sediment return weaker intensities (low backscatter, darker tones).

This part of DS 781 presents data for the acoustic-backscatter map of Offshore of Scott Creek map area, California. Backscatter data are provided as three separate grids depending on mapping system. The raster data files are included in "BackscatterC_SWATH_OffshoreScottCreek.zip," which is accessible from https://doi.org/10.5066/F7CJ8BJW. These data accompany the pamphlet and map sheets of Cochrane, G.R., Dartnell, P., Johnson, S.Y., Greene, H.G., Erdey, M.D., Dieter, B.E., Golden, N.E., Endris, C.A., Hartwell, S.R., Kvitek, R.G., Davenport, C.W., Watt, J.T., Krigsman, L.M., Ritchie, A.C., Sliter, R.W., Finlayson, D.P., and Maier, K.L. (G.R. Cochrane and S.A. Cochran, eds.), 2015, California State Waters Map Series--Offshore of Scott Creek, California: U.S. Geological Survey Open-File Report 2015-1191, pamphlet 40 p., 10 sheets, scale 1:24,000, http://doi.org/10.3133/ofr20151191. The acoustic-backscatter map of the Offshore of Scott Creek map area, California, was generated from backscatter data collected by California State University, Monterey Bay (CSUMB), by Fugro Pelagos, and by the U.S. Geological Survey (USGS). Mapping was completed between 2006 and 2009, using a combination of 400-kHz Reson 7125 (CSUMB) and 244-kHz Reson 8101 (FUGRO) multibeam echosounders, as well as a 234-kHz SWATHplus bathymetric sidescan-sonar system (USGS). These mapping missions combined to collect backscatter data from about the 10-m isobath to beyond the 3-nautical-mile limit of California's State Waters. Within the final imagery, brighter tones indicate higher backscatter intensity, and darker tones indicate lower backscatter intensity. The intensity represents a complex interaction between the acoustic pulse and the seafloor, as well as characteristics within the shallow subsurface, providing a general indication of seafloor texture and composition. Backscatter intensity depends on the acoustic source level; the frequency used to image the seafloor; the grazing angle; the composition and character of the seafloor, including grain size, water content, bulk density, and seafloor roughness; and some biological cover. Harder and rougher bottom types such as rocky outcrops or coarse sediment typically return stronger intensities (high backscatter, lighter tones), whereas softer bottom types such as fine sediment return weaker intensities (low backscatter, darker tones).

This part of DS 781 presents data for the shaded-relief bathymetry map of Offshore Scott Creek, California. The raster data file is included in "BathymetryHS_OffshoreScottCreek.zip", which is accessible from https://doi.org/10.5066/F7CJ8BJW. These data accompany the pamphlet and map sheets of Cochrane, G.R., Dartnell, P., Johnson, S.Y., Greene, H.G., Erdey, M.D., Dieter, B.E., Golden, N.E., Endris, C.A., Hartwell, S.R., Kvitek, R.G., Davenport, C.W., Watt, J.T., Krigsman, L.M., Ritchie, A.C., Sliter, R.W., Finlayson, D.P., and Maier, K.L. (G.R. Cochrane and S.A. Cochran, eds.), 2015, California State Waters Map Series--Offshore of Scott Creek, California: U.S. Geological Survey Open-File Report 2015-1191, pamphlet 40 p., 10 sheets, scale 1:24,000, http://doi.org/10.3133/ofr20151191. The bathymetry and shaded-relief maps of Offshore Scott Creek, California, were generated from bathymetry data collected by California State University, Monterey Bay (CSUMB), by Fugro Pelagos, and by the U.S. Geological Survey (USGS). Mapping was completed between 2006 and 2009, using a combination of 400-kHz Reson 7125 and 244-kHz Reson 8101 multibeam echosounders, as well as a 234-kHz SWATHplus bathymetric sidescan-sonar system. These mapping missions combined to collect bathymetry from about the 10-m isobath to beyond the 3-nautical-mile limit of California's State Waters.

This part of DS 781 presents data for the bathymetry map of Offshore Scott Creek, California. The raster data file is included in "Bathymetry_OffshoreScottCreek.zip", which is accessible from https://doi.org/10.5066/F7CJ8BJW. These data accompany the pamphlet and map sheets of Cochrane, G.R., Dartnell, P., Johnson, S.Y., Greene, H.G., Erdey, M.D., Dieter, B.E., Golden, N.E., Endris, C.A., Hartwell, S.R., Kvitek, R.G., Davenport, C.W., Watt, J.T., Krigsman, L.M., Ritchie, A.C., Sliter, R.W., Finlayson, D.P., and Maier, K.L. (G.R. Cochrane and S.A. Cochran, eds.), 2015, California State Waters Map Series--Offshore of Scott Creek, California: U.S. Geological Survey Open-File Report 2015-1191, pamphlet 40 p., 10 sheets, scale 1:24,000, http://doi.org/10.3133/ofr20151191. The bathymetry and shaded-relief maps of the Offshore Scott Creek map area, California, were generated from bathymetry data collected by California State University, Monterey Bay (CSUMB), by Fugro Pelagos, and by the U.S. Geological Survey (USGS). Mapping was completed between 2006 and 2009, using a combination of 400-kHz Reson 7125 and 244-kHz Reson 8101 multibeam echosounders, as well as a 234-kHz SWATHplus bathymetric sidescan-sonar system. These mapping missions combined to collect bathymetry from about the 10-m isobath to beyond the 3-nautical-mile limit of California's State Waters. NOTE: The horizontal datum of this bathymetry data (NAD83) differs from the horizontal datum of other layers in this data release (WGS84). Some bathymetry grids within this map area were projected horizontally from WGS84 to NAD83 using ESRI tools to be more consistent with the vertical reference of the North American Vertical Datum of 1988 (NAVD88).

This part of DS 781 presents data for the bathymetric contours for several seafloor maps of the Offshore Scott Creek map area, California. The vector data file is included in "Contours_OffshoreScottCreek.zip", which is accessible from https://doi.org/10.5066/F7CJ8BJW. These data accompany the pamphlet and map sheets of Cochrane, G.R., Dartnell, P., Johnson, S.Y., Greene, H.G., Erdey, M.D., Dieter, B.E., Golden, N.E., Endris, C.A., Hartwell, S.R., Kvitek, R.G., Davenport, C.W., Watt, J.T., Krigsman, L.M., Ritchie, A.C., Sliter, R.W., Finlayson, D.P., and Maier, K.L. (G.R. Cochrane and S.A. Cochran, eds.), 2015, California State Waters Map Series--Offshore of Scott Creek, California: U.S. Geological Survey Open-File Report 2015-1191, pamphlet 40 p., 10 sheets, scale 1:24,000, http://doi.org/10.3133/ofr20151191. 10-m interval contours of the Offshore Scott Creek map area, California, were generated from bathymetry data collected by California State University, Monterey Bay (CSUMB), by Fugro Pelagos, and by the U.S. Geological Survey (USGS). Mapping was completed between 2006 and 2009, using a combination of 400-kHz Reson 7125 and 244-kHz Reson 8101 multibeam echosounders, as well as a 234-kHz SWATHplus bathymetric sidescan-sonar system. These mapping missions combined to collect bathymetry from about the 10-m isobath to beyond the 3-nautical-mile limit of California's State Waters. Bathymetric contours at 10-m intervals were generated from a modified 2-m bathymetric surface. The original surface was smoothed using the Focal Mean tool in ArcGIS and a circular neighborhood with a radius of 20 to 30 meters (depending on the area). The contours were generated from this smoothed surface using the ArcGIS Spatial Analyst Contour tool. The most continuous contour segments were preserved while smaller segments and isolated island polygons were excluded from the final output. The contours were then clipped to the boundary of the map area.

This part of DS 781 presents data for the faults for the geologic and geomorphic map of the Offshore of Scott Creek map area, California. The vector data file is included in "Faults_OffshoreScottCreek.zip," which is accessible from https://doi.org/10.5066/F7CJ8BJW. These data accompany the pamphlet and map sheets of Cochrane, G.R., Dartnell, P., Johnson, S.Y., Greene, H.G., Erdey, M.D., Dieter, B.E., Golden, N.E., Endris, C.A., Hartwell, S.R., Kvitek, R.G., Davenport, C.W., Watt, J.T., Krigsman, L.M., Ritchie, A.C., Sliter, R.W., Finlayson, D.P., and Maier, K.L. (G.R. Cochrane and S.A. Cochran, eds.), 2015, California State Waters Map Series--Offshore of Scott Creek, California: U.S. Geological Survey Open-File Report 2015-1191, pamphlet 40 p., 10 sheets, scale 1:24,000, http://doi.org/10.3133/ofr20151191.

This part of DS 781 presents data for the folds for the geologic and geomorphic map of the Offshore of Scott Creek map area, California. The vector data file is included in "Folds_OffshoreScottCreek.zip," which is accessible from https://doi.org/10.5066/F7CJ8BJW. These data accompany the pamphlet and map sheets of Cochrane, G.R., Dartnell, P., Johnson, S.Y., Greene, H.G., Erdey, M.D., Dieter, B.E., Golden, N.E., Endris, C.A., Hartwell, S.R., Kvitek, R.G., Davenport, C.W., Watt, J.T., Krigsman, L.M., Ritchie, A.C., Sliter, R.W., Finlayson, D.P., and Maier, K.L. (G.R. Cochrane and S.A. Cochran, eds.), 2015, California State Waters Map Series--Offshore of Scott Creek, California: U.S. Geological Survey Open-File Report 2015-1191, pamphlet 40 p., 10 sheets, scale 1:24,000, http://doi.org/10.3133/ofr20151191.

This part of DS 781 presents data for the geologic and geomorphic map of the Offshore of Scott Creek map area, California. The vector data file is included in "Geology_OffshoreScottCreek.zip," which is accessible from https://doi.org/10.5066/F7CJ8BJW. These data accompany the pamphlet and map sheets of Cochrane, G.R., Dartnell, P., Johnson, S.Y., Greene, H.G., Erdey, M.D., Dieter, B.E., Golden, N.E., Endris, C.A., Hartwell, S.R., Kvitek, R.G., Davenport, C.W., Watt, J.T., Krigsman, L.M., Ritchie, A.C., Sliter, R.W., Finlayson, D.P., and Maier, K.L. (G.R. Cochrane and S.A. Cochran, eds.), 2015, California State Waters Map Series--Offshore of Scott Creek, California: U.S. Geological Survey Open-File Report 2015-1191, pamphlet 40 p., 10 sheets, scale 1:24,000, http://doi.org/10.3133/ofr20151191. Marine geology and geomorphology were mapped in the Offshore of Scott Creek map area, California, from approximate Mean High Water (MHW) to the 3-nautical-mile limit of California''s State Waters. Offshore geologic units were delineated on the basis of integrated analyses of adjacent onshore geology with multibeam bathymetry and backscatter imagery, seafloor-sediment and rock samples, digital camera and video imagery, and high-resolution seismic-reflection profiles.

This part of DS 781 presents data for the habitat map of the seafloor of the Offshore of Scott Creek map area, California. The vector data file is included in "Habitat_OffshoreScottCreek.zip," which is accessible from https://doi.org/10.5066/F7CJ8BJW. These data accompany the pamphlet and map sheets of Cochrane, G.R., Dartnell, P., Johnson, S.Y., Greene, H.G., Erdey, M.D., Dieter, B.E., Golden, N.E., Endris, C.A., Hartwell, S.R., Kvitek, R.G., Davenport, C.W., Watt, J.T., Krigsman, L.M., Ritchie, A.C., Sliter, R.W., Finlayson, D.P., and Maier, K.L. (G.R. Cochrane and S.A. Cochran, eds.), 2015, California State Waters Map Series--Offshore of Scott Creek, California: U.S. Geological Survey Open-File Report 2015-1191, pamphlet 40 p., 10 sheets, scale 1:24,000, http://doi.org/10.3133/ofr20151191. Potential marine benthic habitat maps were constructed using multibeam echosounder (MBES) bathymetry and backscatter data. The habitats were based on substrate types and documented or "ground truthed" using underwater video images and seafloor samples obtained by the USGS. These maps display various habitat types that range from flat, soft, unconsolidated sediment-covered seafloor to hard, deformed (folded), or highly rugose and differentially eroded bedrock exposures.

This part of DS 781 presents the seafloor-character map of the Offshore of Scott Creek map area, California. The raster data file is included in "SeafloorCharacter_OffshoreScottCreek.zip," which is accessible from https://doi.org/10.5066/F7CJ8BJW. These data accompany the pamphlet and map sheets of Cochrane, G.R., Dartnell, P., Johnson, S.Y., Greene, H.G., Erdey, M.D., Dieter, B.E., Golden, N.E., Endris, C.A., Hartwell, S.R., Kvitek, R.G., Davenport, C.W., Watt, J.T., Krigsman, L.M., Ritchie, A.C., Sliter, R.W., Finlayson, D.P., and Maier, K.L. (G.R. Cochrane and S.A. Cochran, eds.), 2015, California State Waters Map Series--Offshore of Scott Creek, California: U.S. Geological Survey Open-File Report 2015-1191, pamphlet 40 p., 10 sheets, scale 1:24,000, http://doi.org/10.3133/ofr20151191. This raster-format seafloor character map shows four substrate classes offshore of Scott Creek, California. The substrate classes mapped in this area have been further divided into the following California Marine Life Protection Act depth zones and slope classes: Depth Zone 2 (intertidal to 30 m), Depth Zone 3 (30 to 100 m), Slope Class 1 (0 degrees - 5 degrees), and Slope Class 2 (5 degrees - 30 degrees). Depth Zone 1 (intertidal), Depth Zones 4-5 (greater than 100 m), and Slopes Classes 3-4 (greater than 30 degrees) are not present in the region covered by this block. The map is created using a supervised classification method described by Cochrane (2008). Reference Cited: Cochrane, G.R., 2008, Video-supervised classification of sonar data for mapping seafloor habitat, in Reynolds, J.R., and Greene, H.G., eds., Marine habitat mapping technology for Alaska: Fairbanks, University of Alaska, Alaska Sea Grant College Program, p. 185-194, accessed April 5, 2011, at http://doc.nprb.org/web/research/research%20pubs/615_habitat_mapping_workshop/Individual%20Chapters%20High-Res/Ch13%20Cochrane.pdf.

This portion of the USGS data release presents bathymetry data collected during surveys performed in the Columbia River littoral cell, Washington and Oregon, in 2020 (USGS Field Activity Number 2020-622-FA). Bathymetry data were collected using four personal watercraft (PWCs) equipped with single-beam sonar systems and global navigation satellite system (GNSS) receivers. The sonar systems consisted of an Odom Echotrac CV-100 single-beam echosounder and 200 kHz transducer with a 9-degree beam angle. Raw acoustic backscatter returns were digitized by the echosounder with a vertical resolution of 1.25 cm. Depths from the echosounders were computed using sound velocity profiles measured using a YSI CastAway CTD during the survey. Positioning of the survey vessels was determined at 5 to 10 Hz using Trimble R7 GNSS receivers. Output from the GNSS receivers and sonar systems were combined in real time on the PWC by a computer running HYPACK hydrographic survey software. Navigation information was displayed on a video monitor, allowing PWC operators to navigate along survey lines at speeds of 2 to 3 m/s. Survey-grade positions of the PWCs were achieved with a single-base station and differential post-processing. Positioning data from the GNSS receivers were post-processed using Waypoint Grafnav to apply differential corrections from a GNSS base station with known horizontal and vertical coordinates relative to the North American Datum of 1983. Orthometric elevations relative to the NAVD88 vertical datum were computed using National Geodetic Survey Geoid12a offsets. Bathymetric data were merged with post-processed positioning data and spurious soundings were removed using a custom Graphical User Interface (GUI) programmed with the computer program MATLAB. The average estimated vertical uncertainty of the bathymetric measurements is 10 cm. The final point data from the PWCs are provided in a comma-separated text file and are projected in cartesian coordinates using the Washington State Plane South, meters coordinate system. Due to equipment and staffing issues associated with the global pandemic, bathymetric surveys performed at the southern portion of the Clatsop Plains sub-cell (survey lines 71 to 101) and North Beach sub-cell were performed several weeks after the corresponding topographic surveys. The CTD was not available for bathymetric surveys at these locations and an assumed speed of sound of 1,500 and 1,490 m/s was applied to soundings collected in the North Beach sub-cell and southern portion of the Clatsop sub-cell, respectively.

This portion of the USGS data release presents topography data collected during surveys performed in the Columbia River littoral cell, Washington and Oregon, in 2020 (USGS Field Activity Number 2020-622-FA). Topographic profiles were collected by walking along survey lines with global navigation satellite system (GNSS) receivers mounted on backpacks. Prior to data collection, vertical distances between the GNSS antennas and the ground were measured using a tape measure. Hand-held data collectors were used to log raw data and display navigational information allowing surveyors to navigate survey lines spaced at 100- to 1000-m intervals along the beach. Profiles were surveyed from the landward edge of the study area (either the base of a bluff, engineering structure, or just landward of the primary dune) over the beach foreshore, to wading depth on the same series of transects as nearshore bathymetric surveys that were conducted during the same time period. Additional topographic data were collected between survey lines in some areas with an all-terrain vehicle (ATV) equipped with a GNSS receiver to constrain the elevations and alongshore extent of major morphological features. Positioning data from the survey platforms were referenced to a GNSS base station with known horizontal and vertical coordinates relative to the North American Datum of 1983. Differential corrections from the GNSS base stations to the survey platforms were either applied in real-time with a VHF radio link, or post-processed using Trimble Business Center software. Orthometric elevations relative to the NAVD88 vertical datum were computed using National Geodetic Survey Geoid12a offsets. The average estimated vertical uncertainty of the topographic measurements is 4 cm. The final point data are provided in comma-separated text format and are projected in Cartesian coordinates using the Washington State Plane South, meters coordinate system. Due to equipment and staffing issues associated with the global pandemic, topographic surveys performed at the southern portion of the Clatsop Plains sub-cell (lines 71 to 101) and North Beach sub-cell took place several weeks prior the corresponding bathymetric surveys.

This portion of the USGS data release presents bathymetry data collected during surveys performed in the Columbia River littoral cell, Washington and Oregon, in 2021 (USGS Field Activity Number 2021-632-FA). Bathymetry data were collected using four personal watercraft (PWCs) equipped with single-beam sonar systems and global navigation satellite system (GNSS) receivers. The sonar systems consisted of an Odom Echotrac CV-100 single-beam echosounder and 200 kHz transducer with a 9-degree beam angle. Raw acoustic backscatter returns were digitized by the echosounder with a vertical resolution of 1.25 cm. Depths from the echosounders were computed using sound velocity profiles measured using a YSI CastAway CTD during the survey. Positioning of the survey vessels was determined at 5 to 10 Hz using Trimble R7 GNSS receivers. Output from the GNSS receivers and sonar systems were combined in real time on the PWC by a computer running HYPACK hydrographic survey software. Navigation information was displayed on a video monitor, allowing PWC operators to navigate along survey lines at speeds of 2 to 3 m/s. Survey-grade positions of the PWCs were achieved with a single-base station and differential post-processing. Positioning data from the GNSS receivers were post-processed using Waypoint Grafnav to apply differential corrections from a GNSS base station with known horizontal and vertical coordinates relative to the North American Datum of 1983. Orthometric elevations relative to the NAVD88 vertical datum were computed using National Geodetic Survey Geoid12a offsets. Bathymetric data were merged with post-processed positioning data and spurious soundings were removed using a custom Graphical User Interface (GUI) programmed with the computer program MATLAB. The average estimated vertical uncertainty of the bathymetric measurements is 10 cm. The final point data from the PWCs are provided in a comma-separated text file and are projected in cartesian coordinates using the Washington State Plane South, meters coordinate system.

This portion of the USGS data release presents topography data collected during surveys performed in the Columbia River littoral cell, Washington and Oregon, in 2021 (USGS Field Activity Number 2021-632-FA). Topographic profiles were collected by walking along survey lines with global navigation satellite system (GNSS) receivers mounted on backpacks. Prior to data collection, vertical distances between the GNSS antennas and the ground were measured using a tape measure. Hand-held data collectors were used to log raw data and display navigational information allowing surveyors to navigate survey lines spaced at 100- to 1000-m intervals along the beach. Profiles were surveyed from the landward edge of the study area (either the base of a bluff, engineering structure, or just landward of the primary dune) over the beach foreshore, to wading depth on the same series of transects as nearshore bathymetric surveys that were conducted during the same time period. Additional topographic data were collected between survey lines in some areas with an all-terrain vehicle (ATV) equipped with a GNSS receiver to constrain the elevations and alongshore extent of major morphological features. Positioning data from the survey platforms were referenced to a GNSS base station with known horizontal and vertical coordinates relative to the North American Datum of 1983. Differential corrections from the GNSS base stations to the survey platforms were either applied in real-time with a VHF radio link, or post-processed using Trimble Business Center software. Orthometric elevations relative to the NAVD88 vertical datum were computed using National Geodetic Survey Geoid12a offsets. The average estimated vertical uncertainty of the topographic measurements is 4 cm. The final point data are provided in comma-separated text format and are projected in Cartesian coordinates using the Washington State Plane South, meters coordinate system.

Pressure, conductivity, temperature, and water level relative the North American Vertical Datum of 1988 (NAVD88) were measured at seven locations in Puget Sound and Bellingham Bay, Washington, USA, from November 2, 2018 to June 4, 2021. These data were collected using submersible pressure-conductivity-temperature sensors mounted on piers to support studies of extreme water levels and flooding hazards in the region.

This part of DS 781 presents data for the geologic and geomorphic map of the Offshore Pigeon Point map area, California. The vector data file is included in "Geology_OffshorePigeonPoint.zip," which is accessible from https://doi.org/10.5066/F7513W80. Marine geology and geomorphology were mapped in the Offshore Pigeon Point map area, California, from approximate Mean High Water (MHW) to the 3-nautical-mile limit of California'€™s State Waters. Offshore geologic units were delineated on the basis of integrated analyses of adjacent onshore geology with multibeam bathymetry and backscatter imagery, seafloor-sediment and rock samples, digital camera and video imagery, and high-resolution seismic-reflection profiles. These data accompany the pamphlet and map sheets of Cochrane, G.R., Watt, J.T., Dartnell, P., Greene, H.G., Erdey, M.D., Dieter, B.E., Golden, N.E., Johnson, S.Y., Endris, C.A., Hartwell, S.R., Kvitek, R.G., Davenport, C.W., Krigsman, L.M., Ritchie, A.C., Sliter, R.W., Finlayson, D.P., and Maier, K.L. (G.R. Cochrane and S.A. Cochran, eds.), 2015, California State Waters Map Series—Offshore of Pigeon Point, California: U.S. Geological Survey Open-File Report 2015–1232, pamphlet 40 p., 10 sheets, scale 1:24,000, https://doi.org/10.3133/ofr20151232.

This part of DS 781 presents data for the acoustic-backscatter map of Offshore of Aptos map area, California. Backscatter data are provided as two separate grids depending on mapping system and processing method. This metadata file refers to the data included in "BackscatterA_SWATH_OffshoreAptos.zip," which is accessible from https://doi.org/10.5066/F7K35RQB. These data accompany the pamphlet and map sheets of Cochrane, G.R., Johnson, S.Y., Dartnell, P., Greene, H.G., Erdey, M.D, Dieter, B.E., Golden, N.E., Hartwell, S.R., Ritchie, A.C., Kvitek, r.G., Maier, K.L., Endris, C.A., Davenport, C.W., Watt, J.T., Sliter, R.W., Finlayson, D.P., and Krigsman, L.M., (G.R. Cochrane and S.A. Cochran, eds.), 2016, California State Waters Map Series—Offshore of Aptos, California: U.S. Geological Survey Open-File Report 2016-1025, 43 p., 10 sheets, scale 1:24,000, https://doi.org/10.3133/ofr20161025. The acoustic-backscatter map of Offshore of Aptos, California, was generated from backscatter data collected by the U.S. Geological Survey (USGS) and by Monterey Bay Aquarium Research Institute (MBARI). Mapping was completed between 1998 and 2009, using a combination of a 234-kHz SWATHplus bathymetric sidescan-sonar system and a 30-kHz Simrad EM-300 multibeam echosounder. Within the final imagery, brighter tones indicate higher backscatter intensity, and darker tones indicate lower backscatter intensity. The intensity represents a complex interaction between the acoustic pulse and the seafloor, as well as characteristics within the shallow subsurface, providing a general indication of seafloor texture and composition. Backscatter intensity depends on the acoustic source level; the frequency used to image the seafloor; the grazing angle; the composition and character of the seafloor, including grain size, water content, bulk density, and seafloor roughness; and some biological cover. Harder and rougher bottom types such as rocky outcrops or coarse sediment typically return stronger intensities (high backscatter, lighter tones), whereas softer bottom types such as fine sediment return weaker intensities (low backscatter, darker tones).

Underwater images collected near Dollar Point in Lake Tahoe, California, were processed using Structure-from-Motion (SfM) photogrammetry techniques into a classified 3D point cloud. The DEM was derived in Metashape (ver. 1.6.4) from the point cloud, but it excludes the 'high noise' class. The DEM data were output as a geoTIFF raster at 25-mm resolution.

Lakebed orthoimagery was developed from underwater images collected near Dollar Point in Lake Tahoe, California, and processed using Structure-from-Motion (SfM) photogrammetry techniques. The orthoimages were developed using both image-mosaic and image-averaging methods, which were then output as 5-mm resolution rasters. In general, the "Mosaic" product is somewhat sharper in resolution but will include some distinct seam lines and noticeable differences in image quality across the image. The "Average" product, in contrast, is more uniform in color and quality but blurrier overall.

Three-dimensional point clouds (LAZ format) were developed from underwater images collected near Dollar Point in Lake Tahoe, California, and processed using Structure-from-Motion (SfM) photogrammetry techniques. Point cloud data include x,y,z positions, RGB colors, Metashape-computed confidence values, and a two-class classification ('unclassified' and 'high noise') derived from the confidence values. LAZ is an open format developed for the efficient use of point cloud lidar data. A description of the LAZ format and links to software tools for using LAZ files are provided at the USGS website: https://www.usgs.gov/news/3d-elevation-program-distributing-lidar-data-laz-format

This part of DS 781 presents data for the faults for the geologic and geomorphic map of the Offshore Pigeon Point map area, California. The vector data file is included in "Faults_OffshorePigeonPoint.zip," which is accessible from https://doi.org/10.5066/F7513W80. These data accompany the pamphlet and map sheets of Cochrane, G.R., Watt, J.T., Dartnell, P., Greene, H.G., Erdey, M.D., Dieter, B.E., Golden, N.E., Johnson, S.Y., Endris, C.A., Hartwell, S.R., Kvitek, R.G., Davenport, C.W., Krigsman, L.M., Ritchie, A.C., Sliter, R.W., Finlayson, D.P., and Maier, K.L. (G.R. Cochrane and S.A. Cochran, eds.), 2015, California State Waters Map Series—Offshore of Pigeon Point, California: U.S. Geological Survey Open-File Report 2015–1232, pamphlet 40 p., 10 sheets, scale 1:24,000, https://doi.org/10.3133/ofr20151232.

This part of DS 781 presents data for the faults for the geologic and geomorphic map of the Offshore Aptos map area, California. The vector data file is included in "Faults_OffshoreAptos.zip," which is accessible from https://doi.org/10.5066/F7K35RQB. These data accompany the pamphlet and map sheets of Cochrane, G.R., Johnson, S.Y., Dartnell, P., Greene, H.G., Erdey, M.D, Dieter, B.E., Golden, N.E., Hartwell, S.R., Ritchie, A.C., Kvitek, r.G., Maier, K.L., Endris, C.A., Davenport, C.W., Watt, J.T., Sliter, R.W., Finlayson, D.P., and Krigsman, L.M., (G.R. Cochrane and S.A. Cochran, eds.), 2016, California State Waters Map Series—Offshore of Aptos, California: U.S. Geological Survey Open-File Report 2016–1025, 43 p., 10 sheets, scale 1:24,000, https://doi.org/10.3133/ofr20161025. Faults in the Offshore of Aptos map area are identified on seismic-reflection data based on abrupt truncation or warping of reflections and (or) juxtaposition of reflection panels with different seismic parameters such as reflection presence, amplitude, frequency, geometry, continuity, and vertical sequence. Faults were primarily mapped by interpretation of seismic reflection profile data from USGS field activity S-N1-09-MB. The seismic reflection profiles were primarily collected in 2009.

This part of DS 781 presents data for the acoustic-backscatter map of Offshore of Santa Cruz map area, California. Backscatter data are provided as a raster file included in "Backscatter_Swath_OffshoreSantaCruz.zip," which is accessible from https://doi.org/10.5066/F7TM785G. These data accompany the pamphlet and map sheets of Cochrane, G.R., Dartnell, P., Johnson, S.Y., Erdey, M.D., Golden, N.E., Greene, H.G., Dieter, B.E., Hartwell, S.R., Ritchie, A.C., Finlayson, D.P., Endris, C.A., Watt, J.T., Davenport, C.W., Sliter, R.W., Maier, K.L., and Krigsman, L.M. (G.R. Cochrane and S.A. Cochran, eds.), 2016, California State Waters Map Series—Offshore of Santa Cruz, California: U.S. Geological Survey Open-File Report 2016-1024, pamphlet 40 p., 10 sheets, scale 1:24,000, https://doi.org/10.3133/ofr20161024. The acoustic-backscatter map of the Offshore of Santa Cruz, California was generated from backscatter data collected by the U.S. Geological Survey (USGS). Mapping was completed in 2009, using a 234-kHz SWATHplus bathymetric sidescan-sonar system. Within the final imagery, brighter tones indicate higher backscatter intensity, and darker tones indicate lower backscatter intensity. The intensity represents a complex interaction between the acoustic pulse and the seafloor, as well as characteristics within the shallow subsurface, providing a general indication of seafloor texture and composition. Backscatter intensity depends on the acoustic source level; the frequency used to image the seafloor; the grazing angle; the composition and character of the seafloor, including grain size, water content, bulk density, and seafloor roughness; and some biological cover. Harder and rougher bottom types such as rocky outcrops or coarse sediment typically return stronger intensities (high backscatter, lighter tones), whereas softer bottom types such as fine sediment return weaker

This part of DS 781 presents data for the shaded-relief bathymetry map of Offshore Santa Cruz, California. The raster data file is included in "BathymetryHS_OffshoreSantaCruz.zip", which is accessible from https://doi.org/10.5066/F7TM785G. These data accompany the pamphlet and map sheets of Cochrane, G.R., Dartnell, P., Johnson, S.Y., Erdey, M.D., Golden, N.E., Greene, H.G., Dieter, B.E., Hartwell, S.R., Ritchie, A.C., Finlayson, D.P., Endris, C.A., Watt, J.T., Davenport, C.W., Sliter, R.W., Maier, K.L., and Krigsman, L.M. (G.R. Cochrane and S.A. Cochran, eds.), 2016, California State Waters Map Series—Offshore of Santa Cruz, California: U.S. Geological Survey Open-File Report 2016-1024, pamphlet 40 p., 10 sheets, scale 1:24,000, https://doi.org/10.3133/ofr20161024. The shaded-relief bathymetry map of Offshore Santa Cruz, California, was generated from bathymetry data collected by the U.S. Geological Survey (USGS). Mapping was completed in 2009 using a 234-kHz SWATHplus bathymetric sidescan-sonar system. The mapping mission collected bathymetry data from about the 10-m isobath to beyond the 3-nautical-mile limit of California's State Waters. NOTE: The horizontal datum of this bathymetry data (NAD83) differs from the horizontal datum of other layers in this data release (WGS84).

This part of DS 781 presents data for the bathymetry map of Offshore Santa Cruz, California. The raster data file is included in "Bathymetry_OffshoreSantaCruz.zip", which is accessible from https://doi.org/10.5066/F7TM785G. These data accompany the pamphlet and map sheets of Cochrane, G.R., Dartnell, P., Johnson, S.Y., Erdey, M.D., Golden, N.E., Greene, H.G., Dieter, B.E., Hartwell, S.R., Ritchie, A.C., Finlayson, D.P., Endris, C.A., Watt, J.T., Davenport, C.W., Sliter, R.W., Maier, K.L., and Krigsman, L.M. (G.R. Cochrane and S.A. Cochran, eds.), 2016, California State Waters Map Series—Offshore of Santa Cruz, California: U.S. Geological Survey Open-File Report 2016-1024, pamphlet 40 p., 10 sheets, scale 1:24,000, https://doi.org/10.3133/ofr20161024. The bathymetry map of Offshore Santa Cruz, California, was generated from bathymetry data collected by the U.S. Geological Survey (USGS). Mapping was completed in 2009 using a 234-kHz SWATHplus bathymetric sidescan-sonar system. The mapping mission collected bathymetry data from about the 10-m isobath to beyond the 3-nautical-mile limit of California's State Waters. NOTE: The horizontal datum of this bathymetry data (NAD83) differs from the horizontal datum of other layers in this data release (WGS84).

This part of DS 781 presents data for the bathymetric contours for several seafloor maps of the Offshore Santa Cruz map area, California. The vector data file is included in "Contours_OffshoreSantaCruz.zip", which is accessible from https://doi.org/10.5066/F7TM785G. These data accompany the pamphlet and map sheets of Cochrane, G.R., Dartnell, P., Johnson, S.Y., Erdey, M.D., Golden, N.E., Greene, H.G., Dieter, B.E., Hartwell, S.R., Ritchie, A.C., Finlayson, D.P., Endris, C.A., Watt, J.T., Davenport, C.W., Sliter, R.W., Maier, K.L., and Krigsman, L.M. (G.R. Cochrane and S.A. Cochran, eds.), 2016, California State Waters Map Series—Offshore of Santa Cruz, California: U.S. Geological Survey Open-File Report 2016-1024, pamphlet 40 p., 10 sheets, scale 1:24,000, https://doi.org/10.3133/ofr20161024. 10-m interval contours of the Offshore Santa Cruz map area, California, were generated from bathymetry data collected by the U.S. Geological Survey (USGS). Mapping was completed in 2009 using a 234-kHz SWATHplus bathymetric sidescan-sonar system. The mapping mission collected bathymetry data from about the 10-m isobath to beyond the 3-nautical-mile limit of California's State Waters.

This part of DS 781 presents data for the faults for the geologic and geomorphic map of the Offshore of Santa Cruz map area, California. The vector data file is included in "Faults_OffshoreSantaCruz.zip," which is accessible from https://doi.org/10.5066/F7TM785G. These data accompany the pamphlet and map sheets of Cochrane, G.R., Dartnell, P., Johnson, S.Y., Erdey, M.D., Golden, N.E., Greene, H.G., Dieter, B.E., Hartwell, S.R., Ritchie, A.C., Finlayson, D.P., Endris, C.A., Watt, J.T., Davenport, C.W., Sliter, R.W., Maier, K.L., and Krigsman, L.M. (G.R. Cochrane and S.A. Cochran, eds.), 2016, California State Waters Map Series—Offshore of Santa Cruz, California: U.S. Geological Survey Open-File Report 2016-1024, pamphlet 40 p., 10 sheets, scale 1:24,000, https://doi.org/10.3133/ofr20161024.

This part of DS 781 presents data for the folds for the geologic and geomorphic map of the Offshore of Santa Cruz map area, California. The vector data file is included in "Folds_OffshoreSantaCruz.zip," which is accessible from https://doi.org/10.5066/F7TM785G. These data accompany the pamphlet and map sheets of Cochrane, G.R., Dartnell, P., Johnson, S.Y., Erdey, M.D., Golden, N.E., Greene, H.G., Dieter, B.E., Hartwell, S.R., Ritchie, A.C., Finlayson, D.P., Endris, C.A., Watt, J.T., Davenport, C.W., Sliter, R.W., Maier, K.L., and Krigsman, L.M. (G.R. Cochrane and S.A. Cochran, eds.), 2016, California State Waters Map Series—Offshore of Santa Cruz, California: U.S. Geological Survey Open-File Report 2016-1024, pamphlet 40 p., 10 sheets, scale 1:24,000, https://doi.org/10.3133/ofr20161024.

This part of DS 781 presents data for the geologic and geomorphic map of the Offshore Santa Cruz map area, California. The vector data file is included in "Geology_OffshoreSantaCruz.zip," which is accessible from https://doi.org/10.5066/F7TM785G. These data accompany the pamphlet and map sheets of Cochrane, G.R., Dartnell, P., Johnson, S.Y., Erdey, M.D., Golden, N.E., Greene, H.G., Dieter, B.E., Hartwell, S.R., Ritchie, A.C., Finlayson, D.P., Endris, C.A., Watt, J.T., Davenport, C.W., Sliter, R.W., Maier, K.L., and Krigsman, L.M. (G.R. Cochrane and S.A. Cochran, eds.), 2016, California State Waters Map Series—Offshore of Santa Cruz, California: U.S. Geological Survey Open-File Report 2016-1024, pamphlet 40 p., 10 sheets, scale 1:24,000, https://doi.org/10.3133/ofr20161024. Marine geology and geomorphology were mapped in the Offshore Santa Cruz map area, California, from approximate Mean High Water (MHW) to the 3-nautical-mile limit of California''s State Waters. Offshore geologic units were delineated on the basis of integrated analyses of adjacent onshore geology with multibeam bathymetry and backscatter imagery, seafloor-sediment and rock samples, digital camera and video imagery, and high-resolution seismic-reflection profiles.

This part of DS 781 presents the seafloor-character map Offshore of Santa Cruz, California. The raster data file is included in "SeafloorCharacter_OffshoreSantaCruz.zip," which is accessible from https://doi.org/10.5066/F7TM785G. These data accompany the pamphlet and map sheets of Cochrane, G.R., Dartnell, P., Johnson, S.Y., Erdey, M.D., Golden, N.E., Greene, H.G., Dieter, B.E., Hartwell, S.R., Ritchie, A.C., Finlayson, D.P., Endris, C.A., Watt, J.T., Davenport, C.W., Sliter, R.W., Maier, K.L., and Krigsman, L.M. (G.R. Cochrane and S.A. Cochran, eds.), 2016, California State Waters Map Series—Offshore of Santa Cruz, California: U.S. Geological Survey Open-File Report 2016-1024, pamphlet 40 p., 10 sheets, scale 1:24,000, https://doi.org/10.3133/ofr20161024. This raster-format seafloor character map shows five substrate classes Offshore of Santa Cruz, California. The substrate classes mapped in this area have been further divided into the following California Marine Life Protection Act depth zones and slope classes: Depth Zone 2 (intertidal to 30 m), Depth Zone 3 (30 to 100 m), Slope Class 1 (0 degrees - 5 degrees), and Slope Class 2 (5 degrees - 30 degrees). Depth Zone 1 (intertidal), Depth Zones 4-5 (greater than 100 m), and Slopes Classes 3-4 (greater than 30 degrees) are not present in the region covered by this block. The map is created using a supervised classification method described by Cochrane (2008). Reference Cited: Cochrane, G.R., 2008, Video-supervised classification of sonar data for mapping seafloor habitat, in Reynolds, J.R., and Greene, H.G., eds., Marine habitat mapping technology for Alaska: Fairbanks, University of Alaska, Alaska Sea Grant College Program, p. 185-194, accessed April 5, 2011, at http://doc.nprb.org/web/research/research%20pubs/615_habitat_mapping_workshop/Individual%20Chapters%20High-Res/Ch13%20Cochrane.pdf. Sappington, J.M., Longshore, K.M., and Thompson, D.B., 2007, Quantifying landscape ruggedness for animal habitat analysis--A case study using bighorn sheep in the Mojave Desert: Journal of Wildlife Management, v. 71, p. 1419-1426.

Physical, environmental, and biotic observations were derived from underwater video collected by the Monterey Bay Aquarium Research Institute (MBARI) using remotely operated vehicles (ROVs) offshore of Morro Bay, California. The data were acquired during three separate surveys in 2019 in support of the U.S. Geological Survey (USGS)/Bureau of Ocean Energy Management (BOEM) California Deepwater Investigations and Groundtruthing I (Cal DIG I) project. Transect information developed to analyze the data for biotopes (as described in Kuhnz and others, 2021) and the resulting biotope numbers are included in the point data. A joint USGS-BOEM-MBARI cruise, which took place from 19-26 September 2019 on the R/V Bold Horizon (USGS field activity 2019-642-FA), focused on conducting biological surveys using MBARI's MiniROV (dives M137-148). Additional surveys were conducted from 02-14 February 2019 (dives D1120-1131) and from 01-11 November 2019 (dives D1202-1217) using MBARI's R/V Western Flyer and ROV Doc Ricketts. The ROV-video surveys were designed and conducted to collect video ground-truth information about substrate and biota.

Physical, environmental, and substrate observations were derived from underwater video collected by the Monterey Bay Aquarium Research Institute (MBARI) using remotely operated vehicles (ROVs) offshore of Morro Bay, California. A majority of the data were acquired during three separate surveys in 2019 in support of the U.S. Geological Survey (USGS)/Bureau of Ocean Energy Management (BOEM) California Deepwater Investigations and Groundtruthing I (Cal DIG I) project. Additional observations from underwater video data collected by the Ocean Exploration Trust's E/V Nautilus in 2020 are also included. Slope, rugosity, and depth information derived from multibeam echosounder (MBES) bathymetry data, and induration (an indication of substrate hardness) information, are also included in the point data. A joint USGS-BOEM-MBARI cruise, which took place from 19-26 September 2019 on the R/V Bold Horizon (USGS field activity 2019-642-FA), focused on conducting biological surveys using MBARI's MiniROV (dives M137-148). Additional surveys were conducted from 02-14 February 2019 (dives D1120-1131) and from 01-11 November 2019 (dives D1202-1217) using MBARI's R/V Western Flyer and ROV Doc Ricketts. The ROV-video surveys were designed and conducted to collect video ground-truth information about substrate and biota. MBARI-acquired video was analyzed by MBARI. Analysis of Nautilus video data was done by the USGS.

These metadata describe Coastal and Marine Ecological Classification Standard (CMECS; Federal Geographic Data Committee, 2012) attributed polygons derived from multibeam echosounder acoustic bathymetry and backscatter intensity data, and from underwater video collected offshore of Morro Bay, California from 2016 to 2020. The polygons have CMECS substrate, geoform and biotic component attributes. Most of the data were collected in support of the U.S. Geological Survey (USGS)/Bureau of Ocean Energy Management (BOEM) California Deepwater Investigations and Groundtruthing I (Cal DIG I) project through collaborations with the National Oceanic and Atmospheric Administration (NOAA) and the Monterey Bay Aquarium Research Institute (MBARI).

Polycyclic aromatic hydrocarbons (PAHs) are reported for water samples collected from the San Lorenzo River water during the rainy seasons from 2015 to 2016. Samples were collected during 2-, 5- and close to 10 year flood events.

This part of DS 781 presents data for the bathymetry map of Drakes Bay and Vicinity map area, California. The raster data file for the bathymetry map is included in "Bathymetry_DrakesBay.zip," which is accessible from https://pubs.usgs.gov/ds/781/DrakesBay/data_catalog_DrakesBay.html. These data accompany the pamphlet and map sheets of Watt, J.T., Dartnell, P., Golden, N.E., Greene, H.G., Erdey, M.D., Cochrane, G.R., Johnson, S.Y., Hartwell, S.R., Kvitek, R.G., Manson, M.W., Endris, C.A., Dieter, B.E., Sliter, R.W., Krigsman, L.M., Lowe, E.N., and Chin, J.L. (J.T. Watt and S.A. Cochran, eds.), 2015, California State Waters Map Series—Drakes Bay and Vicinity, California: U.S. Geological Survey Open-File Report 2015–1041, pamphlet 36 p., 10 sheets, scale 1:24,000, https://doi.org/10.3133/ofr20151041. The bathymetry map of Drakes Bay and Vicinity map area, California, was generated from bathymetry data collected by California State University, Monterey Bay (CSUMB), and by Fugro Pelagos. Mapping was completed between 2007 and 2010, using a combination of 200-kHz and 400-kHz Reson 7125, and 244-kHz Reson 8101 multibeam echosounders, as well as 468-kHz SEA SWATHPlus interferometric system. These mapping missions combined to collect bathymetry from about the 10-m isobath to beyond the 3-nautical-mile limit of California's State Waters. NOTE: the horizontal datum of the bathymetry data (NAD83) differs from the horizontal datum of other layers in this data series (WGS84). Some bathymetry grids within this map were projected horizontally from WGS84 to NAD83 using Esri tools to be more consistent with the vertical reference of the North American Vertical Datum of 1988 (NAVD88). These data are not intended for navigational purposes.

This U.S. Geological Survey data release provides data on spatial variations in climatological wave parameters (significant wave height, peak wave period, and wave power) for coastal areas along the United States East Coast and Gulf of Mexico. Significant wave height is the average wave height, from crest to trough, of the highest one-third of the waves in a specific time period. Peak wave period is the wave period associated with the most energetic waves in the wave spectrum in a specific time period. Wave power is the energy per unit length generated by the movement of ocean waves. Climatological wave conditions provide the average forcing that can lead to changes in the coastal environment. For the generation of this dataset, we use model simulations. Waves under different climatological wind forcing conditions averaged from the European Centre for Medium Range Weather Forecast (ECMWF) Re-Analysis (ERA-5) were simulated using the coupled ADCIRC/SWAN model system. Coastal areas are resolved with horizontal resolutions on the order of hundreds of meters. The ADCIRC/SWAN simulations provide steady-state wave conditions under constant wind at each computational point of an unstructured grid that covers the entire area of interest—from the open ocean to overland areas up to approximately 15 meters above the North American Vertical Datum of 1988. The simulations provide wave height and period and are also used to calculate wave power. We consider the frequency of occurrence of each wind magnitude and direction bin for each location to reconstruct weighted-average wave conditions. The resulting wave parameters are provided at all points of the computational grid less than 10 meter elevation and shallower than -30 meter bottom depth (North American Vertical Datum of 1988). Grid points above 10 meters and deeper than 30 meters are given a fill value.

This part of the data release presents bathymetry data from the Elwha River delta collected in July 2018 (USGS Field Activity Number 2018-648-FA) using a kayak. The kayak was equipped with a single-beam echosounder and a survey-grade global navigation satellite system (GNSS) receiver.

This portion of the USGS data release presents bathymetric data collected during surveys performed on the Elwha River delta, Washington in 2018 (USGS Field Activity Number 2018-648-FA). Bathymetric data were collected using personal watercraft (PWCs) equipped with single-beam sonar systems and global navigation satellite system (GNSS) receivers.

This portion of the data release presents sediment grain-size data from samples collected on the Elwha River delta, Washington, in July 2018 (USGS Field Activity 2018-648-FA). Surface sediment was collected from 39 locations using a small ponar, or 'grab', sampler from the R/V Frontier in water depths between about 1 and 17 m around the delta. An additional 35 samples were collected by hand at low tide. A hand-held global satellite navigation system (GNSS) receiver was used to determine the locations of sediment samples. The grain size distributions of suitable samples were determined using standard techniques developed by the USGS Pacific Coastal and Marine Science Center sediment lab. Grab samples that yielded less than 50 g of sediment were omitted from analysis. The grain-size data are provided in a comma-delimited spreadsheet (.csv).

This part of the data release presents topography data from the Elwha River delta collected in July 2018. Topography data were collected on foot with global navigation satellite system (GNSS) receivers mounted on backpacks.

This portion of the data release presents the locations of the temporary ground control points (GCPs) used for the structure-from-motion (SfM) processing of the imagery collected during an unoccupied aerial system (UAS) survey of the coral reef off Waiakane, Molokai, Hawaii, on 24 June 2018. Twenty temporary ground control points (GCPs) were distributed throughout the survey area to establish survey control. The GCPs consisted of: nine submerged targets consisting of small (80 centimeter X 80 centimeter) square tarps with black-and-white cross patterns anchored to the shallow (less than 1.5 meters deep) seafloor using 0.9 kilogram fishing weights; nine sub-aerial targets consisting of orange plastic five-gallon bucket lids (32 centimeter diameter) painted with a black “X” pattern and affixed in a horizontal orientation to vertical rebar stakes placed in areas of reef rubble to provide the targets with sufficient elevation to remain above the water surface; and two sub-aerial ground targets consisting of small (80 centimeter X 80 centimeter) square tarps with black-and-white cross patterns placed in the sand at the shoreline. The GCP positions were measured using post-processed kinematic (PPK) GPS, using corrections from a GPS base station (MK02) located approximately 1 kilometer from the study area. Reference coordinates for MK02 were established using the mean position derived from four static GPS occupations with durations greater than 4 hours submitted to the National Geodetic Survey Online Positioning User Service (NGS OPUS). The GCP positions are presented in a comma-delimited text file.

This portion of the data release presents raw aerial imagery collected during an unoccupied aerial system (UAS) survey of the coral reef off Waiakane, Molokai, Hawaii, on 24 June 2018. The imagery was acquired using a Department of Interior-owned 3DR Solo quadcopter fitted with a Ricoh GR II digital camera featuring a global shutter. The camera was mounted in a nadir orientation using a fixed mount. Before each flight, the camera’s digital ISO, aperture, and shutter speed were adjusted for ambient light conditions. The UAS was flown on pre-programmed autonomous flight lines spaced to provide approximately 75 percent overlap between images from adjacent lines. The UAS was flown at an approximate altitude of 100 meters above ground level (AGL), resulting in a nominal ground-sample-distance (GSD) of 2.6 centimeters per pixel. The camera was triggered at 1 Hz using a built-in intervalometer. A total of five flights were conducted for the survey between 16:40 and 17:45 UTC (06:40 and 07:45 HST). Flight F01 was a reconnaissance flight, and no mapping imagery was collected. Flights F02 and F03 were conducted at an approximate altitude of 100 meters above ground level (AGL), resulting in complete coverage of the mapping area with a nominal ground-sample-distance (GSD) of approximately 2.5 centimeters per pixel. Flights F04 and F05 were conducted using the same flight lines and altitudes of F02 and F03, but the camera was fitted with a circular polarizing filter to reduced reflections and provide improved imaging of the seafloor through the water surface. After acquisition, the images were renamed to include flight number and acquisition time in the file name. The coordinates of the approximate image acquisition location were added ('geotagged') to the image metadata (EXIF) using the telemetry log from the UAS onboard single-frequency autonomous GPS. The image EXIF was also updated to include additional information related to the acquisition. Although the images were recorded in both JPG and camera raw (Adobe DNG) formats, only the JPG images are provided in this data release. The data release includes a total of 1,665 JPG images. Images from takeoff and landing sequences were not used for processing and have been omitted from the data release. The images from each flight are provided in a zip file named with the flight number. The zip files containing the imagery collected with the circular polarizing filter are named with '_CPF' to distinguish them from the files with imagery collected without a filter.

This portion of the data release presents a bathymetric point cloud from an unoccupied aerial system (UAS) survey of the coral reef off Waiakane, Molokai, Hawaii, on 24 June 2018. The point cloud has been corrected for the effects of refraction using the techniques described in Dietrich (2017a). The point cloud was created from structure-from-motion (SfM) processing of aerial imagery collected using a UAS with a Ricoh GR II digital camera fitted with a circular polarizing filter. During the survey, a pressure sensor was deployed in the survey area to gain an accurate measurement of the water surface elevation (WSE). After a preliminary dense point cloud was derived from SfM processing, the WSE was used to calculate apparent water depths. These apparent depths along with the estimated camera positions and orientations were used as inputs for the multi-view refraction correction python script (py_sfm_depth.py) described in Dietrich (2017b). The refraction-corrected point cloud showed a substantial improvement in accuracy over the uncorrected point cloud. When compared to the 2013 U.S. Army Corps of Engineers Topobathy Lidar for the area in the central portion of the data set the vertical RMSE for the refraction-corrected point cloud was 0.241 meters with a mean residual of -0.010 meters, whereas the vertical RMSE for the uncorrected point cloud was 0.426 meters with a mean residual of -0.334 meters (see the Vertical Positional Accuracy Report in the metadata for a complete description of the accuracy analysis). For this data release, the final refraction-corrected point cloud is presented in the LAZ format (LAS 1.2 specification). The point cloud has 35,083,205 points with an average point spacing of 0.07 meters. Each point in the point cloud contains an explicit horizontal and vertical coordinate and red, green, and blue (RGB) color values. References Cited: Deitrich, J.R., 2017a, Bathymetric Structure-from-Motion: extracting shallow stream bathymetry from multi-view stereo photogrammetry: Earth Surface Processes and Landforms, https://doi.org/10.1002/esp.4060. Deitrich, J.R., 2017b, py_sfm_depth: Github online repository, https://github.com/geojames/py_sfm_depth.

This data provides river turbidity measurements collected on the Carmel River, CA. Turbidity was measured to study any changes in the Carmel River’s sediment loads following the removal of the San Clemente Dam. The USGS-run DTS-12 turbidity sensor was deployed above the Sleepy Hollow Weir on the Carmel River, CA (instrument was located at 36.445250 degrees North, 121.710494 degrees West). Deployment began on December 9, 2014. After June 16, 2016, the instrument was removed for calibration. A new instrument was re-deployed on October 14, 2016, and continued to record until recovery on July 13, 2017. Due to the instrument removal and calibration, there exists an approximately 4-month long gap in data collection from June 16 to October 14, 2016. The sensor recorded turbidity, water temperature, and battery voltage at 15-minute intervals. The data are in Formazin Nephelometric Units (FNU), which are similar to Nephelometric Turbidity Units (NTU) in that both measure scattered light at 90 degrees from the incident light beam. FNU are measured with an infrared light source (by the ISO 7027 method), whereas NTU requires a white light source (EPA method 180.1). For more information on FNU and water turbidity data, please visit http://or.water.usgs.gov/grapher/fnu.html. Apparently spurious data points were removed during processing. Those included: data points in which the temperature reading dropped abruptly to zero [both temperature and turbidity values were set to "NaN" (Not A Number)], data points in which the turbidity value abruptly dropped from a non-zero value to zero and recovered immediately to near the original non-zero reading (in which case turbidity, but not temperature, values were set to "NaN"), and several points were deleted in the record in which turbidity spiked rapidly by two or three orders of magnitude and then immediately returned to much lower values. This may occur, for example, if a leaf blocked the sensor momentarily, but we considered it was likely not a real increase in turbidity. Several rapid apparent increases in turbidity remain in the record. These may be real or spurious; they did not appear abrupt enough to be clearly spurious, and so are left in the record. Turbidity spikes were also removed that coincided with equipment deployment at the site.

Pebble-count data were collected during summer surveys (2013, 2015, 2016, 2017, 2018, 2019, 2020, and 2021) at ten sites along the Carmel River, California. Grain-size measurements were made at four to six transects per site using a 0.5 by 0.5 m sampling frame, with approximately 100 sediment-particle counts per transect. Each transect was defined by coordinates on the left and right sides of the river, and sediment grain sizes were measured at five equally spaced locations within the bankfull channel on the transect defined by those endpoint coordinates (see accompanying file within this data release for transect endpoint coordinates). The grain-size data file reports spatial information as being the midpoint of the transect. Grain-size diameters were measured using a gravelometer. We report grain-size values to the next size class up in this spreadsheet, that is, the size gradation that the sediment grain passed through on the gravelometer frame, with the data being analogous to the percent passing in sieve analyses. These data supersede grain-size data originally published in 2017 at https://doi.org/10.5066/F74M93HF.

This dataset contains the easting, northing, and elevation values of the river-right and river-left transect endpoint reference benchmarks (RBM and LBM) from survey transects at 10 survey reaches along the Carmel River, central California. Topographic surveys were completed on these transects during eight summer surveys (in 2013, 2014, 2015, 2016, 2017, 2019, 2020 and 2021). See accompanying file within this data release for elevation measurements. All data were collected in NAD83 UTM10N horizontal coordinates and NAVD88 Geoid 12B vertical coordinates, in units of meters. The positions of some reference marks were selected using only horizontal reference; the elevation values for these marks are set to a no data value of "NULL" in the data file.

High-resolution topographic surveys were conducted at two pools on the Carmel River between 2014 and 2019 using a survey-grade total station. The Dam Reach pool (DMPOOL) is located within the Dam Reach, approximately 450 meters downstream of the former site of the San Clemente Dam. The Sleepy Hollow pool (SHPOOL) is located within the Sleepy Hollow reach, approximately 2.25 kilometers downstream of the former site of the San Clemente Dam. Both pools were surveyed in 2014, 2015, 2016, 2017, and 2019 using a total station, in conjunction with the channel cross-section surveys also conducted as part of this study (see accompanying file within this data release for topographic survey transect data). For the 2015 survey, a kayak-mounted single-beam echosounder was also used to augment the data collected with the total station. Horizontal and vertical coordinates are provided for each point surveyed. Vertical coordinates are referenced to the NAVD88 vertical datum, in units of meters. Horizontal coordinates are referenced to the NAD83(2011) reference frame, projected in Universal Transverse Mercator (UTM) coordinates (zone 10N), in units of meters.

This part of DS 781 presents data for the depth-to-transition map of the Point Conception to Hueneme Canyon, California, region. The raster data file is included in "DepthToTransition_PointConceptionToHuenemeCanyon.zip," which is accessible from https://doi.org/10.5066/F7891424. As part of the USGS's California State Waters Mapping Project, a 50-m grid of sediment thickness for the seafloor within the 3-nautical mile limit between Point Conception and Hueneme Canyon was generated from seismic-reflection data collected in 2014 (USGS activity 2014-632-FA) supplemented with outcrop and geologic structure (fault) information following the methodology of Wong (2012). Water depths determined from bathymetry data were added to the sediment thickness data to provide information on the depth to base of the post-LGM unit. This depth-to-transition layer was merged with a previously published depth-to-transition grid for the Santa Barbara Channel region (available at https://pubs.usgs.gov/ds/781/SantaBarbaraChannel/data_catalog_SantaBarbaraChannel.html). Reference Cited: Wong, F. L., Phillips, E.L., Johnson, S.Y., and Sliter, R.W., 2012, Modeling of depth to base of Last Glacial Maximum and seafloor sediment thickness for the California State Waters Map Series, eastern Santa Barbara Channel, California: U.S. Geological Survey Open-File Report 2012-1161, 16 p. (available at https://pubs.usgs.gov/of/2012/1161/)

This part of DS 781 presents data for the isopachs for the Point Conception to Hueneme Canyon, California, region. The vector data file is included in "Isopachs_PointConceptionToHuenemeCanyon.zip," which is accessible from https://doi.org/10.5066/F7891424. As part of the USGS's California State Waters Mapping Project, a 50-m grid of sediment thickness for the seafloor within the 3-nautical mile limit between Point Conception and Hueneme Canyon was generated from seismic-reflection data collected in 2014 (USGS activity 2014-632-FA) supplemented with outcrop and geologic structure (fault) information following the methodology of Wong (2012). This sediment thickness layer was merged with a previously published sediment thickness grid for the Santa Barbara Channel region (available at https://pubs.usgs.gov/ds/781/SantaBarbaraChannel/data_catalog_SantaBarbaraChannel.html). This polyline shapefile provides the sediment-thickness isopachs to accompany the grid layer. Reference Cited: Wong, F. L., Phillips, E.L., Johnson, S.Y., and Sliter, R.W., 2012, Modeling of depth to base of Last Glacial Maximum and seafloor sediment thickness for the California State Waters Map Series, eastern Santa Barbara Channel, California: U.S. Geological Survey Open-File Report 2012-1161, 16 p. (available at https://pubs.usgs.gov/of/2012/1161/)

This part of DS 781 presents data for the sediment-thickness map of the Point Conception to Hueneme Canyon, California, region. The raster data file is included in "SedimentThickness_PointConceptionToHuenemeCanyon.zip," which is accessible from https://doi.org/10.5066/F7891424. As part of the USGS's California State Waters Mapping Project, a 50-m grid of sediment thickness for the seafloor within the 3-nautical mile limit between Point Conception and Hueneme Canyon was generated from seismic-reflection data collected in 2014 (USGS activity 2014-632-FA) supplemented with outcrop and geologic structure (fault) information following the methodology of Wong (2012). This sediment thickness layer was merged with a previously published sediment thickness grid for the Santa Barbara Channel region (available at https://pubs.usgs.gov/ds/781/SantaBarbaraChannel/data_catalog_SantaBarbaraChannel.html). Reference Cited: Wong, F. L., Phillips, E.L., Johnson, S.Y., and Sliter, R.W., 2012, Modeling of depth to base of Last Glacial Maximum and seafloor sediment thickness for the California State Waters Map Series, eastern Santa Barbara Channel, California: U.S. Geological Survey Open-File Report 2012-1161, 16 p. (available at https://pubs.usgs.gov/of/2012/1161/)

This part of DS 781 presents data for the transgressive contours for the Point Conception to Hueneme Canyon, California, region. The vector file is included in "TransgressiveContours_PointConceptionToHuenemeCanyon.zip," which is accessible from https://doi.org/10.5066/F7891424. As part of the USGS's California State Waters Mapping Project, a 50-m grid of sediment thickness for the seafloor within the 3-nautical mile limit between Point Conception and Hueneme Canyon was generated from seismic-reflection data collected in 2014 (USGS activity 2014-632-FA) supplemented with outcrop and geologic structure (fault) information following the methodology of Wong (2012). Water depths determined from bathymetry data were added to the sediment thickness data to provide information on the depth to base of the post-LGM unit. This depth-to-transition layer was merged with a previously published depth-to-transition grid for the Santa Barbara Channel region (available at https://pubs.usgs.gov/ds/781/SantaBarbaraChannel/data_catalog_SantaBarbaraChannel.html). This polyline shapefile provides the depth-to-transition contours to accompany the grid layer. Reference Cited: Wong, F. L., Phillips, E.L., Johnson, S.Y., and Sliter, R.W., 2012, Modeling of depth to base of Last Glacial Maximum and seafloor sediment thickness for the California State Waters Map Series, eastern Santa Barbara Channel, California: U.S. Geological Survey Open-File Report 2012-1161, 16 p. (available at https://pubs.usgs.gov/of/2012/1161/)

This part of DS 781 presents data for the acoustic-backscatter map of Monterey Canyon and Vicinity map area, California. Backscatter data are provided as separate grids depending on mapping system and processing method. These metadata describe acoustic-backscatter data collected and processed by the U.S. Geological Survey. The raster data files are included in "BackscatterA_USGS_SWATH_MontereyCanyon.zip," which is accessible from https://doi.org/10.3133/ds781. These data accompany the pamphlet and map sheets of Dartnell, P., Maier, K.L., Erdey, M.D., Dieter, B.E., Golden, N.E., Johnson, S.Y., Hartwell, S.R., Cochrane, G.R., Ritchie, A.C., Finlayson, D.P., Kvitek, R.G., Sliter, R.W., Greene, H.G., Davenport, C.W., Endris, C.A., and Krigsman, L.M. (P. Dartnell and S.A. Cochran, eds.), 2016, California State Waters Map Series—Monterey Canyon and Vicinity, California: U.S. Geological Survey Open-File Report 2016–1072, 48 p., 10 sheets, scale 1:24,000, https://doi.org/10.3133/ofr20161072. The acoustic-backscatter map of Monterey Canyon and Vicinity, California, was generated from acoustic-backscatter data collected by the U.S. Geological Survey (USGS), by Monterey Bay Aquarium Research Institute (MBARI), and by California State University, Monterey Bay (CSUMB). Mapping for the entire map area was completed between 1998 and 2014 using a combination of 30-kHz Simrad EM-300 and 200-kHz/400-kHz Reson 7125 multibeam echosounders, as well as 234-kHz and 468-kHz SEA SWATHplus bathymetric sidescan-sonar systems. The USGS mapping was completed in 2009 and 2014. Within the final imagery, brighter tones indicate higher backscatter intensity, and darker tones indicate lower backscatter intensity. The intensity represents a complex interaction between the acoustic pulse and the seafloor, as well as characteristics within the shallow subsurface, providing a general indication of seafloor texture and composition. Backscatter intensity depends on the acoustic source level; the frequency used to image the seafloor; the grazing angle; the composition and character of the seafloor, including grain size, water content, bulk density, and seafloor roughness; and some biological cover. Harder and rougher bottom types such as rocky outcrops or coarse sediment typically return stronger intensities (high backscatter, lighter tones), whereas softer bottom types such as fine sediment return weaker intensities (low backscatter, darker tones).

This part of DS 781 presents data for the acoustic-backscatter map of Monterey Canyon and Vicinity map area, California. Backscatter data are provided as separate grids depending on mapping system and processing method. These metadata describe acoustic-backscatter data collected by Monterey Bay Aquarium Research Institute (MBARI) and processed by the U.S. Geological Survey. The raster data files are included in "BackscatterB_EM300_MontereyCanyon.zip," which is accessible from https://doi.org/10.3133/ds781. These data accompany the pamphlet and map sheets of Dartnell, P., Maier, K.L., Erdey, M.D., Dieter, B.E., Golden, N.E., Johnson, S.Y., Hartwell, S.R., Cochrane, G.R., Ritchie, A.C., Finlayson, D.P., Kvitek, R.G., Sliter, R.W., Greene, H.G., Davenport, C.W., Endris, C.A., and Krigsman, L.M. (P. Dartnell and S.A. Cochran, eds.), 2016, California State Waters Map Series—Monterey Canyon and Vicinity, California: U.S. Geological Survey Open-File Report 2016–1072, 48 p., 10 sheets, scale 1:24,000, https://doi.org/10.3133/ofr20161072. The acoustic-backscatter map of Monterey Canyon and Vicinity, California, were generated from acoustic-backscatter data collected by the U.S. Geological Survey (USGS), by Monterey Bay Aquarium Research Institute (MBARI), and by California State University, Monterey Bay (CSUMB). Mapping for the entire map area was completed between 1998 and 2014 using a combination of 30-kHz Simrad EM-300 and 200-kHz/400-kHz Reson 7125 multibeam echosounders, as well as 234-kHz and 468-kHz SEA SWATHplus bathymetric sidescan-sonar systems. The MBARI mapping was completed in 1998, the data were downloaded and reprocessed by the USGS in 2014. Within the final imagery, brighter tones indicate higher backscatter intensity, and darker tones indicate lower backscatter intensity. The intensity represents a complex interaction between the acoustic pulse and the seafloor, as well as characteristics within the shallow subsurface, providing a general indication of seafloor texture and composition. Backscatter intensity depends on the acoustic source level; the frequency used to image the seafloor; the grazing angle; the composition and character of the seafloor, including grain size, water content, bulk density, and seafloor roughness; and some biological cover. Harder and rougher bottom types such as rocky outcrops or coarse sediment typically return stronger intensities (high backscatter, lighter tones), whereas softer bottom types such as fine sediment return weaker intensities (low backscatter, darker tones).

This part of DS 781 presents data for the acoustic-backscatter map of Monterey Canyon and Vicinity map area, California. Backscatter data are provided as separate grids depending on mapping system and processing method. These metadata describe acoustic-backscatter data collected by California State University, Monterey Bay and processed by the U.S. Geological Survey. The raster data files are included in "BackscatterC_7125_MontereyCanyon.zip," which is accessible from https://doi.org/10.5066/F7XD0ZQ4. These data accompany the pamphlet and map sheets of Dartnell, P., Maier, K.L., Erdey, M.D., Dieter, B.E., Golden, N.E., Johnson, S.Y., Hartwell, S.R., Cochrane, G.R., Ritchie, A.C., Finlayson, D.P., Kvitek, R.G., Sliter, R.W., Greene, H.G., Davenport, C.W., Endris, C.A., and Krigsman, L.M. (P. Dartnell and S.A. Cochran, eds.), 2016, California State Waters Map Series—Monterey Canyon and Vicinity, California: U.S. Geological Survey Open-File Report 2016–1072, 48 p., 10 sheets, scale 1:24,000, https://doi.org/10.3133/ofr20161072. The acoustic-backscatter map of Monterey Canyon and Vicinity, California, were generated from acoustic-backscatter data collected by the U.S. Geological Survey (USGS), by Monterey Bay Aquarium Research Institute (MBARI), and by California State University, Monterey Bay (CSUMB). Mapping for the entire map area was completed between 1998 and 2014 using a combination of 30-kHz Simrad EM-300 and 200-kHz/400-kHz Reson 7125 multibeam echosounders, as well as 234-kHz and 468-kHz SEA SWATHplus bathymetric sidescan-sonar systems. The CSUMB mapping missions were completed in 2008 and 2009. Within the final imagery, brighter tones indicate higher backscatter intensity, and darker tones indicate lower backscatter intensity. The intensity represents a complex interaction between the acoustic pulse and the seafloor, as well as characteristics within the shallow subsurface, providing a general indication of seafloor texture and composition. Backscatter intensity depends on the acoustic source level; the frequency used to image the seafloor; the grazing angle; the composition and character of the seafloor, including grain size, water content, bulk density, and seafloor roughness; and some biological cover. Harder and rougher bottom types such as rocky outcrops or coarse sediment typically return stronger intensities (high backscatter, lighter tones), whereas softer bottom types such as fine sediment return weaker intensities (low backscatter, darker tones).

This part of DS 781 presents data for the acoustic-backscatter map of Monterey Canyon and Vicinity map area, California. Backscatter data are provided as separate grids depending on mapping system and processing method. These metadata describe acoustic-backscatter data collected by California State University, Monterey Bay and processed by the U.S. Geological Survey. The raster data files are included in "BackscatterD_CSUMB_SWATH_MontereyCanyon.zip," which is accessible from https://doi.org/10.5066/F7XD0ZQ4. These data accompany the pamphlet and map sheets of Dartnell, P., Maier, K.L., Erdey, M.D., Dieter, B.E., Golden, N.E., Johnson, S.Y., Hartwell, S.R., Cochrane, G.R., Ritchie, A.C., Finlayson, D.P., Kvitek, R.G., Sliter, R.W., Greene, H.G., Davenport, C.W., Endris, C.A., and Krigsman, L.M. (P. Dartnell and S.A. Cochran, eds.), 2016, California State Waters Map Series—Monterey Canyon and Vicinity, California: U.S. Geological Survey Open-File Report 2016–1072, 48 p., 10 sheets, scale 1:24,000, https://doi.org/10.3133/ofr20161072. The acoustic-backscatter map of Monterey Canyon and Vicinity, California, were generated from acoustic-backscatter data collected by the U.S. Geological Survey (USGS), by Monterey Bay Aquarium Research Institute (MBARI), and by California State University, Monterey Bay (CSUMB). Mapping for the entire map area was completed between 1998 and 2014 using a combination of 30-kHz Simrad EM-300 and 200-kHz/400-kHz Reson 7125 multibeam echosounders, as well as 234-kHz and 468-kHz SEA SWATHplus bathymetric sidescan-sonar systems. The CSUMB mapping missions were completed in 2008 and 2009. Within the final imagery, brighter tones indicate higher backscatter intensity, and darker tones indicate lower backscatter intensity. The intensity represents a complex interaction between the acoustic pulse and the seafloor, as well as characteristics within the shallow subsurface, providing a general indication of seafloor texture and composition. Backscatter intensity depends on the acoustic source level; the frequency used to image the seafloor; the grazing angle; the composition and character of the seafloor, including grain size, water content, bulk density, and seafloor roughness; and some biological cover. Harder and rougher bottom types such as rocky outcrops or coarse sediment typically return stronger intensities (high backscatter, lighter tones), whereas softer bottom types such as fine sediment return weaker intensities (low backscatter, darker tones).

This part of DS 781 presents data for 2-m and 5-m bathymetry and shaded-relief maps of Monterey Canyon and Vicinity, California. Bathymetry data are provided as separate grids depending on the mapping resolution. Data collected at shallower depths by the U.S. Geological Survey (USGS) and California State University, Monterey Bay (CSUMB) have a spatial resolution of 2 m per pixel, whereas data collected at deeper depths by the Monterey Bay Aquarium Research Institute (MBARI) have a spatial resolution of 5-m per pixel. This metadata file describes the shaded-relief 2-m data collected by the USGS and CSUMB, and processed by the USGS. The raster data file is included in "BathymetryAHS_2m_MontereyCanyon.zip," which is accessible from https://doi.org/10.5066/F7XD0ZQ4. These data accompany the pamphlet and map sheets of Dartnell, P., Maier, K.L., Erdey, M.D., Dieter, B.E., Golden, N.E., Johnson, S.Y., Hartwell, S.R., Cochrane, G.R., Ritchie, A.C., Finlayson, D.P., Kvitek, R.G., Sliter, R.W., Greene, H.G., Davenport, C.W., Endris, C.A., and Krigsman, L.M. (P. Dartnell and S.A. Cochran, eds.), 2016, California State Waters Map Series—Monterey Canyon and Vicinity, California: U.S. Geological Survey Open-File Report 2016–1072, 48 p., 10 sheets, scale 1:24,000, https://doi.org/10.3133/ofr20161072. The 2-m and 5-m bathymetry and shaded-relief maps of Monterey Canyon and Vicinity, California, were generated from data collected between 1998 and 2014 using a combination of 30-kHz Simrad EM-300 and 200-kHz/400-kHz Reson 7125 multibeam echosounders, as well as 234-kHz and 468-kHz SEA SWATHplus bathymetric sidescan-sonar systems. The mapping missions collected bathymetry data from about the 10-m isobath to beyond the 3-nautical-mile limit of California's State Waters. NOTE: The horizontal datum of this bathymetry data (NAD83) differs from the horizontal datum of other layers in this data release (WGS84).

This part of DS 781 presents data for 2-m and 5-m bathymetry and shaded-relief maps of Monterey Canyon and Vicinity, California. Bathymetry data are provided as separate grids depending on the mapping resolution. Data collected at shallower depths by the U.S. Geological Survey (USGS) and California State University, Monterey Bay (CSUMB) have a spatial resolution of 2 m per pixel, whereas data collected at deeper depths by the Monterey Bay Aquarium Research Institute (MBARI) have a spatial resolution of 5-m per pixel. This metadata file describes the 2-m data collected by the USGS and CSUMB, and processed by the USGS. The raster data file is included in "BathymetryA_2m_MontereyCanyon.zip," which is accessible from https://doi.org/10.5066/F7XD0ZQ4. These data accompany the pamphlet and map sheets of Dartnell, P., Maier, K.L., Erdey, M.D., Dieter, B.E., Golden, N.E., Johnson, S.Y., Hartwell, S.R., Cochrane, G.R., Ritchie, A.C., Finlayson, D.P., Kvitek, R.G., Sliter, R.W., Greene, H.G., Davenport, C.W., Endris, C.A., and Krigsman, L.M. (P. Dartnell and S.A. Cochran, eds.), 2016, California State Waters Map Series—Monterey Canyon and Vicinity, California: U.S. Geological Survey Open-File Report 2016–1072, 48 p., 10 sheets, scale 1:24,000, https://doi.org/10.3133/ofr20161072. The 2-m and 5-m bathymetry and shaded-relief maps of Monterey Canyon and Vicinity, California, were generated from data collected between 1998 and 2014 using a combination of 30-kHz Simrad EM-300 and 200-kHz/400-kHz Reson 7125 multibeam echosounders, as well as 234-kHz and 468-kHz SEA SWATHplus bathymetric sidescan-sonar systems. The mapping missions collected bathymetry data from about the 10-m isobath to beyond the 3-nautical-mile limit of California's State Waters. NOTE: The horizontal datum of this bathymetry data (NAD83) differs from the horizontal datum of other layers in this data release (WGS84).

This part of DS 781 presents data for 2-m and 5-m bathymetry and shaded-relief maps of Monterey Canyon and Vicinity, California. Bathymetry data are provided as separate grids depending on the mapping resolution. Data collected at shallower depths by the U.S. Geological Survey (USGS) and California State University, Monterey Bay (CSUMB) have a spatial resolution of 2 m per pixel, whereas data collected at deeper depths by the Monterey Bay Aquarium Research Institute (MBARI) have a spatial resolution of 5-m per pixel. This metadata file describes the shaded-relief 5-m data collected by MBARI and processed by the USGS. The raster data file is included in "BathymetryBHS_5m_MontereyCanyon.zip," which is accessible from https://doi.org/10.5066/F7XD0ZQ4. These data accompany the pamphlet and map sheets of Dartnell, P., Maier, K.L., Erdey, M.D., Dieter, B.E., Golden, N.E., Johnson, S.Y., Hartwell, S.R., Cochrane, G.R., Ritchie, A.C., Finlayson, D.P., Kvitek, R.G., Sliter, R.W., Greene, H.G., Davenport, C.W., Endris, C.A., and Krigsman, L.M. (P. Dartnell and S.A. Cochran, eds.), 2016, California State Waters Map Series—Monterey Canyon and Vicinity, California: U.S. Geological Survey Open-File Report 2016–1072, 48 p., 10 sheets, scale 1:24,000, https://doi.org/10.3133/ofr20161072. The 2-m and 5-m bathymetry and shaded-relief maps of Monterey Canyon and Vicinity, California, were generated from data collected between 1998 and 2014 using a combination of 30-kHz Simrad EM-300 and 200-kHz/400-kHz Reson 7125 multibeam echosounders, as well as 234-kHz and 468-kHz SEA SWATHplus bathymetric sidescan-sonar systems. The mapping missions collected bathymetry data from about the 10-m isobath to beyond the 3-nautical-mile limit of California's State Waters. NOTE: The horizontal datum of this bathymetry data (NAD83) differs from the horizontal datum of other layers in this data release (WGS84).

This part of DS 781 presents data for 2-m and 5-m bathymetry and shaded-relief maps of Monterey Canyon and Vicinity, California. Bathymetry data are provided as separate grids depending on the mapping resolution. Data collected at shallower depths by the U.S. Geological Survey (USGS) and California State University, Monterey Bay (CSUMB) have a spatial resolution of 2 m per pixel, whereas data collected at deeper depths by the Monterey Bay Aquarium Research Institute (MBARI) have a spatial resolution of 5-m per pixel. This metadata file describes the 5-m data collected by MBARI and processed by the USGS. The raster data file is included in "BathymetryB_5m_MontereyCanyon.zip," which is accessible from https://doi.org/10.5066/F7XD0ZQ4. These data accompany the pamphlet and map sheets of Dartnell, P., Maier, K.L., Erdey, M.D., Dieter, B.E., Golden, N.E., Johnson, S.Y., Hartwell, S.R., Cochrane, G.R., Ritchie, A.C., Finlayson, D.P., Kvitek, R.G., Sliter, R.W., Greene, H.G., Davenport, C.W., Endris, C.A., and Krigsman, L.M. (P. Dartnell and S.A. Cochran, eds.), 2016, California State Waters Map Series—Monterey Canyon and Vicinity, California: U.S. Geological Survey Open-File Report 2016–1072, 48 p., 10 sheets, scale 1:24,000, https://doi.org/10.3133/ofr20161072. The 2-m and 5-m bathymetry and shaded-relief maps of Monterey Canyon and Vicinity, California, were generated from data collected between 1998 and 2014 using a combination of 30-kHz Simrad EM-300 and 200-kHz/400-kHz Reson 7125 multibeam echosounders, as well as 234-kHz and 468-kHz SEA SWATHplus bathymetric sidescan-sonar systems. The mapping missions collected bathymetry data from about the 10-m isobath to beyond the 3-nautical-mile limit of California's State Waters. NOTE: The horizontal datum of this bathymetry data (NAD83) differs from the horizontal datum of other layers in this data release (WGS84).

This part of DS 781 presents fault data for the geologic and geomorphic map of the Monterey Canyon and Vicinity map area, California. The vector data file is included in "Faults_MontereyCanyon.zip," which is accessible from http://pubs.usgs.gov/ds/781/MontereyCanyon/data_catalog_MontereyCanyon.html. These data accompany the pamphlet and map sheets of Dartnell, P., Maier, K.L., Erdey, M.D., Dieter, B.E., Golden, N.E., Johnson, S.Y., Hartwell, S.R., Cochrane, G.R., Ritchie, A.C., Finlayson, D.P., Kvitek, R.G., Sliter, R.W., Greene, H.G., Davenport, C.W., Endris, C.A., and Krigsman, L.M. (P. Dartnell and S.A. Cochran, eds.), 2016, California State Waters Map Series—Monterey Canyon and Vicinity, California: U.S. Geological Survey Open-File Report 2016–1072, 48 p., 10 sheets, scale 1:24,000, https://doi.org/10.3133/ofr20161072. Faults in the Monterey Canyon and Vicinity map area are identified on seismic-reflection data based on abrupt truncation or warping of reflections and (or) juxtaposition of reflection panels with different seismic parameters such as reflection presence, amplitude, frequency, geometry, continuity, and vertical sequence. Faults were primarily mapped by interpretation of seismic reflection profile data from USGS field activities S–N1–09–MB and S–6–11–MB. The seismic reflection profiles were collected in 2009 and 2011.

This part of DS 781 presents fold data for the geologic and geomorphic map of the Monterey Canyon and Vicinity map area, California. The vector data file is included in "Folds_MontereyCanyon.zip," which is accessible from http://pubs.usgs.gov/ds/781/MontereyCanyon/data_catalog_MontereyCanyon.html. These data accompany the pamphlet and map sheets of Dartnell, P., Maier, K.L., Erdey, M.D., Dieter, B.E., Golden, N.E., Johnson, S.Y., Hartwell, S.R., Cochrane, G.R., Ritchie, A.C., Finlayson, D.P., Kvitek, R.G., Sliter, R.W., Greene, H.G., Davenport, C.W., Endris, C.A., and Krigsman, L.M. (P. Dartnell and S.A. Cochran, eds.), 2016, California State Waters Map Series—Monterey Canyon and Vicinity, California: U.S. Geological Survey Open-File Report 2016–1072, 48 p., 10 sheets, scale 1:24,000, https://doi.org/10.3133/ofr20161072. Folds were primarily mapped by interpretation of seismic reflection profile data from USGS field activities S–N1–09–MB and S–6–11–MB. The seismic reflection profiles were collected in 2009 and 2011.

This part of DS 781 presents data for the geologic and geomorphic map of Monterey Canyon and Vicinity, California. The vector data file is included in "Geology_MontereyCanyon.zip," which is accessible from http://pubs.usgs.gov/ds/781/MontereyCanyon/data_catalog_MontereyCanyon.html. These data accompany the pamphlet and map sheets of Dartnell, P., Maier, K.L., Erdey, M.D., Dieter, B.E., Golden, N.E., Johnson, S.Y., Hartwell, S.R., Cochrane, G.R., Ritchie, A.C., Finlayson, D.P., Kvitek, R.G., Sliter, R.W., Greene, H.G., Davenport, C.W., Endris, C.A., and Krigsman, L.M. (P. Dartnell and S.A. Cochran, eds.), 2016, California State Waters Map Series—Monterey Canyon and Vicinity, California: U.S. Geological Survey Open-File Report 2016–1072, 48 p., 10 sheets, scale 1:24,000, https://doi.org/10.3133/ofr20161072. Marine geology and geomorphology were mapped in the Monterey Canyon and Vicinity map area from approximate Mean High Water (MHW) across the continental shelf, as well as in Monterey Canyon to a water depth of about 1,750 m. This map area includes much of central Monterey Bay, extending just beyond the limit of California’s State Waters (note that the California’s State Waters limit, which generally is 3 nautical miles [5.6 km] from shore, extends farther offshore (as much as 12 nautical miles) between Santa Cruz and Monterey, so that it encompasses all of Monterey Bay. Offshore geologic units were delineated on the basis of integrated analyses of adjacent onshore geology with multibeam bathymetry and backscatter imagery, seafloor-sediment and rock samples, digital camera and video imagery, and high-resolution seismic reflection profiles. Aerial photographs taken in multiple years were used to map the nearshore area (0 to 10 m water depth) and to link the offshore and onshore geology.

This part of DS 781 presents data for the paleoshorelines for the geologic and geomorphic map of Monterey Canyon and Vicinity, California. The vector data file is included in "Paleoshorelines_MontereyCanyon.zip," which is accessible from https://doi.org/10.3133/ofr20161072. These data accompany the pamphlet and map sheets of Dartnell, P., Maier, K.L., Erdey, M.D., Dieter, B.E., Golden, N.E., Johnson, S.Y., Hartwell, S.R., Cochrane, G.R., Ritchie, A.C., Finlayson, D.P., Kvitek, R.G., Sliter, R.W., Greene, H.G., Davenport, C.W., Endris, C.A., and Krigsman, L.M. (P. Dartnell and S.A. Cochran, eds.), 2016, California State Waters Map Series—Monterey Canyon and Vicinity, California: U.S. Geological Survey Open-File Report 2016–1072, 48 p., 10 sheets, scale 1:24,000, https://doi.org/10.3133/ofr20161072.

This part of DS 781 presents the seafloor-character map of Monterey Canyon and Vicinity, California. The raster data file is included in "SeafloorCharacter_2m_MontereyCanyon.zip," which is accessible from https://doi.org/10.3133/ds781. These data accompany the pamphlet and map sheets of Dartnell, P., Maier, K.L., Erdey, M.D., Dieter, B.E., Golden, N.E., Johnson, S.Y., Hartwell, S.R., Cochrane, G.R., Ritchie, A.C., Finlayson, D.P., Kvitek, R.G., Sliter, R.W., Greene, H.G., Davenport, C.W., Endris, C.A., and Krigsman, L.M. (P. Dartnell and S.A. Cochran, eds.), 2016, California State Waters Map Series—Monterey Canyon and Vicinity, California: U.S. Geological Survey Open-File Report 2016–1072, 48 p., 10 sheets, scale 1:24,000, https://doi.org/10.3133/ofr20161072. This raster-format seafloor character map shows five substrate classes in Monterey Canyon and Vicinity, California. The substrate classes mapped in this area have been further divided into the following California Marine Life Protection Act depth zones and slope classes: Depth Zone 2 (intertidal to 30 m), Depth Zone 3 (30 to 100 m), Depth Zone 4 (100 to 200 m), Slope Class 1 (0 degrees - 5 degrees), and Slope Class 2 (5 degrees - 30 degrees). Depth Zone 1 (intertidal), Depth Zone 5 (greater than 200 m), and Slopes Classes 3-4 (greater than 30 degrees) are not present in the region covered by this block. The map is created using a supervised classification method described by Cochrane (2008), with multibeam echosounder (MBES) bathymetry and backscatter data collected and processed between 1998 and 2014, along with ground-truth verification from underwater video and sediment samples. Reference Cited: Cochrane, G.R., 2008, Video-supervised classification of sonar data for mapping seafloor habitat, in Reynolds, J.R., and Greene, H.G., eds., Marine habitat mapping technology for Alaska: Fairbanks, University of Alaska, Alaska Sea Grant College Program, p. 185-194, accessed April 5, 2011, at http://doc.nprb.org/web/research/research%20pubs/615_habitat_mapping_workshop/Individual%20Chapters%20High-Res/Ch13%20Cochrane.pdf.

This part of DS 781 presents the seafloor-character map of Monterey Canyon and Vicinity, California. The raster data file is included in "SeafloorCharacter_5m_MontereyCanyon.zip," which is accessible from https://doi.org/10.3133/ds781. These data accompany the pamphlet and map sheets of Dartnell, P., Maier, K.L., Erdey, M.D., Dieter, B.E., Golden, N.E., Johnson, S.Y., Hartwell, S.R., Cochrane, G.R., Ritchie, A.C., Finlayson, D.P., Kvitek, R.G., Sliter, R.W., Greene, H.G., Davenport, C.W., Endris, C.A., and Krigsman, L.M. (P. Dartnell and S.A. Cochran, eds.), 2016, California State Waters Map Series—Monterey Canyon and Vicinity, California: U.S. Geological Survey Open-File Report 2016–1072, 48 p., 10 sheets, scale 1:24,000, https://doi.org/10.3133/ofr20161072. This raster-format seafloor character map shows five substrate classes in Monterey Canyon and Vicinity, California. The substrate classes mapped in this area have been further divided into the following California Marine Life Protection Act depth zones and slope classes: Depth Zone 2 (intertidal to 30 m), Depth Zone 3 (30 to 100 m), Depth Zone 4 (100 to 200 m), Slope Class 1 (0 degrees - 5 degrees), and Slope Class 2 (5 degrees - 30 degrees). Depth Zone 1 (intertidal), Depth Zone 5 (greater than 200 m), and Slopes Classes 3-4 (greater than 30 degrees) are not present in the region covered by this block. The map is created using a supervised classification method described by Cochrane (2008), with multibeam echosounder (MBES) bathymetry and backscatter data collected and processed between 1998 and 2014, along with ground-truth verification from underwater video and sediment samples. Reference Cited: Cochrane, G.R., 2008, Video-supervised classification of sonar data for mapping seafloor habitat, in Reynolds, J.R., and Greene, H.G., eds., Marine habitat mapping technology for Alaska: Fairbanks, University of Alaska, Alaska Sea Grant College Program, p. 185-194, accessed April 5, 2011, at http://doc.nprb.org/web/research/research%20pubs/615_habitat_mapping_workshop/Individual%20Chapters%20High-Res/Ch13%20Cochrane.pdf.

This portion of the data release presents a wave model application developed to simulate wind generated surface gravity waves on an ancient lake on Mars. The phase-averaged wave model, SWAN, was applied within the Delft3D modeling system (Deltares, 2018) with reduced gravity and a range of atmospheric densities and wind speeds to simulate potential conditions that could generate wind waves on Mars. The data release includes model input files for simulations with three different atmospheric densities, compatible with version 4.04.01 of the Delft3D modeling system.

This portion of the data release presents tabulated wave parameter results derived from simulations of wind generated surface gravity waves on an ancient lake on Mars. The phase-averaged wave model, SWAN, was applied within the Delft3D modeling system (Deltares, 2018) with reduced gravity and a range of atmospheric densities and wind speeds to simulate potential conditions that could generate wind waves on Mars.

Seismic-reflection data were collected offshore southern California in 1972 aboard the USNS Bartlett (USGS Field Activity B-1-72-SC). In 2021 these legacy data were reprocessed to improve accuracy and geologic resolvability of California’s continental margin.

This part of USGS Data Series 781 (Golden, 2019) presents 2-m-resolution acoustic backscatter data for the Offshore of Morro Bay, California, map area. Backscatter data were collected by Fugro Pelagos in 2008 using a combination of 400-kHz Reson 7125, 240-kHz Reson 8101, and 100-kHz Reson 8111 multibeam echosounder systems. The data were post-processed by the California State University Monterey Bay Seafloor Mapping Lab and the University of California Santa Cruz Center for Integrated Spatial Research. The acoustic backscatter data are available as a georeferenced TIFF image. Within the final imagery, brighter tones indicate higher backscatter intensity, and darker tones indicate lower backscatter intensity. The intensity represents a complex interaction between the acoustic pulse and the seafloor, as well as characteristics within the shallow subsurface, providing a general indication of seafloor texture and composition. Backscatter intensity depends on the acoustic source level; the frequency used to image the seafloor; the grazing angle; the composition and character of the seafloor, including grain size, water content, bulk density, and seafloor roughness; and some biological cover. Harder and rougher bottom types such as rocky outcrops or coarse sediment typically return stronger intensities (high backscatter, lighter tones), whereas softer bottom types such as fine sediment return weaker intensities (low backscatter, darker tones).

This part of USGS Data Series 781 (Golden, 2019) presents 2-m-resolution bathymetry data for the Offshore of Morro Bay, California, map area. Bathymetry data were collected by Fugro Pelagos in 2008 using a combination of 400-kHz Reson 7125, 240-kHz Reson 8101, and 100-kHz Reson 8111 multibeam echosounder systems. The data were post-processed by the California State University Monterey Bay Seafloor Mapping Lab and the University of California Santa Cruz Center for Integrated Spatial Research. The bathymetry data are available as a georeferenced TIFF image.

Seafloor character, a combination of seafloor induration (surface hardness) and rugosity, was derived from multibeam echosounder (MBES) and annotated underwater video data collected offshore of Morro Bay, California. The MBES and underwater video data were collected in support of the U.S. Geological Survey (USGS) California Seafloor Mapping Program, under a collaboration with the California State University Monterey Bay Seafloor Mapping Lab, the California Ocean Protection Council, and the National Oceanic and Atmospheric Administration (NOAA). Substrate observations from the underwater video were translated into Coastal and Marine Ecological Classification Standard (CMECS; Federal Geographic Data Committee, 2012) induration classes to use as training for a supervised numerical classification of the MBES data. The seafloor character raster is provided as a 2-meter resolution GeoTIFF.

Rainfall measurements were collected in and near the CZU Lightning Complex Fire (hereafter, "CZU Fire") burn area, Santa Cruz Mountains, California. The CZU Fire ignited in the Santa Cruz Mountains, California, on August 16, 2020. By the time of full containment on September 22, 2020, the fire had burned 350 km2 (86,510 acres) in Santa Cruz and San Mateo Counties. The U.S. Geological Survey (USGS) installed four rain gages in and near the CZU Fire burn area to measure rainfall during the post-fire wet seasons. The USGS gratefully acknowledges the cooperation of Big Basin Redwoods State Park, the Big Creek Lumber Co., the McCrary family, and Brookdale Lodge in the collection of these data.

These data are a part of the effort to map geologic substrates of the Stellwagen Bank National Marine Sanctuary region off Boston, Massachusetts. The overall goal is to develop high-resolution (1:25,000) interpretive maps, based on multibeam sonar data and seabed sampling, showing surficial geology and seabed sediment dynamics. The work was conducted in collaboration with the Stellwagen Bank National Marine Sanctuary, and the data collected will aid research on the ecology of fish and invertebrate species that inhabit the region. The Sanctuary's research vessel, R/V Auk, visited a total of 233 locations on Stellwagen Bank during six separate surveys from May 2016 to April 2019. During these surveys a customized Van Veen grab sampler (SEABOSS) equipped with a video camera and a CTD was deployed in drift mode to collect sediment for grain-size analysis, video imagery of the seabed, and measurements of water column properties including temperature, salinity, density, and fluorescence.

These data are a part of the effort to map geologic substrates of the Stellwagen Bank National Marine Sanctuary region off Boston, Massachusetts. The overall goal is to develop high-resolution (1:25,000) interpretive maps, based on multibeam sonar data and seabed sampling, showing surficial geology and seabed sediment dynamics. The work was conducted in collaboration with the Stellwagen Bank National Marine Sanctuary, and the data collected will aid research on the ecology of fish and invertebrate species that inhabit the region. The Sanctuary's research vessel, R/V Auk, visited a total of 233 locations on Stellwagen Bank during six separate surveys from May 2016 to April 2019. During these surveys a customized Van Veen grab sampler (SEABOSS) equipped with a video camera and a CTD was deployed in drift mode to collect sediment for grain-size analysis, video imagery of the seabed, and measurements of water column properties including temperature, salinity, density, and fluorescence.

These data are a part of the effort to map geologic substrates of the Stellwagen Bank National Marine Sanctuary region off Boston, Massachusetts. The overall goal is to develop high-resolution (1:25,000) interpretive maps, based on multibeam sonar data and seabed sampling, showing surficial geology and seabed sediment dynamics. The work was conducted in collaboration with the Stellwagen Bank National Marine Sanctuary, and the data collected will aid research on the ecology of fish and invertebrate species that inhabit the region. The Sanctuary's research vessel, R/V Auk, visited a total of 233 locations on Stellwagen Bank during six separate surveys from May 2016 to April 2019. During these surveys a customized Van Veen grab sampler (SEABOSS) equipped with a video camera and a CTD was deployed in drift mode to collect sediment for grain-size analysis, video imagery of the seabed, and measurements of water column properties including temperature, salinity, density, and fluorescence.

These data are a part of the effort to map geologic substrates of the Stellwagen Bank National Marine Sanctuary region off Boston, Massachusetts. The overall goal is to develop high-resolution (1:25,000) interpretive maps, based on multibeam sonar data and seabed sampling, showing surficial geology and seabed sediment dynamics. The work was conducted in collaboration with the Stellwagen Bank National Marine Sanctuary, and the data collected will aid research on the ecology of fish and invertebrate species that inhabit the region. The Sanctuary's research vessel, R/V Auk, visited a total of 233 locations on Stellwagen Bank during six separate surveys from May 2016 to April 2019. During these surveys a customized Van Veen grab sampler (SEABOSS) equipped with a video camera and a CTD was deployed in drift mode to collect sediment for grain-size analysis, video imagery of the seabed, and measurements of water column properties including temperature, salinity, density, and fluorescence.

These data are a part of the effort to map geologic substrates of the Stellwagen Bank National Marine Sanctuary region off Boston, Massachusetts. The overall goal is to develop high-resolution (1:25,000) interpretive maps, based on multibeam sonar data and seabed sampling, showing surficial geology and seabed sediment dynamics. The work was conducted in collaboration with the Stellwagen Bank National Marine Sanctuary, and the data collected will aid research on the ecology of fish and invertebrate species that inhabit the region. The Sanctuary's research vessel, R/V Auk, visited a total of 233 locations on Stellwagen Bank during six separate surveys from May 2016 to April 2019. During these surveys a customized Van Veen grab sampler (SEABOSS) equipped with a video camera and a CTD was deployed in drift mode to collect sediment for grain-size analysis, video imagery of the seabed, and measurements of water column properties including temperature, salinity, density, and fluorescence.

These data are a part of the effort to map geologic substrates of the Stellwagen Bank National Marine Sanctuary region off Boston, Massachusetts. The overall goal is to develop high-resolution (1:25,000) interpretive maps, based on multibeam sonar data and seabed sampling, showing surficial geology and seabed sediment dynamics. The work was conducted in collaboration with the Stellwagen Bank National Marine Sanctuary, and the data collected will aid research on the ecology of fish and invertebrate species that inhabit the region. The Sanctuary's research vessel, R/V Auk, visited a total of 233 locations on Stellwagen Bank during six separate surveys from May 2016 to April 2019. During these surveys a customized Van Veen grab sampler (SEABOSS) equipped with a video camera and a CTD was deployed in drift mode to collect sediment for grain-size analysis, video imagery of the seabed, and measurements of water column properties including temperature, salinity, density, and fluorescence.

These data are a part of the effort to map geologic substrates of the Stellwagen Bank National Marine Sanctuary region off Boston, Massachusetts. The overall goal is to develop high-resolution (1:25,000) interpretive maps, based on multibeam sonar data and seabed sampling, showing surficial geology and seabed sediment dynamics. The work was conducted in collaboration with the Stellwagen Bank National Marine Sanctuary, and the data collected will aid research on the ecology of fish and invertebrate species that inhabit the region. The Sanctuary's research vessel, R/V Auk, visited a total of 233 locations on Stellwagen Bank during six separate surveys from May 2016 to April 2019. During these surveys a customized Van Veen grab sampler (SEABOSS) equipped with a video camera and a CTD was deployed in drift mode to collect sediment for grain-size analysis, video imagery of the seabed, and measurements of water column properties including temperature, salinity, density, and fluorescence.

This part of DS 781 presents data for the acoustic-backscatter map of the Offshore of Monterey map area, California. Backscatter data are provided as separate grids depending on resolution. This metadata file refers to the data included in "Backscatter_5m_OffshoreMonterey.zip," which is accessible from https://doi.org/10.5066/F70Z71C8. These data accompany the pamphlet and map sheets of Johnson, S.Y., Dartnell, P., Hartwell, S.R., Cochrane, G.R., Golden, N.E., Watt, J.T., Davenport, C.W., Kvitek, R.G., Erdey, M.D., Krigsman, L.M., Sliter, R.W., and Maier, K.L. (S.Y. Johnson and S.A. Cochran, eds.), 2016, California State Waters Map Series—Offshore of Monterey, California: U.S. Geological Survey Open-File Report 2016–1110, pamphlet 44 p., 10 sheets, scale 1:24,000, http://dx.doi.org/10.3133/ofr20161110. The acoustic-backscatter map of the Offshore of Monterey map area in central California was generated from backscatter data collected by California State University, Monterey Bay (CSUMB) and by Monterey Bay Aquarium Research Institute (MBARI). Mapping was completed between 1998 and 2012 using a combination of multibeam echosounders including 200-kHz/400-kHz Reson 7125, 100-kHz Reson 7111, 240 kHz Reson 8101, and 30-kHz Simrad EM-300 as well as 234-kHz and 468-kHz SWATHplus bathymetric sidescan-sonar system. Within the final imagery, brighter tones indicate higher backscatter intensity, and darker tones indicate lower backscatter intensity. The intensity represents a complex interaction between the acoustic pulse and the seafloor, as well as characteristics within the shallow subsurface, providing a general indication of seafloor texture and composition. Backscatter intensity depends on the acoustic source level; the frequency used to image the seafloor; the grazing angle; the composition and character of the seafloor, including grain size, water content, bulk density, and seafloor roughness; and some biological cover. Harder and rougher bottom types such as rocky outcrops or coarse sediment typically return stronger intensities (high backscatter, lighter tones), whereas softer bottom types such as fine sediment return weaker intensities (low backscatter, darker tones).

This part of DS 781 presents data for the acoustic-backscatter map of the Offshore of Monterey map area, California. Backscatter data are provided as separate grids depending on resolution. This metadata file refers to the data included in "Backscatter_7125_OffshoreMonterey.zip," which is accessible from https://doi.org/10.5066/F70Z71C8. These data accompany the pamphlet and map sheets of Johnson, S.Y., Dartnell, P., Hartwell, S.R., Cochrane, G.R., Golden, N.E., Watt, J.T., Davenport, C.W., Kvitek, R.G., Erdey, M.D., Krigsman, L.M., Sliter, R.W., and Maier, K.L. (S.Y. Johnson and S.A. Cochran, eds.), 2016, California State Waters Map Series—Offshore of Monterey, California: U.S. Geological Survey Open-File Report 2016–1110, pamphlet 44 p., 10 sheets, scale 1:24,000, https://doi.org/10.3133/ofr20161110. The acoustic-backscatter map of the Offshore of Monterey map area in central California was generated from backscatter data collected by California State University, Monterey Bay (CSUMB) and by Monterey Bay Aquarium Research Institute (MBARI). Mapping was completed between 1998 and 2012 using a combination of multibeam echosounders including 200-kHz/400-kHz Reson 7125, 100-kHz Reson 7111, 240 kHz Reson 8101, and 30-kHz Simrad EM-300 as well as 234-kHz and 468-kHz SWATHplus bathymetric sidescan-sonar system. Within the final imagery, brighter tones indicate higher backscatter intensity, and darker tones indicate lower backscatter intensity. The intensity represents a complex interaction between the acoustic pulse and the seafloor, as well as characteristics within the shallow subsurface, providing a general indication of seafloor texture and composition. Backscatter intensity depends on the acoustic source level; the frequency used to image the seafloor; the grazing angle; the composition and character of the seafloor, including grain size, water content, bulk density, and seafloor roughness; and some biological cover. Harder and rougher bottom types such as rocky outcrops or coarse sediment typically return stronger intensities (high backscatter, lighter tones), whereas softer bottom types such as fine sediment return weaker intensities (low backscatter, darker tones).

This part of DS 781 presents data for the acoustic-backscatter map of the Offshore of Monterey map area, California. Backscatter data are provided as separate grids depending on resolution. This metadata file refers to the data included in "Backscatter_8101_OffshoreMonterey.zip," which is accessible from https://doi.org/10.5066/F70Z71C8. These data accompany the pamphlet and map sheets of Johnson, S.Y., Dartnell, P., Hartwell, S.R., Cochrane, G.R., Golden, N.E., Watt, J.T., Davenport, C.W., Kvitek, R.G., Erdey, M.D., Krigsman, L.M., Sliter, R.W., and Maier, K.L. (S.Y. Johnson and S.A. Cochran, eds.), 2016, California State Waters Map Series—Offshore of Monterey, California: U.S. Geological Survey Open-File Report 2016–1110, pamphlet 44 p., 10 sheets, scale 1:24,000, https://doi.org/10.3133/ofr20161110. The acoustic-backscatter map of the Offshore of Monterey map area in central California was generated from backscatter data collected by California State University, Monterey Bay (CSUMB) and by Monterey Bay Aquarium Research Institute (MBARI). Mapping was completed between 1998 and 2012 using a combination of multibeam echosounders including 200-kHz/400-kHz Reson 7125, 100-kHz Reson 7111, 240 kHz Reson 8101, and 30-kHz Simrad EM-300 as well as 234-kHz and 468-kHz SWATHplus bathymetric sidescan-sonar system. Within the final imagery, brighter tones indicate higher backscatter intensity, and darker tones indicate lower backscatter intensity. The intensity represents a complex interaction between the acoustic pulse and the seafloor, as well as characteristics within the shallow subsurface, providing a general indication of seafloor texture and composition. Backscatter intensity depends on the acoustic source level; the frequency used to image the seafloor; the grazing angle; the composition and character of the seafloor, including grain size, water content, bulk density, and seafloor roughness; and some biological cover. Harder and rougher bottom types such as rocky outcrops or coarse sediment typically return stronger intensities (high backscatter, lighter tones), whereas softer bottom types such as fine sediment return weaker intensities (low backscatter, darker tones).

This part of DS 781 presents data for the acoustic-backscatter map of the Offshore of Monterey map area, California. Backscatter data are provided as separate grids depending on resolution. This metadata file refers to the data included in "Backscatter_Swath_OffshoreMonterey.zip," which is accessible from https://doi.org/10.5066/F70Z71C8. These data accompany the pamphlet and map sheets of Johnson, S.Y., Dartnell, P., Hartwell, S.R., Cochrane, G.R., Golden, N.E., Watt, J.T., Davenport, C.W., Kvitek, R.G., Erdey, M.D., Krigsman, L.M., Sliter, R.W., and Maier, K.L. (S.Y. Johnson and S.A. Cochran, eds.), 2016, California State Waters Map Series—Offshore of Monterey, California: U.S. Geological Survey Open-File Report 2016–1110, pamphlet 44 p., 10 sheets, scale 1:24,000, https://doi.org/10.3133/ofr20161110. The acoustic-backscatter map of the Offshore of Monterey map area in central California was generated from backscatter data collected by California State University, Monterey Bay (CSUMB) and by Monterey Bay Aquarium Research Institute (MBARI). Mapping was completed between 1998 and 2012 using a combination of multibeam echosounders including 200-kHz/400-kHz Reson 7125, 100-kHz Reson 7111, 240 kHz Reson 8101, and 30-kHz Simrad EM-300 as well as 234-kHz and 468-kHz SWATHplus bathymetric sidescan-sonar system. Within the final imagery, brighter tones indicate higher backscatter intensity, and darker tones indicate lower backscatter intensity. The intensity represents a complex interaction between the acoustic pulse and the seafloor, as well as characteristics within the shallow subsurface, providing a general indication of seafloor texture and composition. Backscatter intensity depends on the acoustic source level; the frequency used to image the seafloor; the grazing angle; the composition and character of the seafloor, including grain size, water content, bulk density, and seafloor roughness; and some biological cover. Harder and rougher bottom types such as rocky outcrops or coarse sediment typically return stronger intensities (high backscatter, lighter tones), whereas softer bottom types such as fine sediment return weaker intensities (low backscatter, darker tones).

This part of DS 781 presents fault data for the geologic and geomorphic map of the Offshore of Monterey map area, California. The vector data file is included in "Faults_OffshoreMonterey.zip," which is accessible from https://doi.org/10.5066/F70Z71C8. These data accompany the pamphlet and map sheets of Johnson, S.Y., Dartnell, P., Hartwell, S.R., Cochrane, G.R., Golden, N.E., Watt, J.T., Davenport, C.W., Kvitek, R.G., Erdey, M.D., Krigsman, L.M., Sliter, R.W., and Maier, K.L. (S.Y. Johnson and S.A. Cochran, eds.), 2016, California State Waters Map Series—Offshore of Monterey, California: U.S. Geological Survey Open-File Report 2016–1110, pamphlet 44 p., 10 sheets, scale 1:24,000, https://doi.org/10.3133/ofr20161110. Faults in the Offshore of Monterey map area are identified on seismic-reflection data based on abrupt truncation or warping of reflections and (or) juxtaposition of reflection panels with different seismic parameters such as reflection presence, amplitude, frequency, geometry, continuity, and vertical sequence. Faults were primarily mapped by interpretation of seismic reflection profile data from USGS field activity S–6–11–MB collected in 2011.

This part of DS 781 presents fold data for the geologic and geomorphic map of the Offshore of Monterey map area, California. The vector data file is included in "Folds_OffshoreMonterey.zip," which is accessible from https://doi.org/10.5066/F70Z71C8. These data accompany the pamphlet and map sheets of Johnson, S.Y., Dartnell, P., Hartwell, S.R., Cochrane, G.R., Golden, N.E., Watt, J.T., Davenport, C.W., Kvitek, R.G., Erdey, M.D., Krigsman, L.M., Sliter, R.W., and Maier, K.L. (S.Y. Johnson and S.A. Cochran, eds.), 2016, California State Waters Map Series—Offshore of Monterey, California: U.S. Geological Survey Open-File Report 2016–1110, pamphlet 44 p., 10 sheets, scale 1:24,000, https://doi.org/10.3133/ofr20161110. Folds in the Offshore of Monterey map area are identified on seismic-reflection data based on abrupt truncation or warping of reflections and (or) juxtaposition of reflection panels with different seismic parameters such as reflection presence, amplitude, frequency, geometry, continuity, and vertical sequence. Faults were primarily mapped by interpretation of seismic reflection profile data from USGS field activity S–6–11–MB collected in 2011.

This part of DS 781 presents data for the geologic and geomorphic map of the Offshore of Monterey map area, California. The vector data file is included in "Geology_OffshoreMonterey.zip," which is accessible from https://doi.org/10.5066/F70Z71C8. These data accompany the pamphlet and map sheets of Johnson, S.Y., Dartnell, P., Hartwell, S.R., Cochrane, G.R., Golden, N.E., Watt, J.T., Davenport, C.W., Kvitek, R.G., Erdey, M.D., Krigsman, L.M., Sliter, R.W., and Maier, K.L. (S.Y. Johnson and S.A. Cochran, eds.), 2016, California State Waters Map Series—Offshore of Monterey, California: U.S. Geological Survey Open-File Report 2016–1110, pamphlet 44 p., 10 sheets, scale 1:24,000, https://doi.org/10.3133/ofr20161110. Marine geology and geomorphology were mapped in the Offshore of Monterey map area, California, from approximate Mean High Water (MHW) to the 3-nautical-mile limit of California''s State Waters. Offshore geologic units were delineated on the basis of integrated analyses of adjacent onshore geology with multibeam bathymetry and backscatter imagery, seafloor-sediment and rock samples, digital camera and video imagery, and high-resolution seismic-reflection profiles.

This part of DS 781 presents data for the habitat map of the seafloor of the Offshore of Monterey map area, California. The vector data file is included in "Habitat_OffshoreMonterey.zip," which is accessible from https://doi.org/10.5066/F70Z71C8. These data accompany the pamphlet and map sheets of Johnson, S.Y., Dartnell, P., Hartwell, S.R., Cochrane, G.R., Golden, N.E., Watt, J.T., Davenport, C.W., Kvitek, R.G., Erdey, M.D., Krigsman, L.M., Sliter, R.W., and Maier, K.L. (S.Y. Johnson and S.A. Cochran, eds.), 2016, California State Waters Map Series—Offshore of Monterey, California: U.S. Geological Survey Open-File Report 2016–1110, pamphlet 44 p., 10 sheets, scale 1:24,000, https://doi.org/10.3133/ofr20161110. This map shows physical marine benthic habitats in the Offshore of Monterey map area. Marine benthic habitats represent a particular type of water quality, substrate, geomorphology, seafloor process, or any other attribute that may provide a habitat for a specific species or an assemblage of organisms. Marine benthic habitats are classified using the Coastal and Marine Ecological Classification Standard (CMECS), developed by representatives from a consortium of federal agencies. CMECS is the U.S. government standard for marine habitat characterization. The standard provides an ecologically-relevant structure for biologic, geologic, chemical, and physical habitat attributes. This map illustrates the geoform and substrate components of the standard. This map was derived from seafloor geology map (sheet 10) units by translation of the unit description into the best-fit values of CMECS classes. The CMECS classes are documented at https://www.fgdc.gov/standards/projects/FGDC-standards-projects/cmecs-folder/CMECS_Version_06-2012_FINAL.pdf. Please refer to Greene and others (2007) for more information regarding the Benthic Marine Potential Habitat Classification Scheme and the codes used to represent various seafloor features. Reference Cited: Greene, H.G., Bizzarro, J.J., O'Connell, V.M., and Brylinsky, C.K., 2007, Construction of digital potential marine benthic habitat maps using a coded classification scheme and its application, in Todd, B.J., and Greene, H.G., eds., Mapping the seafloor for habitat characterization: Geological Association of Canada Special Paper 47, p. 141-155.

Environmental parameters affecting plant productivity and microbial respiration, such as water level, salinity, and groundwater temperature included in these datasets, are key components of wetland carbon cycling, carbon storage, and capacity to maintain elevation. Data were collected to (1) provide background data to evaluate potential differences in water level and carbon flux between wetland sites with differing elevation and tidal inundation and (2) facilitate applications of Blue Carbon projects in coastal wetlands. Associated child pages include continuous water level, salinity, and temperature from shallow wells installed in coastal wetland sites on Cape Cod, Massachusetts. These datasets are grouped by the project they support or by study site. Project study sites include salt marshes with natural tidal flow, salt marshes that were previously tidally restricted and have been restored, impounded coastal wetlands with restricted tidal flow inclusive of various vegetation types, and Phragmites dominated areas fringing natural salt marshes.

Environmental parameters affecting plant productivity and microbial respiration, such as water level, salinity, and groundwater temperature included in these datasets, are key components of wetland carbon cycling, carbon storage, and capacity to maintain elevation. Data were collected to (1) provide background data to evaluate potential differences in water level and carbon flux between wetland sites with differing elevation and tidal inundation and (2) facilitate applications of Blue Carbon projects in coastal wetlands. Associated child pages include continuous water level, salinity, and temperature from shallow wells installed in coastal wetland sites on Cape Cod, Massachusetts. These datasets are grouped by the project they support or by study site. Project study sites include salt marshes with natural tidal flow, salt marshes that were previously tidally restricted and have been restored, impounded coastal wetlands with restricted tidal flow inclusive of various vegetation types, and Phragmites dominated areas fringing natural salt marshes.

More than 1,000 line-km of sub-bottom chirp data were collected with an Edgetech 0.5-16 kHz subscan system by Scripps Institution of Oceanography between 2006 and 2008 in the Salton Sea, California, with assistance from the U.S. Geological Survey (USGS). Data were subsequently donated by Scripps to the USGS for public release (USGS field activity identifier 2006-603-DD).

Vegetation type and density data were collected by the U.S. Geological Survey (USGS) Pacific Coastal and Marine Science Center at three locations in the Sacramento-San Joaquin Delta. Data were collected in Lindsey Slough in April 2017, and Middle River and the Mokelumne River in March 2018. Vegetation samples were collected by divers, and used to determine dry biomass density. These data were collected as part of a cooperative project, with the USGS California Water Science Center and the California Department of Fish and Wildlife, on the effects of invasive aquatic vegetation on sediment transport in the Sacramento-San Joaquin Delta.

Suspended-sediment concentrations are reported in mg/L for water samples collected from the San Lorenzo River during the rainy seasons from 2008 to 2019. Samples were collected during 2-, 5- and 10-year flood events.

This data release contains processed high-resolution multichannel sparker seismic-reflection (MCS) data that were collected aboard Humboldt State University’s R/V Coral Sea in October of 2018 on U.S. Geological Survey cruise 2018-658-FA on the shelf and slope between Cape Blanco, Oregon, and Cape Mendocino, California. MCS data were collected to characterize quaternary deformation and sediment dynamics along the southern Cascadia margin.

The 14 January 2022 eruption of Tonga Hunga-Tonga Ha'apai volcano generated tsunamis that impacted the west coast of the United States on the morning of 15 January 2022. This data release presents runup heights and tsunami heights collected by the U.S. Geological Survey (USGS) and the California Geological Survey (CGS) during surveys at the Santa Cruz Harbor and beaches in Santa Cruz County, California, on January 19th and 20th, 2022 (USGS Field Activity 2022-607-FA). Evidence of tsunami inundation included high water marks on buildings and harbor infrastructure, wrack lines, and flow-direction indicators such as bent vegetation. Elevations surveyed at the maximum inland extent of tsunami inundation are classified as “runup heights.” High-water mark elevations are classified as “tsunami heights.” At Seabright, Beer Can, and Manresa beaches, the inferred tsunami inundation limit was identified as the highest wrack line on each beach. The wrack line coincided with a change in sand color, with the inundated side consisting of darker colored sand. Based on eyewitness and video observations of the tsunami in Santa Cruz Harbor, most of the surveyed tsunami and runup heights are the result of inundation from the highest wave around 08:50 PST (16:50 GMT). Flow-depth markers on buildings and infrastructure from a later wave were measured and reported as lower tsunami heights and are noted. The final point data are provided in a comma-separated text file and are projected in cartesian coordinates using the Universal Transverse Mercator (UTM), Zone 10 North, meters coordinate system.

This part of USGS Data Series 781 (Golden, 2019) presents 2-m-resolution acoustic backscatter data for the Offshore of Point Estero, California, map area. Backscatter data were collected by Fugro Pelagos in 2008 using a combination of 400-kHz Reson 7125, 240-kHz Reson 8101, and 100-kHz Reson 8111 multibeam echosounder systems. The data were post-processed by the California State University Monterey Bay Seafloor Mapping Lab and the University of California Santa Cruz Center for Integrated Spatial Research. The acoustic backscatter data are available as a georeferenced TIFF image. Within the final imagery, brighter tones indicate higher backscatter intensity, and darker tones indicate lower backscatter intensity. The intensity represents a complex interaction between the acoustic pulse and the seafloor, as well as characteristics within the shallow subsurface, providing a general indication of seafloor texture and composition. Backscatter intensity depends on the acoustic source level; the frequency used to image the seafloor; the grazing angle; the composition and character of the seafloor, including grain size, water content, bulk density, and seafloor roughness; and some biological cover. Harder and rougher bottom types such as rocky outcrops or coarse sediment typically return stronger intensities (high backscatter, lighter tones), whereas softer bottom types such as fine sediment return weaker intensities (low backscatter, darker tones).

This part of USGS Data Series 781 (Golden, 2019) presents 2-m-resolution bathymetry data for the Offshore of Point Estero, California, map area. Bathymetry data were collected by Fugro Pelagos in 2008 using a combination of 400-kHz Reson 7125, 240-kHz Reson 8101, and 100-kHz Reson 8111 multibeam echosounder systems. The data were post-processed by the California State University Monterey Bay Seafloor Mapping Lab and the University of California Santa Cruz Center for Integrated Spatial Research. The bathymetry data are available as a georeferenced TIFF image.

Seafloor character, a combination of seafloor induration (surface hardness) and rugosity, was derived from multibeam echosounder (MBES) and annotated underwater video data collected offshore of Point Estero, California. The MBES and underwater video data were collected in support of the U.S. Geological Survey (USGS) California Seafloor Mapping Program, under a collaboration with the California State University Monterey Bay Seafloor Mapping Lab, the California Ocean Protection Council, and the National Oceanic and Atmospheric Administration (NOAA). Substrate observations from the underwater video were translated into Coastal and Marine Ecological Classification Standard (CMECS; Federal Geographic Data Committee, 2012) induration classes to use as training for a supervised numerical classification of the MBES data. The seafloor character raster is provided as a 2-meter resolution GeoTIFF.

Seismic-reflection data were collected offshore central California in 1972 aboard the USNS Bartlett (USGS Field Activity B-1-72-SC). In 2021 these legacy data were reprocessed to improve accuracy and geologic resolvability of California’s continental margin.

The data in this part of the release are images of the beach for use in structure from motion that were taken with a camera attached to a helium filled balloon-kite (Helikite). During September and October 2021, USGS and Woods Hole Oceanographic Institute (WHOI) scientists conducted multiple field surveys to collect an elevation time series at the USGS DUring Nearshore Event eXperiment (DUNEX) site on Pea Island National Wildlife Refuge, NC. Agisoft Metashape (v. 1.8.1) was used to create orthomosaics and digital surface models with the collected imagery. DUNEX is a multi-agency, academic, and non-governmental organization collaborative community experiment designed to study nearshore coastal processes during storm events. USGS participation in DUNEX will contribute new measurements and models that will increase our understanding of storm impacts to coastal environments, including hazards to humans and infrastructure and changes in landscape and natural habitats.

A digital elevation model (DEM) was created from underwater images collected at Eastern Dry Rocks coral reef near Key West, Florida, in May 2021 using the SQUID-5 camera system. The underwater images were processed using Structure-from-Motion (SfM) photogrammetry techniques into a classified two-class ('unclassified' and 'low noise') 3D point cloud. The DEM was derived in Metashape (ver. 1.6.5) from the point cloud, but it excludes the 'low noise' class. The DEM covers a rectangular area of seafloor approximately 800x160 meters (0.12 square kilometers) in size and was saved as a tiled GeoTIFF raster at 1-cm resolution.

This text file (SQUID5_EDR_2021_Image_Locations.txt) provides the GNSS antenna location for underwater images collected at Eastern Dry Rocks coral reef, near Key West, Florida, in May 2021, using the SQUID5 Structure-from-Motion (SfM) system, a towed-surface vehicle with five downward-looking underwater cameras developed by the U.S. Geological Survey. The GNSS antenna location for the time of each image capture is presented with greater precision than is stored in the individual image's EXIF header due to decimal place limitations of the EXIF format.

A three-dimensional point cloud (LAZ format) was developed from underwater images collected at Eastern Dry Rocks coral reef near Key West, Florida, in May 2021 using the SQUID-5 camera system and processed using Structure-from-Motion (SfM) photogrammetry techniques. Point cloud data include x,y,z positions, RGB colors, Metashape-computed confidence values, and a two-class classification ('unclassified' and 'low noise') derived from the confidence values. LAS (and its compressed form, LAZ) is an open format developed for the efficient use of point cloud lidar data. A description of the LAZ format and links to software tools for using LAZ files are provided at the USGS website: https://www.usgs.gov/news/3d-elevation-program-distributing-lidar-data-laz-format

Underwater images totaling 138,733 in number were collected at Eastern Dry Rocks coral reef, near Key West, Florida, in May 2021, using the SQUID5 Structure-from-Motion (SfM) system, a towed-surface vehicle with five downward-looking underwater cameras developed by the U.S. Geological Survey. The images are organized in zipped files grouped by survey line. The SQUID-5 records images as TIFF (.tif) format to maintain the highest resolution and bit depth. Each image includes EXIF metadata, containing GNSS date, time, latitude, longitude, and altitude of the GNSS antenna mounted on the towed surface vehicle, copyright, keywords, and other fields.

An extensive set of physics-based XBeach Non-hydrostatic hydrodynamic model simulations (with input files here included) were used to evaluate the influence of shore-normal reef channels on flooding along fringing reef-lined coasts, specifically during extreme wave conditions when the risk for coastal flooding and the resulting impact to coastal communities is greatest. These input files accompany the modeling conducted for the following publication: Storlazzi, C.D., Rey, A.E., and van Dongeren, A.R., 2022, A numerical study of geomorphic and oceanographic controls on wave-driven runup on fringing reefs with shore-normal channels: Journal of Marine Science and Engineering, 10(6), 828, https://doi.org/10.3390/jmse10060828.

Low-altitude (80-100 meters above ground level) digital images of Town Neck Beach in Sandwich, Massachusetts, were obtained from a camera mounted on a small unmanned aerial system (UAS; also known as a drone). Imagery was collected at close to low tide on seven days to observe changes in beach and dune morphology. The images were geolocated by using the single-frequency geographic positioning system aboard the UAS. Ground control points (GCPs) were established by using temporary targets on the ground, which were located by using a real-time kinematic global navigation satellite system (RTK-GNSS) base station and rovers. The GCPs can be used as constraints during photogrammetric processing. Transect points were collected by using the same RTK-GNSS system; these can be used to evaluate photogrammetric products. This data release includes georeferenced images, image-location files, GCPs, and transect points. Collection of these data was supported by the U.S. Geological Survey Coastal and Marine Geology Program and were conducted under USGS field activity numbers 2017-005-FA, 2017-008-FA, 2017-010-FA, 2017-014-FA, 2017-027-FA, 2017-029-FA, and 2017-050-FA.

Low-altitude (80-100 meters above ground level) digital images of Town Neck Beach in Sandwich, Massachusetts, were obtained from a camera mounted on a small unmanned aerial system (UAS; also known as a drone). Imagery was collected at close to low tide on seven days to observe changes in beach and dune morphology. The images were geolocated by using the single-frequency geographic positioning system aboard the UAS. Ground control points (GCPs) were established by using temporary targets on the ground, which were located by using a real-time kinematic global navigation satellite system (RTK-GNSS) base station and rovers. The GCPs can be used as constraints during photogrammetric processing. Transect points were collected by using the same RTK-GNSS system; these can be used to evaluate photogrammetric products. This data release includes georeferenced images, image-location files, GCPs, and transect points. Collection of these data was supported by the USGS Coastal and Marine Geology Program and were conducted under USGS field activity numbers 2017-005-FA, 2017-008-FA, 2017-010-FA, 2017-014-FA, 2017-027-FA, 2017-029-FA, and 2017-050-FA.

Low-altitude (80-100 meters above ground level) digital images of Town Neck Beach in Sandwich, Massachusetts, were obtained from a camera mounted on a small unmanned aerial system (UAS; also known as a drone). Imagery was collected at close to low tide on seven days to observe changes in beach and dune morphology. The images were geolocated by using the single-frequency geographic positioning system aboard the UAS. Ground control points (GCPs) were established by using temporary targets on the ground, which were located by using a real-time kinematic global navigation satellite system (RTK-GNSS) base station and rovers. The GCPs can be used as constraints during photogrammetric processing. Transect points were collected by using the same RTK-GNSS system; these can be used to evaluate photogrammetric products. This data release includes georeferenced images, image-location files, GCPs, and transect points. Collection of these data was supported by the U.S. Geological Survey Coastal and Marine Geology Program and were conducted under USGS field activity numbers 2017-005-FA, 2017-008-FA, 2017-010-FA, 2017-014-FA, 2017-027-FA, 2017-029-FA, and 2017-050-FA.

Low-altitude (80-100 meters above ground level) digital images of Town Neck Beach in Sandwich, Massachusetts, were obtained from a camera mounted on a small unmanned aerial system (UAS; also known as a drone). Imagery was collected at close to low tide on to observe changes in beach and dune morphology. The images were geolocated by using the single-frequency geographic positioning system aboard the UAS. Ground control points (GCPs) were established by using temporary targets on the ground, which were located by using a real-time kinematic global navigation satellite system (RTK-GNSS) base station and rovers. The GCPs can be used as constraints during photogrammetric processing. Transect points were collected by using the same RTK-GNSS system; these can be used to evaluate photogrammetric products. This data release includes georeferenced images, image-location files, GCPs, and transect points. Collection of these data was supported by the U.S. Geological Survey Coastal and Marine Geology Program and were conducted under USGS field activity numbers 2017-005-FA, 2017-008-FA, 2017-010-FA, 2017-014-FA, 2017-027-FA, 2017-029-FA, and 2017-050-FA.

Low-altitude (80-100 meters above ground level) digital images of Town Neck Beach in Sandwich, Massachusetts, were obtained from a camera mounted on a small unmanned aerial system (UAS; also known as a drone). Imagery was collected at close to low tide to observe changes in beach and dune morphology. The images were geolocated by using the single-frequency geographic positioning system aboard the UAS. Ground control points (GCPs) were established by using temporary targets on the ground, which were located by using a real-time kinematic global navigation satellite system (RTK-GNSS) base station and rovers. The GCPs can be used as constraints during photogrammetric processing. Transect points were collected by using the same RTK-GNSS system; these can be used to evaluate photogrammetric products. This data release includes georeferenced images, image-location files, GCPs, and transect points. Collection of these data was supported by the U.S. Geological Coastal and Marine Geology Program and were conducted under USGS field activity numbers 2017-005-FA, 2017-008-FA, 2017-010-FA, 2017-014-FA, 2017-027-FA, 2017-029-FA, and 2017-050-FA.

Low-altitude (80-100 meters above ground level) digital images of Town Neck Beach in Sandwich, Massachusetts, were obtained from a camera mounted on a small unmanned aerial system (UAS; also known as a drone). Imagery was collected at close to low tide on seven days to observe changes in beach and dune morphology. The images were geolocated by using the single-frequency geographic positioning system aboard the UAS. Ground control points (GCPs) were established by using temporary targets on the ground, which were located by using a real-time kinematic global navigation satellite system (RTK-GNSS) base station and rovers. The GCPs can be used as constraints during photogrammetric processing. Transect points were collected by using the same RTK-GNSS system; these can be used to evaluate photogrammetric products. This data release includes georeferenced images, image-location files, GCPs, and transect points. Collection of these data was supported by the U.S. Geological Survey Coastal and Marine Geology Program and were conducted under USGS field activity numbers 2017-005-FA, 2017-008-FA, 2017-010-FA, 2017-014-FA, 2017-027-FA, 2017-029-FA, and 2017-050-FA.

Low-altitude (80-100 meters above ground level) digital images of Town Neck Beach in Sandwich, Massachusetts, were obtained from a camera mounted on a small unmanned aerial system (UAS; also known as a drone). Imagery was collected at close to low tide to observe changes in beach and dune morphology. The images were geolocated by using the single-frequency geographic positioning system aboard the UAS. Ground control points (GCPs) were established by using temporary targets on the ground, which were located by using a real-time kinematic global navigation satellite system (RTK-GNSS) base station and rovers. The GCPs can be used as constraints during photogrammetric processing. Transect points were collected by using the same RTK-GNSS system; these can be used to evaluate photogrammetric products. This data release includes georeferenced images, image-location files, GCPs, and transect points. Collection of these data was supported by the U.S. Geological Survey Coastal and Marine Geology Program and were conducted under USGS field activity numbers 2017-005-FA, 2017-008-FA, 2017-010-FA, 2017-014-FA, 2017-027-FA, 2017-029-FA, and 2017-050-FA.

Low-altitude (80-100 meters above ground level) digital images of Town Neck Beach in Sandwich, Massachusetts were obtained from a camera mounted on a small unmanned aerial system (UAS; also known as a drone). Imagery was collected at close to low tide on seven days to observe changes in beach and dune morphology. The images were geolocated by using the single-frequency geographic positioning system aboard the UAS. Ground control points (GCPs) were established by using temporary targets on the ground, which were located by using a real-time kinematic global navigation satellite system (RTK-GNSS) base station and rovers. The GCPs can be used as constraints during photogrammetric processing. Transect points were collected by using the same RTK-GNSS system; these can be used to evaluate photogrammetric products. This data release includes georeferenced images, image-location files, GCPs, and transect points. Collection of these data was supported by the U.S. Geological Survey Coastal and Marine Geology Program and were conducted under USGS field activity numbers 2017-005-FA, 2017-008-FA, 2017-010-FA, 2017-014-FA, 2017-027-FA, 2017-029-FA, and 2017-050-FA.

Low-altitude (80-100 meters above ground level) digital images of Town Neck Beach in Sandwich, Massachusetts were obtained with a camera mounted on a small unmanned aerial system (UAS; also known as a drone). Imagery was collected at close to low tide on seven days to observe changes in beach and dune morphology. The images were geolocated by using the single-frequency geographic positioning system aboard the UAS. Ground control points (GCPs) were established by using temporary targets on the ground, which were located by using a real-time kinematic global navigation satellite system (RTK-GNSS) base station and rovers. The GCPs can be used as constraints during photogrammetric processing. Transect points were collected by using the same RTK-GNSS system; these can used to evaluate photogrammetric products. This data release includes georeferenced images, image-location files, GCPs, and transect points. Collection of these data was supported by the USGS Coastal and Marine Geology Program and were conducted under USGS field activity numbers 2017-005-FA, 2017-008-FA, 2017-010-FA, 2017-014-FA, 2017-027-FA, 2017-029-FA, and 2017-050-FA.

Low-altitude (80-100 meters above ground level) digital images of Town Neck Beach in Sandwich, Massachusetts were obtained from a camera mounted on a small unmanned aerial system (UAS; also known as a drone). Imagery was collected at close to low tide on seven days to observe changes in beach and dune morphology. The images were geolocated by using the single-frequency geographic positioning system aboard the UAS. Ground control points (GCPs) were established by using temporary targets on the ground, which were located by using a real-time kinematic global navigation satellite system (RTK-GNSS) base station and rovers. The GCPs can be used as constraints during photogrammetric processing. Transect points were collected by using the same RTK-GNSS system; these can be used to evaluate photogrammetric products. This data release includes georeferenced images, image-location files, GCPs, and transect points. Collection of these data was supported by the U.S. Geological Survey Coastal and Marine Geology Program and were conducted under USGS field activity numbers 2017-005-FA, 2017-008-FA, 2017-010-FA, 2017-014-FA, 2017-029-FA, and 2017-050-FA.

Low-altitude (30-120 meters above ground level) digital images of Town Neck Beach in Sandwich, Massachusetts, were obtained with a series of cameras mounted on small unmanned aerial systems (UAS, also known as a drone). Imagery was collected at close to low tide on five days to observe changes in beach and dune morphology. The images were geolocated by using the single-frequency geographic positioning system aboard the UAS. Ground control points (GCPs) were established by using temporary targets on the ground, which were located by using a real-time kinematic global navigation satellite system (RTK-GNSS) base station and rovers. The GCPs can be used as constraints during photogrammetric processing. Transect points were collected by using the same RTK-GNSS system; these can be used to evaluate photogrammetric products. This data release includes georeferenced images, image-location files, GCPs, and transect points. Collection of these data was supported by the U.S. Geological Survey Coastal/Marine Hazards and Resources Program and conducted under U.S. Geological Survey field activity numbers 2016-013-FA, 2016-043-FA, 2016-053-FA, 2016-054-FA and 2016-055-FA.

Low-altitude (30-120 meters above ground level) digital images of Town Neck Beach in Sandwich, Massachusetts, were obtained with a series of cameras mounted on small unmanned aerial systems (UAS, also known as a drone). Imagery was collected at close to low tide on five days to observe changes in beach and dune morphology. The images were geolocated by using the single-frequency geographic positioning system aboard the UAS. Ground control points (GCPs) were established by using temporary targets on the ground, which were located by using a real-time kinematic global navigation satellite system (RTK-GNSS) base station and rovers. The GCPs can be used as constraints during photogrammetric processing. Transect points were collected by using the same RTK-GNSS system; these can be used to evaluate photogrammetric products. This data release includes georeferenced images, image-location files, GCPs, and transect points. Collection of these data was supported by the U.S. Geological Survey Coastal/Marine Hazards and Resources Program and conducted under U.S. Geological Survey field activity numbers 2016-013-FA, 2016-043-FA, 2016-053-FA, 2016-054-FA and 2016-055-FA.

Low-altitude (30-120 meters above ground level) digital images of Town Neck Beach in Sandwich, Massachusetts, were obtained with a series of cameras mounted on small unmanned aerial systems (UAS, also known as a drone). Imagery was collected at close to low tide on five days to observe changes in beach and dune morphology. The images were geolocated by using the single-frequency geographic positioning system aboard the UAS. Ground control points (GCPs) were established by using temporary targets on the ground, which were located by using a real-time kinematic global navigation satellite system (RTK-GNSS) base station and rovers. The GCPs can be used as constraints during photogrammetric processing. Transect points were collected by using the same RTK-GNSS system; these can be used to evaluate photogrammetric products. This data release includes georeferenced images, image-location files, GCPs, and transect points. Collection of these data was supported by the U.S. Geological Survey Coastal/Marine Hazards and Resources Program and conducted under U.S. Geological Survey field activity numbers 2016-013-FA, 2016-043-FA, 2016-053-FA, 2016-054-FA and 2016-055-FA.

Low-altitude (30-120 meters above ground level) digital images of Town Neck Beach in Sandwich, Massachusetts, were obtained with a series of cameras mounted on small unmanned aerial systems (UAS, also known as a drone). Imagery was collected at close to low tide on five days to observe changes in beach and dune morphology. The images were geolocated by using the single-frequency geographic positioning system aboard the UAS. Ground control points (GCPs) were established by using temporary targets on the ground, which were located by using a real-time kinematic global navigation satellite system (RTK-GNSS) base station and rovers. The GCPs can be used as constraints during photogrammetric processing. Transect points were collected by using the same RTK-GNSS system; these can be used to evaluate photogrammetric products. This data release includes georeferenced images, image-location files, GCPs, and transect points. Collection of these data was supported by the U.S. Geological Survey Coastal/Marine Hazards and Resources Program and conducted under U.S. Geological Survey field activity numbers 2016-013-FA, 2016-043-FA, 2016-053-FA, 2016-054-FA and 2016-055-FA.

Low-altitude (30-120 meters above ground level) digital images of Town Neck Beach in Sandwich, Massachusetts, were obtained with a series of cameras mounted on small unmanned aerial systems (UAS, also known as a drone). Imagery was collected at close to low tide on five days to observe changes in beach and dune morphology. The images were geolocated by using the single-frequency geographic positioning system aboard the UAS. Ground control points (GCPs) were established by using temporary targets on the ground, which were located by using a real-time kinematic global navigation satellite system (RTK-GNSS) base station and rovers. The GCPs can be used as constraints during photogrammetric processing. Transect points were collected by using the same RTK-GNSS system; these can be used to evaluate photogrammetric products. This data release includes georeferenced images, image-location files, GCPs, and transect points. Collection of these data was supported by the U.S. Geological Survey Coastal/Marine Hazards and Resources Program and conducted under U.S. Geological Survey field activity numbers 2016-013-FA, 2016-043-FA, 2016-053-FA, 2016-054-FA and 2016-055-FA.

Low-altitude (30-120 meters above ground level) digital images of Town Neck Beach in Sandwich, Massachusetts, were obtained with a series of cameras mounted on small unmanned aerial systems (UAS, also known as a drone). Imagery was collected at close to low tide on five days to observe changes in beach and dune morphology. The images were geolocated by using the single-frequency geographic positioning system aboard the UAS. Ground control points (GCPs) were established by using temporary targets on the ground, which were located by using a real-time kinematic global navigation satellite system (RTK-GNSS) base station and rovers. The GCPs can be used as constraints during photogrammetric processing. Transect points were collected by using the same RTK-GNSS system; these can be used to evaluate photogrammetric products. This data release includes georeferenced images, image-location files, GCPs, and transect points. Collection of these data was supported by the U.S. Geological Survey Coastal/Marine Hazards and Resources Program and conducted under U.S. Geological Survey field activity numbers 2016-013-FA, 2016-043-FA, 2016-053-FA, 2016-054-FA and 2016-055-FA.

Bathymetric survey data were collected in April 2009 just south of Dumbarton Bridge in south San Francisco Bay, California. Portions of the main channel and western shallows/intertidal mudflats were surveyed using an interferometric sidescan sonar system following procedures detailed in Foxgrover and others, 2011. The bathymetry is provided as a 1-m resolution raster in geoTIFF format, referenced to the vertical datum of mean lower low water (MLLW). To convert to the North American Vertical Datum of 1988 (NAVD88), subtract a static offset of 0.37 m (datum conversions provided in Foxgrover and others, 2007).

Bathymetric survey data were collected in April 2013 just south of Dumbarton Bridge in south San Francisco Bay, California. A portion of the main channel was surveyed using an interferometric sidescan sonar system following procedures detailed in Foxgrover and others, 2011. The bathymetry is provided as a 1-m resolution raster in geoTIFF format, referenced to the vertical datum of mean lower low water (MLLW). To convert to the North American Vertical Datum of 1988 (NAVD88), subtract a static offset of 0.37 m (datum conversions provided in Foxgrover and others, 2007).

Bathymetric survey data were collected in April 2016 just south of Dumbarton Bridge in south San Francisco Bay, California. A portion of the main channel was surveyed using an interferometric sidescan sonar system following procedures detailed in Foxgrover and others, 2011. The bathymetry is provided as a 1-m resolution raster in geoTIFF format, referenced to the vertical datum of mean lower low water (MLLW). To convert to the North American Vertical Datum of 1988 (NAVD88), subtract a static offset of 0.37 m (datum conversions provided in Foxgrover and others, 2007).

Bathymetric survey data were collected in April 2018 just south of Dumbarton Bridge in south San Francisco Bay, California. A portion of the main channel was surveyed using an interferometric sidescan sonar system following procedures detailed in Foxgrover and others, 2011. The bathymetry is provided as a 1-m resolution raster in geoTIFF format, referenced to the vertical datum of mean lower low water (MLLW). To convert to the North American Vertical Datum of 1988 (NAVD88), subtract a static offset of 0.37 m (datum conversions provided in Foxgrover and others, 2007).

Bathymetric survey data were collected in December 2008 just south of Dumbarton Bridge in south San Francisco Bay, California. Portions of the main channel and western shallows/intertidal mudflats were surveyed using an interferometric sidescan sonar system following procedures detailed in Foxgrover and others, 2011. The bathymetry is provided as a 1-m resolution raster in geoTIFF format, referenced to the vertical datum of mean lower low water (MLLW). To convert to the North American Vertical Datum of 1988 (NAVD88), subtract a static offset of 0.37 m (datum conversions provided in Foxgrover and others, 2007).

Bathymetric survey data were collected in February 2009 just south of Dumbarton Bridge in south San Francisco Bay, California. Portions of the main channel and western shallows/intertidal mudflats were surveyed using an interferometric sidescan sonar system following procedures detailed in Foxgrover and others, 2011 . The bathymetry is provided as a 1-m resolution raster in geoTIFF format, referenced to the vertical datum of mean lower low water (MLLW). To convert to the North American Vertical Datum of 1988 (NAVD88), subtract a static offset of 0.37 m (datum conversions provided in Foxgrover and others, 2007).

Bathymetric survey data were collected in January 2010 just south of Dumbarton Bridge in south San Francisco Bay, California. Portions of the main channel and western shallows/intertidal mudflats were surveyed using an interferometric sidescan sonar system following procedures detailed in Foxgrover and others, 2011. The bathymetry is provided as a 1-m resolution raster in geoTIFF format, referenced to the vertical datum of mean lower low water (MLLW). To convert to the North American Vertical Datum of 1988 (NAVD88), subtract a static offset of 0.37 m (datum conversions provided in Foxgrover and others, 2007).

Bathymetric survey data were collected in January 2011 just south of Dumbarton Bridge in south San Francisco Bay, California. Portions of the main channel and western shallows/intertidal mudflats were surveyed using an interferometric sidescan sonar system following procedures detailed in Foxgrover and others, 2011. The bathymetry is provided as a 1-m resolution raster in geoTIFF format, referenced to the vertical datum of mean lower low water (MLLW). To convert to the North American Vertical Datum of 1988 (NAVD88), subtract a static offset of 0.37 m (datum conversions provided in Foxgrover and others, 2007).

Bathymetric survey data were collected in March 2010 just south of Dumbarton Bridge in south San Francisco Bay, California. Portions of the main channel and western shallows/intertidal mudflats were surveyed using an interferometric sidescan sonar system following procedures detailed in Foxgrover and others, 2011. The bathymetry is provided as a 1-m resolution raster in geoTIFF format, referenced to the vertical datum of mean lower low water (MLLW). To convert to the North American Vertical Datum of 1988 (NAVD88), subtract a static offset of 0.37 m (datum conversions provided in Foxgrover and others, 2007).

Bathymetric survey data were collected in March 2019 just south of Dumbarton Bridge in south San Francisco Bay, California. A portion of the main channel was surveyed using an interferometric sidescan sonar system following procedures detailed in Foxgrover and others, 2011. The bathymetry is provided as a 1-m resolution raster in geoTIFF format, referenced to the vertical datum of mean lower low water (MLLW). To convert to the North American Vertical Datum of 1988 (NAVD88), subtract a static offset of 0.37 m (datum conversions provided in Foxgrover and others, 2007).

Bathymetric survey data were collected in November 2013 just south of Dumbarton Bridge in south San Francisco Bay, California. Portions of the main channel and western shallows/intertidal mudflats were surveyed using an interferometric sidescan sonar system following procedures detailed in Foxgrover and others, 2011. The bathymetry is provided as a 1-m resolution raster in geoTIFF format, referenced to the vertical datum of mean lower low water (MLLW). To convert to the North American Vertical Datum of 1988 (NAVD88), subtract a static offset of 0.37 m (datum conversions provided in Foxgrover and others, 2007).

Bathymetric survey data were collected in October 2009 just south of Dumbarton Bridge in south San Francisco Bay, California. Portions of the main channel and western shallows/intertidal mudflats were surveyed using an interferometric sidescan sonar system following procedures detailed in Foxgrover and others, 2011. The bathymetry is provided as a 1-m resolution raster in geoTIFF format, referenced to the vertical datum of mean lower low water (MLLW). To convert to the North American Vertical Datum of 1988 (NAVD88), subtract a static offset of 0.37 m (datum conversions provided in Foxgrover and others, 2007).

Bathymetric survey data were collected in October 2014 just south of Dumbarton Bridge in south San Francisco Bay, California. A portion of the main channel was surveyed using an interferometric sidescan sonar system following procedures detailed in Foxgrover and others, 2011. The bathymetry is provided as a 1-m resolution raster in geoTIFF format, referenced to the vertical datum of mean lower low water (MLLW). To convert to the North American Vertical Datum of 1988 (NAVD88), subtract a static offset of 0.37 m (datum conversions provided in Foxgrover and others, 2007).

Bathymetric survey data were collected in October 2015 just south of Dumbarton Bridge in south San Francisco Bay, California. Portions of the main channel and western shallows/intertidal mudflats were surveyed using an interferometric sidescan sonar system following procedures detailed in Foxgrover and others, 2011. The bathymetry is provided as a 1-m resolution raster in geoTIFF format, referenced to the vertical datum of mean lower low water (MLLW). To convert to the North American Vertical Datum of 1988 (NAVD88), subtract a static offset of 0.37 m (datum conversions provided in Foxgrover and others, 2007).

Bathymetric survey data were collected in October 2016 just south of Dumbarton Bridge in south San Francisco Bay, California. A portion of the main channel was surveyed using an interferometric sidescan sonar system following procedures detailed in Foxgrover and others, 2011. The bathymetry is provided as a 1-m resolution raster in geoTIFF format, referenced to the vertical datum of mean lower low water (MLLW). To convert to the North American Vertical Datum of 1988 (NAVD88), subtract a static offset of 0.37 m (datum conversions provided in Foxgrover and others, 2007).

Bathymetric survey data were collected in September 2010 just south of Dumbarton Bridge in south San Francisco Bay, California. Portions of the main channel and western shallows/intertidal mudflats were surveyed using an interferometric sidescan sonar system following procedures detailed in Foxgrover and others, 2011. The bathymetry is provided as a 1-m resolution raster in geoTIFF format, referenced to the vertical datum of mean lower low water (MLLW). To convert to the North American Vertical Datum of 1988 (NAVD88), subtract a static offset of 0.37 m (datum conversions provided in Foxgrover and others, 2007).

Various water column variables, including salinity, dissolved inorganic nutrients, pH, total alkalinity, dissolved inorganic carbon, radio-carbon isotopes were measured in samples collected using a Niskin-bottle rosette at selected depths from sites offshore of California and Oregon from October to November 2018 during NOAA cruise SH-18-12 on the R/V Bell M. Shimada (USGS field activity 2018-663-FA). CTD (Conductivity Temperature Depth) data were also collected at each depth that a Niskin-bottle sample was collected and are presented along with the water sample data. This data release supersedes version 2.0, published in September 2021 at https://doi.org/10.5066/P99DIQZ5. Versioning details are documented in the accompanying VersionHistory_P99MJ096.txt file.

CTD (Conductivity Temperature Depth) data were collected offshore of California and Oregon from October to November 2018 during NOAA cruise SH-18-12 on the R/V Bell M. Shimada (USGS field activity 2018-663-FA). This data release supersedes version 2.0, published in September 2021 at https://doi.org/10.5066/P99DIQZ5. Versioning details are documented in the accompanying VersionHistory_P99MJ096.txt file.

Topographic surveys were completed during eight summer surveys (in 2013, 2014, 2015, 2016, 2017, 2019, 2020 and 2021) at 10 sites along the Carmel River, CA: Berwick (BW), Control Reach (CR), Crossroads (CRO), DeDampierre Lower (DDL), DeDampierre Upper (DDU), Dam Reach (DM), Reservoir Reach (RS), San Carlos (SC), Sleepy Hollow (SH), and Schulte Road (SR)). Topographic measurements were made at multiple locations along four to six cross-section transects per site using a total station (at sites CR, RS, DM and SH) and with an auto level and survey rod (at sites DDU, DDL, BW, SR, SC and CRO). This dataset contains the cross-sectional elevation measurements for each transect and survey year. The elevation measurement locations along each transect were located by measuring distances between corresponding endpoint coordinates, starting from river left (see accompanying file within this data release for topographic survey transect endpoint coordinates). For the transects measured with a total station (CR, RS, DM and SH), horizontal coordinates, elevation and distance from the left bank end point are provided. For the transects measured with an auto level (DDU, DDL, BW, SR, SC and CRO), only elevation and distance from left bank end point are provided (horizontal coordinates are defined as NULL for these transects). Vertical coordinates are referenced to the NAVD88 vertical datum, in units of meters. Horizontal coordinates are referenced to the NAD83(2011) reference frame, projected in Universal Transverse Mercator (UTM) coordinates (zone 10N). These data supersede grain-size data originally published in 2017 at https://doi.org/10.5066/F74M93HF.

Various water column variables, including salinity, dissolved inorganic nutrients, pH, total alkalinity, dissolved inorganic carbon, radio-carbon isotopes were measured in samples collected using a Niskin-bottle rosette at selected depths from sites offshore of California and Oregon from October to November 2019 during NOAA Ship Lasker R-19-05 (USGS field activity 2019-672-FA). CTD (Conductivity Temperature Depth) data were also collected at each depth that a Niskin-bottle sample was collected and are presented along with the water sample data. This data release supersedes version 1.0, published in August 2020 at https://doi.org/10.5066/P9ZS1JX8. Versioning details are documented in the accompanying VersionHistory_P9JKYWQU.txt file.

CTD (Conductivity Temperature Depth) data were collected offshore of California and Oregon from October to November 2019 during NOAA cruise RL-19-05 (USGS field activity 2019-672-FA). This data release supersedes version 1.0, published in August 2020 at https://doi.org/10.5066/P9ZS1JX8. Versioning details are documented in the accompanying VersionHistory_P9JKYWQU.txt file.

Various water column variables, including salinity, dissolved inorganic nutrients, dissolved inorganic carbon, and radio-carbon isotopes were measured in samples collected using a Niskin-bottle rosette at selected depths from deepwater sites offshore the US Mid- and South Atlantic from September 2017 to April 2019. CTD (Conductivity Temperature Depth) data were also collected at each depth that a Niskin-bottle sample was collected and are presented along with the water sample data during the following five cruises: PC1704 (USGS field activity 2017-004-FA) in September 2017, ENG615 (USGS field activity 2018-024-FA) in June 2018, AT41 (USGS field activity 2018-042-FA) in August 2018, BMCC-2018 (USGS field activity 2018-053-FA) in October 2018, and RB1903 (USGS field activity 2019-620-FA) in April 2019. This data release supersedes version 1.0, published in March 2021 at https://doi.org/10.5066/P9V7ODDR. Versioning details are documented in the accompanying VersionHistory_P9K2GOAL.txt file.

A seabed orthoimage was developed from underwater images collected at Eastern Dry Rocks coral reef near Key West, Florida, in May 2021 using the SQUID-5 camera system. The underwater images were processed using Structure-from-Motion (SfM) photogrammetry techniques. The orthoimage covers a rectangular area of seafloor approximately 800x160 meters (0.12 square kilometers) in size, and it was created using image-mosaicking methods and saved as a tiled, 5-mm resolution raster.

Multibeam acoustic-backscatter data were collected offshore of Morro Bay, California, from 2016 to 2019. The data were collected during five separate multi-agency surveys for the U.S. Geological Survey (USGS)/Bureau of Ocean Energy Management (BOEM) California Deepwater Investigations and Groundtruthing I (Cal DIG I) project, under a collaboration with the National Oceanic and Atmospheric Administration (NOAA), using Simrad 700 series hull-mounted multibeam echosounders. Data in 2017 and 2018 were acquired by the NOAA Hydrographic Vessel Rainier (surveys H1309, H13151, and H13152). The 2018 data acquired by the Ranier were collected during USGS field activity 2018-641-FA. Additional data were collected in 2019 by the NOAA Hydrographic Survey Vessel Fairweather (survey W00479). Data from the Scripps Institution of Oceanography R/V Sally Ride collected in 2016 (survey SR1604) were used to fill in a small gap in the NOAA data. The acoustic-backscatter data from the five surveys were combined into a single raster and are provided as a 10-meter resolution GeoTIFF.

Multibeam acoustic-bathymetry data were collected offshore of Morro Bay, California, from 2016 to 2019. The data were collected during five separate multi-agency surveys for the U.S. Geological Survey (USGS)/Bureau of Ocean Energy Management (BOEM) California Deepwater Investigations and Groundtruthing I (Cal DIG I) project, under a collaboration with the National Oceanic and Atmospheric Administration (NOAA), using Simrad 700 series hull-mounted multibeam echosounders. Data in 2017 and 2018 were acquired by the NOAA Hydrographic Vessel Rainier (surveys H1309, H13151, and H13152). The 2018 data acquired by the Ranier were collected during USGS field activity 2018-641-FA. Additional data were collected in 2019 by the NOAA Hydrographic Survey Vessel Fairweather (survey W00479). Data from the Scripps Institution of Oceanography R/V Sally Ride collected in 2016 (survey SR1604) were used to fill in a small gap in the NOAA data. The acoustic-backscatter data from the five surveys were combined into a single raster and are provided as a 10-meter resolution GeoTIFF.

Seafloor induration (surface hardness) was derived from multibeam echosounder (MBES) and annotated underwater video data collected offshore of Morro Bay, California, from 2016 to 2020. MBES and underwater video data were collected in support of the U.S. Geological Survey (USGS)/Bureau of Ocean Energy Management (BOEM) California Deepwater Investigations and Groundtruthing I (Cal DIG I) project, under a collaboration with the National Oceanic and Atmospheric Administration (NOAA). Substrate observations from the underwater video were translated into Coastal and Marine Ecological Classification Standard (CMECS; Federal Geographic Data Committee, 2012) induration classes to use as training for a supervised classification of the MBES data. The induration raster is provided as a 10-meter resolution GeoTIFF.

Five-stage sequential extractions were performed on ultramafic soil from Bosque Susua and Guanica Dry Forest, stream sediment from Rios Loco and Yauco, and nearshore sediment from Guanica Bay in southwest Puerto Rico. Sequential extractions showed partitioning of geologically enriched metals and other elements among five phases: 1) adsorbed and calcium-bound, 2) iron-, manganese-oxyhydroxide-bound, 3) crystalline iron-oxide-bound, 4) sulfide-bound, and 5) residual. Total element contents are also reported. Soil and sediment were collected between June 19, 2017, and March 27, 2018.

Environmental parameters affecting plant productivity and microbial respiration, such as water level, salinity, and groundwater temperature included in these datasets, are key components of wetland carbon cycling, carbon storage, and capacity to maintain elevation. Data were collected to (1) provide background data to evaluate potential differences in water level and carbon flux between wetland sites with differing elevation and tidal inundation and (2) facilitate applications of Blue Carbon projects in coastal wetlands. Associated child pages include continuous water level, salinity, and temperature from shallow wells installed in coastal wetland sites on Cape Cod, Massachusetts. These datasets are grouped by the project they support or by study site. Project study sites include salt marshes with natural tidal flow, salt marshes that were previously tidally restricted and have been restored, impounded coastal wetlands with restricted tidal flow inclusive of various vegetation types, and Phragmites dominated areas fringing natural salt marshes.

This part of USGS Data Series 781 (Golden, 2019) presents 2-m-resolution acoustic backscatter data for the Offshore of Point Buchon, California, map area. Backscatter data were collected by Fugro Pelagos in 2008 using a combination of 400-kHz Reson 7125, 240-kHz Reson 8101, and 100-kHz Reson 8111 multibeam echosounder systems. The data were post-processed by the California State University Monterey Bay Seafloor Mapping Lab and the University of California Santa Cruz Center for Integrated Spatial Research. The acoustic backscatter data are available as a georeferenced TIFF image. Within the final imagery, brighter tones indicate higher backscatter intensity, and darker tones indicate lower backscatter intensity. The intensity represents a complex interaction between the acoustic pulse and the seafloor, as well as characteristics within the shallow subsurface, providing a general indication of seafloor texture and composition. Backscatter intensity depends on the acoustic source level; the frequency used to image the seafloor; the grazing angle; the composition and character of the seafloor, including grain size, water content, bulk density, and seafloor roughness; and some biological cover. Harder and rougher bottom types such as rocky outcrops or coarse sediment typically return stronger intensities (high backscatter, lighter tones), whereas softer bottom types such as fine sediment return weaker intensities (low backscatter, darker tones).

This part of USGS Data Series 781 (Golden, 2019) presents 2-m-resolution bathymetry data for the Offshore of Point Buchon, California, map area. Bathymetry data were collected by Fugro Pelagos in 2008 using a combination of 400-kHz Reson 7125, 240-kHz Reson 8101, and 100-kHz Reson 8111 multibeam echosounder systems. The data were post-processed by the California State University Monterey Bay Seafloor Mapping Lab and the University of California Santa Cruz Center for Integrated Spatial Research. The bathymetry data are available as a georeferenced TIFF image.

Seafloor character, a combination of seafloor induration (surface hardness) and rugosity, was derived from multibeam echosounder (MBES) and annotated underwater video data collected offshore of Point Buchon, California. The MBES and underwater video data were collected in support of the U.S. Geological Survey (USGS) California Seafloor Mapping Program, under a collaboration with the California State University Monterey Bay Seafloor Mapping Lab, the California Ocean Protection Council, and the National Oceanic and Atmospheric Administration (NOAA). Substrate observations from the underwater video were translated into Coastal and Marine Ecological Classification Standard (CMECS; Federal Geographic Data Committee, 2012) induration classes to use as training for a supervised numerical classification of the MBES data. The seafloor character raster is provided as a 2-meter resolution GeoTIFF.

This publication releases swath bathymetry and backscatter datasets derived from multibeam bathymetric data acquired by the U.S. Geological Survey (USGS) on the R/V Marcus G. Langseth legs MGL1108 (transit) and MGL1109 in the Gulf of Alaska, and MGL1111 in the Bering Sea. These data were acquired with a Kongsberg Simrad EM-122 multibeam echosounder and Seafloor Information System (SIS) acquisition software. The MGL1108 data were combined with the MGL1109 data during processing and are presented as MGL1109. This data set includes 100-m bathymetry grids generated from processed bathymetry soundings, 100-m backscatter grids generated from coregistered processed backscatter values, extracted center beam bathymetry soundings, and associated metadata. The Gulf of Alaska lines extend from 48.27° N to 57.71° N, 137.81° W to 152.38° W in the northeastern Pacific, including portions of the distal Surveyor and Baranof fan systems, Kodiak-Bowie seamounts, and Aleutian trench near Kodiak Island, AK. The Bering Sea lines extend from 54.04° N to 58.88° N, 166.56° W to 174.04° E, including portions of the Umnak Plateau northwest of Dutch Harbor, AK, central Aleutian Basin, distal eastern flank of Shirshov Ridge, and north face of Bowers Ridge. These surveys were designed primarily for acquisition of multichannel seismic-reflection imaging and ocean-bottom seismometer OBS velocity analysis in support of Extended Continental Shelf (ECS) maritime zone definition. The USGS activities in mapping the ECS are coordinated through the interagency ECS Task Force, comprising representatives from the U.S. Department of State, the USGS, the National Oceanographic and Atmospheric Administration (NOAA), and several other governmental agencies. Further information on the U.S. Extended Continental Shelf (ECS) Project and its activities is posted at https://www.state.gov/e/oes/ocns/opa/ecs/index.htm.

This raster dataset represents approximately 69,060 square kilometers of Simrad EM122 multibeam-backscatter data collected in the Gulf of Alaska during U.S. Geological Survey (USGS) cruise MGL1109 aboard the R/V Marcus G. Langseth. The data have been reduced for position, elevation, orientation, water-column sound-speed, and refraction effects.

This raster dataset represents approximately 69,060 square kilometers of Simrad EM122 multibeam-backscatter data collected in the Gulf of Alaska during U.S. Geological Survey (USGS) cruise MGL1109 aboard the R/V Marcus G. Langseth. The data have been reduced for position, elevation, orientation, water-column sound-speed, and refraction effects.

This raster dataset represents approximately 69,060 square kilometers of Simrad EM122 multibeam-backscatter data collected in the Gulf of Alaska during U.S. Geological Survey (USGS) cruise MGL1109 aboard the R/V Marcus G. Langseth. The data have been reduced for position, elevation, orientation, water-column sound-speed, and refraction effects.

This raster dataset represents approximately 69,060 square kilometers of Simrad EM122 multibeam-backscatter data collected in the Gulf of Alaska during U.S. Geological Survey (USGS) cruise MGL1109 aboard the R/V Marcus G. Langseth. The data have been reduced for position, elevation, orientation, water-column sound-speed, and refraction effects.

This raster dataset represents approximately 69,060 square kilometers of Simrad EM122 multibeam-backscatter data collected in the Gulf of Alaska during U.S. Geological Survey (USGS) cruise MGL1109 aboard the R/V Marcus G. Langseth. The data have been reduced for position, elevation, orientation, water-column sound-speed, and refraction effects.

This raster dataset represents approximately 69,060 square kilometers of Simrad EM122 multibeam-backscatter data collected in the Gulf of Alaska during U.S. Geological Survey (USGS) cruise MGL1109 aboard the R/V Marcus G. Langseth. The data have been reduced for position, elevation, orientation, water-column sound-speed, and refraction effects.

This raster dataset represents approximately 69,060 square kilometers of Simrad EM122 multibeam-backscatter data collected in the Gulf of Alaska during U.S. Geological Survey (USGS) cruise MGL1109 aboard the R/V Marcus G. Langseth. The data have been reduced for position, elevation, orientation, water-column sound-speed, and refraction effects.

This raster dataset represents approximately 69,060 square kilometers of Simrad EM122 multibeam-bathymetry data collected in the Gulf of Alaska during U.S. Geological Survey - Coastal and Marine Geology Program cruise MGL1109 aboard the R/V Marcus G. Langseth. The data have been reduced for position, elevation, orientation, water-column sound-speed, and refraction effects.

This raster dataset represents approximately 69,060 square kilometers of Simrad EM122 multibeam-bathymetry data collected in the Gulf of Alaska during U.S. Geological Survey - Coastal and Marine Geology Program cruise MGL1109 aboard the R/V Marcus G. Langseth. The data have been reduced for position, elevation, orientation, water-column sound-speed, and refraction effects.

This raster dataset represents approximately 69,060 square kilometers of Simrad EM122 multibeam-bathymetry data collected in the Gulf of Alaska during U.S. Geological Survey - Coastal and Marine Geology Program cruise MGL1109 aboard the R/V Marcus G. Langseth. The data have been reduced for position, elevation, orientation, water-column sound-speed, and refraction effects.

This raster dataset represents approximately 69,060 square kilometers of Simrad EM122 multibeam-bathymetry data collected in the Gulf of Alaska during U.S. Geological Survey - Coastal and Marine Geology Program cruise MGL1109 aboard the R/V Marcus G. Langseth. The data have been reduced for position, elevation, orientation, water-column sound-speed, and refraction effects.

This raster dataset represents approximately 69,060 square kilometers of Simrad EM122 multibeam-bathymetry data collected in the Gulf of Alaska during U.S. Geological Survey - Coastal and Marine Geology Program cruise MGL1109 aboard the R/V Marcus G. Langseth. The data have been reduced for position, elevation, orientation, water-column sound-speed, and refraction effects.

This raster dataset represents approximately 69,060 square kilometers of Simrad EM122 multibeam-bathymetry data collected in the Gulf of Alaska during U.S. Geological Survey - Coastal and Marine Geology Program cruise MGL1109 aboard the R/V Marcus G. Langseth. The data have been reduced for position, elevation, orientation, water-column sound-speed, and refraction effects.

This raster dataset represents approximately 69,060 square kilometers of Simrad EM122 multibeam-bathymetry data collected in the Gulf of Alaska during U.S. Geological Survey - Coastal and Marine Geology Program cruise MGL1109 aboard the R/V Marcus G. Langseth. The data have been reduced for position, elevation, orientation, water-column sound-speed, and refraction effects.

This raster dataset represents approximately 69,060 square kilometers of Simrad EM122 multibeam-bathymetry data collected in the Gulf of Alaska during U.S. Geological Survey - Coastal and Marine Geology Program cruise MGL1109 aboard the R/V Marcus G. Langseth. The data have been reduced for position, elevation, orientation, water-column sound-speed, and refraction effects.

This shapefile contains center-beam depths for approximately 5804 trackline kilometers of Simrad EM122 multibeam-bathymetry data collected in the Gulf of Alaska during U.S. Geological Survey (USGS) cruise MGL1109 aboard the R/V Marcus G. Langseth. The depth values were extracted from gridded data which were reduced for position, elevation, orientation, water-column sound-speed, and refraction effects.