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.

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.

Access to the U.S. Geological Survey (USGS) Coastal and Marine Geology Program’s (CMGP) vast collection of unique and valuable seafloor and coastal imagery is made available in the CMGP Video and Photograph Portal. The portal provides a single location for data discovery and viewing. The CMGP and our research partners invest immense resources collecting, processing, and archiving seafloor and oblique coastal video and photographs. Until the publication of the CMGP Video and Photograph Portal in 2015, only a small number of these data sets were available to the public through static web interfaces. Prior to development of the data portal, retrieving this imagery most often required internal USGS access with specific hardware and software. Furthermore, it was difficult to manage and challenging to share such a large amount of information. The Coastal and Marine Geology Program (CMGP) Video and Photograph Portal contains imagery spanning from 2003 to the present. Video and photographs originally collected on analog film media have been digitized and processed along with more recently collected digital video and photographs to meet a common standard for all CMGP video/photo imagery. The Portal is based on an interactive map allowing users to zoom into an area of interest and find available USGS imagery. The co-located video and still photographs are displayed simultaneously, just as they were acquired in the field. In the portal, videos are ultimately stored and streamed as embedded YouTube videos, and photographs are stored in Picasa. Presenting the imagery in this way requires multiple processing steps and tools, including video and photo editing, database management, and computer scripting to automate processing, formatting and quality assurance tasks. A robust set of processing tools have been developed to streamline and automate portions of the workflow based on the wide range of data types processed so far. However, sometimes the data received are uniquely organized and formatted, requiring individualized processing. In that case processing tools are updated to accept a wider range of data formats and organizational structures.

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).

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.

The grain size of sediment on the riverbed was measured during 20 surveys on the Elwha River, Washington, between 2006 and 2017. Most data were collected along the same transects where channel topography was measured (see related child item in this data release: https://www.sciencebase.gov/catalog/item/5a989288e4b06990606de04b). Measurements of sediment ranging from medium sand to boulders were made using the CobbleCam digital photographic technique (Warrick and others, 2009), which uses a calibrated autocorrelation algorithm (Rubin, 2004) to calculate the mean grain size of sediment from pixels in downward-looking digital photographs. This technique yields grain-size values accurate to within 14 percent of those obtained by pebble counting (Wolman, 1954; Warrick and others, 2009). For samples finer than medium sand, we measured grain size using a Coulter laser particle-size analyzer at the USGS laboratory in Santa Cruz, California. Grain size was measured along subaerial portions of the survey transects within the bankfull channel. We also measured grain size of some sediment deposited after dam removal that did not coincide with survey transects (these sample locations are labeled “OffTransect” in the data file). References: Rubin, D.M., 2004, A simple autocorrelation algorithm for determining grain size from digital images of sediment: Journal of Sedimentary Research 74, p. 160–165, https://doi.org/10.1306/052203740160. Warrick, J.A., Rubin, D.M., Ruggiero, P., Harney, J.N., Draut, A.E., and Buscombe, D., 2009, Cobble Cam: grain-size measurements of sand to boulder from digital photographs and autocorrelation analyses: Earth Surface Processes and Landforms 34, p. 1811–1821, https://doi.org/10.1002/esp.1877. Wolman, M.G., 1954, A method of sampling coarse river-bed material: Eos Trans. AGU 35, p. 951–956, https://doi.org/10.1029/TR035i006p00951.

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.

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.

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.

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.

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.

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.

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 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.

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.

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.

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.

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.

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.

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.

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.

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.

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 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.

ArcInfo GRID format data generated from the 2004 multibeam sonar survey of the Northeastern Channel Islands, CA Region. The data include high-resolution, acoustic, corrected backscatter.

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.

1-m resolution bathymetry collected in Coyote Creek and Alviso Slough in April 2015. 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 2015. 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 2015. 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 2016. 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 2016. 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 2016. 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 March 2017. 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 March 2017. 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 March 2017. 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 October 2015. 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 October 2015. 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 October 2015. 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 October 2016. 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 October 2016. 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 October 2016. 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.

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

The U.S. Geological Survey (USGS) conducts baseline and storm-response photography missions to document and understand the changes in the vulnerability of the Nation's coasts to extreme storms. On June 6, 2006, the USGS conducted an oblique aerial photographic survey from Navarre, Florida, to the Chandeleur Islands, Louisiana, and from Grand Point, Alabama, to St. Joseph Point, Mississippi, aboard a U.S. Coast Guard HH60 Helicopter aircraft at an altitude of 500 feet (ft) and approximately 1,200 ft offshore. This mission was conducted to collect data for assessing incremental changes in the beach and nearshore area since the last survey, which was flown in March 2006 (https://cmgds.marine.usgs.gov/fan_info.php?fan=06ACH01) and the data can be used to assess future coastal change. The photographs provided are Joint Photographic Experts Group (JPEG) images. The photograph locations are an estimate of the aircraft's position and do not indicate the location of the features in the images. These photographs document the configuration of the barrier islands and other coastal features at the time of the survey. ExifTool (version 4.0) was used to add the following to the header of each photograph: time of collection, GPS latitude, GPS longitude, keywords, credit, artist (photographer), caption, copyright, and contact information. Photographs can be opened with any JPEG-compatible image viewer. All image times are recorded in Coordinated Universal Time (UTC).

The U.S. Geological Survey (USGS) conducts baseline and storm-response photography missions to document and understand the changes in the vulnerability of the Nation's coasts to extreme storms. On September 26-27, 2006, the USGS conducted an oblique aerial photographic survey from Dauphin Island, Alabama, to Breton Island, Louisiana, aboard a U.S. Coast Guard HH60 Helicopter aircraft at an altitude of 500 feet (ft) and approximately 1,200 ft offshore. This mission was conducted to collect data for assessing incremental changes in the beach and nearshore area since the last survey, which was flown in June 2006 (https://cmgds.marine.usgs.gov/fan_info.php?fan=06ACH02) and the data can be used to assess future coastal change. The photographs provided are Joint Photographic Experts Group (JPEG) images. The photograph locations are an estimate of the aircraft's position and do not indicate the location of the features in the images. These photographs document the configuration of the barrier islands and other coastal features at the time of the survey. ExifTool (version 4.0) was used to add the following to the header of each photograph: time of collection, GPS latitude, GPS longitude, keywords, credit, artist (photographer), caption, copyright, and contact information. Photographs can be opened with any JPEG-compatible image viewer. All image times are recorded in Coordinated Universal Time (UTC).

The U.S. Geological Survey (USGS) conducts baseline and storm-response photography missions to document and understand the changes in the vulnerability of the Nation's coasts to extreme storms. On November 14, 2006, the USGS conducted an oblique aerial photographic survey from the Harney River, Everglades National Park, Florida to Anclote Key, Florida, aboard a U.S. Coast Guard HH60 Helicopter aircraft at an altitude of 500 feet (ft) and approximately 1,200 ft offshore. This mission was conducted to collect data for assessing incremental changes in the beach and nearshore area since the last survey, which was flown in October 2005 (https://cmgds.marine.usgs.gov/fan_info.php?fan=05CCH05), and the data can be used to assess future coastal change. The photographs provided are Joint Photographic Experts Group (JPEG) images. The photograph locations are an estimate of the aircraft's position and do not indicate the location of the features in the images. These photographs document the configuration of the barrier islands and other coastal features at the time of the survey. ExifTool (version 4.0) was used to add the following to the header of each photograph: time of collection, GPS latitude, GPS longitude, keywords, credit, artist (photographer), caption, copyright, and contact information. Photographs can be opened with any JPEG-compatible image viewer. All image times are recorded in Coordinated Universal Time (UTC).

The U.S. Geological Survey (USGS) conducts baseline and storm-response photography missions to document and understand the changes in the vulnerability of the Nation's coasts to extreme storms. On July 26-27, 2007, the USGS conducted an oblique aerial photographic survey from Dauphin Island, Alabama, to Breton Island, Louisiana, aboard a U.S. Coast Guard HH60 Helicopter aircraft at an altitude of 500 feet (ft) and approximately 1,200 ft offshore. This mission was conducted to collect data for assessing incremental changes in the beach and nearshore area and can also be used as a baseline to assess future coastal change. The photographs provided are Joint Photographic Experts Group (JPEG) images. The photograph locations are an estimate of the aircraft's position and do not indicate the location of the features in the images. These photographs document the configuration of the barrier islands and other coastal features at the time of the survey. ExifTool (version 4.0) was used to add the following to the header of each photograph: time of collection, GPS latitude, GPS longitude, keywords, credit, artist (photographer), caption, copyright, and contact information. Photographs can be opened with any JPEG-compatible image viewer. All image times are recorded in Coordinated Universal Time (UTC).

The U.S. Geological Survey (USGS) conducts baseline and storm-response photography missions to document and understand the changes in the vulnerability of the Nation's coasts to extreme storms. On May 6, 2008, the USGS conducted an oblique aerial photographic survey from False Cape State Park, Virginia, to Myrtle Beach, South Carolina, aboard a U.S. Coast Guard HH60 Helicopter aircraft at an altitude of 500 feet (ft) and approximately 1,200 ft offshore. This mission (08CH01) was conducted to collect data for assessing incremental changes in the beach and nearshore area since the last survey, which was flown in September 2003 (https://cmgds.marine.usgs.gov/fan_info.php?fan=03CCH01) (Subino 2013), and the data can be used to assess future coastal change. The photographs provided are Joint Photographic Experts Group (JPEG) images. The photograph locations are an estimate of the aircraft's position and do not indicate the location of the features in the images. These photographs document the configuration of the barrier islands and other coastal features at the time of the survey. ExifTool (version 4.0) was used to add the following to the header of each photograph: time of collection, GPS latitude, GPS longitude, keywords, credit, artist (photographer), caption, copyright, and contact information. Photographs can be opened with any JPEG-compatible image viewer. All image times are recorded in Coordinated Universal Time (UTC).

The U.S. Geological Survey (USGS) conducts baseline and storm-response photography missions to document and understand the changes in the vulnerability of the Nation's coasts to extreme storms. On June 24–25, 2008, the USGS conducted an oblique aerial photographic survey from Dog Island, Florida, to Breton Island, Louisiana, aboard a U.S. Coast Guard HH60 Helicopter at an altitude of 500 feet (ft) and approximately 1,200 ft offshore. This mission was conducted to collect data for assessing incremental changes in the beach and nearshore area since the last survey, which was flown in July 2007 (https://cmgds.marine.usgs.gov/fan_info.php?fan=07CCH01) (Morgan and Thornton, 2018 [https://doi.org/10.5066/F7FJ2G2M]), and the data can be used to assess future coastal change. The photographs provided are Joint Photographic Experts Group (JPEG) images. The photograph locations are an estimate of the aircraft's position and do not indicate the location of the features in the images. These photographs document the configuration of the barrier islands and other coastal features at the time of the survey. ExifTool (version 4.0) was used to add the following to the header of each photograph: time of collection, GPS latitude, GPS longitude, keywords, credit, artist (photographer), caption, copyright, and contact information. Photographs can be opened with any JPEG-compatible image viewer. All image times are recorded in Coordinated Universal Time (UTC).

ASCII XYZ point cloud data were produced from remotely sensed, geographically referenced elevation measurements by the U.S. Geological Survey (USGS). Elevation measurements were collected over the area using the National Aeronautics and Space Administration (NASA) Experimental Advanced Airborne Research Lidar (EAARL), a pulsed laser ranging system mounted onboard an aircraft to measure ground elevation, vegetation canopy, and coastal topography. The system uses high-frequency laser beams directed at the Earth's surface through an opening in the bottom of the aircraft's fuselage. The laser system records the time difference between emission of the laser beam and the reception of the reflected laser signal in the aircraft. The plane travels over the target area at approximately 50 meters per second at an elevation of approximately 300 meters, resulting in a laser swath of approximately 240 meters with an average point spacing of 2-3 meters. The EAARL, developed originally by NASA at Wallops Flight Facility in Virginia, measures ground elevation with a vertical resolution of 3 centimeters. A sampling rate of 3 kilohertz or higher results in an extremely dense spatial elevation dataset. Over 100 kilometers of coastline can be surveyed easily within a 3- to 4-hour mission. When resultant elevation maps for an area are analyzed, they provide a useful tool to make management decisions regarding land development.

The U.S. Geological Survey (USGS) conducts baseline and storm-response photography missions to document and understand the changes in the vulnerability of the Nation's coasts to extreme storms. On September 4, 2008, the USGS conducted an oblique aerial photographic survey from the Chandeleur Islands, Louisiana, to Isles Dernieres Barrier Islands Refuge, Louisiana, aboard a Beechcraft Super King Air 200 aircraft at an altitude of 500 feet (ft) and approximately 1,200 ft offshore. This mission was conducted to collect data for assessing incremental changes in the beach and nearshore area since the last survey, which was flown in September 2005 (https://cmgds.marine.usgs.gov/fan_info.php?fan=05CCH03), and the data can be used to assess future coastal change. The photographs provided are Joint Photographic Experts Group (JPEG) images. The photograph locations are an estimate of the aircraft's position and do not indicate the location of the features in the images. These photographs document the configuration of the barrier islands and other coastal features at the time of the survey. ExifTool (version 4.0) was used to add the following to the header of each photograph: time of collection, GPS latitude, GPS longitude, keywords, credit, artist (photographer), caption, copyright, and contact information. Photographs can be opened with any JPEG-compatible image viewer. All image times are recorded in Coordinated Universal Time (UTC).

Data release doi:10.5066/P9RIB5GC associated with this metadata record serves as an archive of single-beam bathymetric (SBB) data collected in July 2004 (Madison Bay) and August 2008 (Bully Camp, Point au Chien, Caminada, Fourchon, and Leeville) at six study areas in the Mississippi River Delta Plain (MRDP), Louisiana. Data were collected from historically formed open-water bodies as part of the U.S. Geological Survey’s (USGS) Gulf Coast Subsidence project to provide more extensive spatial coverage than water depths collected only along coring transects in 2002, 2003, 2006, and 2007 (USGS Open-File Reports [OFR] 2005-1216 and 2009-1158). The bathymetric data were used to estimate magnitudes of one-dimensional (vertical) and three-dimensional (volume) accommodation that formed as a result of extensive historical wetland loss in Barataria and Terrebonne Basins in the MRDP. All bathymetric data are provided as x,y,z point data in the projected coordinate system North American Datum of 1983 (NAD83), Universal Transverse Mercator (UTM) Zone 15 North (15N) and all elevations are North American Vertical Datum of 1988 (NAVD88) orthometric heights, derived using the GEOID03 geoid model.

The U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center conducted research to quantify the combined effect of all constructive and destructive processes on modern coral reef ecosystems by projecting future regional-scale changes in seafloor elevation along the Florida Reef Tract, Florida (FL). USGS staff used historical bathymetric point data from the 1930's (National Oceanic and Atmospheric Administration (NOAA) Office of Coast Survey, see Yates and others, 2017) and light detection and ranging (lidar)-derived data acquired in 2002 (Brock and others, 2006, 2007) to calculate historical seafloor elevation changes in the Upper Florida Keys (UFK) (Yates and others, 2017). Using those changes in seafloor elevation, annual rates of elevation change were calculated for 13 habitat types found in the UFK reef tract. The annual rate of mean elevation change for each habitat type was applied to a digital elevation model (DEM) extending from Port St. Lucie to Marquesas Key, FL that was modified from the NOAA National Centers for Environmental Information (NCEI) U.S. Coastal Relief Model coastal DEM (NOAA, 2001) to project future seafloor elevation (from 2001) along the Florida Reef Tract. Grid resolution for the DEM is 3-arc seconds (approximately 90 meters).

The U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center conducted research to quantify the combined effect of all constructive and destructive processes on modern coral reef ecosystems by projecting future regional-scale changes in seafloor elevation along the Florida Reef Tract, Florida (FL). USGS staff used historical bathymetric point data from the 1930's (National Oceanic and Atmospheric Administration (NOAA) Office of Coast Survey, see Yates and others, 2017) and light detection and ranging (lidar)-derived data acquired in 2002 (Brock and others, 2006, 2007) to calculate historical seafloor elevation changes in the Upper Florida Keys (UFK) (Yates and others, 2017). Using those changes in seafloor elevation, annual rates of erosion were calculated for 13 habitat types found in the UFK reef tract. The annual rate of mean erosion for each habitat type was applied to a digital elevation model (DEM) extending from Port St. Lucie to Marquesas Key, FL that was modified from the NOAA National Centers for Environmental Information (NCEI) U.S. Coastal Relief Model coastal DEM (NOAA, 2001) to project future seafloor elevation (from 2001) along the Florida Reef Tract. Grid resolution for the DEM is 3-arc seconds (approximately 90 meters(m)).

The U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center conducted research to quantify the combined effect of all constructive and destructive processes on modern coral reef ecosystems by projecting future regional-scale changes in seafloor elevation for several sites along the Florida Reef Tract, Florida (FL) including the shallow seafloor along Key West, FL. USGS staff used historical bathymetric point data from the 1930's (National Oceanic and Atmospheric Administration (NOAA) Office of Coast Survey, see Yates and others, 2017) and light detection and ranging (lidar)-derived data acquired in 2002 (Brock and others, 2006, 2007) to calculate historical seafloor elevation changes in the Upper Florida Keys (UFK) (Yates and others, 2017). Using those changes in seafloor elevation, annual rates of elevation change were calculated for 13 habitat types found in the UFK reef tract. The annual rate of mean elevation change for each habitat type was applied to a digital elevation model (DEM) extending from Big Pine Key to Marquesas Key, FL that was modified from the NOAA National Centers for Environmental Information (NCEI) Key West coastal DEM (NOAA, 2011) to project future seafloor elevation (from 2011) along the Key West section of the Florida Reef Tract. Grid resolution for the DEM is 1/3 arc second (approximately 10 meters).

The U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center conducted research to quantify the combined effect of all constructive and destructive processes on modern coral reef ecosystems by projecting future regional-scale changes in seafloor elevation for several sites along the Florida Reef Tract, Florida (FL) including the shallow seafloor along Key West, FL. USGS staff used historical bathymetric point data from the 1930's (National Oceanic and Atmospheric Administration (NOAA) Office of Coast Survey, see Yates and others, 2017) and light detection and ranging (lidar)-derived data acquired in 2002 (Brock and others, 2006, 2007) to calculate historical seafloor elevation changes in the Upper Florida Keys (UFK) (Yates and others, 2017). Using those changes in seafloor elevation, annual rates of erosion were calculated for 13 habitat types found in the UFK reef tract. The annual rate of mean erosion for each habitat type was applied to a digital elevation model (DEM) extending from Big Pine Key to Marquesas Key, FL that was modified from the NOAA National Centers for Environmental Information (NCEI) Key West coastal DEM (NOAA, 2011) to project future seafloor elevation (from 2011) along the Key West section of the Florida Reef Tract. Grid resolution for the DEM is 1/3 arc-second (approximately 10 meters).

The U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center conducted research to quantify the combined effect of all constructive and destructive processes on modern coral reef ecosystems by projecting future regional-scale changes in seafloor elevation for several sites along the Florida Reef Tract, Florida (FL) including the shallow seafloor along the coast of Miami, FL. USGS staff used historical bathymetric point data from the 1930's (National Oceanic and Atmospheric Administration (NOAA) Office of Coast Survey, see Yates and others, 2017) and light detection and ranging (lidar)-derived data acquired in 2002 (Brock and others, 2006, 2007) to calculate historical seafloor elevation changes in the Upper Florida Keys (UFK) (Yates and others, 2017). Using those changes in seafloor elevation, annual rates of elevation change were calculated for 13 habitat types found in the UFK reef tract. The annual rate of mean elevation change for each habitat type was applied to a digital elevation model (DEM) extending from Deerfield Beach to Homestead, FL that was modified from the NOAA National Centers for Environmental Information (NCEI) Miami coastal DEM (NOAA, 2015) to project future seafloor elevation (from 2014) along the Miami section of the Florida Reef Tract. Grid resolution for the DEM is 1/3 arc second (approximately 10 meters).

The U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center conducted research to quantify the combined effect of all constructive and destructive processes on modern coral reef ecosystems by projecting future regional-scale changes in seafloor elevation for several sites along the Florida Reef Tract, Florida (FL) including the shallow seafloor along the coast of Miami, FL. USGS staff used historical bathymetric point data from the 1930's (National Oceanic and Atmospheric Administration (NOAA) Office of Coast Survey, see Yates and others, 2017) and light detection and ranging (lidar)-derived data acquired in 2002 (Brock and others, 2006, 2007) to calculate historical seafloor elevation changes in the Upper Florida Keys (UFK) (Yates and others, 2017). Using those changes in seafloor elevation, annual rates of erosion were calculated for 13 habitat types found in the UFK reef tract. The annual rate of mean erosion for each habitat type was applied to a digital elevation model (DEM) extending from Deerfield Beach to Homestead, FL that was modified from the NOAA National Centers for Environmental Information (NCEI) Miami coastal DEM (NOAA, 2015) to project future seafloor elevation (from 2014) along the Miami section of the Florida Reef Tract. Grid resolution for the DEM is 1/3 arc second (approximately 10 meters).

The U.S. Geological Survey (USGS) conducts baseline and storm-response photography missions to document and understand the changes in the vulnerability of the Nation's coasts to extreme storms. On July 24, 2010, the USGS conducted an oblique aerial photographic survey at the Chandeleur Islands, Louisiana, and Dauphin Island, Alabama, aboard a Beechcraft BE90 King Air aircraft at an altitude of 500 feet (ft) and approximately 1,200 ft offshore. This mission was conducted to collect data for assessing incremental changes in the beach and nearshore area and can be used to assess future coastal change. The photographs provided are Joint Photographic Experts Group (JPEG) images. The photograph locations are an estimate of the aircraft's position and do not indicate the location of the features in the images. These photographs document the configuration of the barrier islands and other coastal features at the time of the survey. ExifTool (version 4.0) was used to add the following to the header of each photograph: time of collection, GPS latitude, GPS longitude, keywords, credit, artist (photographer), caption, copyright, and contact information. Photographs can be opened with any JPEG-compatible image viewer by clicking on a thumbnail on the contact sheet. All image times are recorded in Coordinated Universal Time (UTC).

The U.S. Geological Survey (USGS) conducts baseline and storm-response photography missions to document and understand the changes in the vulnerability of the Nation's coasts to extreme storms. On September 3, 2010, the USGS conducted an oblique aerial photographic survey from Breton Island to the Chandeleur Islands, Louisiana, aboard a Cessna 210 aircraft at an altitude of 500 feet (ft) and approximately 1,200 ft offshore. This mission was conducted to collect data for assessing incremental changes in the beach and nearshore area and can be used to assess future coastal change. The photographs provided are Joint Photographic Experts Group (JPEG) images. The photograph locations are an estimate of the aircraft's position and do not indicate the location of features in the images. These photographs document the configuration of the barrier islands and other coastal features at the time of the survey. ExifTool (version 4.0) was used to add the following to the header of each photograph: time of collection, GPS latitude, GPS longitude, keywords, credit, artist (photographer), caption, copyright, and contact information. Photographs can be opened with any JPEG-compatible image viewer by clicking on a thumbnail on the contact sheet. All image times are recorded in Coordinated Universal Time (UTC).

In 2010, scientists from the U.S. Geological Survey St. Petersburg Coastal and Marine Science Center collected sediment cores from coastal waters offshore of the Mississippi barrier islands. With funding support from the Northern Gulf of Mexico Ecosystem Change and Hazard Susceptibility project (NGOM), subaqueous sediment cores were collected over an area of 480 km2, the distance from Ship Island to Petit Bois Island Pass, Mississippi, within the boundary of the Gulf Islands National Seashore. This represents only a fraction of the total area encompassed by the NGOM project, which extends from Sabine Lake, Louisiana to Perdido Bay, Alabama. The primary objectives of the NGOM project are to understand the evolution of coastal ecosystems on the northern gulf coast, the impact of human activities on these ecosystems, and the vulnerability of ecosystems and human communities to more frequent and intense hurricanes in the future. Selection of the core sites was based on geophysical surveys conducted around the islands from 2008-2010. The surveys, using acoustic systems to image and interpret the nearsurface stratigraphy, were conducted to investigate the geologic controls on island evolution. This data series serves as an archive of sediment data collected from August to September, 2010 offshore of the Mississippi barrier islands. Data products, including descriptive core logs, core photographs, results of sediment grain-size analyses, sample location maps, and Geographic Information System (GIS) data files with accompanying formal Federal Geographic Data Committee (FDGC) metadata, can be downloaded from the data products and downloads page.

In 2010, scientists from the U.S. Geological Survey St. Petersburg Coastal and Marine Science Center collected sediment cores from coastal waters offshore of the Mississippi barrier islands. With funding support from the Northern Gulf of Mexico Ecosystem Change and Hazard Susceptibility project (NGOM), subaqueous sediment cores were collected over an area of 480 km2 the distance from Ship Island to Petit Bois Island Pass, Mississippi, within the boundary of the Gulf Islands National Seashore. This represents only a fraction of the total area encompassed by the NGOM project, which extends from Sabine Lake, Louisiana to Perdido Bay, Alabama. The primary objectives of the NGOM project are to understand the evolution of coastal ecosystems on the northern gulf coast, the impact of human activities on these ecosystems, and the vulnerability of ecosystems and human communities to more frequent and intense hurricanes in the future. Selection of the core sites was based on geophysical surveys conducted around the islands from 2008-2010. The surveys, using acoustic systems to image and interpret the nearsurface stratigraphy, were conducted to investigate the geologic controls on island evolution. This data series serves as an archive of sediment data collected from August to October, 2010 offshore of the Mississippi barrier islands. Data products, including descriptive core logs, core photographs, results of sediment grain-size analyses, sample location maps, and Geographic Information System (GIS) data files with accompanying formal Federal Geographic Data Committee (FDGC) metadata, can be downloaded from the data products and downloads page.

The U.S. Geological Survey (USGS) conducts baseline and storm-response photography missions to document and understand the changes in the vulnerability of the Nation's coasts to extreme storms. On May 19-20, 2010, the USGS conducted an oblique aerial photographic survey from Horseshoe Beach, Florida, to East Cape, Florida, aboard a Piper Navajo Chieftain aircraft at an altitude of 500 feet (ft) and approximately 1,200 ft offshore. This mission was conducted to collect data for assessing incremental changes in the beach and nearshore area and can be used to assess future coastal change. The photographs provided are Joint Photographic Experts Group (JPEG) images. The photograph locations are an estimate of the aircraft's position and do not indicate the location of the feature in the images. These photographs document the configuration of the barrier islands and other coastal features at the time of the survey. ExifTool (version 4.0) was used to add the following to the header of each photograph: time of collection, GPS latitude, GPS longitude, keywords, credit, artist (photographer), caption, copyright, and contact information. Photographs can be opened with any JPEG-compatible image viewer by clicking on a thumbnail on the contact sheet. All image times are recorded in Coordinated Universal Time (UTC).

The U.S. Geological Survey (USGS) conducts baseline and storm-response photography missions to document and understand the changes in the vulnerability of the Nation's coasts to extreme storms. On June 22–23, 2010, the USGS conducted an oblique aerial photographic survey from Tampa Bay to the Marquesas Keys, Florida, aboard a Piper Navajo Chieftain aircraft at an altitude of 500 feet (ft) and approximately 1,200 ft offshore. This mission was conducted to collect data for assessing incremental changes in the beach and nearshore area since the last survey, which was flown in May 2010 (http://cmgds.marine.usgs.gov/fan_info.php?fan=10LME01) (Morgan and Nelson, 2017, [https://doi.org/10.5066/F71G0JDR]), and the data can be used to assess future coastal change. The photographs provided are Joint Photographic Experts Group (JPEG) images. The photograph locations are an estimate of the aircraft's position and do not indicate the location of the features in the images. These photographs document the configuration of the barrier islands and other coastal features at the time of the survey. ExifTool (version 4.0) was used to add the following to the header of each photograph: time of collection, GPS latitude, GPS longitude, keywords, credit, artist (photographer), caption, copyright, and contact information. Photographs can be opened with any JPEG-compatible image viewer. All image times are recorded in Coordinated Universal Time (UTC).

The U.S. Geological Survey (USGS) conducts baseline and storm-response photography missions to document and understand the changes in the vulnerability of the Nation's coasts to extreme storms. On January 22, 2011, the USGS conducted an oblique aerial photographic survey at Breton Island and the Chandeleur Islands, LA, aboard a Cessna 210 aircraft at an altitude of 500 feet (ft) and approximately 1,200 ft offshore. This mission was conducted to collect data for assessing incremental changes in the beach and nearshore area. since the last survey, which was flown in September 2010 (http://cmgds.marine.usgs.gov/fan_info.php?fan=10CCH02) (unpublished), and the data can be used as a baseline to assess future coastal change. The photographs provided are Joint Photographic Experts Group (JPEG) images. The photograph locations are an estimate of the aircraft's position and do not indicate the location of the feature in the images. These photographs document the configuration of the barrier islands and other coastal features at the time of the survey. ExifTool (version 4.0) was used to add the following to the header of each photograph: time of collection, GPS latitude, GPS longitude, keywords, credit, artist (photographer), caption, copyright, and contact information. Photographs can be opened with any JPEG-compatible image viewer by clicking on a thumbnail on the contact sheet. All image times are recorded in Coordinated Universal Time (UTC).

The U.S. Geological Survey (USGS) conducts baseline and storm-response photography missions to document and understand the changes in the vulnerability of the Nation's coasts to extreme storms. On August 24, 2011, the USGS conducted an oblique aerial photographic survey from Ponte Vedra, Florida, to the South Carolina/North Carolina border, aboard a Piper Navajo Chieftain aircraft at an altitude of 500 feet (ft) and approximately 1,200 ft offshore. This mission was conducted to collect data for assessing incremental changes in the beach and nearshore area. since the last survey, which was flown in September 1996 (https://cmgds.marine.usgs.gov/fan_info.php?fan=96ACH05) (unpublished), and the data can be used to assess future coastal change. The photographs provided are Joint Photographic Experts Group (JPEG) images. The photograph locations are an estimate of the aircraft's position and do not indicate the location of the feature in the images. These photographs document the configuration of the barrier islands and other coastal features at the time of the survey. ExifTool (version 4.0) was used to add the following to the header of each photograph: time of collection, GPS latitude, GPS longitude, keywords, credit, artist (photographer), caption, copyright, and contact information. Photographs can be opened with any JPEG-compatible image viewer by clicking on a thumbnail on the contact sheet. All image times are recorded in Coordinated Universal Time (UTC).

As part of the Barrier Island Evolution Research (BIER) project, scientists from the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center (SPCMSC) collected sediment samples from the northern Chandeleur Islands in March and September 2012. The overall objective of this project, which integrates geophysical (bathymetric, seismic, and topographic) and sedimentologic data, is to better understand the depositional and erosional processes that drive the morphologic evolution of barrier islands over annual to interannual timescales (1 to 5 years). Between June 2010 and April 2011, in response to the Deepwater Horizon oil spill, the State of Louisiana constructed a sand berm extending more than 14 kilometers (km) along the northern Chandeleur Islands platform. The construction of the berm provided a unique opportunity to investigate how this new sediment source will interact with and affect the morphologic evolution of the barrier-island system. Data collected from this study will be used to describe differences in the physical characteristics and spatial distribution of sediments both along the axis of the berm and also along transects across the berm and onto the adjacent barrier island. Comparison of these data with data from subsequent sampling efforts will provide information about sediment interactions and movement between the berm and the natural island platform, improving our understanding of short-term morphologic change and processes in this barrier-island system. This data series serves as an archive of sediment data collected in March and September 2012 from the Chandeleur Islands sand berm and adjacent barrier-island environments. Data products, including descriptive core logs, core photographs and x-radiographs, results of sediment grain-size analyses, sample location maps, and Geographic Information System (GIS) data files with accompanying formal Federal Geographic Data Committee (FDGC) metadata, can be downloaded from http://pubs.usgs.gov/ds/0850/html/ds850_data.html.

As part of the Barrier Island Evolution Research (BIER) project, scientists from the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center (SPCMSC) collected sediment samples from the northern Chandeleur Islands in March and September 2012. The overall objective of this project, which integrates geophysical (bathymetric, seismic, and topographic) and sedimentologic data, is to better understand the depositional and erosional processes that drive the morphologic evolution of barrier islands over annual to interannual timescales (1 to 5 years). Between June 2010 and April 2011, in response to the Deepwater Horizon oil spill, the State of Louisiana constructed a sand berm extending more than 14 kilometers (km) along the northern Chandeleur Islands platform. The construction of the berm provided a unique opportunity to investigate how this new sediment source will interact with and affect the morphologic evolution of the barrier-island system. Data collected from this study will be used to describe differences in the physical characteristics and spatial distribution of sediments both along the axis of the berm and also along transects across the berm and onto the adjacent barrier island. Comparison of these data with data from subsequent sampling efforts will provide information about sediment interactions and movement between the berm and the natural island platform, improving our understanding of short-term morphologic change and processes in this barrier-island system. This data series serves as an archive of sediment data collected in March and September 2012 from the Chandeleur Islands sand berm and adjacent barrier-island environments. Data products, including descriptive core logs, core photographs and x-radiographs, results of sediment grain-size analyses, sample location maps, and Geographic Information System (GIS) data files with accompanying formal Federal Geographic Data Committee (FDGC) metadata, can be downloaded from http://pubs.usgs.gov/ds/850/data.html.

In July of 2012, the U.S. Geological Survey conducted a geophysical survey offshore of the Chandeleur Islands, La. to investigate the geologic controls on barrier island framework. This report serves as an archive of unprocessed digital chirp subbottom data, trackline maps, navigation files, Geographic Information System (GIS) information, and formal Federal Geographic Data Committee (FGDC) metadata. Gained digital images of the seismic profiles are also provided. The archived trace data are in standard Society of Exploration Geophysicists (SEG) SEG Y revision 0 format (Barry and others, 1975) and may be downloaded and processed with commercial or public domain software such as Seismic Unix (SU). Examples of SU processing scripts and USGS software for viewing the SEG Y files (Zihlman, 1992) are also provided. These data are available for viewing using GeoMapApp (<http://www.geomapapp.org/>) and Virtual Ocean (<http://www.virtualocean.org/>) multi-platform open source software. In addition, the SEG Y files can also be downloaded from the USGS Coastal and Marine Geoscience Data System (<http://cmgds.marine.usgs.gov>).

As part of the Barrier Island Evolution Research (BIER) project, scientists from the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center (SPCMSC) collected sediment samples from the northern Chandeleur Islands in March and September 2012. The overall objective of this project, which integrates geophysical (bathymetric, seismic, and topographic) and sedimentologic data, is to better understand the depositional and erosional processes that drive the morphologic evolution of barrier islands over annual to interannual timescales (1 to 5 years). Between June 2010 and April 2011, in response to the Deepwater Horizon oil spill, the State of Louisiana constructed a sand berm extending more than 14 kilometers (km) along the northern Chandeleur Islands platform. The construction of the berm provided a unique opportunity to investigate how this new sediment source will interact with and affect the morphologic evolution of the barrier-island system. Data collected from this study will be used to describe differences in the physical characteristics and spatial distribution of sediments both along the axis of the berm and also along transects across the berm and onto the adjacent barrier island. Comparison of these data with data from subsequent sampling efforts will provide information about sediment interactions and movement between the berm and the natural island platform, improving our understanding of short-term morphologic change and processes in this barrier-island system. This data series serves as an archive of sediment data collected in March and September 2012 from the Chandeleur Islands sand berm and adjacent barrier-island environments. Data products, including descriptive core logs, core photographs and x-radiographs, results of sediment grain-size analyses, sample location maps, and Geographic Information System (GIS) data files with accompanying formal Federal Geographic Data Committee (FDGC) metadata, can be downloaded from http://pubs.usgs.gov/ds/850/data.html.

As part of the Barrier Island Evolution Research (BIER) project, scientists from the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center (SPCMSC) collected sediment samples from the northern Chandeleur Islands in March and September 2012. The overall objective of this project, which integrates geophysical (bathymetric, seismic, and topographic) and sedimentologic data, is to better understand the depositional and erosional processes that drive the morphologic evolution of barrier islands over annual to interannual timescales (1 to 5 years). Between June 2010 and April 2011, in response to the Deepwater Horizon oil spill, the State of Louisiana constructed a sand berm extending more than 14 kilometers (km) along the northern Chandeleur Islands platform. The construction of the berm provided a unique opportunity to investigate how this new sediment source will interact with and affect the morphologic evolution of the barrier-island system. Data collected from this study will be used to describe differences in the physical characteristics and spatial distribution of sediments both along the axis of the berm and also along transects across the berm and onto the adjacent barrier island. Comparison of these data with data from subsequent sampling efforts will provide information about sediment interactions and movement between the berm and the natural island platform, improving our understanding of short-term morphologic change and processes in this barrier-island system. This data series serves as an archive of sediment data collected in March and September 2012 from the Chandeleur Islands sand berm and adjacent barrier-island environments. Data products, including descriptive core logs, core photographs and x-radiographs, results of sediment grain-size analyses, sample location maps, and Geographic Information System (GIS) data files with accompanying formal Federal Geographic Data Committee (FDGC) metadata, can be downloaded from http://pubs.usgs.gov/ds/850/data.html.

From April 13-20, 2013, scientists from the U.S. Geological Survey St. Petersburg Coastal and Marine Science Center (USGS-SPCMSC) conducted geophysical surveys and collected sediment samples from Dauphin Island, Alabama. This dataset, Ground Penetrating Radar (GPR) Trackline Locations Collected from Dauphin Island, Alabama, in April 2013, contains geospatial data and raster images of the GPR data. The GPR trackline locations are presented as Geographic Information System (GIS) files and the subsurface profile data are provided as images in Joint Photographic Experts Group (JPEG) format.

From April 13-20, 2013, scientists from the U.S. Geological Survey St. Petersburg Coastal and Marine Science Center (USGS-SPCMSC) conducted geophysical and sediment sampling surveys on Dauphin Island, Alabama, as part of field activity number 13BIM01. This dataset, Ground Penetrating Radar (GPR) Profile Trace Data Collected from Dauphin Island, Alabama in April 2013, contains the unprocessed, raw profile trace data obtained during this survey.

From April 13-20, 2013, scientists from the U.S. Geological Survey St. Petersburg Coastal and Marine Science Center (USGS-SPCMSC) conducted geophysical and sediment sampling surveys on Dauphin Island, Alabama as part of field activity number 13BIM01. This dataset, Ground Penetrating Radar (GPR) Navigation Data Collected from Dauphin Island, Alabama, in April 2013, contains the processed, differentially corrected position data obtained during this survey.

From April 13 to 20, 2013, scientists from the U.S. Geological Survey St. Petersburg Coastal and Marine Science Center (USGS SPCMSC) collected push cores and vibracores on Dauphin Island, Alabama, along with push and auger cores in salt marshes at several locations in southwestern coastal Alabama. This work, a component of the SPCMSC’s Barrier Island Evolution Research (BIER) project, was conducted as part of USGS field activity number (FAN) 13BIM01. The objectives of the study were to evaluate processes affecting the development and evolution of certain back-barrier environments (marsh, flats, ponds, etc.) and to assist in developing geologic controls on barrier island breaching. In addition to the collection of sediment cores, marsh surface sediments were collected for micropaleontological analysis (included in this report). Ground penetrating radar (GPR) was collected on Dauphin Island and adjacent barrier-island environments. Elevation-corrected subsurface profile images of the processed GPR data, unprocessed digital GPR trace data, post-processed differential Global Positioning System (DGPS) data, and Geographic Information System (GIS) files are reported in Forde and others (2016, https://doi.org/10.3133/ds982). This data report is an archive of field-collected and laboratory analytical data for the sediment cores and surface sediments. Data products include: GPS-derived site locations and elevations; core logs and photographs; lithologic, radiochemical, elemental composition, stable isotopic composition, micropaleontological data; and Federal Geographic Data Committee (FGDC) metadata.

On July 5–19 (13BIM02) and August 22–September 1 (13BIM07) of 2013, the U.S. Geological Survey (USGS) conducted geophysical surveys to investigate the geologic controls on barrier island evolution and medium-term and interannual sediment transport along the oil spill mitigation sand berm constructed at the north end and offshore of the Chandeleur Islands, La. This investigation is part of a broader USGS study, which seeks to understand barrier island evolution better over medium time scales (months to years). This report serves as an archive of unprocessed, digital chirp subbottom data, trackline maps, navigation files, Geographic Information System (GIS) information, and formal Federal Geographic Data Committee (FGDC) metadata. Gained digital images of the seismic profiles are provided. The archived trace data are in standard Society of Exploration Geophysicists (SEG) SEG Y revision 0 format (Barry and others, 1975) and may be downloaded and processed with commercial or public domain software such as Seismic Unix (SU). Examples of SU processing scripts and USGS software for viewing the SEG Y files (Zihlman, 1992) are provided. These data are available for viewing using GeoMapApp (<http://www.geomapapp.org/>) and Virtual Ocean (<http://www.virtualocean.org/>) multi-platform open source software. In addition, the SEG Y files can also be downloaded from the USGS Coastal and Marine Geoscience Data System (<http://cmgds.marine.usgs.gov>).

As part of the Barrier Island Evolution Research (BIER) project, scientists from the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center (SPCMSC) collected sediment samples from the northern Chandeleur Islands in July 2013. The overall objective of this project, which integrates geophysical (bathymetric, seismic, and topographic) and sedimentologic data, is to understand better the depositional and erosional processes that drive the morphologic evolution of barrier islands over annual to interannual timescales (1 to 5 years). Between June 2010 and April 2011, in response to the Deepwater Horizon oil spill, the State of Louisiana constructed a sand berm extending more than 14 kilometers (km) along the northern Chandeleur Islands platform. The construction of the berm provided a unique opportunity for scientists to investigate how this new sediment source interacts with and affects the morphologic evolution of the barrier-island system. Data collected from this study can be used to describe differences in the physical characteristics and spatial distribution of sediments both along the axis of the berm and also along transects across the berm and onto the adjacent barrier island. Comparison of these data with data from prior sampling efforts can provide information about sediment interactions and movement between the berm and the natural island platform, improving insight into short-term morphologic change and processes in this barrier-island system. This data series serves as an archive of sediment data collected in July 2013 from the Chandeleur Islands sand berm and adjacent barrier-island environments. Data products, including descriptive core logs, core photographs and x-radiographs, results of sediment grain-size analyses, sample location maps, and Geographic Information System (GIS) data files with accompanying formal Federal Geographic Data Committee (FDGC) metadata, can be downloaded from https://pubs.usgs.gov/ds/894/ds894_data.html.

As part of the Barrier Island Evolution Research (BIER) project, scientists from the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center (SPCMSC) collected sediment samples from the northern Chandeleur Islands in July 2013. The overall objective of this project, which integrates geophysical (bathymetric, seismic, and topographic) and sedimentologic data, is to understand better the depositional and erosional processes that drive the morphologic evolution of barrier islands over annual to interannual timescales (1 to 5 years). Between June 2010 and April 2011, in response to the Deepwater Horizon oil spill, the State of Louisiana constructed a sand berm extending more than 14 kilometers (km) along the northern Chandeleur Islands platform. The construction of the berm provided a unique opportunity to investigate how this new sediment source interacts with and affects the morphologic evolution of the barrier-island system. Data collected from this study can be used to describe differences in the physical characteristics and spatial distribution of sediments both along the axis of the berm and also along transects across the berm and onto the adjacent barrier island. Comparison of these data with data from prior sampling efforts can provide information about sediment interactions and movement between the berm and the natural island platform, improving our understanding of short-term morphologic change and processes in this barrier-island system. This data series serves as an archive of sediment data collected in July 2013 from the Chandeleur Islands sand berm and adjacent barrier-island environments. Data products, including descriptive core logs, core photographs and x-radiographs, results of sediment grain-size analyses, sample location maps, and Geographic Information System (GIS) data files with accompanying formal Federal Geographic Data Committee (FDGC) metadata, can be downloaded from https://pubs.usgs.gov/ds/894/downloads.html.

As part of the Barrier Island Evolution Research (BIER) project, scientists from the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center (SPCMSC) collected sediment samples from the northern Chandeleur Islands in July 2013. The overall objective of this project, which integrates geophysical (bathymetric, seismic, and topographic) and sedimentologic data, is to better understand the depositional and erosional processes that drive the morphologic evolution of barrier islands over annual to interannual timescales (1 to 5 years). Between June 2010 and April 2011, in response to the Deepwater Horizon oil spill, the State of Louisiana constructed a sand berm extending more than 14 kilometers (km) along the northern Chandeleur Islands platform. The construction of the berm provided a unique opportunity to investigate how this new sediment source will interact with and affect the morphologic evolution of the barrier-island system. Data collected from this study will be used to describe differences in the physical characteristics and spatial distribution of sediments both along the axis of the berm and also along transects across the berm and onto the adjacent barrier island. Comparison of these data with data from prior sampling efforts will provide information about sediment interactions and movement between the berm and the natural island platform, improving our understanding of short-term morphologic change and processes in this barrier-island system. This data series serves as an archive of sediment data collected in July 2013 from the Chandeleur Islands sand berm and adjacent barrier-island environments. Data products, including descriptive core logs, core photographs and x-radiographs, results of sediment grain-size analyses, sample location maps, and Geographic Information System (GIS) data files with accompanying formal Federal Geographic Data Committee (FDGC) metadata, can be downloaded from https://pubs.usgs.gov/ds/894/ds894_data.html.

In August of 2013, the U.S. Geological Survey conducted a geophysical survey offshore of Petit Bois Island, Mississippi to investigate the geologic controls on barrier island framework and long-term sediment transport. This report serves as an archive of unprocessed digital chirp subbottom data, trackline maps, navigation files, GIS information, and formal FGDC metadata. Gained digital images of the seismic profiles are provided. The archived trace data are in standard Society of Exploration Geophysicists (SEG) SEG Y revision 0 format (Barry and others, 1975) and may be downloaded and processed with commercial or public domain software such as Seismic Unix (SU). Example SU processing scripts and USGS software for viewing the SEG Y files (Zihlman, 1992) are also provided. These data are available for viewing using GeoMapApp (<http://www.geomapapp.org/>) and Virtual Ocean (<http://www.virtualocean.org/>) multi-platform open source software. In addition, the SEG Y files can also be downloaded from the USGS Coastal and Marine Geoscience Data System (<http://cmgds.marine.usgs.gov>).

From March 16 - 31, 2013, the U.S. Geological Survey conducted a geophysical survey to investigate sediment deposits and long-term sediment transport within the Snake River from Brownlee Dam to Hells Canyon Reservoir, Idaho; this effort will help the USGS to better understand geologic processes. This report serves as an archive of unprocessed digital chirp subbottom data, trackline maps, navigation files, GIS information, and formal FGDC metadata. Gained digital images of the seismic profiles are also provided. The archived trace data are in standard Society of Exploration Geophysicists (SEG) SEG Y revision 0 format (Barry and others, 1975) and may be downloaded and processed with commercial or public domain software such as Seismic Unix (SU). Example SU processing scripts and USGS software for viewing the SEG Y files (Zihlman, 1992) are also provided. These data are also available for viewing using GeoMapApp (<http://www.geomapapp.org/>) and Virtual Ocean (<http://www.virtualocean.org/>) multi-platform open source software. In addition, the SEG Y files can also be downloaded from the USGS Coastal and Marine Geoscience Data System (<http://cmgds.marine.usgs.gov>)

Breton Island, located at the southern end of the Chandeleur Islands, supports one of Louisiana’s largest historical brown pelican (Pelecanus occidentalis) nesting colonies. Although the brown pelican was delisted as an endangered species in 2009, nesting areas are threatened by continued land loss and are extremely vulnerable to storm impacts. The U.S. Fish and Wildlife Service proposed to restore Breton Island to pre-Hurricane Katrina conditions through rebuilding the shoreface, dune, and back-barrier marsh environments. Prior to restoration, scientists from the U.S. Geological Survey’s (USGS) St. Petersburg Coastal and Marine Science Center Geologic and Morphologic Evolution of Coastal Margins project collected high-resolution geophysical (topography, bathymetry, and sub-bottom profiles) and sedimentologic data from around Breton Island to characterize the geologic framework of the island platform, nearshore, and shelf environments. These data will be used to characterize the geologic framework around Breton Island, identify potential borrow areas for restoration efforts, quantify seafloor change, and provide information for sediment transport and morphologic change models to asses island response to restoration and natural processes. Data release doi:10.5066/F79C6VKF associated with this metadata record serves as an archive of sediment data from vibracores, push cores, and submerged grab samples collected from around Breton and Gosier Islands, Louisiana, during two surveys conducted in July 2014 and January 2015 (USGS Field Activity Numbers 2014–314–FA and 2014–336–FA, respectively). Sedimentologic and stratigraphic metrics (for example, sediment texture or unit thicknesses) derived from these data can be used to ground-truth the geophysical data and characterize potential sand resources or can be incorporated into sediment transport or morphologic change models. Data collection and processing methods are described in Data Series 1037 (https://doi.org/10.3133/ds1037). Data products, including sample location tables, descriptive core logs, core photographs and x-radiographs, results of sediment grain-size analyses, and geographic information system (GIS) data files with accompanying formal Federal Geographic Data Committee (FGDC) metadata can be downloaded from https://doi.org/10.5066/F79C6VKF.

This data release is an archive of sedimentary field and laboratory analytical data collected in Grand Bay, Alabama/Mississippi from 2014-2016 by scientists from the U.S. Geological Survey St. Petersburg Coastal and Marine Science Center (USGS SPCMSC). This work, a component of the SPCMSC’s Sea-level and Storm Impacts on Estuarine Environments and Shorelines (SSIEES) project, provides the necessary data to quantify sedimentation rates and sediment sources for the marsh and estuary. The SSIEES project objective is to evaluate the exchange of sediment material between the marsh and estuary due to extreme storms and sea-level rise. Micropaleontological data from select cores and surface samples are available in Haller and others (2018, https://doi.org/10.5066/F7MC8X5F, https://doi.org/10.5066/F7445KSG). Single-beam bathymetry of Grand Bay proper and multi-beam bathymetry of several marsh-edge eroding shorelines are reported in Dewitt and others (2017, https://doi.org/10.3133/ds1070) and Stalk and others (2018, https://doi.org/10.5066/F7MC8Z9N), respectively. Subbottom and sidescan sonar data for Grand Bay proper are reported in Locker and others (2018, https://doi.org/10.5066/P9374DKQ). This publication includes data for the sediment cores and surface sediments taken in Grand Bay marsh and estuary during five sampling periods of this study, which were designated as USGS Field Activity Numbers (FAN) 2014-323-FA (project ID 14CCT01), 2015-315-FA (project ID 15CCT02), 2016-331-FA (project ID 16CCT03), 2016-348-FA (project ID 16CCT04), and 2016-358-FA (project ID 16CCT07). Data products include: GPS-derived site locations and elevations; core photographs,logs, and x-radiographs; lithologic, radiochemical, elemental composition, stable isotopic composition, and radiocarbon data; and Federal Geographic Data Committee (FGDC) metadata.

Foraminiferal samples were collected from Chincoteague Bay, Newport Bay, and Tom’s Cove as well as the marshes on the back-barrier side of Assateague Island and the Delmarva (Delaware-Maryland-Virginia) mainland by U.S. Geological Survey (USGS) researchers from the St. Petersburg Coastal and Marine Science Center in March, April (14CTB01), and October (14CTB02) 2014. Samples were also collected by the Woods Hole Coastal and Marine Science Center (WHCMSC) in July 2014 and shipped to the St. Petersburg office for processing. The dataset includes raw foraminiferal and normalized counts for the estuarine grab samples (G), terrestrial surface samples (S), and inner shelf grab samples (G). For further information regarding data collection and sample site coordinates, processing methods, or related datasets, please refer to USGS Data Series 1060 (https://doi.org/10.3133/ds1060), USGS Open-File Report 2015–1219 (https://doi.org/10.3133/ofr20151219), and USGS Open-File Report 2015-1169 (https://doi.org/10.3133/ofr20151169). Downloadable data are available as Excel spreadsheets, comma-separated values text files, and formal Federal Geographic Data Committee metadata.

Foraminiferal samples were collected from Chincoteague Bay, Newport Bay, and Tom’s Cove as well as the marshes on the back-barrier side of Assateague Island and the Delmarva (Delaware-Maryland-Virginia) mainland by U.S. Geological Survey (USGS) researchers from the St. Petersburg Coastal and Marine Science Center in March, April (14CTB01), and October (14CTB02) 2014. Samples were also collected by the Woods Hole Coastal and Marine Science Center (WHCMSC) in July 2014 and shipped to the St. Petersburg office for processing. The dataset includes raw foraminiferal and normalized counts for the estuarine grab samples (G), terrestrial surface samples (S), and inner shelf grab samples (G). For further information regarding data collection and sample site coordinates, processing methods, or related datasets, please refer to USGS Data Series 1060 (https://doi.org/10.3133/ds1060), USGS Open-File Report 2015–1219 (https://doi.org/10.3133/ofr20151219), and USGS Open-File Report 2015-1169 (https://doi.org/10.3133/ofr20151169). Downloadable data are available as Excel spreadsheets, comma-separated values text files, and formal Federal Geographic Data Committee metadata.

Foraminiferal samples were collected from Chincoteague Bay, Newport Bay, and Tom’s Cove as well as the marshes on the back-barrier side of Assateague Island and the Delmarva (Delaware-Maryland-Virginia) mainland by U.S. Geological Survey (USGS) researchers from the St. Petersburg Coastal and Marine Science Center in March, April (14CTB01), and October (14CTB02) 2014. Samples were also collected by the Woods Hole Coastal and Marine Science Center (WHCMSC) in July 2014 and shipped to the St. Petersburg office for processing. The dataset includes raw foraminiferal and normalized counts for the estuarine grab samples (G), terrestrial surface samples (S), and inner shelf grab samples (G). For further information regarding data collection and sample site coordinates, processing methods, or related datasets, please refer to USGS Data Series 1060 (https://doi.org/10.3133/ds1060), USGS Open-File Report 2015–1219 (https://doi.org/10.3133/ofr20151219), and USGS Open-File Report 2015-1169 (https://doi.org/10.3133/ofr20151169). Downloadable data are available as Excel spreadsheets, comma-separated values text files, and formal Federal Geographic Data Committee metadata.

Foraminiferal samples were collected from Chincoteague Bay, Newport Bay, and Tom’s Cove as well as the marshes on the back-barrier side of Assateague Island and the Delmarva (Delaware-Maryland-Virginia) mainland by U.S. Geological Survey (USGS) researchers from the St. Petersburg Coastal and Marine Science Center in March, April (14CTB01), and October (14CTB02) 2014. Samples were also collected by the Woods Hole Coastal and Marine Science Center (WHCMSC) in July 2014 and shipped to the St. Petersburg office for processing. The dataset includes raw foraminiferal and normalized counts for the estuarine grab samples (G), terrestrial surface samples (S), and inner shelf grab samples (G). For further information regarding data collection and sample site coordinates, processing methods, or related datasets, please refer to USGS Data Series 1060 (https://doi.org/10.3133/ds1060), USGS Open-File Report 2015–1219 (https://doi.org/10.3133/ofr20151219), and USGS Open-File Report 2015-1169 (https://doi.org/10.3133/ofr20151169). Downloadable data are available as Excel spreadsheets, comma-separated values text files, and formal Federal Geographic Data Committee metadata.

Foraminiferal samples were collected from Chincoteague Bay, Newport Bay, and Tom’s Cove as well as the marshes on the back-barrier side of Assateague Island and the Delmarva (Delaware-Maryland-Virginia) mainland by U.S. Geological Survey (USGS) researchers from the St. Petersburg Coastal and Marine Science Center in March, April (14CTB01), and October (14CTB02) 2014. Samples were also collected by the Woods Hole Coastal and Marine Science Center (WHCMSC) in July 2014 and shipped to the St. Petersburg office for processing. The dataset includes raw foraminiferal and normalized counts for the estuarine grab samples (G), terrestrial surface samples (S), and inner shelf grab samples (G). For further information regarding data collection and sample site coordinates, processing methods, or related datasets, please refer to USGS Data Series 1060 (https://doi.org/10.3133/ds1060), USGS Open-File Report 2015–1219 (https://doi.org/10.3133/ofr20151219), and USGS Open-File Report 2015-1169 (https://doi.org/10.3133/ofr20151169). Downloadable data are available as Excel spreadsheets, comma-separated values text files, and formal Federal Geographic Data Committee metadata.

This project is a collaborative effort between the U.S. Geological Survey (USGS), U.S. Army Corps of Engineers (USACE), and the state of Alabama funded by the National Fish and Wildlife Foundation (NFWF) to investigate viable, sustainable restoration options that protect and restore the natural resources of Dauphin Island, Alabama. Scientists from the USGS, St. Petersburg Coastal and Marine Science Center collected push cores and water quality data from the marshes of Dauphin Island, Little Dauphin Island, and Cedar Key, Alabama in August, 2015 (U.S. Geological Survey Field Activity Number (FAN) 2015-322-FA; referred to as 15BIM09) as well as in April, 2013 (13BIM01). Sample sites varied between high marshes, low salt marshes, and sand flats. This report serves as an archive for the sedimentological and geochemical data derived from the marsh cores and select surface data from the corresponding marsh core sites collected in August, 2015 (15BIM09). Downloadable data are available and include Excel spreadsheets, JPEG files, and formal Federal Geographic Data Committee metadata. For further information regarding data collection and/or processing methods refer to USGS OFR Ellis and others 2017–1165 (https://doi.org/10.3133/OFR20171165), and Data Series Ellis and others 2017–1046 (https://doi.org/10.3133/DS20171046).

This data release is an archive of sedimentary field and laboratory analytical data collected in Grand Bay, Alabama/Mississippi from 2014-2016 by scientists from the U.S. Geological Survey St. Petersburg Coastal and Marine Science Center (USGS SPCMSC). This work, a component of the SPCMSC’s Sea-level and Storm Impacts on Estuarine Environments and Shorelines (SSIEES) project, provides the necessary data to quantify sedimentation rates and sediment sources for the marsh and estuary. The SSIEES project objective is to evaluate the exchange of sediment material between the marsh and estuary due to extreme storms and sea-level rise. Micropaleontological data from select cores and surface samples are available in Haller and others (2018, https://doi.org/10.5066/F7MC8X5F, https://doi.org/10.5066/F7445KSG). Single-beam bathymetry of Grand Bay proper and multi-beam bathymetry of several marsh-edge eroding shorelines are reported in Dewitt and others (2017, https://doi.org/10.3133/ds1070) and Stalk and others (2018, https://doi.org/10.5066/F7MC8Z9N), respectively. Subbottom and sidescan sonar data for Grand Bay proper are reported in Locker and others (2018, https://doi.org/10.5066/P9374DKQ). This publication includes data for the sediment cores and surface sediments taken in Grand Bay marsh and estuary during five sampling periods of this study, which were designated as USGS Field Activity Numbers (FAN) 2014-323-FA (project ID 14CCT01), 2015-315-FA (project ID 15CCT02), 2016-331-FA (project ID 16CCT03), 2016-348-FA (project ID 16CCT04), and 2016-358-FA (project ID 16CCT07). Data products include: GPS-derived site locations and elevations; core photographs,logs, and x-radiographs; lithologic, radiochemical, elemental composition, stable isotopic composition, and radiocarbon data; and Federal Geographic Data Committee (FGDC) metadata.

The toxic dinoflagellate Pyrodinium bahamense (P. bahamense) produces recurring, persistent summer algal blooms in Old Tampa Bay, Florida, which degrade water quality and are potentially harmful to humans if contaminated shellfish is consumed. As part of its life cycle, P. bahamense produces dormant cysts, which settle to the seafloor, forming seed beds that may initiate future blooms if favorable conditions for germination occur. From August 2015 to September 2016, the U. S. Geological Survey (USGS) and Florida Fish and Wildlife Conservation Commission (FWC) collaborated to conduct seasonal sediment sampling at in Old Tampa Bay, Florida. Sediment cores were collected at three sites. The USGS characterized bottom sediment texture and measured profiles of naturally-occurring radionuclides in the uppermost five centimeters of the sediment column. This information will provide an assessment of sediment accumulation, depositional focusing, and resuspension in relation to the potential impact on the seeding potential of P. bahamense cysts. This data will be used in conjunction with FWC research on the vertical distribution of cyst abundance and viability to estimate the seeding potential of future blooms (Lopez and others, 2015). This project was funded by the Tampa Bay Environmental Restoration Fund. This data release is an archive of USGS field data and laboratory analytical results for the five sampling periods in this study, designated as USGS Field Activity Numbers 2015-329-FA (project ID 15FWR02), 2015-341-FA (project ID 15FWR03), 2016-312-FA (project ID 16FWR04), 2016-327-FA (project ID 16FWR05), and 2016-350-FA (project ID 16FWR06).

The toxic dinoflagellate Pyrodinium bahamense (P. bahamense) produces recurring, persistent summer algal blooms in Old Tampa Bay, Florida, which degrade water quality and are potentially harmful to humans if contaminated shellfish is consumed. As part of its life cycle, P. bahamense produces dormant cysts, which settle to the seafloor, forming seed beds that may initiate future blooms if favorable conditions for germination occur. From August 2015 to September 2016, the U. S. Geological Survey (USGS) and Florida Fish and Wildlife Conservation Commission (FWC) collaborated to conduct seasonal sediment sampling at in Old Tampa Bay, Florida. Sediment cores were collected at three sites. The USGS characterized bottom sediment texture and measured profiles of naturally-occurring radionuclides in the uppermost five centimeters of the sediment column. This information will provide an assessment of sediment accumulation, depositional focusing, and resuspension in relation to the potential impact on the seeding potential of P. bahamense cysts. This data will be used in conjunction with FWC research on the vertical distribution of cyst abundance and viability to estimate the seeding potential of future blooms (Lopez and others, 2015). This project was funded by the Tampa Bay Environmental Restoration Fund. This data release is an archive of USGS field data and laboratory analytical results for the five sampling periods in this study, designated as USGS Field Activity Numbers 2015-329-FA (project ID 15FWR02), 2015-341-FA (project ID 15FWR03), 2016-312-FA (project ID 16FWR04), 2016-327-FA (project ID 16FWR05), and 2016-350-FA (project ID 16FWR06).

Microfossil (benthic foraminifera) samples were obtained from surficial grab (denoted with “G”) and push core (denoted with “M”) sediments collected in Grand Bay estuary, Mississippi and Alabama, to aid in the paleoenvironmental understanding of Grand Bay estuary. The data presented here were collected as part of the U.S. Geological Survey’s Sea-level and Storm Impacts on Estuarine Environments and Shorelines (SSIEES) project, and Barrier Island Evolution Research (BIER) project. Sampling was conducted in May 2016 [field activity number (FAN) 2016-331-FA, alternate FAN 16CCT03]. In the field, 15 cores were collected in tidal creek mouths, proximal to tidal creek mouths, in protected coves, and in the open Grand Bay estuary. Surface samples were collected at each core site location. At the St. Petersburg Coastal and Marine Science Center (SPCMSC), 13 of the 15 cores were selectively subsampled for foraminifera, resulting in a total of 64 push core subsamples. Estuarine surface grab samples and push core subsamples were processed in the laboratory to three size fractions (63–125 micrometers (μm), 125–850 μm, and >850 μm), of which the 125–850 μm fraction was picked. The raw foraminiferal count data from the picked subsamples are provided below. For further information regarding foraminiferal collection and/or processing methods, refer to Ellis and others (2017a, https://doi.org/10.3133/ds1060). For information regarding 16CCT03 site locations, water quality parameters and sediment properties, refer to Marot and others (2019, https://doi.org/10.5066/P9FO8R3Y). For related datasets from the Mississippi Sound area, please refer to Haller and others (2018a, https://doi.org/10.5066/F7MC8X5F; and 2018b, https://doi.org/10.5066/F7445KSG), Ellis and others (2018, https://doi.org/10.3133/ofr20171165), Ellis and others (2017b, https://doi.org/10.3133/ds1046), and DeWitt and others (2017, https://doi.org/10.3133/ds1070). Downloadable data are available as Excel spreadsheets, comma-separated values text files, and formal Federal Geographic Data Committee metadata.

This data release is an archive of sedimentary field and laboratory analytical data collected in Grand Bay, Alabama/Mississippi from 2014-2016 by scientists from the U.S. Geological Survey St. Petersburg Coastal and Marine Science Center (USGS SPCMSC). This work, a component of the SPCMSC’s Sea-level and Storm Impacts on Estuarine Environments and Shorelines (SSIEES) project, provides the necessary data to quantify sedimentation rates and sediment sources for the marsh and estuary. The SSIEES project objective is to evaluate the exchange of sediment material between the marsh and estuary due to extreme storms and sea-level rise. Micropaleontological data from select cores and surface samples are available in Haller and others (2018, https://doi.org/10.5066/F7MC8X5F, https://doi.org/10.5066/F7445KSG). Single-beam bathymetry of Grand Bay proper and multi-beam bathymetry of several marsh-edge eroding shorelines are reported in Dewitt and others (2017, https://doi.org/10.3133/ds1070) and Stalk and others (2018, https://doi.org/10.5066/F7MC8Z9N), respectively. Subbottom and sidescan sonar data for Grand Bay proper are reported in Locker and others (2018, https://doi.org/10.5066/P9374DKQ). This publication includes data for the sediment cores and surface sediments taken in Grand Bay marsh and estuary during five sampling periods of this study, which were designated as USGS Field Activity Numbers (FAN) 2014-323-FA (project ID 14CCT01), 2015-315-FA (project ID 15CCT02), 2016-331-FA (project ID 16CCT03), 2016-348-FA (project ID 16CCT04), and 2016-358-FA (project ID 16CCT07). Data products include: GPS-derived site locations and elevations; core photographs,logs, and x-radiographs; lithologic, radiochemical, elemental composition, stable isotopic composition, and radiocarbon data; and Federal Geographic Data Committee (FGDC) metadata.

This data release is an archive of sedimentary field and laboratory analytical data collected in Grand Bay, Alabama/Mississippi from 2014-2016 by scientists from the U.S. Geological Survey St. Petersburg Coastal and Marine Science Center (USGS SPCMSC). This work, a component of the SPCMSC’s Sea-level and Storm Impacts on Estuarine Environments and Shorelines (SSIEES) project, provides the necessary data to quantify sedimentation rates and sediment sources for the marsh and estuary. The SSIEES project objective is to evaluate the exchange of sediment material between the marsh and estuary due to extreme storms and sea-level rise. Micropaleontological data from select cores and surface samples are available in Haller and others (2018, https://doi.org/10.5066/F7MC8X5F, https://doi.org/10.5066/F7445KSG). Single-beam bathymetry of Grand Bay proper and multi-beam bathymetry of several marsh-edge eroding shorelines are reported in Dewitt and others (2017, https://doi.org/10.3133/ds1070) and Stalk and others (2018, https://doi.org/10.5066/F7MC8Z9N), respectively. Subbottom and sidescan sonar data for Grand Bay proper are reported in Locker and others (2018, https://doi.org/10.5066/P9374DKQ). This publication includes data for the sediment cores and surface sediments taken in Grand Bay marsh and estuary during five sampling periods of this study, which were designated as USGS Field Activity Numbers (FAN) 2014-323-FA (project ID 14CCT01), 2015-315-FA (project ID 15CCT02), 2016-331-FA (project ID 16CCT03), 2016-348-FA (project ID 16CCT04), and 2016-358-FA (project ID 16CCT07). Data products include: GPS-derived site locations and elevations; core photographs,logs, and x-radiographs; lithologic, radiochemical, elemental composition, stable isotopic composition, and radiocarbon data; and Federal Geographic Data Committee (FGDC) metadata.

This data release is an archive of sedimentary field and laboratory analytical data collected in Grand Bay, Alabama/Mississippi from 2014-2016 by scientists from the U.S. Geological Survey St. Petersburg Coastal and Marine Science Center (USGS SPCMSC). This work, a component of the SPCMSC’s Sea-level and Storm Impacts on Estuarine Environments and Shorelines (SSIEES) project, provides the necessary data to quantify sedimentation rates and sediment sources for the marsh and estuary. The SSIEES project objective is to evaluate the exchange of sediment material between the marsh and estuary due to extreme storms and sea-level rise. Micropaleontological data from select cores and surface samples are available in Haller and others (2018, https://doi.org/10.5066/F7MC8X5F, https://doi.org/10.5066/F7445KSG). Single-beam bathymetry of Grand Bay proper and multi-beam bathymetry of several marsh-edge eroding shorelines are reported in Dewitt and others (2017, https://doi.org/10.3133/ds1070) and Stalk and others (2018, https://doi.org/10.5066/F7MC8Z9N), respectively. Subbottom and sidescan sonar data for Grand Bay proper are reported in Locker and others (2018, https://doi.org/10.5066/P9374DKQ). This publication includes data for the sediment cores and surface sediments taken in Grand Bay marsh and estuary during five sampling periods of this study, which were designated as USGS Field Activity Numbers (FAN) 2014-323-FA (project ID 14CCT01), 2015-315-FA (project ID 15CCT02), 2016-331-FA (project ID 16CCT03), 2016-348-FA (project ID 16CCT04), and 2016-358-FA (project ID 16CCT07). Data products include: GPS-derived site locations and elevations; core photographs, logs, and x-radiographs; lithologic, radiochemical, elemental composition, stable isotopic composition, and radiocarbon data; and Federal Geographic Data Committee (FGDC) metadata.

The toxic dinoflagellate Pyrodinium bahamense (P. bahamense) produces recurring, persistent summer algal blooms in Old Tampa Bay, Florida, which degrade water quality and are potentially harmful to humans if contaminated shellfish is consumed. As part of its life cycle, P. bahamense produces dormant cysts, which settle to the seafloor, forming seed beds that may initiate future blooms if favorable conditions for germination occur. From August 2015 to September 2016, the U. S. Geological Survey (USGS) and Florida Fish and Wildlife Conservation Commission (FWC) collaborated to conduct seasonal sediment sampling at in Old Tampa Bay, Florida. Sediment cores were collected at three sites. The USGS characterize bottom sediment texture and measured profiles of naturally-occurring radionuclides in the uppermost five centimeters of the sediment column. This information will provide an assessment of sediment accumulation, depositional focusing, and resuspension in relation to the potential impact on the seeding potential of P. bahamense cysts. This data will be used in conjunction with FWC research on the vertical distribution of cyst abundance and viability to estimate the seeding potential of future blooms (Lopez and others, 2015). This project was funded by the Tampa Bay Environmental Restoration Fund. This data release is an archive of USGS field data and laboratory analytical results for the five sampling periods in this study, designated as USGS Field Activity Numbers 2015-329-FA (project ID 15FWR02), 2015-341-FA (project ID 15FWR03), 2016-312-FA (project ID 16FWR04), 2016-327-FA (project ID 16FWR05), and 2016-350-FA (project ID 16FWR06).

The toxic dinoflagellate Pyrodinium bahamense (P. bahamense) produces recurring, persistent summer algal blooms in Old Tampa Bay, Florida, which degrade water quality and are potentially harmful to humans if contaminated shellfish is consumed. As part of its life cycle, P. bahamense produces dormant cysts, which settle to the seafloor, forming seed beds that may initiate future blooms if favorable conditions for germination occur. From August 2015 to September 2016, the U. S. Geological Survey (USGS) and Florida Fish and Wildlife Conservation Commission (FWC) collaborated to conduct seasonal sediment sampling at in Old Tampa Bay, Florida. Sediment cores were collected at three sites. The USGS characterized bottom sediment texture and measured profiles of naturally-occurring radionuclides in the uppermost five centimeters of the sediment column. This information will provide an assessment of sediment accumulation, depositional focusing, and resuspension in relation to the potential impact on the seeding potential of P. bahamense cysts. This data will be used in conjunction with FWC research on the vertical distribution of cyst abundance and viability to estimate the seeding potential of future blooms (Lopez and others, 2015). This project was funded by the Tampa Bay Environmental Restoration Fund. This data release is an archive of USGS field data and laboratory analytical results for the five sampling periods in this study, designated as USGS Field Activity Numbers 2015-329-FA (project ID 15FWR02), 2015-341-FA (project ID 15FWR03), 2016-312-FA (project ID 16FWR04), 2016-327-FA (project ID 16FWR05), and 2016-350-FA (project ID 16FWR06).

The toxic dinoflagellate Pyrodinium bahamense (P. bahamense) produces recurring, persistent summer algal blooms in Old Tampa Bay, Florida, which degrade water quality and are potentially harmful to humans if contaminated shellfish is consumed. As part of its life cycle, P. bahamense produces dormant cysts, which settle to the seafloor, forming seed beds that may initiate future blooms if favorable conditions for germination occur. From August 2015 to September 2016, the U. S. Geological Survey (USGS) and Florida Fish and Wildlife Conservation Commission (FWC) collaborated to conduct seasonal sediment sampling at in Old Tampa Bay, Florida. Sediment cores were collected at three sites. The USGS characterize bottom sediment texture and measured profiles of naturally-occurring radionuclides in the uppermost five centimeters of the sediment column. This information will provide an assessment of sediment accumulation, depositional focusing, and resuspension in relation to the potential impact on the seeding potential of P. bahamense cysts. This data will be used in conjunction with FWC research on the vertical distribution of cyst abundance and viability to estimate the seeding potential of future blooms (Lopez and others, 2015). This project was funded by the Tampa Bay Environmental Restoration Fund. This data release is an archive of USGS field data and laboratory analytical results for the five sampling periods in this study, designated as USGS Field Activity Numbers 2015-329-FA (project ID 15FWR02), 2015-341-FA (project ID 15FWR03), 2016-312-FA (project ID 16FWR04), 2016-327-FA (project ID 16FWR05), and 2016-350-FA (project ID 16FWR06).

Breton Island, Louisiana Transects with Shoreline Change Rates (1869 - 2014) (Geographic, NAD83) consists of vector transect data that were derived from the Digital Shoreline Analysis System (DSAS) version 4.0. Rates from the DSAS statistical output table were joined to the transects to provide a visual representation of the shoreline change rates on a transect-by-transect basis.

In order to characterize coastal change, historical maps and complementary records were compiled including: topographic sheets (T-sheets), hydrographic sheets (H-sheets, smooth sheets), shorelines, and bathymetric soundings surrounding the Mississippi and Alabama (MSAL) barrier islands. One goal of this work was to create a time-series of bathymetric change maps around the islands between 1916 and 2016.

To characterize coastal change, historical maps and complementary records were compiled including: topographic sheets (T-sheets), hydrographic sheets (H-sheets, smooth sheets), shorelines, and bathymetric soundings surrounding the Mississippi (MS) barrier islands over several time periods (1916-1920, 2008-2009 and 2016). One goal of this work was to create a time-series of bathymetric change maps around the islands. Datasets include 1916 through 1920 soundings collected by the United States Coast and Geodetic Survey as downloaded H-sheets and some digitized soundings from the National Oceanic and Atmospheric Administration (NOAA), 2008 to 2009 soundings collected by the United States Geological Survey St. Petersburg and Woods Hole Coastal and Marine Science Center (USGS SPCMSC and WHCMSC, respectively), and 2016 soundings collected by the USGS SPCMSC in the nearshore environment of Ship, Horn, and Petit Bois Islands. This USGS data release includes three digital elevation models (DEMs) for 1916 to 1920, 2008 to 2009, and 2016; however, this metadata file pertains only to the 1916 to 1920 DEM (1916_1920_MS_NAD83NAVD88g12B_50m.tif). This work was completed in cooperation with the United States Army Corps of Engineers (Mobile, Alabama) and the National Park Service as part of the Mississippi Coastal Improvements Program (MsCIP).

The U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center (SPCMSC) conducted research to identify areas of seafloor elevation stability and instability based on elevation changes between the 1930’s and 2002 in the Upper Florida Keys (UFK) from Triumph Reef to Pickles Reef within a 234.2 square-kilometer area. USGS SPCMSC staff used seafloor elevation-change data from Yates and others (2017a) derived from an elevation-change analysis between two elevation datasets acquired in the 1930’s and 2001/2002 using the methods of Yates and others (2017b). Most of the elevation data from the 2001/2002 time period were collected during 2002, so as an abbreviated naming convention, we refer to this time period as 2002. A seafloor stability threshold was determined for the 1930’s-2002 UFK elevation-change dataset based on the vertical uncertainty of the 1930’s historical hydrographic surveys and 2002 digital elevation models (DEMs). Five stability categories (which include, Stable: 0.0 meters (m) to ±0.24 m or 0.0 m to ±0.49 m; Moderately stable: ±0.25 m to ±0.49 m; Moderately unstable: ±0.50 m to ±0.74 m; Mostly unstable: ±0.75 m to ±0.99 m; and Unstable: ±1.00 m to Max/Min elevation change) were created and used to define levels of stability and instability for each elevation-change value (25,982 data points) based on the amount of erosion and accretion during the 1930’s to 2002 time period. Seafloor-stability point and triangulated irregular network (TIN) surface models were created at five different elevation-change data resolutions (1st order through 5th order) with each resolution becoming increasingly more detailed. The stability models were used to determine the level of seafloor stability at potential areas of interest for coral restoration and 13 habitat types found in the UFK. Stability surface (TIN) models were used for areas defined by specific XY geographic points, while stability point models were used for areas defined by bounding box coordinate locations. This data release includes ArcGIS map packages containing the binned and color-coded stability point and surface (TIN) models, potential coral restoration locations, and habitat files; maps of each stability model; and data tables containing stability and elevation-change data for the potential coral restoration locations and habitat types. Data were collected under Florida Keys National Marine Sanctuary permit FKNMS-2016-068.

The U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center (SPCMSC) conducted research to identify areas of seafloor elevation stability and instability based on elevation changes between the 1930’s and 2016 along the Florida Reef Tract (FRT) from Miami to Key West within a 982.4 square-kilometer area. USGS SPCMSC staff used seafloor elevation-change data from Yates and others (2021) derived from an elevation-change analysis between two elevation datasets acquired in the 1930’s and 2016/2017 using the methods of Yates and others (2017). Most of the elevation data from the 2016/2017 time period were collected during 2016, so as an abbreviated naming convention, we refer to this time period as 2016. A seafloor stability threshold was determined for the 1930’s-2016 FRT elevation-change dataset based on the vertical uncertainty of the 1930’s historical hydrographic surveys and 2016 digital elevation models (DEMs). Five stability categories (which include, Stable: 0.0 meters (m) to ±0.24 m or 0.0 m to ±0.49 m; Moderately stable: ±0.25 m to ±0.49 m; Moderately unstable: ±0.50 m to ±0.74 m; Mostly unstable: ±0.75 m to ±0.99 m; and Unstable: ±1.00 m to Max/Min elevation change) were created and used to define levels of stability and instability for each elevation-change value (85,253 data points) based on the amount of erosion and accretion during the 1930’s to 2016 time period. Seafloor-stability point and triangulated irregular network (TIN) surface models were created at five different elevation-change data resolutions (1st order through 5th order) with each resolution becoming increasingly more detailed. In order to view the stability models at a larger extent, the stability point and surface (TIN) models were divided into four sub-regions: Biscayne Bay, Upper Key, Middle Keys, and Lower Keys. The stability models were used to determine the level of seafloor stability at potential areas of interest for coral restoration and 14 habitat types found along the FRT. Stability surface (TIN) models were used for areas defined by specific XY geographic points, while stability point models were used for areas defined by bounding box coordinate locations. This data release includes ArcGIS map packages containing the binned and color-coded stability point and surface (TIN) models, potential coral restoration locations, habitat files, and sub-region boundaries; maps of each stability model at full extent and for each sub-region; and data tables containing stability and elevation-change data for the potential coral restoration locations and habitat types. Data were collected under Florida Keys National Marine Sanctuary permit FKNMS-2016-068. Coral restoration locations were provided by Mote Marine Laboratory under Special Activity License SAL-18-1724-SCRP.

The U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center conducted research to quantify bathymetric changes along the Florida Reef Tract (FRT) from Miami to Key West within a 982.4 square-kilometer area. USGS staff calculated changes in seafloor elevation from the 1930’s to 2016 using digitized historical hydrographic surveys (H-sheets) acquired by the U.S. Coast and Geodetic Survey (USC&GS) in the 1930’s and light detection and ranging (lidar)-derived digital elevation models (DEMs) acquired by the National Oceanic and Atmospheric Administration (NOAA) in 2016 and 2017. Most of the elevation data from the 2016/2017 time period were collected during 2016, so as an abbreviated naming convention, we refer to this time frame as 2016. An elevation change analysis between the 1930’s and 2016 data was performed to quantify and map impacts to seafloor elevation and to determine elevation and volume change statistics for 14 habitat types found within the study area along the FRT. Data were collected under Florida Keys National Marine Sanctuary permit FKNMS-2016-068.

The U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center (SPCMSC) conducted research to identify areas of seafloor elevation stability and instability based on elevation changes between the years of 1938 and 2004 at Looe Key coral reef near Big Pine Key, Florida (FL), within a 19.06 square-kilometer area. USGS SPCMSC staff used seafloor elevation-change data from Yates and others (2017a) derived from an elevation-change analysis between two elevation datasets acquired in 1938 and 2004 using the methods of Yates and others (2017b). A seafloor stability threshold was determined for the 1938-2004 Looe Key elevation-change dataset based on the vertical uncertainty of the 1938 historical hydrographic survey and 2004 digital elevation model (DEM). Five stability categories (which include, Stable: 0.0 meters (m) to ±0.24 m or 0.0 m to ±0.49 m; Moderately stable: ±0.25 m to ±0.49 m; Moderately unstable: ±0.50 m to ±0.74 m; Mostly unstable: ±0.75 m to ±0.99 m; and Unstable: ±1.00 m to Max/Min elevation change) were created and used to define levels of stability and instability for each elevation-change value (1,687 data points) based on the amount of erosion and accretion during the 1938 to 2004 time period. Seafloor-stability point and triangulated irregular network (TIN) surface models were created at five different elevation-change data resolutions (1st order through 5th order) with each resolution becoming increasingly more detailed. The stability models were used to determine the level of seafloor stability at potential areas of interest for coral restoration and ten habitat types found at Looe Key. Stability surface (TIN) models were used for areas defined by specific XY geographic points, while stability point models were used for areas defined by bounding box coordinate locations. This data release includes ArcGIS map packages containing the binned and color-coded stability point and surface (TIN) models, potential coral restoration locations, and habitat files; maps of each stability model; and data tables containing stability and elevation-change data for the potential coral restoration locations and habitat types. Data were collected under Florida Keys National Marine Sanctuary permit FKNMS-2016-068. Coral restoration locations were provided by Mote Marine Laboratory under Special Activity License SAL-18-1724-SCRP.

The U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center conducted research to quantify bathymetric changes in the Upper Florida Keys (UFK) from Triumph Reef to Pickles Reef within a 242.4 square-kilometer area. USGS staff calculated changes in seafloor elevation from 2002 to 2016 using light detection and ranging (lidar)-derived data acquired by the USGS in 2001 and 2002 and lidar-derived data acquired by the National Oceanic and Atmospheric Administration (NOAA) in 2016 and 2017. Most of the elevation data from these two time periods was collected during 2002 and 2016. As an abbreviated naming convention, we refer to this study time period and dataset as 2002-2016. An elevation change analysis between the 2002 and 2016 lidar data was performed to quantify and map impacts to seafloor elevation and to determine elevation and volume change statistics for 13 habitat types found in the UFK. This elevation change study was conducted under Florida Keys National Marine Sanctuary permit FKNMS-2016-068.

The U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center (SPCMSC) conducted research to identify areas of seafloor elevation stability and instability based on elevation changes between the years of 2002 and 2016 in the Upper Florida Keys (UFK) from Triumph Reef to Pickles Reef within a 242.4 square-kilometer area. USGS SPCMSC staff used seafloor elevation-change data from Murphy and others (2021) derived from an elevation-change analysis between two elevation datasets acquired in 2001/2002 and 2016/2017 using the methods of Yates and others (2017). Most of the elevation data from these two time periods were collected during 2002 and 2016, so as an abbreviated naming convention, we refer to this study time period as 2002-2016. A seafloor stability threshold was determined for the 2002-2016 UFK elevation-change dataset based on the vertical uncertainty of the 2002 and 2016 digital elevation models (DEMs). Five stability categories (which include, Stable: 0.0 meters (m) to ±0.24 m or 0.0 m to ±0.49 m; Moderately stable: ±0.25 m to ±0.49 m; Moderately unstable: ±0.50 m to ±0.74 m; Mostly unstable: ±0.75 m to ±0.99 m; and Unstable: ±1.00 m to Max/Min elevation change) were created and used to define levels of stability and instability for each elevation-change value (60,585,610 data points at 2-m horizontal resolution) based on the amount of erosion and accretion during the 2002 to 2016 time period. Seafloor-stability point and triangulated irregular network (TIN) surface models were created at five different elevation-change data resolutions (1st order through 5th order) with each resolution becoming increasingly more detailed. The stability models were used to determine the level of seafloor stability at potential areas of interest for coral restoration and 13 habitat types found in the UFK. Stability surface (TIN) models were used for areas defined by specific XY geographic points, while stability point models were used for areas defined by bounding box coordinate locations. This data release includes ArcGIS Pro map packages containing the binned and color-coded stability point and surface (TIN) models, potential coral restoration locations, and habitat files; maps of each stability model; and data tables containing stability and elevation-change data for the potential coral restoration locations and habitat types. Data were collected under Florida Keys National Marine Sanctuary permit FKNMS-2016-068.

Data release doi:10.5066/P9RIB5GC associated with this metadata record serves as an archive of single-beam bathymetric (SBB) data collected in July 2004 (Madison Bay) and August 2008 (Bully Camp, Point au Chien, Caminada, Fourchon, and Leeville) at six study areas in the Mississippi River Delta Plain (MRDP), Louisiana. Data were collected from historically formed open-water bodies as part of the U.S. Geological Survey’s (USGS) Gulf Coast Subsidence project to provide more extensive spatial coverage than water depths collected only along coring transects in 2002, 2003, 2006, and 2007 (USGS Open-File Reports [OFR] 2005-1216 and 2009-1158). The bathymetric data were used to estimate magnitudes of one-dimensional (vertical) and three-dimensional (volume) accommodation that formed as a result of extensive historical wetland loss in Barataria and Terrebonne Basins in the MRDP. All bathymetric data are provided as x,y,z point data in the projected coordinate system North American Datum of 1983 (NAD83), Universal Transverse Mercator (UTM) Zone 15 North (15N) and all elevations are North American Vertical Datum of 1988 (NAVD88) orthometric heights, derived using the GEOID03 geoid model.

The U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center (SPCMSC) conducted research to identify areas of seafloor elevation stability and instability based on elevation changes between the years of 2004 and 2016 at Looe Key coral reef near Big Pine Key, Florida (FL), within a 16.37 square-kilometer area. USGS SPCMSC staff used seafloor elevation-change data from Yates and others (2019) derived from an elevation-change analysis between two elevation datasets acquired in 2004 and 2016 using the methods of Yates and others (2017). A seafloor stability threshold was determined for the 2004-2016 Looe Key elevation-change dataset based on the vertical uncertainty of the 2004 and 2016 digital elevation models (DEMs). Five stability categories (which include, Stable: 0.0 meters (m) to ±0.24 m or 0.0 m to ±0.49 m; Moderately stable: ±0.25 m to ±0.49 m; Moderately unstable: ±0.50 m to ±0.74 m; Mostly unstable: ±0.75 m to ±0.99 m; and Unstable: ±1.00 m to Max/Min elevation change) were created and used to define levels of stability and instability for each elevation-change value (4,086,712 data points at 2-m horizontal resolution) based on the amount of erosion and accretion during the 2004 to 2016 time period. Seafloor-stability point and triangulated irregular network (TIN) surface models were created at five different elevation-change data resolutions (1st order through 5th order) with each resolution becoming increasingly more detailed. The stability models were used to determine the level of seafloor stability at potential areas of interest for coral restoration and ten habitat types found at Looe Key. Stability surface (TIN) models were used for areas defined by specific XY geographic points, while stability point models were used for areas defined by bounding box coordinate locations. This data release includes ArcGIS map packages containing the binned and color-coded stability point and surface (TIN) models, potential coral restoration locations, and habitat files; maps of each stability model; and data tables containing stability and elevation-change data for the potential coral restoration locations and habitat types. Data were collected under Florida Keys National Marine Sanctuary permit FKNMS-2016-068. Coral restoration locations were provided by Mote Marine Laboratory under Special Activity License SAL-18-1724-SCRP.

To characterize coastal change, historical maps and complementary records were compiled including: topographic sheets (T-sheets), hydrographic sheets (H-sheets, smooth sheets), shorelines, and bathymetric soundings surrounding the Mississippi (MS) barrier islands over several time periods (1916-1920, 2008-2009 and 2016). One goal of this work was to create a time-series of bathymetric change maps around the islands. Datasets include 1916 through 1920 soundings collected by the United States Coast and Geodetic Survey as downloaded H-sheets and some digitized soundings from the National Oceanic and Atmospheric Administration (NOAA), 2008 to 2009 soundings collected by the United States Geological Survey St. Petersburg and Woods Hole Coastal and Marine Science Center (USGS SPCMSC and WHCMSC, respectively), and 2016 soundings collected by the USGS SPCMSC in the nearshore environment of Ship, Horn, and Petit Bois Islands. This USGS data release includes three digital elevation models (DEMs) for 1916 to 1920, 2008 to 2009, and 2016; however, this metadata file pertains only to the 2008 to 2009 DEM (2008_2009_MS_NAD83NAVD88g12B_50m.tif). This work was completed in cooperation with the United States Army Corps of Engineers (Mobile, Alabama) and the National Park Service as part of the Mississippi Coastal Improvements Program (MsCIP).

During the summers of 2008 and 2009 the United States Geological Survey (USGS) conducted bathymetric surveys from West Ship Island, Mississippi, to Dauphin Island, Alabama, as part of the Northern Gulf of Mexico (NGOM) Ecosystem Change and Hazard Susceptibility project. The survey area extended from the shoreline out to approximately two kilometers and included the adjacent passes. These findings were originally published in Dewitt and others (2012). This USGS data release includes updated elevation point data (xyz) in which NOAA's Vdatum version 3.6 was used convert the 2008-2009 XYZ soundings to Universal Transverse Mercator (UTM) North American Datum of 1983 (NAD83) horizontal datum and North American Vertical Datum of 1988 (NAVD88) GEOID12B vertical datum. One goal of this work was to create time-series of bathymetric change maps around the islands using data collected between 1916 to 1920, 2008 to 2009, and in 2016.

To characterize coastal change, historical maps and complementary records were compiled including: topographic sheets (T-sheets), hydrographic sheets (H-sheets, smooth sheets), shorelines, and bathymetric soundings surrounding the Mississippi (MS) barrier islands over several time periods (1916-1920, 2008-2009 and 2016). One goal of this work was to create a time-series of bathymetric change maps around the islands. Data sets include 1916 through 1920 soundings collected by the United States Coast and Geodetic Survey as downloaded H-sheets and some digitized soundings from the National Oceanic and Atmospheric Administration (NOAA), 2008 to 2009 soundings collected by the United States Geological Survey St. Petersburg and Woods Hole Coastal and Marine Science Centers (USGS SPCMSC and WHCMSC, respectively), and 2016 soundings collected by the USGS SPCMSC in the nearshore environment of Ship, Horn, and Petit Bois Islands. This USGS data release includes three interpolated bathymetric change maps created by comparing the 1916 to 1920, 2008 to 2009, and 2016 bathymetry data; however, this metadata file pertains to the bathymetric change digital elevation model (DEM) observed between the 1916-1920 and 2008-2009 bathymetry (2008to2009minus1916to1920_MS_NAD83NAVD88g12B_50m.tif). This work was completed in cooperation with the United States Army Corps of Engineers (Mobile, Alabama) and the National Park Service as part of the Mississippi Coastal Improvements Program (MsCIP).

The United States Geological Survey (USGS) is conducting a study on the effects of climate change on ocean acidification within the Gulf of Mexico; dealing specifically with the effect of ocean acidification on marine organisms and habitats. To investigate this, the USGS participated in cruises in the West Florida Shelf and northern Gulf of Mexico regions aboard the R/V Weatherbird II, a ship of opportunity lead by Dr. Kendra Daly, of the University of South Florida (USF). This cruise occurred February 17-23, 2011, leaving from and returned to Saint Petersburg, Florida. The USGS collected data pertaining to pH, dissolved inorganic carbon (DIC), and total alkalinity in discrete samples. Seventy-one underway discrete samples were collected approximately hourly over a span of 1628 kilometer (km) track line, additionally 34 samples were taken at 10 stations. Flow-through conductivity-temperature-depth (CTD) data were collected, which includes temperature, salinity, and pH. Corroborating the USGS data are the vertical CTD profiles collected by USF, using the following sensors: CTD, oxygen, chlorophyll fluorescence, optical backscatter, and transmissometer. Additionally, discrete depth samples for nutrients, chlorophyll, and particulate organic carbon/nitrogen were collected.

The United States Geological Survey (USGS) is conducting a study on the effects of climate change on ocean acidification within the Gulf of Mexico; dealing specifically with the effect of ocean acidification on marine organisms and habitats. To investigate this, the USGS participated in cruises in the West Florida Shelf and northern Gulf of Mexico regions aboard the R/V Weatherbird II, a ship of opportunity lead by Dr. Kendra Daly, of the University of South Florida (USF). This cruise occurred February 17-23, 2011, leaving from and returned to Saint Petersburg, Florida. The USGS collected data pertaining to pH, dissolved inorganic carbon (DIC), and total alkalinity in discrete samples. Seventy-one underway discrete samples were collected approximately hourly over a span of 1628 kilometer (km) track line, additionally 34 samples were taken at 10 stations. Flow-through conductivity-temperature-depth (CTD) data were collected, which includes temperature, salinity, and pH. Corroborating the USGS data are the vertical CTD profiles collected by USF, using the following sensors: CTD, oxygen, chlorophyll fluorescence, optical backscatter, and transmissometer. Additionally, discrete depth samples for nutrients, chlorophyll, and particulate organic carbon/nitrogen were collected.

The United States Geological Survey (USGS) is conducting a study on the effects of climate change on ocean acidification within the Gulf of Mexico; dealing specifically with the effect of ocean acidification on marine organisms and habitats. To investigate this, the USGS participated in cruises in the West Florida Shelf and northern Gulf of Mexico regions aboard the R/V Weatherbird II, a ship of opportunity lead by Dr. Kendra Daly, of the University of South Florida (USF). This cruise occurred February 17-23, 2011, leaving from and returned to Saint Petersburg, Florida. The USGS collected data pertaining to pH, dissolved inorganic carbon (DIC), and total alkalinity in discrete samples. Seventy-one underway discrete samples were collected approximately hourly over a span of 1628 kilometer (km) track line, additionally 34 samples were taken at 10 stations. Flow-through conductivity-temperature-depth (CTD) data were collected, which includes temperature, salinity, and pH. Corroborating the USGS data are the vertical CTD profiles collected by USF, using the following sensors: CTD, oxygen, chlorophyll fluorescence, optical backscatter, and transmissometer. Additionally, discrete depth samples for nutrients, chlorophyll, and particulate organic carbon/nitrogen were collected.

The United States Geological Survey (USGS) is conducting a study on the effects of climate change on ocean acidification within the Gulf of Mexico; dealing specifically with the effect of ocean acidification on marine organisms and habitats. To investigate this, the USGS participated in cruises in the West Florida Shelf and northern Gulf of Mexico regions aboard the R/V Weatherbird II, a ship of opportunity lead by Dr. Kendra Daly, of the University of South Florida (USF). This cruise occurred January 3-7, 2011, leaving from and returned to Saint Petersburg, Florida. The USGS collected data pertaining to pH, dissolved inorganic carbon (DIC), and total alkalinity in discrete samples. Thirty-six underway discrete samples in January were collected approximately hourly over a span of 745.3 km Flow-through conductivity-temperature-depth (CTD) data were collected, which includes temperature, salinity, and pH. Corroborating the USGS data are the vertical CTD profiles collected by USF, using the following sensors: CTD, oxygen, chlorophyll fluorescence, optical backscatter, and transmissometer. Additionally, discrete depth samples for nutrients, chlorophyll, and particulate organic carbon/nitrogen were collected.

The United States Geological Survey (USGS) is conducting a study on the effects of climate change on ocean acidification within the Gulf of Mexico; dealing specifically with the effect of ocean acidification on marine organisms and habitats. To investigate this, the USGS participated in cruises in the West Florida Shelf and northern Gulf of Mexico regions aboard the R/V Weatherbird II, a ship of opportunity lead by Dr. Kendra Daly, of the University of South Florida (USF). This cruise occurred January 3-7, 2011, leaving from and returned to Saint Petersburg, Florida. The USGS collected data pertaining to pH, dissolved inorganic carbon (DIC), and total alkalinity in discrete samples. Thirty-six underway discrete samples in January were collected approximately hourly over a span of 745.3 km Flow-through conductivity-temperature-depth (CTD) data were collected, which includes temperature, salinity, and pH. Corroborating the USGS data are the vertical CTD profiles collected by USF, using the following sensors: CTD, oxygen, chlorophyll fluorescence, optical backscatter, and transmissometer. Additionally, discrete depth samples for nutrients, chlorophyll, and particulate organic carbon/nitrogen were collected.

The United States Geological Survey (USGS) is conducting a study on the effects of climate change on ocean acidification within the Gulf of Mexico; dealing specifically with the effect of ocean acidification on marine organisms and habitats. To investigate this, the USGS participated in cruises in the West Florida Shelf and northern Gulf of Mexico regions aboard the R/V Weatherbird II, a ship of opportunity lead by Dr. Kendra Daly, of the University of South Florida (USF). This cruise occurred January 3-7, 2011, leaving from and returned to Saint Petersburg, Florida. The USGS collected data pertaining to pH, dissolved inorganic carbon (DIC), and total alkalinity in discrete samples. Thirty-six underway discrete samples in January were collected approximately hourly over a span of 745.3 km Flow-through conductivity-temperature-depth (CTD) data were collected, which includes temperature, salinity, and pH. Corroborating the USGS data are the vertical CTD profiles collected by USF, using the following sensors: CTD, oxygen, chlorophyll fluorescence, optical backscatter, and transmissometer. Additionally, discrete depth samples for nutrients, chlorophyll, and particulate organic carbon/nitrogen were collected.

The United States Geological Survey (USGS) is conducting a study on the effects of climate change on ocean acidification within the Gulf of Mexico; dealing specifically with the effect of ocean acidification on marine organisms and habitats. To investigate this, the USGS participated in cruises in the West Florida Shelf and northern Gulf of Mexico regions aboard the R/V Weatherbird II, a ship of opportunity lead by Dr. Kendra Daly, of the University of South Florida (USF). This cruise occurred June 25-30, 2011, leaving from and returned to Saint Petersburg, Florida. The USGS collected data pertaining to pH, dissolved inorganic carbon (DIC), and total alkalinity in discrete samples. Thirty-six discrete samples were collected at ten stations. Flow-through conductivity-temperature-depth (CTD) data were collected, which includes temperature, salinity, and pH. Corroborating the USGS data are the vertical CTD profiles collected by USF, using the following sensors: CTD, oxygen, chlorophyll fluorescence, optical backscatter, and transmissometer. Additionally, discrete depth samples for nutrients, chlorophyll, and particulate organic carbon/nitrogen were collected.

The United States Geological Survey (USGS) is conducting a study on the effects of climate change on ocean acidification within the Gulf of Mexico; dealing specifically with the effect of ocean acidification on marine organisms and habitats. To investigate this, the USGS participated in cruises in the West Florida Shelf and northern Gulf of Mexico regions aboard the R/V Weatherbird II, a ship of opportunity lead by Dr. Kendra Daly, of the University of South Florida (USF). This cruise occurred June 25-30, 2011, leaving from and returned to Saint Petersburg, Florida. The USGS collected data pertaining to pH, dissolved inorganic carbon (DIC), and total alkalinity in discrete samples. Forty-eight underway discrete samples were collected approximately hourly over a span of 1130 kilometer (km) track line. Flow-through conductivity-temperature-depth (CTD) data were collected, which includes temperature, salinity, and pH. Corroborating the USGS data are the vertical CTD profiles collected by USF, using the following sensors: CTD, oxygen, chlorophyll fluorescence, optical backscatter, and transmissometer. Additionally, discrete depth samples for nutrients, chlorophyll, and particulate organic carbon/nitrogen were collected.

The United States Geological Survey (USGS) is conducting a study on the effects of climate change on ocean acidification within the Gulf of Mexico; dealing specifically with the effect of ocean acidification on marine organisms and habitats. To investigate this, the USGS participated in cruises in the West Florida Shelf and northern Gulf of Mexico regions aboard the R/V Weatherbird II, a ship of opportunity lead by Dr. Kendra Daly, of the University of South Florida (USF). This cruise occurred May 03 - 09, 2011, leaving from and returned to Saint Petersburg, Florida. The USGS collected data pertaining to pH, dissolved inorganic carbon (DIC), and total alkalinity in discrete samples. Thirty-four underway discrete samples were collected approximately hourly over a span of 1632 kilometer (km) track line, additionally 44 discrete samples were taken at four stations, these were taken at various depths. Flow-through conductivity-temperature-depth (CTD) data were collected, which includes temperature, salinity, and pH. Corroborating the USGS data are the vertical CTD profiles collected by USF, using the following sensors: CTD, oxygen, chlorophyll fluorescence, optical backscatter, and transmissometer. Additionally, discrete depth samples for nutrients, chlorophyll, and particulate organic carbon/nitrogen were collected.

The United States Geological Survey (USGS) is conducting a study on the effects of climate change on ocean acidification within the Gulf of Mexico; dealing specifically with the effect of ocean acidification on marine organisms and habitats. To investigate this, the USGS participated in cruises in the West Florida Shelf and northern Gulf of Mexico regions aboard the R/V Weatherbird II, a ship of opportunity lead by Dr. Kendra Daly, of the University of South Florida (USF). This cruise occurred May 03 - 09, 2011, leaving from and returned to Saint Petersburg, Florida. The USGS collected data pertaining to pH, dissolved inorganic carbon (DIC), and total alkalinity in discrete samples. Thirty-four underway discrete samples were collected approximately hourly over a span of 1632 kilometer (km) track line, additionally 44 discrete samples were taken at four stations, these were taken at various depths. Flow-through conductivity-temperature-depth (CTD) data were collected, which includes temperature, salinity, and pH. Corroborating the USGS data are the vertical CTD profiles collected by USF, using the following sensors: CTD, oxygen, chlorophyll fluorescence, optical backscatter, and transmissometer. Additionally, discrete depth samples for nutrients, chlorophyll, and particulate organic carbon/nitrogen were collected.

The United States Geological Survey (USGS) is conducting a study on the effects of climate change on ocean acidification within the Gulf of Mexico; dealing specifically with the effect of ocean acidification on marine organisms and habitats. To investigate this, the USGS participated in two cruises in the West Florida Shelf and northern Gulf of Mexico regions aboard the R/V Weatherbird II, a ship of opportunity lead by Dr. Kendra Daly, of the University of South Florida (USF). The cruises occurred September 20 - 28 and November 2 - 4, 2011. Both left from and returned to Saint Petersburg, Florida, but followed different routes (see Trackline). On both cruises the USGS collected data pertaining to pH, dissolved inorganic carbon (DIC), and total alkalinity in discrete samples. Discrete surface samples were taken during transit approximatly hourly on both cruises, 95 in September were collected over a span of 2127 km, and 7 over a trackline of 732 km line on the November cruise. Along with the surface samples, another set of samples were taken at various depths at stations; 27 in September at four stations and 15 in November at five stations. In addition to the discrete samples flow-through data was also collected on both cruises in a variety of forms. Surface CTD data was collected every five minutes which includes temperature, salinity, and pH. In addition, two more flow-through instruments were setup on both cruises that recorded pH and CO2 every 15 minutes. Corroborating the USGS data is the vertical CTD profiles collected by USF, using the following sensors: CTD, oxygen, chlorophyll fluorescence, optical backscatter, and transmissometer. Additionally, discrete depth samples for nutrients, chlorophyll, and particulate organic carbon/nitrogen were collected.

The United States Geological Survey (USGS) is conducting a study on the effects of climate change on ocean acidification within the Gulf of Mexico; dealing specifically with the effect of ocean acidification on marine organisms and habitats. To investigate this, the USGS participated in two cruises in the West Florida Shelf and northern Gulf of Mexico regions aboard the R/V Weatherbird II, a ship of opportunity lead by Dr. Kendra Daly, of the University of South Florida (USF). The cruises occurred September 20 - 28 and November 2 - 4, 2011. Both left from and returned to Saint Petersburg, Florida, but followed different routes (see Trackline). On both cruises the USGS collected data pertaining to pH, dissolved inorganic carbon (DIC), and total alkalinity in discrete samples. Discrete surface samples were taken during transit approximatly hourly on both cruises, 95 in September were collected over a span of 2127 km, and 7 over a trackline of 732 km line on the November cruise. Along with the surface samples, another set of samples were taken at various depths at stations; 27 in September at four stations and 15 in November at five stations. In addition to the discrete samples flow-through data was also collected on both cruises in a variety of forms. Surface CTD data was collected every five minutes which includes temperature, salinity, and pH. In addition, two more flow-through instruments were setup on both cruises that recorded pH and CO2 every 15 minutes. Corroborating the USGS data is the vertical CTD profiles collected by USF, using the following sensors: CTD, oxygen, chlorophyll fluorescence, optical backscatter, and transmissometer. Additionally, discrete depth samples for nutrients, chlorophyll, and particulate organic carbon/nitrogen were collected.

The United States Geological Survey (USGS) is conducting a study on the effects of climate change on ocean acidification within the Gulf of Mexico; dealing specifically with the effect of ocean acidification on marine organisms and habitats. To investigate this, the USGS participated in two cruises in the West Florida Shelf and northern Gulf of Mexico regions aboard the R/V Weatherbird II, a ship of opportunity lead by Dr. Kendra Daly, of the University of South Florida (USF). The cruises occurred September 20 - 28 and November 2 - 4, 2011. Both left from and returned to Saint Petersburg, Florida, but followed different routes (see Trackline). On both cruises the USGS collected data pertaining to pH, dissolved inorganic carbon (DIC), and total alkalinity in discrete samples. Discrete surface samples were taken during transit approximatly hourly on both cruises, 95 in September were collected over a span of 2127 km, and 7 over a trackline of 732 km line on the November cruise. Along with the surface samples, another set of samples were taken at various depths at stations; 27 in September at four stations and 15 in November at five stations. In addition to the discrete samples flow-through data was also collected on both cruises in a variety of forms. Surface CTD data was collected every five minutes which includes temperature, salinity, and pH. In addition, two more flow-through instruments were setup on both cruises that recorded pH and CO2 every 15 minutes. Corroborating the USGS data is the vertical CTD profiles collected by USF, using the following sensors: CTD, oxygen, chlorophyll fluorescence, optical backscatter, and transmissometer. Additionally, discrete depth samples for nutrients, chlorophyll, and particulate organic carbon/nitrogen were collected.

The United States Geological Survey (USGS) is conducting a study on the effects of climate change on ocean acidification within the Gulf of Mexico; dealing specifically with the effect of ocean acidification on marine organisms and habitats. To investigate this, the USGS participated in two cruises in the West Florida Shelf and northern Gulf of Mexico regions aboard the R/V Weatherbird II, a ship of opportunity lead by Dr. Kendra Daly, of the University of South Florida (USF). The cruises occurred September 20 - 28 and November 2 - 4, 2011. Both left from and returned to Saint Petersburg, Florida, but followed different routes (see Trackline). On both cruises the USGS collected data pertaining to pH, dissolved inorganic carbon (DIC), and total alkalinity in discrete samples. Discrete surface samples were taken during transit approximatly hourly on both cruises, 95 in September were collected over a span of 2127 km, and 7 over a trackline of 732 km line on the November cruise. Along with the surface samples, another set of samples were taken at various depths at stations; 27 in September at four stations and 15 in November at five stations. In addition to the discrete samples flow-through data was also collected on both cruises in a variety of forms. Surface CTD data was collected every five minutes which includes temperature, salinity, and pH. In addition, two more flow-through instruments were setup on both cruises that recorded pH and CO2 every 15 minutes. Corroborating the USGS data is the vertical CTD profiles collected by USF, using the following sensors: CTD, oxygen, chlorophyll fluorescence, optical backscatter, and transmissometer. Additionally, discrete depth samples for nutrients, chlorophyll, and particulate organic carbon/nitrogen were collected.

The United States Geological Survey (USGS) is conducting a study on the effects of climate change on ocean acidification within the Gulf of Mexico; dealing specifically with the effect of ocean acidification on marine organisms and habitats. To investigate this, the USGS participated in two cruises in the West Florida Shelf and northern Gulf of Mexico regions aboard the R/V Weatherbird II, a ship of opportunity lead by Dr. Kendra Daly, of the University of South Florida (USF). The cruises occurred September 20 - 28 and November 2 - 4, 2011. Both left from and returned to Saint Petersburg, Florida, but followed different routes (see Trackline). On both cruises the USGS collected data pertaining to pH, dissolved inorganic carbon (DIC), and total alkalinity in discrete samples. Discrete surface samples were taken during transit approximatly hourly on both cruises, 95 in September were collected over a span of 2127 km, and 7 over a trackline of 732 km line on the November cruise. Along with the surface samples, another set of samples were taken at various depths at stations; 27 in September at four stations and 15 in November at five stations. In addition to the discrete samples flow-through data was also collected on both cruises in a variety of forms. Surface CTD data was collected every five minutes which includes temperature, salinity, and pH. In addition, two more flow-through instruments were setup on both cruises that recorded pH and CO2 every 15 minutes. Corroborating the USGS data is the vertical CTD profiles collected by USF, using the following sensors: CTD, oxygen, chlorophyll fluorescence, optical backscatter, and transmissometer. Additionally, discrete depth samples for nutrients, chlorophyll, and particulate organic carbon/nitrogen were collected.

The United States Geological Survey (USGS) is conducting a study on the effects of climate change on ocean acidification within the Gulf of Mexico; dealing specifically with the effect of ocean acidification on marine organisms and habitats. To investigate this, the USGS participated in two cruises in the West Florida Shelf and northern Gulf of Mexico regions aboard the R/V Weatherbird II, a ship of opportunity lead by Dr. Kendra Daly, of the University of South Florida (USF). The cruises occurred September 20 - 28 and November 2 - 4, 2011. Both left from and returned to Saint Petersburg, Florida, but followed different routes (see Trackline). On both cruises the USGS collected data pertaining to pH, dissolved inorganic carbon (DIC), and total alkalinity in discrete samples. Discrete surface samples were taken during transit approximatly hourly on both cruises, 95 in September were collected over a span of 2127 km, and 7 over a trackline of 732 km line on the November cruise. Along with the surface samples, another set of samples were taken at various depths at stations; 27 in September at four stations and 15 in November at five stations. In addition to the discrete samples flow-through data was also collected on both cruises in a variety of forms. Surface CTD data was collected every five minutes which includes temperature, salinity, and pH. In addition, two more flow-through instruments were setup on both cruises that recorded pH and CO2 every 15 minutes. Corroborating the USGS data is the vertical CTD profiles collected by USF, using the following sensors: CTD, oxygen, chlorophyll fluorescence, optical backscatter, and transmissometer. Additionally, discrete depth samples for nutrients, chlorophyll, and particulate organic carbon/nitrogen were collected.

The United States Geological Survey (USGS) is conducting a study on the effects of climate change on ocean acidification within the Gulf of Mexico; dealing specifically with the effect of ocean acidification on marine organisms and habitats. To investigate this, the USGS participated in two cruises in the West Florida Shelf and northern Gulf of Mexico regions aboard the R/V Weatherbird II, a ship of opportunity lead by Dr. Kendra Daly, of the University of South Florida (USF). The cruises occurred September 20 - 28 and November 2 - 4, 2011. Both left from and returned to Saint Petersburg, Florida, but followed different routes (see Trackline). On both cruises the USGS collected data pertaining to pH, dissolved inorganic carbon (DIC), and total alkalinity in discrete samples. Discrete surface samples were taken during transit approximatly hourly on both cruises, 95 in September were collected over a span of 2127 km, and 7 over a trackline of 732 km line on the November cruise. Along with the surface samples, another set of samples were taken at various depths at stations; 27 in September at four stations and 15 in November at five stations. In addition to the discrete samples flow-through data was also collected on both cruises in a variety of forms. Surface CTD data was collected every five minutes which includes temperature, salinity, and pH. In addition, two more flow-through instruments were setup on both cruises that recorded pH and CO2 every 15 minutes. Corroborating the USGS data is the vertical CTD profiles collected by USF, using the following sensors: CTD, oxygen, chlorophyll fluorescence, optical backscatter, and transmissometer. Additionally, discrete depth samples for nutrients, chlorophyll, and particulate organic carbon/nitrogen were collected.

The United States Geological Survey (USGS) is conducting a study on the effects of climate change on ocean acidification within the Gulf of Mexico; dealing specifically with the effect of ocean acidification on marine organisms and habitats. To investigate this, the USGS participated in two cruises in the West Florida Shelf and northern Gulf of Mexico regions aboard the R/V Weatherbird II, a ship of opportunity lead by Dr. Kendra Daly, of the University of South Florida (USF). The cruises occurred September 20 - 28 and November 2 - 4, 2011. Both left from and returned to Saint Petersburg, Florida, but followed different routes (see Trackline). On both cruises the USGS collected data pertaining to pH, dissolved inorganic carbon (DIC), and total alkalinity in discrete samples. Discrete surface samples were taken during transit approximatly hourly on both cruises, 95 in September were collected over a span of 2127 km, and 7 over a trackline of 732 km line on the November cruise. Along with the surface samples, another set of samples were taken at various depths at stations; 27 in September at four stations and 15 in November at five stations. In addition to the discrete samples flow-through data was also collected on both cruises in a variety of forms. Surface CTD data was collected every five minutes which includes temperature, salinity, and pH. In addition, two more flow-through instruments were setup on both cruises that recorded pH and CO2 every 15 minutes. Corroborating the USGS data is the vertical CTD profiles collected by USF, using the following sensors: CTD, oxygen, chlorophyll fluorescence, optical backscatter, and transmissometer. Additionally, discrete depth samples for nutrients, chlorophyll, and particulate organic carbon/nitrogen were collected.

Breton Island, located at the southern end of the Chandeleur Islands, supports one of Louisiana’s largest historical brown pelican (Pelecanus occidentalis) nesting colonies. Although the brown pelican was delisted as an endangered species in 2009, nesting areas are threatened by continued land loss and are extremely vulnerable to storm impacts. The U.S. Fish and Wildlife Service proposed to restore Breton Island to pre-Hurricane Katrina conditions through rebuilding the shoreface, dune, and back-barrier marsh environments. Prior to restoration, scientists from the U.S. Geological Survey’s (USGS) St. Petersburg Coastal and Marine Science Center Geologic and Morphologic Evolution of Coastal Margins project collected high-resolution geophysical (topography, bathymetry, and sub-bottom profiles) and sedimentologic data from around Breton Island to characterize the geologic framework of the island platform, nearshore, and shelf environments. These data will be used to characterize the geologic framework around Breton Island, identify potential borrow areas for restoration efforts, quantify seafloor change, and provide information for sediment transport and morphologic change models to asses island response to restoration and natural processes. Data release doi:10.5066/F79C6VKF associated with this metadata record serves as an archive of sediment data from vibracores, push cores, and submerged grab samples collected from around Breton and Gosier Islands, Louisiana, during two surveys conducted in July 2014 and January 2015 (USGS Field Activity Numbers 2014–314–FA and 2014–336–FA, respectively). Sedimentologic and stratigraphic metrics (for example, sediment texture or unit thicknesses) derived from these data can be used to ground-truth the geophysical data and characterize potential sand resources or can be incorporated into sediment transport or morphologic change models. Data collection and processing methods are described in Data Series 1037 (https://doi.org/10.3133/ds1037). Data products, including sample location tables, descriptive core logs, core photographs and x-radiographs, results of sediment grain-size analyses, and geographic information system (GIS) data files with accompanying formal Federal Geographic Data Committee (FGDC) metadata can be downloaded from https://doi.org/10.5066/F79C6VKF.

Scientists from the United States Geological Survey, St. Petersburg Coastal and Marine Science Center (USGS-SPCMS) acquired sediment cores, sediment surface grab samples, Ground Penetrating Radar (GPR) and Differential Global Positioning System (DGPS) data from Assateague Island, Maryland, in October (FAN 2014-322-FA) 2014. The objectives were to identify washover deposits in the stratigraphic record to aid in understanding barrier island evolution. The report associated with this metadata record serves as an archive of GPR and DGPS data collected from Assateague Island in October 2014. Data products and accompanying Federal Geographic Data Committee (FGDC) metadata can be downloaded from the Data Downloads page located at, http://pubs.usgs.gov/publication/ds989/ds_data_downloads.html.

Data release doi:10.5066/F71G0KKD associated with this metadata record serves as an archive of sediment data for samples collected in 2015, 2016, and 2017 from coastal Louisiana. In 2015 and 2016, sediment grab samples (N=874) were collected coast-wide along shore-perpendicular transects that included back-barrier, emergent (beach and barrier island), shoreface, and nearshore environments. Sample locations were selected to re-occupy locations previously sampled in 2008 (U.S. Geological Survey [USGS] Open-File Report 2013-1083). The 2008 and 2015-2016 datasets were collected under the Barrier Island Coastal Monitoring (BICM) program (CPRA project LA-0226; https://cims.coastal.louisiana.gov/outreach/ProjectView.aspx?projID=LA-0226), an ongoing collaboration between the State of Louisiana Coastal Protection and Restoration Authority (CPRA), the University of New Orleans (UNO) Pontchartrain Institute for Environmental Sciences (PIES), and the USGS. In 2017, additional grab samples (N=77) were collected along shore-perpendicular transects from West Belle Pass to Caminada Pass as part of the CPRA (CPRA project BA-0045; https://cims.coastal.louisiana.gov/outreach/ProjectView.aspx?projID=BA-0045). The 2015, 2016, and 2017 sediment samples were collected by personnel from UNO–PIES and provided to the USGS St. Petersburg and Coastal Marine Science Center (SPCMSC) sediment laboratory. Textural characteristics were analyzed using a Coulter LS 200 particle-size analyzer. Following SPCMSC data management and archiving protocols, the samples were assigned a USGS field activity number (FAN) (https://cmgds.marine.usgs.gov/fan_info.php?fan=2015-301-CNT). Data products, including sample location tables, results of sediment grain-size analyses, and geographic information system (GIS) data files with accompanying formal Federal Geographic Data Committee (FGDC) metadata can be downloaded from https://doi.org/10.5066/F71G0KKD.

As part of the Sea level and Storm Impacts on Estuarine Environments and Shorelines project (SSIEES), scientists from the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center conducted a single-beam bathymetry survey within the estuarine, open bay and tidal creek environments of Grand Bay Alabama/Mississippi, from May to June 2015. The goal of the SSIEES project is to assess the physical controls of sediment and material exchange between wetlands and estuarine environments along the northern Gulf of Mexico, specifically Grand Bay AL/MS and Vermilion Bay, Louisiana, as well as along the east coast in Chincoteague Bay Virginia/Maryland. The data included in this data release provide baseline bathymetric information for future research investigating wetland/marsh evolution, sediment transport, recent and long term geomorphic change, and can also support modeling of future changes in response to restoration and storm impacts. The survey area encompasses more than 40 square kilometers (km2) of Grand Bay’s incorporated waters. This data release serves as an archive of processed single-beam bathymetry data, collected from May 28 to June 3, 2015 (USGS Field Activity Number [FAN] 2015-315-FA). Geographic information system (GIS) data products include: a 10- and 30-meter cell size interpolated bathymetry grid, trackline maps, and point data files. Additional files include error analysis maps, Field Activity Collection System (FACS) logs, and formal Federal Geographic Data Committee (FGDC) metadata.

As part of the Louisiana Coastal Protection and Restoration Authority (CPRA) Barrier Island Comprehensive Monitoring Program (BICM), scientists from the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center conducted a single-beam bathymetry survey around the Chandeleur Islands, Louisiana in June 2015. The goal of the program is to provide long-term data on Louisiana’s barrier islands and use this data to plan, design, evaluate, and maintain current and future barrier island restoration projects. The data described in this report, along with USGS bathymetry data collected in 2013 as a part of the Barrier Island Evolution Research project covering the northern Chandeleur Islands, and data collected in 2014 in collaboration with the Louisiana CPRA Barrier Island Comprehensive Monitoring project around Breton Island, will be used to assess bathymetric change since 2006-2007 and serve as a bathymetric control in supporting modeling of future changes in response to restoration and storm impacts. The survey area encompasses approximately 435 square kilometers (km2) of nearshore and back-barrier environments around Hewes Point, the Chandeleur Islands, and Curlew and Grand Gosier Shoals. This data series serves as an archive of processed single-beam bathymetry data, collected in the nearshore of the Chandeleur Islands, Louisiana from June 17-24, 2015 during USGS Field Activity Number 2015-317-FA. Geographic information system data products include: a 200 meter-cell-size interpolated bathymetry grid, trackline maps, and xyz point data files. Additional files include error analysis maps, Field Activity Collection System logs, and formal Federal Geographic Data Committee metadata.

As part of the Louisiana Coastal Protection and Restoration Authority (CPRA) Barrier Island Comprehensive Monitoring (BICM) Program, scientists from the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center conducted a single-beam bathymetry survey around the Chandeleur Islands, Louisiana in June 2015. The goal of the program is to provide long-term data on Louisiana’s barrier islands and use this data to plan, design, evaluate, and maintain current and future barrier island restoration projects. The data described in this report, along with USGS bathymetry data collected in 2013 as a part of the Barrier Island Evolution Research project covering the northern Chandeleur Islands, and data collected in 2014 in collaboration with the Louisiana CPRA Barrier Island Comprehensive Monitoring project around Breton Island, will be used to assess bathymetric change since 2006-2007 and serve as a bathymetric control in supporting modeling future changes in response to restoration and storm impacts. The survey area encompasses approximately 435 square kilometers (km2) of nearshore and back-barrier environments around Hewes Point, the Chandeleur Islands, and Curlew and Grand Gosier Shoals. This Data Series serves as an archive of processed single-beam bathymetry data, collected in the nearshore of the Chandeleur Islands, Louisiana from June 17-24, 2015 during USGS Field Activity Number (FAN) 2015-317-FA. Geographic information system data products include: a 200 meter-cell-size interpolated bathymetry grid, trackline maps, and xyz point data files. Additional files include error analysis maps, Field Activity Collection System logs, and formal Federal Geographic Data Committee metadata.

As part of the Louisiana Coastal Protection and Restoration Authority (CPRA) Barrier Island Comprehensive Monitoring (BICM) Program, scientists from the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center conducted a single beam bathymetry survey around the Chandeleur Islands, Louisiana in June 2015. The goal of the program is to provide long-term data on Louisiana’s barrier islands and use this data to plan, design, evaluate, and maintain current and future barrier island restoration projects. The data described in this report, along with USGS bathymetry data collected in 2013 as a part of the Barrier Island Evolution Research project covering the northern Chandeleur Islands, and data collected in 2014 in collaboration with the Louisiana CPRA Barrier Island Comprehensive Monitoring project around Breton Island, will be used to assess bathymetric change since 2006-2007 and serve as a bathymetric control in supporting modeling of future changes in response to restoration and storm impacts. The survey area encompasses approximately 435 square kilometers (km2) of nearshore and back-barrier environments around Hewes Point, the Chandeleur Islands, and Curlew and Grand Gosier Shoals. This Data Series serves as an archive of processed single beam bathymetry data, collected in the nearshore of the Chandeleur Islands, Louisiana from June 17-24, 2015 during USGS Field Activity Number (FAN) 2015-317-FA. Geographic information system data products include: a 200 meter-cell-size interpolated bathymetry grid, trackline maps, and xyz point data files. Additional files include error analysis maps, Field Activity Collection System logs, and formal Federal Geographic Data Committee metadata.

As part of the Barrier Island Comprehensive Monitoring Program (BICM), scientists from the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center conducted a nearshore single-beam bathymetry survey along the south-central coast of Louisiana, from Raccoon Point to Point Au Fer Island, in July 2015. The goal of the BICM program is to provide long-term data on Louisiana’s coastline and use this data to plan, design, evaluate, and maintain current and future barrier island restoration projects. The data described in this report will provide baseline bathymetric information for future research investigating island evolution, sediment transport, and recent and long term geomorphic change, and will support modeling of future changes in response to restoration and storm impacts. The survey area encompasses more than 300 square kilometers (km2) of nearshore environment from Raccoon Point to Point Au Fer Island. This data series serves as an archive of processed single-beam bathymetry data, collected from July 22–29, 2015, under USGS Field Activity Number 2015-320-FA. Geographic information system data products include a 200-meter-cell-size interpolated bathymetry grid, trackline maps, and point data files.

From September 14 to 28, 2015, the U.S. Geological Survey (USGS) conducted a geophysical survey to investigate the geologic controls on barrier island evolution and medium-term and interannual sediment transport along the sand berm constructed in 2011 (offshore, at the northern end of the Chandeleur Islands, Louisiana) as mitigation of the Deepwater Horizon oil spill. This investigation is part of a broader USGS project, which seeks to better understand barrier island evolution over medium time scales (months to years). This publication serves as an archive of unprocessed, digital chirp subbottom data, survey trackline map, navigation files, geographic information system (GIS) data, and formal Federal Geographic Data Committee (FGDC) metadata. Processed subbottom profile images are also provided. The archived trace data are in standard Society of Exploration Geophysicists (SEG) SEG Y revision 0 format (Barry and others, 1975) and may be downloaded and processed with commercial or public domain software such as Seismic Unix (SU). These data are available for viewing using GeoMapApp (http://www.geomapapp.org/) and Virtual Ocean (http://www.virtualocean.org/) multi-platform open source software. In addition, the SEG Y files can be downloaded from the USGS Coastal and Marine Geoscience Data System (http://cmgds.marine.usgs.gov).

The U.S. Geological Survey St. Petersburg Coastal and Marine Science Center (USGS SPCMSC), collected swath bathymetry data offshore of the Northern Chandeleur Islands, Louisiana in September 2015. This USGS Data Release includes the resulting processed elevation point data (xyz) and an interpolated digital elevation model (DEM). For further information regarding data collection and/or processing methods, refer to previously published USGS Data Series 847 and 848 (https://doi.org/10.3133/ds8487 and https://doi.org10.3133/ds848)

The U.S. Geological Survey St. Petersburg Coastal and Marine Science Center (USGS SPCMSC), collected swath bathymetry data offshore of the Northern Chandeleur Islands, Louisiana in September 2015. This USGS Data Release includes the resulting processed elevation point data (xyz) and an interpolated digital elevation model (DEM). For further information regarding data collection and/or processing methods, refer to previously published USGS Data Series 847 and 848 (https://doi.org/10.3133/ds8487 and https://doi.org10.3133/ds848)

The U.S. Geological Survey St. Petersburg Coastal and Marine Science Center (USGS SPCMSC), collected swath bathymetry data offshore of the Northern Chandeleur Islands, Louisiana in September 2015. This USGS Data Release includes the resulting processed elevation point data (xyz) and an interpolated digital elevation model (DEM). For further information regarding data collection and/or processing methods, refer to previously published USGS Data Series 847 and 848 (https://doi.org/10.3133/ds8487 and https://doi.org10.3133/ds848)

Researchers from the U.S. Geological Survey (USGS) conducted a long-term, coastal morphologic-change study at Fire Island, New York, prior to and after Hurricane Sandy impacted the area in October 2012. The Fire Island Coastal Change project (https://coastal.er.usgs.gov/fire-island/) objectives include understanding the morphologic evolution of the barrier island system on a variety of time scales (months to centuries) and resolving storm-related impacts, post-storm beach response, and recovery. In April 2016, scientists from the USGS St. Petersburg Coastal and Marine Science Center conducted geophysical and sediment sampling surveys on Fire Island to characterize and quantify spatial variability in the subaerial geology with the goal of subsequently integrating onshore geology with other surf zone and nearshore datasets.

Researchers from the U.S. Geological Survey (USGS) conducted a long-term coastal morphologic-change study at Fire Island, New York, prior to and after Hurricane Sandy impacted the area in October 2012. The Fire Island Coastal Change project objectives include understanding the morphologic evolution of the barrier island system on a variety of time scales (months to centuries) and resolving storm-related impacts, post-storm beach response, and recovery. In April 2016, scientists from the USGS St. Petersburg Coastal and Marine Science Center (SPCMSC) conducted sediment sampling and geophysical surveys on Fire Island to characterize and quantify spatial variability in the subaerial geology with the goal of subsequently integrating onshore geology with other surf zone and nearshore datasets. Data release doi:10.5066/F7FN15GX associated with this metadata record serves as an archive of sediment data from 14 vibracores collected on April 10 and 11, 2016 (USGS Field Activity Number [FAN] 2016-322-FA) along 6 transects at Fire Island, New York, that extend from the upper to lower subaerial shoreface. Sedimentologic and stratigraphic metrics (for example, sediment texture or unit thicknesses) derived from these data can be used to assess spatial and temporal trends and may aid in understanding beach evolution. Data collection and processing methods are described in Data Series 1100. Data products, including sample location tables, descriptive core logs, core photographs, results of sediment grain-size analyses, and geographic information system (GIS) data files with accompanying formal Federal Geographic Data Committee (FGDC) metadata, can be downloaded from https://doi.org/10.5066/F7FN15GX.

From June 10 to 19, 2016, the U.S. Geological Survey (USGS) conducted a geophysical survey to investigate the geologic controls on barrier island evolution and medium-term and interannual sediment transport along the sand berm constructed in 2011 (offshore, at the northern end of the Chandeleur Islands, Louisiana) as mitigation of the Deepwater Horizon oil spill. This investigation is part of a broader USGS project, which seeks to better understand barrier island evolution over medium time scales (months to years). This publication serves as an archive of unprocessed, digital chirp subbottom data, survey trackline map, navigation files, geographic information system (GIS) data, and formal Federal Geographic Data Committee (FGDC) metadata. Processed subbottom profile images are also provided. The archived trace data are in standard Society of Exploration Geophysicists (SEG) SEG Y revision 0 format (Barry and others, 1975) and may be downloaded and processed with commercial or public domain software such as Seismic Unix (SU). These data are available for viewing using GeoMapApp (http://www.geomapapp.org/) and Virtual Ocean (http://www.virtualocean.org/) multi-platform open source software. In addition, the SEG Y files can be downloaded from the USGS Coastal and Marine Geoscience Data System (CMGDS, https://cmgds.marine.usgs.gov).

The U.S. Geological Survey (USGS) conducts baseline and storm-response photography missions to document and understand the changes in the vulnerability of the Nation's coasts to extreme storms. On September 7, 2016, the USGS conducted an oblique aerial photographic survey from Navarre Beach, Florida, to Breton Island, Louisiana, aboard a Maule MT57 aircraft at an altitude of 500 feet (ft) and approximately 1,200 ft offshore. This mission was conducted to collect data for assessing incremental changes in the beach and nearshore area since the last survey, which was flown in September 2016 (https://cmgds.marine.usgs.gov/fan_info.php?fan=2015-335-FA) (Morgan, 2016, [https://dx.doi.org/10.3133/ds1008]), and the data can be used as a baseline to assess future coastal change. The photographs provided are Joint Photographic Experts Group (JPEG) images. The photograph locations are an estimate of the aircraft's position and do not indicate the location of the features in the images. These photographs document the configuration of the barrier islands and other coastal features at the time of the survey. ExifTool (version 4.0) was used to add the following to the header of each photograph: time of collection, GPS latitude, GPS longitude, keywords, credit, artist (photographer), caption, copyright, and contact information. Photographs can be opened with any JPEG-compatible image viewer by clicking on a thumbnail on the contact sheet. All image times are recorded in Coordinated Universal Time (UTC).

The U.S. Geological Survey (USGS) conducts baseline and storm-response photography missions to document and understand the changes in the vulnerability of the Nation's coasts to extreme storms. On October 13–15, 2016, the USGS conducted an oblique aerial photographic survey from Port St. Lucie, Florida, to Kitty Hawk, North Carolina, aboard a Cessna 182 aircraft at an altitude of 500 feet (ft) and approximately 1,200 ft offshore. This mission was conducted to collect data for assessing incremental changes in the beach and nearshore area. The photographs provided are Joint Photographic Experts Group (JPEG) images. The photograph locations are an estimate of the aircraft's position and do not indicate the location of the features seen in the image. These photographs document the configuration of the barrier islands and other coastal features at the time of the survey. ExifTool (version 4.0) was used to add the following to the header of each photograph: time of collection, GPS latitude, GPS longitude, keywords, credit, artist (photographer), caption, copyright, and contact information. Photographs can be opened with any JPEG-compatible image viewer by clicking on a thumbnail on the contact sheet. All image times are recorded in Coordinated Universal Time (UTC).

The U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center (SPCMSC) conducted research to identify areas of seafloor elevation stability and instability based on elevation changes between the years of 2016 and 2017 at Crocker Reef near Islamorada, Florida (FL), within a 33.62 square-kilometer area. USGS SPCMSC staff used seafloor elevation-change data from Yates and others (2019) derived from an elevation-change analysis between two elevation datasets acquired in 2016 and 2017 using the methods of Yates and others (2017). A seafloor stability threshold was determined for the 2016-2017 Crocker Reef elevation-change dataset based on the vertical uncertainty of the 2016 and 2017 digital elevation models (DEMs). Five stability categories (which include, Stable: 0.0 meters (m) to ±0.24 m or 0.0 m to ±0.49 m; Moderately stable: ±0.25 m to ±0.49 m; Moderately unstable: ±0.50 m to ±0.74 m; Mostly unstable: ±0.75 m to ±0.99 m; and Unstable: ±1.00 m to Max/Min elevation change) were created and used to define levels of stability and instability for each elevation-change value (8,402,223 data points at 2-m horizontal resolution) based on the amount of erosion and accretion during the 2016 to 2017 time period. Seafloor-stability point and triangulated irregular network (TIN) surface models were created at five different elevation-change data resolutions (1st order through 5th order) with each resolution becoming increasingly more detailed. The stability point models were used to determine the level of seafloor stability at nine habitat types found at Crocker Reef. This data release includes ArcGIS map packages containing the binned and color-coded stability point and surface (TIN) models and habitat files; maps of each stability model; and data tables containing stability and elevation-change data for the habitat types. Data were collected under Florida Keys National Marine Sanctuary permit FKNMS-2016-068.

The U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center (SPCMSC) conducted research to identify areas of seafloor elevation stability and instability based on elevation changes between the years of 2016 and 2017 at Looe Key coral reef near Big Pine Key, Florida (FL), within a 19.74 square-kilometer area. USGS SPCMSC staff used seafloor elevation-change data from Yates and others (2019) derived from an elevation-change analysis between two elevation datasets acquired in 2016 and 2017 using the methods of Yates and others (2017). A seafloor stability threshold was determined for the 2016-2017 Looe Key elevation-change dataset based on the vertical uncertainty of the 2016 and 2017 digital elevation models (DEMs). Five stability categories (which include, Stable: 0.0 meters (m) to ±0.24 m or 0.0 m to ±0.49 m; Moderately stable: ±0.25 m to ±0.49 m; Moderately unstable: ±0.50 m to ±0.74 m; Mostly unstable: ±0.75 m to ±0.99 m; and Unstable: ±1.00 m to Max/Min elevation change) were created and used to define levels of stability and instability for each elevation-change value (4,934,364 data points at 2-m horizontal resolution) based on the amount of erosion and accretion during the 2016 to 2017 time period. Seafloor-stability point and triangulated irregular network (TIN) surface models were created at five different elevation-change data resolutions (1st order through 5th order) with each resolution becoming increasingly more detailed. The stability models were used to determine the level of seafloor stability at potential areas of interest for coral restoration and ten habitat types found at Looe Key. Stability surface (TIN) models were used for areas defined by specific XY geographic points, while stability point models were used for areas defined by bounding box coordinate locations. This data release includes ArcGIS map packages containing the binned and color-coded stability point and surface (TIN) models, potential coral restoration locations, and habitat files; maps of each stability model; and data tables containing stability and elevation-change data for the potential coral restoration locations and habitat types. Data were collected under Florida Keys National Marine Sanctuary permit FKNMS-2016-068. Coral restoration locations were provided by Mote Marine Laboratory under Special Activity License SAL-18-1724-SCRP.

The U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center (SPCMSC) conducted research to identify areas of seafloor elevation stability and instability based on elevation changes between the years of 2016 and 2019 along the Florida Reef Tract (FRT) from Miami to Key West within a 939.4 square-kilometer area. USGS SPCMSC staff used seafloor elevation-change data from Fehr and others (2021) derived from an elevation-change analysis between two elevation datasets acquired in 2016/2017 and 2019 using the methods of Yates and others (2017). Most of the elevation data from the 2016/2017 time period were collected during 2016, so as an abbreviated naming convention, we refer to this time period as 2016. Due to file size limitations, the elevation-change data was divided into five blocks. A seafloor stability threshold was determined for the 2016-2019 FRT elevation-change datasets based on the vertical uncertainty of the 2016 and 2019 digital elevation models (DEMs). Five stability categories (which include, Stable: 0.0 meters (m) to ±0.24 m or 0.0 m to ±0.49 m; Moderately stable: ±0.25 m to ±0.49 m; Moderately unstable: ±0.50 m to ±0.74 m; Mostly unstable: ±0.75 m to ±0.99 m; and Unstable: ±1.00 m to Max/Min elevation change) were created and used to define levels of stability and instability for each elevation-change value (total of 235,153,117 data points at 2-m horizontal resolution) based on the amount of erosion and accretion during the 2016 to 2019 time period. Seafloor-stability point and triangulated irregular network (TIN) surface models were created for each block at five different elevation-change data resolutions (1st order through 5th order) with each resolution becoming increasingly more detailed. The stability models were used to determine the level of seafloor stability at potential areas of interest for coral restoration and 14 habitat types found along the FRT. Stability surface (TIN) models were used for areas defined by specific XY geographic points, while stability point models were used for areas defined by bounding box coordinate locations. This data release includes ArcGIS Pro map packages containing the binned and color-coded stability point and surface (TIN) models, potential coral restoration locations, and habitat files for each block; maps of each stability model; and data tables containing stability and elevation-change data for the potential coral restoration locations and habitat types. Data were collected under Florida Keys National Marine Sanctuary permit FKNMS-2016-068. Coral restoration locations were provided by Mote Marine Laboratory under Special Activity License SAL-18-1724-SCRP.

To characterize coastal change, historical maps and complementary records were compiled including: topographic sheets (T-sheets), hydrographic sheets (H-sheets, smooth sheets), shorelines, and bathymetric soundings surrounding the Mississippi (MS) barrier islands over several time periods (1916-1920, 2008-2009 and 2016). One goal of this work was to create a time-series of bathymetric change maps around the islands. Datasets include 1916 through 1920 soundings collected by the United States Coast and Geodetic Survey as downloaded H-sheets and some digitized soundings from the National Oceanic and Atmospheric Administration (NOAA), 2008 to 2009 soundings collected by the United States Geological Survey St. Petersburg and Woods Hole Coastal and Marine Science Center (USGS SPCMSC and WHCMSC, respectively), and 2016 soundings collected by the USGS SPCMSC in the nearshore environment of Ship, Horn, and Petit Bois Islands. This USGS data release includes three digital elevation models (DEMs) for 1916 to 1920, 2008 to 2009, and 2016; however, this metadata file pertains to the 2016 DEM (2016_MS_NAD83NAVD88g12B_50m.tif). This work was completed in cooperation with the United States Army Corps of Engineers (Mobile, Alabama) and the National Park Service as part of the Mississippi Coastal Improvements Program (MsCIP).

To characterize coastal change, historical maps and complementary records were compiled including: topographic sheets (T-sheets), hydrographic sheets (H-sheets, smooth sheets), shorelines, and bathymetric soundings surrounding the Mississippi (MS) barrier islands over several time periods (1916-1920, 2008-2009 and 2016). One goal of this work was to create a time-series of bathymetric change maps around the islands. Datasets include 1916 through 1920 soundings collected by the United States Coast and Geodetic Survey as downloaded H-sheets and some digitized soundings from the National Oceanic and Atmospheric Administration (NOAA), 2008 to 2009 soundings collected by the United States Geological Survey (USGS) St. Petersburg and Woods Hole Coastal and Marine Science Centers (SPCMSC and WHCMSC respectively), and 2016 soundings collected by the SPCMSC in the nearshore environment of Ship, Horn, and Petit Bois Islands. This USGS data release includes three interpolated bathymetric change maps created by comparing the 1916 to 1920, 2008 to 2009, and 2016 bathymetry data. This metadata file pertains to the historical bathymetric change digital elevation model (DEM) resulting by subtracting the 1916-1920 bathymetry from the 2016 bathymetry (2016minus1916to1920_MS_NAD83NAVD88g12B_50m.tif). This work was completed in cooperation with the United States Army Corps of Engineers (USACE) Mobile, Alabama and the National Park Service (NPS) as part of the Mississippi Coastal Improvements Program (MsCIP).

To characterize coastal change, historical maps and complementary records were compiled including: topographic sheets (T-sheets), hydrographic sheets (H-sheets, smooth sheets), shorelines, and bathymetric soundings surrounding the Mississippi (MS) barrier islands over several time periods (1916-1920, 2008-2009 and 2016). One goal of this work was to create a time-series of bathymetric change maps around the islands. Data sets include 1916 through 1920 soundings collected by the United States Coast and Geodetic Survey as downloaded H-sheets and some digitized soundings from the National Oceanic and Atmospheric Administration (NOAA), 2008 to 2009 soundings collected by the United States Geological Survey St. Petersburg and Woods Hole Coastal and Marine Science Centers (USGS SPCMSC and WHCMSC, respectively), and 2016 soundings collected by the USGS SPCMSC in the nearshore environment of Ship, Horn, and Petit Bois Islands. This USGS data release includes three interpolated bathymetric change maps created by comparing the 1916 to 1920, 2008 to 2009, and 2016 bathymetry data; however, this metadata file pertains to the bathymetric change digital elevation model (DEM) observed between the 2008-2009 and 2016 bathymetry (2016minus2008to2009_MS_NAD83NAVD88g12B_50m.tif). This work was completed in cooperation with the United States Army Corps of Engineers (Mobile, Alabama) and the National Park Service as part of the Mississippi Coastal Improvements Program (MsCIP).

The U.S. Geological Survey (USGS) conducts baseline and storm-response photography missions to document and understand the changes in the vulnerability of the Nation's coasts to extreme storms. On June 09, 2017, the USGS conducted an oblique aerial photographic survey of the U.S. Army Corps of Engineers Field Research Facility (USACE FRF), located in Duck, North Carolina, aboard a Cessna 182 aircraft at an altitude of approximately 1000 feet (ft). This mission was conducted to collect data for USACE FRF Duck Unmanned Aerial Systems (UAS) Open Field Experiment, carried out June 5–21, 2017. The photographs provided are Joint Photographic Experts Group (JPEG) and Nikon Electronic Format (NEF) images. The photograph locations are an estimate of the aircraft's position and do not indicate the location of the feature in the images. These photographs document the configuration of the USACE FRF at the time of the survey. ExifTool (version 4.0) was used to add the following to the header of each photograph: time of collection, GPS latitude, GPS longitude, keywords, credit, artist (photographer), caption, copyright, and contact information. JPEG photographs can be opened with any JPEG-compatible image viewer. All image times are recorded in Coordinated Universal Time (UTC). In addition to the photographs, a Google Earth Keyhole Markup Language (KML) file is provided and can be used to view the images by clicking on the marker and then the thumbnail or the link above the thumbnail. This KML, 2017-033-FA.kml, can be found in 2017-033-FA-SupplementalFiles.zip.

Aerial imagery acquired with a small unmanned aircraft system (sUAS), in conjunction with surveyed ground control points (GCPs) visible in the imagery, can be processed with structure-from-motion (SfM) photogrammetry techniques to produce high-resolution orthomosaics, three-dimensional (3D) point clouds and digital elevation models (DEMs). This dataset, prepared by the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center (SPCMSC), provides UAS survey data consisting of aerial imagery and GCP positions and elevations collected at Madeira Beach, Florida, monthly from July 2017 to June 2018 in order to observe seasonal and storm-induced changes in beach topography.

From August 7 to 16, 2017, the U.S. Geological Survey (USGS) conducted a geophysical survey to investigate the geologic controls on barrier island evolution and medium-term and interannual sediment transport along the sand berm constructed in 2011 (offshore, at the northern end of the Chandeleur Islands, Louisiana) as mitigation of the Deepwater Horizon oil spill. This investigation is part of a broader USGS project, which seeks to better understand barrier island evolution over medium time scales (months to years). This publication serves as an archive of unprocessed, digital chirp subbottom data, survey trackline map, navigation files, geographic information system (GIS) data, and formal Federal Geographic Data Committee (FGDC) metadata. Processed subbottom profile images are also provided. The archived trace data are in standard Society of Exploration Geophysicists (SEG) SEG Y revision 0 format (Barry and others, 1975) and may be downloaded and processed with commercial or public domain software such as Seismic Unix (SU). These data are available for viewing using GeoMapApp (http://www.geomapapp.org/) and Virtual Ocean (http://www.virtualocean.org/) multi-platform open source software. In addition, the SEG Y files can be downloaded from the USGS Coastal and Marine Geoscience Data System (CMGDS, https://cmgds.marine.usgs.gov).

The U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center (SPCMSC) conducted research to identify areas of seafloor elevation stability and instability based on elevation changes between the years of 2017 and 2018 at Crocker Reef near Islamorada, Florida (FL), within a 6.11 square-kilometer area. USGS SPCMSC staff used seafloor elevation-change data from Yates and others (2019) derived from an elevation-change analysis between two elevation datasets acquired in 2017 and 2018 using the methods of Yates and others (2017). A seafloor stability threshold was determined for the 2017-2018 Crocker Reef elevation-change dataset based on the vertical uncertainty of the 2017 and 2018 digital elevation models (DEMs). Five stability categories (which include, Stable: 0.0 meters (m) to ±0.24 m or 0.0 m to ±0.49 m; Moderately stable: ±0.25 m to ±0.49 m; Moderately unstable: ±0.50 m to ±0.74 m; Mostly unstable: ±0.75 m to ±0.99 m; and Unstable: ±1.00 m to Max/Min elevation change) were created and used to define levels of stability and instability for each elevation-change value (1,525,339 data points at 2-m horizontal resolution) based on the amount of erosion and accretion during the 2017 to 2018 time period. Seafloor-stability point and triangulated irregular network (TIN) surface models were created at five different elevation-change data resolutions (1st order through 5th order) with each resolution becoming increasingly more detailed. The stability point models were used to determine the level of seafloor stability at seven habitat types found at Crocker Reef. This data release includes ArcGIS map packages containing the binned and color-coded stability point and surface (TIN) models and habitat files; maps of each stability model; and data tables containing stability and elevation-change data for the habitat types. Data were collected under Florida Keys National Marine Sanctuary permit FKNMS-2016-068.

June 2–10 and July 2, 2017, the U.S. Geological Survey (USGS) conducted geophysical surveys offshore of the Louisiana Chenier Plain to document the changing morphology of the coastal environment. Data were collected under the Barrier Island Coastal Monitoring (BICM) program, an ongoing collaboration between the State of Louisiana Coastal Protection and Restoration Authority (CPRA), the University of New Orleans (UNO) Pontchartrain Institute for Environmental Sciences (PIES), and the USGS. Project objectives include compiling historical shoreline bathymetric datasets and comparing them to other bathymetric data collected during the BICM project. At the same time, subsurface geophysical data were collected to investigate the geomorphology and geologic controls on barrier-shoreline evolution. This publication serves as an archive of unprocessed, digital chirp subbottom data, survey trackline maps, navigation files, geographic information system (GIS) data, and formal Federal Geographic Data Committee (FGDC) metadata. Processed subbottom profile images are also provided. The archived trace data are in standard Society of Exploration Geophysicists (SEG) SEG Y revision 0 format (Barry and others, 1975) and may be downloaded and processed with commercial or public domain software such as Seismic Unix (SU). These data are available for viewing using GeoMapApp (http://www.geomapapp.org/) and Virtual Ocean (http://www.virtualocean.org/) multi-platform open source software. In addition, the SEG Y files can be downloaded from the USGS Coastal and Marine Geoscience Data System (CMGDS, https://cmgds.marine.usgs.gov).

The U.S. Geological Survey (USGS) Remote Sensing Coastal Change (RSCC) project collects aerial imagery along coastal swaths, in response to storm events, with optimized endlap/sidelap and precise position information to create high-resolution orthomosaics, three-dimensional (3D) point clouds, and digital elevation/surface models (DEMs/DSMs) using Structure-from-Motion (SfM) photogrammetry methods. These products are valuable for measuring topographic change, and for understanding coastal vulnerability and response to disturbance events. A nadir (vertical) aerial imagery survey was conducted from Cape Fear to Duck, North Carolina on October 6-8, 2018, in response to Hurricane Florence. The observations along the coastline cover an area approximately 275 kilometers long and 300 to 700 meters (m) wide and encompass both highly developed towns as well as natural undeveloped areas, including the federal lands of Cape Lookout National Seashore and Cape Hatteras National Seashore. Low-altitude (300 m above ground level) digital aerial imagery were acquired from a manned, fixed-wing Piper P28A aircraft using a Sony A7R 36 Megapixel digital camera, along with precise aircraft navigation Global Navigation Satellite System (GNSS) data. Data were collected in shore-parallel lines, flying at approximately 50 meters per second (m/s) and capturing true color imagery at 1 Hertz (Hz), resulting in image footprints with approximately 75-80% endlap, 60-70% sidelap, and 5.3-centimeter (cm) ground sample distance (GSD). The precise time of each image capture (flash event) was recorded, and the corresponding aircraft position was computed in post-processing from the aircraft navigation GNSS data; precise image positions can then be determined by accounting for the lever arm offsets between the aircraft GNSS antenna and the camera lens. Position data, provided as latitude/longitude/ellipsoid height, is referenced to the North American Datum of 1983 (NAD83 (2011)).

Researchers from the U.S. Geological Survey (USGS) conducted a long-term, coastal morphologic-change study at Fire Island, New York, prior to and after Hurricane Sandy impacted the area in October 2012. The Fire Island Coastal System Change project (https://coastal.er.usgs.gov/fire-island/) objectives include understanding the morphologic evolution of the barrier island system on a variety of time scales (months to centuries) and resolving storm-related impacts, post-storm beach response, and recovery. From June 2-16, 2018, scientists from the USGS St. Petersburg Coastal and Marine Science Center conducted geophysical surveys on Fire Island to characterize and quantify spatial variability in the subaerial geology with the goal of subsequently integrating onshore geology with other surf zone and nearshore datasets. This publication serves as an archive of high-resolution subbottom profile images, survey trackline map, navigation files, geographic information system (GIS) data, and formal Federal Geographic Data Committee (FGDC) metadata. Additionally, in April 2016, geophysical and sediment sampling data were collected as part of the Fire Island project. The ground penetrating radar and vibracore datasets are available from Forde and others, 2018; Buster and others, 2018; and Bernier and others, 2018.

From August 16 to 21, 2018, the U.S. Geological Survey (USGS) conducted a geophysical survey to investigate the geologic controls on barrier island evolution and medium-term and interannual sediment transport along the sand berm constructed in 2011 (offshore, at the northern end of the Chandeleur Islands, Louisiana) as mitigation of the Deepwater Horizon oil spill. This investigation is part of a broader USGS project, which seeks to better understand barrier island evolution over medium time scales (months to years). This publication serves as an archive of unprocessed, digital chirp subbottom data, survey trackline map, navigation files, geographic information system (GIS) data, and formal Federal Geographic Data Committee (FGDC) metadata. Processed subbottom profile images are also provided. The archived trace data are in standard Society of Exploration Geophysicists (SEG) SEG Y revision 0 format (Barry and others, 1975) and may be downloaded and processed with commercial or public domain software such as Seismic Unix (SU). These data are available for viewing using GeoMapApp (http://www.geomapapp.org/) and Virtual Ocean (http://www.virtualocean.org/) multi-platform open source software. In addition, the SEG Y files can be downloaded from the USGS Coastal and Marine Geoscience Data System (CMGDS, https://cmgds.marine.usgs.gov).

This dataset, prepared by the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center (SPCMSC), provides beach profile data collected at Madeira Beach, Florida. Data were collected on foot by a person equipped with a Global Positioning System (GPS) antenna affixed to a backpack outfitted for surveying location and elevation data (XYZ) along pre-determined transects. The horizontal position data are given in the Universal Transverse Mercator (UTM) projected coordinate system, Zone 17 North (17N), referenced to the North American Datum of 1983 (NAD 83); the elevation data are referenced to the North American Vertical Datum of 1988 (NAVD 88), GEOID12B.

This dataset, prepared by the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center (SPCMSC), provides beach profile data collected at Madeira Beach, Florida. Data were collected on foot by a person equipped with a Global Positioning System (GPS) antenna affixed to a backpack outfitted for surveying location and elevation data (XYZ) along pre-determined transects. The horizontal position data are given in the Universal Transverse Mercator (UTM) projected coordinate system, Zone 17 North (17N), referenced to the North American Datum of 1983 (NAD 83); the elevation data are referenced to the North American Vertical Datum of 1988 (NAVD 88), GEOID12B.

This data release is an archive of sedimentary laboratory analytical data produced by scientists from the U.S. Geological Survey St. Petersburg Coastal and Marine Science Center (USGS SPCMSC) for sediment cores and surface samples collected from coastal marshes in Georgia (GA), Virginia (VA), and Massachusetts (MA). Collaborators from USGS Patuxent Wildlife Research Center (PWRC) and the Virginia Institute of Marine Science (VIMS) collected these samples in South Altamaha, GA, Mockhorn Island, VA, Goodwin Island, VA and Laws Point, Plum Island Estuary, MA during a period spanning 2015 to 2019. This work provides the USGS and VIMS with the necessary data needed to assess the emergence rates (chronology and sedimentation rates) of coastal marshes at these locations. This publication includes data for marsh sediment cores and surface samples collected by collaborators at the USGS PWRC and VIMS from 2015-2019. Data products include site locations, loss on ignition and radiochemical data (alpha and gamma spectrometry), and Federal Geographic Data Committee (FGDC) metadata.

From August 9 to 14, 2019, researchers from the U.S. Geological Survey (USGS) conducted a geophysical survey to investigate shoreface morphology and geology near Cedar Island, Virginia. The Coastal Sediment Availability and Flux project objectives include understanding the morphologic evolution of the barrier island system on a variety of time scales (months to centuries) and resolving storm-related impacts, post-storm beach response, and recovery. This publication serves as an archive of high-resolution chirp subbottom data, survey trackline map, navigation files, geographic information system (GIS) data, and formal Federal Geographic Data Committee (FGDC) metadata. Processed subbottom profile images are also provided. The archived trace data are in standard Society of Exploration Geophysicists (SEG) SEG Y revision 0 format (Barry and others, 1975). In addition to this data release, the SEG Y files can be downloaded from the USGS Coastal and Marine Geoscience Data System (CMGDS) at, https://cmgds.marine.usgs.gov. Bathymetry and backscatter data were also collected during this survey are available in Stalk and others (2020).

From September 27 through October 5, 2019, the U.S. Geological Survey (USGS) conducted geophysical surveys to investigate the geologic controls on barrier island evolution and sediment transport offshore of the Rockaway Peninsula, New York. This shapefile represents a point dataset of field activity number (FAN) 2019-333-FA chirp subbottom profile 1,000-shot-interval locations.

From September 27 through October 5, 2019, the U.S. Geological Survey (USGS) conducted geophysical surveys to investigate the geologic controls on barrier island evolution and sediment transport offshore of the Rockaway Peninsula, New York. This shapefile represents a point dataset of field activity number (FAN) 2019-333-FA chirp subbottom profile start of trackline locations.

From September 27 through October 5, 2019, the U.S. Geological Survey (USGS) conducted geophysical surveys to investigate the geologic controls on barrier island evolution and sediment transport offshore of the Rockaway Peninsula, New York. This shapefile represents a line dataset of field activity number (FAN) 2019-333-FA chirp tracklines.

This dataset, prepared by the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center (SPCMSC), provides beach profile data collected at Madeira Beach, Florida. Data were collected on foot by a person equipped with a Global Positioning System (GPS) antenna affixed to a backpack outfitted for surveying location and elevation data (XYZ) along pre-determined transects. The horizontal position data are given in the Universal Transverse Mercator (UTM) projected coordinate system, Zone 17 North (17N), referenced to the North American Datum of 1983 (NAD 83); the elevation data are referenced to the North American Vertical Datum of 1988 (NAVD 88), GEOID12B.

The U.S. Geological Survey (USGS) Remote Sensing Coastal Change (RSCC) project surveyed 34 features visible from the air to be used as ground control points (GCP) on the Outer Banks, North Carolina, on September 24 and 25, 2019, after the passing of Hurricane Dorian (U.S. landfall on September 6, 2019). Global Positioning System (GPS) data were collected in support of aerial imagery surveys documenting the storm impacts and subsequent recovery along the coast and will be used as control and check points in Structure-from-Motion (SfM) photogrammetry processing to produce topographic maps. This dataset consists of horizontal and vertical positions of permanent GCPs, measured using Real-Time Kinematic (RTK) Global Navigation Satellite System (GNSS) equipment. The data are provided in comma-separated values (.csv) delimited text format, in both geographic and projected (Universal Transverse Mercator Zone 18N) coordinates, and vertical measurements are provided as both ellipsoid and orthometric heights. All horizontal coordinates and ellipsoid heights are referenced to the North American Datum of 1983 (NAD83(2011)), and orthometric heights are referenced to the North American Vertical Datum of 1988 (NAVD88), GEOID12B. Additional information as well as photographs for each GCP (120 photos were collected, in total) are also included.

This dataset, prepared by the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center (SPCMSC), provides beach profile data collected at Madeira Beach, Florida. Data were collected on foot by a person equipped with a Global Positioning System (GPS) antenna affixed to a backpack outfitted for surveying location and elevation data (XYZ) along pre-determined transects. The horizontal position data are given in the Universal Transverse Mercator (UTM) projected coordinate system, Zone 17 North (17N), referenced to the North American Datum of 1983 (NAD 83); the elevation data are referenced to the North American Vertical Datum of 1988 (NAVD 88), GEOID12B.

This dataset, prepared by the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center (SPCMSC), provides beach profile data collected at Madeira Beach, Florida. Data were collected on foot by a person equipped with a Global Positioning System (GPS) antenna affixed to a backpack outfitted for surveying location and elevation data (XYZ) along pre-determined transects. The horizontal position data are given in the Universal Transverse Mercator (UTM) projected coordinate system, Zone 17 North (17N), referenced to the North American Datum of 1983 (NAD 83); the elevation data are referenced to the North American Vertical Datum of 1988 (NAVD 88), GEOID12B.

The U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center conducted research to quantify the combined effect of all constructive and destructive processes on modern coral reef ecosystems by projecting future regional-scale changes in seafloor elevation along the Florida Reef Tract, Florida (FL). USGS staff used historical bathymetric point data from the 1930's (National Oceanic and Atmospheric Administration (NOAA) Office of Coast Survey, see Yates and others, 2017) and light detection and ranging (lidar)-derived data acquired in 2002 (Brock and others, 2006, 2007) to calculate historical seafloor elevation changes in the Upper Florida Keys (UFK) (Yates and others, 2017). Using those changes in seafloor elevation, annual rates of elevation change were calculated for 13 habitat types found in the UFK reef tract. The annual rate of mean elevation change for each habitat type was applied to a digital elevation model (DEM) extending from Port St. Lucie to Marquesas Key, FL that was modified from the NOAA National Centers for Environmental Information (NCEI) U.S. Coastal Relief Model coastal DEM (NOAA, 2001) to project future seafloor elevation (from 2001) along the Florida Reef Tract. Grid resolution for the DEM is 3-arc seconds (approximately 90 meters).

The U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center conducted research to quantify the combined effect of all constructive and destructive processes on modern coral reef ecosystems by projecting future regional-scale changes in seafloor elevation along the Florida Reef Tract, Florida (FL). USGS staff used historical bathymetric point data from the 1930's (National Oceanic and Atmospheric Administration (NOAA) Office of Coast Survey, see Yates and others, 2017) and light detection and ranging (lidar)-derived data acquired in 2002 (Brock and others, 2006, 2007) to calculate historical seafloor elevation changes in the Upper Florida Keys (UFK) (Yates and others, 2017). Using those changes in seafloor elevation, annual rates of erosion were calculated for 13 habitat types found in the UFK reef tract. The annual rate of mean erosion for each habitat type was applied to a digital elevation model (DEM) extending from Port St. Lucie to Marquesas Key, FL that was modified from the NOAA National Centers for Environmental Information (NCEI) U.S. Coastal Relief Model coastal DEM (NOAA, 2001) to project future seafloor elevation (from 2001) along the Florida Reef Tract. Grid resolution for the DEM is 3-arc seconds (approximately 90 meters (m)).

The U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center conducted research to quantify the combined effect of all constructive and destructive processes on modern coral reef ecosystems by projecting future regional-scale changes in seafloor elevation for several sites along the Florida Reef Tract, Florida (FL) including the shallow seafloor along Key West, FL. USGS staff used historical bathymetric point data from the 1930's (National Oceanic and Atmospheric Administration (NOAA) Office of Coast Survey, see Yates and others, 2017) and light detection and ranging (lidar)-derived data acquired in 2002 (Brock and others, 2006, 2007) to calculate historical seafloor elevation changes in the Upper Florida Keys (UFK) (Yates and others, 2017). Using those changes in seafloor elevation, annual rates of elevation change were calculated for 13 habitat types found in the UFK reef tract. The annual rate of mean elevation change for each habitat type was applied to a digital elevation model (DEM) extending from Big Pine Key to Marquesas Key, FL that was modified from the NOAA National Centers for Environmental Information (NCEI) Key West coastal DEM (NOAA, 2011) to project future seafloor elevation (from 2011) along the Key West section of the Florida Reef Tract. Grid resolution for the DEM is 1/3 arc second (approximately 10 meters).

The U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center conducted research to quantify the combined effect of all constructive and destructive processes on modern coral reef ecosystems by projecting future regional-scale changes in seafloor elevation for several sites along the Florida Reef Tract, Florida (FL) including the shallow seafloor along Key West, FL. USGS staff used historical bathymetric point data from the 1930's (National Oceanic and Atmospheric Administration (NOAA) Office of Coast Survey, see Yates and others, 2017) and light detection and ranging (lidar)-derived data acquired in 2002 (Brock and others, 2006, 2007) to calculate historical seafloor elevation changes in the Upper Florida Keys (UFK) (Yates and others, 2017). Using those changes in seafloor elevation, annual rates of erosion were calculated for 13 habitat types found in the UFK reef tract. The annual rate of mean erosion for each habitat type was applied to a digital elevation model (DEM) extending from Big Pine Key to Marquesas Key, FL that was modified from the NOAA National Centers for Environmental Information (NCEI) Key West coastal DEM (NOAA, 2011) to project future seafloor elevation (from 2011) along the Key West section of the Florida Reef Tract. Grid resolution for the DEM is 1/3 arc-second (approximately 10 meters).

The U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center conducted research to quantify the combined effect of all constructive and destructive processes on modern coral reef ecosystems by projecting future regional-scale changes in seafloor elevation for several sites along the Florida Reef Tract, Florida (FL) including the shallow seafloor along the coast of Miami, FL. USGS staff used historical bathymetric point data from the 1930's (National Oceanic and Atmospheric Administration (NOAA) Office of Coast Survey, see Yates and others, 2017) and light detection and ranging (lidar)-derived data acquired in 2002 (Brock and others, 2006, 2007) to calculate historical seafloor elevation changes in the Upper Florida Keys (UFK) (Yates and others, 2017). Using those changes in seafloor elevation, annual rates of elevation change were calculated for 13 habitat types found in the UFK reef tract. The annual rate of mean elevation change for each habitat type was applied to a digital elevation model (DEM) extending from Deerfield Beach to Homestead, FL that was modified from the NOAA National Centers for Environmental Information (NCEI) Miami coastal DEM (NOAA, 2015) to project future seafloor elevation (from 2014) along the Miami section of the Florida Reef Tract. Grid resolution for the DEM is 1/3 arc second (approximately 10 meters).

The U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center conducted research to quantify the combined effect of all constructive and destructive processes on modern coral reef ecosystems by projecting future regional-scale changes in seafloor elevation for several sites along the Florida Reef Tract, Florida (FL) including the shallow seafloor along the coast of Miami, FL. USGS staff used historical bathymetric point data from the 1930's (National Oceanic and Atmospheric Administration (NOAA) Office of Coast Survey, see Yates and others, 2017) and light detection and ranging (lidar)-derived data acquired in 2002 (Brock and others, 2006, 2007) to calculate historical seafloor elevation changes in the Upper Florida Keys (UFK) (Yates and others, 2017). Using those changes in seafloor elevation, annual rates of erosion were calculated for 13 habitat types found in the UFK reef tract. The annual rate of mean erosion for each habitat type was applied to a digital elevation model (DEM) extending from Deerfield Beach to Homestead, FL that was modified from the NOAA National Centers for Environmental Information (NCEI) Miami coastal DEM (NOAA, 2015) to project future seafloor elevation (from 2014) along the Miami section of the Florida Reef Tract. Grid resolution for the DEM is 1/3 arc second (approximately 10 meters).

The U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center conducted research to quantify the combined effect of all constructive and destructive processes on modern coral reef ecosystems by projecting future regional-scale changes in seafloor elevation along the Florida Reef Tract, Florida (FL). USGS staff used historical bathymetric point data from the 1930's (National Oceanic and Atmospheric Administration (NOAA) Office of Coast Survey, see Yates and others, 2017) and light detection and ranging (lidar)-derived data acquired in 2002 (Brock and others, 2006, 2007) to calculate historical seafloor elevation changes in the Upper Florida Keys (UFK) (Yates and others, 2017). Using those changes in seafloor elevation, annual rates of elevation change were calculated for 13 habitat types found in the UFK reef tract. The annual rate of mean elevation change for each habitat type was applied to a digital elevation model (DEM) extending from Port St. Lucie to Marquesas Key, FL that was modified from the NOAA National Centers for Environmental Information (NCEI) U.S. Coastal Relief Model coastal DEM (NOAA, 2001) to project future seafloor elevation (from 2001) along the Florida Reef Tract. Grid resolution for the DEM is 3-arc seconds (approximately 90 meters).

The U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center conducted research to quantify the combined effect of all constructive and destructive processes on modern coral reef ecosystems by projecting future regional-scale changes in seafloor elevation along the Florida Reef Tract, Florida (FL). USGS staff used historical bathymetric point data from the 1930's (National Oceanic and Atmospheric Administration (NOAA) Office of Coast Survey, see Yates and others, 2017) and light detection and ranging (lidar)-derived data acquired in 2002 (Brock and others, 2006, 2007) to calculate historical seafloor elevation changes in the Upper Florida Keys (UFK) (Yates and others, 2017). Using those changes in seafloor elevation, annual rates of erosion were calculated for 13 habitat types found in the UFK reef tract. The annual rate of mean erosion for each habitat type was applied to a digital elevation model (DEM) extending from Port St. Lucie to Marquesas Key, FL that was modified from the NOAA National Centers for Environmental Information (NCEI) U.S. Coastal Relief Model coastal DEM (NOAA, 2001) to project future seafloor elevation (from 2001) along the Florida Reef Tract. Grid resolution for the DEM is 3-arc seconds (approximately 90 meters (m)).

The U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center conducted research to quantify the combined effect of all constructive and destructive processes on modern coral reef ecosystems by projecting future regional-scale changes in seafloor elevation for several sites along the Florida Reef Tract, Florida (FL) including the shallow seafloor along Key West, FL. USGS staff used historical bathymetric point data from the 1930's (National Oceanic and Atmospheric Administration (NOAA) Office of Coast Survey, see Yates and others, 2017) and light detection and ranging (lidar)-derived data acquired in 2002 (Brock and others, 2006, 2007) to calculate historical seafloor elevation changes in the Upper Florida Keys (UFK) (Yates and others, 2017). Using those changes in seafloor elevation, annual rates of elevation change were calculated for 13 habitat types found in the UFK reef tract. The annual rate of mean elevation change for each habitat type was applied to a digital elevation model (DEM) extending from Big Pine Key to Marquesas Key, FL that was modified from the NOAA National Centers for Environmental Information (NCEI) Key West coastal DEM (NOAA, 2011) to project future seafloor elevation (from 2011) along the Key West section of the Florida Reef Tract. Grid resolution for the DEM is 1/3 arc second (approximately 10 meters).

The U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center conducted research to quantify the combined effect of all constructive and destructive processes on modern coral reef ecosystems by projecting future regional-scale changes in seafloor elevation for several sites along the Florida Reef Tract, Florida (FL) including the shallow seafloor along Key West, FL. USGS staff used historical bathymetric point data from the 1930's (National Oceanic and Atmospheric Administration (NOAA) Office of Coast Survey, see Yates and others, 2017) and light detection and ranging (lidar)-derived data acquired in 2002 (Brock and others, 2006, 2007) to calculate historical seafloor elevation changes in the Upper Florida Keys (UFK) (Yates and others, 2017). Using those changes in seafloor elevation, annual rates of erosion were calculated for 13 habitat types found in the UFK reef tract. The annual rate of mean erosion for each habitat type was applied to a digital elevation model (DEM) extending from Big Pine Key to Marquesas Key, FL that was modified from the NOAA National Centers for Environmental Information (NCEI) Key West coastal DEM (NOAA, 2011) to project future seafloor elevation (from 2011) along the Key West section of the Florida Reef Tract. Grid resolution for the DEM is 1/3 arc-second (approximately 10 meters).

The U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center conducted research to quantify the combined effect of all constructive and destructive processes on modern coral reef ecosystems by projecting future regional-scale changes in seafloor elevation for several sites along the Florida Reef Tract, Florida (FL) including the shallow seafloor along the coast of Miami, FL. USGS staff used historical bathymetric point data from the 1930's (National Oceanic and Atmospheric Administration (NOAA) Office of Coast Survey, see Yates and others, 2017) and light detection and ranging (lidar)-derived data acquired in 2002 (Brock and others, 2006, 2007) to calculate historical seafloor elevation changes in the Upper Florida Keys (UFK) (Yates and others, 2017). Using those changes in seafloor elevation, annual rates of elevation change were calculated for 13 habitat types found in the UFK reef tract. The annual rate of mean elevation change for each habitat type was applied to a digital elevation model (DEM) extending from Deerfield Beach to Homestead, FL that was modified from the NOAA National Centers for Environmental Information (NCEI) Miami coastal DEM (NOAA, 2015) to project future seafloor elevation (from 2014) along the Miami section of the Florida Reef Tract. Grid resolution for the DEM is 1/3 arc second (approximately 10 meters).

The U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center conducted research to quantify the combined effect of all constructive and destructive processes on modern coral reef ecosystems by projecting future regional-scale changes in seafloor elevation for several sites along the Florida Reef Tract, Florida (FL) including the shallow seafloor along the coast of Miami, FL. USGS staff used historical bathymetric point data from the 1930's (National Oceanic and Atmospheric Administration (NOAA) Office of Coast Survey, see Yates and others, 2017) and light detection and ranging (lidar)-derived data acquired in 2002 (Brock and others, 2006, 2007) to calculate historical seafloor elevation changes in the Upper Florida Keys (UFK) (Yates and others, 2017). Using those changes in seafloor elevation, annual rates of erosion were calculated for 13 habitat types found in the UFK reef tract. The annual rate of mean erosion for each habitat type was applied to a digital elevation model (DEM) extending from Deerfield Beach to Homestead, FL that was modified from the NOAA National Centers for Environmental Information (NCEI) Miami coastal DEM (NOAA, 2015) to project future seafloor elevation (from 2014) along the Miami section of the Florida Reef Tract. Grid resolution for the DEM is 1/3 arc second (approximately 10 meters).

The U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center conducted research to quantify the combined effect of all constructive and destructive processes on modern coral reef ecosystems by projecting future regional-scale changes in seafloor elevation along the Florida Reef Tract, Florida (FL). USGS staff used historical bathymetric point data from the 1930's (National Oceanic and Atmospheric Administration (NOAA) Office of Coast Survey, see Yates and others, 2017) and light detection and ranging (lidar)-derived data acquired in 2002 (Brock and others, 2006, 2007) to calculate historical seafloor elevation changes in the Upper Florida Keys (UFK) (Yates and others, 2017). Using those changes in seafloor elevation, annual rates of elevation change were calculated for 13 habitat types found in the UFK reef tract. The annual rate of mean elevation change for each habitat type was applied to a digital elevation model (DEM) extending from Port St. Lucie to Marquesas Key, FL that was modified from the NOAA National Centers for Environmental Information (NCEI) U.S. Coastal Relief Model coastal DEM (NOAA, 2001) to project future seafloor elevation (from 2001) along the Florida Reef Tract. Grid resolution for the DEM is 3-arc seconds (approximately 90 meters).

The U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center conducted research to quantify the combined effect of all constructive and destructive processes on modern coral reef ecosystems by projecting future regional-scale changes in seafloor elevation along the Florida Reef Tract, Florida (FL). USGS staff used historical bathymetric point data from the 1930's (National Oceanic and Atmospheric Administration (NOAA) Office of Coast Survey, see Yates and others, 2017) and light detection and ranging (lidar)-derived data acquired in 2002 (Brock and others, 2006, 2007) to calculate historical seafloor elevation changes in the Upper Florida Keys (UFK) (Yates and others, 2017). Using those changes in seafloor elevation, annual rates of erosion were calculated for 13 habitat types found in the UFK reef tract. The annual rate of mean erosion for each habitat type was applied to a digital elevation model (DEM) extending from Port St. Lucie to Marquesas Key, FL that was modified from the NOAA National Centers for Environmental Information (NCEI) U.S. Coastal Relief Model coastal DEM (NOAA, 2001) to project future seafloor elevation (from 2001) along the Florida Reef Tract. Grid resolution for the DEM is 3-arc seconds (approximately 90 meters (m)).

The U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center conducted research to quantify the combined effect of all constructive and destructive processes on modern coral reef ecosystems by projecting future regional-scale changes in seafloor elevation for several sites along the Florida Reef Tract, Florida (FL) including the shallow seafloor along Key West, FL. USGS staff used historical bathymetric point data from the 1930's (National Oceanic and Atmospheric Administration (NOAA) Office of Coast Survey, see Yates and others, 2017) and light detection and ranging (lidar)-derived data acquired in 2002 (Brock and others, 2006, 2007) to calculate historical seafloor elevation changes in the Upper Florida Keys (UFK) (Yates and others, 2017). Using those changes in seafloor elevation, annual rates of elevation change were calculated for 13 habitat types found in the UFK reef tract. The annual rate of mean elevation change for each habitat type was applied to a digital elevation model (DEM) extending from Big Pine Key to Marquesas Key, FL that was modified from the NOAA National Centers for Environmental Information (NCEI) Key West coastal DEM (NOAA, 2011) to project future seafloor elevation (from 2011) along the Key West section of the Florida Reef Tract. Grid resolution for the DEM is 1/3 arc second (approximately 10 meters).

The U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center conducted research to quantify the combined effect of all constructive and destructive processes on modern coral reef ecosystems by projecting future regional-scale changes in seafloor elevation for several sites along the Florida Reef Tract, Florida (FL) including the shallow seafloor along Key West, FL. USGS staff used historical bathymetric point data from the 1930's (National Oceanic and Atmospheric Administration (NOAA) Office of Coast Survey, see Yates and others, 2017) and light detection and ranging (lidar)-derived data acquired in 2002 (Brock and others, 2006, 2007) to calculate historical seafloor elevation changes in the Upper Florida Keys (UFK) (Yates and others, 2017). Using those changes in seafloor elevation, annual rates of erosion were calculated for 13 habitat types found in the UFK reef tract. The annual rate of mean erosion for each habitat type was applied to a digital elevation model (DEM) extending from Big Pine Key to Marquesas Key, FL that was modified from the NOAA National Centers for Environmental Information (NCEI) Key West coastal DEM (NOAA, 2011) to project future seafloor elevation (from 2011) along the Key West section of the Florida Reef Tract. Grid resolution for the DEM is 1/3 arc-second (approximately 10 meters).

The U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center conducted research to quantify the combined effect of all constructive and destructive processes on modern coral reef ecosystems by projecting future regional-scale changes in seafloor elevation for several sites along the Florida Reef Tract, Florida (FL) including the shallow seafloor along the coast of Miami, FL. USGS staff used historical bathymetric point data from the 1930's (National Oceanic and Atmospheric Administration (NOAA) Office of Coast Survey, see Yates and others, 2017) and light detection and ranging (lidar)-derived data acquired in 2002 (Brock and others, 2006, 2007) to calculate historical seafloor elevation changes in the Upper Florida Keys (UFK) (Yates and others, 2017). Using those changes in seafloor elevation, annual rates of elevation change were calculated for 13 habitat types found in the UFK reef tract. The annual rate of mean elevation change for each habitat type was applied to a digital elevation model (DEM) extending from Deerfield Beach to Homestead, FL that was modified from the NOAA National Centers for Environmental Information (NCEI) Miami coastal DEM (NOAA, 2015) to project future seafloor elevation (from 2014) along the Miami section of the Florida Reef Tract. Grid resolution for the DEM is 1/3 arc second (approximately 10 meters).

The U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center conducted research to quantify the combined effect of all constructive and destructive processes on modern coral reef ecosystems by projecting future regional-scale changes in seafloor elevation for several sites along the Florida Reef Tract, Florida (FL) including the shallow seafloor along the coast of Miami, FL. USGS staff used historical bathymetric point data from the 1930's (National Oceanic and Atmospheric Administration (NOAA) Office of Coast Survey, see Yates and others, 2017) and light detection and ranging (lidar)-derived data acquired in 2002 (Brock and others, 2006, 2007) to calculate historical seafloor elevation changes in the Upper Florida Keys (UFK) (Yates and others, 2017). Using those changes in seafloor elevation, annual rates of erosion were calculated for 13 habitat types found in the UFK reef tract. The annual rate of mean erosion for each habitat type was applied to a digital elevation model (DEM) extending from Deerfield Beach to Homestead, FL that was modified from the NOAA National Centers for Environmental Information (NCEI) Miami coastal DEM (NOAA, 2015) to project future seafloor elevation (from 2014) along the Miami section of the Florida Reef Tract. Grid resolution for the DEM is 1/3 arc second (approximately 10 meters).

The U.S. Geological Survey (USGS) conducts baseline and storm-response photography missions to document and understand the changes in the vulnerability of the Nation's coasts to extreme storms. On March 27, 1998, the USGS conducted an oblique aerial photographic survey from Fenwick Island State Park, Delaware, to Corolla, North Carolina, aboard a U.S. Coast Guard HH60 Helicopter at an altitude of 500 feet (ft) and approximately 1,000 ft offshore. This mission was conducted to collect data for assessing incremental changes in the beach and nearshore area and can also be used as a baseline to assess future coastal change. The photographs provided are Joint Photographic Experts Group (JPEG) images. The photograph locations are an estimate of the aircraft's position and do not indicate the location of the features in the images. These photographs document the configuration of the barrier islands and other coastal features at the time of the survey. ExifTool (version 4.0) was used to add the following to the header of each photograph: time of collection, GPS latitude, GPS longitude, keywords, credit, artist (photographer), caption, copyright, and contact information. Photographs can be opened with any JPEG-compatible image viewer. All image times are recorded in Coordinated Universal Time (UTC).

This dataset includes Attenuation Factor (AF; Rao and others, 1985) model results for Upper Floridan aquifer vulnerability to Bromacil and 1,2-Dibromoethane or Ethylene Dibromide (EDB). The AF value serves as an index for assessing the transport of pesticide mass from the vadose zone. The AF model setup requires the input of raster soil bulk density, soil organic carbon content, soil field capacity, soil air filled porosity, recharge to the aquifer, depth to groundwater, the pesticide sorption coefficient, pesticide Henry's Law Constant, and pesticide half-life. These variables were entered into the AF equation using the raster calculator tool in ArcGIS. The resulting AF values are dimensionless and range between 0 and 1. A value of 1 indicates that all of the pesticides in the vadose zone will leach into the groundwater; conversely, a value of 0 suggests that no pesticides will leach into the groundwater.

A digital elevation model (DEM) mosaic was produced for Anegada, British Virgin Islands, from remotely sensed, geographically referenced elevation measurements collected by Watershed Sciences, Inc. (WSI)/Quantum Spatial using an Optech Orion M300 (1064-nm wavelength) lidar sensor on January 21, 2014.

ASCII XYZ point cloud data for a portion of the environs of Anegada, British Virgin Islands, was produced from remotely sensed, geographically referenced elevation measurements collected March 19-20, 2014 by the U.S. Geological Survey. Elevation measurements were collected over the area using the second-generation Experimental Advanced Airborne Research Lidar (EAARL-B), a pulsed laser ranging system mounted onboard an aircraft to measure ground elevation, vegetation canopy, and coastal topography. The system uses high-frequency laser beams directed at the Earth's surface through an opening in the bottom of the aircraft's fuselage. The laser system records the time difference between emission of the laser beam and the reception of the reflected laser signal in the aircraft. The plane travels over the target area at approximately 55 meters per second at an elevation of approximately 300 meters, resulting in a laser swath of approximately 240 meters with an average point spacing of 0.5-1.6 meters. The nominal vertical elevation accuracy expressed as the root mean square error (RMSE) is 20 centimeters. A peak sampling rate of 15-30 kilohertz results in an extremely dense spatial elevation dataset. More than 100 kilometers of coastline can be surveyed easily within a 3- to 4-hour mission. When resultant elevation maps for an area are analyzed, they provide a useful tool to make management decisions regarding land development.

A seamless (bare earth and submerged) topography Digital Elevation Model (DEM) mosaic for a portion of the submerged environs of Anegada, British Virgin Islands, was produced from remotely sensed, geographically referenced elevation measurements collected March 19-20, 2014 by the U.S. Geological Survey. Elevation measurements were collected over the area using the second-generation Experimental Advanced Airborne Research Lidar (EAARL-B), a pulsed laser ranging system mounted onboard an aircraft to measure ground elevation, vegetation canopy, and coastal topography. The system uses high-frequency laser beams directed at the Earth's surface through an opening in the bottom of the aircraft's fuselage. The laser system records the time difference between emission of the laser beam and the reception of the reflected laser signal in the aircraft. The plane travels over the target area at approximately 55 meters per second at an elevation of approximately 300 meters, resulting in a laser swath of approximately 240 meters with an average point spacing of 0.5-1.6 meters. The nominal vertical elevation accuracy expressed as the root mean square error (RMSE) is 20 centimeters. A peak sampling rate of 15-30 kilohertz results in an extremely dense spatial elevation dataset. More than 100 kilometers of coastline can be surveyed easily within a 3- to 4-hour mission. When resultant elevation maps for an area are analyzed, they provide a useful tool to make management decisions regarding land development.

ASCII XYZ point cloud data were produced from remotely sensed, geographically referenced elevation measurements acquired cooperatively by the U.S. Geological Survey (USGS) and the National Park Service (NPS). Elevation measurements were collected over northern Assateague Island National Seashore using the first-generation National Aeronautics and Space Administration (NASA) Experimental Advanced Airborne Research Lidar (EAARL), a pulsed laser ranging system mounted onboard an aircraft to measure ground elevation, vegetation canopy, and coastal topography. The system uses high-frequency laser beams directed at the Earth's surface through an opening in the bottom of the aircraft's fuselage. The laser system records the time difference between emission of the laser beam and the reception of the reflected laser signal in the aircraft. The plane travels over the target area at approximately 60 meters per second at an elevation of approximately 300 meters, resulting in a laser swath of approximately 240 meters with an average point spacing of 2-3 meters. The EAARL, developed originally by NASA at Wallops Flight Facility in Virginia, measures ground elevation with a vertical resolution of 3 centimeters. A sampling rate of 3 kilohertz or higher results in an extremely dense spatial elevation dataset. Over 100 kilometers of coastline can be surveyed easily within a 3- to 4-hour mission. When resultant elevation maps for an area are analyzed, they provide a useful tool to make management decisions regarding land development.

A bare-earth topography Digital Elevation Model (DEM) mosaic for the northern half of Assateague Island National Seashore was produced from remotely sensed, geographically referenced elevation measurements acquired cooperatively by the U.S. Geological Survey (USGS) and the National Park Service (NPS). Elevation measurements were collected over northern Assateague Island National Seashore using the first-generation National Aeronautics and Space Administration (NASA) Experimental Advanced Airborne Research Lidar (EAARL), a pulsed laser ranging system mounted onboard an aircraft to measure ground elevation, vegetation canopy, and coastal topography. The system uses high-frequency laser beams directed at the Earth's surface through an opening in the bottom of the aircraft's fuselage. The laser system records the time difference between emission of the laser beam and the reception of the reflected laser signal in the aircraft. The plane travels over the target area at approximately 60 meters per second at an elevation of approximately 300 meters, resulting in a laser swath of approximately 240 meters with an average point spacing of 2-3 meters. The EAARL, developed originally by NASA at Wallops Flight Facility in Virginia, measures ground elevation with a vertical resolution of 3 centimeters. A sampling rate of 3 kilohertz or higher results in an extremely dense spatial elevation dataset. Over 100 kilometers of coastline can be surveyed easily within a 3- to 4-hour mission. When resultant elevation maps for an area are analyzed, they provide a useful tool to make management decisions regarding land development.

ASCII XYZ point cloud data were produced from remotely sensed, geographically referenced elevation measurements acquired cooperatively by the U.S. Geological Survey (USGS) and the National Park Service (NPS). Elevation measurements were collected over Assateague Island National Seashore using the first-generation National Aeronautics and Space Administration (NASA) Experimental Advanced Airborne Research Lidar (EAARL), a pulsed laser ranging system mounted onboard an aircraft to measure ground elevation, vegetation canopy, and coastal topography. The system uses high-frequency laser beams directed at the Earth's surface through an opening in the bottom of the aircraft's fuselage. The laser system records the time difference between emission of the laser beam and the reception of the reflected laser signal in the aircraft. The plane travels over the target area at approximately 60 meters per second at an elevation of approximately 300 meters, resulting in a laser swath of approximately 240 meters with an average point spacing of 2-3 meters. The EAARL, developed originally by NASA at Wallops Flight Facility in Virginia, measures ground elevation with a vertical resolution of 3 centimeters. A sampling rate of 3 kilohertz or higher results in an extremely dense spatial elevation dataset. Over 100 kilometers of coastline can be surveyed easily within a 3- to 4-hour mission. When resultant elevation maps for an area are analyzed, they provide a useful tool to make management decisions regarding land development.

A bare-earth topography Digital Elevation Model (DEM) mosaic for the Assateague Island National Seashore was produced from remotely sensed, geographically referenced elevation measurements acquired cooperatively by the U.S. Geological Survey (USGS) and the National Park Service (NPS). Elevation measurements were collected over Assateague Island National Seashore using the first-generation National Aeronautics and Space Administration (NASA) Experimental Advanced Airborne Research Lidar (EAARL), a pulsed laser ranging system mounted onboard an aircraft to measure ground elevation, vegetation canopy, and coastal topography. The system uses high-frequency laser beams directed at the Earth's surface through an opening in the bottom of the aircraft's fuselage. The laser system records the time difference between emission of the laser beam and the reception of the reflected laser signal in the aircraft. The plane travels over the target area at approximately 60 meters per second at an elevation of approximately 300 meters, resulting in a laser swath of approximately 240 meters with an average point spacing of 2-3 meters. The EAARL, developed originally by NASA at Wallops Flight Facility in Virginia, measures ground elevation with a vertical resolution of 3 centimeters. A sampling rate of 3 kilohertz or higher results in an extremely dense spatial elevation dataset. Over 100 kilometers of coastline can be surveyed easily within a 3- to 4-hour mission. When resultant elevation maps for an area are analyzed, they provide a useful tool to make management decisions regarding land development.

ASCII XYZ point cloud data were produced from remotely sensed, geographically referenced elevation measurements acquired cooperatively by the U.S. Geological Survey (USGS) and the National Park Service (NPS). Elevation measurements were collected over Assateague Island National Seashore using the first-generation National Aeronautics and Space Administration (NASA) Experimental Advanced Airborne Research Lidar (EAARL), a pulsed laser ranging system mounted onboard an aircraft to measure ground elevation, vegetation canopy, and coastal topography. The system uses high-frequency laser beams directed at the Earth's surface through an opening in the bottom of the aircraft's fuselage. The laser system records the time difference between emission of the laser beam and the reception of the reflected laser signal in the aircraft. The plane travels over the target area at approximately 60 meters per second at an elevation of approximately 300 meters, resulting in a laser swath of approximately 240 meters with an average point spacing of 2-3 meters. The EAARL, developed originally by NASA at Wallops Flight Facility in Virginia, measures ground elevation with a vertical resolution of 3 centimeters. A sampling rate of 3 kilohertz or higher results in an extremely dense spatial elevation dataset. Over 100 kilometers of coastline can be surveyed easily within a 3- to 4-hour mission. When resultant elevation maps for an area are analyzed, they provide a useful tool to make management decisions regarding land development.

A first-surface topography Digital Elevation Model (DEM) mosaic for the Assateague Island National Seashore was produced from remotely sensed, geographically referenced elevation measurements acquired cooperatively by the U.S. Geological Survey (USGS) and the National Park Service (NPS). Elevation measurements were collected over Assateague Island National Seashore using the first-generation National Aeronautics and Space Administration (NASA) Experimental Advanced Airborne Research Lidar (EAARL), a pulsed laser ranging system mounted onboard an aircraft to measure ground elevation, vegetation canopy, and coastal topography. The system uses high-frequency laser beams directed at the Earth's surface through an opening in the bottom of the aircraft's fuselage. The laser system records the time difference between emission of the laser beam and the reception of the reflected laser signal in the aircraft. The plane travels over the target area at approximately 60 meters per second at an elevation of approximately 300 meters, resulting in a laser swath of approximately 240 meters with an average point spacing of 2-3 meters. The EAARL, developed originally by NASA at Wallops Flight Facility in Virginia, measures ground elevation with a vertical resolution of 3 centimeters. A sampling rate of 3 kilohertz or higher results in an extremely dense spatial elevation dataset. Over 100 kilometers of coastline can be surveyed easily within a 3- to 4-hour mission. When resultant elevation maps for an area are analyzed, they provide a useful tool to make management decisions regarding land development.

A digital elevation model (DEM) mosaic was produced for Assateague Island, Maryland and Virginia, post-Hurricane Joaquin, from remotely sensed, geographically referenced elevation measurements collected by Quantum Spatial using a Leica ALS70 (1064-nm wavelength) lidar sensor.

Binary point-cloud data were produced for Assateague Island, Maryland and Virginia, post-Hurricane Hermine, from remotely sensed, geographically referenced elevation measurements collected by Quantum Spatial using a Riegl VQ-880-G (532-nm wavelength circular scan and 1064-nm wavelength linear scan) lidar sensor.

A digital elevation model (DEM) mosaic was produced for Assateague Island, Maryland and Virginia, post-Hurricane Hermine, from remotely sensed, geographically referenced elevation measurements collected by Quantum Spatial using a Riegl VQ-880-G (532-nm wavelength circular scan and 1064-nm wavelength linear scan) lidar sensor.

The U.S. Geological Survey (USGS) Coastal and Marine Geology Program has actively collected geophysical and sedimentological data in the northern Gulf of Mexico for several decades, including shallow subsurface data in the form of high-resolution seismic-reflection profiles (HRSP). Prior to the mid-1990s most HRSP data were collected in analog format as paper rolls of continuous profiles up to 25 meters long. A large portion of this data resides in a single repository with minimal metadata. As part of the National Geological and Geophysical Data Preservation Program, scientists at the USGS St. Petersburg Coastal and Marine Science Center are converting the analog paper records to digital format using a large-format continuous scanner. This data release serves as an archive of seismic profiles with headers, converted Society of Exploration Geophysicists Y format (SEG-Y) files, navigation data, and geographic information system (GIS) data files for digitized boomer seismic-reflection data collected from the Research Vessel (R/V) Acadiana. The Acadiana 87-2 geophysical cruise included seismic data collected in the northern Gulf of Mexico, Chandeleur Sound, and Mississippi Sound from June 15 to June 26, 1987.

Scientists from the South West Florida Management District (SWFWMD) acquired and analyzed over 20 years of seasonally-sampled hydrochemical data from five first-order-magnitude (springs that discharge 2.83 m3 s-1 or more) coastal springs located in west-central Florida. These data were subsequently obtained by the U.S. Geological Survey (USGS) for further analyses and interpretation. The spring study sites (Chassahowitzka, Homosassa, Kings Bay, Rainbow, and Weeki Wachee), which are fed by the Floridan Aquifer system and discharge into the Gulf of Mexico were investigated to identify temporal and spatial trends of pH, alkalinity, partial pressure of carbon dioxide (pCO2) and CO2 flux.

Aerial_WDL_Shorelines.zip features digitized historic shorelines for the Dauphin Island coastline from October 1940 to November 2015. This dataset contains 10 Wet Dry Line (WDL) shorelines separated into 58 shoreline segments alongshore Dauphin Island, AL. The individual sections are divided according to location along the island and shoreline type: open-ocean, back-barrier, marsh shoreline. Imagery of Dauphin Island, Alabama was acquired from several sources including the United States Geological Survey (USGS), the National Agriculture Imagery Program (NAIP), the United States Department of Agriculture's Farm Service Agency (USDA, FSA), and the University of Alabama. These images were downloaded directly from the source's website or received as a hard copy via mail. Using ArcMap 10.3.1, the imagery was used to delineate and digitize historical shorelines at the wet-dry line along sandy beaches and the mean high water line where vegetation indicated. These shorelines were digitized for use in long-term shoreline and wetland analyses and physical change assessments. Shorelines for all 10 dates were compiled into a database for use with the Digital Shoreline Analysis System (DSAS; Thieler and others, 2009) to quantify rates of shoreline change over the 1940-2015 time period. The migration of shorelines through time is presented as the linear regression rate (LRR) in the associated back-barrier and open ocean transect files, which are also included in the USGS data release (https://coastal.er.usgs.gov/data-release/provisional/ip086178/).

This dataset defines storm-induced coastal erosion hazards for the Louisiana, Mississippi, Alabama and Florida coastline. The analysis was based on a storm-impact scaling model that used observations of beach morphology combined with sophisticated hydrodynamic models to predict how the coast would respond to the direct landfall of Subtropical Storm Alberto in May 2018. Storm-induced water levels, due to both surge and waves, were compared to beach and dune elevations to determine the probabilities of the three types of coastal change: collision (dune erosion), overwash, and inundation. All hydrodynamic and morphologic variables are included in this dataset.

Sedimentologic and topographic data from Hurricane Sandy washover deposits were collected from Southern Long Beach Island, New Jersey, in order to document changes to the barrier-island beaches, dunes, and coastal wetlands due to Hurricane Sandy and subsequent storm events. These data will provide a baseline dataset for use in future coastal change descriptive and predictive studies and assessments. The data presented here were collected as part of the U.S. Geological Survey’s Barrier Island and Estuarine Wetland Physical Change Assessment project (http://coastal.er.usgs.gov/sandy-wetland-assessment/), which aims to assess ecological and societal vulnerability that results from long- and short-term physical changes to barrier islands and coastal wetlands. This metadata record describes data that were collected in April 2015, approximately two and a half years after Hurricane Sandy’s landfall on 29 October 2012. During the field campaign, washover deposits were photographed, and described. In addition, sediment samples, cores, and surface elevations were collected. Data collected during this study including sample locations and elevations, core photographs, computed tomography (CT) scans, descriptive core logs, sediment grain-size data, and accompanying Federal Geographic Data Committee (FGDC) metadata are provided in the associated USGS Data Release, available at https://doi.org/10.5066/F7PK0D7S.

Sedimentologic and topographic data from Hurricane Sandy (HS) washover deposits were collected from Southern Long Beach Island, New Jersey, in order to document changes to the barrier-island beaches, dunes, and coastal wetlands due to HS and subsequent storm events. These data will provide a baseline dataset for use in future coastal change descriptive and predictive studies and assessments. The data presented here were collected as part of the U.S. Geological Survey’s Barrier Island and Estuarine Wetland Physical Change Assessment Project (http://coastal.er.usgs.gov/sandy-wetland-assessment/), which aims to assess ecological and societal vulnerability that results from long- and short-term physical changes to barrier islands and coastal wetlands. This metadata record describes data that were collected in April 2015, approximately two and a half years after HS’s landfall on 29 October 2012. During the field campaign, washover deposits were photographed and described. In addition, sediment samples, cores, and surface elevations were collected. Data products provided in the associated USGS Data Release (available at https://doi.org/10.5066/F7PK0D7S) include, sample locations and elevations, core photographs, computed tomography (CT) scans, descriptive core logs, sediment grain-size data, and accompanying Federal Geographic Data Committee (FGDC) metadata.

A first-surface topography Digital Elevation Model (DEM) mosaic for the Canyonlands and Upper Neches River Corridor Units of Big Thicket National Preserve in Texas was produced from remotely sensed, geographically referenced elevation measurements collected on January 11, 15, 17, 18, 21, 23, 25, and 29, 2014 by the U.S. Geological Survey, in cooperation with the National Park Service - Gulf Coast Network. Elevation measurements were collected over the area using the second-generation Experimental Advanced Airborne Research Lidar (EAARL-B), a pulsed laser ranging system mounted onboard an aircraft to measure ground elevation, vegetation canopy, and coastal topography. The system uses high-frequency laser beams directed at the Earth's surface through an opening in the bottom of the aircraft's fuselage. The laser system records the time difference between emission of the laser beam and the reception of the reflected laser signal in the aircraft. The plane travels over the target area at approximately 55 meters per second at an elevation of approximately 300 meters, resulting in a laser swath of approximately 240 meters with an average point density of 1.4 points per square meter. A peak sampling rate of 15-30 kilohertz results in an extremely dense spatial elevation dataset. More than 100 kilometers of coastline can be surveyed easily within a 3- to 4-hour mission. When resultant elevation maps for an area are analyzed, they provide a useful tool to make management decisions regarding land development.

Analysis of shoreline change for Dauphin Island, Alabama was conducted using the U.S. Geological Survey (USGS) Digital Shoreline Analysis System (DSAS) v.4.3 for ArcMap (Thieler and others, 2009) and vector shorelines derived from air photos and lidar elevation surveys. DSAS-generated transects were cast at 100-meter intervals along a user defined shore-parallel baseline. The intersections of transects with the mean high water (MHW) shoreline positions are identified by intercept points. The rate of shoreline change was determined by measuring the differences in the distance to each historical shoreline position from the baseline along each transect. Three analyses of change rates were conducted using a combination of shorelines derived from different data sources. Shoreline change rates from the wet dry line (WDL) shoreline were derived from 10 sets of air photos from 1940 - 2015. Rates of change were also calculated using MHW shorelines extracted from 14 lidar datasets from 1998 - 2014. A final change analysis was conducted using a combination of all WDL (aerial) and MHW (lidar) shorelines, from 1940 - 2015.

Coastal marshes are highly dynamic and ecologically important ecosystems that are subject to pervasive and often harmful disturbances, including shoreline erosion. Shoreline erosion can result in an overall loss of coastal marsh, particularly in estuaries with moderate- or high-wave energy. Not only can waves be important physical drivers of shoreline change they can also influence shore-proximal vertical accretion through sediment delivery. For these reasons, estimates of wave energy can provide a quantitative measure of wave effects on marsh shorelines. Since wave energy is difficult to measure at all locations, scientists and managers often rely on hydrodynamic models to estimate wave properties at different locations. The Wave Exposure Model (WEMo) is a simple tool that uses linear wave theory to estimate wave energy characteristics for enclosed and semi-enclosed estuaries (Malhotra and Fonseca, 2007). The interpretation of hydrodynamic models is improved if model results can be validated against measured data. The data presented in this publication are input and validation data for modeled and observed mean wave height for two temporary oceanographic stations established by the U.S. Geological Survey (USGS) in the Grand Bay National Estuarine Research Reserve, Mississippi.

This dataset defines storm-induced coastal erosion hazards for the Texas and Louisiana coastline. The analysis was based on a storm-impact scaling model that used observations of beach morphology combined with sophisticated hydrodynamic models to predict how the coast would respond to the direct landfall of Tropical Storm Bill in June 2015. Storm-induced water levels, due to both surge and waves, were compared to beach and dune elevations to determine the probabilities of the three types of coastal change: collision (dune erosion), overwash, and inundation. All hydrodynamic and morphologic variables are included in this dataset.

1869 Digitized Shoreline for Breton Island, Louisiana (Geographic, NAD83) consists of vector shoreline data that were derived from a set of National Ocean Service (NOS) raster shoreline maps (often called T-sheet or TP-sheet maps) created for Breton Island in 1869. In 2002, NOAA published digitized shorelines for T-sheet (T-1097), which were subsequently edited by USGS staff for input into the Digital Shoreline Analysis System (DSAS) Version 4.0, where area and shoreline change analyses could be conducted.

1922 Digitized Shoreline for Breton Island, Louisiana (Geographic, NAD83) consists of vector shoreline data that were derived from a set of National Ocean Service (NOS) raster shoreline maps (often called T-sheet or TP-sheet maps) created for Breton Island in 1922. In 2002, NOAA published digitized shorelines for T-sheet (T-3920), which were subsequently edited by USGS staff for input into the Digital Shoreline Analysis System (DSAS) Version 4.0, where area and shoreline change analyses could be conducted.

1950 Digitized Shoreline for Breton Island, Louisiana (Geographic, NAD83) consists of vector shoreline data that were derived from a set of National Ocean Service (NOS) raster shoreline maps (often called T-sheet or TP-sheet maps) created for Breton Island in 1950. In 2002, NOAA published digitized shorelines for T-sheet (T-9393), which were subsequently edited by USGS staff for input into the Digital Shoreline Analysis System (DSAS) Version 4.0, where area and shoreline change analyses could be conducted.

Shorelines were derived from the National High Altitude Photography (NHAP) program. The NHAP was coordinated by the U.S. Geological Survey as an interagency project to acquire cloud-free aerial photographs at a specific altitude above mean terrain elevation. Two different camera systems were used to obtain simultaneous coverage of black-and-white (BW) and color infrared (CIR) aerial photographs over the conterminous United States. Black-and-white aerial photographs were obtained on 9-inch film from an altitude of 40,000 feet above mean terrain elevation and are centered over USGS 7.5-minute quadrangles. Images are at a scale of 1:80,000 (1 inch equals about 1.26 miles). All NHAP flights where flown in a north to south direction. Imagery was collected over Breton Island on November 17, 1983. This dataset contains digitized shorelines created from the NHAP imagery for Breton Island, Louisiana. Shorelines were digitized in ArcMap 10.2.2 so they could be used for area and shoreline change analysis, using the Digital Shoreline Analysis System (DSAS) Version 4.0.

Shorelines were derived from the U.S. Geological Survey Earth Resources Observation and Science (EROS) Center's Digital Orthophoto Quarter Quads (DOQQ) images collected on January 24, 1998. This dataset contains digitized shorelines created from the USGS imagery for Breton Island, Louisiana. Shorelines were digitized in ArcMap 10.2.2 so they could be used for area and shoreline change analysis using the Digital Shoreline Analysis System (DSAS) Version 4.0.

A first-surface elevation map was produced cooperatively from remotely sensed, geographically referenced elevation measurements collected by the U.S. Geological Survey (USGS) and National Aeronautics and Space Administration (NASA) on September 07-09, 2001. Elevation measurements were collected over the area using the NASA Airborne Topographic Mapper (ATM), a scanning lidar system that measures high-resolution topography of the land surface. The ATM system is deployed on a Twin Otter or P-3 Orion aircraft and incorporates a green-wavelength laser operating at pulse rates of 2 to 10 kilohertz. Measurements from the laser-ranging device are coupled with data acquired from inertial navigation system (INS) attitude sensors and differentially corrected global positioning system (GPS) receivers to measure topography of the surface at accuracies of +/-15 centimeters. This dataset contains vectorized shorelines created from data acquired from Breton Island, Louisiana. Shorelines were vectorized in ArcMap 10.2.2 so they could be used for area and shoreline change analysis, using the Digital Shoreline Analysis System (DSAS) Version 4.0.

Shorelines were derived from the U.S. Geological Survey Earth Resources Observation and Science (EROS) Center’s Digital Orthophoto Quarter Quads (DOQQ) images collected on January 20, 2004. This dataset contains digitized shorelines created from the USGS imagery for Breton Island, Louisiana. Shorelines were digitized in ArcMap 10.2.2 so they could be used for area and shoreline change analysis, using the Digital Shoreline Analysis System (DSAS) version 4.0.

Shorelines were derived from the U.S. Geological Survey Earth Resources Observation and Science (EROS) Center’s Digital Orthophoto Quadrangle (DOQ) images collected on November 17, 2005. This dataset contains digitized shorelines created from the USGS imagery for Breton Island, Louisiana. Shorelines were digitized in ArcMap 10.2.2 so they could be used for area and shoreline change analysis, using the Digital Shoreline Analysis System (DSAS) version 4.0.

Shorelines were derived from the National Agriculture Imagery Program (NAIP) digital ortho imagery collected on October 11, 2007. This dataset contains digitized shorelines created from the NAIP imagery for Breton Island, Louisiana. Shorelines were digitized in ArcMap 10.2.2 so they could be used for area and shoreline change analysis using the Digital Shoreline Analysis System (DSAS) Version 4.0.

Shorelines were derived from the National Agriculture Imagery Program (NAIP) digital ortho imagery collected on May 10, 2010. This dataset contains digitized shorelines created from the NAIP imagery for Breton Island, Louisiana. Shorelines were digitized in ArcMap 10.2.2 so they could be used for area and shoreline change analysis using the Digital Shoreline Analysis System (DSAS) version 4.0.

Shorelines were derived from a U.S. Geological Survey Earth Resources Observations and Science Center (EROS) high-resolution orthorectified image that was collected on October 20, 2012 over Breton Island, Louisiana. Shorelines were digitized in ArcMap 10.2.2 so they could be used for area and shoreline change analysis using the Digital Shoreline Analysis System (DSAS) version 4.0.

Shorelines were derived from a U.S. Geological Survey topographic lidar survey that was conducted on July 12-14, 2013 over Dauphin Island, Alabama and Chandeleur, Stake, Grand Gosier and Breton Islands, Louisiana and published in USGS Data Series 838. Photo Science, Inc., was contracted by the USGS to collect and process these data. Lidar data were acquired around portions of both the Alabama and Louisiana barrier islands; however, this dataset only contains shorelines created from data acquired from Breton Island, Louisiana. Shorelines were vectorized in ArcMap 10.2.2 so they could be used for area and shoreline change analysis, using the Digital Shoreline Analysis System (DSAS) version 4.0.

Shorelines were derived from a U.S. Geological Survey topographic lidar survey that was conducted on January 16-18, 2014 over Breton Island, Louisiana and released under USGS field activity number 14LGC01. Quantum Spatial was contracted by the USGS to collect and process these data. This dataset contains vectorized shorelines created from data acquired from Breton Island, Louisiana. Shorelines were vectorized in ArcMap 10.2.2 so they could be used for area and shoreline change analysis, using the Digital Shoreline Analysis System (DSAS) version 4.0.

ASCII XYZ point cloud data were produced from remotely sensed, geographically referenced elevation measurements acquired cooperatively by the U.S. Geological Survey (USGS) and the National Park Service (NPS). Elevation measurements were collected over Cape Cod National Seashore using the first-generation National Aeronautics and Space Administration (NASA) Experimental Advanced Airborne Research Lidar (EAARL), a pulsed laser ranging system mounted onboard an aircraft to measure ground elevation, vegetation canopy, and coastal topography. The system uses high-frequency laser beams directed at the Earth's surface through an opening in the bottom of the aircraft's fuselage. The laser system records the time difference between emission of the laser beam and the reception of the reflected laser signal in the aircraft. The plane travels over the target area at approximately 60 meters per second at an elevation of approximately 300 meters, resulting in a laser swath of approximately 240 meters with an average point spacing of 2-3 meters. The EAARL, developed originally by NASA at Wallops Flight Facility in Virginia, measures ground elevation with a vertical resolution of 3 centimeters. A sampling rate of 3 kilohertz or higher results in an extremely dense spatial elevation dataset. Over 100 kilometers of coastline can be surveyed easily within a 3- to 4-hour mission. When resultant elevation maps for an area are analyzed, they provide a useful tool to make management decisions regarding land development.

A bare-earth topography Digital Elevation Model (DEM) mosaic for the Cape Cod National Seashore was produced from remotely sensed, geographically referenced elevation measurements acquired cooperatively by the U.S. Geological Survey (USGS) and the National Park Service (NPS). Elevation measurements were collected over Cape Cod National Seashore using the first-generation National Aeronautics and Space Administration (NASA) Experimental Advanced Airborne Research Lidar (EAARL), a pulsed laser ranging system mounted onboard an aircraft to measure ground elevation, vegetation canopy, and coastal topography. The system uses high-frequency laser beams directed at the Earth's surface through an opening in the bottom of the aircraft's fuselage. The laser system records the time difference between emission of the laser beam and the reception of the reflected laser signal in the aircraft. The plane travels over the target area at approximately 60 meters per second at an elevation of approximately 300 meters, resulting in a laser swath of approximately 240 meters with an average point spacing of 2-3 meters. The EAARL, developed originally by NASA at Wallops Flight Facility in Virginia, measures ground elevation with a vertical resolution of 3 centimeters. A sampling rate of 3 kilohertz or higher results in an extremely dense spatial elevation dataset. Over 100 kilometers of coastline can be surveyed easily within a 3- to 4-hour mission. When resultant elevation maps for an area are analyzed, they provide a useful tool to make management decisions regarding land development.

A first-surface topography Digital Elevation Model (DEM) mosaic for the Cape Cod National Seashore was produced from remotely sensed, geographically referenced elevation measurements acquired cooperatively by the U.S. Geological Survey (USGS) and the National Park Service (NPS). Elevation measurements were collected over Cape Cod National Seashore using the first-generation National Aeronautics and Space Administration (NASA) Experimental Advanced Airborne Research Lidar (EAARL), a pulsed laser ranging system mounted onboard an aircraft to measure ground elevation, vegetation canopy, and coastal topography. The system uses high-frequency laser beams directed at the Earth's surface through an opening in the bottom of the aircraft's fuselage. The laser system records the time difference between emission of the laser beam and the reception of the reflected laser signal in the aircraft. The plane travels over the target area at approximately 60 meters per second at an elevation of approximately 300 meters, resulting in a laser swath of approximately 240 meters with an average point spacing of 2-3 meters. The EAARL, developed originally by NASA at Wallops Flight Facility in Virginia, measures ground elevation with a vertical resolution of 3 centimeters. A sampling rate of 3 kilohertz or higher results in an extremely dense spatial elevation dataset. Over 100 kilometers of coastline can be surveyed easily within a 3- to 4-hour mission. When resultant elevation maps for an area are analyzed, they provide a useful tool to make management decisions regarding land development.

ASCII XYZ data for Cape Hatteras, North Carolina, were produced from remotely sensed, geographically referenced elevation measurements collected post-Hurricane Isabel on September 21, 2003 by the U.S. Geological Survey, in cooperation with the National Aeronautics and Space Administration (NASA). Elevation measurements were collected over the area using the first-generation Experimental Advanced Airborne Research Lidar (EAARL-A), a pulsed laser ranging system mounted onboard an aircraft to measure ground elevation, vegetation canopy, and coastal topography. The system uses high-frequency laser beams directed at the Earth's surface through an opening in the bottom of the aircraft's fuselage. The laser system records the time difference between emission of the laser beam and the reception of the reflected laser signal in the aircraft. The plane travels over the target area at approximately 55 meters per second at an elevation of approximately 300 meters, resulting in a laser swath of approximately 240 meters. More than 100 kilometers of coastline can be surveyed easily within a 3- to 4-hour mission. When resultant elevation maps for an area are analyzed, they provide a useful tool to make management decisions regarding land development.

ASCII XYZ data for Cape Hatteras, North Carolina, were produced from remotely sensed, geographically referenced elevation measurements collected pre-Hurricane Isabel on September 16, 2003 by the U.S. Geological Survey, in cooperation with the National Aeronautics and Space Administration (NASA). Elevation measurements were collected over the area using the first-generation Experimental Advanced Airborne Research Lidar (EAARL-A), a pulsed laser ranging system mounted onboard an aircraft to measure ground elevation, vegetation canopy, and coastal topography. The system uses high-frequency laser beams directed at the Earth's surface through an opening in the bottom of the aircraft's fuselage. The laser system records the time difference between emission of the laser beam and the reception of the reflected laser signal in the aircraft. The plane travels over the target area at approximately 55 meters per second at an elevation of approximately 300 meters, resulting in a laser swath of approximately 240 meters. More than 100 kilometers of coastline can be surveyed easily within a 3- to 4-hour mission. When resultant elevation maps for an area are analyzed, they provide a useful tool to make management decisions regarding land development.

Binary point-cloud data were produced for the Chandeleur Islands, Louisiana, from remotely sensed, geographically referenced elevation measurements collected by Leading Edge Geomatics (LEG) using a Leica Chiroptera II Bathymetric and Topographic Sensor. Dewberry reports that the nominal pulse spacing for this project was 1 point every 0.7 meters. Dewberry used proprietary procedures to classify the LAS according to project specifications: 0-Never Classified, 1-Unclassified, 2-Ground (includes model key point bit for points identified as Model Key Point), 7-Low Noise, 17-Bridges, 18-High Noise, 40-Bathymetric point or submerged topography (includes model key point bit for points identified as Model Key Point), 41-Water Surface, and 42-Derived water surface.

A digital elevation model (DEM) mosaic was produced for the Chandeleur Islands, Louisiana, from remotely sensed, geographically referenced elevation measurements collected by Leading Edge Geomatics (LEG) using a Leica Chiroptera II Bathymetric and Topographic Sensor. Dewberry reports that the nominal pulse spacing for this project was 1 point every 0.7 meters. Dewberry used proprietary procedures to classify the LAS according to project specifications: 0-Never Classified, 1-Unclassified, 2-Ground (includes model key point bit for points identified as Model Key Point), 7-Low Noise, 17-Bridges, 18-High Noise, 40-Bathymetric point or submerged topography (includes model key point bit for points identified as Model Key Point), 41-Water Surface, and 42-Derived water surface.

Aerial imagery acquired with a small unmanned aircraft system (sUAS), in conjunction with surveyed ground control points (GCP) visible in the imagery, can be processed with structure-from-motion (SfM) photogrammetry techniques to produce high-resolution orthomosaics, three-dimensional (3D) point clouds and digital elevation models (DEMs). This dataset, prepared by the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center (SPCMSC), provides UAS survey data products consisting of DEMs produced from imagery collected at Little Dauphin Island and Pelican Island, Alabama, from September 2018 to April 2019 in order to develop integrated models linking geomorphology, habitat characteristics, and non-breeding shorebird species usage. Photogrammetry software was used to perform SfM processing on low-altitude digital aerial imagery acquired with a 3DR Solo UAS quadcopter equipped with a Ricoh GR II digital camera and MicaSense RedEdge 3 multispectral camera, using surveyed temporary targets (black and white, 4-square checked pattern) distributed uniformly throughout the UAS flight operations area as GCPs. The following SfM products are produced for each UAS survey over the northern half of Little Dauphin Island and all of Pelican Island: * georeferenced red-green-blue (RGB) orthomosaic image with 5-centimeter (cm) resolution * georeferenced multispectral (MS) orthomosaic image with 5-cm resolution * DEM with 5-cm horizontal resolution * 3D RGB-colored point cloud All horizontal data are provided in the Universal Transverse Mercator (UTM) projected coordinate system, Zone 16 North (16N), referenced to the North American Datum of 1983 (NAD83(2011)), and elevation is referenced to the North American Vertical Datum of 1988 (NAVD88), GEOID12B.

Aerial imagery acquired with a small unmanned aircraft system (sUAS), in conjunction with surveyed ground control points (GCP) visible in the imagery, can be processed with structure-from-motion (SfM) photogrammetry techniques to produce high-resolution orthomosaics, three-dimensional (3D) point clouds and digital elevation models (DEMs). This dataset, prepared by the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center (SPCMSC), provides UAS survey data products consisting of multispectral (MS) orthomosaics produced from imagery collected at Little Dauphin Island and Pelican Island, Alabama, from September 2018 to April 2019 in order to develop integrated models linking geomorphology, habitat characteristics, and non-breeding shorebird species usage. Photogrammetry software was used to perform SfM processing on low-altitude digital aerial imagery acquired with a 3DR Solo UAS quadcopter equipped with a Ricoh GR II digital camera and MicaSense RedEdge 3 multispectral camera, using surveyed temporary targets (black and white, 4-square checked pattern) distributed uniformly throughout the UAS flight operations area as GCPs. The following SfM products are produced for each UAS survey over the northern half of Little Dauphin Island and all of Pelican Island: * georeferenced red-green-blue (RGB) orthomosaic image with 5-centimeter (cm) resolution * georeferenced MS orthomosaic image with 5-cm resolution * DEM with 5-cm horizontal resolution * 3D RGB-colored point cloud All horizontal data are provided in the Universal Transverse Mercator (UTM) projected coordinate system, Zone 16 North (16N), referenced to the North American Datum of 1983 (NAD83(2011)), and elevation is referenced to the North American Vertical Datum of 1988 (NAVD88), GEOID12B.

Aerial imagery acquired with a small unmanned aircraft system (sUAS), in conjunction with surveyed ground control points (GCP) visible in the imagery, can be processed with structure-from-motion (SfM) photogrammetry techniques to produce high-resolution orthomosaics, three-dimensional (3D) point clouds and digital elevation models (DEMs). This dataset, prepared by the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center (SPCMSC), provides UAS survey data products consisting of red-green-blue (RGB) orthomosaics produced from imagery collected at Little Dauphin Island and Pelican Island, Alabama, from September 2018 to April 2019 in order to develop integrated models linking geomorphology, habitat characteristics, and non-breeding shorebird species usage. Photogrammetry software was used to perform SfM processing on low-altitude digital aerial imagery acquired with a 3DR Solo UAS quadcopter equipped with a Ricoh GR II digital camera and MicaSense RedEdge 3 multispectral camera, using surveyed temporary targets (black and white, 4-square checked pattern) distributed uniformly throughout the UAS flight operations area as GCPs. The following SfM products are produced for each UAS survey over the northern half of Little Dauphin Island and all of Pelican Island: * georeferenced red-green-blue (RGB) orthomosaic image with 5-centimeter (cm) resolution * georeferenced multispectral (MS) orthomosaic image with 5-cm resolution * DEM with 5-cm horizontal resolution * 3D RGB-colored point cloud All horizontal data are provided in the Universal Transverse Mercator (UTM) projected coordinate system, Zone 16 North (16N), referenced to the North American Datum of 1983 (NAD83(2011)), and elevation is referenced to the North American Vertical Datum of 1988 (NAVD88), GEOID12B.

Aerial imagery acquired with a small unmanned aircraft system (sUAS), in conjunction with surveyed ground control points (GCP) visible in the imagery, can be processed with structure-from-motion (SfM) photogrammetry techniques to produce high-resolution orthomosaics, three-dimensional (3D) point clouds and digital elevation models (DEMs). This dataset, prepared by the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center (SPCMSC), provides UAS survey data products consisting of point cloud data produced from imagery collected at Little Dauphin Island and Pelican Island, Alabama, from September 2018 to April 2019 in order to develop integrated models linking geomorphology, habitat characteristics, and non-breeding shorebird species usage. Photogrammetry software was used to perform SfM processing on low-altitude digital aerial imagery acquired with a 3DR Solo UAS quadcopter equipped with a Ricoh GR II digital camera and MicaSense RedEdge 3 multispectral camera, using surveyed temporary targets (black and white, 4-square checked pattern) distributed uniformly throughout the UAS flight operations area as GCPs. The following SfM products are produced for each UAS survey over the northern half of Little Dauphin Island and all of Pelican Island: * georeferenced red-green-blue (RGB) orthomosaic image with 5-centimeter (cm) resolution * georeferenced multispectral (MS) orthomosaic image with 5-cm resolution * DEM with 5-cm horizontal resolution * 3D RGB-colored point cloud All horizontal data are provided in the Universal Transverse Mercator (UTM) projected coordinate system, Zone 16 North (16N), referenced to the North American Datum of 1983 (NAD83(2011)), and elevation is referenced to the North American Vertical Datum of 1988 (NAVD88), GEOID12B.

Understanding the processes that govern whether a coral reef is accreting (growing) or dissolving are fundamental to questions of reef health and resiliency. A total of 52 surficial sediment samples were collected within a 1-km x 1-km area around Crocker Reef in the Florida Keys, USA, between 2013 and 2014. Samples 1-35 were collected in July 2013 and samples 36-52 were collected in July 2014. The samples were processed using conventional, published techniques (see process step section) to yield grain size and mineralogical data. The dataset, CRKR2013-2014_SEDIMENT_Images.zip contains photomicrograph images that correspond to each sediment sample. These images also include additional, survey-specific EXchangable Image File format (EXIF) header information.

Understanding the processes that govern whether a coral reef is accreting (growing) or dissolving are fundamental to questions of reef health and resiliency. A total of 52 surficial sediment samples were collected within a 1-km x 1-km area around Crocker Reef in the Florida Keys, USA, between 2013 and 2014. Samples 1-35 were collected in July 2013 and samples 36-52 were collected in July 2014. The samples were processed using conventional, published techniques (see process step 2) to yield grain size and mineralogical data. The dataset, CRKR2013-2014_SEDIMENT_Mineralogy.zip contains a spreadsheet with mineralogical data for each sample. The dataset, CRKR2013-2014_SEDIMENT_GrainSize.zip contains a spreadsheet with grain size data for each sample.

Underwater digital images, single-beam bathymetry, and global positioning system (GPS) data were collected June 24-25, 2014, within a 1-kilkometer (km) x 1-km area around Crocker Reef in the Florida Keys, USA. A total of 91,206 images of the seafloor and water column were collected along pre-defined transect lines and organized into three sets: track1, track2, and track3. This data release contains a subset of those images (25,485 images), all of which were used for benthic habitat classification, and contain GPS data. The data were collected using the U.S. Geological Survey (USGS) shallow Along-Track Reef-Imaging System (sATRIS), a boat-based, pole-mounted sensor package for mapping shallow-water benthic environments. Two other implementations exist, a towed system called Deep ATRIS and a profiling system called Drift ATRIS. All three ATRIS implementations incorporate a digital still camera and an acoustic depth sounder.

ASCII XYZ point cloud data for a portion of the submerged environs of Crocker Reef, Florida, were produced from remotely sensed, geographically referenced elevation measurements collected on April 13 and 22, 2014 by the U.S. Geological Survey. Elevation measurements were collected over the area using the second-generation Experimental Advanced Airborne Research Lidar (EAARL-B), a pulsed laser ranging system mounted onboard an aircraft to measure ground elevation, vegetation canopy, and coastal topography. The system uses high-frequency laser beams directed at the Earth's surface through an opening in the bottom of the aircraft's fuselage. The laser system records the time difference between emission of the laser beam and the reception of the reflected laser signal in the aircraft. The plane travels over the target area at approximately 55 meters per second at an elevation of approximately 300 meters, resulting in a laser swath of approximately 240 meters with an average point density of 0.9 points per square meter. A peak sampling rate of 15-30 kilohertz results in an extremely dense spatial elevation dataset. More than 100 kilometers of coastline can be surveyed easily within a 3- to 4-hour mission. When resultant elevation maps for an area are analyzed, they provide a useful tool to make management decisions regarding land development.

A submerged topography digital elevation model (DEM) mosaic for a portion of the submerged environs of Crocker Reef, Florida, was produced from remotely sensed, geographically referenced elevation measurements collected on April 13 and 22, 2014 by the U.S. Geological Survey. Elevation measurements were collected over the area using the second-generation Experimental Advanced Airborne Research Lidar (EAARL-B), a pulsed laser ranging system mounted onboard an aircraft to measure ground elevation, vegetation canopy, and coastal topography. The system uses high-frequency laser beams directed at the Earth's surface through an opening in the bottom of the aircraft's fuselage. The laser system records the time difference between emission of the laser beam and the reception of the reflected laser signal in the aircraft. The plane travels over the target area at approximately 55 meters per second at an elevation of approximately 300 meters, resulting in a laser swath of approximately 240 meters with an average point density of 0.9 points per square meter. A peak sampling rate of 15–30 kilohertz results in an extremely dense spatial elevation dataset. More than 100 kilometers of coastline can be surveyed easily within a 3- to 4-hour mission. When resultant elevation maps for an area are analyzed, they provide a useful tool to make management decisions regarding land development.

The distribution of benthic habitats for a 1-kilometer (km) x 1-km area around Crocker Reef in the Florida Keys, USA, is based upon underwater digital images of the seafloor collected on June 24 and 25, 2014 (Zawada and others, 2016). The imagery was collected using the U.S. Geological Survey (USGS) shallow Along-Track Reef-Imaging System (sATRIS), a boat-based, pole-mounted sensor package for mapping shallow-water benthic environments. The polygons contained in the shapefile included in this data release, Habitat.shp, represent the four general habitat types found at Crocker Reef: hardbottom, rubble, sand, and seagrass.

A geophysical survey was conducted offshore Cape Canaveral, Florida by Coastal Carolina University offshore of Cape Canaveral, Florida using high-resolution chirp sub-bottom, multibeam bathymetry and side scan sonar (SSS) systems on June 13, 14, 16, and 17 of 2016. This USGS data release includes the resulting processed elevation point data (xyz), an interpolated digital elevation model (DEM), with processed backscatter, side scan sonar, and seismic chirp data.

A geophysical survey was conducted offshore Cape Canaveral, Florida by Coastal Carolina University offshore of Cape Canaveral, Florida using high-resolution chirp sub-bottom, multibeam bathymetry and side scan sonar (SSS) systems on June 13, 14, 16, and 17 of 2016. This USGS data release includes the resulting processed elevation point data (xyz), an interpolated digital elevation model (DEM), with processed backscatter, side scan sonar, and seismic chirp data.

A geophysical survey was conducted offshore Cape Canaveral, Florida by Coastal Carolina University offshore of Cape Canaveral, Florida using high-resolution chirp sub-bottom, multibeam bathymetry and side scan sonar (SSS) systems on June 13, 14, 16, and 17 of 2016. This USGS data release includes the resulting processed elevation point data (xyz), an interpolated digital elevation model (DEM), with processed backscatter, side scan sonar, and seismic chirp data.

A geophysical survey was conducted offshore Cape Canaveral, Florida by Coastal Carolina University offshore of Cape Canaveral, Florida using high-resolution chirp sub-bottom, multibeam bathymetry and side scan sonar (SSS) systems on June 13, 14, 16, and 17 of 2016. This USGS data release includes the resulting processed elevation point data (xyz), an interpolated digital elevation model (DEM), with processed backscatter, side scan sonar, and seismic chirp data.

A geophysical survey was conducted offshore Cape Canaveral, Florida by Coastal Carolina University offshore of Cape Canaveral, Florida using high-resolution chirp sub-bottom, multibeam bathymetry and side scan sonar (SSS) systems on June 13, 14, 16, and 17 of 2016. This USGS data release includes the resulting processed elevation point data (xyz), an interpolated digital elevation model (DEM), with processed backscatter, side scan sonar, and seismic chirp data.

An Ellipsoidally Referenced Survey (ERS) using two Teledyne Reson SeaBat T50-P multibeam echosounders, in dual-head configuration, was conducted by the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center (SPCMSC) offshore of Cedar Key, Florida (FL) during two legs, November 27-30, and December 10-13, 2018. This dataset, Cedar_Key_MBB_2018_xyz.zip, includes the processed elevation point data (x,y,z), as derived from a 1-meter (m) bathymetric grid.

The U.S. Geological Survey St. Petersburg Coastal and Marine Science Center (USGS SPCMSC), collected single beam and swath bathymetry data from the northern Chandeleur Islands, Louisiana, in June of 2016. This USGS data release includes the resulting processed elevation point data (xyz) and an interpolated digital elevation model (DEM). This USGS data release provides 437-line kilometers (km) of processed single beam bathymetry (SBB) and interferometric bathymetry (IFB) data collected under Field Activity Number (FAN) 2016-335-FA. This FAN encompasses two subfans each of which represents one survey vessel; the research vessel (RV) Sallenger (subFAN 16BIM01) collected IFB data and the RV Jabba Jaw (subFAN 16BIM02) acquired SB) data. SBB and IFB point data (xyz) are provided in two datums; the International Terrestrial Reference Frame of 2008 (ITRF08) and ellipsoid height and the North American Datum of 1983 (NAD83) in the CORS96 realization for the horizontal and the North American Vertical Datum of 1988 (NAVD88), using the geoid model of 2009 (GEOID09), orthometric height for the vertical. Additional files provided in this data release include: trackline shapefiles, digital and handwritten field logs, and a comprehensive 50-meter (m) DEM. For additional information regarding data collection and processing of the sounding data, please refer to the field logs and formal Federal Geographic Data Committee (FGDC) metadata for the individual IFB and SBB point data (xyz) files and survey trackline shapefiles also included within this publication and available at, https://doi.org/10.5066/P993MBJK.

An Ellipsoidally Referenced Survey (ERS) using two Teledyne Reson SeaBat T50-P multibeam echosounders, in dual-head configuration, was conducted by the U.S Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center (SPCMSC) offshore of the Chandeleur Islands, Louisiana, August 9-12, 2017. This dataset, Chandeleur_Islands_2017_MBB_xyz.zip, includes the processed elevation point data (x,y,z), as derived from a 1-meter (m) bathymetric grid.

An Ellipsoidally Referenced Survey (ERS) using two Teledyne Reson SeaBat T50-P multibeam echosounders, in dual-head configuration, was conducted by the U.S Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center (SPCMSC) offshore of the Chandeleur Islands, Louisiana, August 16-21, 2018. This dataset, Chandeleur_ Islands_2018_MBES_xyz.zip, includes the processed elevation point data (x,y,z), as derived from a 1-meter (m) bathymetric grid.

As a part of the Barrier Island Comprehensive Monitoring Program (BICM), scientists from the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center conducted a nearshore single-beam bathymetry survey along the Chenier Plain, Louisiana from Marsh Island to Sabine Pass. The goal of the BICM program is to provide long-term data on Louisiana's coastline and use this data to plan, design, evaluate, and maintain current and future barrier island restoration projects. The data described in this metadata record will provide baseline bathymetric information for future research investigating island evolution, sediment transport, recent- and long-term geomorphic change and will support modeling of future changes in response to restoration and storm impacts. Over 3,300-line kilometers of single-beam data were acquired during two field missions: USGS Field Activity numbers (FAN) 2017-323-FA, conducted June 2-14, and 2017-324-FA, which occurred July 8-16, 2017 aboard four separate survey vessels. The final x,y,z data are provided in the native World Geodetic System of 1984 (WGS84) G1150 acquisition format (which is equivalent to the International Terrestrial Reference Frame of 2000 [ITRF00]), with values ranging from -26.62 m to -56.83m ellipsoid height, as well as the North American Datum of 1983 (NAD83) reference frame and the North American Vertical Datum of 1988 (NAVD88) GEOID12A , with values ranging from 0.00 to -30.13 m .

As a part of the Barrier Island Comprehensive Monitoring Program (BICM), scientists from the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center conducted a nearshore single-beam bathymetry survey along the Chenier Plain, Louisiana from Marsh Island to Sabine Pass. The goal of the BICM program is to provide long-term data on Louisiana’s coastline and use this data to plan, design, evaluate, and maintain current and future barrier island restoration projects. The data described in this metadata record will provide baseline bathymetric information for future research investigating island evolution, sediment transport, recent- and long-term geomorphic change and will support modeling of future changes in response to restoration and storm impacts. Over 3,300-line kilometers of single-beam data were acquired during two field missions: USGS Field Activity numbers (FAN) 2017-323-FA, conducted June 2-14, and 2017-324-FA, which occurred July 8-16, 2017 aboard four separate survey vessels. The final x,y,z data are provided in the native World Geodetic System of 1984 (WGS84) G1150 acquisition format (which is equivalent to the International Terrestrial Reference Frame of 2000 [ITRF00]), with values ranging from -26.62 m to -68.94 m ellipsoid height, as well as the North American Datum of 1983 (NAD83) reference frame and the North American Vertical Datum of 1988 (NAVD88) GEOID12A , with values ranging from 0.00 to -30.13 m.

As a part of the Barrier Island Comprehensive Monitoring Program (BICM), scientists from the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center conducted a nearshore single-beam bathymetry survey along the Chenier Plain, Louisiana from Marsh Island to Sabine Pass. The goal of the BICM program is to provide long-term data on Louisiana's coastline and use this data to plan, design, evaluate, and maintain current and future barrier island restoration projects. The data described in this metadata record will provide baseline bathymetric information for future research investigating island evolution, sediment transport, recent- and long-term geomorphic change and will support modeling of future changes in response to restoration and storm impacts. Over 3,300-line kilometers of single-beam data were acquired during two field missions: USGS Field Activity numbers (FAN) 2017-323-FA, conducted June 2-14, and 2017-324-FA, which occurred July 8-16, 2017 aboard four separate survey vessels. The final x,y,z data are provided in the native World Geodetic System of 1984 (WGS84) G1150 acquisition format (which is equivalent to the International Terrestrial Reference Frame of 2000 [ITRF00]), with values ranging from -26.62 m to -68.94 m ellipsoid height, as well as the North American Datum of 1983 (NAD83) reference frame and the North American Vertical Datum of 1988 (NAVD88) GEOID12A , with values ranging from 0.00 to -30.13 m.

The National Assessment of Coastal Change Hazards project aims to understand and forecast coastal landscape change. This dataset consists of delineated coastal cliff toes that may be used to assess the hazard posed by eroding coastal cliffs on the islands of Puerto Rico, Culebra, and Vieques. The delineation of cliff tops and toes can be used as an input into cliff hazard metrics and to measure overall cliff changes over time. Cliff tops and cliff toes were identified along three-dimensional (3D) transects using the Cliff Feature Delineation Tool (Seymour and others, 2020), which assigned coordinate locations (X, Y, Z) of cliff features over a 140,244-meter (m) stretch of the Puerto Rican coastline at 10-m intervals and output them as either polyline (cliff transects) or point (cliff top or toe) shapefiles. Feature delineation was performed using post-Hurricane Maria (landfall was September 20, 2017) rasterized topobathy lidar elevation data collected by the U.S. Army Corps of Engineers and published by the National Oceanic and Atmospheric Administration National Centers for Environmental Information (2018) as bare earth digital elevation model (DEM) files. The delineation tool (Seymour and others, 2020) was used to generate 3D point features in Esri ArcGIS shapefile format representing the cliff toes; these files should be opened in a 3D geographic information system (GIS) viewer.

The National Assessment of Coastal Change Hazards project aims to understand and forecast coastal landscape change. This dataset consists of delineated coastal cliff tops that may be used to assess the hazard posed by eroding coastal cliffs on the islands of Puerto Rico, Culebra, and Vieques. The delineation of cliff tops and toes can be used as an input into cliff hazard metrics and to measure overall cliff changes over time. Cliff tops and cliff toes were identified along three-dimensional (3D) transects using the Cliff Feature Delineation Tool (Seymour and others, 2020), which assigned coordinate locations (X, Y, Z) of cliff features over a 140,244-meter (m) stretch of the Puerto Rican coastline at 10-m intervals and output them as either polyline (cliff transects) or point (cliff top or toe) shapefiles. Feature delineation was performed using post-Hurricane Maria (landfall was September 20, 2017) rasterized topobathy lidar elevation data collected by the U.S. Army Corps of Engineers and published by the National Oceanic and Atmospheric Administration National Centers for Environmental Information (2018) as bare earth digital elevation model (DEM) files. The delineation tool (Seymour and others, 2020) was used to generate 3D point features in Esri ArcGIS shapefile format representing the cliff tops; these files should be opened in a 3D geographic information system (GIS) viewer.

The National Assessment of Coastal Change Hazards project aims to understand and forecast coastal landscape change. This dataset consists of delineated coastal cliff transects that may be used to assess the hazard posed by eroding coastal cliffs on the islands of Puerto Rico, Culebra, and Vieques. The delineation of cliff tops and toes can be used as an input into cliff hazard metrics and to measure overall cliff changes over time. Cliff tops and cliff toes were identified along three-dimensional (3D) transects using the Cliff Feature Delineation Tool (Seymour and others, 2020), which assigned coordinate locations (X, Y, Z) of cliff features over a 140,244-meter (m) stretch of the Puerto Rican coastline at 10-m intervals and output them as either polyline (cliff transects) or point (cliff top or toe) shapefiles. Feature delineation was preformed using post-Hurricane Maria (landfall was September 20, 2017) rasterized topobathy lidar elevation data collected by the U.S. Army Corps of Engineers and published by the National Oceanic and Atmospheric Administration National Centers for Environmental Information (2018) as bare earth digital elevation model (DEM) files. The delineation tool (Seymour and others, 2020) was used to generate 3D point features in Esri ArcGIS shapefile format representing the cliff transects, these files should be opened in a 3D geographic information system (GIS) viewer.

This dataset defines storm-induced coastal erosion hazards for the Florida coastline. The analysis was based on a storm-impact scaling model that used observations of beach morphology combined with sophisticated hydrodynamic models to predict how the coast would respond to the direct landfall of Tropical Storm Colin in June 2016. Storm-induced water levels, due to both surge and waves, are compared to beach and dune elevations to determine the probabilities of the three types of coastal change: collision (dune erosion), overwash, and inundation. All hydrodynamic and morphologic variables are included in this dataset.

Coral reefs around the world have degraded over the last half-century as evidenced by loss of live coral cover. This ubiquitous observation led to the establishment of long-term, ecological monitoring programs in several regions with sizable coral-reef resources. As part of the U.S. Geological Survey (USGS) John Wesley Powell Center for Analysis and Synthesis working group "Local-scale ecosystem resilience amid global-scale ocean change: the coral reef example," scientists gathered resultant data from four of these programs in the main Hawaiian Islands, the Florida Keys, Mo'orea in French Polynesia, and St. John in the U.S. Virgin Islands to examine among-site, within-region spatial and temporal variation in coral cover. Data from the four focal regions represent spatial scales ranging from ~80 to 17,000 km2. The surveys chosen for the analysis were carried out at fixed sites between 1992 and 2015. Survey durations differed among focal regions and extended from 11 years at Mo'orea to 24 years at some of the sites in St. John. One hundred and twenty-three fixed sites (defined here as distinct surveys carried out within a defined reef habitat, depth range, or area of shoreline) were surveyed repeatedly (annually or every few years) in each focal region. Only sites with surveys extending over a decade or more and with at least 3 years of surveys were used so as to capture a variety of disturbance events (for example, El Niño events, major storms, etc.). Each focal region has experienced disturbances such as overfishing, disease pandemics, thermal stress, pollution, invasive species, predator outbreaks, and major storms. The data gathered for analysis are provided in this data release and are interpreted in Guest and others (2018).

This dataset contains carbonate system data collected by scientists from the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center to investigate the effects of carbon cycling, coastal and ocean acidification at Crocker Reef located along the Florida Keys Reef Tract, in Southeast Florida, USA. These data were collected using an autonomous instrument called the Ocean Carbon System version 1 (OCSv1) deployed on the seafloor at Crocker Reef. The OCSv1 consists of five sensors integrated into a Sea-Bird Scientific STOR-X submersible data logger including a Seabird SeapHOx sensor for measurement of pH; a Sea-Bird 16 Plus CTD Recorder for measurement of conductivity (for calculation of salinity), temperature, and depth an Aanderaa oxygen optode for measurement of dissolved oxygen; a Pro-Oceanus CO2-Pro for measurement of CO2; and a Wetlabs Eco-PAR sensor for measurement of photosynthetically active radiation. The dataset is a time series of carbonate system parameters including: water temperature (degrees Celsius, °C), pressure (decibar, dbar), salinity, pHT (pH on the total scale), carbon dioxide (parts per million, ppm), pressure from the CO2-Pro Infrared Gas Analyzer (IRGA) (millibars, mbar), dissolved oxygen (milligrams per liter, mg/L) and photosynthetically active radiation (microEinsteins). Each parameter was measured every hour for 24-hour time periods throughout the duration of deployment. Data were collected under Florida Keys National Marine Sanctuary Permit FKNMS-2013-097. For further information regarding data collection and/or processing methods refer to USGS Open-File Report 2019–1003 (https://doi.org/10.3133/ofr20191003).

The U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center conducted research to quantify bathymetric changes at Crocker Reef near Islamorada, Florida (FL), within a 33.6 square-kilometer area following the landfall of Hurricane Irma in September 2017. USGS staff used light detection and ranging (lidar)-derived data acquired by the National Oceanic and Atmospheric Administration (NOAA) between July 21 and November 21, 2016 and USGS multibeam data collected between October 10 and December 8, 2017 (Fredericks and others, 2019) to assess changes in seafloor elevation and structure that occurred after the passage of Hurricane Irma. An elevation change analysis between the 2016 NOAA lidar data and the 2017 multibeam data was performed to quantify and map impacts to seafloor elevations and to determine elevation and volume change statistics for nine habitat types found at Crocker Reef, FL. Data were collected under Florida Keys National Marine Sanctuary permit FKNMS-2016-068.

An Ellipsoidally Referenced Survey (ERS) using two Teledyne Reson SeaBat T50-P multibeam echosounders, in dual-head configuration, was conducted by the U.S Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center (SPCMSC) at Crocker Reef, the Florida Keys October 10-28, and December 5-8, 2017. This dataset, Crocker_2017_MBB_xyz.zip, includes the processed elevation point data (x,y,z), as derived from a 1-meter (m) bathymetric grid.

An Ellipsoidally Referenced Survey (ERS) using two Teledyne Reson SeaBat T50-P multibeam echosounders, in dual-head configuration, was conducted by the U.S Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center (SPCMSC) at Crocker Reef, the Florida Keys March 8-15, 2018. This dataset, Crocker_2018_MBB_xyz.zip, includes the processed elevation point data (x,y,z), as derived from a 1-meter (m) bathymetric grid.

This dataset includes DRASTIC (Aller and others, 1987) model results for Upper Floridan aquifer vulnerability to contamination. The DRASTIC value serves as an intrinsic vulnerability index for assessing the transport of contaminants from the surface. The DRASTIC model setup requires the input of raster data for depth to groundwater, aquifer recharge, aquifer media, soil media, topography, vadose zone media, and aquifer hydraulic conductivity. These variables were entered into the DRASTIC equation using the raster calculator tool in ArcGIS.

Underwater digital images, single-beam bathymetry, and global positioning system (GPS) data were collected June 13-14, 2009 at Pulaski Shoal within Dry Tortugas National Park, Florida, USA. A total of 195,406 images of the seafloor and water column were collected along pre-defined transect lines and organized into 3 sets: track1, track2, and track3. This data release contains a subset of those images (32,135 images), all of which were used for benthic habitat classification, and contain GPS data. The data were collected using the U.S. Geological Survey (USGS) shallow Along Track Reef Imaging System (sATRIS), a boat-based, pole-mounted sensor package for mapping shallow-water benthic environments. Two other implementations exist: A towed system called Deep ATRIS and a profiling system called Drift ATRIS. All three ATRIS implementations incorporate a digital still camera and an acoustic depth sounder.

Underwater digital images, single-beam bathymetry, and global positioning system (GPS) data were collected July 13 to July 17, 2011 within Dry Tortugas National Park, Florida, USA. A total of 272,828 images of the seafloor and water column were collected along pre-defined transect lines and organized into 14 sets, track1-track14. This data release contains a subset of those images (43,991 images), all of which were used for benthic habitat classification and contain GPS data. The data were collected using the U.S. Geological Survey (USGS) shallow Along Track Reef Imaging System (sATRIS), a boat-based, pole-mounted sensor package for mapping shallow-water benthic environments. Two other implementations exist: A towed system called Deep ATRIS and a profiling system called Drift ATRIS. All three ATRIS implementations incorporate a digital still camera and an acoustic depth sounder.

In July of 2006, the U.S. Geological Survey conducted geophysical surveys offshore of Chandeleur Islands, LA, and in nearby waterbodies. This report serves as an archive of unprocessed digital CHIRP seismic reflection data, trackline maps, navigation files, GIS information, Field Activity Collection System (FACS) logs, observer's logbook, and formal FGDC metadata. Gained digital images of the seismic profiles are also provided. The archived trace data are in standard Society of Exploration Geophysicists (SEG) SEG-Y format (Barry and others, 1975) and may be downloaded and processed with commercial or public domain software such as Seismic Unix (SU). Example SU processing scripts and USGS software for viewing the SEG-Y files (Zihlman, 1992) are also provided.

In 2000, the U.S. Geological Survey (USGS), in cooperation with the University of New Orleans (UNO) and the U.S. Army Corps of Engineers (USACE), conducted geophysical surveys in Barataria Bight from Sandy Point to Belle Pass, LA (Study Area Map). Sediment cores were collected as part of the USGS Subsidence and Coastal Change (SCC) Project, which included the Barataria Sand-Resource Study (bss) vibracore surveys (Kindinger and others, 2001). This report also contains information from other cruise data sets, including the Cheniere Ronquille, LA, data (CR83) and the Plaquemines, LA, data (P86). The sediment data for these cruises were obtained by the Louisiana Geological Survey (LGS), the Louisiana Department of Natural Resources (LDNR) and Alpine Ocean Seismic Survey, Inc., as part of the near shore sand resource inventory of "Louisiana Sand Resource Inventory 1985 Vibracore Services" (Suter and others, 1991; Alpine Ocean Seismic Survey, Inc., 1986). Additionally, this report also includes the U.S. Army Corps of Engineers EUSTIS borehole cores (B-#). EUSTIS is the type of drill rig used to obtain the borehole cores and is used as name identifier for the USACE borehole cores presented herein. These cores are presented on a separate map with links to the description profiles and grain-size data that can be found by clicking on the USACE EUSTIS link. This report serves as an archive of vibracore data collected during field activities of Subsidence and Coastal Change (SCC) 00SCC01, 00SCC03, 00SCC05 (collectively noted in the report as 00SCC) by the U.S. Geological Survey, CR83 (Cheniere Ronquille, LA) and P86 (Plaquemines, LA) by the Louisiana Geological Survey and the Louisiana Department of Natural Resources, and borehole data collected in 2000 for the U.S. Army Core of Engineers (USACE-EUSTIS). Data presented here include the sections Vibracore Description Sheets, Interpreted Core Classification Profiles, Grain-Size and Penetrometer Data, core location Maps and Core Data Table of all core data analysis files, and vibracore Photographs. Additional data include Field Activity Collection System (FACS) logs and scanned observer's logbooks (Field Logs), as well as formal Federal Geographical Data Committee (FGDC) Metadata.

In September and October of 2003, the U.S. Geological Survey (USGS), in cooperation with the Florida Geological Survey, conducted geophysical surveys of the Atlantic Ocean offshore northeast Florida from St. Augustine, Florida, to the Florida-Georgia border. This report serves as an archive of unprocessed digital boomer subbottom profile data, trackline maps, navigation files, Geographic Information System (GIS) files, Field Activity Collection System (FACS) logs, and formal Federal Geographic Data Committee (FGDC) metadata. Filtered and gained (a relative increase in signal amplitude) digital images of the seismic profiles are also provided.

Scientists from the U.S. Geological Survey, St. Petersburg Coastal and Marine Science Center collected 303 surface sediment samples from Dauphin Island, Alabama, and the surrounding water bodies in August 2015. These sediments were processed to determine physical characteristics such as organic content, bulk density, and grain-size. The environments where the sediments were collected include high and low salt marshes, over-wash deposits, dunes, beaches, sheltered bays, and open water. Sampling by the USGS was part of a larger study to assess the feasibility and sustainability of proposed restoration efforts for Dauphin Island, Alabama, and assess the island’s resilience to rising sea level and storm events. The data presented in this publication can be used by modelers to attempt validation of hindcast models and create predictive forecast models for both baseline conditions and storms. This study was funded by the National Fish and Wildlife Foundation, via the Gulf Environmental Benefit Fund. This report serves as an archive for sedimentological data derived from surface sediments. Downloadable data are available as Excel spreadsheets, JPEG files, and formal Federal Geographic Data Committee metadata (data downloads).

Scientists from the U.S. Geological Survey, St. Petersburg Coastal and Marine Science Center collected 303 surface sediment samples from Dauphin Island, Alabama, and the surrounding water bodies in August 2015. These sediments were processed to determine physical characteristics such as organic content, bulk density, and grain-size. The environments where the sediments were collected include high and low salt marshes, over-wash deposits, dunes, beaches, sheltered bays, and open water. Sampling by the USGS was part of a larger study to assess the feasibility and sustainability of proposed restoration efforts for Dauphin Island, Alabama, and assess the island’s resilience to rising sea level and storm events. The data presented in this publication can be used by modelers to attempt validation of hindcast models and create predictive forecast models for both baseline conditions and storms. This study was funded by the National Fish and Wildlife Foundation, via the Gulf Environmental Benefit Fund. This report serves as an archive for sedimentological data derived from surface sediments. Downloadable data are available as Excel spreadsheets, JPEG files, and formal Federal Geographic Data Committee metadata (data downloads).

Single-beam bathymetry data along with DGPS navigation data was collected as part of the U.S. Geological Survey cruise A-1-00-SC. The cruise was conducted from Port Hueneme, California, to the Mexican border from June 5 to June 29, 2000. The chief scientists were Chris Gutmacher, Stephanie Ross, Brian Edwards all from the USGS Coastal and Marine Geology office in Menlo Park, CA. The purpose of this cruise was to identify and map active and potentially active faults, folds, and submarine slide-prone areas that may threaten densely populated areas of Southern California. This survey was also taken to determine the pathways through which sea-water is intruding into aquifers of Los Angeles County in the area of the Long Beach and Los Angeles harbors. The geophysical source was a Knudsen 12 kilohertz (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/a/a100sc/html/a-1-00-sc.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 data along with GPS navigation data was collected as part of the U.S. Geological Survey cruise A-1-93-YB. The cruise was conducted in Yakukat Bay, Alaska from August 21 to August 27, 1993. The chief scientists were Paul Carlson of the USGS Coastal and Marine Geology office in Menlo Park, CA and Ellen Cowan of Appalachian State University and Ross Powell of Northern Illinois University. The overall purpose of this study was to characterize seismic facies for interpreting past glacier behavior, especially during the Last Glacial Maximum (LGM). The geophysical source was a hull-mounted 12 kilohertz (kHz) bathymetry 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/a/a193yb/html/a-1-93-yb.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 data along with GPS navigation data was collected as part of the U.S. Geological Survey cruise A-1-94-GB. The cruise was conducted in Prince William Sound, Yakutat Bay, Glacier Bay and Icy Strait, Alaska from August 8 to August 17, 1994. The chief scientists were Paul Carlson and Rob Kayen from the USGS Coastal and Marine Geology office in Menlo Park, CA, Ellen Cowan (Appalachian State University), and Ross Powell (Northern Illinois University). The overall purpose of this study was to study high resolution seismic facies to interpret glacial fluctuations in Gulf of Alaska region. The geophysical source was a 12 kilohertz (kHz) bathymetry 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/a/a194gb/html/a-1-94-gb.sc.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 data along with GPS navigation data was collected as part of the U.S. Geological Survey cruise A-1-94-YB. The cruise was conducted in Yakutat Bay and Yakutat Sea Valley, Alaska from August 5 to August 8, 1994. The chief scientists were Paul Carlson, Rob Kayen from the USGS Coastal and Marine Geology office in Menlo Park, CA and Ellen Cowan (Appalachian State University) and Ross Powell(North Illinois University). The purpose of this cruise was to study Hi-Res seismic facies to interpret glacial fluctuations in Gulf of Alaska region. The geophysical source was a 12 kilohertz (kHz) 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/a/a194yb/html/a-1-94-yb.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 data along with GPS navigation data was collected as part of the U.S. Geological Survey cruise A-2-98-SC. The cruise was conducted in Santa Monica Bay from August 23 to August 31, 1998. The chief scientists were Homa Lee and Brian Edwards from the USGS Coastal and Marine Geology office in Menlo Park, CA. The purpose of this cruise was to collect box core, gravity and piston core samples to understand anthropogenic affects on sedimentation. The geophysical source was an ODEC 3.5 kilohertz (kHz) 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/a/a298sc/html/a-2-98-sc.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 data along with miniranger navigation data was collected as part of the U.S. Geological Survey cruise C-1-79-NC. The cruise was conducted in Northern California from May 1 to May 2 1979. The chief scientist was John Dingler from the USGS Coastal and Marine Geology office in Menlo Park, CA. The purpose of this cruise is unknown. The geophysical source is also unknown. 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/c179nc/html/c-1-79-nc.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 data along with SINS navigation data was collected as part of the U.S. Geological Survey cruise D-1-79-EG. The cruise was conducted in the Eastern Gulf of Alaska from May 24 to June 1, 1979. The chief scientists were Bruce Molnia from the USGS Coastal and Marine Geology office in Menlo Park, CA and Mark Wheeler. The purpose of this cruise was to collect sediment samples and cores for a microfossil study. The geophysical source was a 3.5 kilohertz (kHz) bathymetry system. 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/d/d179eg/html/d-1-79-eg.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 data along with Loran-C RHO-RHO and GPS navigation data was collected as part of the U.S. Geological Survey cruise F-3-89-SC. The cruise was conducted in Monterey Bay, California from February 2 to February 15, 1989. The chief scientists were Mike Field and Jim Gardner from the USGS Coastal and Marine Geology office in Menlo Park, CA. The purpose of this cruise is ground truthing of the Southern Monterey Fan. The geophysical sources are 10 kilohertz (kHz) and 3.5 kHz systems. 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/f389sc/html/f-3-89-sc.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 data along with GPS navigation data was collected as part of the U.S. Geological Survey cruise F-3-92-SC. The cruise was conducted in Southern California from April 22 to May 15, 1992. The chief scientists were Herman Karl and Monty Hampton from the USGS Coastal and Marine Geology office in Menlo Park, CA. The geophysical source was 10 kilohertz (kHz) and 3.5 kHz systems. 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/f392sc/html/f-3-92-sc.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 data along with Loran-C RHO-RHO and GPS navigation data was collected as part of the U.S. Geological Survey cruise F-6-90-SC. The cruise was conducted in Southern California, Monterey Canyon from June 19 to July 12, 1990. The chief scientists were Jim Gardner from the USGS Coastal and Marine Geology office in Menlo Park, CA and Doug Masson. The purpose of this cruise was to survey with midrange sidescan sonar (TOBI: towed ocean bottom instrument).The geophysical source was 12 kilohertz (kHz), 7 kHz, and 3.5 kHz systems. 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/f690sc/html/f-6-90-sc.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 data along with GPS navigation data was collected as part of the U.S. Geological Survey cruise F-7-86-HW. The cruise was conducted in Hawaii from November 28 to December 20, 1986. The chief scientists were Jim Hein from the USGS Coastal and Marine Geology office in Menlo Park, CA and Bill Schwab from the USGS Coastal and Marine Geology office in Woods Hole, Mass. This cruise had many purposes, the bathymetric data is a survey of a small area of the south Johnston Island ridge. The geophysical source was 3.5 kilohertz (kHz) system. 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/f786hw/html/f-7-86-hw.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 data along with Loran-C RHO-RHO and GPS navigation data was collected as part of the U.S. Geological Survey cruise F-7-90-NC. The cruise was conducted in the Gulf of Farallones, Northern California from July 19 to August 3, 1990. The chief scientists were Herman Karl and Dave Drake from the USGS Coastal and Marine Geology office in Menlo Park, CA and Bill Schwab from the USGS Coastal and Marine Geology office in Woods Hole, Mass. The purpose of this cruise was a slope stability survey of the Farallones Escarpment. The geophysical sources were 10 kilohertz (kHz), 4.5 kHz, and 3.5 kHz systems. 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/f790nc/html/f-7-90-nc.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 data along with Loran-C RHO-RHO and GPS navigation data was collected as part of the U.S. Geological Survey cruise F-8-90-NC. The cruise was conducted in the Gulf of Farallones, Northern California from August 5 to August 17, 1990. The chief scientists were Herman Karl and Dave Drake from the USGS Coastal and Marine Geology office in Menlo Park, CA and Bill Schwab from the USGS Coastal and Marine Geology office in Woods Hole, Mass. The purpose of this cruise was a slope stability survey of the Farallones Escarpment. The geophysical sources were 12 kilohertz (kHz), 10 kHz, and 3.5 kHz systems. 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/f890nc/html/f-8-90-nc.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 data along with GPS navigation data was collected as part of the U.S. Geological Survey cruise F-9-91-CP. The cruise was conducted in the Central Pacific from September 24 to September 25, 1991. The chief scientists was Jim Gardner from the USGS Coastal and Marine Geology office in Menlo Park, CA. The geophysical source was a 10 kilohertz (kHz) and 3.5 kHz system. 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/f991cp/html/f-9-91-cp.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 data along with dead reckoning navigation data was collected as part of the U.S. Geological Survey cruise G-1-77-EG. The cruise was conducted in Yakutat Bay, Eastern Gulf of Alaska from April 27 to May 22, 1977. The chief scientist was Paul Carlson from the USGS Coastal and Marine Geology office in Menlo Park, CA. The geophysical source was a 3.5 kilohertz (kHz) system. 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/g177eg/html/g-1-77-eg.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 data along with GPS navigation data was collected as part of the U.S. Geological Survey cruise G-2-95-SF. The cruise was conducted in San Francisco Bay, Golden Gate area from May 30 to June 10, 1995. The chief scientists were Terry Bruns, Paul Carlson, and Dennis Mann all from the USGS Coastal and Marine Geology office in Menlo Park, CA. 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/g295sf/html/g-2-95-sf.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 data along with GPS navigation data was collected as part of the U.S. Geological Survey cruise J-1-00-SF. The cruise was conducted in Grizzly Bay and Suisun Bay in the San Francisco Bay area, California from March 13 to March 14, 2000. The chief scientist was John Chin from the USGS Coastal and Marine Geology office in Menlo Park, CA. The purpose of this cruise was to study San Francisco Bay's response of bed morphology and surficial sediment texture to flow events. 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/j/j100sf/html/j-1-00-sf.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 data along with GPS navigation data was collected as part of the U.S. Geological Survey cruise J-2-00-SF. The cruise was conducted in Grizzly Bay and San Pablo Bay in the San Francisco Bay area, California from March 22 to March 27, 2000. The chief scientist was Bruce Jaffe from the USGS Coastal and Marine Geology office in Menlo Park, CA. The purpose of this cruise was for ground truthing, and to collect box cores and gravity cores. 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/j/j200sf/html/j-2-00-sf.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 data along with miniranger navigation data was collected as part of the U.S. Geological Survey cruise J-2-81-NC. The cruise was conducted in Carmel Bay, Monterey Bay, Northern California from June 23 to June 30, 1981. The chief scientist was John Dingler from the USGS Coastal and Marine Geology office in Menlo Park, CA. The geophysical source was a 12 kilohertz (kHz) system. 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/j/j281nc/html/j-2-81-nc.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 data along with DGPS navigation data was collected as part of the U.S. Geological Survey cruise J-2-95-MB. The cruise was conducted from in Monterey Bay, California from March 6 to April 15, 1995. The chief scientists were Roberto Anima, Andy Stevenson, and Steve Eittreim all from the USGS Coastal and Marine Geology office in Menlo Park, CA. The purpose of this cruise was to compile a side-scan sonar mosaic of the offshore area of Monterey Bay Marine Santuary. The geophysical source was a Lowrance fathometer. 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/j/j295mb/html/j-2-95-mb.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 data along with DGPS navigation data was collected as part of the U.S. Geological Survey cruise J-2-99-SF. The cruise was conducted in Grizzly Bay and San Francisco Bay, California from February 24 to March 8, 1999. The chief scientist was John Chin from the USGS Coastal and Marine Geology office in Menlo Park, CA. The purpose of this cruise was to survey Grizzly Bay and adjacent areas for seasonal changes in bottom morphology and sediment texture. The geophysical source was a 200 kilohertz (kHz) system. 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/j/j299sf/html/j-2-99-sf.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 data along with DGPS navigation data was collected as part of the U.S. Geological Survey cruise J-3-99-SF. The cruise was conducted in Grizzly Bay and San Francisco Bay, California from November 8 to November 18, 1999. The chief scientist was John Chin from the USGS Coastal and Marine Geology office in Menlo Park, CA. The purpose of this cruise was to survey Grizzly Bay and adjacent areas for seasonal changes in bottom morphology and sediment texture. The geophysical source was a 200 kilohertz (kHz) system. 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/j/j399sf/html/j-3-99-sf.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 data along with miniranger navigation data was collected as part of the U.S. Geological Survey cruise J-4-83-HB. The cruise was conducted in Humboldt Bay, California from August 16 to August 19, 1983. The chief scientist was John Dingler from the USGS Coastal and Marine Geology office in Menlo Park, CA. The purpose of the cruise was to conduct a survey of the underwater exterior and related features of both Humboldt Bay jetties and the Crescent City Outer Breakwater. The geophysical source was a Raytheon 7 kilohertz (kHz) system. 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/j/j483hb/html/j-4-83-hb.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 data along with DGPS navigation data was collected as part of the U.S. Geological Survey cruise J-6-95-MB. The cruise was conducted from in Monterey Bay, California from October 16 to November 30, 1995. The chief scientists were Roberto Anima, Andy Stevenson, and Steve Eittreim all from the USGS Coastal and Marine Geology office in Menlo Park, CA. The purpose of this cruise was to produce a mosaic of the northern Monterey Bay Santuary continental shelf area from as near shore out to the continental slope, and to collect digital subbottom profile data to better understand the shallow tectonics and paleomorphology of the sanctuary. The geophysical source was a Lowrance fathometer. 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/j/j695mb/html/j-6-95-mb.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 data along with SINS navigation data was collected as part of the U.S. Geological Survey cruise K-1-85-AR. The cruise was conducted in the Arctic on September 4, 1993. The chief scientists were Erk Reimnitz and Peter Barnes from the USGS Coastal and Marine Geology office in Menlo Park, CA. The overall purpose of this study and the geophysical source are unknown. 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/k185ar/html/k-1-85-ar.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 data along with SINS navigation data was collected as part of the U.S. Geological Survey cruise K-1-90-GB. The cruise was conducted in Glacier Bay, Alaska from June 14 to June 24, 1990. The chief scientist was Paul Carlson from the USGS Coastal and Marine Geology office in Menlo Park, CA. The overall purpose of this study was to look at glacial discharge streams and morainal banks of tidewater glaciers and imaging of gulleys and chutes on a pro-delta face in Queen Inlet and ice gouges on the moraine at the mouth of Muir Inlet.The geophysical source was a 7 kilohertz (kHz) and 3.5 kHz system. 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/k190gb/html/k-1-90-gb.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 data along with radar and GPS navigation data was collected as part of the U.S. Geological Survey cruise K-1-91-YB. The cruise was conducted in Yakutat Bay, Alaska from June 22 to June 28, 1991. The chief scientists were Paul Carlson from the USGS Coastal and Marine Geology office in Menlo Park, CA, and Ross Powell from Northern Illinois University. The overall purpose of this study is a continuation of previous studies of morainal bank and proximal environments of tidewater glaciers in Glacier Bay, Alaska. The geophysical source was a 7 kilohertz (kHz) Rayheon RTT 1000 system. 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/k191yb/html/k-1-91-yb.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 data along with DGPS navigation data was collected as part of the U.S. Geological Survey cruise K-1-93-HW. The cruise was conducted in Oahu, Hawaii from February 20 to February 26, 1993. The chief scientist was Mike Torresan from the USGS Coastal and Marine Geology office in Menlo Park, CA. The purpose of this cruise was to create a detailed bathymetric map of the Mamala Bay seafloor that delimits the general extent of the acoustically-resolvable dredged material deposits. The geophysical source was a 12 kilohertz (kHz) Raytheon system. 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/k193hw/html/k-1-93-hw.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 data along with DGPS navigation data was collected as part of the U.S. Geological Survey cruise K-1-94-HW. The cruise was conducted in Oahu, Hawaii from May 10 to May 16, 1994. The chief scientists were Mike Torresan and Monty Hampton from the USGS Coastal and Marine Geology office in Menlo Park, CA. The purpose of this cruise was to create a detailed bathymetric map of the Mamala Bay seafloor that delimits the general extent of the acoustically-resolvable dredged material deposits. The geophysical source was a Raytheon 12 kilohertz (kHz) DSF-6000 fathometer. 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/k194hw/html/k-1-94-hw.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 data along with DGPS navigation data was collected as part of the U.S. Geological Survey cruise K-1-95-HW. The cruise was conducted in Oahu, Hawaii from June 14 to June 18, 1995. The chief scientist was Mike Torresan from the USGS Coastal and Marine Geology office in Menlo Park, CA. The purpose of this cruise was to conduct an integrated study on the distribution and character of dredged materials as well as the effects of dredged material on the marine environment. A three phase study provided information to evaluate the effects on seafloor substrate and the benthic fauna. The studies include geophysical profiling and imaging, bottom photography, sampling, chemical and physical analyses of sediment, and evaluations of the benthic population, population density, and adverse impacts to the benthic fauna. The geophysical source was an ODEC 3.5 kilohertz (kHz) 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/k195hw/html/k-1-95-hw.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 data along with radar and GPS navigation data was collected as part of the U.S. Geological Survey cruise K-2-91-BG. The cruise was conducted in Bering Glacier, Alaska from July 2 to July 6, 1991. The chief scientists were Paul Carlson from the USGS Coastal and Marine Geology office in Menlo Park, CA. The overall purpose of this study is to collect bathymetry, sidescan and samples from Icy Bay to Vitus Lake, Alaska. The geophysical source is 7 kilohertz (kHz) and 3.5 kHz systems. 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/k191yb/html/k-1-91-yb.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 data along with GPS navigation data was collected as part of the U.S. Geological Survey cruise K-2-93-HW. The cruise was conducted in Kauai, Hawaii from February 27 to March 2, 1993. The chief scientist was Monty Hampton from the USGS Coastal and Marine Geology office in Menlo Park, CA. The purpose of this cruise was to create a detailed bathymetric map of the Mamala Bay seafloor that delimits the general extent of the acoustically-resolvable dredged material deposits. The geophysical source is unknown. 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/k293hw/html/k-2-93-hw.meta.htmlinto 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 the U.S. Geological Survey cruise K-2-94-HW. The cruise was conducted in Mamala Bay, Offshore Honolulu, Oahu, Hawaii from May 16 to May 23, 1994. The chief scientists were Mike Torresan and Monty Hampton from the USGS Coastal and Marine Geology office in Menlo Park, CA. The purpose of this cruise was to sample and groundtruth the 1993 Acoustic data of the seafloor of Mamala Bay over the US Corps of Engineers Deep Ocean dredged material disposal sites used by Pearl and Honolulu Harbors. The geophysical source is a 12 kilohertz (kHz) Raytheon DSF-6000 fathometer. 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/k294hw/html/k-2-94-hw.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 data along with Loran-C RHO-RHO and GPS navigation data was collected as part of the U.S. Geological Survey cruise L-4-86-NC. The cruise was conducted in Northern California from August 21 to September 5, 1986. The chief scientists were Dave Cacchione and Dave Drake from the USGS Coastal and Marine Geology office in Menlo Park, CA. The purpose of this cruise is unknown. The geophysical sources were 12 kilohertz (kHz) and 3.5 kHz systems. 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/l486nc/html/l-4-86-nc.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 data along with DGPS and GPS navigation data were collected as part of the U.S. Geological Survey cruise M-1-97-WO. The cruise was conducted in Southwest Washington Inner Shelf from July 7 to July 15, 1997. The chief scientists were Pat McCrory and Dave Twitchell from the USGS Coastal and Marine Geology office in Menlo Park, CA. The purpose of this cruise was to collect geophysical data to aid in characterizing seismic hazard of nearshore faults & coastal erosion hazards. The geophysical source is a 3.5 kilohertz (kHz) system. 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/m/m197wo/html/m-1-97-wo.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 data along with GPS navigation data was collected as part of the U.S. Geological Survey cruise O-1-00-SC. The cruise was conducted in San Pedro Bay, Santa Monica, California from April 9 to April 14, 2000. The chief scientists were Brian Edwards and Homa Lee from the USGS Coastal and Marine Geology office in Menlo Park, CA. The purpose of this cruise was to study pollution transport and accumulation in Santa Monica Bay. The geophysical source is unknown. 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/o/o100sc/html/o-1-00-sc.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 data along with DGPS navigation data was collected as part of the U.S. Geological Survey cruise O-1-99-SC. The cruise was conducted in Southern California from June 5 to June 17, 1999. The chief scientist was Bill Normark from the USGS Coastal and Marine Geology office in Menlo Park, CA. The purpose of this cruise was to study pollution transport and accumulation in Santa Monica Bay. The geophysical source was an ODEC 12 kilohertz (kHz) 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/o/o199sc/html/o-1-99-sc.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 data along with GPS navigation data was collected as part of the U.S. Geological Survey cruise O-3-99-MB. The cruise was conducted in Point Sur, Monterey Canyon, California from June 25 to June 29, 1999. The chief scientists were Homa Lee from the USGS Coastal and Marine Geology office in Menlo Park, CA and Charlie Paull from the Monterey Bay Aquarium Research Institute. The overall purpose of this study was to provide samples to use in collaborative studies of sedimentology and geochemistry with Monterey Bay Aquarium Research Institute. The geophysical source was a 3.5 kilohertz (kHz) system. 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/o/o399mb/html/o-3-99-mb.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 data along with SINS navigation data was collected as part of the U.S. Geological Survey cruise P-1-92-MB. The cruise was conducted in Monterey Bay from March 20 to March 22, 1992. The chief scientist was Gary Greene from the USGS Coastal and Marine Geology office in Menlo Park, CA. The overall purpose of this study and the geophysical source are unknown. 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/p/p192mb/html/p-1-92-mb.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 data along with Loran-C and GPS navigation data was collected as part of the U.S. Geological Survey cruise P-1-92-SC. The cruise was conducted in Santa Monica Basin, Southern California from January 30 to February 4, 1992. The chief scientist was Bill Normark from the USGS Coastal and Marine Geology office in Menlo Park, CA and Dave Piper from the Geological Survey of Canada (GSC). The purpose of this cruise was to define the growth pattern of Navy Fan (offshore from San Diego in the California Continental Borderland) over the past few hundred thousand years. Specifically, the goals were to better understand the processes that lead to the formation of sandy submarine fans and the role of sea level changes in their formation. .The geophysical source was a Raytheon 12 kilohertz (kHz) PTR echosounder and ORE 3.5 kHz 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/p/p192sc/html/p-1-92-sc.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 data along with SINS navigation data was collected as part of the U.S. Geological Survey cruise P-1-94-AR. The cruise was conducted in Monterey Bay from July 25 to August 30, 1994. The chief scientist was Art Grantz from the USGS Coastal and Marine Geology office in Menlo Park, CA. The overall purpose of this study was to study climatic history of the western Arctic Ocean basin. The geophysical source is unknown. 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/p/p194ar/html/p-1-94-ar.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 data along with GPS navigation data was collected as part of the U.S. Geological Survey cruise S-1-96-WO. The cruise was conducted in Cascadia, Washington from April 14 to June 6, 1996. The chief scientists were Mike Fisher from the USGS Coastal and Marine Geology office in Menlo Park, CA and Ernest Flueh from GEOMAR in Germany. The purpose of this cruise was for seismic studies of earthquake hazards posed by the subduction zone off Washington and Oregon. 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/s/s196wo/html/s-1-96-wo.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 data along with radar and Loran-C navigation data was collected as part of the U.S. Geological Survey cruise G-1-77-EG. The cruise was conducted in Southern California from May 24 to June 1, 1978. The chief scientists were Bill Normark and Gordon Hess from the USGS Coastal and Marine Geology office in Menlo Park, CA. The geophysical source was a 12 kilohertz (kHz) and 3.5 kHz system. 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/g177eg/html/g-1-77-eg.meta.htmlinto 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 the U.S. Geological Survey cruise T-1-98-GB. The cruise was conducted in Glacier Bay, Alaska from August 21 to September 1, 1998. The chief scientists were Paul Carlson, Guy Cochrane, and Philip Hooge all from the USGS Coastal and Marine Geology office in Menlo Park, CA. The overall purpose of this study was to add the geophysical surveying done in this and previous studies with existing population and sonic-tracking data sets as well as future sediment sampling, scuba, submersible, and bottom video camera observations to better understand Dungeness crab and Pacific halibut habitat relationships. The geophysical source was a 3.5 kilohertz (kHz) system. 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/t/t198gb/html/t-1-98-gb.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.

As part of the Barrier Island Evolution Research (BIER) project, scientists from the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center (SPCMSC) collected sediment samples from the northern Chandeleur Islands in March and September 2012. The overall objective of this project, which integrates geophysical (bathymetric, seismic, and topographic) and sedimentologic data, is to better understand the depositional and erosional processes that drive the morphologic evolution of barrier islands over annual to interannual timescales (1 to 5 years). Between June 2010 and April 2011, in response to the Deepwater Horizon oil spill, the State of Louisiana constructed a sand berm extending more than 14 kilometers (km) along the northern Chandeleur Islands platform. The construction of the berm provided a unique opportunity to investigate how this new sediment source will interact with and affect the morphologic evolution of the barrier-island system. Data collected from this study will be used to describe differences in the physical characteristics and spatial distribution of sediments both along the axis of the berm and also along transects across the berm and onto the adjacent barrier island. Comparison of these data with data from subsequent sampling efforts will provide information about sediment interactions and movement between the berm and the natural island platform, improving our understanding of short-term morphologic change and processes in this barrier-island system. This data series serves as an archive of sediment data collected in March and September 2012 from the Chandeleur Islands sand berm and adjacent barrier-island environments. Data products, including descriptive core logs, core photographs and x-radiographs, results of sediment grain-size analyses, sample location maps, and Geographic Information System (GIS) data files with accompanying formal Federal Geographic Data Committee (FDGC) metadata, can be downloaded from https://pubs.usgs.gov/ds/0850/data.html.

Assessing the physical change to shorelines and wetlands is critical in determining the resiliency of wetland systems that protect adjacent habitat and communities. The wetland and back-barrier shorelines of Assateague Island, located in Maryland and Virginia, changed as a result of wave action and storm surge that occurred during Hurricane Sandy, which made landfall on October 29, 2012. The impact of Hurricane Sandy will be assessed and placed in its historical context to understand the future vulnerability of wetland systems. Making these assessments will rely on data extracted from current and historical resources such as maps, aerial photographs, satellite imagery, and lidar elevation data, which document physical changes over time. This USGS Data Series publication includes includes several open-ocean shorelines, back-island shorelines, back-island shoreline points, sand area polygons, and sand lines for Assateague Island that were extracted from orthoimagery (ortho aerial photography)dated from April 12, 1989 to September 5, 2013.

Assessing the physical change to shorelines and wetlands is critical in determining the resiliency of wetland systems that protect adjacent habitat and communities. The wetland and back-barrier shorelines of Assateague Island, located in Maryland and Virginia, changed as a result of wave action and storm surge that occurred during Hurricane Sandy, which made landfall on October 29, 2012. The impact of Hurricane Sandy will be assessed and placed in its historical context to understand the future vulnerability of wetland systems. Making these assessments will rely on data extracted from current and historical resources such as maps, aerial photographs, satellite imagery, and lidar elevation data, which document physical changes over time. This USGS Data Series publication includes several open-ocean shorelines, back-island shorelines, back-island shoreline points, sand area polygons, and sand lines for Assateague Island that were extracted from orthoimagery (orthoaerial photography) dated from April 12, 1989 to September 5, 2013. This data set consists of lines that were digitized at the intersection of the back-island shoreline and a set of transects spaced at 20-meter (m) intervals. The transects, asis_transects_ln_20m_utm18.shp, are included in this Data Series publication and can be accessed via the Data Download page. The lines falling between the transects do not follow the natural back-island shoreline. Only one back-island shoreline/transect intersection line vector was digitized per transect. Orthoimagery of Assateague Island were acquired in digital format from U.S. Department of Agriculture (USDA), U.S. Geological Survey (USGS) and Virginia Geographic Information Network (VGIN) courtesy of the Commonwealth of Virginia. The following list provides additional details about the orthoimagery used. The digitized back-island shorelines for all dates have been compiled into one data set (shapefile) named asis_bshrln_1989_2013_guided.shp. The orthoimage date for each line is included in the shapefile attribute table Date_ field. Date State Type Source Resolution 198904129(1) MD DOQQ USGS 1 meter (m) 19940320 VA DOQQ USGS 1 m 20041105 VA NAIP USDA 2 m 20050608 VA NAIP USDA 2 m 20050615 MD NAIP USDA 1 m 20060528 VA NAIP USDA 2 m 20060701 MD NAIP USDA 2 m 20070622 MD NAIP USDA 1 m 20080525 VA NAIP USDA 1 m 20090626 MD NAIP USDA 1 m 20090726 VA NAIP USDA 1 m 20090807 VA NAIP USDA 1 m 20110530 VA NAIP USDA 1 m 20110602 MD NAIP USDA 1 m 20120512 VA NAIP USDA 1 m 20130315 VA VBMP VGIN(2) 1 m(3) 20130905 MD NAIP USDA 1 m DOQQ Digital Orthophoto Quarter Quads NAIP National Agriculture Imagery Program VBMP Virginia Base Mapping Program (1)Color Infrared orthoimagery; all others are natural color. (2)Imagery courtesy of the Commonwealth of Virginia. (3)Resampled from 1-foot resolution imagery.

Assessing the physical change to shorelines and wetlands is critical in determining the resiliency of wetland systems that protect adjacent habitat and communities. The wetland and back-barrier shorelines of Assateague Island, located in Maryland and Virginia, changed as a result of wave action and storm surge that occurred during Hurricane Sandy, which made landfall on October 29, 2012. The impact of Hurricane Sandy will be assessed and placed in its historical context to understand the future vulnerability of wetland systems. Making these assessments will rely on data extracted from current and historical resources such as maps, aerial photographs, satellite imagery, and lidar elevation data, which document physical changes over time. This USGS Data Series publication includes several open-ocean shorelines, back-island shorelines, back-island shoreline points, sand area polygons, and sand lines for Assateague Island that were extracted from orthoimagery (orthoaerial photography) dated from April 12, 1989 to September 5, 2013. This dataset consists of points that were digitized at the intersection of the back-island shoreline and a set of transects spaced at 20 meter (m) intervals. The transects, asis_transects_ln_20m_utm18.shp, are included in this Data Series publication and can be accessed via the Data Download page. Only one back-island shoreline/transect intersection point was digitized per transect. Orthoimagery of Assateague Island were acquired in digital format from U.S. Department of Agriculture (USDA), U.S. Geological Survey (USGS) and Virginia Geographic Information Network (VGIN) courtesy of the Commonwealth of Virginia. The following list provides additional details about the orthoimagery used. The back-island shoreline points for all dates have been compiled into one dataset (shapefile) named asis_bshrln_1989_2013_transect_guided.shp. The orthoimage date for each line is included in the shapefile attribute table Date_ field. Date State Type Source Resolution 198904129(1) MD DOQQ USGS 1 meter (m) 19940320 VA DOQQ USGS 1 m 20041105 VA NAIP USDA 2 m 20050608 VA NAIP USDA 2 m 20050615 MD NAIP USDA 1 m 20060528 VA NAIP USDA 2 m 20060701 MD NAIP USDA 2 m 20070622 MD NAIP USDA 1 m 20080525 VA NAIP USDA 1 m 20090626 MD NAIP USDA 1 m 20090726 VA NAIP USDA 1 m 20090807 VA NAIP USDA 1 m 20110530 VA NAIP USDA 1 m 20110602 MD NAIP USDA 1 m 20120512 VA NAIP USDA 1 m 20130315 VA VBMP VGIN(2) 1 m(3) 20130905 MD NAIP USDA 1 m DOQQ Digital Orthophoto Quarter Quads NAIP National Agriculture Imagery Program VBMP Virginia Base Mapping Program (1)Color Infrared orthoimagery; all others are natural color. (2)Imagery courtesy of the Commonwealth of Virginia. (3)Resampled from 1-foot resolution imagery.

Assessing the physical change to shorelines and wetlands is critical in determining the resiliency of wetland systems that protect adjacent habitat and communities. The wetland and back-barrier shorelines of Assateague Island, located in Maryland and Virginia, changed as a result of wave action and storm surge that occurred during Hurricane Sandy, which made landfall on October 29, 2012. The impact of Hurricane Sandy will be assessed and placed in its historical context to understand the future vulnerability of wetland systems. Making these assessments will rely on data extracted from current and historical resources such as maps, aerial photographs, satellite imagery, and lidar elevation data, which document physical changes over time. This USGS Data Series publication includes several open-ocean shorelines, back-island shorelines, back-island shoreline points, sand area polygons, and sand lines for Assateague Island that were extracted from orthoimagery (ortho aerial photography) dated from April 12, 1989 to September 5, 2013. This dataset consists of polygons that represent the sand areas found in orthoimagery taken on the date specified in the filename and in the Date_ field in the feature attribute table. Orthoimagery of Assateague Island were acquired in digital format from U.S. Department of Agriculture (USDA), U.S. Geological Survey (USGS) and Virginia Geographic Information Network (VGIN) courtesy of the Commonwealth of Virginia. Prior to processing, the images were trimmed to the Assateague Island area. Using ERDAS Imagine 9.3, the images were classified into 10-40 classes that were coded either sand or not sand. Using ArcGIS 10.1, the resulting coded raster datasets were converted to polygons and edited. The following list provides additional details about the orthoimagery used. The sand areas for each date is in a separate dataset (shapefile) named asis_sand_po_*.shp where the date, in YYYYMMDD format, replaces the asterisk. The orthoimage date for each polygon is also included in the shapefile attribute table Date_ field. Date State Type Source Resolution 198904129(1) MD DOQQ USGS 1 meter (m) 19940320 VA DOQQ USGS 1 m 20041105 VA NAIP USDA 2 m 20050608 VA NAIP USDA 2 m 20050615 MD NAIP USDA 1 m 20060528 VA NAIP USDA 2 m 20060701 MD NAIP USDA 2 m 20070622 MD NAIP USDA 1 m 20080525 VA NAIP USDA 1 m 20090626 MD NAIP USDA 1 m 20090726 VA NAIP USDA 1 m 20090807 VA NAIP USDA 1 m 20110530 VA NAIP USDA 1 m 20110602 MD NAIP USDA 1 m 20120512 VA NAIP USDA 1 m 20130315 VA VBMP VGIN(2) 1 m(3) 20130905 MD NAIP USDA 1 m DOQQ Digital Orthophoto Quarter Quads NAIP National Agriculture Imagery Program VBMP Virginia Base Mapping Program (1)Color Infrared orthoimagery; all others are natural color. (2)Imagery courtesy of the Commonwealth of Virginia. (3)Resampled from 1-foot resolution imagery.

Assessing the physical change to shorelines and wetlands is critical in determining the resiliency of wetland systems that protect adjacent habitat and communities. The wetland and back-barrier shorelines of Assateague Island, located in Maryland and Virginia, changed as a result of wave action and storm surge that occurred during Hurricane Sandy, which made landfall on October 29, 2012. The impact of Hurricane Sandy will be assessed and placed in its historical context to understand the future vulnerability of wetland systems. Making these assessments will rely on data extracted from current and historical resources such as maps, aerial photographs, satellite imagery, and lidar elevation data, which document physical changes over time. This USGS Data Series publication includes several open-ocean shorelines, back-island shorelines, back-island shoreline points, sand area polygons, and sand lines for Assateague Island that were extracted from orthoimagery (ortho aerial photography) dated from April 12, 1989 to September 5, 2013. This dataset consists of lines that were hand-digitized at the approximate open-ocean water line at a scale of approximately 1:2,000. The lines were visually generalized through waves and swash zones by the photointerpreter. Orthoimagery of Assateague Island were acquired in digital format from U.S. Department of Agriculture (USDA), U.S. Geological Survey (USGS) and Virginia Geographic Information Network (VGIN) courtesy of the Commonwealth of Virginia. The following list provides additional details about the orthoimagery used. The open-ocean shorelines for all dates have been compiled in one dataset (shapefile) named asis_sshrln_1989_2013.shp. The orthoimage date for each line is in the shapefile attribute table Date_ field. Date State Type Source Resolution 198904129(1) MD DOQQ USGS 1 meter (m) 19940320 VA DOQQ USGS 1 m 20041105 VA NAIP USDA 2 m 20050608 VA NAIP USDA 2 m 20050615 MD NAIP USDA 1 m 20060528 VA NAIP USDA 2 m 20060701 MD NAIP USDA 2 m 20070622 MD NAIP USDA 1 m 20080525 VA NAIP USDA 1 m 20090626 MD NAIP USDA 1 m 20090726 VA NAIP USDA 1 m 20090807 VA NAIP USDA 1 m 20110530 VA NAIP USDA 1 m 20110602 MD NAIP USDA 1 m 20120512 VA NAIP USDA 1 m 20130315 VA VBMP VGIN(2) 1 m(3) 20130905 MD NAIP USDA 1 m DOQQ Digital Orthophoto Quarter Quads NAIP National Agriculture Imagery Program VBMP Virginia Base Mapping Program (1)Color Infrared orthoimagery; all others are natural color. (2)Imagery courtesy of the Commonwealth of Virginia. (3)Resampled from 1-foot resolution imagery.

Assessing the physical change to shorelines and wetlands is critical in determining the resiliency of wetland systems that protect adjacent habitat and communities. The wetland and back-barrier shorelines of Assateague Island, located in Maryland and Virginia, changed as a result of wave action and storm surge that occurred during Hurricane Sandy, which made landfall on October 29, 2012. The impact of Hurricane Sandy will be assessed and placed in its historical context to understand the future vulnerability of wetland systems. Making these assessments will rely on data extracted from current and historical resources such as maps, aerial photographs, satellite imagery, and lidar elevation data, which document physical changes over time. This USGS Data Series publication includes several open-ocean shorelines, back-island shorelines, back-island shoreline points, sand area polygons, and sand lines for Assateague Island that were extracted from ortho imagery (ortho aerial photography)dated from April 12, 1989 to September 5, 2013.

Assessing the physical change to shorelines and wetlands is critical in determining the resiliency of wetland systems that protect adjacent habitat and communities. The wetland and back-barrier shorelines of New Jersey changed as a result of wave action and storm surge that occurred during Hurricane Sandy, which made landfall on October 29, 2012. The impact of Hurricane Sandy will be assessed and placed in its historical context to understand the future vulnerability of wetland systems. Making these assessments will rely on data extracted from current and historical resources such as maps, aerial photographs, satellite imagery, and lidar elevation data, which document physical changes over time. This USGS Data Series publication includes several open-ocean shorelines, back-island shorelines, back-island shoreline points, sand area polygons, and sand lines for the undeveloped areas of New Jersey's barrier islands that were extracted from orthoimagery (ortho aerial photography)dated from March 9, 1991 to July 30, 2013.

Assessing the physical change to shorelines and wetlands is critical in determining the resiliency of wetland systems that protect adjacent habitat and communities. The wetland and back-barrier shorelines of New Jersey's barrier islands changed as a result of wave action and storm surge that occurred during Hurricane Sandy, which made landfall on October 29, 2012. The impact of Hurricane Sandy will be assessed and placed in its historical context to understand the future vulnerability of wetland systems. Making these assessments will rely on data extracted from current and historical resources such as maps, aerial photographs, satellite imagery, and lidar elevation data, which document physical changes over time. This USGS Data Series publication includes several open-ocean shorelines, back-island shorelines, back-island shoreline points, sand area polygons, and sand lines for the undeveloped areas of New Jersey's barrier islands that were extracted from orthoimagery (orthoaerial photography) dated from March 9, 1991 to July 30, 2013. This data set consists of lines that were digitized at the intersection of the back-island shoreline and a set of transects spaced at 20-meter (m) intervals. The transects, nj_transects_ln_20m_utm18.shp, are included in this Data Series publication and can be accessed via the Data Download page, located at http://pubs.usgs.gov/ds/0960/_ds_data-products.html. The lines falling between the transects do not follow the natural back-island shoreline. Only one back-island shoreline/transect intersection line vector was digitized per transect. Orthoimagery of New Jersey were acquired in digital format from U.S. Department of Agriculture (USDA), U.S. Geological Survey (USGS), National Oceanic and Atmospheric Administration (NOAA), and New Jersey Geographic Information Network (NJGIN). The following list provides additional details about the orthoimagery used. The digitized back-island shorelines are organized by area (Sandy Hook, Barnegat Bay, Great Bay, Ludlum Bay, Great Channel, and Cape May) with all dates for each area compiled into one data-set (shapefile) named bshrln_<range of dates>_<area name>.shp. The orthoimage date for each line is included in the shapefile attribute table "Date" field. Date Type Source Resolution 19910309-19910313 Pan USGS, DOQQ 1 m (meter) 19950325-19950407 CIR USGS, DOQQ 1 m 20020218-20020411 CIR NJGIN 1 m* 20060805-20060613 Natural USDA, NAIP 1 m 20070318-20070415 Natural NJGIN 1 m* 20080808-20080826 Natural USDA, NAIP 1 m 20100703-20100726 Natural USDA, NAIP 1 m 20120314-20120416 RGBI NJGIN 1 m* 20121031-20121106 Natural NOAA 1 m* 20130707-20130730 Natural USDA, NAIP 1 m Pan - Panchromatic (1 band, gray scale) CIR - Color Infrared (infrared, red, green) Natural - Natural Color (red, green, blue) RGBI - Natural Color and Infrared (red, green, blue, and infrared) DOQQ - Digital Orthophoto Quarter Quads NAIP - National Agriculture Imagery Program *Resampled from 1-foot resolution imagery.

Assessing the physical change to shorelines and wetlands is critical in determining the resiliency of wetland systems that protect adjacent habitat and communities. The wetland and back-barrier shorelines of New Jersey changed as a result of wave action and storm surge that occurred during Hurricane Sandy, which made landfall on October 29, 2012. The impact of Hurricane Sandy will be assessed and placed in its historical context to understand the future vulnerability of wetland systems. Making these assessments will rely on data extracted from current and historical resources such as maps, aerial photographs, satellite imagery, and lidar elevation data, which document physical changes over time. This USGS Data Series publication includes several open-ocean shorelines, back-island shorelines, back-island shoreline points, sand area polygons, and sand lines for the undeveloped areas of New Jersey's barrier islands that were extracted from orthoimagery (orthoaerial photography) dated from March 9, 1991 to July 30, 2013. This data-set consists of points that were digitized at the intersection of the back-island shoreline and a set of transects spaced at 20-meter (m) intervals. The transects, nj_transects_ln_20m_utm18.shp, are included in this Data Series publication and can be accessed via the Data Download page. Only one back-island shoreline/transect intersection point was digitized per transect. Orthoimagery of New Jersey were acquired in digital format from U.S. Department of Agriculture (USDA), U.S. Geological Survey (USGS), National Oceanic and Atmospheric Administration (NOAA), and New Jersey Geographic Information Network (NJGIN). The following list provides additional details about the orthoimagery used. The back-island shoreline points are organized by area with all dates for each area compiled into one data-set (shapefile) named nj_bshrpt_<range of dates>_<area name>.shp. The orthoimage date for each line is included in the shapefile attribute table "Date_" field. Date Type Source Resolution 19910309-19910313 Pan USGS, DOQQ 1 m (meter) 19950325-19950407 CIR USGS, DOQQ 1 m 20020218-20020411 CIR NJGIN 1 m* 20060805-20060613 Natural USDA, NAIP 1 m 20070318-20070415 Natural NJGIN 1 m* 20080808-20080826 Natural USDA, NAIP 1 m 20100703-20100726 Natural USDA, NAIP 1 m 20120314-20120416 RGBI NJGIN 1 m* 20121031-20121106 Natural NOAA 1 m* 20130707-20130730 Natural USDA, NAIP 1 m Pan - Panchromatic (1 band, gray scale) CIR - Color Infrared (infrared, red, green) Natural - Natural Color (red, green, blue) RGBI - Natural Color and Infrared (red, green, blue, and infrared) DOQQ - Digital Orthophoto Quarter Quads NAIP - National Agriculture Imagery Program *Resampled from 1-foot resolution imagery.

Assessing the physical change to shorelines and wetlands is critical in determining the resiliency of wetland systems that protect adjacent habitat and communities. The wetland and back-barrier shorelines of New Jersey changed as a result of wave action and storm surge that occurred during Hurricane Sandy, which made landfall on October 29, 2012. The impact of Hurricane Sandy will be assessed and placed in its historical context to understand the future vulnerability of wetland systems. Making these assessments will rely on data extracted from current and historical resources such as maps, aerial photographs, satellite imagery, and lidar elevation data, which document physical changes over time. This USGS Data Series publication includes several open-ocean shorelines, back-island shorelines, back-island shoreline points, sand area polygons, and sand lines for the undeveloped areas of New Jersey that were extracted from orthoimagery (ortho aerial photography) dated from March 9, 1991 to July 30, 2013. This data-set consists of lines that comprise the inland extent of the main body of sand (beach/dune/overwash area) found in the orthoimagery taken on the date specified in the filename and in the "Date_" field in the feature attribute table. They are based on the sand area polygons, nj_sandpo_*.shp, that are included in this Data Series publication and can be accessed via the Data Download page. Orthoimagery of New Jersey were acquired in digital format from U.S. Department of Agriculture (USDA), U.S. Geological Survey (USGS), National Oceanic and Atmospheric Administration (NOAA), and New Jersey Geographic Information Network (NJGIN). The following list provides additional details about the orthoimagery used. The sand lines are organized by area with all dates for each area compiled into one data-set (shapefile) named nj_sandln_<range of dates>_<area name>.shp. The orthoimage date for each line is included in the shapefile attribute table "Date_" field. Date Type Source Resolution 19910309-19910313 Pan USGS, DOQQ 1 m (meter) 19950325-19950407 CIR USGS, DOQQ 1 m 20020218-20020411 CIR NJGIN 1 m* 20060805-20060613 Natural USDA, NAIP 1 m 20070318-20070415 Natural NJGIN 1 m* 20080808-20080826 Natural USDA, NAIP 1 m 20100703-20100726 Natural USDA, NAIP 1 m 20120314-20120416 RGBI NJGIN 1 m* 20130707-20130730 Natural USDA, NAIP 1 m Pan - Panchromatic (1 band, gray scale) CIR - Color Infrared (infrared, red, green) Natural - Natural Color (red, green, blue) RGBI - Natural Color and Infrared (red, green, blue, and infrared) DOQQ - Digital Orthophoto Quarter Quads NAIP - National Agriculture Imagery Program *Resampled from 1-foot resolution imagery then smoothed using a 3 m by 3 m focal mean.

Assessing the physical change to shorelines and wetlands is critical in determining the resiliency of wetland systems that protect adjacent habitat and communities. The wetland and back-barrier shorelines of New Jersey changed as a result of wave action and storm surge that occurred during Hurricane Sandy, which made landfall on October 29, 2012. The impact of Hurricane Sandy will be assessed and placed in its historical context to understand the future vulnerability of wetland systems. Making these assessments will rely on data extracted from current and historical resources such as maps, aerial photographs, satellite imagery, and lidar elevation data, which document physical changes over time. This USGS Data Series publication includes several open-ocean shorelines, back-island shorelines, back-island shoreline points, sand area polygons, and sand lines for the undeveloped areas of New Jersey's barrier islands that were extracted from orthoimagery (ortho aerial photography) dated from March 9, 1991 to July 30, 2013. This data-set consists of polygons that represent the sand areas found in orthoimagery taken on the date specified in the filename and in the "Date_" field in the feature attribute table. Orthoimagery of New Jersey were acquired in digital format from U.S. Department of Agriculture (USDA), U.S. Geological Survey (USGS), National Oceanic and Atmospheric Administration (NOAA), and New Jersey Geographic Information Network (NJGIN). Prior to processing, the images were trimmed to remove the bulk of the areas that were not going to be analyzed. Using ERDAS Imagine 9.3, the images were classified into 40 classes that were coded as either sand or not sand. Due to varying conditions within the imagery, the coding occasionally varied from one area to another. Using ArcGIS 10.2, the resulting coded raster data-sets were converted to polygons and edited. The following list provides additional details about the orthoimagery used. The sand areas for each date and area is in a separate data-set (shapefile) named nj_sandpo_<range of dates>_<area name>.shp. The orthoimage date for each polygon is included in the shapefile attribute table's "Date_" field. Date Type Source Resolution 19910309-19910313 Pan USGS, DOQQ 1 m (meter) 19950325-19950407 CIR USGS, DOQQ 1 m 20020218-20020411 CIR NJGIN 1 m* 20060805-20060613 Natural USDA, NAIP 1 m 20070318-20070415 Natural NJGIN 1 m* 20080808-20080826 Natural USDA, NAIP 1 m 20100703-20100726 Natural USDA, NAIP 1 m 20120314-20120416 RGBI NJGIN 1 m* 20130707-20130730 Natural USDA, NAIP 1 m Pan - Panchromatic (1 band, gray scale) CIR - Color Infrared (infrared, red, green) Natural - Natural Color (red, green, blue) RGBI - Natural Color and Infrared (red, green, blue, and infrared) DOQQ - Digital Orthophoto Quarter Quads NAIP - National Agriculture Imagery Program *Resampled from 1-foot resolution imagery then smoothed using a 3 pixel by 3 pixel focal mean.

Assessing the physical change to shorelines and wetlands is critical in determining the resiliency of wetland systems that protect adjacent habitat and communities. The wetland and back-barrier shorelines of New Jersey changed as a result of wave action and storm surge that occurred during Hurricane Sandy, which made landfall on October 29, 2012. The impact of Hurricane Sandy will be assessed and placed in its historical context to understand the future vulnerability of wetland systems. Making these assessments will rely on data extracted from current and historical resources such as maps, aerial photographs, satellite imagery, and lidar elevation data, which document physical changes over time. This USGS Data Series publication includes several open-ocean shorelines, back-island shorelines, back-island shoreline points, sand area polygons, and sand lines for the undeveloped areas of New Jersey's barrier islands that were extracted from orthoimagery (ortho aerial photography) dated from March 9, 1991 to July 30, 2013. This data-set consists of lines that were hand-digitized at the approximate open-ocean water line at a scale of approximately 1:2,000. The lines were visually generalized through waves and swash zones by the photointerpreter. Orthoimagery of New Jersey were acquired in digital format from U.S. Department of Agriculture (USDA), U.S. Geological Survey (USGS), National Oceanic and Atmospheric Administration (NOAA), and New Jersey Geographic Information Network (NJGIN). The following list provides additional details about the orthoimagery used. The open-ocean shorelines are organized by area with all dates for each area compiled into one data-set (shapefile) named nj_sshrln_<range of dates>_<area name>.shp. The orthoimage date for each line is in the shapefile attribute table "Date_" field. Date Type Source Resolution 19910309-19910313 Pan USGS, DOQQ 1 m (meter) 19950325-19950407 CIR USGS, DOQQ 1 m 20020218-20020411 CIR NJGIN 1 m* 20060805-20060613 Natural USDA, NAIP 1 m 20070318-20070415 Natural NJGIN 1 m* 20080808-20080826 Natural USDA, NAIP 1 m 20100703-20100726 Natural USDA, NAIP 1 m 20120314-20120416 RGBI NJGIN 1 m* 20121031-20121106 Natural NOAA 1 m* 20130707-20130730 Natural USDA, NAIP 1 m Pan - Panchromatic (1 band, gray scale) CIR - Color Infrared (infrared, red, green) Natural - Natural Color (red, green, blue) RGBI - Natural Color and Infrared (red, green, blue, and infrared) DOQQ - Digital Orthophoto Quarter Quads NAIP - National Agriculture Imagery Program *Resampled from 1-foot resolution imagery.

Assessing the physical change to shorelines and wetlands is critical in determining the resiliency of wetland systems that protect adjacent habitat and communities. The wetland and back-barrier shorelines of New Jersey changed as a result of wave action and storm surge that occurred during Hurricane Sandy, which made landfall on October 29, 2012. The impact of Hurricane Sandy will be assessed and placed in its historical context to understand the future vulnerability of wetland systems. Making these assessments will rely on data extracted from current and historical resources such as maps, aerial photographs, satellite imagery, and lidar elevation data, which document physical changes over time. This USGS Data Series publication includes several open-ocean shorelines, back-island shorelines, back-island shoreline points, sand area polygons, and sand lines the undeveloped areas of New Jersey that were extracted from ortho imagery (ortho aerial photography) dated from March 9, 1991 to July 30, 2013.

The U.S. Geological Survey has a long history of responding to and documenting the impacts of storms along the Nation’s coasts and incorporating these data into storm impact and coastal change vulnerability assessments. These studies, however, have traditionally focused on sandy shorelines and sandy barrier-island systems, without consideration of impacts to coastal wetlands. The goal of the Barrier Island and Estuarine Wetland Physical Change Assessment project is to integrate a wetland-change assessment with existing coastal-change assessments for the adjacent sandy dunes and beaches, initially focusing on Assateague Island along the Maryland and Virginia coastline. Assateague Island was impacted by waves and storm surge associated with the passage of Hurricane Sandy in October 2012, including erosion and overwash along the ocean-facing sandy shoreline as well as erosion and overwash deposition in the back-barrier and estuarine bay environments. This report serves as an archive of data that were derived from Landsat 5 and Landsat 8 imagery from 1984 to 2014, including wetland and terrestrial habitat extents; open-ocean, back-barrier, and estuarine mainland shoreline positions; and sand-line positions along the estuarine mainland and barrier shorelines from Assateague Island, Maryland to Metompkin Island, Virginia. The geographic information system data files with accompanying formal Federal Geographic Data Committee (FGDC) metadata can be downloaded from http://pubs.usgs.gov/ds/0968/ds968_data.html.

The U.S. Geological Survey has a long history of responding to and documenting the impacts of storms along the Nation’s coasts and incorporating these data into storm impact and coastal change vulnerability assessments. These studies, however, have traditionally focused on sandy shorelines and sandy barrier-island systems, without consideration of impacts to coastal wetlands. The goal of the Barrier Island and Estuarine Wetland Physical Change Assessment project is to integrate a wetland-change assessment with existing coastal-change assessments for the adjacent sandy dunes and beaches, initially focusing on Assateague Island along the Maryland and Virginia coastline. Assateague Island was impacted by waves and storm surge associated with the passage of Hurricane Sandy in October 2012, including erosion and overwash along the ocean-facing sandy shoreline as well as erosion and overwash deposition in the back-barrier and estuarine bay environments. This report serves as an archive of data that were derived from Landsat 5 and Landsat 8 imagery from 1984 to 2014, including wetland and terrestrial habitat extents; open-ocean, back-barrier, and estuarine mainland shoreline positions; and sand-line positions along the estuarine mainland and barrier shorelines from Assateague Island, Maryland to Metompkin Island, Virginia. The geographic information system data files with accompanying formal Federal Geographic Data Committee (FGDC) metadata can be downloaded from http://pubs.usgs.gov/ds/0968/ds968_data.html.

The U.S. Geological Survey has a long history of responding to and documenting the impacts of storms along the Nation’s coasts and incorporating these data into storm impact and coastal change vulnerability assessments. These studies, however, have traditionally focused on sandy shorelines and sandy barrier-island systems, without consideration of impacts to coastal wetlands. The goal of the Barrier Island and Estuarine Wetland Physical Change Assessment project is to integrate a wetland-change assessment with existing coastal-change assessments for the adjacent sandy dunes and beaches, initially focusing on Assateague Island along the Maryland and Virginia coastline. Assateague Island was impacted by waves and storm surge associated with the passage of Hurricane Sandy in October 2012, including erosion and overwash along the ocean-facing sandy shoreline as well as erosion and overwash deposition in the back-barrier and estuarine bay environments. This report serves as an archive of data that were derived from Landsat 5 and Landsat 8 imagery from 1984 to 2014, including wetland and terrestrial habitat extents; open-ocean, back-barrier, and estuarine mainland shoreline positions; and sand-line positions along the estuarine mainland and barrier shorelines from Assateague Island, Maryland to Metompkin Island, Virginia. The geographic information system data files with accompanying formal Federal Geographic Data Committee (FGDC) metadata can be downloaded from http://pubs.usgs.gov/ds/0968/ds968_data.html.

In 2006 and 2007, the U.S. Geological Survey (USGS) and collaborators at the University of New Orleans (UNO) collected high-resolution seismic profiles and subsurface cores around the Chandeleur and Breton Islands, Louisiana. To ground-truth the acoustic seismic surveys conducted in 2006, 124 vibracores were acquired during the 07SCC04 and 07SCC05 cruises in 2007. These cores were collected within the back-barrier, nearshore, and offshore environments. The surveys were conducted as part of a post-hurricane assessment and sediment resource inventory for the Barrier Island Coastal Monitoring (BICM) project. Vibracores were collected offshore using the USGS R/V G.K. Gilbert, while the terrestrial, back-barrier, and nearshore vibracores were collected from the UNO R/V Greenhead. This report serves as an archive of sediment data from two concurrent vibracore surveys (cruises 07SCC04 and 07SCC05) from around the Breton and Chandeleur Islands in 2007 and also documents sediment data from vibracores collected offshore of the Chandeleur Islands in 1987 (cruise 87039). The 1987 vibracores were collected through the collaborated efforts of the USGS,Louisiana Geological Survey (LGS), and Alpine Ocean Seismic.

Dauphin Island, Alabama is a barrier island located in the Gulf of Mexico that supports local residence, tourism, commercial infrastructure, and the historical Fort Gaines. During the past decade the island has been impacted by several major hurricanes (Ivan, 2004; Katrina, 2005; Isaac 2012). Storms along with sea level rise, presents a continued threat to island stability. State and federal managers are taking a scientific investigative approach to identify the best options available to formulate and implement a long-term plan to properly restore Dauphin Island and provide resilience against future storms and sea-level rise. Island morphology, including current bathymetry data, is one of several aspects being investigated and funded through a grant from National Fish and Wildlife Foundation Gulf Environmental Benefit Fund. In August 2015, the United States Geological Survey Saint Petersburg Coastal and Marine Science Center (USGS SPCMSC) in cooperation with the United States Army Corps of Engineers (USACE) and the state of Alabama conducted bathymetric surveys of the nearshore waters surrounding Dauphin Island. This data release provides 1,165-line kilometers (km) of processed single-beam bathymetry (SBB) data collected by the USGS SPCMSC in August 2015 (Field Activity Number [FAN] 2015-326-FA). Data were acquired aboard 4 separate survey vessels; the RV Sallenger (subFAN, 15BIM10), the RV Jabba Jaw (subFAN, 15BIM11), the RV Shark (subFAN, 15BIM12), and the RV Chum (subFAN, 15BIM13). The data are provided in three datums: 1) the International Terrestrial Reference Frame of 2000 (ITRF00), ellipsoid height (-47.04 meters [m] to -29.36 m); 2) the North American Datum of 1983, realization of CORS96 (NAD83 (CORS96)) horizontal, and the North American Vertical Datum 1988 (NAVD88) vertical (-0.24 m to -17.33 m); and 3) the NAD83 (CORS96) horizontal, and Mean Lower Low Water (MLLW) vertical (-0.12 m to -17.93 m). Additional files include trackline shapefiles, digital and handwritten Field Activity Collection Systems (FACS) logs, a comprehensive 50-meter Digital Elevation Model (DEM), and formal Federal Geographic Data Committee (FGDC) metadata.

Dauphin Island, Alabama is a barrier island located in the Gulf of Mexico that supports local residence, tourism, commercial infrastructure, and the historical Fort Gaines. During the past decade the island has been impacted by several major hurricanes (Ivan, 2004; Katrina, 2005; Isaac 2012). Storms along with sea level rise, presents a continued threat to island stability. State and federal managers are taking a scientific investigative approach to identify the best options available to formulate and implement a long-term plan to properly restore Dauphin Island and provide resilience against future storms and sea-level rise. Island morphology, including current bathymetry data, is one of several aspects being investigated and funded through a grant from National Fish and Wildlife Foundation Gulf Environmental Benefit Fund. In August 2015, the United States Geological Survey Saint Petersburg Coastal and Marine Science Center (USGS SPCMSC) in cooperation with the United States Army Corps of Engineers (USACE) and the state of Alabama conducted bathymetric surveys of the nearshore waters surrounding Dauphin Island. This data release provides 1,165-line kilometers (km) of processed single-beam bathymetry (SBB) data collected by the USGS SPCMSC in August 2015 (Field Activity Number [FAN] 2015-326-FA). Data were acquired aboard 4 separate survey vessels; the RV Sallenger (subFAN, 15BIM10), the RV Jabba Jaw (subFAN, 15BIM11), the RV Shark (subFAN, 15BIM12), and the RV Chum (subFAN, 15BIM13). The data are provided in three datums: 1) the International Terrestrial Reference Frame of 2000 (ITRF00), ellipsoid height (-47.04 meters [m] to -29.36 m); 2) the North American Datum of 1983, realization of CORS96 (NAD83 (CORS96)) horizontal, and the North American Vertical Datum 1988 (NAVD88) vertical (-0.24 m to -17.33 m); and 3) the NAD83 (CORS96) horizontal, and Mean Lower Low Water (MLLW) vertical (-0.12 m to -17.93 m). Additional files include trackline shapefiles, digital and handwritten Field Activity Collection Systems (FACS) logs, a comprehensive 50-meter Digital Elevation Model (DEM), and formal Federal Geographic Data Committee (FGDC) metadata.

The National Assessment of Coastal Change Hazards project exists to understand and predict storm impacts to our nation's coastlines. This geospatial dataset defines the alongshore extent of overwash sediments deposited along the Louisiana coast and attributed to coastal processes during [Atlantic Basin] Hurricane Delta, which made landfall in the U.S. on October 9, 2020.

The numerical model Delft3D (developed by Deltares) was developed to evaluate the potential effects of proposed navigation channel deepening and widening in Mobile Harbor, Alabama (AL). The Delft3D model setup requires the input of a merged topographic and bathymetric elevations, a wave climate based on significant wave heights, peak wave period and mean wave direction, and a tidal-time series. The model was validated by comparing model outputs from deterministic runs with observations of water levels and velocities. The validated model was used to simulate scenarios of existing conditions and proposed with-project conditions (for example, channel deepening and widening). Simulations were performed for one year (the year 2010) and ten years, with and without 0.5 meters (m) of sea level rise. Model inputs and outputs in the form of topography and bathymetry for the scenario runs as well as output water levels and velocities for the deterministic runs are provided in this data release. For further information regarding model input generation and visualization of model output topography and bathymetry, refer to USGS Open-File Report 2018-1123.

The numerical model Delft3D (developed by Deltares) was developed to evaluate the potential effects of proposed navigation channel deepening and widening in Mobile Harbor, Alabama (AL). The Delft3D model setup requires the input of a merged topographic and bathymetric elevations, a wave climate based on significant wave heights, peak wave period and mean wave direction, and a tidal-time series. The model was validated by comparing model outputs from deterministic runs with observations of water levels and velocities. The validated model was used to simulate scenarios of existing conditions and proposed with-project conditions (for example, channel deepening and widening). Simulations were performed for one year (the year 2010) and ten years, with and without 0.5 meters (m) of sea level rise. Model inputs and outputs in the form of topography and bathymetry for the scenario runs as well as output water levels and velocities for the deterministic runs are provided in this data release. For further information regarding model input generation and visualization of model output topography and bathymetry, refer to USGS Open-File Report 2018-1123.

The numerical model Delft3D (developed by Deltares) was developed to evaluate the potential effects of proposed navigation channel deepening and widening in Mobile Harbor, Alabama (AL). The Delft3D model setup requires the input of a merged topographic and bathymetric elevations, a wave climate based on significant wave heights, peak wave period and mean wave direction, and a tidal-time series. The model was validated by comparing model outputs from deterministic runs with observations of water levels and velocities. The validated model was used to simulate scenarios of existing conditions and proposed with-project conditions (for example, channel deepening and widening). Simulations were performed for one year (the year 2010) and ten years, with and without 0.5 meters (m) of sea level rise. Model inputs and outputs in the form of topography and bathymetry for the scenario runs as well as output water levels and velocities for the deterministic runs are provided in this data release. For further information regarding model input generation and visualization of model output topography and bathymetry, refer to USGS Open-File Report 2018-1123.

This data release includes new, sub-annual Strontium/Calcium (Sr/Ca) and annual linear extension rates covering a period between 1980 and 2012 for five colonies of the massive coral, Orbicella faveolata (O. faveolata). All five coral colonies were collected live by U.S. Geological Survey (USGS) scientists from the Dry Tortugas National Park (DTNP), Florida (FL) in August 2008 and May 2012.

This data release includes new sub annual and mean annual Sr/Ca records from two species of massive coral, Orbicella faveolata (coral B3) and Siderastrea siderea (coral CG2), from the Dry Tortugas National Park, FL (DTNP). We combine these new records with published Sr/Ca data from three additional S. siderea coral (DeLong et al., 2014) to generate a 278-year long multi-species stacked Sr/Ca-SST record from DRTO.

ASCII XYZ point cloud data were produced from remotely sensed, geographically referenced elevation measurements by the U.S. Geological Survey (USGS). Elevation measurements were collected over some of the eastern Louisiana barrier islands in cooperation with the National Park Service (NPS), using the Experimental Advanced Airborne Research Lidar (EAARL), a pulsed laser ranging system mounted onboard an aircraft to measure ground elevation, vegetation canopy, and coastal topography. The system uses high-frequency laser beams directed at the Earth's surface through an opening in the bottom of the aircraft's fuselage. The laser system records the time difference between emission of the laser beam and the reception of the reflected laser signal in the aircraft. The plane travels over the target area at approximately 60 meters per second at an elevation of approximately 300 meters, resulting in a laser swath of approximately 240 meters with an average point spacing of 2-3 meters. The EAARL, developed originally by NASA at Wallops Flight Facility in Virginia, measures ground elevation with a vertical resolution of 3 centimeters. A sampling rate of 3 kilohertz or higher results in an extremely dense spatial elevation dataset. Over 100 kilometers of coastline can be surveyed easily within a 3- to 4-hour mission. When resultant elevation maps for an area are analyzed, they provide a useful tool to make management decisions regarding land development.

A Digital Elevation Model (DEM) mosaic was produced from remotely sensed, geographically referenced elevation measurements by the U.S. Geological Survey (USGS). Elevation measurements were collected over some of the eastern Louisiana barrier islands in cooperation with the National Park Service (NPS), using the Experimental Advanced Airborne Research Lidar (EAARL), a pulsed laser ranging system mounted onboard an aircraft to measure ground elevation, vegetation canopy, and coastal topography. The system uses high-frequency laser beams directed at the Earth's surface through an opening in the bottom of the aircraft's fuselage. The laser system records the time difference between emission of the laser beam and the reception of the reflected laser signal in the aircraft. The plane travels over the target area at approximately 60 meters per second at an elevation of approximately 300 meters, resulting in a laser swath of approximately 240 meters with an average point spacing of 2-3 meters. The EAARL, developed originally by NASA at Wallops Flight Facility in Virginia, measures ground elevation with a vertical resolution of 3 centimeters. A sampling rate of 3 kilohertz or higher results in an extremely dense spatial elevation dataset. Over 100 kilometers of coastline can be surveyed easily within a 3- to 4-hour mission. When resultant elevation maps for an area are analyzed, they provide a useful tool to make management decisions regarding land development.

ASCII XYZ point cloud data were produced from remotely sensed, geographically referenced elevation measurements by the U.S. Geological Survey (USGS). Elevation measurements were collected over some of the eastern Louisiana barrier islands in cooperation with the National Park Service (NPS), using the Experimental Advanced Airborne Research Lidar (EAARL), a pulsed laser ranging system mounted onboard an aircraft to measure ground elevation, vegetation canopy, and coastal topography. The system uses high-frequency laser beams directed at the Earth's surface through an opening in the bottom of the aircraft's fuselage. The laser system records the time difference between emission of the laser beam and the reception of the reflected laser signal in the aircraft. The plane travels over the target area at approximately 60 meters per second at an elevation of approximately 300 meters, resulting in a laser swath of approximately 240 meters with an average point spacing of 2-3 meters. The EAARL, developed originally by NASA at Wallops Flight Facility in Virginia, measures ground elevation with a vertical resolution of 3 centimeters. A sampling rate of 3 kilohertz or higher results in an extremely dense spatial elevation dataset. Over 100 kilometers of coastline can be surveyed easily within a 3- to 4-hour mission. When resultant elevation maps for an area are analyzed, they provide a useful tool to make management decisions regarding land development.

A Digital Elevation Model (DEM) mosaic was data were produced from remotely sensed, geographically referenced elevation measurements by the U.S. Geological Survey (USGS). Elevation measurements were collected over some of the eastern Louisiana barrier islands in cooperation with the National Park Service (NPS), using the Experimental Advanced Airborne Research Lidar (EAARL), a pulsed laser ranging system mounted onboard an aircraft to measure ground elevation, vegetation canopy, and coastal topography. The system uses high-frequency laser beams directed at the Earth's surface through an opening in the bottom of the aircraft's fuselage. The laser system records the time difference between emission of the laser beam and the reception of the reflected laser signal in the aircraft. The plane travels over the target area at approximately 60 meters per second at an elevation of approximately 300 meters, resulting in a laser swath of approximately 240 meters with an average point spacing of 2-3 meters. The EAARL, developed originally by NASA at Wallops Flight Facility in Virginia, measures ground elevation with a vertical resolution of 3 centimeters. A sampling rate of 3 kilohertz or higher results in an extremely dense spatial elevation dataset. Over 100 kilometers of coastline can be surveyed easily within a 3- to 4-hour mission. When resultant elevation maps for an area are analyzed, they provide a useful tool to make management decisions regarding land development.

The U.S. Geological Survey (USGS) Coastal and Marine Geology Program has actively collected geophysical and sedimentological data in the northern Gulf of Mexico for several decades, including shallow subsurface data in the form of high-resolution seismic reflection profiles (HRSP). Prior to the mid-1990s most HRSP data were collected in analog format as paper rolls of continuous profiles up to 25 meters long. As part of the National Geological and Geophysical Data Preservation Program (NGGDPP) (https://datapreservation.usgs.gov/), and in collaboration with the Bureau of Ocean Energy Management, Marine Minerals Program, scientists at the USGS St. Petersburg Coastal and Marine Science Center converted analog paper records to digital format using a large-format continuous scanner. The scanned image files were subsequently processed to fix distortions and crop out blank spaces prior to exporting as industry standard Society of Exploration Geophysicists date exchange (SEG-Y) formatted files. This data release serves as an archive of HRSP profiles annotated with header information, and converted SEG-Y files. The HRSP data were collected using a Huntec boomer seismic system onboard the Research Vessel (R/V) Erda. The geophysical cruises were completed in two segments within Mississippi Sound. On the first leg, geophysical surveys were conducted in June with the data being acquired from waterbodies surrounding Grand, Cat, and Horn Island (Erda 92-2). During the second leg, geophysical surveys were collected in August off the coast of Mississippi and Alabama and between Horn and Petit Bois Island (92-4). Data collection and processing methods are described in USGS Data Series 1047.

The Barrier Island and Estuarine Wetland Physical Change Assessment Dataset was created to calibrate and test probability models of barrier island sandline and estuarine shoreline change for study areas in Virginia, Maryland, and New Jersey. The models examined the influence of hydrologic and physical variables related to storm-derived overwash and estuarine shoreline change. Variables were calculated using a transect-based method in a geographic information system (GIS) by creating shoreline-perpendicular lines at regular intervals along the oceanfront shoreline and extrapolating the features from geospatial data, including lidar, bathymetry and aerial imagery. In addition, the dataset provides storm-derived barrier island change for Hurricane Sandy, as well as linear rates of change for long-term sandline and estuarine shorelines.

The numerical model Delft3D (developed by Deltares) was developed to evaluate the potential effects of proposed navigation channel deepening and widening in Mobile Harbor, Alabama (AL). The Delft3D model setup requires the input of a merged topographic and bathymetric elevations, a wave climate based on significant wave heights, peak wave period and mean wave direction, and a tidal-time series. The model was validated by comparing model outputs from deterministic runs with observations of water levels and velocities. The validated model was used to simulate scenarios of existing conditions and proposed with-project conditions (for example, channel deepening and widening). Simulations were performed for one year (the year 2010) and ten years, with and without 0.5 meters (m) of sea level rise. Model inputs and outputs in the form of topography and bathymetry for the scenario runs as well as output water levels and velocities for the deterministic runs are provided in this data release. For further information regarding model input generation and visualization of model output topography and bathymetry, refer to USGS Open-File Report 2018-1123.

The numerical model Delft3D (developed by Deltares) was developed to evaluate the potential effects of proposed navigation channel deepening and widening in Mobile Harbor, Alabama (AL). The Delft3D model setup requires the input of a merged topographic and bathymetric elevations, a wave climate based on significant wave heights, peak wave period and mean wave direction, and a tidal-time series. The model was validated by comparing model outputs from deterministic runs with observations of water levels and velocities. The validated model was used to simulate scenarios of existing conditions and proposed with-project conditions (for example, channel deepening and widening). Simulations were performed for one year (the year 2010) and ten years, with and without 0.5 meters (m) of sea level rise. Model inputs and outputs in the form of topography and bathymetry for the scenario runs as well as output water levels and velocities for the deterministic runs are provided in this data release. For further information regarding model input generation and visualization of model output topography and bathymetry, refer to USGS Open-File Report 2018-1123.

The numerical model Delft3D (developed by Deltares) was developed to evaluate the potential effects of proposed navigation channel deepening and widening in Mobile Harbor, Alabama (AL). The Delft3D model setup requires the input of a merged topographic and bathymetric elevations, a wave climate based on significant wave heights, peak wave period and mean wave direction, and a tidal-time series. The model was validated by comparing model outputs from deterministic runs with observations of water levels and velocities. The validated model was used to simulate scenarios of existing conditions and proposed with-project conditions (for example, channel deepening and widening). Simulations were performed for one year (the year 2010) and ten years, with and without 0.5 meters (m) of sea level rise. Model inputs and outputs in the form of topography and bathymetry for the scenario runs as well as output water levels and velocities for the deterministic runs are provided in this data release. For further information regarding model input generation and visualization of model output topography and bathymetry, refer to USGS Open-File Report 2018-1123.

The numerical model Delft3D (developed by Deltares) was developed to evaluate the potential effects of proposed navigation channel deepening and widening in Mobile Harbor, Alabama (AL). The Delft3D model setup requires the input of a merged topographic and bathymetric elevations, a wave climate based on significant wave heights, peak wave period and mean wave direction, and a tidal-time series. The model was validated by comparing model outputs from deterministic runs with observations of water levels and velocities. The validated model was used to simulate scenarios of existing conditions and proposed with-project conditions (for example, channel deepening and widening). Simulations were performed for one year (the year 2010) and ten years, with and without 0.5 meters (m) of sea level rise. Model inputs and outputs in the form of topography and bathymetry for the scenario runs as well as output water levels and velocities for the deterministic runs are provided in this data release. For further information regarding model input generation and visualization of model output topography and bathymetry, refer to USGS Open-File Report 2018-1123.

The numerical model Delft3D (developed by Deltares) was developed to evaluate the potential effects of proposed navigation channel deepening and widening in Mobile Harbor, Alabama (AL). The Delft3D model setup requires the input of a merged topographic and bathymetric elevations, a wave climate based on significant wave heights, peak wave period and mean wave direction, and a tidal-time series. The model was validated by comparing model outputs from deterministic runs with observations of water levels and velocities. The validated model was used to simulate scenarios of existing conditions and proposed with-project conditions (for example, channel deepening and widening). Simulations were performed for one year (the year 2010) and ten years, with and without 0.5 meters (m) of sea level rise. Model inputs and outputs in the form of topography and bathymetry for the scenario runs as well as output water levels and velocities for the deterministic runs are provided in this data release. For further information regarding model input generation and visualization of model output topography and bathymetry, refer to USGS Open-File Report 2018-1123.

Breton Island, Louisiana Baseline (Geographic, NAD83) consists of vector line data that were input into the Digital Shoreline Analysis System (DSAS) version 4.0, which is computer software used to compute rate of change statistics. A baseline was acquired from the Barrier Island Comprehensive Monitoring Program (BICM) 2009 report (http://lacoast.gov/reports/project/3890772~1.pdf). The baseline included in the BICM report covered the entire Louisiana coastline, so the baseline representing Breton Island had to be clipped then exported to a new shapefile named Breton_Baseline.shp. The position relative to Breton Island was determined to be the best possible baseline to use for the study. The baseline file was used to create the transect files, which are required by the DSAS program to calculate rate of change statistics.

ASCII XYZ data for Fire Island, New York, was produced from remotely sensed, geographically referenced elevation measurements collected October 25 and November 8, 2002 by the U.S. Geological Survey, in cooperation with the National Park Service (NPS) and the National Aeronautics and Space Administration (NASA). Elevation measurements were collected over the area using the first-generation Experimental Advanced Airborne Research Lidar (EAARL-A), a pulsed laser ranging system mounted onboard an aircraft to measure ground elevation, vegetation canopy, and coastal topography. The system uses high-frequency laser beams directed at the Earth's surface through an opening in the bottom of the aircraft's fuselage. The laser system records the time difference between emission of the laser beam and the reception of the reflected laser signal in the aircraft. The plane travels over the target area at approximately 55 meters per second at an elevation of approximately 300 meters, resulting in a laser swath of approximately 240 meters. More than 100 kilometers of coastline can be surveyed easily within a 3- to 4-hour mission. When resultant elevation maps for an area are analyzed, they provide a useful tool to make management decisions regarding land development.

A digital elevation model (DEM) mosaic for Fire Island, New York, was produced from remotely sensed, geographically referenced elevation measurements collected October 25 and November 8, 2002 by the U.S. Geological Survey, in cooperation with the National Park Service (NPS) and the National Aeronautics and Space Administration (NASA). Elevation measurements were collected over the area using the first-generation Experimental Advanced Airborne Research Lidar (EAARL-A), a pulsed laser ranging system mounted onboard an aircraft to measure ground elevation, vegetation canopy, and coastal topography. The system uses high-frequency laser beams directed at the Earth's surface through an opening in the bottom of the aircraft's fuselage. The laser system records the time difference between emission of the laser beam and the reception of the reflected laser signal in the aircraft. The plane travels over the target area at approximately 55 meters per second at an elevation of approximately 300 meters, resulting in a laser swath of approximately 240 meters. More than 100 kilometers of coastline can be surveyed easily within a 3- to 4-hour mission. When resultant elevation maps for an area are analyzed, they provide a useful tool to make management decisions regarding land development.

Hurricane Sandy made U.S. landfall, coincident with astronomical high tides, near Atlantic City, New Jersey, on October 29, 2012. The storm, the largest on historical record in the Atlantic basin, affected an extensive area of the east coast of the United States. The highest waves and storm surge were focused along the heavily populated New York and New Jersey coasts. At the height of the storm, a record significant wave height of 9.6 meters (m) was recorded at the wave buoy offshore of Fire Island, New York. During the storm an overwash channel opened a breach in the location of Old Inlet, in the Otis Pike High Dunes Wilderness Area. This breach is referred to as the wilderness breach (fig 1). Fire Island, New York is the site of a long term coastal morphologic change and processes project conducted by the U.S. Geological Survey (USGS). One of the objectives of the project was to understand the morphologic evolution of the barrier system on a variety of time scales (days - years - decades - centuries). In response to Hurricane Sandy, this effort continued with the intention of resolving storm impact and the response and recovery of the beach. The day before Hurricane Sandy made landfall (October 28, 2012), a USGS field team conducted differential global positioning system (DGPS) surveys at Fire Island to quantify the pre-storm morphologic state of the beach and dunes. The area was re-surveyed after the storm, as soon as access to the island was possible. In order to fully capture the recovery of the barrier system, the USGS Hurricane Sandy Supplemental Fire Island Study was established to include collection in the weeks, months, and years following the storm. As part of the USGS Hurricane Sandy Supplemental Fire Island Study, the beach is monitored periodically to enable better understanding of post-Sandy recovery. The alongshore state of the beach is recorded using a DGPS to collect data around the mean high water elevation (MHW; 0.46 meter North American Vertical Datum of 1988) to derive a shoreline, and the cross-shore response and recovery are measured along a series of 15 profiles. Monitoring continued in the weeks following Hurricane Sandy with additional monthly collection through April 2013 and repeat surveys every 2–3 months thereafter until October 2014. Bi-annual surveys have been collected through September 2016. Beginning in October 2014 the USGS also began collecting shoreline data at the Wilderness breach. The shoreline collected was an approximation of the MHW shoreline. The operator walked an estimated MHW elevation above the water line and below the berm crest, using knowledge of tides and local conditions to interpret a consistent shoreline. See below for survey collection dates for all data types. This shapefile FIIS_Breach_Shorelines.shp consists of Fire Island, NY breach shorelines collected following an interpreted MHW shoreline as identified in the field. Oct 28 2012 (MHW shoreline/Cross-shore data) Nov 01 2012 (MHW shoreline/Cross-shore data) Nov 04 2012 (Cross-shore data only) Dec 01 2012 (MHW shoreline/Cross-shore data) Dec 12 2012 (MHW shoreline/Cross-shore data) Jan 10 2013 (MHW shoreline/Cross-shore data) Feb 13 2013 (MHW shoreline/Cross-shore data) Mar 13 2013 (MHW shoreline/Cross-shore data) Apr 09 2013 (MHW shoreline/Cross-shore data) Jun 24 2013 (MHW shoreline/Cross-shore data) Sep 18 2013 (MHW shoreline/Cross-shore data) Dec 03 2013 (MHW shoreline/Cross-shore data) Jan 29 2014 (MHW shoreline/Cross-shore data) Jun 11 2014 (Cross-shore data only) Sep 09 2014 (MHW shoreline/Cross-shore data) Oct 07 2014 (Cross-shore data/MHW Breach shoreline) Jan 21 2015 (MHW shoreline/Cross-shore data/Breach shoreline) Mar 19 2015 (MHW shoreline/Cross-shore data) May 16 2015 (MHW shoreline/Cross-shore data/Breach shoreline) Set 28 2015 (MHW shoreline/Cross-shore data/Breach shoreline) Jan 21 2016 (MHW shoreline/Cross-shore data) Jan 25 2016 (MHW shoreline/Cross-shore data) Apr 06 2016 (Cross-shore data only) Apr 11 2016 (MHW shoreline/Cross-shore data/Breach shoreline) Jun 16 2016 (Cross-shore data only) Sep 27 2016 (MHW shoreline/Cross-shore data/Breach shoreline)

Hurricane Sandy made U.S. landfall, coincident with astronomically high tides, near Atlantic City, New Jersey, on October 29, 2012. The storm, the largest on historical record in the Atlantic basin, affected an extensive area of the east coast of the United States. The highest waves and storm surge were focused along the heavily populated New York and New Jersey coasts. At the height of the storm, a record significant wave height of 9.6 meters (m) was recorded at the wave buoy offshore of Fire Island, New York. During the storm, an overwash channel opened a breach in the location of Old Inlet, in the Otis Pike High Dunes Wilderness Area. This breach is referred to as the Wilderness Breach (fig. 1). Fire Island, New York is the site of a long term coastal morphologic change and processes project conducted by the U.S. Geological Survey (USGS). One of the objectives of the project was to understand the morphologic evolution of the barrier system on a variety of time scales (days - years - decades - centuries). In response to Hurricane Sandy, this effort continued with the intention of resolving storm impact and the response and recovery of the beach. The day before Hurricane Sandy made landfall (October 28, 2012), a USGS field team conducted differential global positioning system (DGPS) surveys at Fire Island to quantify the pre-storm morphologic state of the beach and dunes. The area was re-surveyed after the storm, as soon as access to the island was possible. In order to fully capture the recovery of the barrier system, the USGS Hurricane Sandy Supplemental Fire Island Study was established to include collection in the weeks, months, and years following the storm. As part of the USGS Hurricane Sandy Supplemental Fire Island Study, the beach is monitored periodically to enable better understanding of post-Hurricane Sandy recovery. The alongshore state of the beach is recorded using a DGPS to collect data around the mean high water elevation (MHW; 0.46 meters, North American Vertical Datum of 1988 [NAVD88]) to derive a shoreline, and the cross-shore response and recovery are measured along a series of 15 profiles. Monitoring continued in the weeks following Hurricane Sandy with additional monthly data collection through April 2013 and repeat surveys every 2–3 months thereafter until October 2014. Recurring surveys have been conducted through October 2017. Beginning in October 2014 the USGS also began collecting shoreline data at the Wilderness Breach. The shoreline data collected was an approximation of the MHW shoreline. The operator walked an estimated MHW elevation above the water line and below the berm crest, using knowledge of tides and local conditions to interpret a consistent shoreline. See below for survey collection dates for all data types. The shapefile, FIIS_Breach_Shorelines.shp, consists of Fire Island, NY breach shorelines collected by following an interpreted MHW shoreline as identified in the field. Oct 28 2012 (MHW shoreline/Cross-shore data) Nov 01 2012 (MHW shoreline/Cross-shore data) Nov 04 2012 (Cross-shore data only) Dec 01 2012 (MHW shoreline/Cross-shore data) Dec 12 2012 (MHW shoreline/Cross-shore data) Jan 10 2013 (MHW shoreline/Cross-shore data) Feb 13 2013 (MHW shoreline/Cross-shore data) Mar 13 2013 (MHW shoreline/Cross-shore data) Apr 09 2013 (MHW shoreline/Cross-shore data) Jun 24 2013 (MHW shoreline/Cross-shore data) Sep 18 2013 (MHW shoreline/Cross-shore data) Dec 03 2013 (MHW shoreline/Cross-shore data) Jan 29 2014 (MHW shoreline/Cross-shore data) Jun 11 2014 (Cross-shore data only) Sep 09 2014 (MHW shoreline/Cross-shore data) Oct 07 2014 (Cross-shore data/MHW Breach shoreline) Jan 21 2015 (MHW shoreline/Cross-shore data/Breach shoreline) Mar 19 2015 (MHW shoreline/Cross-shore data) May 16 2015 (MHW shoreline/Cross-shore data/Breach shoreline) Set 28 2015 (MHW shoreline/Cross-shore data/Breach shoreline) Jan 21 2016 (MHW shoreline/Cross-shore data) Jan 25 2016 (MHW shoreline/Cross-shore data) Apr 06 2016 (Cross-shore data only) Apr 11 2016 (MHW shoreline/Cross-shore data/Breach shoreline) Jun 16 2016 (Cross-shore data only) Sep 27 2016 (MHW shoreline/Cross-shore data/Breach shoreline) Jan 24 2017 (MHW shoreline/Cross-shore data/Breach shoreline) May 23 2017 (MHW shoreline/Cross-shore data/Breach shoreline) Oct 17 2017 (MHW shoreline/Cross-shore data/Breach shoreline)

Hurricane Sandy made U.S. landfall, coincident with astronomical high tides, near Atlantic City, New Jersey, on October 29, 2012. The storm, the largest on historical record in the Atlantic basin, affected an extensive area of the east coast of the United States. The highest waves and storm surge were focused along the heavily populated New York and New Jersey coasts. At the height of the storm, a record significant wave height of 9.6 meters (m) was recorded at the wave buoy offshore of Fire Island, New York (fig. 1, inset). During the storm an overwash channel opened a breach in the location of Old Inlet, in the Otis Pike High Dunes Wilderness area. This breach is now referred to as the Wilderness Breach (fig. 1). Fire Island, New York is the site of a long term coastal morphologic change and processes project conducted by the U.S. Geological Survey (USGS). One of the objectives of the project was to understand the morphologic evolution of the barrier system on a variety of time scales (days - years - decades - centuries). In response to Hurricane Sandy, this effort continued with the intention of resolving storm impacts post-storm beach response and recovery. The day before Hurricane Sandy made landfall (October 28, 2012), a USGS field team conducted differential global positioning system (DGPS) surveys at Fire Island to quantify the pre-storm morphologic state of the beach and dunes. The area was re-surveyed after the storm, as soon as access to the island was possible. In order to fully capture the recovery of the barrier system, the USGS Hurricane Sandy Supplemental Fire Island Study was established include collection in the weeks, months, and years following the storm. As part of the USGS Hurricane Sandy Supplemental Fire Island Study, the beach is monitored periodically to enable better understanding of post-Sandy recovery. The alongshore state of the beach is recorded using a DGPS to collect data around the mean high water (MHW; 0.46 meter North American Vertical Datum of 1988) to derive a shoreline, and the cross-shore response and recovery are measured along a series of 15 profiles. Monitoring continued in the weeks following Hurricane Sandy with additional monthly collection through April 2013 and repeat surveys every 2-3 months thereafter until October 2014. Bi-annual surveys have been collected through September 2016. Beginning in October 2014 the USGS also began collecting shoreline data at the Wilderness Breach. See below for survey collection dates for all data types. For along shore shoreline data, the MHW shoreline (0.46 m [NAVD 88]; Weber and others, 2005) is derived from the field data using an interpolation method that creates a series of equally-spaced cross-shore profiles between the two survey lines that flank the MHW contour. The foreshore slope is assumed to be uniform on each profile. Using this slope and the two surveyed positions on each cross-shore profile, a simple geometric calculation is done to find where each profile line intersects the MHW contour. This shapefile FIIS_Shorelines_Oct2012_Sept2016.shp consists of Fire Island, NY pre- and post-storm shoreline data collected from October 2012 to September 2016. This dataset contains 20 Mean High Water (MHW) shorelines for Fire Island, NY (A total of 22 dates, where two shorelines were collected over multiple days). Prior to and following Hurricane Sandy in October, 2012, continuous alongshore DGPS data were collected to assess the positional changes of the shoreline (MHW - 0.46 m NAVD88) and the upper portion of the beach. In the four years following Sandy, 22 surveys were conducted collecting data along shore-parallel tracks to capture the base of the dune, the mid-beach, and the upper and lower foreshore. The alongshore tracks extend from just west of Fire Island Lighthouse to the western flank of the storm-induced breach in the location of Old Inlet, in the Otis Pike High Dunes Wilderness area. Oct 28 2012 (MHW shoreline/Cross-shore data) Nov 01 2012 (MHW shoreline/Cross-shore data) Nov 04 2012 (Cross-shore data only) Dec 01 2012 (MHW shoreline/Cross-shore data) Dec 12 2012 (MHW shoreline/Cross-shore data) Jan 10 2013 (MHW shoreline/Cross-shore data) Feb 13 2013 (MHW shoreline/Cross-shore data) Mar 13 2013 (MHW shoreline/Cross-shore data) Apr 09 2013 (MHW shoreline/Cross-shore data) Jun 24 2013 (MHW shoreline/Cross-shore data) Sep 18 2013 (MHW shoreline/Cross-shore data) Dec 03 2013 (MHW shoreline/Cross-shore data) Jan 29 2014 (MHW shoreline/Cross-shore data) Jun 11 2014 (Cross-shore data only) Sep 09 2014 (MHW shoreline/Cross-shore data) Oct 07 2014 (Cross-shore data/Breach shoreline) Jan 21 2015 (MHW shoreline/Cross-shore data/Breach shoreline) Mar 19 2015 (MHW shoreline/Cross-shore data) May 16 2015 (MHW shoreline/Cross-shore data/Breach shoreline) Set 28 2015 (MHW shoreline/Cross-shore data/Breach shoreline) Jan 21 2016 (MHW shoreline/Cross-shore data) Jan 25 2016 (MHW shoreline/Cross-shore data) Apr 06 2016 (Cross-shore data only) Apr 11 2016 (MHW shoreline/Cross-shore data/Breach shoreline) Jun 16 2016 (Cross-shore data only) Sep 27 2016 (MHW shoreline/Cross-shore data/Breach shoreline)

Hurricane Sandy made U.S. landfall, coincident with astronomically high tides, near Atlantic City, New Jersey, on October 29, 2012. The storm, the largest on historical record in the Atlantic basin, affected an extensive area of the east coast of the United States. The highest waves and storm surge were focused along the heavily populated New York and New Jersey coasts. At the height of the storm, a record significant wave height of 9.6 meters (m) was recorded at the wave buoy offshore of Fire Island, New York (fig. 1, inset). During the storm, an overwash channel opened a breach in the location of Old Inlet, in the Otis Pike High Dunes Wilderness area. This breach is now referred to as the Wilderness Breach (fig. 1). Fire Island, New York is the site of a long term coastal morphologic change and processes project conducted by the U.S. Geological Survey (USGS). One of the objectives of the project was to understand the morphologic evolution of the barrier system on a variety of time scales (days - years - decades - centuries). In response to Hurricane Sandy, this effort continued with the intention of resolving storm impacts, post-storm beach response, and recovery. The day before Hurricane Sandy made landfall (October 28, 2012), a USGS field team conducted differential global positioning system (DGPS) surveys at Fire Island to quantify the pre-storm morphologic state of the beach and dunes. The area was re-surveyed after the storm, as soon as access to the island was possible. In order to fully capture the recovery of the barrier system, the USGS Hurricane Sandy Supplemental Fire Island Study was established to include collection in the weeks, months, and years following the storm. As part of the USGS Hurricane Sandy Supplemental Fire Island Study, the beach is monitored periodically to enable better understanding of post-Hurricane Sandy recovery. The alongshore state of the beach is recorded using a DGPS to collect data around the mean high water (MHW; 0.46 meters, North American Vertical Datum of 1988 [NAVD88]) to derive a shoreline, and the cross-shore response and recovery are measured along a series of 15 profiles. Monitoring continued in the weeks following Hurricane Sandy with additional monthly data collection through April 2013 and repeat surveys every 2-3 months thereafter until October 2014. Bi-annual surveys have been collected through September 2016. Beginning in October 2014 the USGS also began collecting shoreline data at the Wilderness Breach. See below for survey collection dates for all data types. For along shore shoreline data, the MHW shoreline (0.46 m [NAVD88]; Weber and others, 2005) is derived from the field data using an interpolation method that creates a series of equally-spaced cross-shore profiles between the two survey lines that flank the MHW contour. The foreshore slope is assumed to be uniform on each profile. Using this slope and the two surveyed positions on each cross-shore profile, a simple geometric calculation is done to find where each profile line intersects the MHW contour. This shapefile, FIIS_Shorelines_Oct2012_Oct2017.shp, consists of Fire Island, NY pre- and post-storm shoreline data collected from October 2012 to October 2017. This dataset contains 25 Mean High Water (MHW) shorelines for Fire Island, NY (A total of 23 full shorelines, where two shorelines were collected over multiple days). Prior to and following Hurricane Sandy in October 2012, continuous alongshore DGPS data were collected to assess the positional changes of the shoreline (MHW - 0.46 m NAVD88) and the upper portion of the beach. In the five years following Sandy, 24 surveys were conducted collecting data along shore-parallel tracks to capture the base of the dune, the mid-beach, and the upper and lower foreshore. The alongshore tracks extend from just west of Fire Island Lighthouse to the western flank of the storm-induced breach in the location of Old Inlet, in the Otis Pike High Dunes Wilderness area. Oct 28 2012 (MHW shoreline/Cross-shore data) Nov 01 2012 (MHW shoreline/Cross-shore data) Nov 04 2012 (Cross-shore data only) Dec 01 2012 (MHW shoreline/Cross-shore data) Dec 12 2012 (MHW shoreline/Cross-shore data) Jan 10 2013 (MHW shoreline/Cross-shore data) Feb 13 2013 (MHW shoreline/Cross-shore data) Mar 13 2013 (MHW shoreline/Cross-shore data) Apr 09 2013 (MHW shoreline/Cross-shore data) Jun 24 2013 (MHW shoreline/Cross-shore data) Sep 18 2013 (MHW shoreline/Cross-shore data) Dec 03 2013 (MHW shoreline/Cross-shore data) Jan 29 2014 (MHW shoreline/Cross-shore data) Jun 11 2014 (Cross-shore data only) Sep 09 2014 (MHW shoreline/Cross-shore data) Oct 07 2014 (Cross-shore data/Breach shoreline) Jan 21 2015 (MHW shoreline/Cross-shore data/Breach shoreline) Mar 19 2015 (MHW shoreline/Cross-shore data) May 16 2015 (MHW shoreline/Cross-shore data/Breach shoreline) Sep 28 2015 (MHW shoreline/Cross-shore data/Breach shoreline) Jan 21 2016 (MHW shoreline/Cross-shore data) Jan 25 2016 (MHW shoreline/Cross-shore data) Apr 06 2016 (Cross-shore data only) Apr 11 2016 (MHW shoreline/Cross-shore data/Breach shoreline) Jun 16 2016 (Cross-shore data only) Sep 27 2016 (MHW shoreline/Cross-shore data/Breach shoreline) Jan 24 2017 (MHW shoreline/Cross-shore data/Breach shoreline) May 23 2017 (MHW shoreline/Cross-shore data/Breach shoreline) Oct 17 2017 (MHW shoreline/Cross-shore data/Breach shoreline)

Binary point-cloud data of a portion of the submerged environs of Fort Lauderdale, Florida, were produced from remotely sensed, geographically referenced elevation measurements by the U.S. Geological Survey (USGS). Elevation measurements were collected over the area using the second-generation Experimental Advanced Airborne Research Lidar (EAARL-B), a pulsed laser ranging system mounted onboard an aircraft to measure ground elevation, vegetation canopy, and coastal topography. The system uses high-frequency laser beams directed at the Earth's surface through an opening in the bottom of the aircraft's fuselage. The laser system records the time difference between emission of the laser pulse and the reception of the reflected laser signal in the aircraft. The plane travels over the target area at approximately 60 meters per second at an elevation of approximately 300 meters, resulting in a laser swath of approximately 240 meters with an average point spacing of 0.5-1.6 meters. The EAARL, developed originally by the National Aeronautics and Space Administration (NASA) at Wallops Flight Facility in Virginia, measures ground elevation with a vertical resolution of 3 centimeters. A peak sampling rate of 15-30 kilohertz results in an extremely dense spatial elevation dataset. Over 100 kilometers of coastline can be surveyed easily within a 3- to 4-hour mission. When resultant elevation maps for an area are analyzed, they provide a useful tool to make management decisions regarding land development.

Binary point-cloud data of a portion of the submerged environs of Fort Lauderdale, Florida, were produced from remotely sensed, geographically referenced elevation measurements by the U.S. Geological Survey (USGS). Elevation measurements were collected over the area using the second-generation Experimental Advanced Airborne Research Lidar (EAARL-B), a pulsed laser ranging system mounted onboard an aircraft to measure ground elevation, vegetation canopy, and coastal topography. The system uses high-frequency laser beams directed at the Earth's surface through an opening in the bottom of the aircraft's fuselage. The laser system records the time difference between emission of the laser pulse and the reception of the reflected laser signal in the aircraft. The plane travels over the target area at approximately 60 meters per second at an elevation of approximately 300 meters, resulting in a laser swath of approximately 240 meters with an average point spacing of 0.5-1.6 meters. The EAARL, developed originally by the National Aeronautics and Space Administration (NASA) at Wallops Flight Facility in Virginia, measures ground elevation with a vertical resolution of 3 centimeters. A peak sampling rate of 15-30 kilohertz results in an extremely dense spatial elevation dataset. Over 100 kilometers of coastline can be surveyed easily within a 3- to 4-hour mission. When resultant elevation maps for an area are analyzed, they provide a useful tool to make management decisions regarding land development.

Binary point-cloud data of a portion of the submerged environs of Fort Lauderdale, Florida, were produced from remotely sensed, geographically referenced elevation measurements by the U.S. Geological Survey (USGS). Elevation measurements were collected over the area using the second-generation Experimental Advanced Airborne Research Lidar (EAARL-B), a pulsed laser ranging system mounted onboard an aircraft to measure ground elevation, vegetation canopy, and coastal topography. The system uses high-frequency laser beams directed at the Earth's surface through an opening in the bottom of the aircraft's fuselage. The laser system records the time difference between emission of the laser pulse and the reception of the reflected laser signal in the aircraft. The plane travels over the target area at approximately 60 meters per second at an elevation of approximately 300 meters, resulting in a laser swath of approximately 240 meters with an average point spacing of 0.5-1.6 meters. The EAARL, developed originally by the National Aeronautics and Space Administration (NASA) at Wallops Flight Facility in Virginia, measures ground elevation with a vertical resolution of 3 centimeters. A peak sampling rate of 15-30 kilohertz results in an extremely dense spatial elevation dataset. Over 100 kilometers of coastline can be surveyed easily within a 3- to 4-hour mission. When resultant elevation maps for an area are analyzed, they provide a useful tool to make management decisions regarding land development.

Binary point-cloud data of a portion of the submerged environs of Fort Lauderdale, Florida, were produced from remotely sensed, geographically referenced elevation measurements by the U.S. Geological Survey (USGS). Elevation measurements were collected over the area using the second-generation Experimental Advanced Airborne Research Lidar (EAARL-B), a pulsed laser ranging system mounted onboard an aircraft to measure ground elevation, vegetation canopy, and coastal topography. The system uses high-frequency laser beams directed at the Earth's surface through an opening in the bottom of the aircraft's fuselage. The laser system records the time difference between emission of the laser pulse and the reception of the reflected laser signal in the aircraft. The plane travels over the target area at approximately 60 meters per second at an elevation of approximately 300 meters, resulting in a laser swath of approximately 240 meters with an average point spacing of 0.5-1.6 meters. The EAARL, developed originally by the National Aeronautics and Space Administration (NASA) at Wallops Flight Facility in Virginia, measures ground elevation with a vertical resolution of 3 centimeters. A peak sampling rate of 15-30 kilohertz results in an extremely dense spatial elevation dataset. Over 100 kilometers of coastline can be surveyed easily within a 3- to 4-hour mission. When resultant elevation maps for an area are analyzed, they provide a useful tool to make management decisions regarding land development.

The prevalence of antibiotic resistance genes in microbial communities from sewage wastewater streams and from offshore marine sediments in the vicinity of sewage wastewater outfalls in Southeast Florida was investigated from June 2018 to March 2019. Sediment and wastewater samples were analyzed for 15 different antibiotic resistant gene targets via polymerase chain reaction (PCR) presence/absence assays in Southeast Florida coral reef environments. Data collected from five sites (Broward North Wastewater Treatment Plant (WWTP), Broward North WWTP Outfall, Haµlover (Miami-Dade North) Outfall, Hollywood Outfall, Hollywood WWTP, and Miami-Dade North WWTP) illustrated widespread prevalence of antibiotic resistance genes in these microbial communities with the highest concentrations occurring in the sewage wastewater stream and in close proximity to the outfall pipe and outfall. Data indicated seasonal (wet versus dry season) trends and potential public and ecosystem health risks. Additionally, a reef in the Florida Keys was evaluated during the wet season using the same tools and approach prior to and after diseased corals were treated with amoxicillin. Resistance to amoxicillin was only observed in the post-treatment sample set.

The U.S. Geological Survey (USGS) Coral Reef Ecosystems Studies (CREST) project (http://coastal.er.usgs.gov/crest/) provides science that helps resource managers tasked with the stewardship of coral reef resources. Coral reef organisms are very sensitive to high and low water-temperature extremes. It is critical to precisely know water temperatures experienced by corals and associated plants and animals that live in the dynamic nearshore environment to document thresholds in temperature tolerance. This dataset provides underwater temperature data recorded every fifteen minutes from 2009 to 2015 at five off-shore coral reefs in the Florida Keys, USA. From northeast to southwest, these sites are Fowey Rocks (Biscayne National Park), Molasses Reef (Florida Keys National Marine Sanctuary, FKNMS), Crocker Reef (FKNMS), Sombrero Reef (FKNMS), and Pulaski Shoal (Dry Tortugas National Park). A portion of the dataset included here was interpreted in conjunction with coral and algal calcification rates in Kuffner et al. (2013).

The U.S. Geological Survey (USGS) Coral Reef Ecosystems Studies (CREST) project (http://coastal.er.usgs.gov/crest/) provides science that helps resource managers tasked with the stewardship of coral reef resources. Coral reef organisms are very sensitive to high and low water-temperature extremes. It is critical to precisely know water temperatures experienced by corals and associated plants and animals that live in the dynamic nearshore environment to document thresholds in temperature tolerance. This dataset provides underwater temperature data recorded every fifteen minutes from 2009 to 2016 at five off-shore coral reefs in the Florida Keys, USA. From northeast to southwest, these sites are Fowey Rocks (Biscayne National Park), Molasses Reef (Florida Keys National Marine Sanctuary, FKNMS), Crocker Reef (FKNMS), Sombrero Reef (FKNMS), and Pulaski Shoal (Dry Tortugas National Park). A portion of the dataset included here was interpreted in conjunction with coral and algal calcification rates in Kuffner et al. (2013).

The U.S. Geological Survey (USGS) Coral Reef Ecosystems Studies (CREST) project (https://coastal.er.usgs.gov/crest/) provides science that helps resource managers tasked with the stewardship of coral reef resources. Coral reef organisms are very sensitive to high and low water-temperature extremes. It is critical to precisely know water temperatures experienced by corals and associated plants and animals that live in the dynamic nearshore environment to document thresholds in temperature tolerance. This dataset provides underwater temperature data recorded every fifteen minutes from 2009 to 2017 at five off-shore coral reefs in the Florida Keys, USA. From northeast to southwest, these sites are Fowey Rocks (Biscayne National Park), Molasses Reef (Florida Keys National Marine Sanctuary, FKNMS), Crocker Reef (FKNMS), Sombrero Reef (FKNMS), and Pulaski Shoal (Dry Tortugas National Park). A portion of the dataset included here was interpreted in conjunction with coral and algal calcification rates in Kuffner and others (2013).

The U.S. Geological Survey (USGS) Coral Reef Ecosystems Studies (CREST) project (https://coastal.er.usgs.gov/crest/) provides science that helps resource managers tasked with the stewardship of coral reef resources. Coral reef organisms are very sensitive to high and low water-temperature extremes. It is critical to precisely know water temperatures experienced by corals and associated plants and animals that live in the dynamic nearshore environment to document thresholds in temperature tolerance. This dataset provides underwater temperature data recorded every fifteen minutes from 2009 to 2018 at five off-shore coral reefs in the Florida Keys, USA. From northeast to southwest, these sites are Fowey Rocks (Biscayne National Park), Molasses Reef (Florida Keys National Marine Sanctuary, FKNMS), Crocker Reef (FKNMS), Sombrero Reef (FKNMS), and Pulaski Shoal (Dry Tortugas National Park). A portion of the dataset included here was interpreted in conjunction with coral and algal calcification rates in Kuffner and others (2013).

The U.S. Geological Survey (USGS) Coral Reef Ecosystems Studies (CREST) project (https://coastal.er.usgs.gov/crest/) provides science that helps resource managers tasked with the stewardship of coral reef resources. Coral reef organisms are very sensitive to high and low water-temperature extremes. It is critical to precisely know water temperatures experienced by corals and associated plants and animals that live in the dynamic nearshore environment to document thresholds in temperature tolerance. This dataset provides underwater temperature data recorded every fifteen minutes from 2009 to 2019 at six off-shore coral reefs in the Florida Keys, USA. From northeast to southwest, these sites are Fowey Rocks (Biscayne National Park), Molasses Reef (Florida Keys National Marine Sanctuary, FKNMS, site terminated in 2013), Crocker Reef (FKNMS, site added in 2013), Sombrero Reef (FKNMS), Pulaski Shoal Light(Dry Tortugas National Park), and Pulaski Shoal West (Dry Tortugas National Park, site added in 2016). A portion of the dataset included here was interpreted in conjunction with coral and algal calcification rates in Kuffner and others (2013).

The U.S. Geological Survey (USGS) Coral Reef Ecosystems Studies (CREST) project (https://coastal.er.usgs.gov/crest/) provides science that helps resource managers tasked with the stewardship of coral reef resources. Coral reef organisms are very sensitive to high and low water-temperature extremes. It is critical to precisely know water temperatures experienced by corals and associated plants and animals that live in the dynamic nearshore environment to document thresholds in temperature tolerance. This dataset provides underwater temperature data recorded every fifteen minutes from 2009 to 2020 at six off-shore coral reefs in the Florida Keys, USA. From northeast to southwest, these sites are Fowey Rocks (Biscayne National Park), Molasses Reef (Florida Keys National Marine Sanctuary, FKNMS, site terminated in 2013), Crocker Reef (FKNMS, site added in 2013), Sombrero Reef (FKNMS), Pulaski Shoal Light (Dry Tortugas National Park), and Pulaski Shoal West (Dry Tortugas National Park, site added in 2016). A portion of the dataset included here was interpreted in conjunction with coral and algal calcification rates in Kuffner and others (2013).

This data release contains time-series photographs taken of corals and coral habitats in the Florida Keys between 1959 and 2015 at Carysfort Reef and Grecian Rocks (a total of six sites). The original intent was to show coral reef recovery after Hurricane Donna devastated the area in 1960. Corals, especially elkhorn and staghorn coral, grew prolifically after the storm until the late 1970s, then began to decline, with the maximum period of decline centered around 1983 and 1984. These time-series photographs, showing the same individual coral colonies year after year, document the decline in coral health observed at these locations, mirroring patterns seen region-wide across the western Atlantic. A selection of the photographs was previously published (in low resolution) in Lidz and others (2006), wherein findings and conclusions related to these data were discussed. Lidz, B. H., Reich, C. D., Peterson, R. L., and Shinn, E. A. (2006). New maps, new information: Coral reefs of the Florida Keys. Journal of Coastal Research, 22(2), 260-282, https://doi.org/10.2112/05A-0023.1

Underwater digital images, single-beam bathymetry, and global-positioning system (GPS) data were collected September 29-30, 2011 around Dustan Rocks Patch Reef, Thor Patch Reef, West Turtle Shoal Patch Reef, and Rawa Patch Reef in the Florida Keys. A total of 101,734 images were collected, covering 4672 square meteres (m2) of reef habitat. This data release contains a subset of 1,420 images, organized into four sets: Track1, Track2, Track3, and Track4. These images were used for coral bleaching assessments, contain GPS data and also include additional, survey-specific Exchangable Image File format (EXIF) header information. The data were collected using the USGS shallow Along-Track Reef-Imaging System (sATRIS), a boat-based, pole-mounted sensor package for mapping shallow-water benthic environments. Two other implementations exist: A towed system called Deep ATRIS and a profiling system called Drift ATRIS. All three ATRIS implementations incorporate a digital still camera, a video camera, and an acoustic depth sounder. In this study, sATRIS images were collected at a rate of 10 hertz (Hz), the single-beam depth soundings at 10 Hz, and the GPS data at 1 Hz. The survey was conducted using the USGS research vessel (R/V) Halimeda, running at a nominal speed of 2 knots.

The model output of bathymetry and topography values resulting from a deterministic simulation at Dauphin Island, Alabama, as described in USGS Open-File Report 2019–1139 (https://doi.org/10.3133/ofr20191139), are provided here. For further information regarding model input generation and visualization of model output topography and bathymetry refer to Mickey and others (2020).

Scientists from the U.S. Geological Survey St. Petersburg Coastal and Marine Science Center in St. Petersburg, Florida, conducted a bathymetric survey of Fire Island, New York, June 2?17, 2018. The U.S. Geological Survey is involved in a post-Hurricane Sandy effort to map and monitor the morphologic evolution of the wilderness breach and the adjacent shoreface environment. During this study, bathymetry data were collected aboard two personal watercraft (PWC) outfitted with single-beam echosounders, as well as a towed seismic sled with similar instrumentation. Additional elevation data were collected using a backpack- mounted Global Positioning System (GPS) on flood shoals and in shallow channels within the wilderness breach.

Topographic sheets (t-sheets) produced by the National Ocean Service (NOS) during the 1800s provide the position of past shorelines. The shoreline data can be vectorized into a geographic information system (GIS) and compared to modern shoreline data to calculate estimates of long-term shoreline rates of change. Many t-sheets were scanned and digitized by the National Oceanic and Atmospheric Administration (NOAA) and are available on the NOAA Shoreline website (https://shoreline.noaa.gov/data/datasheets/t-sheets.html). However, some t-sheets were not scanned by NOAA and are only available via the National Archives and Records Administration (NARA). The data included within this data release were previously unavailable or not published in digital format. These data were produced to provide a more comprehensive record of shoreline position for Fire Island and Great South Bay, New York, to aid geologic and coastal hazards studies. This data release includes previously unavailable georeferenced t-sheets and digital vector shorelines for the Fire Island and Great South Bay, New York, coastline from 1834, 1838, and 1874/1875. The original t-sheets were scanned by the NARA-authorized vendor and sent to the Unites States Geological Survey St. Petersburg Coastal and Marine Science Center (USGS SPCMSC) as non-georeferenced digital raster files. Upon arrival at the SPCMSC, USGS staff performed the following procedures: rasters were georeferenced, projected to a modern datum, and shorelines were digitized to create a vector polyline depicting the historical shoreline position. The t-sheets included in this data release are: 1) T-479a, T-479b, T-1 (Parts 2 and 3) (1834); 2) T-58 (Parts 1 and 2) (1838); 3) T-1374a, T-1374b, T-1375a, T-1375b (1874); and 4) T-1402 (1875). All shorelines, including the ocean-facing barrier island shoreline, back-barrier island shoreline, mainland and islands were digitized. Please read the full metadata for details on data collection, dataset variables, and data quality.

Topographic sheets (t-sheets) produced by the National Ocean Service (NOS) during the 1800s provide the position of past shorelines. The shoreline data can be vectorized into a geographic information system (GIS) and compared to modern shoreline data to calculate estimates of long-term shoreline rates of change. Many t-sheets were scanned and digitized by the National Oceanic and Atmospheric Administration (NOAA) and are available on the NOAA Shoreline web site (https://shoreline.noaa.gov/data/datasheets/t-sheets.html). However, some t-sheets were not scanned by NOAA and are only available via the National Archives and Records Administration (NARA). The data included within this data release were previously unavailable or not published in digital format. These data were produced to provide a more comprehensive record of shoreline position for Fire Island and Great South Bay, New York, to aid geologic and coastal hazards studies. This data release includes previously unavailable georeferenced t-sheets and digital vector shorelines for the Fire Island and Great South Bay, New York, coastline from 1834, 1838, and 1874/1875. The original t-sheets were scanned by the NARA-authorized vendor and sent to the Unites States Geological Survey St. Petersburg Coastal and Marine Science Center (USGS SPCMSC) as non-georeferenced digital raster files. Upon arrival at the SPCMSC, USGS staff performed the following procedures: rasters were georeferenced, projected to a modern datum, and shorelines were digitized to create a vector polyline depicting the historical shoreline position. The t-sheets included in this data release are: 1) T-479a, T-479b, T-1 (Parts 2 and 3) (1834); 2) T-58 (Parts 1 and 2) (1838); 3) T-1374a, T-1374b, T-1375a, T-1375b (1874); and 4) T-1402 (1875). All shorelines, including the ocean-facing barrier island shoreline, back-barrier island shoreline, mainland and islands were digitized. Please read the full metadata for details on data collection, dataset variables, and data quality.

Fire in the Everglades National Park (ENP) has historically been influential in shaping the Everglades ecosystem. As a result, ENP has been documenting fire events since 1948, and these data have been incorporated into an Esri ArcGIS geodatabase (Smith, T.J. III, and others, 2015). According to this geodatabase, 757,078 hectares of wetlands burned from 1948 to 2011. The main type of vegetation that has burned is comprised of palustrine and estuarine wetlands; however, there are areas in ENP that are comprised of these wetlands but have no documented fire events. Consequently, scientists at the USGS St. Petersburg Coastal and Marine Science Center question the accuracy of the data in this geodatabase. The abundance of fossil charcoal in sediment cores has been used, historically, as a fire proxy so to test the accuracy of this data, USGS scientists examined fossil charcoal in sediment cores taken from six locations in ENP. Two of the cores were taken from areas with well-documented fire events and four cores where taken from areas with no documented fire events. USGS scientists also dated three of the cores using excess Lead-210 (210Pb). Based on charcoal abundance in these cores, USGS scientists were able to verify documented fire events in the geodatabase. Furthermore, the presence of fossil charcoal in cores from areas with no documented fire events indicate that fire events did, in fact, occur in these areas in 1948-1964 and 1950-1980. These findings indicate the presence of fire events that are undocumented in the Esri geodatabase and suggest that 210Pb-dating is a promising method for reconstructing a regional fire history.

The National Assessment of Coastal Change Hazards project exists to understand and predict storm impacts to our nation's coastlines. This geospatial dataset defines the alongshore extent of overwash sediments deposited along the southeast coast of the United States from North Carolina to Virginia and attributed to coastal processes during [Atlantic Basin] Hurricane Florence, which made landfall in the U.S. on September 14, 2018.

This dataset defines storm-induced coastal erosion hazards for the Georgia, South Carolina, North Carolina, Virginia, Maryland, Delaware, New Jersey and New York coastline. The analysis was based on a storm-impact scaling model that used observations of beach morphology combined with sophisticated hydrodynamic models to predict how the coast would respond to the direct landfall of Hurricane Florence in September 2018. Storm-induced water levels, due to both surge and waves, were compared to beach and dune elevations to determine the probabilities of the three types of coastal change: collision (dune erosion), overwash, and inundation. . All hydrodynamic and morphologic variables are included in this dataset.

The Simulating WAves Nearshore (SWAN) wave model input grid 4 bathymetry with pit 3 configuration (G4_P3_grid.shp) and output of significant wave height, dominant wave period, and mean wave direction resulting from simulation of wave scenarios at Breton Island, LA, as described in USGS Open-File Report 20151055 are provided here.

The U.S. Geological Survey (USGS) Coastal and Marine Geology Program has actively collected geophysical and sedimentological data in the northern Gulf of Mexico for several decades, including shallow subsurface data in the form of high-resolution seismic reflection profiles (HRSP). Prior to the mid-1990s most HRSP data were collected in analog format as paper rolls of continuous profiles up to 25 meters long. As part of the National Geological and Geophysical Data Preservation Program (https://datapreservation.usgs.gov/), and in collaboration with the Bureau of Ocean Energy Management, Marine Minerals Program, scientists at the USGS St. Petersburg Coastal and Marine Science Center were converting the analog paper records to digital format using a large-format continuous scanner. The image files created by scanning were further processed to fix distortions and crop out blank spaces to create industry standard Society of Exploration Geophysicists date exchange (SEG-Y) format. This data release serves as an archive of HRSP profiles annotated with header information, converted SEG-Y files, navigation data, and cruise trackline shapefiles. The HRSP data were collected using a minisparker/hydrophone system onboard the research vessel Gyre and a Huntec boomer seismic system onboard research vessels (R/V) Carancahua and Kit Jones. Data collection dates and locations varied between surveys: (1) R/V Carancahua (legs 1 and 2) surveys were conducted July 1–15, 1981, within Chandeleur and Mississippi Sounds, (2) the R/V Gyre 81-6 cruise occurred April 9–23, 1981, in the Gulf of Mexico (south of Mississippi and east of Louisiana), (3) R/V Kit Jones 90 (legs 1 and 2) data were acquired from Mississippi Sound and the Gulf of Mexico (south of Mississippi and Alabama) June 21–27, 1990, and (4) R/V Kit Jones 91-2 HRSP data came from Mississippi Sound (south of Alabama) and the Gulf of Mexico (south of Alabama and Florida) from July 10 to 11 and July 21–27, 1991. Data collection and processing methods are described in USGS Data Series 1047.

Shoreline change analysis is an important environmental monitoring tool for evaluating coastal exposure to erosion hazards, particularly for vulnerable habitats such as coastal wetlands where habitat loss is problematic world-wide. The increasing availability of high-resolution satellite imagery and emerging developments in analysis techniques support the implementation of these data into coastal management, including shoreline monitoring and change analysis. Geospatial shoreline data were created from a semi-automated methodology using WorldView (WV) satellite data between 2013 and 2020. The data were compared to contemporaneous field-surveyed Real-time Kinematic (RTK) Global Positioning System (GPS) data collected by the Grand Bay National Estuarine Research Reserve (GBNERR) and digitized shorelines from U.S. Department of Agriculture National Agriculture Imagery Program (NAIP) orthophotos. Field data for shoreline monitoring sites was also collected to aid interpretation of results. This data release contains digital vector shorelines, shoreline change calculations for all three remote sensing data sets, and field surveyed data. The data will aid managers and decision-makers in the adoption of high-resolution satellite imagery into shoreline monitoring activities, which will increase the spatial scale of shoreline change monitoring, provide rapid response to evaluate impacts of coastal erosion, and reduce cost of labor-intensive practices. For further information regarding data collection and/or processing methods, refer to the associated journal article (Smith and others, 2021)

This dataset defines storm-induced coastal erosion hazards for the Louisiana, Mississippi, Alabama and Florida coastline. The analysis was based on a storm-impact scaling model that used observations of beach morphology combined with sophisticated hydrodynamic models to predict how the coast would respond to the direct landfall of Tropical Storm Gordon in September 2018. Storm-induced water levels, due to both surge and waves, were compared to beach and dune elevations to determine the probabilities of the three types of coastal change: collision (dune erosion), overwash, and inundation. All hydrodynamic and morphologic variables are included in this dataset.

This data release serves as an archive of sediment physical properties and grain-size data for surficial samples collected offshore of Assateague Island, Maryland and Virginia, for comparison with surficial estuarine and subaerial sedimentological samples collected and assessed following Hurricane Sandy (Ellis and others, 2015 (http://doi.org/10.3133/ofr20151219); Smith and others, 2015 (http://doi.org/10.3133/ofr20151169); Bernier and others, 2016 (https://pubs.usgs.gov/ds/0999/)). The sediment samples were collected by scientists from the U.S. Geological Survey (USGS) office in Woods Hole, Massachusetts while aboard the motor vessel (M/V) Scarlett Isabella as part of a larger effort to map the inner continental shelf (Pendleton and others, 2016 (http://doi.org/10.5066/F7MW2F60)). Following field work, the sediment samples were shipped to the USGS Coastal and Marine Science Center in St. Petersburg, Florida, where they were renamed for consistency with a previously existing naming scheme and processed for bulk density, loss on ignition (LOI), and grain-size. The grain-size subsamples were processed on a Coulter LS200 particle-size analyzer for consistency regarding methods and output statistics with related data sets from Chincoteague Bay and Assateague Island. For more information regarding sample collection and site information or the related data sets, refer to USGS data release Pendleton and others, 2016 (https://doi.org/10.5066/F7MW2F60); for more information regarding processing methods refer to USGS Open-File Report 2015–1219 (http://doi.org/10.3133/ofr20151219). Downloadable data are available as Excel spreadsheets (.xlsx), comma-separated values text files (.csv), and formal Federal Geographic Data Committee (FGDC) metadata.

GrandBay_2010_Shoreline.zip features a digitized historical shoreline for the Grand Bay, Mississippi (MS) coastline (Pascagoula, MS to Point aux Pins, Alabama [AL]) derived from 2010 aerial imagery. Imagery of the Mississippi and Alabama coastlines was acquired from the National Agriculture Imagery Program (NAIP) and the city of Mobile, AL. Using ArcMap 10.3.1, the imagery was used to delineate and digitize the historical shoreline as either the Wet Dry Line (WDL) along sandy beaches or the vegetation edge along the marsh coastline at a scale of 1:1000. This shoreline was digitized for use in long-term shoreline and wetland analyses and physical change assessment.

GrandBay_2012_Shoreline.zip features a digitized historical shoreline for the Grand Bay, Mississippi (MS) coastline (Pascagoula, MS to Bayou La Fourche Bay, Alabama [AL]) derived from 2012 aerial imagery. Imagery of the Mississippi and Alabama coastlines was acquired from the National Agriculture Imagery Program (NAIP). Using ArcMap 10.3.1, the imagery was used to delineate and digitize a coarse historical shoreline as either proximal Wet Dry Line along sandy beaches or proximal vegetation edge along the marsh coastline at a scale of 1:2000 or 1:6000.

From May 28 to June 3, 2015, the U.S. Geological Survey (USGS) conducted a geophysical survey to investigate the geologic evolution and estuarine sediment thickness in Grand Bay, Alabama and Mississippi. Specific objectives were to document the age and accumulation patterns of estuarine sediment to advance our understanding of sediment exchange with the adjacent marsh and sources of sediment to the coastal ocean. This investigation is part of the USGS Sea-level and Storm Impacts on Estuarine Environments and Shorelines (SSIEES) project. SSIEES seeks to better understand material exchange between marshes and adjacent estuarine water bodies along the northern Gulf of Mexico and the Atlantic coast, and determine the role extreme events (hurricanes, floods, and strong frontal systems) and sea-level change have on coastal change. This publication serves as an archive of unprocessed, digital chirp subbottom and sidescan sonar data, geographic information system (GIS) data and formal Federal Geographic Data Committee (FGDC) metadata, as well as processed sidescan sonar mosaics. Processed subbottom profile images are also provided in the "images" folder of 2015-315-FA_arc.zip. The archived subbottom trace data are in standard Society of Exploration Geophysicists (SEG) SEG Y revision 0 format (Barry and others, 1975) and may be downloaded and processed with commercial or public domain software such as Seismic Unix (SU). The raw sidescan sonar backscatter data are in standard eXtensible Triton Framework (XTF)format. These data are available for viewing using GeoMapApp (http://www.geomapapp.org/) and Virtual Ocean (http://www.virtualocean.org/) multi-platform open source software. In addition, these data files can be downloaded from the USGS Coastal and Marine Geoscience Data System (https://cmgds.marine.usgs.gov).

Scientists from the U.S. Geological Survey St. Petersburg Coastal and Marine Science Center (USGS SPCMSC) in St. Petersburg, Florida, conducted a bathymetric survey of Point Aux Chenes Bay and a small portion of Grand Bay, Mississippi/Alabama, from March 3-6, 2021. Efforts were supported by the Estuarine and MaRsh Geology project (EMRG), and the data described will provide baseline bathymetric information for future research investigating wetland/marsh evolution, sediment transport, and recent and long-term geomorphic change. The data will also support modeling of future changes in response to restoration efforts and storm impacts. During this study, bathymetry data were collected aboard two personal watercrafts (PWC) outfitted with single-beam echosounders.

As part of the Sea level and Storm Impacts on Estuarine Environments and Shorelines project (SSIEES), scientists from the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center conducted a single-beam bathymetry survey within the estuarine, open bay and tidal creek environments of Grand Bay Alabama/Mississippi, in May-June 2015. The goal of the SSIEES project is to assess the physical controls of sediment and material exchange between wetlands and estuarine environments along the northern Gulf of Mexico, specifically Grand Bay AL/MS and Vermilion Bay, Louisiana, as well as along the east coast in Chincoteague Bay Virginia/Maryland. The data included in this data release will provide baseline bathymetric information for future research investigating wetland/marsh evolution, sediment transport, recent and long term geomorphic change, and will support modeling of future changes in response to restoration and storm impacts. The survey area encompasses more than 40 square kilometers (km2) of Grand Bay’s incorporated waters. This data release archives processed single-beam bathymetry data, collected from May 28-June 3, 2015 (USGS Field Activity Number [FAN] 2015-315-FA). Geographic information system (GIS) data products include: a 10 and 30-meter cell size interpolated bathymetry grid, trackline maps, and point data files. Additional files include error analysis maps, Field Activity Collection System (FACS) logs, and formal Federal Geographic Data Committee (FGDC) metadata.

As part of the Sea level and Storm Impacts on Estuarine Environments and Shorelines project (SSIEES), scientists from the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center conducted a single-beam bathymetry survey within the estuarine, open bay and tidal creek environments of Grand Bay Alabama/Mississippi, in May-June 2015. The goal of the SSIEES project is to assess the physical controls of sediment and material exchange between wetlands and estuarine environments along the northern Gulf of Mexico, specifically Grand Bay MS/AL and Vermilion Bay, Louisiana, as well as along the US east coast in Chincoteague Bay Virginia/Maryland. The data described in this report will provide baseline bathymetric information for future research investigating wetland/marsh evolution, sediment transport, recent and long term geomorphic change, and will support modeling of future changes in response to restoration and storm impacts. The survey area encompasses more than 40 square kilometers (km2) of Grand Bay?s incorporated waters. This data release archives processed single-beam bathymetry data, collected from May 28-June 3, 2015 (USGS Field Activity Number 2015-315-FA). Geographic information system data products include: a 10 and 30-meter cell size interpolated bathymetry grid, trackline maps, and point data files. Additional files include error analysis maps, Field Activity Collection System (FACS) logs, and formal Federal Geographic Data Committee (FGDC) metadata.

As part of the Sea level and Storm Impacts on Estuarine Environments and Shorelines project (SSIEES), scientists from the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center conducted a single-beam bathymetry survey within the estuarine, open bay and tidal creek environments of Grand Bay Alabama/Mississippi, in May-June 2015. The goal of the SSIEES project is to assess the physical controls of sediment and material exchange between wetlands and estuarine environments along the northern Gulf of Mexico, specifically Grand Bay AL/MS and Vermilion Bay, Louisiana, as well as along the US east coast in Chincoteague Bay Virginia/Maryland. The data included in this data release will provide baseline bathymetric information for future research investigating wetland/marsh evolution, sediment transport, recent and long term geomorphic change, and will support modeling of future changes in response to restoration and storm impacts. The survey area encompasses more than 40 square kilometers (km2) of Grand Bay’s incorporated waters. This data release archives processed single-beam bathymetry data, collected from May 28-June 3, 2015 (USGS Field Activity Number [FAN] 2015-315-FA). Geographic information system (GIS) data products include: a 10 and 30-meter cell size interpolated bathymetry grid, trackline maps, and point data files. Additional files include error analysis maps, Field Activity Collection System (FACS) logs, and formal Federal Geographic Data Committee (FGDC) metadata.

A reconnaissance multibeam bathymetry survey was conducted by the U.S Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center (SPCMSC) in Grand Bay Alabama/Mississippi on May 12, 2016 as an assessment of the shallow water capabilities of the Teledyne Reson SeaBat T50-P multibeam echosounder, and as an attempt to map the eroding marsh edges at locations of interest around the bay. This dataset, Grand_Bay_2016_MBB_Adjusted_xyz.zip, includes the resulting processed elevation point data (x,y,z), as derived from a half meter resolution surface.

A reconnaissance multibeam bathymetry survey was conducted by the U.S Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center (SPCMSC) in Grand Bay Alabama/Mississippi on May 12, 2016 as an assessment of the shallow water capabilities of the Teledyne Reson SeaBat T50-P multibeam echosounder, and as an attempt to map the eroding marsh edges at locations of interest around the bay. This dataset, Grand_Bay_2016_MBB_Tracklines.zip, includes the trackline vector file derived from the acquisition software at the time of survey.

A reconnaissance multibeam bathymetry survey was conducted by the U.S Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center (SPCMSC) in Grand Bay Alabama/Mississippi (AL/MS) on May 12, 2016 as an assessment of the shallow water capabilities of the Teledyne Reson SeaBat T50-P multibeam echosounder, and as an attempt to map the eroding marsh edges at locations of interest around the bay. This dataset, Grand_Bay_2016_MBB_Unadjusted_xyz.zip, includes the resulting [unadjusted] processed elevation point data (x,y,z), as derived from a half meter resolution surface. For this dataset, a static offset was not applied to vertically adjusted the data to match USGS bathymetry data previously acquired in 2015 (DeWitt and others, 2016).

An Ellipsoidally Referenced Survey (ERS) using two Teledyne Reson SeaBat T50-P multibeam echosounders, in dual-head configuration, was conducted by the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center (SPCMSC) in Grand Bay Alabama/Mississippi (AL/MS) October 22-23, 2018. This dataset, Grand_Bay_2018_MBB_xyz.zip, includes the processed point data (x,y,z), as derived from a 1-meter (m) bathymetric grid.

An Ellipsoidally Referenced Survey (ERS) using a Teledyne Reson SeaBat T50-P multibeam echosounder was conducted by the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center (SPCMSC) in Grand Bay Alabama/Mississippi (AL/MS) May 7-10, 2019. This dataset, Grand_Bay_2019_MBES_xyz.zip, includes the processed point data (x,y,z), as derived from a 1-meter (m) bathymetric grid from two separate sensor configurations, which were acquired independently. One configuration utilized a tilted sonar head (62.5 degrees to port), and the other utilized a flat configuration.

To understand sediment deposition in marsh environments, scientists from the U.S. Geological Survey, St. Petersburg Coastal and Marine Science Center (USGS-SPCMSC) selected four study sites in the Grand Bay National Estuarine Research Reserve, Mississippi (GNDNERR). Each site consisted of four plots located along a transect perpendicular to the marsh-estuary shoreline at 5-meter (m) increments (5, 10, 15, and 20 m from the shoreline). Each plot contained four net sedimentation tiles (NST) that were secured flush to the marsh surface using polyvinyl chloride (PVC) pipe. NST are an inexpensive and simple tool to assess short- and long-term deposition that can be deployed in highly dynamic environments without the compaction associated with traditional coring methods. The NST were deployed for three months, measuring quarterly sediment deposition for one year from October 2016 to October 2017. In addition, three NST were deployed at the 10-m plot on October 5th prior to the landfall of Hurricane Nate (October 8, 2017) and retrieved after 12 days, providing measurements of storm deposition. Sediment deposited on the NST were processed to determine physical characteristics, such as deposition thickness, volume, wet weight/dry weight, and organic content (loss-on-ignition [LOI]). When available, additional data collected at each site including water level, elevation, and turbidity data are provided in this data release. Data were collected during Field Activities Numbers (FAN) 2017-303-FA, 2017-315-FA, 2017-333-FA, 2017-346-FA, and 2017-363-FA (also known as subFANs 17CCT01, 17CCT02, 17CCT03, 17CCT04, and 17CCT05, respectively). Additional survey and data details are available from the U.S. Geological Survey Coastal and Marine Geoscience Data System (CMGDS) at, https://cmgds.marine.usgs.gov/. Please read the full metadata for details on data collection, data set variables, and data quality.

To better understand sediment deposition in marsh environments, scientists from the U.S. Geological Survey, St. Petersburg Coastal and Marine Science Center (USGS-SPCMSC) selected four study sites (Sites 5, 6, 7, and 8) along the Point Aux Chenes Bay shoreline of the Grand Bay National Estuarine Research Reserve (GNDNERR), Mississippi. These datasets were collected to serve as baseline data prior to the installation of a living shoreline (a subtidal sill). Each site consisted of five plots located along a transect perpendicular to the marsh-estuary shoreline at 5-meter (m) increments (5, 10, 15, 20, and 25 m from the shoreline). Each plot contained six net sedimentation tiles (NST) that were secured flush to the marsh surface using polyvinyl chloride (PVC) pipe. NST are an inexpensive and simple tool to assess short- and long-term deposition that can be deployed in highly dynamic environments without the compaction associated with traditional coring methods. The NST were deployed for three month sampling periods, measuring sediment deposition from July 2018 to January 2020, with one set of NST being deployed for six months. Sediment deposited on the NST were processed to determine physical characteristics, such as deposition thickness, volume, wet weight/dry weight, grain size, and organic content (loss-on-ignition [LOI]). For select sampling periods, ancillary data (water level, elevation, and wave data) are also provided in this data release. Data were collected during USGS Field Activities Numbers (FAN) 2018-332-FA (18CCT01), 2018-358-FA (18CCT10), 2019-303-FA (19CCT01, 19CCT02, 19CCT03, and 19CCT04, respectively), and 2020-301-FA (20CCT01). Additional survey and data details are available from the U.S. Geological Survey Coastal and Marine Geoscience Data System (CMGDS) at, https://cmgds.marine.usgs.gov/. Data collected between 2016 and 2017 from a related NST study in the GNDNERR (Middle Bay and North Rigolets) can be found at https://doi.org/10.5066/P9BFR2US. Please read the full metadata for details on data collection, dataset variables, and data quality.

To understand sediment deposition in marsh environments, scientists from the U.S. Geological Survey, St. Petersburg Coastal and Marine Science Center (USGS-SPCMSC) selected four study sites in the Grand Bay National Estuarine Research Reserve, Mississippi (GNDNERR). Each site consisted of four plots located along a transect perpendicular to the marsh-estuary shoreline at 5-meter (m) increments (5, 10, 15, and 20 m from the shoreline). Each plot contained four net sedimentation tiles (NST) that were secured flush to the marsh surface using polyvinyl chloride (PVC) pipe. NST are an inexpensive and simple tool to assess short- and long-term deposition that can be deployed in highly dynamic environments without the compaction associated with traditional coring methods. The NST were deployed for three months, measuring quarterly sediment deposition for one year from October 2016 to October 2017. In addition, three NST were deployed at the 10-m plot on October 5th prior to the landfall of Hurricane Nate (October 8, 2017) and retrieved after 12 days, providing measurements of storm deposition. Sediment deposited on the NST were processed to determine physical characteristics, such as deposition thickness, volume, wet weight/dry weight, and organic content (loss-on-ignition [LOI]). When available, additional data collected at each site including water level, elevation, and turbidity data are provided in this data release. Data were collected during Field Activities Numbers (FAN) 2017-303-FA, 2017-315-FA, 2017-333-FA, 2017-346-FA, and 2017-363-FA (also known as subFANs 17CCT01, 17CCT02, 17CCT03, 17CCT04, and 17CCT05, respectively). Additional survey and data details are available from the U.S. Geological Survey Coastal and Marine Geoscience Data System (CMGDS) at, https://cmgds.marine.usgs.gov/. Please read the full metadata for details on data collection, dataset variables, and data quality.

To better understand sediment deposition in marsh environments, scientists from the U.S. Geological Survey, St. Petersburg Coastal and Marine Science Center (USGS-SPCMSC) selected four study sites (Sites 5, 6, 7, and 8) along the Point Aux Chenes Bay shoreline of the Grand Bay National Estuarine Research Reserve (GNDNERR), Mississippi. These datasets were collected to serve as baseline data prior to the installation of a living shoreline (a subtidal sill). Each site consisted of five plots located along a transect perpendicular to the marsh-estuary shoreline at 5-meter (m) increments (5, 10, 15, 20, and 25 m from the shoreline). Each plot contained six net sedimentation tiles (NST) that were secured flush to the marsh surface using polyvinyl chloride (PVC) pipe. NST are an inexpensive and simple tool to assess short- and long-term deposition that can be deployed in highly dynamic environments without the compaction associated with traditional coring methods. The NST were deployed for three month sampling periods, measuring sediment deposition from July 2018 to January 2020, with one set of NST being deployed for six months. Sediment deposited on the NST were processed to determine physical characteristics, such as deposition thickness, volume, wet weight/dry weight, grain size, and organic content (loss-on-ignition [LOI]). For select sampling periods, ancillary data (water level, elevation, and wave data) are also provided in this data release. Data were collected during USGS Field Activities Numbers (FAN) 2018-332-FA (18CCT01), 2018-358-FA (18CCT10), 2019-303-FA (19CCT01, 19CCT02, 19CCT03, and 19CCT04, respectively), and 2020-301-FA (20CCT01). Additional survey and data details are available from the U.S. Geological Survey Coastal and Marine Geoscience Data System (CMGDS) at, https://cmgds.marine.usgs.gov/. Data collected between 2016 and 2017 from a related NST study in the GNDNERR (Middle Bay and North Rigolets) can be found at https://doi.org/10.5066/P9BFR2US. Please read the full metadata for details on data collection, dataset variables, and data quality.

To understand sediment deposition in marsh environments, scientists from the U.S. Geological Survey, St. Petersburg Coastal and Marine Science Center (USGS-SPCMSC) selected four study sites in the Grand Bay National Estuarine Research Reserve, Mississippi (GNDNERR). Each site consisted of four plots located along a transect perpendicular to the marsh-estuary shoreline at 5-meter (m) increments (5, 10, 15, and 20 m from the shoreline). Each plot contained four net sedimentation tiles (NST) that were secured flush to the marsh surface using polyvinyl chloride (PVC) pipe. NST are an inexpensive and simple tool to assess short- and long-term deposition that can be deployed in highly dynamic environments without the compaction associated with traditional coring methods. The NST were deployed for three months, measuring quarterly sediment deposition for one year from October 2016 to October 2017. In addition, three NST were deployed at the 10-m plot on October 5th prior to the landfall of Hurricane Nate (October 8, 2017) and retrieved after 12 days, providing measurements of storm deposition. Sediment deposited on the NST were processed to determine physical characteristics, such as deposition thickness, volume, wet weight/dry weight, and organic content (loss-on-ignition [LOI]). When available, additional data collected at each site including water level, elevation, and turbidity data are provided in this data release. Data were collected during Field Activities Numbers (FAN) 2017-303-FA, 2017-315-FA, 2017-333-FA, 2017-346-FA, and 2017-363-FA (also known as subFANs 17CCT01, 17CCT02, 17CCT03, 17CCT04, and 17CCT05, respectively). Additional survey and data details are available from the U.S. Geological Survey Coastal and Marine Geoscience Data System (CMGDS) at, https://cmgds.marine.usgs.gov/. Please read the full metadata for details on data collection, dataset variables, and data quality.

To understand sediment deposition in marsh environments, scientists from the U.S. Geological Survey, St. Petersburg Coastal and Marine Science Center (USGS-SPCMSC) selected four study sites in the Grand Bay National Estuarine Research Reserve, Mississippi (GNDNERR). Each site consisted of four plots located along a transect perpendicular to the marsh-estuary shoreline at 5-meter (m) increments (5, 10, 15, and 20 m from the shoreline). Each plot contained four net sedimentation tiles (NST) that were secured flush to the marsh surface using polyvinyl chloride (PVC) pipe. NST are an inexpensive and simple tool to assess short- and long-term deposition that can be deployed in highly dynamic environments without the compaction associated with traditional coring methods. The NST were deployed for three months, measuring quarterly sediment deposition for one year from October 2016 to October 2017. In addition, three NST were deployed at the 10-m plot on October 5th prior to the landfall of Hurricane Nate (October 8, 2017) and retrieved after 12 days, providing measurements of storm deposition. Sediment deposited on the NST were processed to determine physical characteristics, such as deposition thickness, volume, wet weight/dry weight, and organic content (loss-on-ignition [LOI]). When available, additional data collected at each site including water level, elevation, and turbidity data are provided in this data release. Data were collected during Field Activities Numbers (FAN) 2017-303-FA, 2017-315-FA, 2017-333-FA, 2017-346-FA, and 2017-363-FA (also known as subFANs 17CCT01, 17CCT02, 17CCT03, 17CCT04, and 17CCT05, respectively). Additional survey and data details are available from the U.S. Geological Survey Coastal and Marine Geoscience Data System (CMGDS) at, https://cmgds.marine.usgs.gov/. Please read the full metadata for details on data collection, dataset variables, and data quality.

To better understand sediment deposition in marsh environments, scientists from the U.S. Geological Survey, St. Petersburg Coastal and Marine Science Center (USGS-SPCMSC) selected four study sites (Sites 5, 6, 7, and 8) along the Point Aux Chenes Bay shoreline of the Grand Bay National Estuarine Research Reserve (GNDNERR), Mississippi. These datasets were collected to serve as baseline data prior to the installation of a living shoreline (a subtidal sill). Each site consisted of five plots located along a transect perpendicular to the marsh-estuary shoreline at 5-meter (m) increments (5, 10, 15, 20, and 25 m from the shoreline). Each plot contained six net sedimentation tiles (NST) that were secured flush to the marsh surface using polyvinyl chloride (PVC) pipe. NST are an inexpensive and simple tool to assess short- and long-term deposition that can be deployed in highly dynamic environments without the compaction associated with traditional coring methods. The NST were deployed for three month sampling periods, measuring sediment deposition from July 2018 to January 2020, with one set of NST being deployed for six months. Sediment deposited on the NST were processed to determine physical characteristics, such as deposition thickness, volume, wet weight/dry weight, grain size, and organic content (loss-on-ignition [LOI]). For select sampling periods, ancillary data (water level, elevation, and wave data) are also provided in this data release. Data were collected during USGS Field Activities Numbers (FAN) 2018-332-FA (18CCT01), 2018-358-FA (18CCT10), 2019-303-FA (19CCT01, 19CCT02, 19CCT03, and 19CCT04, respectively), and 2020-301-FA (20CCT01). Additional survey and data details are available from the U.S. Geological Survey Coastal and Marine Geoscience Data System (CMGDS) at, https://cmgds.marine.usgs.gov/. Data collected between 2016 and 2017 from a related NST study in the GNDNERR (Middle Bay and North Rigolets) can be found at https://doi.org/10.5066/P9BFR2US. Please read the full metadata for details on data collection, dataset variables, and data quality.

Shoreline change analysis is an important environmental monitoring tool for evaluating coastal exposure to erosion hazards, particularly for vulnerable habitats such as coastal wetlands where habitat loss is problematic world-wide. The increasing availability of high-resolution satellite imagery and emerging developments in analysis techniques support the implementation of these data into coastal management, including shoreline monitoring and change analysis. Geospatial shoreline data were created from a semi-automated methodology using WorldView (WV) satellite data between 2013 and 2020. The data were compared to contemporaneous field-surveyed Real-time Kinematic (RTK) Global Positioning System (GPS) data collected by the Grand Bay National Estuarine Research Reserve (GBNERR) and digitized shorelines from U.S. Department of Agriculture National Agriculture Imagery Program (NAIP) orthophotos. Field data for shoreline monitoring sites was also collected to aid interpretation of results. This data release contains digital vector shorelines, shoreline change calculations for all three remote sensing data sets, and field surveyed data. The data will aid managers and decision-makers in the adoption of high-resolution satellite imagery into shoreline monitoring activities, which will increase the spatial scale of shoreline change monitoring, provide rapid response to evaluate impacts of coastal erosion, and reduce cost of labor-intensive practices. For further information regarding data collection and/or processing methods, refer to the associated journal article (Smith and others, 2021).

This dataset contains carbonate system data collected by scientists from the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center to investigate the effects of carbon cycling, coastal and ocean acidification in the Gulf of Mexico near the Tampa Bay estuary located in west central Florida, USA. These data were collected using an autonomous instrument called the Ocean Carbon System version 3 (OCSv3) deployed on the University of South Florida (USF), Coastal Ocean Monitoring and Prediction System (COMPS) Buoy C12. The OCSv3 consists of four sensors integrated into a Sea-Bird Scientific STOR-X submersible data logger including a Sea-Bird SeapHOx sensor for measurement of pH that incorporates a Sea-Bird SBE 37-SMP-ODO MicroCAT C-T-ODO (P) Recorder for measurement of conductivity (for calculation of salinity), temperature, depth, and dissolved oxygen; a Pro-Oceanus CO2-Pro CV CO2 sensor; and a Wetlabs Eco-PAR sensor for measurement of photosynthetically active radiation. The dataset is a time series of carbonate system parameters including: water temperature (Celsius, °C), pressure (decibar, dbar), salinity, pHT (pH on the total scale), carbon dioxide (parts per million, ppm), pressure from the CO2-Pro Infrared Gas Analyzer (IRGA) (millibars, mbar), dissolved oxygen (milligrams per liter, mg/L) and photosynthetically active radiation (microEinsteins). Each parameter was measured every hour for 24-hour time periods throughout the duration of deployment.

Models project the Arctic Ocean will become undersaturated with respect to carbonate minerals in the next decade. Recent field results indicate parts may already be undersaturated in late summer months when ice melt is at its greatest extent. However, few comprehensive data sets of carbonate system parameters in the Arctic Ocean exist. Researchers from the U.S. Geological Survey (USGS) and University of South Florida (USF) collected high-resolution measurements of pCO2, pH, total dissolved inorganic carbon (DIC), total alkalinity (TA), and carbonate (CO3-2) from the Canada Basin that fill critical information gaps concerning Arctic carbon variability. A Multiparameter Inorganic Carbon Analyzer (MICA) was used to collect approximately 9,000 measurements of air and sea pCO2, pH, and DIC along a 11,447-km trackline in August and September 2011. In addition, over 500 discrete surface water samples were taken. These data are being used to characterize and model regional pCO2, pH, and carbonate mineral saturation state. A high-resolution, three-dimensional map of these results will be presented.

This dataset defines storm-induced coastal erosion hazards for the Texas and Louisiana coastline. The analysis was based on a storm-impact scaling model that used observations of beach morphology combined with sophisticated hydrodynamic models to predict how the coast would respond to the direct landfall of Hurricane Harvey in August 2017. Storm-induced water levels, due to both surge and waves, were compared to beach and dune elevations to determine the probabilities of the three types of coastal change: collision (dune erosion), overwash, and inundation. All hydrodynamic and morphologic variables are included in this dataset.

This dataset defines storm-induced coastal erosion hazards for the Florida coastline. The analysis was based on a storm-impact scaling model that used observations of beach morphology combined with sophisticated hydrodynamic models to predict how the coast would respond to the direct landfall of Tropical Storm Hermine in September 2016. Storm-induced water levels, due to both surge and waves, were compared to beach and dune elevations to determine the probabilities of the three types of coastal change: collision (dune erosion), overwash, and inundation. All hydrodynamic and morphologic variables are included in this dataset.

The National Assessment of Coastal Change Hazards project exists to understand and predict storm impacts to our nation's coastlines. This geospatial dataset defines the alongshore extent of overwash sediments deposited along the southeast coast of the United States from Florida to North Carolina and attributed to coastal processes during [Atlantic Basin] Hurricane Matthew, which made landfall in the U.S. on October 8, 2016.

ASCII XYZ point cloud data were produced from remotely sensed, geographically referenced elevation measurements by the U.S. Geological Survey (USGS). Elevation measurements were collected over a portion of the Texas coastline, post-Hurricane Ike (September 2008 hurricane), using the Experimental Advanced Airborne Research Lidar (EAARL), a pulsed laser ranging system mounted onboard an aircraft to measure ground elevation, vegetation canopy, and coastal topography. The system uses high-frequency laser beams directed at the Earth's surface through an opening in the bottom of the aircraft's fuselage. The laser system records the time difference between emission of the laser beam and the reception of the reflected laser signal in the aircraft. The plane travels over the target area at approximately 60 meters per second at an elevation of approximately 300 meters, resulting in a laser swath of approximately 240 meters with an average point spacing of 2-3 meters. The EAARL, developed originally by the National Aeronautics and Space Administration (NASA) at Wallops Flight Facility in Virginia, measures ground elevation with a vertical resolution of 3 centimeters. A sampling rate of 3 kilohertz or higher results in an extremely dense spatial elevation dataset. Over 100 kilometers of coastline can be surveyed easily within a 3- to 4-hour mission. When resultant elevation maps for an area are analyzed, they provide a useful tool to make management decisions regarding land development.

ASCII XYZ point cloud data were produced from remotely sensed, geographically referenced elevation measurements by the U.S. Geological Survey (USGS). Elevation measurements were collected over a portion of the Texas coastline, post-Hurricane Ike (September 2008 hurricane), using the Experimental Advanced Airborne Research Lidar (EAARL), a pulsed laser ranging system mounted onboard an aircraft to measure ground elevation, vegetation canopy, and coastal topography. The system uses high-frequency laser beams directed at the Earth's surface through an opening in the bottom of the aircraft's fuselage. The laser system records the time difference between emission of the laser beam and the reception of the reflected laser signal in the aircraft. The plane travels over the target area at approximately 60 meters per second at an elevation of approximately 300 meters, resulting in a laser swath of approximately 240 meters with an average point spacing of 2-3 meters. The EAARL, developed originally by the National Aeronautics and Space Administration (NASA) at Wallops Flight Facility in Virginia, measures ground elevation with a vertical resolution of 3 centimeters. A sampling rate of 3 kilohertz or higher results in an extremely dense spatial elevation dataset. Over 100 kilometers of coastline can be surveyed easily within a 3- to 4-hour mission. When resultant elevation maps for an area are analyzed, they provide a useful tool to make management decisions regarding land development.

Indian River Lagoon (IRL) is one of the most biologically diverse estuarine systems in the continental United States, extending 200 kilometers (km) along the Atlantic coast of central Florida. The lagoon is characterized by shallow, brackish waters with significant human development along both shores and a width that varies between 0.5-9.0 km. Scientists from the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center, working in collaboration with the St. Johns River Water Management District, mapped surface water radon-222 (radon-in-water) and basic physical water column properties (for example, salinity and temperature) to examine submarine groundwater discharge at two locations, Eau Gallie North and Riverwalk Park, along the western shore of IRL. Eau Gallie North is near the central section of IRL while Riverwalk Park is approximately 20 km north of Eau Gallie site. At each study site, a radon mapping survey was performed over seven north–south shore parallel transects (EA–EG and RA–RG, respectively), positioned between 75–1000 meters offshore, and approximately 1.5 km in length. Each transect was mapped three times in an alternating north–south direction. Surface water was continuously pumped on-board into an air-water exchanger. Dissolved radon-222 was purged from the water into a gaseous phase inside the exchanger. Radon-222 in the exchanger was continuously pumped into and measured by commercially available radon-in-air detectors (RAD7, Durridge, Inc.). In situ surface water temperature and salinity, as well as the water temperature in the exchanger, were also measured. Radon-in-air measurements were corrected to radon-in-water activities using the temperature-salinity dependent air-water partitioning coefficient (Schubert and others, 2012). Starting in September 2016, the USGS conducted surveys bimonthly along the same transects to determine seasonal and temporal variability of radon-222. A previous data release (https://doi.org/10.5066/F7QF8S05) contains the raw radon-222 data and physical water column data collected from September 2016 through July 2017. The last survey, the subject of this data release, had to be divided into two different trips for each study site due to unfavorable weather conditions for radon-222 mapping. This data release contains the raw radon-222 data, physical water column data, Esri GIS data files and data distribution maps of the radon-222 activity and surface water salinity collected during the final IRL trips in September 2017 and November 2017.

Indian River Lagoon (IRL) is one of the most biologically diverse estuarine systems in the continental United States, stretching 200 kilometers (km) along the Atlantic coast of central Florida. The width of the lagoon varies between 0.5-9.0 km and is characterized by shallow, brackish waters with significant human development along both shores. Scientists from the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center, working in collaboration with the St. Johns River Water Management District, mapped surface water radon-222 (radon in water) and basic physical water column properties (for example, salinity and temperature) to investigate submarine groundwater discharge at two locations, Eau Gallie North and Riverwalk Park, along the western shore of IRL. Eau Gallie North is near the central section of IRL. Riverwalk Park is approximately 20 km north of Eau Gallie site. At each study site, the radon mapping surveys were performed over seven north–south shore parallel transects (EA–EG and RA–RG, respectively), situated between 75–1000 meters offshore, and approximately 1.5 km in length. Each transect was mapped three times in an alternating north–south direction. Surface water was continuously pumped on-board into an air-water exchanger; dissolved radon-222 was purged from the water into a gaseous phase inside the exchanger. Radon-222 in the exchanger was continuously pumped into and measured by commercially available radon-in-air detectors (RAD7, Durridge, Inc.). In situ surface water temperature and salinity, as well as the water temperature in the exchanger, were also measured. Radon in air measurements were corrected to radon in water activities using the temperature-salinity dependent air-water partitioning coefficient (Schubert and others, 2012). Starting in September 2016, surveys were conducted bimonthly along the same transects to determine seasonal and temporal variability of radon-222. This data release contains the raw radon-222 data, physical water column data, Esri GIS data files and data distribution maps of the radon-222 activity and surface water salinity collected from September 2016 through July 2017.

Extending 200 kilometers (km) along the Atlantic Coast of Central Florida, Indian River Lagoon (IRL) is one of the most biologically diverse estuarine systems in the continental United States. The lagoon is characterized by shallow, brackish waters and a width that varies between 0.5 and 9.0 km; there is significant human development along both shores. Scientists from the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center used continuous resistivity profiling (CRP, a towed electronic array) measurements, electrical resistivity tomography (ERT), and basic physical water column properties (for example, depth and temperature) to investigate submarine groundwater discharge at two locations, Eau Gallie North and Riverwalk Park, along the western shore of IRL. Eau Gallie North is near the central section of IRL and Riverwalk Park is approximately 20 km north of the Eau Gallie site. At each CRP study site, an 11-electrode marine resistivity array was towed over seven north–south shore parallel transects (EA–EG and RA–RG, respectively), situated between 75–1000 meters offshore, and approximately 1.5 km in length. Each transect was mapped three times in an alternating north–south direction to account for data collected by the concurrently-operating radon mapping system (Everhart and others, 2018). Repeat streaming resistivity surveys were collected bimonthly along these same tracklines, between March and November 2017, to determine seasonal and temporal variability. Since resistivity is a function of both geology and salinity, it is assumed that temporal shifts will reflect salinity changes, as the underlying geology will be presumed to remain constant. ERT study areas consisted of land- and shallow water-based surveys, where [DC] electrical current was injected into the ground via two current electrodes and received by nine potential electrodes. Electrode positions for both sites were recorded along six transects (T01-T06) and are provided in this data release as supplemental information (please see the ERT location map files included in, ERT_survey_maps.zip).

Antecedent topography is an important aspect of coastal morphology when studying and forecasting coastal change hazards. The uncertainty in morphologic response of storm-impact models and their use in short-term hazard forecasting and decadal forecasting is important to account for when considering a coupled model framework. Mickey and others (2020) provided a methodology to investigate uncertainty of profile response within the storm impact model, XBeach, related to varying antecedent topographies. A parameterized island Gaussian fit (PIGF) model generated an idealized baseline profile and a suite of idealized profiles that vary specific characteristics based on collated observed light detection and ranging (lidar) data collected from Dauphin Island, AL, between 2005 and 2015. The model inputs of idealized topography and bathymetry values for simulation of synthetic storm evolution with XBeach are described in Mickey and others (2020) and included in this USGS data release.

The model input for the bathymetry and topography values resulting from a deterministic simulation at Dauphin Island, Alabama, as described in U.S. Geological Survey (USGS) Open-File Report 2019-1139 (https://doi.org/10.3133/ofr20191139), are provided here. For further information regarding model input generation and visualization of model output topography and bathymetry refer to Mickey and others (2020).

The model input and output of topography and bathymetry values resulting from forecast simulations of coupled modeling scenarios occurring between 2015 and 2025 at Dauphin Island, Alabama, and described in U.S. Geological Survey (USGS) Open-File Report 2020–1001 (https://doi.org/10.3133/ofr20201001), are provided here. For further information regarding model input generation and visualization of model output topography and bathymetry, refer to Mickey and others (2020).

Using version 5527 of the XBeach numerical model (Roelvink and others, 2009), barrier island morphological change was simulated at Dauphin Island, Alabama (AL), for a 30-year forecast of multiple storms and sea level rise, considering scenarios of no-action and beach and dune nourishment as described in Passeri and others (2021). The two-dimensional XBeach model can be applied to barrier islands to solve for time-dependent topography and bathymetry. The XBeach model setup requires the input of topographic and bathymetric elevations at each grid cell. Model inputs and outputs in the form of topography and bathymetry at each grid cell are provided in this data release. For further information regarding model input generation and visualization of model output, refer to Passeri and others (2021).

Using the numerical model XBeach version 4926 (Roelvink and others, 2009), hurricanes Ivan (2004) and Katrina (2005) were simulated at Dauphin Island, Alabama, under present-day conditions and future sea level rise scenarios as described in Passeri and others, 2018. The XBeach model setup requires the input of a merged topographic and bathymetric digital elevation model (DEM), and inputs of wave spectra (based on significant wave height, peak wave period and wave direction) and water level (tide and surge) time series. Model inputs and outputs in the form of topography and bathymetry are provided in this data release. For further information regarding model input generation and visualization of model output topography and bathymetry, refer to Passeri and others, 2018.

The U.S. Geological Survey (USGS) Coastal and Marine Hazards and Resources Program (CMHRP) has actively collected geophysical and sedimentological data in the northern Gulf of Mexico for several decades, including shallow subsurface data in the form of high-resolution seismic reflection profiles (HRSP). Prior to the mid-1990s most HRSP data were collected in analog format as paper rolls of continuous profiles up to 25 meters (m) long. As part of the National Geological and Geophysical Data Preservation Program (NGGDPP, https://datapreservation.usgs.gov/), and in collaboration with the Bureau of Ocean Energy Management, Marine Minerals Program, scientists from the USGS St. Petersburg Coastal and Marine Science Center (USGS-SPCMSC) converted analog paper records to digital format using a large-format continuous scanner. The scanned image files were subsequently processed to fix distortions and crop out blank spaces prior to exporting as industry standard Society of Exploration Geophysicists data exchange (SEG-Y) formatted files. This data release serves as an archive of HRSP profiles annotated with header information, converted SEG-Y files, navigation data, and cruise trackline shapefiles. The HRSP data were collected using a sparker seismic system onboard the research vessel (R/V) Amarillo and R/V Sea Raider. The two vessels collected seismic data along the northern Gulf of Mexico ranging from west of Sabine Pass, Texas to south of Marsh Island, Louisiana. The survey occurred from July 9 to September 26, 1980. Data collection and processing methods are described in USGS Data Series 1047 (Bosse and others, 2017).

The National Assessment of Coastal Change Hazards project exists to understand and predict storm impacts to our nation's coastlines. This geospatial dataset defines the alongshore extent of overwash sediments deposited along the Florida coast and attributed to coastal processes during [Atlantic Basin] Hurricane Irma, which made landfall in the U.S. on September 9, 2017.

This dataset defines storm-induced coastal erosion hazards for the Florida, Georgia and South Carolina coastline. The analysis was based on a storm-impact scaling model that used observations of beach morphology combined with sophisticated hydrodynamic models to predict how the coast would respond to the direct landfall of Hurricane Irma in September 2017. Storm-induced water levels, due to both surge and waves, were compared to beach and dune elevations to determine the probabilities of the three types of coastal change: collision (dune erosion), overwash, and inundation. All hydrodynamic and morphologic variables are included in this dataset.

Using the numerical model XBeach version 4926 (Roelvink and others, 2009), various bottom friction scenarios were simulated for hurricanes Ivan (2004) and Katrina (2005) at Dauphin Island, Alabama as described in Passeri and others, 2018. The XBeach model setup requires the input of a merged topographic and bathymetric digital elevation model (DEM), and inputs of wave spectra (based on significant wave height, peak wave period and wave direction) and water level (tide and surge) time series. Model inputs and outputs in the form of topography and bathymetry are provided in this data release. For further information regarding model input generation and visualization of model output topography and bathymetry, refer to Passeri and others, 2018.

Using the numerical model XBeach version 4926 (Roelvink and others, 2009), various bottom friction scenarios were simulated for hurricanes Ivan (2004) and Katrina (2005) at Dauphin Island, Alabama as described in Passeri and others, 2018. The XBeach model setup requires the input of a merged topographic and bathymetric digital elevation model (DEM), and inputs of wave spectra (based on significant wave height, peak wave period and wave direction) and water level (tide and surge) time series. Model inputs and outputs in the form of topography and bathymetry are provided in this data release. For further information regarding model input generation and visualization of model output topography and bathymetry, refer to Passeri and others, 2018.

Using the numerical model XBeach version 4926 (Roelvink and others, 2009), various bottom friction scenarios were simulated for hurricanes Ivan (2004) and Katrina (2005) at Dauphin Island, Alabama as described in Passeri and others, 2018. The XBeach model setup requires the input of a merged topographic and bathymetric digital elevation model (DEM), and inputs of wave spectra (based on significant wave height, peak wave period and wave direction) and water level (tide and surge) time series. Model inputs and outputs in the form of topography and bathymetry are provided in this data release. For further information regarding model input generation and visualization of model output topography and bathymetry, refer to Passeri and others, 2018.

Using the numerical model XBeach version 4926 (Roelvink and others, 2009), hurricanes Ivan (2004) and Katrina (2005) were simulated at Dauphin Island, Alabama, under present-day conditions and future sea level rise scenarios as described in Passeri and others, 2018. The XBeach model setup requires the input of a merged topographic and bathymetric digital elevation model (DEM), and inputs of wave spectra (based on significant wave height, peak wave period and wave direction) and water level (tide and surge) time series. Model inputs and outputs in the form of topography and bathymetry are provided in this data release. For further information regarding model input generation and visualization of model output topography and bathymetry, refer to Passeri and others, 2018.

Using the numerical model XBeach version 4926 (Roelvink and others, 2009), hurricanes Ivan (2004) and Katrina (2005) were simulated at Dauphin Island, Alabama, under present-day conditions and future sea level rise scenarios as described in Passeri and others, 2018. The XBeach model setup requires the input of a merged topographic and bathymetric digital elevation model (DEM), and inputs of wave spectra (based on significant wave height, peak wave period and wave direction) and water level (tide and surge) time series. Model inputs and outputs in the form of topography and bathymetry are provided in this data release. For further information regarding model input generation and visualization of model output topography and bathymetry, refer to Passeri and others, 2018.

Using the numerical model XBeach version 4926 (Roelvink and others, 2009), hurricanes Ivan (2004) and Katrina (2005) were simulated at Dauphin Island, Alabama, under present-day conditions and future sea level rise scenarios as described in Passeri and others, 2018. The XBeach model setup requires the input of a merged topographic and bathymetric digital elevation model (DEM), and inputs of wave spectra (based on significant wave height, peak wave period and wave direction) and water level (tide and surge) time series. Model inputs and outputs in the form of topography and bathymetry are provided in this data release. For further information regarding model input generation and visualization of model output topography and bathymetry, refer to Passeri and others, 2018.

Using the numerical model XBeach version 4926 (Roelvink and others, 2009), various bottom friction scenarios were simulated for hurricanes Ivan (2004) and Katrina (2005) at Dauphin Island, Alabama as described in Passeri and others, 2018. The XBeach model setup requires the input of a merged topographic and bathymetric digital elevation model (DEM), and inputs of wave spectra (based on significant wave height, peak wave period and wave direction) and water level (tide and surge) time series. Model inputs and outputs in the form of topography and bathymetry are provided in this data release. For further information regarding model input generation and visualization of model output topography and bathymetry, refer to Passeri and others, 2018.

Using the numerical model XBeach version 4926 (Roelvink and others, 2009), hurricanes Ivan (2004) and Katrina (2005) were simulated at Dauphin Island, Alabama, under present-day conditions and future sea level rise scenarios as described in Passeri and others, 2018. The XBeach model setup requires the input of a merged topographic and bathymetric digital elevation model (DEM), and inputs of wave spectra (based on significant wave height, peak wave period and wave direction) and water level (tide and surge) time series. Model inputs and outputs in the form of topography and bathymetry are provided in this data release. For further information regarding model input generation and visualization of model output topography and bathymetry, refer to Passeri and others, 2018.

Using the numerical model XBeach version 4926 (Roelvink and others, 2009), hurricanes Ivan (2004) and Katrina (2005) were simulated at Dauphin Island, Alabama, under present-day conditions and future sea level rise scenarios as described in Passeri and others, 2018. The XBeach model setup requires the input of a merged topographic and bathymetric digital elevation model (DEM), and inputs of wave spectra (based on significant wave height, peak wave period and wave direction) and water level (tide and surge) time series. Model inputs and outputs in the form of topography and bathymetry are provided in this data release. For further information regarding model input generation and visualization of model output topography and bathymetry, refer to Passeri and others, 2018.

This dataset defines storm-induced coastal erosion hazards for the Virginia, Maryland, Delaware, New Jersey and New York coastline. The analysis was based on a storm-impact scaling model that used observations of beach morphology combined with sophisticated hydrodynamic models to predict how the coast would respond to the direct impact of the Extratropical Storm in January 2016. Storm-induced water levels, due to both surge and waves, were compared to beach and dune elevations to determine the probabilities of the three types of coastal change: collision (dune erosion), overwash, and inundation. All hydrodynamic and morphologic variables are included in this dataset.

This dataset defines storm-induced coastal erosion hazards for the North Carolina, Virginia, Maryland, Delaware, New Jersey, New York, Rhode Island and Massachusetts coastline. The analysis was based on a storm-impact scaling model that used observations of beach morphology combined with sophisticated hydrodynamic models to predict how the coast would respond to the direct landfall of Hurricane Joaquin in October 2015. Storm-induced water levels, due to both surge and waves, were compared to beach and dune elevations to determine the probabilities of the three types of coastal change: collision (dune erosion), overwash, and inundation. All hydrodynamic and morphologic variables are included in this dataset.

ASCII XYZ point cloud data were produced from remotely sensed, geographically referenced elevation measurements by the U.S. Geological Survey (USGS). Elevation measurements were collected over northwest Florida, post-Hurricane Katrina (August 2005 hurricane), using the National Aeronautics and Space Administration (NASA) Experimental Advanced Airborne Research Lidar (EAARL), a pulsed laser ranging system mounted onboard an aircraft to measure ground elevation, vegetation canopy, and coastal topography. The system uses high-frequency laser beams directed at the Earth's surface through an opening in the bottom of the aircraft's fuselage. The laser system records the time difference between emission of the laser beam and the reception of the reflected laser signal in the aircraft. The plane travels over the target area at approximately 60 meters per second at an elevation of approximately 300 meters, resulting in a laser swath of approximately 240 meters with an average point spacing of 2-3 meters. The EAARL, developed originally by NASA at Wallops Flight Facility in Virginia, measures ground elevation with a vertical resolution of 3 centimeters. A sampling rate of 3 kilohertz or higher results in an extremely dense spatial elevation dataset. Over 100 kilometers of coastline can be surveyed easily within a 3- to 4-hour mission. When resultant elevation maps for an area are analyzed, they provide a useful tool to make management decisions regarding land development.

ASCII XYZ point cloud data were produced from remotely sensed, geographically referenced elevation measurements by the U.S. Geological Survey (USGS). Elevation measurements were collected over northwest Florida, post-Hurricane Katrina (August 2005 hurricane), using the National Aeronautics and Space Administration (NASA) Experimental Advanced Airborne Research Lidar (EAARL), a pulsed laser ranging system mounted onboard an aircraft to measure ground elevation, vegetation canopy, and coastal topography. The system uses high-frequency laser beams directed at the Earth's surface through an opening in the bottom of the aircraft's fuselage. The laser system records the time difference between emission of the laser beam and the reception of the reflected laser signal in the aircraft. The plane travels over the target area at approximately 60 meters per second at an elevation of approximately 300 meters, resulting in a laser swath of approximately 240 meters with an average point spacing of 2-3 meters. The EAARL, developed originally by NASA at Wallops Flight Facility in Virginia, measures ground elevation with a vertical resolution of 3 centimeters. A sampling rate of 3 kilohertz or higher results in an extremely dense spatial elevation dataset. Over 100 kilometers of coastline can be surveyed easily within a 3- to 4-hour mission. When resultant elevation maps for an area are analyzed, they provide a useful tool to make management decisions regarding land development.

Binary point-cloud data were produced for a portion of the upper Florida Keys reef tract, Florida, from remotely sensed, geographically referenced elevation measurements collected by Leading Edge Geomatics (LEG) using a Leica Chiroptera II Bathymetric and Topographic Sensor. Dewberry reports that the nominal pulse spacing for this project was 1 point every 0.7 meters. Dewberry used proprietary procedures to classify the LAS according to project specifications: 0-Never Classified, 1-Unclassified, 2-Ground (includes model key point bit for points identified as Model Key Point), 7-Low Noise, 17-Bridges, 18-High Noise, 40-Bathymetric point or submerged topography (includes model key point bit for points identified as Model Key Point), 41-Water Surface, and 42-Derived water surface.

A digital elevation model (DEM) mosaic was produced for a portion of the upper Florida Keys reef tract, Florida, from remotely sensed, geographically referenced elevation measurements collected by Leading Edge Geomatics (LEG) using a Leica Chiroptera II Bathymetric and Topographic Sensor. Dewberry reports that the nominal pulse spacing for this project was 1 point every 0.7 meters. Dewberry used proprietary procedures to classify the LAS according to project specifications: 0-Never Classified, 1-Unclassified, 2-Ground (includes model key point bit for points identified as Model Key Point), 7-Low Noise, 17-Bridges, 18-High Noise, 40-Bathymetric point or submerged topography (includes model key point bit for points identified as Model Key Point), 41-Water Surface, and 42-Derived water surface.

Using the numerical model XBeach version 4926 (Roelvink and others, 2009), various bottom friction scenarios were simulated for hurricanes Ivan (2004) and Katrina (2005) at Dauphin Island, Alabama as described in Passeri and others, 2018. The XBeach model setup requires the input of a merged topographic and bathymetric digital elevation model (DEM), and inputs of wave spectra (based on significant wave height, peak wave period and wave direction) and water level (tide and surge) time series. Model inputs and outputs in the form of topography and bathymetry are provided in this data release. For further information regarding model input generation and visualization of model output topography and bathymetry, refer to Passeri and others, 2018.

Using the numerical model XBeach version 4926 (Roelvink and others, 2009), various bottom friction scenarios were simulated for hurricanes Ivan (2004) and Katrina (2005) at Dauphin Island, Alabama as described in Passeri and others, 2018. The XBeach model setup requires the input of a merged topographic and bathymetric digital elevation model (DEM), and inputs of wave spectra (based on significant wave height, peak wave period and wave direction) and water level (tide and surge) time series. Model inputs and outputs in the form of topography and bathymetry are provided in this data release. For further information regarding model input generation and visualization of model output topography and bathymetry, refer to Passeri and others, 2018.

Using the numerical model XBeach version 4926 (Roelvink and others, 2009), various bottom friction scenarios were simulated for hurricanes Ivan (2004) and Katrina (2005) at Dauphin Island, Alabama as described in Passeri and others, 2018. The XBeach model setup requires the input of a merged topographic and bathymetric digital elevation model (DEM), and inputs of wave spectra (based on significant wave height, peak wave period and wave direction) and water level (tide and surge) time series. Model inputs and outputs in the form of topography and bathymetry are provided in this data release. For further information regarding model input generation and visualization of model output topography and bathymetry, refer to Passeri and others, 2018.

Using the numerical model XBeach version 4926 (Roelvink and others, 2009), various bottom friction scenarios were simulated for hurricanes Ivan (2004) and Katrina (2005) at Dauphin Island, Alabama as described in Passeri and others, 2018. The XBeach model setup requires the input of a merged topographic and bathymetric digital elevation model (DEM), and inputs of wave spectra (based on significant wave height, peak wave period and wave direction) and water level (tide and surge) time series. Model inputs and outputs in the form of topography and bathymetry are provided in this data release. For further information regarding model input generation and visualization of model output topography and bathymetry, refer to Passeri and others, 2018.

Using the numerical model XBeach version 4926 (Roelvink and others, 2009), hurricanes Ivan (2004) and Katrina (2005) were simulated at Dauphin Island, Alabama, under present-day conditions and future sea level rise scenarios as described in Passeri and others, 2018. The XBeach model setup requires the input of a merged topographic and bathymetric digital elevation model (DEM), and inputs of wave spectra (based on significant wave height, peak wave period and wave direction) and water level (tide and surge) time series. Model inputs and outputs in the form of topography and bathymetry are provided in this data release. For further information regarding model input generation and visualization of model output topography and bathymetry, refer to Passeri and others, 2018.

Using the numerical model XBeach version 4926 (Roelvink and others, 2009), hurricanes Ivan (2004) and Katrina (2005) were simulated at Dauphin Island, Alabama, under present-day conditions and future sea level rise scenarios as described in Passeri and others, 2018. The XBeach model setup requires the input of a merged topographic and bathymetric digital elevation model (DEM), and inputs of wave spectra (based on significant wave height, peak wave period and wave direction) and water level (tide and surge) time series. Model inputs and outputs in the form of topography and bathymetry are provided in this data release. For further information regarding model input generation and visualization of model output topography and bathymetry, refer to Passeri and others, 2018.

Using the numerical model XBeach version 4926 (Roelvink and others, 2009), hurricanes Ivan (2004) and Katrina (2005) were simulated at Dauphin Island, Alabama, under present-day conditions and future sea level rise scenarios as described in Passeri and others, 2018. The XBeach model setup requires the input of a merged topographic and bathymetric digital elevation model (DEM), and inputs of wave spectra (based on significant wave height, peak wave period and wave direction) and water level (tide and surge) time series. Model inputs and outputs in the form of topography and bathymetry are provided in this data release. For further information regarding model input generation and visualization of model output topography and bathymetry, refer to Passeri and others, 2018.

Using the numerical model XBeach version 4926 (Roelvink and others, 2009), various bottom friction scenarios were simulated for hurricanes Ivan (2004) and Katrina (2005) at Dauphin Island, Alabama as described in Passeri and others, 2018. The XBeach model setup requires the input of a merged topographic and bathymetric digital elevation model (DEM), and inputs of wave spectra (based on significant wave height, peak wave period and wave direction) and water level (tide and surge) time series. Model inputs and outputs in the form of topography and bathymetry are provided in this data release. For further information regarding model input generation and visualization of model output topography and bathymetry, refer to Passeri and others, 2018.

Coral reefs serve as natural barriers that protect adjacent shorelines from coastal hazards such as storms, waves and erosion but projections indicate global degradation of coral reefs due to anthropogenic impacts and climate change will cause a transition to net erosion by mid-century. The U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center conducted research to quantify the combined effect of all constructive and destructive processes on modern coral reef ecosystems by measuring regional-scale changes in seafloor elevation. USGS staff assessed five coral reef ecosystems in the Atlantic Ocean (Upper and Lower Florida Keys), Caribbean Sea (U.S. Virgin Islands: St. Thomas and Buck Island, St. Croix), and Pacific Ocean (Maui, Hawaii), including both coral-dominated and adjacent, non-coral dominated habitats. Scientists used historical bathymetric data from the 1930s to 1980s and contemporary light detection and ranging (lidar) digital elevation models (DEMs) from the late 1990s to 2000s to calculate changes in seafloor elevation for each study site over time periods reflecting low to high anthropogenic impacts. LFK_ElevationChange.zip contains the location, elevation, and elevation change data for the Lower Florida Keys. Using these changes in elevation, further analysis was done to calculate corresponding changes in seafloor volume for all study areas and habitat types within each site.

Binary point-cloud data were produced for Long Island, New York, from remotely sensed, geographically referenced elevation measurements collected by Woolpert, Inc. using an Leica ALS50-II lidar sensor flown on a Cessna 404 aircraft. These data were collected post-Hurricane Irene on August 30, 2011.

A digital elevation model (DEM) mosaic was produced for Long Island, New York, from remotely sensed, geographically referenced elevation measurements collected by Woolpert, Inc. using an Leica ALS50-II lidar sensor flown on a Cessna 404 aircraft. These data were collected post-Hurricane Irene on August 30, 2011.

The U.S. Geological Survey (USGS) Coastal and Marine Geology Program has actively collected geophysical and sedimentological data in the northern Gulf of Mexico for several decades, including shallow subsurface data in the form of high-resolution seismic reflection profiles (HRSP). Prior to the mid-1990s most HRSP data were collected in analog format as paper rolls of continuous profiles up to 25 meters long. As part of the National Geological and Geophysical Data Preservation Program (https://datapreservation.usgs.gov/), and in collaboration with the Bureau of Ocean Energy Management, Marine Minerals Program, scientists at the USGS St. Petersburg Coastal and Marine Science Center are converting the analog paper records to digital format using a large-format continuous scanner. The image files created by scanning were further processed to fix distortions and crop out blank spaces to create industry standard Society of Exploration Geophysicists date exchange (SEG-Y) format. This data release serves as an archive of HRSP profiles annotated with header information, converted Society of Exploration Geophysicists SEG-Y files, navigation data, and cruise trackline shapefiles. The HRSP data were collected using a Huntec boomer seismic system onboard the Research Vessels (R/V) R.J. Russell and Carancahua. While on the R/V R.J. Russel, geophysical surveys were conducted at various times between December 1982 and July 1984 with the data being acquired from waterbodies surrounding Isles Dernieres (Lacoss 82-2, 83-3 and 83-4), within Terrebonne and Caillou Bay (82-2), and offshore of Port Fourchon, Louisiana (84-5). While on the R/V Carancahua, geophysical surveys were collected between August and September 1982 off the coast of Holly Beach, Louisiana (82-3). Data collection and processing methods are described in USGS Data Series 1047.

The National Assessment of Coastal Change Hazards project exists to understand and predict storm impacts to our nation's coastlines. This geospatial dataset defines the alongshore extent of overwash sediments deposited along the Louisiana coast and attributed to coastal processes during [Atlantic Basin] Hurricane Laura, which made landfall in the U.S. on August 27, 2020.

This shapefile consists of Dauphin Island, AL shorelines extracted from lidar data collected from November 1998 to January 2014. This dataset contains 14 Mean High Water (MHW) shorelines separated into 37 shoreline segments alongshore Dauphin Island, AL. The individual sections are divided according to location along the island and shoreline type: open ocean, back-barrier, marsh shoreline. Raw lidar point data was converted to a gridded surface, from which a contour of the operational MHW shoreline (0.24 m North American Vertical Datum of 1988 [NAVD 88]; Weber and others, 2005) was identified and extracted. This produced a continuous MHW shoreline for each of the lidar datasets from 1998 – 2014. Shorelines for all 14 dates were compiled into a database for use with the Digital Shoreline Analysis System (DSAS; Thieler and others, 2009) to quantify rates of shoreline change over the 1998-2014 time period. The migration of shorelines through time is presented as the linear regression rate (LRR) in the associated transect files (https://coastal.er.usgs.gov/data-release/provisional/ip086178/).

The U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center conducted research to quantify bathymetric changes at Looe Key near Big Pine Key, Florida (FL), within a 16.4 square-kilometer area between 2004 and 2016. USGS staff used light detection and ranging (lidar)-derived data acquired by the U.S. Army Corps of Engineers (USACE) Joint Airborne Lidar Bathymetry Technical Center of eXpertise (JALBTCX) between December 1 and 31, 2004 (USACE-JALBTCX) and the National Oceanic and Atmospheric Administration (NOAA) between July 21 and November 21, 2016 (NOAA, 2017) to assess changes in seafloor elevation and structure that occurred during this time. An elevation change analysis between the 2004 USACE and 2016 NOAA lidar data was performed to quantify and map impacts to seafloor elevation and determine elevation and volume change statistics for ten habitat types found at Looe Key. Data were collected under Florida Keys National Marine Sanctuary permit FKNMS-2016-068.

The U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center conducted research to quantify bathymetric changes at Looe Key near Big Pine Key, Florida (FL), within a 19.7 square-kilometer area following Hurricane Irma's landfall in September 2017. USGS staff used light detection and ranging (lidar)-derived data acquired by the National Oceanic and Atmospheric Administration (NOAA) between July 21 and November 21, 2016 and USGS multibeam data collected December 12-17, 2017 (Fredericks and others, 2019) to assess changes in seafloor elevation and structure that occurred after the passage of Hurricane Irma. An elevation change analysis between the 2016 NOAA lidar data and the 2017 USGS multibeam data was performed to quantify and map impacts to seafloor elevation and determine elevation and volume change statistics for ten habitat types found at Looe Key. Data were collected under Florida Keys National Marine Sanctuary permit FKNMS-2016-068.

An Ellipsoidally Referenced Survey (ERS) using two Teledyne Reson SeaBat T50-P multibeam echosounders, in dual-head configuration, was conducted by the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center (SPCMSC) at Looe Key, the Florida Keys, during three separate survey legs: December 14-16, 2017, February 2-9, 2018 and March 9-11, 2018. This dataset, Looe_Key_2017_2018_MBB_xyz.zip, includes the processed elevation point data (x,y,z), as derived from a 1-meter (m) bathymetric grid.

The National Assessment of Coastal Change Hazards project derives beach morphology features from lidar elevation data for the purpose of understanding and predicting storm impacts to our nation's coastlines. This dataset defines mean beach slopes for Massachusetts for data collected at various times between 2000 and 2013.

The National Assessment of Coastal Change Hazards project derives beach morphology features from lidar elevation data for the purpose of understanding and predicting storm impacts to our nation's coastlines. This dataset defines beach slopes along the United States Northeast Atlantic Ocean for Massachusetts for data collected at various times between 2000 and 2013

To characterize coastal change, historical maps and complementary records were compiled including: topographic sheets (T-sheets), hydrographic sheets (H-sheets, smooth sheets), shorelines, and bathymetric soundings surrounding the Mississippi (MS) barrier islands over several time periods (1916-1920, 2008-2009 and 2016). One goal of this work was to create a time-series of bathymetric change maps around the islands. This USGS data release includes three digital elevation models (DEMs) for 1916 to 1920, 2008 to 2009, and 2016; however, this metadata file pertains to the snap raster (MS2016_snapraster_50m.tif) used to define pixel locations during raster DEM creation. This work was completed in cooperation with the United States Army Corps of Engineers (Mobile, Alabama) and the National Park Service as part of the Mississippi Coastal Improvements Program (MsCIP).

Microfossil (benthic foraminifera) and coordinate/elevation data were obtained from sediments collected in the coastal zones of Mississippi and Alabama, including marsh and estuarine environments of eastern Mississippi Sound and Mobile Bay, in order to develop a census for coastal environments and to aid in paleoenvironmental reconstruction. These data provide a baseline dataset for use in future wetland and estuarine change studies and assessments, both descriptive and predictive types. The data presented here were collected as part of the U.S. Geological Survey’s Sea-level and Storm Impacts on Estuarine Environments and Shorelines (SSIEES) project (https://coastal.er.usgs.gov/ssiees), Barrier Island Evolution Research (BIER) project (https://coastal.er.usgs.gov/bier), and National Fish and Wildlife Foundation-funded Alabama Barrier Island Restoration Feasibility Study (a collaborative study between the U.S. Army Corp of Engineers, Mobile District; the State of Alabama; and the USGS [https://www.usgs.gov/centers/spcmsc/science/alabama-barrier-island-restoration-study]). These projects aim to assess ecological and societal vulnerability that results from long- and short-term physical changes to barrier islands and coastal wetlands. Four sampling surveys were conducted between 2013 and 2016: 13BIM01 (14–18 April 2013; no FA numbering), 14CCT01 (15–19 September 2014; 2014-323-FA), 15BIM09 (18–20 August 2015; 2015-322-FA), and 16CCT03 (16–17 May 2016; 2016-331-FA). During the four trips, 168 replicate sedimentary samples were collected from 86 marsh and estuarine locations. The sediment samples were collected from various coastal environments, stained in the field with rose Bengal (rB) to indicate life, processed in the laboratory to four size fractions (63–125 μm, 125–250 μm, 250–850 μm, and >850 μm), of which the 125–250 μm and 250–850 μm fractions were picked at equal proportions of total sample and reported combined (125–850 μm). Foraminifera were identified to species level under a binocular microscope and counted to establish a census. For further information regarding foraminiferal collection and/or processing methods, refer to Ellis and others (2017). For related datasets from the Mississippi Sound area, please refer to Ellis and others (2017) and DeWitt and others (2017).

Microfossil (benthic foraminifera) data from coastal areas were collected from state and federally managed lands within the Grand Bay National Estuarine Research Reserve and Grand Bay National Wildlife Refuge, Grand Bay, Mississippi/Alabama; federally managed lands of Bon Secour National Wildlife Refuge on Cedar Island and Little Dauphin Island, Alabama; and municipally managed land around Dauphin Island, Alabama. Samples were analyzed and quantified for foraminiferal census in order to document changes to the coastal wetlands, estuarine environments, and to aid in paleoenvironmental reconstruction. These data provide a baseline dataset for use in future wetland change descriptive and predictive studies and assessments. The data presented here were collected as part of the U.S. Geological Survey’s Sea-level and Storm Impacts on Estuarine Environments and Shorelines (SSIEES) project (https://coastal.er.usgs.gov/ssiees), Barrier Island Evolution Research (BIER) project (https://coastal.er.usgs.gov/bier), and National Fish and Wildlife Foundation-funded Alabama Barrier Island Restoration Feasibility Study (a collaborative study between the U.S. Army Corp of Engineers, Mobile District; the State of Alabama; and the USGS [https://www.usgs.gov/centers/spcmsc/science/alabama-barrier-island-restoration-study]). These projects aim to assess ecological and societal vulnerability that results from long- and short-term physical changes to barrier islands and coastal wetlands. Two sampling surveys were conducted between 2014 and 2015: 14CCT01 (15–19 September 2014; 2014-323-FA), and 15BIM09 (18–20 August 2015; 2015-322-FA). During those two trips, seven Russian peat auger cores were taken from marsh locations. Three cores from Dauphin Island were subsampled and stained with rose Bengal (rB) in the field to indicate life. Four further cores from Dauphin Island and Grand Bay were not stained. At the St. Petersburg Coastal and Marine Science Center all cores were subsampled resulting in a total of 74 subsamples. Samples were processed in the laboratory to four size fractions (63–125 μm, 125–250 μm, 250–850 μm, and >850 μm), of which the 125–250 μm and 250–850 μm fractions were picked at equal proportions of total sample and reported combined (125–850 μm). For additional information regarding foraminiferal collection and/or processing methods, refer to Ellis and others (2017). Further data collected on and surrounding Dauphin Island is presented in Ellis and others (2017) and Ellis and others (2018).

Microfossil (benthic foraminifera) data from coastal areas were collected from state and federally managed lands within the Grand Bay National Estuarine Research Reserve and Grand Bay National Wildlife Refuge, Grand Bay, Mississippi/Alabama; federally managed lands of Bon Secour National Wildlife Refuge on Cedar Island and Little Dauphin Island, Alabama; and municipally managed land around Dauphin Island, Alabama. Samples were analyzed and quantified for foraminiferal census in order to document changes to the coastal wetlands, estuarine environments, and to aid in paleoenvironmental reconstruction. These data provide a baseline dataset for use in future wetland change descriptive and predictive studies and assessments. The data presented here were collected as part of the U.S. Geological Survey’s Sea-level and Storm Impacts on Estuarine Environments and Shorelines (SSIEES) project (https://coastal.er.usgs.gov/ssiees), Barrier Island Evolution Research (BIER) project (https://coastal.er.usgs.gov/bier), and National Fish and Wildlife Foundation-funded Alabama Barrier Island Restoration Feasibility Study (a collaborative study between the U.S. Army Corp of Engineers, Mobile District; the State of Alabama; and the USGS [https://www.usgs.gov/centers/spcmsc/science/alabama-barrier-island-restoration-study]). These projects aim to assess ecological and societal vulnerability that results from long- and short-term physical changes to barrier islands and coastal wetlands. Two sampling surveys were conducted between 2014 and 2015: 14CCT01 (15–19 September 2014; 2014-323-FA), and 15BIM09 (18–20 August 2015; 2015-322-FA). During those two trips, seven Russian peat auger cores were taken from marsh locations. Three cores from Dauphin Island were subsampled and stained with rose Bengal (rB) in the field to indicate life. Four further cores from Dauphin Island and Grand Bay were not stained. At the St. Petersburg Coastal and Marine Science Center all cores were subsampled resulting in a total of 74 subsamples. Samples were processed in the laboratory to four size fractions (63–125 μm, 125–250 μm, 250–850 μm, and >850 μm), of which the 125–250 μm and 250–850 μm fractions were picked at equal proportions of total sample and reported combined (125–850 μm). For additional information regarding foraminiferal collection and/or processing methods, refer to Ellis and others (2017). Further data collected on and surrounding Dauphin Island is presented in Ellis and others (2017) and Ellis and others (2018).

Microfossil (benthic foraminifera) and coordinate/elevation data were obtained from sediments collected in the coastal zones of Mississippi and Alabama, including marsh and estuarine environments of eastern Mississippi Sound and Mobile Bay, in order to develop a census for coastal environments and to aid in paleoenvironmental reconstruction. These data provide a baseline dataset for use in future wetland and estuarine change studies and assessments, both descriptive and predictive types. The data presented here were collected as part of the U.S. Geological Survey’s Sea-level and Storm Impacts on Estuarine Environments and Shorelines (SSIEES) project (https://coastal.er.usgs.gov/ssiees), Barrier Island Evolution Research (BIER) project (https://coastal.er.usgs.gov/bier), and National Fish and Wildlife Foundation-funded Alabama Barrier Island Restoration Feasibility Study (a collaborative study between the U.S. Army Corp of Engineers, Mobile District; the State of Alabama; and the USGS [https://www.usgs.gov/centers/spcmsc/science/alabama-barrier-island-restoration-study]). These projects aim to assess ecological and societal vulnerability that results from long- and short-term physical changes to barrier islands and coastal wetlands. Four sampling surveys were conducted between 2013 and 2016: 13BIM01 (14–18 April 2013; no FA numbering), 14CCT01 (15–19 September 2014; 2014-323-FA), 15BIM09 (18–20 August 2015; 2015-322-FA), and 16CCT03 (16–17 May 2016; 2016-331-FA). During the four trips, 168 replicate sedimentary samples were collected from 86 marsh and estuarine locations. The sediment samples were collected from various coastal environments, stained in the field with rose Bengal (rB) to indicate life, processed in the laboratory to four size fractions (63–125 μm, 125–250 μm, 250–850 μm, and >850 μm), of which the 125–250 μm and 250–850 μm fractions were picked at equal proportions of total sample and reported combined (125–850 μm). Foraminifera were identified to species level under a binocular microscope and counted to establish a census. For further information regarding foraminiferal collection and/or processing methods, refer to Ellis and others (2017). For related datasets from the Mississippi Sound area, please refer to Ellis and others (2017) and DeWitt and others (2017).

Aerial imagery acquired with a small unmanned aircraft system (sUAS), in conjunction with surveyed ground control points (GCP) visible in the imagery, can be processed with structure-from-motion (SfM) photogrammetry techniques to produce high-resolution orthomosaics, three-dimensional (3D) point clouds and digital elevation models (DEMs). This dataset, prepared by the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center (SPCMSC), provides UAS survey data products consisting of DEMs collected at Madeira Beach, Florida, monthly from July 2017 to June 2018 in order to observe seasonal and storm-induced changes in beach topography. Photogrammetry software was used to perform SfM processing on low-altitude digital aerial imagery acquired with a 3DR Solo UAS quadcopter equipped with a Ricoh GR II digital camera, using surveyed permanent features (for example, parking lot stripes, concrete groin blocks) and temporary targets (black and white, 4-square checked pattern) distributed uniformly throughout the UAS flight operations area as GCPs. The following SfM products are produced for each UAS survey over the approximately 700-meter-long and 100-meter-wide stretch of coastline: * georeferenced orthomosaic image with 5-centimeter (cm) resolution * DEM with 5-cm horizontal resolution * 3D RGB-colored point cloud All horizontal data are provided in Universal Transverse Mercator (UTM) projected coordinate system, Zone 17 North (17N), referenced to the North American Datum of 1983 (NAD83 (2011)), and elevation is referenced to the North American Vertical Datum of 1988 (NAVD88), GEOID 12B.

Aerial imagery acquired with a small unmanned aircraft system (sUAS), in conjunction with surveyed ground control points (GCP) visible in the imagery, can be processed with structure-from-motion (SfM) photogrammetry techniques to produce high-resolution orthomosaics, three-dimensional (3D) point clouds and digital elevation models (DEMs). This dataset, prepared by the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center (SPCMSC), provides UAS survey data products consisting of point clouds collected at Madeira Beach, Florida, monthly from July 2017 to June 2018 in order to observe seasonal and storm-induced changes in beach topography. Photogrammetry software was used to perform SfM processing on low-altitude digital aerial imagery acquired with a 3DR Solo UAS quadcopter equipped with a Ricoh GR II digital camera, using surveyed permanent features (for example, parking lot stripes, concrete groin blocks) and temporary targets (black and white, 4-square checked pattern) distributed uniformly throughout the UAS flight operations area as GCPs. The following SfM products are produced for each UAS survey over the approximately 700-meter-long and 100-meter-wide stretch of coastline: * georeferenced orthomosaic image with 5-centimeter (cm) resolution * DEM with 5-cm horizontal resolution * 3D RGB-colored point cloud All horizontal data are provided in Universal Transverse Mercator (UTM) projected coordinate system, Zone 17 North (17N), referenced to the North American Datum of 1983 (NAD83 (2011)), and elevation is referenced to the North American Vertical Datum of 1988 (NAVD88), GEOID12B.

Aerial imagery acquired with a small unmanned aircraft system (sUAS), in conjunction with surveyed ground control points (GCPs) visible in the imagery, can be processed with structure-from-motion (SfM) photogrammetry techniques to produce high-resolution orthomosaics, three-dimensional (3D) point clouds and digital elevation models (DEMs). This dataset, prepared by the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center (SPCMSC), provides UAS survey data consisting of aerial imagery and GCP positions and elevations collected at Madeira Beach, Florida, monthly from July 2017 to June 2018 in order to observe seasonal and storm-induced changes in beach topography.

This dataset defines storm-induced coastal erosion hazards for the North Carolina, Virginia, Maryland, Delaware, New Jersey, New York, Rhode Island, Massachusetts, New Hampshire and Maine coastline. The analysis was based on a storm-impact scaling model that used observations of beach morphology combined with sophisticated hydrodynamic models to predict how the coast would respond to the direct landfall of an Extratropical Storm in March 2018. Storm-induced water levels, due to both surge and waves, were compared to beach and dune elevations to determine the probabilities of the three types of coastal change: collision (dune erosion), overwash, and inundation. All hydrodynamic and morphologic variables are included in this dataset.

The National Assessment of Coastal Change Hazards project exists to understand and predict storm impacts to our nation's coastlines. This geospatial dataset defines the alongshore extent of overwash sediments deposited along the Florida, Georgia, North Carolina,and South Carolina coasts and attributed to coastal processes during [Atlantic Basin] Hurricane Matthew, which made landfall in the U.S. on October 8, 2018.

This dataset defines storm-induced coastal erosion hazards for the Florida, Georgia, South Carolina and North Carolina coastline. The analysis was based on a storm-impact scaling model that used observations of beach morphology combined with sophisticated hydrodynamic models to predict how the coast would respond to the direct landfall of Hurricane Matthew in October 2016. Storm-induced water levels, due to both surge and waves, were compared to beach and dune elevations to determine the probabilities of the three types of coastal change: collision (dune erosion), overwash, and inundation. All hydrodynamic and morphologic variables are included in this dataset.

Coral reefs serve as natural barriers that protect adjacent shorelines from coastal hazards such as storms, waves and erosion but projections indicate global degradation of coral reefs due to anthropogenic impacts and climate change will cause a transition to net erosion by mid-century. The U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center conducted research to quantify the combined effect of all constructive and destructive processes on modern coral reef ecosystems by measuring regional-scale changes in seafloor elevation. USGS staff assessed five coral reef ecosystems in the Atlantic Ocean (Upper and Lower Florida Keys), Caribbean Sea (U.S. Virgin Islands: St. Thomas and Buck Island, St. Croix), and Pacific Ocean (Maui, Hawaii), including both coral-dominated and adjacent, non-coral dominated habitats. Scientists used historical bathymetric data from the 1930s to 1980s and contemporary light detection and ranging (lidar) digital elevation models (DEMs) from the late 1990s to 2000s to calculate changes in seafloor elevation for each study site over time periods reflecting low to high anthropogenic impacts. Maui_ElevationChange.zip contains the location, elevation, and elevation change data for Maui, Hawaii. Using these changes in elevation, further analysis was done to calculate corresponding changes in seafloor volume for all study areas and habitat types within each site

Coastal marshes are highly dynamic and ecologically important ecosystems that are subject to pervasive and often harmful disturbances, including shoreline erosion. Shoreline erosion can result in an overall loss of coastal marsh, particularly in estuaries with moderate- or high-wave energy. Not only can waves be important physical drivers of shoreline change, they can also influence shore-proximal vertical accretion through sediment delivery. For these reason, estimates of wave energy can provide a quantitative measure of wave effects on marsh shorelines. Since wave energy is difficult to measure at all locations, scientists and managers often rely on hydrodynamic models to estimate wave properties at different locations. The Wave Exposure Model (WEMo) is a simple tool that uses linear wave theory to estimate wave energy characteristics for enclosed and semi-enclosed estuaries(Malhotra and Fonseca, 2007). The interpretation of hydrodynamic models is improved if model results can be validated against measured data. The data presented in this publication are input and validation data for modeled and observed mean wave height for two temporary oceanographic stations established by the U.S. Geological Survey (USGS) in the Grand Bay National Estuarine Research Reserve, Mississippi.

Stretching along approximately 200 kilometers (km) of the Atlantic Coast of central Florida, Indian River Lagoon is one of the most biologically diverse estuarine systems in the continental United States. This shallow, brackish lagoon varies in width from about 0.5–9.0 km, with substantial human infrastructure lining both shores. Scientists from the U. S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center used continuous resistivity profiling (CRP), a towed electronic array, to investigate submarine groundwater discharge at Eau Gallie North, a study site located along the western shore of the central section of Indian River Lagoon. The CRP array was towed behind a boat along five shore-parallel transects located between 125-750 meters (m) offshore and traversing ~ 1.5 km along north-south transects. Additional, subsequent resistivity surveys will be conducted along these same tracklines, at various times to determine temporal variability. Since resistivity is a function of both geology and salinity, it is assumed that temporal shifts will reflect salinity changes, as the underlying geology will be presumed to remain constant.

The U.S. Geological Survey (USGS) Coastal and Marine Geology Program has actively collected geophysical and sedimentological data in the northern Gulf of Mexico for several decades, including shallow subsurface data in the form of high-resolution seismic-reflection profiles (HRSP). Prior to the mid-1990s most HRSP data were collected in analog format as paper rolls of continuous profiles up to 25 meters long. A large portion of this data resides in a single repository with minimal metadata. As part of the National Geological and Geophysical Data Preservation Program, scientists at the USGS St. Petersburg Coastal and Marine Science Center are converting the analog paper records to digital format using a large-format continuous scanner. This publication serves as an archive of seismic profiles with headers, converted Society of Exploration Geophysicists Y format (SEG-Y) files, navigation data, and geographic information system (GIS) data files for digitized boomer seismic-reflection data collected from the Research Vessel (R/V) Erda during two cruises in 1990 and 1991. The Erda 90-1 geophysical cruise was conducted in two legs. The first leg included seismic data collected from the Hancock County region of the Mississippi Sound (Erda 90-1_HC) from June 4 to June 6, 1990. The second leg included seismic data collected from the Petit Bois Pass area of Mississippi Sound (Erda 90-1_PBP) from June 8 to June 9, 1990. The Erda 91-3 cruise occurred between September 12 and September 23, 1991 and surveyed the Mississippi Sound region just west of Horn Island, Mississippi.

The U.S. Geological Survey (USGS) Coastal and Marine Geology Program has actively collected geophysical and sedimentological data in the northern Gulf of Mexico for several decades, including shallow subsurface data in the form of high-resolution seismic reflection profiles (HRSP). Prior to the mid-1990s most HRSP data were collected in analog format as paper rolls of continuous profiles up to 25 meters long. As part of the National Geological and Geophysical Data Preservation Program (NGGDPP; https://datapreservation.usgs.gov/), and in collaboration with the Bureau of Ocean Energy Management, Marine Minerals Program, scientists at the USGS St. Petersburg Coastal and Marine Science Center converted analog paper records to digital format using a large-format continuous scanner. The scanned image files were subsequently processed to fix distortions and crop out blank spaces prior to exporting as industry standard Society of Exploration Geophysicists date exchange (SEG-Y) formatted files. This data release serves as an archive of HRSP profiles annotated with header information, converted SEG-Y files, navigation data, and cruise trackline shapefiles. The HRSP data were collected using a Huntec boomer seismic system onboard research vessel (R/V) Kit Jones. The geophysical surveys were conducted from July 7-19, 1992 with data being acquired along the Florida shelf near Pensacola Beach, St. Joseph Bay aquatic Preserve, and St. George Island. Data collection and processing methods are described in USGS Data Series 1047.

Scientists from the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center extracted sediment and surface samples along transects at three saltmarsh sites situated on the lower end of the Waccasassa River in north-west Florida in order to increase understanding of the region’s environmental history and the ongoing soil chemical processes. To this end, they obtained 17 (ten long and seven short) sediment cores and seven surface samples from saltmarshes along the margins of the river, during field trips in November 2014 and February 2015. Site names are WC01, WC04, WC05, WC10, WC11, WC12, WC20, WC21, WC22, and WC23. Long cores from each location are given the location name and the suffix “R”, surface samples are noted with a “S”, and short cores are given the location name and the suffix “D”, except for the cores taken at sites WC22 and WC23. At those two sites, the short cores were obtained by extracting two parallel peat auger samples to a depth of 50 cm, named WC22Ra-b, and WC23Ra-b, respectively.

The National Assessment of Coastal Change Hazards project exists to understand and predict storm impacts to our nation's coastlines. This geospatial dataset defines the Florida coast and attributed to coastal processes during [Atlantic Basin] Hurricane Michael, which made landfall in the U.S. on October 10, 2018.

This dataset defines storm-induced coastal erosion hazards for the Alabama and Florida coastline. The analysis was based on a storm-impact scaling model that used observations of beach morphology combined with sophisticated hydrodynamic models to predict how the coast would respond to the direct landfall of Hurricane Michael in October 2018. Storm-induced water levels, due to both surge and waves, were compared to beach and dune elevations to determine the probabilities of the three types of coastal change: collision (dune erosion), overwash, and inundation. All hydrodynamic and morphologic variables are included in this dataset.

Crocker Reef was the site of an integrated reefscape characterization effort focused on calcification and related biogeochemical processes as part of the USGS Coral Reef Ecosystem Study (CREST) project. This effort included two intensive seasonal sampling trips to capture summer (July 8 to 17, 2014) and winter (January 29 to February 5, 2015) conditions. This data release represents water column microbial and environmental data collected for use as metadata in future publications examining reef metabolic processes via metagenomes derived from water samples and fine-scale temporal and spatial carbonate chemistry measurements. Microbial data are total bacterial counts per milliliter seawater, total viral counts per milliliter of seawater, and plate counts using Thiosulfate Citrate Bile Salts Sucrose (TCBS) agar of Vibrio (bacteria) per milliliter of seawater. Environmental data are water temperature, salinity, dissolved oxygen, and pH.

This dataset contains carbonate system data collected by scientists from the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center to investigate the effects of carbon cycling, coastal and ocean acidification on the Tampa Bay estuary located in west central Florida and eastern Gulf of Mexico. Discrete seawater samples were collected periodically (every few weeks to months) at repeat monitoring locations. Water samples were analyzed by the USGS Carbon Analytical Laboratory in St. Petersburg Florida. This dataset contains time series measurements of carbonate system parameters including: water temperature (Celsius, C), salinity, dissolved oxygen (milligrams/L), total alkalinity (TA, micromoles/kg), dissolved inorganic carbon (DIC, micromoles/kg), pHT (pH on the total scale), nitrate + nitrite (NO3+NO2, micromoles/L), nitrite (NO2, micromoles/L), silicate (SIL, micromoles/L), ammonium (NH4, micromoles/L) and phosphate (PO4, micromoles/L).

This dataset contains carbonate system data collected by scientists from the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center to investigate the effects of carbon cycling, coastal and ocean acidification on the Tampa Bay estuary located in west central Florida, USA. These data were collected using an autonomous instrument called the Ocean Carbon System version 2 (OCSv2) deployed on the seafloor in Tampa Bay. The OCSv2 consists of four sensors integrated into a Sea-Bird Scientific (Satlantic) STOR-X submersible data logger including a Satlantic SeapHOx sensor for measurement of pH that incorporates a Sea-Bird SBE 37-SMP-ODO MicroCAT C-T-ODO (P) Recorder for measurement of conductivity (for calculation of salinity), temperature, depth, and dissolved oxygen; a Pro-Oceanus CO2-Pro CV CO2 sensor; and a Wetlabs Eco-PAR sensor for measurement of photosynthetically active radiation. The dataset is a time series of carbonate system parameters including: water temperature (Celsius, °C), pressure (decibar, dbar), salinity, pHT (pH on the total scale), carbon dioxide (parts per million, ppm), pressure from the CO2-Pro Infrared Gas Analyzer (IRGA) (millibars, mbar), dissolved oxygen (milligrams per liter, mg/L) and photosynthetically active radiation (microEinsteins). Each parameter was measured every hour for 24-hour time periods throughout the duration of deployment.

This dataset contains carbonate system data collected by scientists from the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center to investigate the effects of carbon cycling, coastal and ocean acidification on the Tampa Bay estuary located in west central Florida, USA. These data were collected using an autonomous instrument called the Ocean Carbon System version 2 (OCSv2) deployed on the seafloor in Tampa Bay. The OCSv2 consists of four sensors integrated into a Sea-Bird Scientific (Satlantic) STOR-X submersible data logger including a Satlantic SeapHOx sensor for measurement of pH that incorporates a Sea-Bird SBE 37-SMP-ODO MicroCAT C-T-ODO (P) Recorder for measurement of conductivity (for calculation of salinity), temperature, depth, and dissolved oxygen; a Pro-Oceanus CO2-Pro CV CO2 sensor; and a Wetlabs Eco-PAR sensor for measurement of photosynthetically active radiation. The dataset is a time series of carbonate system parameters including: water temperature (Celsius, °C), pressure (decibar, dbar), salinity, pHT (pH on the total scale), carbon dioxide (parts per million, ppm), pressure from the CO2-Pro Infrared Gas Analyzer (IRGA) (millibars, mbar), dissolved oxygen (milligrams per liter, mg/L) and photosynthetically active radiation (microEinsteins). Each parameter was measured every hour for 24-hour time periods throughout the duration of deployment.

This dataset contains carbonate system data collected by scientists from the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center to investigate the effects of carbon cycling, coastal and ocean acidification on the Tampa Bay estuary located in west central Florida, USA. These data were collected using an autonomous instrument called the Ocean Carbon System version 2 (OCSv2) deployed on the seafloor in Tampa Bay. The OCSv2 consists of four sensors integrated into a Sea-Bird Scientific (Satlantic) STOR-X submersible data logger including a Satlantic SeapHOx sensor for measurement of pH that incorporates a Sea-Bird SBE 37-SMP-ODO MicroCAT C-T-ODO (P) Recorder for measurement of conductivity (for calculation of salinity), temperature, depth, and dissolved oxygen; a Pro-Oceanus CO2-Pro CV CO2 sensor; and a Wetlabs Eco-PAR sensor for measurement of photosynthetically active radiation. The dataset is a time series of carbonate system parameters including: water temperature (Celsius, °C), pressure (decibar, dbar), salinity, pHT (pH on the total scale), carbon dioxide (parts per million, ppm), pressure from the CO2-Pro Infrared Gas Analyzer (IRGA) (millibars, mbar), dissolved oxygen (milligrams per liter, mg/L) and photosynthetically active radiation (microEinsteins). Each parameter was measured every hour for 24-hour time periods throughout the duration of deployment.

In 2010, a video camera was installed near the northern boundary of the National Aeronautics and Space Administration-Kennedy Space Center (NASA-KSC) property along the Atlantic coast of Florida. A region extending 1 kilometer (km) to the south of the camera was established as the region of interest for the video image observations. During every daylight hour of camera operation from August 8, 2011 to July 24, 2012, a time exposure (timex) image product was created by averaging pixel color intensity for all frames collected during a 10-minute video at 2 frames per second (hertz, Hz). One timex image per day was used for analysis. The timex selected for each day was the product that was created when the tide level was closest to the Mean High Water (MHW) at the study site. Based on observed water levels from a nearby National Oceanic and Atmospheric Administration (NOAA) station, the MHW was determined to be 0.23 meters (m) above the North American Vertical Datum of 1988 (NAVD88) (NOAA, 2018). The shoreline was manually identified as the wet-dry line within the region of interest of each available timex product. Each day’s MHW timex product was rectified to a horizontal map and converted to local and world coordinate systems, with the camera centered at the origin, using the established photogrammetric techniques outlined in Holland and others (1997). However, timex products were not available for about half of this timeframe due to camera malfunctions, adverse weather conditions (for example, fog), and/or a lack of daylight during the timing of MHW. The average gap between observations is 2 days, with the largest gap being 12 days. Please carefully review the metadata for more information.

The National Assessment of Coastal Change Hazards project derives beach morphology features from lidar elevation data for the purpose of understanding and predicting storm impacts to our nation's coastlines. This dataset defines mean beach slopes for New Jersey for data collected at various times between 2007 and 2014.

The National Assessment of Coastal Change Hazards project derives beach morphology features from lidar elevation data for the purpose of understanding and predicting storm impacts to our nation's coastlines. This dataset defines beach slopes along the United States Northeast Atlantic Ocean for New Jersey for data collected at various times between 2007 and 2014

This data set represents vector shorelines for the New Jersey coastline (Point Pleasant, NJ to Longport, NJ) from 1839 to 2012. Data were obtained from multiple data sources, including the U.S. Geological Survey (USGS), National Oceanic and Atmospheric Administration (NOAA), and New Jersey Department of Environmental Protection (NJDEP). Shorelines were obtained from the original provider and merged into a single file in order to conduct shoreline change analysis for the open-ocean and estuarine shorelines of Barnegat and Great Bay, New Jersey.

This dataset represents shoreline change rates for the New Jersey coastline (Point Pleasant, NJ to Longport, NJ) from 1839 to 2012. Shoreline data were obtained from multiple data sources, including the U.S. Geological Survey (USGS), National Oceanic and Atmospheric Administration (NOAA), and New Jersey Department of Environmental Protection (NJDEP). Datasets were compiled and analyzed using the R package Analyzing Moving Boundaries Using R (AMBUR) program. Rates of shoreline change can be used for evaluating living shoreline resources, decision-making for future resource and urban planning, and restoration of both protected and open-ocean shorelines.

This dataset defines storm-induced coastal erosion hazards for the Louisiana, Mississippi, Alabama and Florida coastline. The analysis was based on a storm-impact scaling model that used observations of beach morphology combined with sophisticated hydrodynamic models to predict how the coast would respond to the direct landfall of Hurricane Nate in October 2017. Storm-induced water levels, due to both surge and waves, were compared to beach and dune elevations to determine the probabilities of the three types of coastal change: collision (dune erosion), overwash, and inundation. All hydrodynamic and morphologic variables are included in this dataset.

Historical shoreline surveys were conducted by the National Ocean Service (NOS), dating back to the early 1800s. The maps resulting from these surveys, often called t-sheets, provide a reference of historical shoreline position that can be compared to modern data to identify shoreline change. The t-sheets are stored at the National Archives and many have been scanned by the National Oceanic and Atmospheric Administration (NOAA) and are available on the NOAA Shoreline Web site (http://www.shoreline.noaa.gov/data/datasheets/t-sheets.html). While some scanned t-sheets were georeferenced and digitized by NOAA, still others remain as non-georeferenced raster files (http://nosimagery.noaa.gov/images/shoreline_surveys/survey_scans/NOAA_Shoreline_Survey_Scans.html). New_Jersey_1839_75_Digitized_Shoreline.zip features a digitized historic shoreline for the New Jersey coastline from 1839 to 1875. The data were scanned by NOAA, but were not georeferenced. The t-sheets included in this data release are: T-121 (1839), T-119 Part 1 (1841), T-1084 (1868), T-1166 (1870), T-1333 (1871), T-1315a (1872), T-1371 (1874), T-1407 (1875). Digital files were georeferenced, corrected to a modern datum, and shorelines digitized to provide a vector polyline depicting the historical shoreline position. All shorelines, including the foreshore, backshore, mainland and island shorelines were delineated and digitized for each survey using ArcMap 10.3.1. These shorelines were digitized for use in long-term shoreline and wetland analyses for Hurricane Sandy wetland physical change assessment.

Historical shoreline surveys were conducted by the National Ocean Service (NOS), dating back to the early 1800s. The maps resulting from these surveys, often called t-sheets, provide a reference of historical shoreline position that can be compared to modern data to identify shoreline change. The t-sheets are stored at the National Archives and many have been scanned by the National Oceanic and Atmospheric Administration (NOAA) and are available on the NOAA Shoreline Web site (http://www.shoreline.noaa.gov/data/datasheets/t-sheets.html). While some scanned t-sheets were georeferenced and digitized by NOAA, still others remain as non-georeferenced raster files (http://nosimagery.noaa.gov/images/shoreline_surveys/survey_scans/NOAA_Shoreline_Survey_Scans.html). New_Jersey_1839_75_t-sheets.zip features 8 georeferenced raster t-sheets for the New Jersey coastline from 1839 to 1875. The data were scanned by NOAA, but were not georeferenced. The t-sheets included in this data release are: T-121 (1839), T-119 Part 1 (1841), T-1084 (1868), T-1166 (1870), T-1333 (1871), T-1315a (1872), T-1371 (1874), T-1407 (1875). Digital files were georeferenced, corrected to a modern datum, and shorelines digitized to provide a vector polyline depicting the historical shoreline position using ArcGIS 10.3.1. GEoreferenced t-sheets were used to delineate and shorelines for use in long-term shoreline and wetland analyses for Hurricane Sandy wetland physical change assessment.

New_Jersey_1971_78_Digitized_Shoreline.zip features a digitized historic shoreline for the New Jersey coastline (Point Pleasant, NJ to Longport, NJ) from 1971 to 1978. Imagery of the New Jersey coastline was acquired from the New Jersey Geographic Information Network (NJGIN) as two images: “1970 NJDEP Wetlands Basemap” (1971-78) and the “1977 Tidelands Basemaps” (1977-78). These images are available as a web mapping service (WMS) through the NJGIN website (https://njgin.state.nj.us/NJ_NJGINExplorer/jviewer.jsp?pg=wms_instruct). To reduce digitizing error, the imagery was acquired on a hard drive from the NJGIN via personal communication. Using ArcMap 10.3.1, the "1970 NJDEP Wetlands Basemap" was used to delineate and digitize historical foreshore, backshore, mainland, and island shoreline positions, with the “1977 Tidelands Basemaps” being used to fill in missing shorelines and clarify areas of uncertainty from the 1970s imagery. These shorelines were digitized for use in long-term shoreline and wetland analyses for Hurricane Sandy wetland physical change assessment.

Scientists from the U.S. Geological Survey (USGS), St. Petersburg Coastal and Marine Science Center (SPCMSC) conducted a time-series collection of shallow sediment cores from the back-barrier environments along the Chandeleur Islands, Louisiana from March 2012 through July 2013. The sampling efforts were part of a larger USGS study to evaluate the effects on the geomorphology of the Chandeleur Islands following the construction of an artificial sand berm in response to the Deep Water Horizon oil spill. The objective of this study was to evaluate the response of the back-barrier tidal and wetland environments to the berm. This report serves as an archive for sedimentological, radiochemical, and microbiological data derived from the sediment cores. Data is available for a time-series of four sampling periods: March 2012; July 2013; September 2012; and July 2013. Data is available in downloadable spreadsheet, Joint Photographic Experts Group and Portable Document File formats. Additional files included: ArcGIS shape files of the sample locations, detailed results of sediment grain size analyses, and formal Federal Geographic Data Committee (FDGC) metadata.

Scientists from the U.S. Geological Survey (USGS), St. Petersburg Coastal and Marine Science Center (SPCMSC) conducted a time-series collection of shallow sediment cores from the back-barrier environments along the Chandeleur Islands, Louisiana from March 2012 through July 2013. The sampling efforts were part of a larger USGS study to evaluate the effects on the geomorphology of the Chandeleur Islands following the construction of an artificial sand berm in response to the Deep Water Horizon oil spill. The objective of this study was to evaluate the response of the back-barrier tidal and wetland environments to the berm. This report serves as an archive for sedimentological, radiochemical, and microbiological data derived from the sediment cores. Data is available for a time-series of four sampling periods: March 2012; July 2013; September 2012; and July 2013. Data is available in downloadable spreadsheet, Joint Photographic Experts Group and Portable Document File formats. Additional files included: ArcGIS shape files of the study sites, detailed results of sediment grain size analyses, and formal Federal Geographic Data Committee (FDGC) metadata.

Scientists from the U.S. Geological Survey (USGS), St. Petersburg Coastal and Marine Science Center (SPCMSC) conducted a time-series collection of shallow sediment cores from the back-barrier environments along the Chandeleur Islands, Louisiana from March 2012 through July 2013. The sampling efforts were part of a larger USGS study to evaluate the effects on the geomorphology of the Chandeleur Islands following the construction of an artificial sand berm in response to the Deep Water Horizon oil spill. The objective of this study was to evaluate the response of the back-barrier tidal and wetland environments to the berm. This report serves as an archive for sedimentological, radiochemical, and microbiological data derived from the sediment cores. Data is available for a time-series of four sampling periods: March 2012; July 2013; September 2012; and July 2013. Data is available in downloadable spreadsheet, Joint Photographic Experts Group and Portable Document File formats. Additional files included: ArcGIS shape files of the study sites, detailed results of sediment grain size analyses, and formal Federal Geographic Data Committee (FDGC) metadata.

Scientists from the U.S. Geological Survey (USGS), St. Petersburg Coastal and Marine Science Center (SPCMSC) conducted a time-series collection of shallow sediment cores from the back-barrier environments along the Chandeleur Islands, Louisiana from March 2012 through July 2013. The sampling efforts were part of a larger USGS study to evaluate the effects on the geomorphology of the Chandeleur Islands following the construction of an artificial sand berm in response to the Deep Water Horizon oil spill. The objective of this study was to evaluate the response of the back-barrier tidal and wetland environments to the berm. This report serves as an archive for sedimentological, radiochemical, and microbiological data derived from the sediment cores. Data is available for a time-series of four sampling periods: March 2012; July 2013; September 2012; and July 2013. Data is available in downloadable spreadsheet, Joint Photographic Experts Group and Portable Document File formats. Additional files included: ArcGIS shape files of the sample locations, detailed results of sediment grain size analyses, and formal Federal Geographic Data Committee (FDGC) metadata.

The shoreline of Cape Hatteras, North Carolina, is experiencing long-term coastal erosion. In order to better understand and monitor the changing coastline, historical aerial imagery is used to map shoreline change. For the area of Hatteras Island from Cape Point to Oregon Inlet, fourteen aerial datasets from 1978-2002 were scanned and georeferenced for use in a Geographic Information System (GIS). Shoreline positions (high water line) were digitized from georeferenced imagery. The shoreline vectors were then compiled for use in the Digital Shoreline Analysis System (DSAS) ArcGIS extension in order to generate rates of shoreline change.

The influence of tropical and extratropical cyclones on coastal wetlands and marshes is highly variable in both space and time and depends on a number of climatic, geologic, and physical variables. The impacts storms can be either positive or negative with respect to the wetland and marsh ecosystems. Small to moderate amounts of inorganic sediment added during storms or other events helps to abate pressure from sea-level rise. However, if the volume of sediment is large and the resulting deposits thick, the organic substrate may compact causing submergence and a loss in elevation. Similarly, thick deposits of coarse inorganic sediment may also alter the hydrology of the site and impede vegetative processes. Alternative impacts associated with storms include shoreline erosion at the marsh edge as well as potential emergence. Predicting the outcome of these various responses and potential long-term implications can be obtained from a systematic assessment of both historical and recent event deposits. The objectives of this study are to 1) characterize the surficial sediment of the relict to recent washover fans and back-barrier marshes, and 2) characterize the sediment of 6 marsh cores from the back-barrier marshes and a single marsh island core near the mainland. These geologic data will be integrated with other remote sensing data collected along Assateague Island, Maryland / Virginia and assimilated into an assessment of coastal wetland response to storms.

The influence of tropical and extratropical cyclones on coastal wetlands and marshes is highly variable in both space and time and depends on a number of climatic, geologic, and physical variables. The impacts storms can be either positive or negative with respect to the wetland and marsh ecosystems. Small to moderate amounts of inorganic sediment added during storms or other events helps to abate pressure from sea-level rise. However, if the volume of sediment is large and the resulting deposits thick, the organic substrate may compact causing submergence and a loss in elevation. Similarly, thick deposits of coarse inorganic sediment may also alter the hydrology of the site and impede vegetative processes. Alternative impacts associated with storms include shoreline erosion at the marsh edge as well as potential emergence. Predicting the outcome of these various responses and potential long-term implications can be obtained from a systematic assessment of both historical and recent event deposits. The objectives of this study are to 1) characterize the surficial sediment of the relict to recent washover fans and back-barrier marshes, and 2) characterize the sediment of 6 marsh cores from the back-barrier marshes and a single marsh island core near the mainland. These geologic data will be integrated with other remote sensing data collected along Assateague Island, Maryland / Virginia and assimilated into an assessment of coastal wetland response to storms.

The influence of tropical and extratropical cyclones on coastal wetlands and marshes is highly variable in both space and time and depends on a number of climatic, geologic, and physical variables. The impacts storms can be either positive or negative with respect to the wetland and marsh ecosystems. Small to moderate amounts of inorganic sediment added during storms or other events helps to abate pressure from sea-level rise. However, if the volume of sediment is large and the resulting deposits thick, the organic substrate may compact causing submergence and a loss in elevation. Similarly, thick deposits of coarse inorganic sediment may also alter the hydrology of the site and impede vegetative processes. Alternative impacts associated with storms include shoreline erosion at the marsh edge as well as potential emergence. Predicting the outcome of these various responses and potential long-term implications can be obtained from a systematic assessment of both historical and recent event deposits. The objectives of this study are to 1) characterize the surficial sediment of the relict to recent washover fans and back-barrier marshes, and 2) characterize the sediment of 6 marsh cores from the back-barrier marshes and a single marsh island core near the mainland. These geologic data will be integrated with other remote sensing data collected along Assateague Island, Maryland / Virginia and assimilated into an assessment of coastal wetland response to storms.

The influence of tropical and extratropical cyclones on coastal wetlands and marshes is highly variable in both space and time and depends on a number of climatic, geologic, and physical variables. The impacts storms can be either positive or negative with respect to the wetland and marsh ecosystems. Small to moderate amounts of inorganic sediment added during storms or other events helps to abate pressure from sea-level rise. However, if the volume of sediment is large and the resulting deposits thick, the organic substrate may compact causing submergence and a loss in elevation. Similarly, thick deposits of coarse inorganic sediment may also alter the hydrology of the site and impede vegetative processes. Alternative impacts associated with storms include shoreline erosion at the marsh edge as well as potential emergence. Predicting the outcome of these various responses and potential long-term implications can be obtained from a systematic assessment of both historical and recent event deposits. The objectives of this study are to 1) characterize the surficial sediment of the relict to recent washover fans and back-barrier marshes, and 2) characterize the sediment of 6 marsh cores from the back-barrier marshes and a single marsh island core near the mainland. These geologic data will be integrated with other remote sensing data collected along Assateague Island, Maryland / Virginia and assimilated into an assessment of coastal wetland response to storms.

Scientists from the U.S. Geological Survey, St. Petersburg Coastal and Marine Science Center conducted a seasonal collection of surficial sediments from Chincoteague Bay and Tom's Cove, located between Assateague Island and the Delmarva Peninsula in March/April 2014 (2014-301-FA) and October 2014 (2014-322-FA). The sampling efforts were part of a larger U.S. Geological Survey study to assess the effects of storm events on sediment distribution. The objective of this study was to characterize the sediments of Chincoteague Bay in order to create baseline conditions to incorporate with hydrodynamic and sediment transport models in order to evaluate pre- and post-storm (Hurricane Sandy) change. This report serves as an archive for sedimentological data derived from the surface sediment. Data are available for a seasonal comparison between March/April 2014 and October 2014. Downloadable data are available as Excel spreadsheets (sediment samples) and as JPEG files (maps). Additional files include: detailed results of sediment grain size analyses, and formal Federal Geographic Data Committee metadata (data downloads).

Scientists from the U.S. Geological Survey, St. Petersburg Coastal and Marine Science Center conducted a seasonal collection of surficial sediments from Chincoteague Bay and Tom's Cove, located between Assateague Island and the Delmarva Peninsula in March/April 2014 (2014-301-FA) and October 2014 (2014-322-FA). The sampling efforts were part of a larger U.S. Geological Survey study to assess the effects of storm events on sediment distribution. The objective of this study was to characterize the sediments of Chincoteague Bay in order to create baseline conditions to incorporate with hydrodynamic and sediment transport models in order to evaluate pre- and post-storm (Hurricane Sandy) change. This report serves as an archive for sedimentological data derived from the surface sediment. Data are available for a seasonal comparison between March/April 2014 and October 2014. Downloadable data are available as Excel spreadsheets (sediment samples) and as JPEG files (maps). Additional files include: detailed results of sediment grain size analyses, and formal Federal Geographic Data Committee metadata (data downloads).

Scientists from the U.S. Geological Survey, St. Petersburg Coastal and Marine Science Center conducted a seasonal collection of surficial sediments from Chincoteague Bay and Tom's Cove, located between Assateague Island and the Delmarva Peninsula in March/April 2014 (2014-301-FA) and October 2014 (2014-322-FA). The sampling efforts were part of a larger U.S. Geological Survey study to assess the effects of storm events on sediment distribution. The objective of this study was to characterize the sediments of Chincoteague Bay in order to create baseline conditions to incorporate with hydrodynamic and sediment transport models in order to evaluate pre- and post-storm (Hurricane Sandy) change. This report serves as an archive for sedimentological data derived from the surface sediment. Data are available for a seasonal comparison between March/April 2014 and October 2014. Downloadable data are available as Excel spreadsheets (sediment samples) and as JPEG files (maps). Additional files include: detailed results of sediment grain size analyses, and formal Federal Geographic Data Committee metadata (data downloads).

Scientists from the U.S. Geological Survey, St. Petersburg Coastal and Marine Science Center conducted a seasonal collection of surficial sediments from Chincoteague Bay and Tom's Cove, located between Assateague Island and the Delmarva Peninsula in March/April 2014 (2014-301-FA) and October 2014 (2014-322-FA). The sampling efforts were part of a larger U.S. Geological Survey study to assess the effects of storm events on sediment distribution. The objective of this study was to characterize the sediments of Chincoteague Bay in order to create baseline conditions to incorporate with hydrodynamic and sediment transport models in order to evaluate pre- and post-storm (Hurricane Sandy) change. This report serves as an archive for sedimentological data derived from the surface sediment. Data are available for a seasonal comparison between March/April 2014 and October 2014. Downloadable data are available as Excel spreadsheets (sediment samples) and as JPEG files (maps). Additional files include: detailed results of sediment grain size analyses, and formal Federal Geographic Data Committee metadata (data downloads).

The USGS Coral Reef Ecosystems Studies project (https://coastal.er.usgs.gov/crest/) provides science that helps resource managers tasked with the stewardship of coral reef resources. This data release contains data on coral-growth rates for Orbicella sp. coral colonies grown at five sites on the Florida Keys reef tract from 2013 to 2015, survey data for census-based carbonate budgeting at Hen and Chickens Reef (Islamorada, Florida) collected in 2017, and time-series photographs taken of permanent markers used to measure reef erosion at Hen and Chickens Reef in 1998 and 2015. The time-series photographs document a loss in coral-reef elevation over 17 years at this site. The data will be used to inform resource managers of the capacity for future growth (or loss) of reefs dominated by genus Orbicella in the Florida Keys so that the reef ecosystem might be better understood and managed. The datasets included here were interpreted in Kuffner and others (2019). Kuffner, I.B., Toth, L.T., Hudson, J.H., Goodwin, W.B., Stathakopoulos, A., Bartlett, L.A. and Whitcher, E.M. (2019), Improving estimates of coral reef construction and erosion with in situ measurements. Limnol Oceanogr. doi:10.1002/lno.11184

The USGS Coral Reef Ecosystems Studies project (https://coastal.er.usgs.gov/crest/) provides science that helps resource managers tasked with the stewardship of coral reef resources. This data release contains data on coral-growth rates and time-series photographs taken of colonies of the elkhorn coral, Acropora palmata, grown at five sites on the Florida Keys reef tract from Spring 2018 to Autumn 2019. The data will be used to inform resource managers of the capacity for restoration and growth of this threatened species of coral along 350 kilometers of the Florida reef tract to aid species recovery throughout the western Atlantic. The datasets included here were interpreted in Kuffner and others (2020). Kuffner, I.B., Stathakopoulos, A., Toth, L.T., and Bartlett, L.A. In press. Reestablishing a stepping-stone population of the threatened coral, Acropora palmata, to aid regional recovery. Endangered Species Research.

The U.S. Geological Survey (USGS) Coral Reef Ecosystems Studies project (https://coastal.er.usgs.gov/crest/) provides science that helps resource managers tasked with the stewardship of coral reef resources. This data release contains data on coral-growth rates and time-series photographs taken of colonies of the mustard hill coral, Porites astreoides, grown at four sites on the Florida Keys reef tract from Spring 2015 to Spring 2017. The data will be used to inform resource managers on the spatial and temporal variability in heat stress experienced on coral reefs and the response to it by P. astreoides along 350 kilometers (km) of the Florida reef tract. These data will be used to draw conclusions about the capacity of this species to persist in Florida and throughout the western Atlantic as ocean conditions continue to change. The datasets included here were interpreted in Lenz and others (2021). Lenz, E.A., Bartlett, L.A., Stathakopoulos, A., and Kuffner, I.B. (2021), Physiological differences in bleaching response of the coral Porites astreoides along the Florida Keys reef tract during high-temperature stress.

Shoreline change analysis is an important environmental monitoring tool for evaluating coastal exposure to erosion hazards, particularly for vulnerable habitats such as coastal wetlands where habitat loss is problematic world-wide. The increasing availability of high-resolution satellite imagery and emerging developments in analysis techniques support the implementation of these data into coastal management, including shoreline monitoring and change analysis. Geospatial shoreline data were created from a semi-automated methodology using WorldView (WV) satellite data between 2013 and 2020. The data were compared to contemporaneous field-surveyed Real-time Kinematic (RTK) Global Positioning System (GPS) data collected by the Grand Bay National Estuarine Research Reserve (GBNERR) and digitized shorelines from U.S. Department of Agriculture National Agriculture Imagery Program (NAIP) orthophotos. Field data for shoreline monitoring sites was also collected to aid interpretation of results. This data release contains digital vector shorelines, shoreline change calculations for all three remote sensing data sets, and field surveyed data. The data will aid managers and decision-makers in the adoption of high-resolution satellite imagery into shoreline monitoring activities, which will increase the spatial scale of shoreline change monitoring, provide rapid response to evaluate impacts of coastal erosion, and reduce cost of labor-intensive practices. For further information regarding data collection and/or processing methods, refer to the associated journal article (Smith and others, 2021).

Breton Island, Louisiana Transects with Shoreline Change Rates (Post-1950s) (Geographic, NAD83) consists of vector transect data that were derived from the Digital Shoreline Analysis System (DSAS) version 4.0. Rates from the DSAS statistical output table were joined to the transects to provide a visual representation of the shoreline change rates on a transect-by-transect basis.

Breton Island, Louisiana Transects with Shoreline Change Rates (Post Hurricane Katrina) (Geographic, NAD83) consists of vector transect data that was derived from the Digital Shoreline Analysis System (DSAS) version 4.0. Rates from the DSAS statistical output table were joined to the transects to provide a visual representation of the shoreline change rates on a transect-by-transect basis.

Breton Island, Louisiana Transects with Shoreline Change Rates (Pre-1950s) (Geographic, NAD83) consists of vector transect data that were derived from the Digital Shoreline Analysis System (DSAS) version 4.0. Rates from the DSAS statistical output table were joined to the transects to provide a visual representation of the shoreline change rates on a transect-by-transect basis.

Breton Island, Louisiana Transects with Shoreline Change Rates (Pre/Post Hurricane Katrina) (Geographic, NAD83) consists of vector transect data that were derived from the Digital Shoreline Analysis System (DSAS) version 4.0. Rates from the DSAS statistical output table were joined to the transects to provide a visual representation of the shoreline change rates on a transect-by-transect basis.

The Barrier Island and Estuarine Wetland Physical Change Assessment was created to calibrate and test probability models of barrier island estuarine shoreline (backshore) and beach sandline change for study areas in Virginia, Maryland, and New Jersey. The models examined the influence of hydrologic and physical variables related to long-term and storm-derived overwash and back-barrier shoreline change. Input variables were constructed into a Bayesian Network (BN) using Netica, a computer program created by NORSYS Software Corporation that allows users to work with belief networks and influence diagrams. Each model is tested on its ability to predict changes in long-term and event-driven (for example, Hurricane Sandy-induced) backshore and sandline change based on learned correlations from the input variables across the domain. Using the input hydrodynamic and geomorphic data, the BN is constrained to produce a prediction of an updated conditional probability of backshore or sandline change at each location. To evaluate the ability of the BN to reproduce the observations used to train the model, the skill, log likelihood ratio and probability predictions were utilized. These data are the probability and skill metrics for the event-driven estuarine back-barrier shoreline change model.

The Barrier Island and Estuarine Wetland Physical Change Assessment was created to calibrate and test probability models of barrier island estuarine shoreline (backshore) and beach sandline change for study areas in Virginia, Maryland, and New Jersey. The models examined the influence of hydrologic and physical variables related to long-term and storm-derived overwash and back-barrier shoreline change. Input variables were constructed into a Bayesian Network (BN) using Netica, a computer program created by NORSYS Software Corporation that allows users to work with belief networks and influence diagrams. Each model is tested on its ability to predict changes in long-term and event-driven (i.e., Hurricane Sandy-induced) backshore and sandline change based on learned correlations from the input variables across the domain. Using the input hydrodynamic and geomorphic data, the BN is constrained to produce a prediction of an updated conditional probability of backshore or sandline change at each location. To evaluate the ability of the BN to reproduce the observations used to train the model, the skill, log likelihood ratio and probability predictions were utilized. These data are the probability and skill metrics for the event-driven beach sandline change model.

The Barrier Island and Estuarine Wetland Physical Change Assessment was created to calibrate and test probability models of barrier island estuarine shoreline (backshore) and beach sandline change for study areas in Virginia, Maryland, and New Jersey. The models examined the influence of hydrologic and physical variables related to long-term and storm-derived overwash and back-barrier shoreline change. Input variables were constructed into a Bayesian Network (BN) using Netica, a computer program created by NORSYS Software Corporation that allows users to work with belief networks and influence diagrams. Each model is tested on its ability to predict changes in long-term and event-driven (i.e., Hurricane Sandy-induced) backshore and sandline change based on learned correlations from the input variables across the domain. Using the input hydrodynamic and geomorphic data, the BN is constrained to produce a prediction of an updated conditional probability of backshore or sandline change at each location. To evaluate the ability of the BN to reproduce the observations used to train the model, the skill, log likelihood ratio and probability predictions were utilized. These data are the probability and skill metrics for the long-term estuarine back-barrier shoreline change model.

The Barrier Island and Estuarine Wetland Physical Change Assessment was created to calibrate and test probability models of barrier island estuarine shoreline (backshore) and beach sandline change for study areas in Virginia, Maryland, and New Jersey. The models examined the influence of hydrologic and physical variables related to long-term and storm-derived overwash and back-barrier shoreline change. Input variables were constructed into a Bayesian Network (BN) using Netica, a computer program created by NORSYS Software Corporation that allows users to work with belief networks and influence diagrams. Each model is tested on its ability to predict changes in long-term and event-driven (i.e., Hurricane Sandy-induced) backshore and sandline change based on learned correlations from the input variables across the domain. Using the input hydrodynamic and geomorphic data, the BN is constrained to produce a prediction of an updated conditional probability of backshore or sandline change at each location. To evaluate the ability of the BN to reproduce the observations used to train the model, the skill, log likelihood ratio and probability predictions were utilized. These data are the probability and skill metrics for the long-term beach sandline change model.

The numerical model Delft3D (developed by Deltares) was developed to evaluate the potential effects of proposed navigation channel deepening and widening in Mobile Harbor, Alabama (AL). The Delft3D model setup requires the input of a merged topographic and bathymetric elevations, a wave climate based on significant wave heights, peak wave period and mean wave direction, and a tidal-time series. The model was validated by comparing model outputs from deterministic runs with observations of water levels and velocities. The validated model was used to simulate scenarios of existing conditions and proposed with-project conditions (for example, channel deepening and widening). Simulations were performed for one year (the year 2010) and ten years, with and without 0.5 meters (m) of sea level rise. Model inputs and outputs in the form of topography and bathymetry for the scenario runs as well as output water levels and velocities for the deterministic runs are provided in this data release. For further information regarding model input generation and visualization of model output topography and bathymetry, refer to USGS Open-File Report 2018-1123.

The numerical model Delft3D (developed by Deltares) was developed to evaluate the potential effects of proposed navigation channel deepening and widening in Mobile Harbor, Alabama (AL). The Delft3D model setup requires the input of a merged topographic and bathymetric elevations, a wave climate based on significant wave heights, peak wave period and mean wave direction, and a tidal-time series. The model was validated by comparing model outputs from deterministic runs with observations of water levels and velocities. The validated model was used to simulate scenarios of existing conditions and proposed with-project conditions (for example, channel deepening and widening). Simulations were performed for one year (the year 2010) and ten years, with and without 0.5 meters (m) of sea level rise. Model inputs and outputs in the form of topography and bathymetry for the scenario runs as well as output water levels and velocities for the deterministic runs are provided in this data release. For further information regarding model input generation and visualization of model output topography and bathymetry, refer to USGS Open-File Report 2018-1123.

The numerical model Delft3D (developed by Deltares) was developed to evaluate the potential effects of proposed navigation channel deepening and widening in Mobile Harbor, Alabama (AL). The Delft3D model setup requires the input of a merged topographic and bathymetric elevations, a wave climate based on significant wave heights, peak wave period and mean wave direction, and a tidal-time series. The model was validated by comparing model outputs from deterministic runs with observations of water levels and velocities. The validated model was used to simulate scenarios of existing conditions and proposed with-project conditions (for example, channel deepening and widening). Simulations were performed for one year (the year 2010) and ten years, with and without 0.5 meters (m) of sea level rise. Model inputs and outputs in the form of topography and bathymetry for the scenario runs as well as output water levels and velocities for the deterministic runs are provided in this data release. For further information regarding model input generation and visualization of model output topography and bathymetry, refer to USGS Open-File Report 2018-1123.

The numerical model Delft3D (developed by Deltares) was developed to evaluate the potential effects of proposed navigation channel deepening and widening in Mobile Harbor, Alabama (AL). The Delft3D model setup requires the input of a merged topographic and bathymetric elevations, a wave climate based on significant wave heights, peak wave period and mean wave direction, and a tidal-time series. The model was validated by comparing model outputs from deterministic runs with observations of water levels and velocities. The validated model was used to simulate scenarios of existing conditions and proposed with-project conditions (for example, channel deepening and widening). Simulations were performed for one year (the year 2010) and ten years, with and without 0.5 meters (m) of sea level rise. Model inputs and outputs in the form of topography and bathymetry for the scenario runs as well as output water levels and velocities for the deterministic runs are provided in this data release. For further information regarding model input generation and visualization of model output topography and bathymetry, refer to USGS Open-File Report 2018-1123.

The numerical model Delft3D (developed by Deltares) was developed to evaluate the potential effects of proposed navigation channel deepening and widening in Mobile Harbor, Alabama (AL). The Delft3D model setup requires the input of a merged topographic and bathymetric elevations, a wave climate based on significant wave heights, peak wave period and mean wave direction, and a tidal-time series. The model was validated by comparing model outputs from deterministic runs with observations of water levels and velocities. The validated model was used to simulate scenarios of existing conditions and proposed with-project conditions (for example, channel deepening and widening). Simulations were performed for one year (the year 2010) and ten years, with and without 0.5 meters (m) of sea level rise. Model inputs and outputs in the form of topography and bathymetry for the scenario runs as well as output water levels and velocities for the deterministic runs are provided in this data release. For further information regarding model input generation and visualization of model output topography and bathymetry, refer to USGS Open-File Report 2018-1123.

The model input and output of topography and bathymetry values resulting from forecast simulations of coupled modeling scenarios occurring between 2015 and 2025 at Dauphin Island, Alabama, and described in U.S. Geological Survey (USGS) Open-File Report 2020–1001 (https://doi.org/10.3133/ofr20201001), are provided here. For further information regarding model input generation and visualization of model output topography and bathymetry, refer to Mickey and others (2020).

The model input and output of topography and bathymetry values resulting from forecast simulations of coupled modeling scenarios occurring between 2015 and 2025 at Dauphin Island, Alabama, and described in U.S. Geological Survey (USGS) Open-File Report 2020–1001 (https://doi.org/10.3133/ofr20201001), are provided here. For further information regarding model input generation and visualization of model output topography and bathymetry, refer to Mickey and others (2020).

The model input and output of topography and bathymetry values resulting from forecast simulations of coupled modeling scenarios occurring between 2015 and 2025 at Dauphin Island, Alabama, and described in U.S. Geological Survey (USGS) Open-File Report 2020–1001 (https://doi.org/10.3133/ofr20201001), are provided here. For further information regarding model input generation and visualization of model output topography and bathymetry, refer to Mickey and others (2020).

The model input and output of topography and bathymetry values resulting from forecast simulations of coupled modeling scenarios occurring between 2015 and 2025 at Dauphin Island, Alabama, and described in U.S. Geological Survey (USGS) Open-File Report 2020–1001 (https://doi.org/10.3133/ofr20201001), are provided here. For further information regarding model input generation and visualization of model output topography and bathymetry, refer to Mickey and others (2020).

The model input and output of topography and bathymetry values resulting from forecast simulations of coupled modeling scenarios occurring between 2015 and 2025 at Dauphin Island, Alabama, and described in U.S. Geological Survey (USGS) Open-File Report 2020–1001 (https://doi.org/10.3133/ofr20201001), are provided here. For further information regarding model input generation and visualization of model output topography and bathymetry, refer to Mickey and others (2020).

The model input and output of topography and bathymetry values resulting from forecast simulations of coupled modeling scenarios occurring between 2015 and 2025 at Dauphin Island, Alabama, and described in U.S. Geological Survey (USGS) Open-File Report 2020–1001 (https://doi.org/10.3133/ofr20201001), are provided here. For further information regarding model input generation and visualization of model output topography and bathymetry, refer to Mickey and others (2020).

ASCII XYZ point cloud data were produced from remotely sensed, geographically referenced elevation measurements by the U.S. Geological Survey (USGS). Elevation measurements were collected over a portion of the Texas coastline, post-Hurricane Rita (September 2005 hurricane), using the Experimental Advanced Airborne Research Lidar (EAARL), a pulsed laser ranging system mounted onboard an aircraft to measure ground elevation, vegetation canopy, and coastal topography. The system uses high-frequency laser beams directed at the Earth's surface through an opening in the bottom of the aircraft's fuselage. The laser system records the time difference between emission of the laser beam and the reception of the reflected laser signal in the aircraft. The plane travels over the target area at approximately 60 meters per second at an elevation of approximately 300 meters, resulting in a laser swath of approximately 240 meters with an average point spacing of 2-3 meters. The EAARL, developed originally by the National Aeronautics and Space Administration (NASA) at Wallops Flight Facility in Virginia, measures ground elevation with a vertical resolution of 3 centimeters. A sampling rate of 3 kilohertz or higher results in an extremely dense spatial elevation dataset. Over 100 kilometers of coastline can be surveyed easily within a 3- to 4-hour mission. When resultant elevation maps for an area are analyzed, they provide a useful tool to make management decisions regarding land development.

ASCII XYZ point cloud data were produced from remotely sensed, geographically referenced elevation measurements by the U.S. Geological Survey (USGS). Elevation measurements were collected over a portion of the Texas coastline, post-Hurricane Rita (September 2005 hurricane), using the Experimental Advanced Airborne Research Lidar (EAARL), a pulsed laser ranging system mounted onboard an aircraft to measure ground elevation, vegetation canopy, and coastal topography. The system uses high-frequency laser beams directed at the Earth's surface through an opening in the bottom of the aircraft's fuselage. The laser system records the time difference between emission of the laser beam and the reception of the reflected laser signal in the aircraft. The plane travels over the target area at approximately 60 meters per second at an elevation of approximately 300 meters, resulting in a laser swath of approximately 240 meters with an average point spacing of 2-3 meters. The EAARL, developed originally by the National Aeronautics and Space Administration (NASA) at Wallops Flight Facility in Virginia, measures ground elevation with a vertical resolution of 3 centimeters. A sampling rate of 3 kilohertz or higher results in an extremely dense spatial elevation dataset. Over 100 kilometers of coastline can be surveyed easily within a 3- to 4-hour mission. When resultant elevation maps for an area are analyzed, they provide a useful tool to make management decisions regarding land development.

Coastal marshes are highly dynamic and ecologically important ecosystems that are subject to pervasive and often harmful disturbances, including shoreline erosion. Shoreline erosion can result in an overall loss of coastal marsh, particularly in estuaries with moderate- or high-wave energy. Not only can waves be important physical drivers of shoreline change, they can also influence shore-proximal vertical accretion through sediment delivery. For these reason, estimates of wave energy can provide a quantitative measure of wave effects on marsh shorelines. Since wave energy is difficult to measure at all locations, scientists and managers often rely on hydrodynamic models to estimate wave properties at different locations. The Wave Exposure Model (WEMo) is a simple tool that uses linear wave theory to estimate wave energy characteristics for enclosed and semi-enclosed estuaries(Malhotra and Fonseca, 2007). The interpretation of hydrodynamic models is improved if model results can be validated against measured data. The data presented in this publication are input and validation data for modeled and observed mean wave height for two temporary oceanographic stations established by the U.S. Geological Survey (USGS) in the Grand Bay National Estuarine Research Reserve, Mississippi.

An Ellipsoidally Referenced Survey (ERS) using two Teledyne Reson SeaBat T50-P multibeam echosounders, in dual-head configuration, was conducted by the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center (SPCMSC) covering the nearshore, seaward side of Rockaway Peninsula, New York (NY), from October 4-6, 2019. This dataset, Rockaway_2019_MBES_Leg1_xyz.zip, includes the processed elevation point data (x,y,z), as derived from a 1-meter (m) bathymetric grid from the first leg of the survey. Additionally, the dataset Rockaway_2019_MBES_Leg1_Backscatter.zip includes the acoustic backscatter intensity data in 32-bit floating point GeoTIFF (.tif) format. Survey efforts were cut short due to inclement weather and the survey was resumed later in the month (Leg 2). Both survey legs have been made available in this publication independently as there is evidence of morphologic change, and therefore the legs could not accurately be combined into a comprehensive product.

An Ellipsoidally Referenced Survey (ERS) using two Teledyne Reson SeaBat T50-P multibeam echosounders, in dual-head configuration, was conducted by the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center (SPCMSC) covering the nearshore, seaward side of Rockaway Peninsula, New York (NY), from October 24-29, 2019. This dataset, Rockaway_2019_MBES_Leg2_xyz.zip, includes the processed elevation point data (x,y,z), as derived from a 1-meter (m) bathymetric grid from the second leg of the survey. Additionally, the dataset Rockaway_2019_MBES_Leg2_Backscatter.zip includes the acoustic backscatter intensity data in 32-bit floating point GeoTIFF (.tif) format. Leg 1 survey efforts were cut short due to inclement weather and the survey was resumed later in the month (Leg 2). Both survey legs have been made available in this publication independently as there is evidence of morphologic change, and therefore the legs could not accurately be combined into a comprehensive product.

The model input and output of topography and bathymetry values resulting from forecast simulations of coupled modeling scenarios occurring between 2015 and 2025 at Dauphin Island, Alabama, and described in U.S. Geological Survey (USGS) Open-File Report 2020–1001 (https://doi.org/10.3133/ofr20201001), are provided here. For further information regarding model input generation and visualization of model output topography and bathymetry, refer to Mickey and others (2020).

The model input and output of topography and bathymetry values resulting from forecast simulations of coupled modeling scenarios occurring between 2015 and 2025 at Dauphin Island, Alabama, and described in U.S. Geological Survey (USGS) Open-File Report 2020–1001 (https://doi.org/10.3133/ofr20201001), are provided here. For further information regarding model input generation and visualization of model output topography and bathymetry, refer to Mickey and others (2020).

The model input and output of topography and bathymetry values resulting from forecast simulations of coupled modeling scenarios occurring between 2015 and 2025 at Dauphin Island, Alabama, and described in U.S. Geological Survey (USGS) Open-File Report 2020–1001 (https://doi.org/10.3133/ofr20201001), are provided here. For further information regarding model input generation and visualization of model output topography and bathymetry, refer to Mickey and others (2020).

The model input and output of topography and bathymetry values resulting from forecast simulations of coupled modeling scenarios occurring between 2015 and 2025 at Dauphin Island, Alabama, and described in U.S. Geological Survey (USGS) Open-File Report 2020–1001 (https://doi.org/10.3133/ofr20201001), are provided here. For further information regarding model input generation and visualization of model output topography and bathymetry, refer to Mickey and others (2020).

ASCII XYZ point cloud data for a portion of the submerged environs of Saint Croix, U.S. Virgin Islands, was produced from remotely sensed, geographically referenced elevation measurements collected on March 11, 19, and 21, 2014 by the U.S. Geological Survey, in collaboration with the National Oceanic and Atmospheric Administration (NOAA) Coral Reef Conservation Program. Elevation measurements were collected over the area using the second-generation Experimental Advanced Airborne Research Lidar (EAARL-B), a pulsed laser ranging system mounted onboard an aircraft to measure ground elevation, vegetation canopy, and coastal topography. The system uses high-frequency laser beams directed at the Earth's surface through an opening in the bottom of the aircraft's fuselage. The laser system records the time difference between emission of the laser beam and the reception of the reflected laser signal in the aircraft. The plane travels over the target area at approximately 55 meters per second at an elevation of approximately 300 meters, resulting in a laser swath of approximately 240 meters with an average point spacing of 0.5?1.6 meters. The nominal vertical elevation accuracy expressed as the root mean square error (RMSE) is 13.5 centimeters. A peak sampling rate of 15?30 kilohertz results in an extremely dense spatial elevation dataset. More than 100 kilometers of coastline can be surveyed easily within a 3- to 4-hour mission. When resultant elevation maps for an area are analyzed, they provide a useful tool to make management decisions regarding land development.

A submerged topography Digital Elevation Model (DEM) mosaic for a portion of the submerged environs of Saint Croix, U.S. Virgin Islands, was produced from remotely sensed, geographically referenced elevation measurements collected on March 11, 19, and 21, 2014 by the U.S. Geological Survey, in collaboration with the National Oceanic and Atmospheric Administration (NOAA) Coral Reef Conservation Program. Elevation measurements were collected over the area using the second-generation Experimental Advanced Airborne Research Lidar (EAARL-B), a pulsed laser ranging system mounted onboard an aircraft to measure ground elevation, vegetation canopy, and coastal topography. The system uses high-frequency laser beams directed at the Earth's surface through an opening in the bottom of the aircraft's fuselage. The laser system records the time difference between emission of the laser beam and the reception of the reflected laser signal in the aircraft. The plane travels over the target area at approximately 55 meters per second at an elevation of approximately 300 meters, resulting in a laser swath of approximately 240 meters with an average point spacing of 0.5?1.6 meters. The nominal vertical elevation accuracy expressed as the root mean square error (RMSE) is 13.5 centimeters. A peak sampling rate of 15?30 kilohertz results in an extremely dense spatial elevation dataset. More than 100 kilometers of coastline can be surveyed easily within a 3- to 4-hour mission. When resultant elevation maps for an area are analyzed, they provide a useful tool to make management decisions regarding land development.

Coral reefs serve as natural barriers that protect adjacent shorelines from coastal hazards such as storms, waves and erosion but projections indicate global degradation of coral reefs due to anthropogenic impacts and climate change will cause a transition to net erosion by mid-century. The U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center conducted research to quantify the combined effect of all constructive and destructive processes on modern coral reef ecosystems by measuring regional-scale changes in seafloor elevation. USGS staff assessed five coral reef ecosystems in the Atlantic Ocean (Upper and Lower Florida Keys), Caribbean Sea (U.S. Virgin Islands: Saint Thomas and Buck Island, St. Croix), and Pacific Ocean (Maui, Hawaii), including both coral-dominated and adjacent, non-coral dominated habitats. Scientists used historical bathymetric data from the 1930s to 1980s and contemporary light detection and ranging (lidar) digital elevation models (DEMs) from the late 1990s to 2000s to calculate changes in seafloor elevation for each study site over time periods reflecting low to high anthropogenic impacts. STC_ElevationChange.zip contains the location, elevation, and elevation change data for Buck Island-St.Croix, U.S. Virgin Islands. Using these changes in elevation, further analysis was done to calculate corresponding changes in seafloor volume for all study areas and habitat types within each site.

Coral reefs serve as natural barriers that protect adjacent shorelines from coastal hazards such as storms, waves and erosion but projections indicate global degradation of coral reefs due to anthropogenic impacts and climate change will cause a transition to net erosion by mid-century. The U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center conducted research to quantify the combined effect of all constructive and destructive processes on modern coral reef ecosystems by measuring regional-scale changes in seafloor elevation. USGS staff assessed five coral reef ecosystems in the Atlantic Ocean (Upper and Lower Florida Keys), Caribbean Sea (U.S. Virgin Islands: St. Thomas and Buck Island, St. Croix), and Pacific Ocean (Maui, Hawaii), including both coral-dominated and adjacent, non-coral dominated habitats. Scientists used historical bathymetric data from the 1930s to 1980s and contemporary light detection and ranging (lidar) digital elevation models (DEMs) from the late 1990s to 2000s to calculate changes in seafloor elevation for each study site over time periods reflecting low to high anthropogenic impacts. STT_ElevationChange.zip contains the location, elevation, and elevation change data for St. Thomas, U.S. Virgin Islands. Using these changes in elevation, further analysis was done to calculate corresponding changes in seafloor volume for all study areas and habitat types within each site.

The National Assessment of Coastal Change Hazards project exists to understand and predict storm impacts to our nation's coastlines. This geospatial dataset defines the alongshore extent of overwash sediments deposited along the Florida and Alabama coast and attributed to coastal processes during [Atlantic Basin] Hurricane Sally, which made landfall in the U.S. on September 16, 2020.

An Ellipsoidally Referenced Survey (ERS) using two Teledyne Reson SeaBat T50-P multibeam echosounders, in dual-head configuration, was conducted by the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center (SPCMSC) offshore of Santa Rosa Island, Florida (FL), June 15-29, 2019. This dataset, Santa_Rosa_Island_2019_MBES_UTM16N_xyz.zip, includes the processed elevation point data (XYZ) as derived from a 1-meter (m) bathymetric grid.

The U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center conducted research to quantify bathymetric changes along the Florida Reef Tract (FRT) from Miami to Marquesas Keys within a 939.4 square-kilometer area between 2016 and 2019. USGS staff used light detection and ranging (lidar)-derived data acquired by the National Oceanic and Atmospheric Administration (NOAA) during two separate lidar surveys. The first is dataset is referenced as "2016 lidar" data and was collected between July 21, 2016 and February 20, 2017 (NOAA, 2017a-c and 2018) and the second is denoted as "2019 lidar" data and was collected between November 20, 2018 and March 23, 2019 (NOAA, 2020). The 2016 and 2019 NOAA lidar datasets were used to assess changes in elevation and structure that occurred during this period along the FRT. An elevation change analysis between the 2016 and 2019 lidar data was performed to quantify and map impacts to seafloor elevation and determine elevation and volume change statistics for individual habitats found within the FRT. Data were collected under Florida Keys National Marine Sanctuary permit FKNMS-2016-068.

This data release serves as an archive of sediment physical properties and grain-size data for surficial samples collected offshore of Assateague Island, Maryland and Virginia, for comparison with surficial estuarine and subaerial sedimentological samples collected and assessed following Hurricane Sandy (Ellis and others, 2015 (http://doi.org/10.3133/ofr20151219); Smith and others, 2015 (http://doi.org/10.3133/ofr20151169); Bernier and others, 2016 (https://pubs.usgs.gov/ds/0999/)). The sediment samples were collected by scientists from the U.S. Geological Survey (USGS) office in Woods Hole, Massachusetts while aboard the motor vessel (M/V) Scarlett Isabella as part of a larger effort to map the inner continental shelf (Pendleton and others, 2016 (http://doi.org/10.5066/F7MW2F60)). Following field work, the sediment samples were shipped to the USGS Coastal and Marine Science Center in St. Petersburg, Florida, where they were renamed for consistency with a previously existing naming scheme and processed for bulk density, loss on ignition (LOI), and grain-size. The grain-size subsamples were processed on a Coulter LS200 particle-size analyzer for consistency regarding methods and output statistics with related data sets from Chincoteague Bay and Assateague Island. For more information regarding sample collection and site information or the related data sets, refer to USGS data release Pendleton and others, 2016 (https://doi.org/10.5066/F7MW2F60); for more information regarding processing methods refer to USGS Open-File Report 2015Ð1219 (http://doi.org/10.3133/ofr20151219). Downloadable data are available as Excel spreadsheets (.xlsx), comma-separated values text files (.csv), and formal Federal Geographic Data Committee (FGDC) metadata.

An Ellipsoidally Referenced Survey (ERS) using two Teledyne Reson SeaBat T50-P multibeam echosounders, in dual-head configuration, was conducted by the U.S Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center (SPCMSC) offshore of Seven Mile Island, New Jersey, September 6-8, 2018 and September 21-23, 2018. This dataset, presented as Seven_Mile_Island_2018_MBES_WGS84_UTM18N_xyz.zip and Seven_Mile_Island_2018_MBES_NAD83_NAVD88_GEOID12B_xyz.zip, includes the processed elevation point data (x,y,z), as derived from a 1-meter (m) bathymetric grid.

The United States Geological Survey Saint Petersburg Coastal and Marine Science Center (USGS SPCMSC), in cooperation with the United States Army Corps of Engineers (USACE) conducted bathymetric surveys of the nearshore waters surrounding Ship and Horn Islands, Gulf Islands National Seashore, Mississippi (GUIS). Camille Cut separates Ship Island into East Ship Island and West Ship Island. The objective of this study was to establish base-level elevation conditions around West Ship Island, East Ship Island, Horn Island and their associated active littoral system prior to restoration activities. These activities include the closure of Camille Cut and the placement of sediment in the littoral zone of West Ship Island. This survey will be used to verify sediment migration patterns by monitoring elevation change over time. The dataset produced by this survey will also be compared with historic bathymetric datasets to help further understand island elevation over time. This data release provides 667-line kilometers (km) of processed Single-Beam Bathymetry (SBB) data and 773-line km of processed Interferometric Bathymetry (IFB) collected by the USGS SPCMSC in July 2016 (field activity number [FAN] 2016-347-FA). The IFB data were acquired aboard the Research Vessel (RV) Sallenger (subFAN, 16BIM04), and the SBB data were acquired aboard the RV Jabba Jaw (subFAN, 16BIM05) and the RV Mako (subFAN, 16BIM06). The IFB and SBB point data are provided in three datums: 1) the International Terrestrial Reference Frame of 2000 (ITRF00), ellipsoid height (-49.70 meters [m] to -28.87 m); 2) the North American Datum of 1983 (NAD83) CORS96 realization and the North American Vertical Datum 1988 (NAVD88) with respect to the GEOID12B model (-0.07 m to -20.69 m); and 3) NAD83 (CORS96) and Mean Lower Low Water (MLLW) (-0.04 m to -20.60 m). This metadata record describes the comprehensive, 50-meter (m) Digital Elevation Model (DEM) created from the IFB and SBB point data and provided in NAD83 NAVD88 GEOID12B. For additional information regarding data collection and processing, please refer to the field logs and formal Federal Geographic Data Committee (FGDC) metadata for the individual XYZ point data files and survey trackline shapefiles also included within this data release (https://doi.org/10.5066/F7B8571Q).

The United States Geological Survey Saint Petersburg Coastal and Marine Science Center (USGS SPCMSC), in cooperation with the United States Army Corps of Engineers (USACE) conducted bathymetric surveys of the nearshore waters surrounding Ship and Horn Islands, Gulf Islands National Seashore, Mississippi (GUIS). Camille Cut separates Ship Island into East Ship Island and West Ship Island. The objective of this study was to establish base-level elevation conditions around West Ship Island, East Ship Island, Horn Island and their associated active littoral system prior to restoration activities. These activities include the closure of Camille Cut and the placement of sediment in the littoral zone of West Ship Island. This survey will be used to verify sediment migration patterns by monitoring elevation change over time. The dataset produced by this survey will also be compared with historic bathymetric datasets to help further understand island elevation over time. This data release provides 667-line kilometers (km) of processed Single-Beam Bathymetry (SBB) data and 773-line km of processed Interferometric Bathymetry (IFB) collected by the USGS SPCMSC in July 2016 (field activity number [FAN] 2016-347-FA). The IFB data were acquired aboard the Research Vessel (RV) Sallenger (subFAN, 16BIM04), and the SBB data were acquired aboard the RV Jabba Jaw (subFAN, 16BIM05) and the RV Mako (subFAN, 16BIM06). The IFB and SBB point data are provided in three datums: 1) the International Terrestrial Reference Frame of 2000 (ITRF00), ellipsoid height (-49.70 meters [m] to -28.87 m); 2) the North American Datum of 1983 (NAD83) CORS96 realization and the North American Vertical Datum 1988 (NAVD88) with respect to the GEOID12B model (-0.07 m to -20.69 m); and 3) NAD83 (CORS96) and Mean Lower Low Water (MLLW) (-0.04 m to -20.60 m). This metadata record describes the comprehensive, 50-meter (m) Digital Elevation Model (DEM) created from the IFB and SBB point data and provided in NAD83 NAVD88 GEOID12B. For additional information regarding data collection and processing, please refer to the field logs and formal Federal Geographic Data Committee (FGDC) metadata for the individual XYZ point data files and survey trackline shapefiles also included within this data release (https://doi.org/10.5066/F7B8571Q).

The United States Geological Survey Saint Petersburg Coastal and Marine Science Center (USGS SPCMSC), in cooperation with the United States Army Corps of Engineers (USACE) conducted bathymetric surveys of the nearshore waters surrounding Ship and Horn Islands, Gulf Islands National Seashore, Mississippi (GUIS). Camille Cut separates Ship Island into East Ship Island and West Ship Island. The objective of this study was to establish base-level elevation conditions around West Ship Island, East Ship Island, Horn Island and their associated active littoral system prior to restoration activities. These activities include the closure of Camille Cut and the placement of sediment in the littoral zone of West Ship Island. This survey will be used to verify sediment migration patterns by monitoring elevation change over time. The dataset produced by this survey will also be compared with historic bathymetric datasets to help further understand island elevation over time. This data release provides 667-line kilometers (km) of processed Single-Beam Bathymetry (SBB) data and 773-line km of processed Interferometric Bathymetry (IFB) collected by the USGS SPCMSC in July 2016 (field activity number [FAN] 2016-347-FA). The IFB data were acquired aboard the Research Vessel (RV) Sallenger (subFAN, 16BIM04), and the SBB data were acquired aboard the RV Jabba Jaw (subFAN, 16BIM05) and the RV Mako (subFAN, 16BIM06). The IFB and SBB point data are provided in three datums: 1) the International Terrestrial Reference Frame of 2000 (ITRF00), ellipsoid height (-49.70 meters [m] to -28.87 m); 2) the North American Datum of 1983 (NAD83) CORS96 realization and the North American Vertical Datum 1988 (NAVD88) with respect to the GEOID12B model (-0.07 m to -20.69 m); and 3) NAD83 (CORS96) and Mean Lower Low Water (MLLW) (-0.04 m to -20.60 m). This metadata record describes the comprehensive, 50-meter (m) Digital Elevation Model (DEM) created from the IFB and SBB point data and provided in NAD83 NAVD88 GEOID12B. For additional information regarding data collection and processing, please refer to the field logs and formal Federal Geographic Data Committee (FGDC) metadata for the individual XYZ point data files and survey trackline shapefiles also included within this data release (https://doi.org/10.5066/F7B8571Q).

This dataset represents a compilation of vector shorelines in the Grand Bay National Estuarine Research Reserve (Mississippi and Alabama) from 1848 to 2017. Shoreline data were obtained from multiple data sources, including the U.S. Geological Survey (USGS), the National Oceanic and Atmospheric Administration (NOAA), the Grand Bay National Estuarine Research Reserve (GBNERR), and the Mississippi Office of Geology (MOG). All shoreline data types have uncertainty associated with delineating the shoreline location, particularly with vegetated coastlines. For this study, the "apparent shoreline" was mapped for all data sources. The "apparent shoreline" is defined as "where the actual shoreline is obscured by marsh, mangrove, cypress, or other type of marine vegetation, the outer edge of the vegetation is mapped” (Shalowitz, 1964). In the case of aerial imagery, vegetation-water boundary was digitized. Field-surveys identified the edge of the dominate vegetation or the eroding scarp line. Shorelines were obtained from the original provider, or digitized, and merged into a single file, in order to conduct shoreline change analyses. Datasets were compiled and analyzed using the R package Analyzing Moving Boundaries Using R (AMBUR) program. Rates of shoreline change can be used for evaluating living shoreline resources, decision-making for future resource planning, and restoration of both protected and open-ocean shorelines. This data release contains shorelines from 1848-2017 along with transects with rates of change joined to the data table. This metadata record should be reviewed in its entirety to ensure specific data is suitable for other studies as some shorelines were specifically digitized for use with transects in this study. Shorelines from 1942, 1975, 1986, 1992, 2004, 2006, 2014 have limited spatial resolution. All shorelines labeled GBNERR in the “Source” field of the attribute table, and 2016 and 2017 GPS shorelines from the USGS, are previously unpublished data sets.

Shoreline change analysis is an important environmental monitoring tool for evaluating coastal exposure to erosion hazards, particularly for vulnerable habitats such as coastal wetlands where habitat loss is problematic world-wide. The increasing availability of high-resolution satellite imagery and emerging developments in analysis techniques support the implementation of these data into coastal management, including shoreline monitoring and change analysis. Geospatial shoreline data were created from a semi-automated methodology using WorldView (WV) satellite data between 2013 and 2020. The data were compared to contemporaneous field-surveyed Real-time Kinematic (RTK) Global Positioning System (GPS) data collected by the Grand Bay National Estuarine Research Reserve (GBNERR) and digitized shorelines from U.S. Department of Agriculture National Agriculture Imagery Program (NAIP) orthophotos. Field data for shoreline monitoring sites was also collected to aid interpretation of results. This data release contains digital vector shorelines, shoreline change calculations for all three remote sensing data sets, and field surveyed data. The data will aid managers and decision-makers in the adoption of high-resolution satellite imagery into shoreline monitoring activities, which will increase the spatial scale of shoreline change monitoring, provide rapid response to evaluate impacts of coastal erosion, and reduce cost of labor-intensive practices. For further information regarding data collection and/or processing methods, refer to the associated journal article (Smith and others, 2021).

Shoreline change analysis is an important environmental monitoring tool for evaluating coastal exposure to erosion hazards, particularly for vulnerable habitats such as coastal wetlands where habitat loss is problematic world-wide. The increasing availability of high-resolution satellite imagery and emerging developments in analysis techniques support the implementation of these data into coastal management, including shoreline monitoring and change analysis. Geospatial shoreline data were created from a semi-automated methodology using WorldView (WV) satellite data between 2013 and 2020. The data were compared to contemporaneous field-surveyed Real-time Kinematic (RTK) Global Position System (GPS) data collected by the Grand Bay National Estuarine Research Reserve and digitized shorelines from U.S. Department of Agriculture National Agriculture Imagery Program (NAIP) orthophotos. Field data for shoreline monitoring sites was also collected to aid interpretation of results. This data release contains digital vector shorelines, shoreline change calculations for all three remote sensing data sets, and field surveyed data. The data will aid managers and decision-makers in the adoption of high-resolution satellite imagery into shoreline monitoring activities, which will increase the spatial scale of shoreline change monitoring, provide rapid response to evaluate impacts of coastal erosion, and reduce cost of labor-intensive practices. For further information regarding data collection and/or processing methods, refer to the associated journal article (Smith and others, 2021).

Shoreline change analysis is an important environmental monitoring tool for evaluating coastal exposure to erosion hazards, particularly for vulnerable habitats such as coastal wetlands where habitat loss is problematic world-wide. The increasing availability of high-resolution satellite imagery and emerging developments in analysis techniques support the implementation of these data into coastal management, including shoreline monitoring and change analysis. Geospatial shoreline data were created from a semi-automated methodology using WorldView (WV) satellite data between 2013 and 2020. The data were compared to contemporaneous field-surveyed Real-time Kinematic (RTK) Global Positioning System (GPS) data collected by the Grand Bay National Estuarine Research Reserve (GBNERR) and digitized shorelines from U.S. Department of Agriculture National Agriculture Imagery Program (NAIP) orthophotos. Field data for shoreline monitoring sites was also collected to aid interpretation of results. This data release contains digital vector shorelines, shoreline change calculations for all three remote sensing data sets, and field surveyed data. The data will aid managers and decision-makers in the adoption of high-resolution satellite imagery into shoreline monitoring activities, which will increase the spatial scale of shoreline change monitoring, provide rapid response to evaluate impacts of coastal erosion, and reduce cost of labor-intensive practices. For further information regarding data collection and/or processing methods, refer to the associated journal article (Smith and others, 2021).

This data release serves as an archive of sediment physical properties and grain-size data for surficial samples collected offshore of Assateague Island, Maryland and Virginia, for comparison with surficial estuarine and subaerial sedimentological samples collected and assessed following Hurricane Sandy (Ellis and others, 2015 (http://doi.org/10.3133/ofr20151219); Smith and others, 2015 (http://doi.org/10.3133/ofr20151169); Bernier and others, 2016 (https://pubs.usgs.gov/ds/0999/)). The sediment samples were collected by scientists from the U.S. Geological Survey (USGS) office in Woods Hole, Massachusetts while aboard the motor vessel (M/V) Scarlett Isabella as part of a larger effort to map the inner continental shelf (Pendleton and others, 2016 (http://doi.org/10.5066/F7MW2F60)). Following field work, the sediment samples were shipped to the USGS Coastal and Marine Science Center in St. Petersburg, Florida, where they were renamed for consistency with a previously existing naming scheme and processed for bulk density, loss on ignition (LOI), and grain-size. The grain-size subsamples were processed on a Coulter LS200 particle-size analyzer for consistency regarding methods and output statistics with related data sets from Chincoteague Bay and Assateague Island. For more information regarding sample collection and site information or the related data sets, refer to USGS data release Pendleton and others, 2016 (https://doi.org/10.5066/F7MW2F60); for more information regarding processing methods refer to USGS Open-File Report 2015Ð1219 (http://doi.org/10.3133/ofr20151219). Downloadable data are available as Excel spreadsheets (.xlsx), comma-separated values text files (.csv), and formal Federal Geographic Data Committee (FGDC) metadata.

Staghorn coral, Acropora cervicornis, is a threatened species and the primary focus of western Atlantic reef-restoration efforts to date. As part of the USGS Coral Reef Ecosystems Studies project (http://coastal.er.usgs.gov/crest/), scientists investigated skeletal characteristics of nursery-grown staghorn coral reared using two commonly used grow-out methods at Mote Tropical Research Laboratory’s offshore nursery. USGS staff compared linear extension, calcification rate, and skeletal density of nursery-raised A. cervicornis branches reared for six months either on blocks attached to substratum or hanging from monofilament line (on PVC “trees”) in the water column. The results demonstrated that branches grown on the substratum had significantly higher skeletal density, measured using computerized tomography (CT), and lower linear extension rates compared to water-column fragments. Calcification rates determined with buoyant weighing were not statistically different between the two grow-out methods, but did vary among coral genotypes. Whereas skeletal density and extension rates were plastic traits that depended on environment, the calcification rate was conserved. Results show that the two rearing methods generate the same amount of calcium-carbonate skeleton but produce colonies with different skeletal characteristics, and suggest that genetically based variability in coral-calcification performance exists. The data resulting from this experiment are provided in this data release and are interpreted in Kuffner et al. (2017).

Using version 5527 of the XBeach numerical model (Roelvink and others, 2009), barrier island morphological change was simulated at Dauphin Island, Alabama (AL), for a 30-year forecast of multiple storms and sea level rise, considering scenarios of no-action and beach and dune nourishment as described in Passeri and others (2021). The two-dimensional XBeach model can be applied to barrier islands to solve for time-dependent topography and bathymetry. The XBeach model setup requires the input of topographic and bathymetric elevations at each grid cell. Model inputs and outputs in the form of topography and bathymetry at each grid cell are provided in this data release. For further information regarding model input generation and visualization of model output, refer to Passeri and others (2021).

Using version 5527 of the XBeach numerical model (Roelvink and others, 2009), barrier island morphological change was simulated at Dauphin Island, Alabama (AL), for a 30-year forecast of multiple storms and sea level rise, considering scenarios of no-action and beach and dune nourishment as described in Passeri and others (2021). The two-dimensional XBeach model can be applied to barrier islands to solve for time-dependent topography and bathymetry. The XBeach model setup requires the input of topographic and bathymetric elevations at each grid cell. Model inputs and outputs in the form of topography and bathymetry at each grid cell are provided in this data release. For further information regarding model input generation and visualization of model output, refer to Passeri and others (2021).

Using version 5527 of the XBeach numerical model (Roelvink and others, 2009), barrier island morphological change was simulated at Dauphin Island, Alabama (AL), for a 30-year forecast of multiple storms and sea level rise, considering scenarios of no-action and beach and dune nourishment as described in Passeri and others (2021). The two-dimensional XBeach model can be applied to barrier islands to solve for time-dependent topography and bathymetry. The XBeach model setup requires the input of topographic and bathymetric elevations at each grid cell. Model inputs and outputs in the form of topography and bathymetry at each grid cell are provided in this data release. For further information regarding model input generation and visualization of model output, refer to Passeri and others (2021).

Using version 5527 of the XBeach numerical model (Roelvink and others, 2009), barrier island morphological change was simulated at Dauphin Island, Alabama (AL), for a 30-year forecast of multiple storms and sea level rise, considering scenarios of no-action and beach and dune nourishment as described in Passeri and others (2021). The two-dimensional XBeach model can be applied to barrier islands to solve for time-dependent topography and bathymetry. The XBeach model setup requires the input of topographic and bathymetric elevations at each grid cell. Model inputs and outputs in the form of topography and bathymetry at each grid cell are provided in this data release. For further information regarding model input generation and visualization of model output, refer to Passeri and others (2021).

Using version 5527 of the XBeach numerical model (Roelvink and others, 2009), barrier island morphological change was simulated at Dauphin Island, Alabama (AL), for a 30-year forecast of multiple storms and sea level rise, considering scenarios of no-action and beach and dune nourishment as described in Passeri and others (2021). The two-dimensional XBeach model can be applied to barrier islands to solve for time-dependent topography and bathymetry. The XBeach model setup requires the input of topographic and bathymetric elevations at each grid cell. Model inputs and outputs in the form of topography and bathymetry at each grid cell are provided in this data release. For further information regarding model input generation and visualization of model output, refer to Passeri and others (2021).

Using version 5527 of the XBeach numerical model (Roelvink and others, 2009), barrier island morphological change was simulated at Dauphin Island, Alabama (AL), for a 30-year forecast of multiple storms and sea level rise, considering scenarios of no-action and beach and dune nourishment as described in Passeri and others (2021). The two-dimensional XBeach model can be applied to barrier islands to solve for time-dependent topography and bathymetry. The XBeach model setup requires the input of topographic and bathymetric elevations at each grid cell. Model inputs and outputs in the form of topography and bathymetry at each grid cell are provided in this data release. For further information regarding model input generation and visualization of model output, refer to Passeri and others (2021).

Using version 5527 of the XBeach numerical model (Roelvink and others, 2009), barrier island morphological change was simulated at Dauphin Island, Alabama (AL), for a 30-year forecast of multiple storms and sea level rise, considering scenarios of no-action and beach and dune nourishment as described in Passeri and others (2021). The two-dimensional XBeach model can be applied to barrier islands to solve for time-dependent topography and bathymetry. The XBeach model setup requires the input of topographic and bathymetric elevations at each grid cell. Model inputs and outputs in the form of topography and bathymetry at each grid cell are provided in this data release. For further information regarding model input generation and visualization of model output, refer to Passeri and others (2021).

Using version 5527 of the XBeach numerical model (Roelvink and others, 2009), barrier island morphological change was simulated at Dauphin Island, Alabama (AL), for a 30-year forecast of multiple storms and sea level rise, considering scenarios of no-action and beach and dune nourishment as described in Passeri and others (2021). The two-dimensional XBeach model can be applied to barrier islands to solve for time-dependent topography and bathymetry. The XBeach model setup requires the input of topographic and bathymetric elevations at each grid cell. Model inputs and outputs in the form of topography and bathymetry at each grid cell are provided in this data release. For further information regarding model input generation and visualization of model output, refer to Passeri and others (2021).

Using version 5527 of the XBeach numerical model (Roelvink and others, 2009), barrier island morphological change was simulated at Dauphin Island, Alabama (AL), for a 30-year forecast of multiple storms and sea level rise, considering scenarios of no-action and beach and dune nourishment as described in Passeri and others (2021). The two-dimensional XBeach model can be applied to barrier islands to solve for time-dependent topography and bathymetry. The XBeach model setup requires the input of topographic and bathymetric elevations at each grid cell. Model inputs and outputs in the form of topography and bathymetry at each grid cell are provided in this data release. For further information regarding model input generation and visualization of model output, refer to Passeri and others (2021).

Using version 5527 of the XBeach numerical model (Roelvink and others, 2009), barrier island morphological change was simulated at Dauphin Island, Alabama (AL), for a 30-year forecast of multiple storms and sea level rise, considering scenarios of no-action and beach and dune nourishment as described in Passeri and others (2021). The two-dimensional XBeach model can be applied to barrier islands to solve for time-dependent topography and bathymetry. The XBeach model setup requires the input of topographic and bathymetric elevations at each grid cell. Model inputs and outputs in the form of topography and bathymetry at each grid cell are provided in this data release. For further information regarding model input generation and visualization of model output, refer to Passeri and others (2021).

Using version 5527 of the XBeach numerical model (Roelvink and others, 2009), barrier island morphological change was simulated at Dauphin Island, Alabama (AL), for a 30-year forecast of multiple storms and sea level rise, considering scenarios of no-action and beach and dune nourishment as described in Passeri and others (2021). The two-dimensional XBeach model can be applied to barrier islands to solve for time-dependent topography and bathymetry. The XBeach model setup requires the input of topographic and bathymetric elevations at each grid cell. Model inputs and outputs in the form of topography and bathymetry at each grid cell are provided in this data release. For further information regarding model input generation and visualization of model output, refer to Passeri and others (2021).

Using version 5527 of the XBeach numerical model (Roelvink and others, 2009), barrier island morphological change was simulated at Dauphin Island, Alabama (AL), for a 30-year forecast of multiple storms and sea level rise, considering scenarios of no-action and beach and dune nourishment as described in Passeri and others (2021). The two-dimensional XBeach model can be applied to barrier islands to solve for time-dependent topography and bathymetry. The XBeach model setup requires the input of topographic and bathymetric elevations at each grid cell. Model inputs and outputs in the form of topography and bathymetry at each grid cell are provided in this data release. For further information regarding model input generation and visualization of model output, refer to Passeri and others (2021).

Using version 5527 of the XBeach numerical model (Roelvink and others, 2009), barrier island morphological change was simulated at Dauphin Island, Alabama (AL), for a 30-year forecast of multiple storms and sea level rise, considering scenarios of no-action and beach and dune nourishment as described in Passeri and others (2021). The two-dimensional XBeach model can be applied to barrier islands to solve for time-dependent topography and bathymetry. The XBeach model setup requires the input of topographic and bathymetric elevations at each grid cell. Model inputs and outputs in the form of topography and bathymetry at each grid cell are provided in this data release. For further information regarding model input generation and visualization of model output, refer to Passeri and others (2021).

Using version 5527 of the XBeach numerical model (Roelvink and others, 2009), barrier island morphological change was simulated at Dauphin Island, Alabama (AL), for a 30-year forecast of multiple storms and sea level rise, considering scenarios of no-action and beach and dune nourishment as described in Passeri and others (2021). The two-dimensional XBeach model can be applied to barrier islands to solve for time-dependent topography and bathymetry. The XBeach model setup requires the input of topographic and bathymetric elevations at each grid cell. Model inputs and outputs in the form of topography and bathymetry at each grid cell are provided in this data release. For further information regarding model input generation and visualization of model output, refer to Passeri and others (2021).

Using version 5527 of the XBeach numerical model (Roelvink and others, 2009), barrier island morphological change was simulated at Dauphin Island, Alabama (AL), for a 30-year forecast of multiple storms and sea level rise, considering scenarios of no-action and beach and dune nourishment as described in Passeri and others (2021). The two-dimensional XBeach model can be applied to barrier islands to solve for time-dependent topography and bathymetry. The XBeach model setup requires the input of topographic and bathymetric elevations at each grid cell. Model inputs and outputs in the form of topography and bathymetry at each grid cell are provided in this data release. For further information regarding model input generation and visualization of model output, refer to Passeri and others (2021).

Using version 5527 of the XBeach numerical model (Roelvink and others, 2009), barrier island morphological change was simulated at Dauphin Island, Alabama (AL), for a 30-year forecast of multiple storms and sea level rise, considering scenarios of no-action and beach and dune nourishment as described in Passeri and others (2021). The two-dimensional XBeach model can be applied to barrier islands to solve for time-dependent topography and bathymetry. The XBeach model setup requires the input of topographic and bathymetric elevations at each grid cell. Model inputs and outputs in the form of topography and bathymetry at each grid cell are provided in this data release. For further information regarding model input generation and visualization of model output, refer to Passeri and others (2021).

Using version 5527 of the XBeach numerical model (Roelvink and others, 2009), barrier island morphological change was simulated at Dauphin Island, Alabama (AL), for a 30-year forecast of multiple storms and sea level rise, considering scenarios of no-action and beach and dune nourishment as described in Passeri and others (2021). The two-dimensional XBeach model can be applied to barrier islands to solve for time-dependent topography and bathymetry. The XBeach model setup requires the input of topographic and bathymetric elevations at each grid cell. Model inputs and outputs in the form of topography and bathymetry at each grid cell are provided in this data release. For further information regarding model input generation and visualization of model output, refer to Passeri and others (2021).

Using version 5527 of the XBeach numerical model (Roelvink and others, 2009), barrier island morphological change was simulated at Dauphin Island, Alabama (AL), for a 30-year forecast of multiple storms and sea level rise, considering scenarios of no-action and beach and dune nourishment as described in Passeri and others (2021). The two-dimensional XBeach model can be applied to barrier islands to solve for time-dependent topography and bathymetry. The XBeach model setup requires the input of topographic and bathymetric elevations at each grid cell. Model inputs and outputs in the form of topography and bathymetry at each grid cell are provided in this data release. For further information regarding model input generation and visualization of model output, refer to Passeri and others (2021).

Using version 5527 of the XBeach numerical model (Roelvink and others, 2009), barrier island morphological change was simulated at Dauphin Island, Alabama (AL), for a 30-year forecast of multiple storms and sea level rise, considering scenarios of no-action and beach and dune nourishment as described in Passeri and others (2021). The two-dimensional XBeach model can be applied to barrier islands to solve for time-dependent topography and bathymetry. The XBeach model setup requires the input of topographic and bathymetric elevations at each grid cell. Model inputs and outputs in the form of topography and bathymetry at each grid cell are provided in this data release. For further information regarding model input generation and visualization of model output, refer to Passeri and others (2021).

Using version 5527 of the XBeach numerical model (Roelvink and others, 2009), barrier island morphological change was simulated at Dauphin Island, Alabama (AL), for a 30-year forecast of multiple storms and sea level rise, considering scenarios of no-action and beach and dune nourishment as described in Passeri and others (2021). The two-dimensional XBeach model can be applied to barrier islands to solve for time-dependent topography and bathymetry. The XBeach model setup requires the input of topographic and bathymetric elevations at each grid cell. Model inputs and outputs in the form of topography and bathymetry at each grid cell are provided in this data release. For further information regarding model input generation and visualization of model output, refer to Passeri and others (2021).

Using version 5527 of the XBeach numerical model (Roelvink and others, 2009), barrier island morphological change was simulated at Dauphin Island, Alabama (AL), for a 30-year forecast of multiple storms and sea level rise, considering scenarios of no-action and beach and dune nourishment as described in Passeri and others (2021). The two-dimensional XBeach model can be applied to barrier islands to solve for time-dependent topography and bathymetry. The XBeach model setup requires the input of topographic and bathymetric elevations at each grid cell. Model inputs and outputs in the form of topography and bathymetry at each grid cell are provided in this data release. For further information regarding model input generation and visualization of model output, refer to Passeri and others (2021).

Using version 5527 of the XBeach numerical model (Roelvink and others, 2009), barrier island morphological change was simulated at Dauphin Island, Alabama (AL), for a 30-year forecast of multiple storms and sea level rise, considering scenarios of no-action and beach and dune nourishment as described in Passeri and others (2021). The two-dimensional XBeach model can be applied to barrier islands to solve for time-dependent topography and bathymetry. The XBeach model setup requires the input of topographic and bathymetric elevations at each grid cell. Model inputs and outputs in the form of topography and bathymetry at each grid cell are provided in this data release. For further information regarding model input generation and visualization of model output, refer to Passeri and others (2021).

Using version 5527 of the XBeach numerical model (Roelvink and others, 2009), barrier island morphological change was simulated at Dauphin Island, Alabama (AL), for a 30-year forecast of multiple storms and sea level rise, considering scenarios of no-action and beach and dune nourishment as described in Passeri and others (2021). The two-dimensional XBeach model can be applied to barrier islands to solve for time-dependent topography and bathymetry. The XBeach model setup requires the input of topographic and bathymetric elevations at each grid cell. Model inputs and outputs in the form of topography and bathymetry at each grid cell are provided in this data release. For further information regarding model input generation and visualization of model output, refer to Passeri and others (2021).

Using version 5527 of the XBeach numerical model (Roelvink and others, 2009), barrier island morphological change was simulated at Dauphin Island, Alabama (AL), for a 30-year forecast of multiple storms and sea level rise, considering scenarios of no-action and beach and dune nourishment as described in Passeri and others (2021). The two-dimensional XBeach model can be applied to barrier islands to solve for time-dependent topography and bathymetry. The XBeach model setup requires the input of topographic and bathymetric elevations at each grid cell. Model inputs and outputs in the form of topography and bathymetry at each grid cell are provided in this data release. For further information regarding model input generation and visualization of model output, refer to Passeri and others (2021).

The U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center conducted research to quantify bathymetric changes at a subsection of Crocker Reef near Islamorada, Florida (FL), within a 6.1 square-kilometer area following the landfall of Hurricane Irma in September 2017. USGS staff used USGS multibeam data collected between October 10 and December 8, 2017 (Fredericks and others, 2019) and March 8-15, 2018 (Fredericks and others, 2019) to assess changes in seafloor elevation and structure in the months following the passage of Hurricane Irma. An elevation change analysis between the 2017 and 2018 USGS multibeam data was performed to quantify and map impacts to seafloor elevations and to determine elevation and volume change statistics for seven habitat types found within a subsection of Crocker Reef, FL.

This dataset contains carbonate system data collected by scientists from the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center to investigate the effects of carbon cycling, coastal and ocean acidification on the Tampa Bay estuary located in west central Florida. Discrete seawater samples were collected along spatial transects at one to four hour intervals over 24-hour time periods. Water samples were analyzed at the USGS Carbon Analytical Laboratory in St. Petersburg Florida. This data set contains time series measurements of carbonate system parameters including: water temperature (Celsius, C), salinity, dissolved oxygen (milligrams/L), total alkalinity (TA, micromoles/kg), dissolved inorganic carbon (DIC, micromoles/kg), pHT (pH on the total scale), nitrate + nitrite (NO3+NO2, micromols/L), nitrite (NO2, micromols/L), silicate (SIL, micromols/L), ammonium (NH4, micromols/L) and phosphate (PO4, micromols/L).

This dataset contains carbonate system data collected by scientists from the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center to investigate the effects of carbon cycling, coastal and ocean acidification on the Tampa Bay estuary located in west central Florida, USA. These data were collected using an autonomous instrument called the Ocean Carbon System (OCS) deployed on the seafloor in Tampa Bay. The OCS consists of five sensors integrated into a Sea-Bird Scientific (Satlantic) STOR-X submersible data logger including a Seabird 16plus CTD, a Satlantic SeaFET pH sensor, a Pro-Oceanus CO2-Pro CO2 sensor, an Aanderaa oxygen optode, and a Wetlabs Eco-PAR sensor. The dataset is a time series of carbonate system parameters including: water temperature (Celsius, °C), conductivity (siemens, S), pressure (decibar, dbar), salinity, pHT (pH on the total scale), carbon dioxide (ppm), pressure from the CO2-Pro Infrared Gas Analyzer (IRGA) (millibars), dissolved oxygen (micromoles) and photosynthetically available radiation (microEinsteins). Each parameter was measured every hour for 24-hour time periods during extended deployments ranging from weeks to months.

Rates of shoreline change for Dauphin Island, Alabama were generated for three analysis periods, using two different shoreline proxy datasets. Mean High Water line (MHW) shorelines were generated from 14 lidar datasets (1998-2014) and Wet Dry Line (WDL) shorelines were digitized from ten sets of georeferenced aerial images (1940-2015). Rates of change were generated for three groups of shorelines: MHW (lidar), WDL (aerial) and MHW and WDL shorelines combined. These data will aid in developing an understanding of the evolution of the barrier island position, size and shape as well as documenting spatially-variable patterns in erosion and accretion of different sections of the island.

Rates of shoreline change for Dauphin Island, Alabama were generated for three analysis periods, using two different shoreline proxy datasets. Mean High Water line (MHW) shorelines were generated from 14 lidar datasets (1998-2014) and Wet Dry Line (WDL) shorelines were digitized from ten sets of georeferenced aerial images (1940-2015). Rates of change were generated for three groups of shorelines: MHW (lidar), WDL (aerial) and MHW and WDL shorelines combined. These data will aid in developing an understanding of the evolution of the barrier island position, size and shape as well as documenting spatially-variable patterns in erosion and accretion of different sections of the island.

This dataset contains shoreline change rates for the Grand Bay National Estuarine Research Reserve from 1848 to 2017. Shoreline data were obtained from multiple data sources, including the U.S. Geological Survey (USGS), National Oceanic and Atmospheric Administration (NOAA), the Grand Bay National Estuarine Research Reserve(GBNERR), and the Mississippi Office of Geology (MOG). Datasets were compiled and analyzed using the R package Analyzing Moving Boundaries Using R (AMBUR) program. Rates of shoreline change can be used for evaluating living shoreline resources, decision-making for future resource planning, and restoration of both protected and open-ocean shorelines.

Three Acoustic Doppler current profilers (ADCP), a current meter and a pressure logger were deployed at Crocker Reef, a senile (dead) barrier reef located in the northern portion of the Florida Reef Tract from December 12, 2014 to January 30, 2015 to quantify flow characteristics in various sub-regions. A Nortek Aquadopp current meter was deployed on the reef flat and configured to measure three-dimensional flow velocities at the middle of the water column. Current measurements were taken at a rate of 1 hertz (Hz) for 120 seconds (s) every 10 minutes; each 120-s sampling was internally averaged to provide data at 10-minute resolution.

Coral reefs serve as natural barriers that protect adjacent shorelines from coastal hazards such as storms, waves and erosion but projections indicate global degradation of coral reefs due to anthropogenic impacts and climate change will cause a transition to net erosion by mid-century. The U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center conducted research to quantify the combined effect of all constructive and destructive processes on modern coral reef ecosystems by measuring regional-scale changes in seafloor elevation. USGS staff assessed five coral reef ecosystems in the Atlantic Ocean (Upper and Lower Florida Keys), Caribbean Sea (U.S. Virgin Islands: St. Thomas and Buck Island, St. Croix), and Pacific Ocean (Maui, Hawaii), including both coral-dominated and adjacent, non-coral dominated habitats. Scientists used historical bathymetric data from the 1930s to 1980s and contemporary light detection and ranging (lidar) digital elevation models (DEMs) from the late 1990s to 2000s to calculate changes in seafloor elevation for each study site over time periods reflecting low to high anthropogenic impacts. UFK_ElevationChange.zip contains the location, elevation, and elevation change data for the Upper Florida Keys. Using these changes in elevation, further analysis was done to calculate corresponding changes in seafloor volume for all study areas and habitat types within each site.

The U.S. Geological Survey (USGS) Coastal and Marine Hazards and Resources Program (CMHRP) has actively collected geophysical and sedimentological data in the northern Gulf of Mexico for several decades, including shallow subsurface data in the form of high-resolution seismic reflection profiles (HRSP). Prior to the mid-1990s most HRSP data were collected in analog format as paper rolls of continuous profiles up to 25 meters long. As part of the National Geological and Geophysical Data Preservation Program (NGGDPP, https://datapreservation.usgs.gov/), and in collaboration with the Bureau of Ocean Energy Management, Marine Minerals Program, scientists from the USGS St. Petersburg Coastal and Marine Science Center (USGS-SPCMSC) converted analog paper records (located in the archives of the Department of Earth and Environmental Sciences at the University of New Orleans (UNO)) to digital format using a large-format continuous scanner. The scanned image files were subsequently processed to fix distortions and crop out blank spaces prior to exporting as industry standard Society of Exploration Geophysicists data exchange (SEG-Y) formatted files. This data release serves as an archive of HRSP profiles annotated with header information, converted SEG-Y files, navigation data, and cruise trackline shapefiles. The HRSP data were collected using a boomer seismic system onboard research vessel (R/V) Acadiana, R/V R.J. Russell (Lacoss) and R/V Kit Jones. The first set of Acadiana geophysical cruise data (89-1) was collected southeast of Grand Isle, Louisiana (LA), east of the Chandeleur Islands, LA and south of Petit Bois Island, Mississippi from July 28 to August 6, 1989. The second set of Acadiana geophysical cruise data (92-1) was collected along Holly Beach, LA and south of the Louisiana/Texas border from July 23-27, 1992. The first set of Lacoss geophysical cruise data (Lacoss 82-1) was collected along the southwestern coast of Louisiana offshore of Holly Beach from August 28 to September 18, 1982. The second set of Lacoss geophysical cruise data (Lacoss 85-6) was collected along the Chandeleur Islands from May 15-21, 1985. The first set of Kit Jones geophysical cruise data (Kit Jones 91-1) was collected south of Gulf Shores, Alabama and extends along the panhandle of Florida (FL) from May 15-19, 1991. The second set of Kit Jones geophysical cruise data (Kit Jones 92-3) was collected in a similar region as 91-1, as well as off the coast of St. Vincent Island, FL from July 15-20, 1992. Data collection and processing methods are described in USGS Data Series 1047.

Shoreline change analysis is an important environmental monitoring tool for evaluating coastal exposure to erosion hazards, particularly for vulnerable habitats such as coastal wetlands where habitat loss is problematic world-wide. The increasing availability of high-resolution satellite imagery and emerging developments in analysis techniques support the implementation of these data into coastal management, including shoreline monitoring and change analysis. Geospatial shoreline data were created from a semi-automated methodology using WorldView (WV) satellite data between 2013 and 2020. The data were compared to contemporaneous field-surveyed Real-time Kinematic (RTK) Global Positioning System (GPS) data collected by the Grand Bay National Estuarine Research Reserve (GBNERR) and digitized shorelines from U.S. Department of Agriculture National Agriculture Imagery Program (NAIP) orthophotos. Field data for shoreline monitoring sites was also collected to aid interpretation of results. This data release contains digital vector shorelines, shoreline change calculations for all three remote sensing data sets, and field surveyed data. The data will aid managers and decision-makers in the adoption of high-resolution satellite imagery into shoreline monitoring activities, which will increase the spatial scale of shoreline change monitoring, provide rapid response to evaluate impacts of coastal erosion, and reduce cost of labor-intensive practices. For further information regarding data collection and/or processing methods, refer to the associated journal article (Smith and others, 2021).

The U.S. Army Corps of Engineers(USACE) contracted a beach survey of Fire Island, New York from September 17–October 6, 2013, for the purpose of planning a beach reconstruction project following Hurricane Sandy. This dataset contains elevation data of subaerial morphology and nearshore bathymetry collected using real time kinematic global positioning system (RTK-GPS) and hydrography techniques. The data were provided to the U.S. Geological Survey(USGS) to contribute to an existing monitoring dataset of the beach and dune morphology at Fire Island.

Using version 5527 of the XBeach numerical model (Roelvink and others, 2009), barrier island morphological change was simulated at Dauphin Island, Alabama (AL), for a 30-year forecast of multiple storms and sea level rise, considering scenarios of no-action and beach and dune nourishment as described in Passeri and others (2021). The two-dimensional XBeach model can be applied to barrier islands to solve for time-dependent topography and bathymetry. The XBeach model setup requires the input of topographic and bathymetric elevations at each grid cell. Model inputs and outputs in the form of topography and bathymetry at each grid cell are provided in this data release. For further information regarding model input generation and visualization of model output, refer to Passeri and others (2021).

Using version 5527 of the XBeach numerical model (Roelvink and others, 2009), barrier island morphological change was simulated at Dauphin Island, Alabama (AL), for a 30-year forecast of multiple storms and sea level rise, considering scenarios of no-action and beach and dune nourishment as described in Passeri and others (2021). The two-dimensional XBeach model can be applied to barrier islands to solve for time-dependent topography and bathymetry. The XBeach model setup requires the input of topographic and bathymetric elevations at each grid cell. Model inputs and outputs in the form of topography and bathymetry at each grid cell are provided in this data release. For further information regarding model input generation and visualization of model output, refer to Passeri and others (2021).

The National Assessment of Coastal Change Hazards project exists to understand and predict storm impacts to our nation's coastlines. This geospatial dataset defines the alongshore extent of overwash sediments deposited along the Louisiana coast and attributed to coastal processes during [Atlantic Basin] Hurricane Zeta, which made landfall in the U.S. on October 28, 2020.

Weathered oil in the surf-zone after an oil spill may mix with suspended sediments to form sand and oil agglomerates (SOA). Sand and oil agglomerates may form in mats on the scale of tens of meters (m), and may break apart into pieces between 1 and 10 centimeters (cm) in diameter. These more mobile pieces are susceptible to alongshore and cross-shore transport, and lead to beach re-oiling on the time scale of months to years following a spill. The U.S. Geological Survey (USGS) conducted experiments March 10 - 13, 2014, to expand the available data on sand and oil agglomerate motion; test shear stress based incipient motion parameterizations in a controlled, laboratory setting; and directly observe SOA exhumation and burial processes. Artificial sand and oil agglomerates (aSOA) were created and deployed in a small-oscillatory flow tunnel in two sets of experiments, during which, video and velocity data were obtained. The first experiment, which was set up to help researchers investigate incipient motion, used with an immobile, rough bottom (referred to as false-floor) and the second–testing seafloor interactions–utilized with a coarse grain sand bottom (movable sand bed). Detailed information regarding the creation of the aSOA can be found in Dalyander et al. (2015). More information about the USGS laboratory experiment conducted in collaboration with the Naval Research Laboratory can be found in the associated Open File Report (OFR Number Unknown).

Weathered oil in the surf-zone after an oil spill may mix with suspended sediments to form sand and oil agglomerates (SOA). Sand and oil agglomerates may form in mats on the scale of tens of meters (m), and may break apart into pieces between 1 and 10 centimeters (cm) in diameter. These more mobile pieces are susceptible to alongshore and cross-shore transport, and lead to beach re-oiling on the time scale of months to years following a spill. The U.S. Geological Survey (USGS) conducted experiments March 10 - 13, 2014, to expand the available data on sand and oil agglomerate motion; test shear stress based incipient motion parameterizations in a controlled, laboratory setting; and directly observe SOA exhumation and burial processes. Artificial sand and oil agglomerates (aSOA) were created and deployed in a small-oscillatory flow tunnel in two sets of experiments, during which, video and velocity data were obtained. The first experiment, which was set up to help researchers investigate incipient motion, used with an immobile, rough bottom (referred to as false-floor) and the second–testing seafloor interactions–utilized with a coarse grain sand bottom (movable sand bed). Detailed information regarding the creation of the aSOA can be found in Dalyander et al. (2015). More information about the USGS laboratory experiment conducted in collaboration with the Naval Research Laboratory can be found in the associated Open File Report (OFR Number Unknown).

Weathered oil in the surf-zone after an oil spill may mix with suspended sediments to form sand and oil agglomerates (SOA). Sand and oil agglomerates may form in mats on the scale of tens of meters (m), and may break apart into pieces between 1 and 10 centimeters (cm) in diameter. These more mobile pieces are susceptible to alongshore and cross-shore transport, and lead to beach re-oiling on the time scale of months to years following a spill. The U.S. Geological Survey (USGS) conducted experiments March 10 - 13, 2014, to expand the available data on sand and oil agglomerate motion; test shear stress based incipient motion parameterizations in a controlled, laboratory setting; and directly observe SOA exhumation and burial processes. Artificial sand and oil agglomerates (aSOA) were created and deployed in a small-oscillatory flow tunnel in two sets of experiments, during which, video and velocity data were obtained. The first experiment, which was set up to help researchers investigate incipient motion, used with an immobile, rough bottom (referred to as false-floor) and the second–testing seafloor interactions–utilized with a coarse grain sand bottom (movable sand bed). Detailed information regarding the creation of the aSOA can be found in Dalyander et al. (2015). More information about the USGS laboratory experiment conducted in collaboration with the Naval Research Laboratory can be found in the associated Open File Report (OFR Number Unknown).

Weathered oil in the surf-zone after an oil spill may mix with suspended sediments to form sand and oil agglomerates (SOA). Sand and oil agglomerates may form in mats on the scale of tens of meters (m), and may break apart into pieces between 1 and 10 centimeters (cm) in diameter. These more mobile pieces are susceptible to alongshore and cross-shore transport, and lead to beach re-oiling on the time scale of months to years following a spill. The U.S. Geological Survey (USGS) conducted experiments March 10 - 13, 2014, to expand the available data on sand and oil agglomerate motion; test shear stress based incipient motion parameterizations in a controlled, laboratory setting; and directly observe SOA exhumation and burial processes. Artificial sand and oil agglomerates (aSOA) were created and deployed in a small-oscillatory flow tunnel in two sets of experiments, during which, video and velocity data were obtained. The first experiment, which was set up to help researchers investigate incipient motion, used with an immobile, rough bottom (referred to as false-floor) and the second–testing seafloor interactions–utilized with a coarse grain sand bottom (movable sand bed). Detailed information regarding the creation of the aSOA can be found in Dalyander et al. (2015). More information about the USGS laboratory experiment conducted in collaboration with the Naval Research Laboratory can be found in the associated Open File Report (OFR Number Unknown).

Weathered oil in the surf-zone after an oil spill may mix with suspended sediments to form sand and oil agglomerates (SOA). Sand and oil agglomerates may form in mats on the scale of tens of meters (m), and may break apart into pieces between 1 and 10 centimeters (cm) in diameter. These more mobile pieces are susceptible to alongshore and cross-shore transport, and lead to beach re-oiling on the time scale of months to years following a spill. The U.S. Geological Survey (USGS) conducted experiments March 10 - 13, 2014, to expand the available data on sand and oil agglomerate motion; test shear stress based incipient motion parameterizations in a controlled, laboratory setting; and directly observe SOA exhumation and burial processes. Artificial sand and oil agglomerates (aSOA) were created and deployed in a small-oscillatory flow tunnel in two sets of experiments, during which, video and velocity data were obtained. The first experiment, which was set up to help researchers investigate incipient motion, used with an immobile, rough bottom (referred to as false-floor) and the second–testing seafloor interactions–utilized with a coarse grain sand bottom (movable sand bed). Detailed information regarding the creation of the aSOA can be found in Dalyander et al. (2015). More information about the USGS laboratory experiment conducted in collaboration with the Naval Research Laboratory can be found in the associated Open File Report (OFR Number Unknown).

Weathered oil in the surf-zone after an oil spill may mix with suspended sediments to form sand and oil agglomerates (SOA). Sand and oil agglomerates may form in mats on the scale of tens of meters (m), and may break apart into pieces between 1 and 10 centimeters (cm) in diameter. These more mobile pieces are susceptible to alongshore and cross-shore transport, and lead to beach re-oiling on the time scale of months to years following a spill. The U.S. Geological Survey (USGS) conducted experiments March 10 - 13, 2014, to expand the available data on sand and oil agglomerate motion; test shear stress based incipient motion parameterizations in a controlled, laboratory setting; and directly observe SOA exhumation and burial processes. Artificial sand and oil agglomerates (aSOA) were created and deployed in a small-oscillatory flow tunnel in two sets of experiments, during which, video and velocity data were obtained. The first experiment, which was set up to help researchers investigate incipient motion, used with an immobile, rough bottom (referred to as false-floor) and the second–testing seafloor interactions–utilized with a coarse grain sand bottom (movable sand bed). Detailed information regarding the creation of the aSOA can be found in Dalyander et al. (2015). More information about the USGS laboratory experiment conducted in collaboration with the Naval Research Laboratory can be found in the associated Open File Report (OFR Number Unknown).

Weathered oil in the surf-zone after an oil spill may mix with suspended sediments to form sand and oil agglomerates (SOA). Sand and oil agglomerates may form in mats on the scale of tens of meters (m), and may break apart into pieces between 1 and 10 centimeters (cm) in diameter. These more mobile pieces are susceptible to alongshore and cross-shore transport, and lead to beach re-oiling on the time scale of months to years following a spill. The U.S. Geological Survey (USGS) conducted experiments March 10 - 13, 2014, to expand the available data on sand and oil agglomerate motion; test shear stress based incipient motion parameterizations in a controlled, laboratory setting; and directly observe SOA exhumation and burial processes. Artificial sand and oil agglomerates (aSOA) were created and deployed in a small-oscillatory flow tunnel in two sets of experiments, during which, video and velocity data were obtained. The first experiment, which was set up to help researchers investigate incipient motion, used with an immobile, rough bottom (referred to as false-floor) and the second–testing seafloor interactions–utilized with a coarse grain sand bottom (movable sand bed). Detailed information regarding the creation of the aSOA can be found in Dalyander et al. (2015). More information about the USGS laboratory experiment conducted in collaboration with the Naval Research Laboratory can be found in the associated Open File Report (OFR Number Unknown).

Bacillus species and B. anthracis presence/absence data were determined in 4,770 soil samples collected across the contiguous United States, in cooperation with the U.S. Environmental Protection Agency (EPA). Polymerase Chain Reaction (PCR) data for Bacillus species and B. anthracis rpoB gene PCR amplicon detection were reported as non-detect (n), low (l), medium (m), and high (h). Results for both pag and lef genes of the pX01 plasmid were reported by the University of South Florida's Center for Biological Defense. This data was recorded as negative or positive for each of the genes and included the following combinations: neg/neg, pos/neg, neg/pos, and pos/pos. Data for the pX02 plasmid were recorded as negative (neg) or positive (pos).

On July 5–19 (13BIM02) and August 22–September 1 (13BIM07) of 2013, the U.S. Geological Survey (USGS) conducted geophysical surveys to investigate the geologic controls on barrier island evolution and medium-term and interannual sediment transport along the oil spill mitigation sand berm constructed at the north end and offshore of the Chandeleur Islands, La. This investigation is part of a broader USGS study, which seeks to understand barrier island evolution better over medium time scales (months to years). This report serves as an archive of unprocessed, digital chirp subbottom data, trackline maps, navigation files, Geographic Information System (GIS) information, and formal Federal Geographic Data Committee (FGDC) metadata. Gained digital images of the seismic profiles are provided. The archived trace data are in standard Society of Exploration Geophysicists (SEG) SEG Y revision 0 format (Barry and others, 1975) and may be downloaded and processed with commercial or public domain software such as Seismic Unix (SU). Examples of SU processing scripts and USGS software for viewing the SEG Y files (Zihlman, 1992) are provided. These data are available for viewing using GeoMapApp (<http://www.geomapapp.org/>) and Virtual Ocean (<http://www.virtualocean.org/>) multi-platform open source software. In addition, the SEG Y files can also be downloaded from the USGS Coastal and Marine Geoscience Data System (<http://cmgds.marine.usgs.gov>).

In August and September of 1993 and January of 1994, the U.S. Geological Survey, under a cooperative agreement with the St. Johns River Water Management District (SJRWMD), conducted geophysical surveys of Kingsley Lake, Orange Lake, and Lowry Lake in northeast Florida. This report serves as an archive of unprocessed digital boomer seismic reflection data, trackline maps, navigation files, GIS information, observer's logbook, Field Activity Collection System (FACS) logs, and formal FGDC metadata. A filtered and gained GIF image of each seismic profile is also provided. The archived trace data are in standard Society of Exploration Geophysicists (SEG) SEG-Y format (Barry and others, 1975) and may be downloaded and processed with commercial or public domain software such as Seismic Unix (SU). Examples of SU processing scripts and in-house (USGS) software for viewing SEG-Y files (Zihlman, 1992) are also provided. For more information on the seismic surveys see http://walrus.wr.usgs.gov/infobank/p/p193fl/html/p-1-93-fl.meta.html and http://walrus.wr.usgs.gov/infobank/p/p194fl/html/p-1-94-fl.meta.html These data are also available via GeoMapApp (http://www.geomapapp.org/) and Virtual Ocean (http://www.virtualocean.org/) earth science exploration and visualization applications.

In March of 2004, the U.S. Geological Survey conducted a geophysical survey in the Withlacoochee River of west-central Florida. This report serves as an archive of unprocessed digital boomer seismic reflection data, trackline maps, navigation files, GIS information, Field Activity Collection System (FACS) logs, observer's logbook, and FGDC metadata. Filtered and gained digital images of the seismic profiles are also provided. The archived trace data are in standard Society of Exploration Geophysicists (SEG) SEG-Y format (Barry and others, 1975) and may be downloaded and processed with commercial or public domain software such as Seismic Unix (SU). Example SU processing scripts and USGS software for viewing the SEG-Y files (Zihlman, 1992) are also provided. For more information on the seismic surveys see http://walrus.wr.usgs.gov/infobank/w/w104fl/html/w-1-04-fl.meta.html These data are also available via GeoMapApp (http://www.geomapapp.org/) and Virtual Ocean ( http://www.virtualocean.org/) earth science exploration and visualization applications.

In August of 1994, the U.S. Geological Survey, in cooperation with Coastal Carolina University, conducted marine geophysical surveys in numerous water bodies adjacent to the south-central South Carolina coastal region. Data were collected aboard the MS Coastal in the Ashley, North Edisto, Wadmalaw, Dawho, South Edisto, and Ashepoo Rivers; the Wappoo, North, Steamboat, Bohicket, and Toogoodoo Creeks; Charleston Harbor; Wadmalaw Sound; Fenwick Cut; and the Atlantic Ocean from offshore Isle of Palms to Kiawah Island. This report serves as an archive of unprocessed digital boomer seismic reflection data, trackline maps, navigation files, GIS information, observers' logbooks, Field Activity Collection System (FACS) logs, and FGDC metadata. Filtered and gained digital images of the seismic profiles are also provided. The archived trace data are in standard Society of Exploration Geophysicists (SEG) SEG-Y format (Barry and others, 1975) and may be downloaded and processed with commercial or public domain software such as Seismic Unix (SU). Example SU processing scripts and USGS software for viewing the SEG-Y files (Zihlman, 1992) are also provided. For more information on the seismic surveys see http://walrus.wr.usgs.gov/infobank/c/c294sr/html/c-2-94-sr.meta.html These data are also available via GeoMapApp (http://www.geomapapp.org/) and Virtual Ocean ( http://www.virtualocean.org/) earth science exploration and visualization applications.

In June of 1997, the U.S. Geological Survey, in cooperation with Coastal Carolina University, conducted a geophysical survey of the shallow geologic framework of the continental shelf offshore of central South Carolina from the Isle of Palms to Bull Island. This report serves as an archive of unprocessed digital boomer seismic reflection data, trackline maps, navigation files, GIS information, observers' logbooks, Field Activity Collection System (FACS) logs, and formal FGDC metadata. Filtered and gained digital images of the seismic profiles are also provided. The archived trace data are in standard Society of Exploration Geophysicists (SEG) SEG-Y format (Barry and others, 1975) and may be downloaded and processed with commercial or public domain software such as Seismic Unix (SU). Example SU processing scripts and USGS software for viewing the SEG-Y files (Zihlman, 1992) are also provided. For more information on the seismic surveys see http://walrus.wr.usgs.gov/infobank/g/g197sr/html/g-1-97-sr.meta.html These data are also available via GeoMapApp (http://www.geomapapp.org/) and Virtual Ocean ( http://www.virtualocean.org/) earth science exploration and visualization applications.

In October and November of 1995 and February of 1996, the U.S. Geological Survey, in cooperation with the Southwest Florida Water Management District, conducted geophysical surveys of the Peace River in west-central Florida from east of Bartow to west of Arcadia. This report serves as an archive of unprocessed digital boomer seismic reflection data, trackline maps, navigation files, GIS files, Field Activity Collection System (FACS) logs, observers' logbooks, and formal FGDC metadata. Filtered and gained digital images of the seismic profiles are also provided. The archived trace data are in standard Society of Exploration Geophysicists (SEG) SEG-Y format (Barry and others, 1975) and may be downloaded and processed with commercial or public domain software such as Seismic Unix (SU). Example SU processing scripts and USGS software for viewing the SEG-Y files (Zihlman, 1992) are also provided. For more information on the seismic surveys see http://walrus.wr.usgs.gov/infobank/j/j395fl/html/j-3-95-fl.meta.html and http://walrus.wr.usgs.gov/infobank/j/j296fl/html/j-2-96-fl.meta.html These data are also available via GeoMapApp (http://www.geomapapp.org/) and Virtual Ocean ( http://www.virtualocean.org/) earth science exploration and visualization applications.

In June and July of 2004, the U.S. Geological Survey, in cooperation with the University of New Orleans, conducted geophysical surveys in Terrebonne Bay, Timbalier Bay, Lake Pelto, and Barataria Bay, Louisiana, and nearby waterbodies. This report serves as an archive of unprocessed digital boomer and chirp seismic reflection data, trackline maps, navigation files, GIS information, Field Activity Collection System (FACS) logs, observer's logbook, and formal FGDC metadata. Filtered and gained digital images of the seismic profiles are also provided. The archived trace data are in standard Society of Exploration Geophysicists (SEG) SEG-Y format (Barry and others, 1975) and may be downloaded and processed with commercial or public domain software such as Seismic Unix (SU). Example SU processing scripts and USGS software for viewing the SEG-Y files (Zihlman, 1992) are also provided. For more information on the seismic surveys see http://walrus.wr.usgs.gov/infobank/g/g104la/html/g-1-04-la.meta.html These data are also available via GeoMapApp (http://www.geomapapp.org/) and Virtual Ocean ( http://www.virtualocean.org/) earth science exploration and visualization applications.

In September of 2003, the U.S. Geological Survey conducted geophysical surveys in Lake Pelto, Timbalier Bay, Terrebonne Bay, and nearby waterbodies offshore south-central Louisiana. This report serves as an archive of unprocessed digital boomer and chirp seismic reflection data, trackline maps, navigation files, GIS information, Field Activity Collection System (FACS) logs, observer's logbook, and formal FGDC metadata. Filtered and gained digital images of the seismic profiles are also provided. The archived trace data are in standard Society of Exploration Geophysicists (SEG) SEG-Y format (Barry and others, 1975) and may be downloaded and processed with commercial or public domain software such as Seismic Unix (SU). Example SU processing scripts and USGS software for viewing the SEG-Y files (Zihlman, 1992) are also provided. For more information on the seismic surveys see http://walrus.wr.usgs.gov/infobank/g/g103la/html/g-1-03-la.meta.html These data are also available via GeoMapApp (http://www.geomapapp.org/) and Virtual Ocean ( http://www.virtualocean.org/) earth science exploration and visualization applications.

In August of 2005, the U.S. Geological Survey conducted geophysical surveys offshore of Port Fourchon and Timbalier Bay, Louisiana, and in nearby waterbodies. This report serves as an archive of unprocessed digital Chirp seismic reflection data, trackline maps, navigation files, GIS information, Field Activity Collection System (FACS) logs, observer's logbook, and formal FGDC metadata. Filtered and gained digital images of the seismic profiles are also provided. The archived trace data are in standard Society of Exploration Geophysicists (SEG) SEG-Y format (Barry and others, 1975) and may be downloaded and processed with commercial or public domain software such as Seismic Unix (SU). Example SU processing scripts and USGS software for viewing the SEG-Y files (Zihlman, 1992) are also provided. For more information on the seismic surveys see http://walrus.wr.usgs.gov/infobank/g/g105la/html/g-1-05-la.meta.html These data are also available via GeoMapApp (http://www.geomapapp.org/) and Virtual Ocean ( http://www.virtualocean.org/) earth science exploration and visualization applications.

In May of 2006, the U.S. Geological Survey conducted geophysical surveys offshore of Siesta Key, Florida. This report serves as an archive of unprocessed digital Chirp seismic reflection data, trackline maps, navigation files, GIS information, Field Activity Collection System (FACS) logs, observer's logbook, and formal FGDC metadata. Gained digital images of the seismic profiles are also provided. The archived trace data are in standard Society of Exploration Geophysicists (SEG) SEG-Y format (Barry and others, 1975) and may be downloaded and processed with commercial or public domain software such as Seismic Unix (SU). Example SU processing scripts and USGS software for viewing the SEG-Y files (Zihlman, 1992) are also provided. For more information on the seismic surveys see http://walrus.wr.usgs.gov/infobank/g/g106fl/html/g-1-06-fl.meta.html These data are also available via GeoMapApp (http://www.geomapapp.org/) and Virtual Ocean ( http://www.virtualocean.org/) earth science exploration and visualization applications.

In June of 2006, the U.S. Geological Survey conducted a geophysical survey offshore of Isles Dernieres, Louisiana. This report serves as an archive of unprocessed digital Chirp seismic reflection data, trackline maps, navigation files, GIS information, Field Activity Collection System (FACS) logs, observer's logbook, and formal FGDC metadata. Gained digital images of the seismic profiles are also provided. The archived trace data are in standard Society of Exploration Geophysicists (SEG) SEG-Y format (Barry and others, 1975) and may be downloaded and processed with commercial or public domain software such as Seismic UNIX (SU). Example SU processing scripts and USGS software for viewing the SEG-Y files (Zihlman, 1992) are also provided. For more information on the seismic surveys see http://walrus.wr.usgs.gov/infobank/g/g106la/html/g-1-06-la.meta.html These data are also available via GeoMapApp (http://www.geomapapp.org/) and Virtual Ocean ( http://www.virtualocean.org/) earth science exploration and visualization applications.

In June of 1996, the U.S. Geological Survey conducted geophysical surveys from Nueces to Copano Bays, Texas. This report serves as an archive of unprocessed digital boomer seismic reflection data, trackline maps, navigation files, GIS information, cruise log, and formal FGDC metadata. Filtered and gained digital images of the seismic profiles and high resolution scanned TIFF images of the original paper printouts are also provided. The archived trace data are in standard Society of Exploration Geophysicists (SEG) SEG-Y format (Barry and others, 1975) and may be downloaded and processed with commercial or public domain software such as Seismic Unix (SU). Example SU processing scripts and USGS software for viewing the SEG-Y files (Zihlman, 1992) are also provided. For more information on the seismic surveys see http://walrus.wr.usgs.gov/infobank/b/b0296tx/html/b-02-96-tx.meta.html These data are also available via GeoMapApp (http://www.geomapapp.org/) and Virtual Ocean (http://www.virtualocean.org/) earth science exploration and visualization applications.

In September of 2006, the U.S. Geological Survey conducted geophysical surveys offshore of Fort Lauderdale, FL. This report serves as an archive of unprocessed digital boomer and Chirp seismic reflection data, trackline maps, navigation files, GIS information, Field Activity Collection System (FACS) logs, observer's logbook, and formal FGDC metadata. Filtered and gained digital images of the seismic profiles are also provided. The archived trace data are in standard Society of Exploration Geophysicists (SEG) SEG-Y format (Barry and others, 1975) and may be downloaded and processed with commercial or public domain software such as Seismic Unix (SU). Example SU processing scripts and USGS software for viewing the SEG-Y files (Zihlman, 1992) are also provided. For more information on the seismic surveys see http://walrus.wr.usgs.gov/infobank/c/c106fl/html/c-1-06-fl.meta.html These data are also available via GeoMapApp (http://www.geomapapp.org/) and Virtual Ocean (http://www.virtualocean.org/) earth science exploration and visualization applications

In July of 2006, the U.S. Geological Survey conducted geophysical surveys offshore of Chandeleur Islands, LA, and in nearby waterbodies. This report serves as an archive of unprocessed digital CHIRP seismic reflection data, trackline maps, navigation files, GIS information, Field Activity Collection System (FACS) logs, observer's logbook, and formal FGDC metadata. Gained digital images of the seismic profiles are also provided. The archived trace data are in standard Society of Exploration Geophysicists (SEG) SEG-Y format (Barry and others, 1975) and may be downloaded and processed with commercial or public domain software such as Seismic Unix (SU). Example SU processing scripts and USGS software for viewing the SEG-Y files (Zihlman, 1992) are also provided.

In July of 2006, the U.S. Geological Survey conducted a geophysical survey offshore of Cheniere Caminada, Louisiana. This report serves as an archive of unprocessed digital Chirp seismic reflection data, trackline maps, navigation files, GIS information, Field Activity Collection System (FACS) logs, observer's logbook, and formal FGDC metadata. Gained digital images of the seismic profiles are also provided. The archived trace data are in standard Society of Exploration Geophysicists (SEG) SEG-Y format (Barry and others, 1975) and may be downloaded and processed with commercial or public domain software such as Seismic Unix (SU). Example SU processing scripts and USGS software for viewing the SEG-Y files (Zihlman, 1992) are also provided. For more information on the seismic surveys see http://walrus.wr.usgs.gov/infobank/g/g306la/html/g-3-06-la.meta.html These data are also available via GeoMapApp (http://www.geomapapp.org/) and Virtual Ocean ( http://www.virtualocean.org/) earth science exploration and visualization applications.

In August of 1996, the U.S. Geological Survey conducted geophysical surveys in Lakes Mabel and Starr, Florida. This report serves as an archive of unprocessed digital boomer seismic reflection data, trackline maps, navigation files, GIS information, cruise log, and formal FGDC metadata. For detailed information about the hydrologic setting of Lake Starr and the interpretation of some of these seismic reflection data, see Swancar and others (2000) at http://fl.water.usgs.gov/publications/Abstracts/wri00_4030_swancar.html. Filtered and gained digital images of the seismic profiles are also provided. The archived trace data are in standard Society of Exploration Geophysicists (SEG) SEG-Y format (Barry and others, 1975) and may be downloaded and processed with commercial or public domain software such as Seismic Unix (SU). Example SU processing scripts and USGS software for viewing the SEG-Y files (Zihlman, 1992) are also provided. For more information on the seismic surveys see http://walrus.wr.usgs.gov/infobank/b/b496fl/html/b-4-96-fl.meta.html These data are also available via GeoMapApp (http://www.geomapapp.org/) and Virtual Ocean ( http://www.virtualocean.org/) earth science exploration and visualization applications.

In July of 2002, the U.S. Geological Survey and St. Johns River Water Management District (SJRWMD) conducted geophysical surveys in Lakes Ada, Crystal, Jennie, Mary, Rice, and Sylvan, Florida. This report serves as an archive of unprocessed digital boomer seismic reflection data, trackline maps, navigation files, GIS information, FACS logs, and formal FGDC metadata. Filtered and gained digital images of the seismic profiles are also provided. The archived trace data are in standard Society of Exploration Geophysicists (SEG) SEG-Y format (Barry and others, 1975) and may be downloaded and processed with commercial or public domain software such as Seismic Unix (SU). Example SU processing scripts and USGS software for viewing the SEG-Y files (Zihlman, 1992) are also provided. For more information on the seismic surveys see http://walrus.wr.usgs.gov/infobank/b/b402fl/html/b-4-02-fl.meta.html These data are also available via GeoMapApp (http://www.geomapapp.org/) and Virtual Ocean ( http://www.virtualocean.org/) earth science exploration and visualization applications.

In June and August of 1992, the U.S. Geological Survey (USGS) conducted geophysical surveys to investigate the shallow geologic framework from Lake Pontchartrain, Louisiana, to Mobile Bay, Alabama. This work was conducted onboard the Argonne National Laboratorys R/V ERDA-1 as part of the Mississippi/Alabama Pollution Project. This report is part of a series to digitally archive the legacy analog data collected from the Mississippi-Alabama SHelf (MASH). The MASH data rescue project is a cooperative effort by the USGS and the Minerals Management Service (MMS). This report serves as an archive of high resolution scanned Tagged Image File Format (TIFF) and Graphics Interchange Format (GIF) images of the original boomer paper records, navigation files, trackline maps, Geographic Information System (GIS) files, cruise logs, and formal Federal Geographic Data Committee (FGDC) metadata. For more information on the seismic surveys see http://woodshole.er.usgs.gov/operations/ia/public_ds_info.php?fa=1992-010-FA and http://woodshole.er.usgs.gov/operations/ia/public_ds_info.php?fa=1992-037-FA These data are also available via GeoMapApp (http://www.geomapapp.org/) and Virtual Ocean ( http://www.virtualocean.org/) earth science exploration and visualization applications.

From May 13 to May 14 of 2008, the U.S. Geological Survey conducted geophysical surveys in Lake Panasoffkee, Florida. Thisreport serves as an archive of unprocessed digital boomer and CHIRP seismic reflection data, trackline maps, navigation files, GIS information, FACS logs, and formal FGDC metadata. Filtered and (or) gained digital images of the seismic profiles are also provided. The archived trace data are in standard Society of Exploration Geophysicists (SEG) SEG-Y format (Barry and others, 1975) and may be downloaded and processed with commercial or public domain software such as Seismic Unix (SU). Example SU processing scripts and USGS software for viewing the SEG-Y files (Zihlman, 1992) are also provided. For more information on the seismic surveys see http://walrus.wr.usgs.gov/infobank/j/j308fl/html/j-3-08-fl.meta.html These data are also available via GeoMapApp (http://www.geomapapp.org/) and Virtual Ocean ( http://www.virtualocean.org/) earth science exploration and visualization applications.

In March of 2008, the U.S. Geological Survey and St. Johns River Water Management District (SJRWMD) conducted geophysical surveys in Lakes Avalon, Big, Colby, Helen, Johns, Prevatt, Searcy, Saunders, Three Island, and Trout, located in central Florida. This report serves as an archive of unprocessed digital boomer seismic reflection data, trackline maps, navigation files, GIS information, FACS logs, and formal FGDC metadata. Filtered and gained digital images of the seismic profiles are also provided. The archived trace data are in standard Society of Exploration Geophysicists (SEG) SEG-Y format (Barry and others, 1975) and may be downloaded and processed with commercial or public domain software such as Seismic Unix (SU). Example SU processing scripts and USGS software for viewing the SEG-Y files (Zihlman, 1992) are also provided. For more information on the seismic surveys see http://walrus.wr.usgs.gov/infobank/j/j108fl/html/j-1-08-fl.meta.html These data are also available via GeoMapApp (http://www.geomapapp.org/) and Virtual Ocean ( http://www.virtualocean.org/) earth science exploration and visualization applications.

In April and July of 1981, the U.S. Geological Survey (USGS) conducted geophysical surveys to investigate the shallow geologic framework of the Alabama-Mississippi-Louisiana Shelf in the northern Gulf of Mexico. Work was conducted onboard the Texas A&M University R/V Carancahua and the R/V Gyre to develop a geologic understanding of the study area and to locate potential hazards related to offshore oil and gas production. While the R/V Carancahua only collected boomer data, the R/V Gyre used a 400-Joule minisparker, 3.5-kilohertz (kHz) subbottom profiler, 12-kHz precision depth recorder, and two air guns. The authors selected the minisparker data set because, unlike with the boomer data, it provided the most complete record. This report is part of a series to digitally archive the legacy analog data collected from the Mississippi-Alabama SHelf (MASH). The MASH data rescue project is a cooperative effort by the USGS and the Minerals Management Service (MMS). This report serves as an archive of high-resolution scanned Tagged Image File Format (TIFF) and Graphics Interchange Format (GIF) images of the original boomer and minisparker paper records, navigation files, trackline maps, Geographic Information System (GIS) files, cruise logs, and formal Federal Geographic Data Committee (FGDC) metadata.

In June of 1990 and July of 1991, the U.S. Geological Survey (USGS) conducted geophysical surveys to investigate the shallow geologic framework of the Mississippi-Alabama-Florida shelf in the northern Gulf of Mexico, from Mississippi Sound to the Florida Panhandle. Work was done onboard the Mississippi Mineral Resources Institute R/V Kit Jones as part of a project to study coastal erosion and offshore sand resources. This report is part of a series to digitally archive the legacy analog data collected from the Mississippi-Alabama SHelf (MASH). The MASH data rescue project is a cooperative effort by the USGS and the Minerals Management Service (MMS). This report serves as an archive of high-resolution scanned Tagged Image File Format (TIFF) and Graphics Interchange Format (GIF) images of the original boomer paper records, navigation files, trackline maps, Geographic Information System (GIS) files, cruise logs, and formal Federal Geographic Data Committee (FGDC) metadata.

From September 2 through 4, 2008, the U.S. Geological Survey and St. Johns River Water Management District (SJRWMD) conducted geophysical surveys in Lakes Cherry, Helen, Hiawassee, Louisa, and Prevatt, central Florida. This report serves as an archive of unprocessed digital boomer seismic reflection data, trackline maps, navigation files, GIS information, FACS logs, and formal FGDC metadata. Filtered and gained digital images of the seismic profiles are also provided. The archived trace data are in standard Society of Exploration Geophysicists (SEG) SEG-Y format (Barry and others, 1975) and may be downloaded and processed with commercial or public domain software such as Seismic Unix (SU). Example SU processing scripts and USGS software for viewing the SEG-Y files (Zihlman, 1992) are also provided. For more information on the seismic surveys see http://walrus.wr.usgs.gov/infobank/j/j408fl/html/j-4-08-fl.meta.html These data are also available via GeoMapApp (http://www.geomapapp.org/) and Virtual Ocean ( http://www.virtualocean.org/) earth science exploration and visualization applications.

In July of 2000, the U.S. Geological Survey (USGS), in cooperation with the Florida Geological Survey (FGS), conducted a geophysical survey of the Atlantic Ocean offshore Florida's east coast from Brevard County to northern Martin County. This report serves as an archive of unprocessed digital boomer seismic reflection data, trackline maps, navigation files, GIS information, digital and handwritten Field Activity Collection System (FACS) logs, and Federal Geographic Data Committee (FGDC) metadata. A filtered and gained digital image of each seismic profile is also provided. The archived trace data are in standard Society of Exploration Geophysicists (SEG) SEG-Y format (Barry and others, 1975) and may be downloaded and processed with commercial or public domain software such as Seismic Unix (SU). Examples of SU processing scripts and (USGS) software for viewing SEG-Y files (Zihlman, 1992) are also provided. For more information on the seismic surveys see http://walrus.wr.usgs.gov/infobank/g/g100fl/html/g-1-00-fl.meta.html These data are also available via GeoMapApp (http://www.geomapapp.org/) and Virtual Ocean ( http://www.virtualocean.org/) earth science exploration and visualization applications.

In March of 2009, the U.S. Geological Survey and Texas A&M University at Galveston conducted geophysical surveys to investigate the shallow geologic framework from Sabine Pass to Galveston, TX, as part of the USGS's Coastal Change and Transport (CCT) study. This report serves as an archive of unprocessed digital Chirp sub-bottom profile data, trackline maps, navigation files, GIS information, FACS logs, observer's logbook, and formal FGDC metadata. Gained digital images of the sub-bottom profiles are also provided. The archived trace data are in standard SEG-Y format (Barry and others, 1975) and may be downloaded and processed with commercial or public domain software such as Seismic Unix (SU). Example SU processing scripts and USGS software for viewing the SEG-Y files (Zihlman, 1992) are also provided. For more information on the seismic surveys see http://walrus.wr.usgs.gov/infobank/m/m109gm/html/m-1-09-gm.meta.html These data are also available via GeoMapApp (http://www.geomapapp.org/) and Virtual Ocean ( http://www.virtualocean.org/) earth science exploration and visualization applications.

From March 16 - 31, 2013, the U.S. Geological Survey conducted a geophysical survey to investigate sediment deposits and long-term sediment transport within the Snake River from Brownlee Dam to Hells Canyon Reservoir, Idaho; this effort will help the USGS to better understand geologic processes. This report serves as an archive of unprocessed digital chirp subbottom data, trackline maps, navigation files, GIS information, and formal FGDC metadata. Gained digital images of the seismic profiles are also provided. The archived trace data are in standard Society of Exploration Geophysicists (SEG) SEG Y revision 0 format (Barry and others, 1975) and may be downloaded and processed with commercial or public domain software such as Seismic Unix (SU). Example SU processing scripts and USGS software for viewing the SEG Y files (Zihlman, 1992) are also provided. These data are also available for viewing using GeoMapApp (<http://www.geomapapp.org/>) and Virtual Ocean (<http://www.virtualocean.org/>) multi-platform open source software. In addition, the SEG Y files can also be downloaded from the USGS Coastal and Marine Geoscience Data System (<http://cmgds.marine.usgs.gov>)

In July of 2012, the U.S. Geological Survey conducted a geophysical survey offshore of the Chandeleur Islands, La. to investigate the geologic controls on barrier island framework. This report serves as an archive of unprocessed digital chirp subbottom data, trackline maps, navigation files, Geographic Information System (GIS) information, and formal Federal Geographic Data Committee (FGDC) metadata. Gained digital images of the seismic profiles are also provided. The archived trace data are in standard Society of Exploration Geophysicists (SEG) SEG Y revision 0 format (Barry and others, 1975) and may be downloaded and processed with commercial or public domain software such as Seismic Unix (SU). Examples of SU processing scripts and USGS software for viewing the SEG Y files (Zihlman, 1992) are also provided. These data are available for viewing using GeoMapApp (<http://www.geomapapp.org/>) and Virtual Ocean (<http://www.virtualocean.org/>) multi-platform open source software. In addition, the SEG Y files can also be downloaded from the USGS Coastal and Marine Geoscience Data System (<http://cmgds.marine.usgs.gov>).

In August of 2013, the U.S. Geological Survey conducted a geophysical survey offshore of Petit Bois Island, Mississippi to investigate the geologic controls on barrier island framework and long-term sediment transport. This report serves as an archive of unprocessed digital chirp subbottom data, trackline maps, navigation files, GIS information, and formal FGDC metadata. Gained digital images of the seismic profiles are provided. The archived trace data are in standard Society of Exploration Geophysicists (SEG) SEG Y revision 0 format (Barry and others, 1975) and may be downloaded and processed with commercial or public domain software such as Seismic Unix (SU). Example SU processing scripts and USGS software for viewing the SEG Y files (Zihlman, 1992) are also provided. These data are available for viewing using GeoMapApp (<http://www.geomapapp.org/>) and Virtual Ocean (<http://www.virtualocean.org/>) multi-platform open source software. In addition, the SEG Y files can also be downloaded from the USGS Coastal and Marine Geoscience Data System (<http://cmgds.marine.usgs.gov>).

In June of 1994 and August and September of 1995, the U.S. Geological Survey, in cooperation with the University of Texas Bureau of Economic Geology, conducted geophysical surveys of the Sabine and Calcasieu Lake areas and the Gulf of Mexico offshore eastern Texas and western Louisiana. This report serves as an archive of unprocessed digital boomer seismic reflection data, trackline maps, navigation files, observers' logbooks, GIS information, and formal FGDC metadata. In addition, a filtered and gained GIF image of each seismic profile is provided. The archived trace data are in standard Society of Exploration Geophysicists (SEG) SEG-Y format (Barry and others, 1975) and may be downloaded and processed with commercial or public domain software such as Seismic Unix (SU). Examples of SU processing scripts and in-house (USGS) software for viewing SEG-Y files (Zihlman, 1992) are also provided. Processed profile images, trackline maps, navigation files, and formal metadata may be viewed with a web browser. Scanned handwritten logbooks and Field Activity Collection System (FACS) logs may be viewed with Adobe Reader. For more information on the seismic surveys see http://walrus.wr.usgs.gov/infobank/g/g194gm/html/g-1-94-gm.meta.html and http://walrus.wr.usgs.gov/infobank/g/g195gm/html/g-1-95-gm.meta.html These data are also available via GeoMapApp (http://www.geomapapp.org/) and Virtual Ocean ( http://www.virtualocean.org/) earth science exploration and visualization applications.

In October of 2001 and August of 2002, the U.S. Geological Survey conducted geophysical surveys of the Lower Atchafalaya River, the Mississippi River Delta, Barataria Bay, and the Gulf of Mexico south of East Timbalier Island, Louisiana. This report serves as an archive of unprocessed digital marine seismic reflection data, trackline maps, navigation files, observers' logbooks, GIS information, and formal FGDC metadata. In addition, a filtered and gained GIF image of each seismic profile is provided. The archived trace data are in standard Society of Exploration Geophysicists (SEG) SEG-Y format (Barry and othes, 1975) and may be downloaded and processed with commercial or public domain software such as Seismic Unix (SU). Examples of SU processing scripts and in-house (USGS) software for viewing SEG-Y files (Zihlman, 1992) are also provided. Processed profile images, trackline maps, navigation files, and formal metadata may be viewed with a web browser. Scanned handwritten logbooks and Field Activity Collection System (FACS) logs may be viewed with Adobe Reader. For more information on the seismic surveys see http://walrus.wr.usgs.gov/infobank/g/g601la/html/g-6-01-la.meta.html and http://walrus.wr.usgs.gov/infobank/g/g102gm/html/g-1-02-gm.meta.html These data are also available via GeoMapApp (http://www.geomapapp.org/) and Virtual Ocean ( http://www.virtualocean.org/) earth science exploration and visualization applications.

Scientists from the U.S. Geological Survey (USGS), St. Petersburg Coastal and Marine Science Center collected a set of sediment cores from the back-barrier environments along the Chandeleur Islands, Louisiana, in March 2012. The sampling efforts were part of a larger USGS study to evaluate effects on the geomorphology of the Chandeleur Islands following the construction of an artificial sand berm to reduce oil transport onto federally managed lands. The objective of this study was to evaluate the response of the back-barrier tidal and wetland environments to the berm. This report serves as an archive for sedimentological and radiochemical data derived from the sediment cores. The data described in this report is available for download.

Scientists from the U.S. Geological Survey (USGS), St. Petersburg Coastal and Marine Science Center collected a set of sediment cores from the back-barrier environments along the Chandeleur Islands, Louisiana, in March 2012. The sampling efforts were part of a larger USGS study to evaluate effects on the geomorphology of the Chandeleur Islands following the construction of an artificial sand berm to reduce oil transport onto federally managed lands. The objective of this study was to evaluate the response of the back-barrier tidal and wetland environments to the berm. This report serves as an archive for sedimentological and radiochemical data derived from the sediment cores. The data described in this report is available for download.

Scientists from the U.S. Geological Survey (USGS), St. Petersburg Coastal and Marine Science Center collected a set of sediment cores from the back-barrier environments along the Chandeleur Islands, Louisiana, in March 2012. The sampling efforts were part of a larger USGS study to evaluate effects on the geomorphology of the Chandeleur Islands following the construction of an artificial sand berm to reduce oil transport onto federally managed lands. The objective of this study was to evaluate the response of the back-barrier tidal and wetland environments to the berm. This report serves as an archive for sedimentological and radiochemical data derived from the sediment cores. The data described in this report is available for download.

Scientists from the U.S. Geological Survey (USGS), St. Petersburg Coastal and Marine Science Center collected a set of sediment cores from the back-barrier environments along the Chandeleur Islands, Louisiana, in March 2012. The sampling efforts were part of a larger USGS study to evaluate effects on the geomorphology of the Chandeleur Islands following the construction of an artificial sand berm to reduce oil transport onto federally managed lands. The objective of this study was to evaluate the response of the back-barrier tidal and wetland environments to the berm. This report serves as an archive for sedimentological and radiochemical data derived from the sediment cores. The data described in this report is available for download.

In August of 2014, the U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service (USFWS), conducted a geophysical survey offshore of Breton Island, Louisiana to investigate the geologic controls on barrier island framework and long-term sediment transport. Additional details related to this activity can be found by searching the USGS's Coastal and Marine Geoscience Data System (CMGDS), for field activity 2014-317-FA (also known as 14BIM05). This report serves as an archive of unprocessed digital chirp subbottom data, trackline maps, navigation files, GIS information, and formal FGDC metadata. Gained digital images of the seismic profiles are also provided. The archived trace data are in standard Society of Exploration Geophysicists (SEG) SEG Y revision 0 format (Barry and others, 1975) and may be downloaded and processed with commercial or public domain software such as Seismic Unix (SU). Example SU processing scripts and USGS software for viewing the SEG Y files (Zihlman, 1992) are also provided. These data are available for viewing using GeoMapApp (http://www.geomapapp.org/) and Virtual Ocean (http://www.virtualocean.org/) multi-platform open source software. In addition, the SEG Y files can also be downloaded from the USGS Coastal and Marine Geoscience Data System (http://cmgds.marine.usgs.gov).

In response to the 2010 Governor’s Action Plan to clean up the Barnegat Bay–Little Egg Harbor (BBLEH) estuary in New Jersey, the U.S. Geological Survey (USGS) partnered with the New Jersey Department of Environmental Protection in 2011 to begin a multidisciplinary research project to understand the physical controls on water quality in the bay. Between 2011 and 2013, USGS scientists mapped the geological and morphological characteristics of the seafloor of the BBLEH estuary using a suite of geophysical tools. However, this mapping effort included only surficial characterization of bay sediments; to verify the sub-surface geophysical data, sediment cores were required. Data Series 985 associated with this metadata record serves as an archive of sedimentologic data from 18 vibracores collected from Barnegat Bay between May and August of 2014 by the U.S. Department of Agriculture Natural Resources Conservation Service (NRCS) on behalf of the USGS. The vibracores were collected in conjunction with an ongoing NRCS subaqueous soil survey for the BBLEH estuary. The data presented in this report, including descriptive core logs, core photographs, processed grain-size data, and Geographic Information System (GIS) data files with accompanying formal Federal Geographic Data Committee metadata, can be viewed or downloaded from https://pubs.usgs.gov/ds/0985/ds985_data/.

In response to the 2010 Governor’s Action Plan to clean up the Barnegat Bay–Little Egg Harbor (BBLEH) estuary in New Jersey, the U.S. Geological Survey (USGS) partnered with the New Jersey Department of Environmental Protection in 2011 to begin a multidisciplinary research project to understand the physical controls on water quality in the bay. Between 2011 and 2013, USGS scientists mapped the geological and morphological characteristics of the seafloor of the BBLEH estuary using a suite of geophysical tools. However, this mapping effort included only surficial characterization of bay sediments; to verify the sub-surface geophysical data, sediment cores were required. Data Series 985 associated with this metadata record serves as an archive of sedimentologic data from 18 vibracores collected from Barnegat Bay between May and August of 2014 by the U.S. Department of Agriculture Natural Resources Conservation Service (NRCS) on behalf of the USGS. The vibracores were collected in conjunction with an ongoing NRCS subaqueous soil survey for the BBLEH estuary. The data presented in this report, including descriptive core logs, core photographs, processed grain-size data, and Geographic Information System (GIS) data files with accompanying formal Federal Geographic Data Committee metadata, can be viewed or downloaded from https://pubs.usgs.gov/ds/985/ds985_data/.

Barnegat Bay, located along the eastern shore of New Jersey, was significantly impacted by Hurricane Sandy in October 2012. Scientists from the U.S. Geological Survey (USGS) developed a multidisciplinary study of sediment transport and hydrodynamics to understand the mechanisms that govern estuarine and wetland responses to storm forcing. This report details the physical and chemical characteristics of surficial and downcore sediments from two areas within the bay. Eleven sites were sampled in both the central portion of the bay near Barnegat Inlet and in the southern portion of the bay in Little Egg Harbor. Laboratory analyses include Be-7, Pb-210, bulk density, porosity, x-radiographs, and grain-size distribution. These data will serve as a critical baseline dataset for understanding the current sedimentological regime and can be applied to future storms for understanding estuarine and wetland evolution. This report serves as an archive for sedimentological and radiochemical data derived from the surface sediments and box cores. Downloadable data are available as Excel spreadsheets, PDF files, and JPEG files, and includes sediment core data plots and x-radiographs, as well as, physical-properties, grain-size, alpha-spectoscopy, and gamma-spectroscopy data. Federal Geographic Data Committee metadata are available for analytical datasets in the data downloads page of this report.

Barnegat Bay, located along the eastern shore of New Jersey, was significantly impacted by Hurricane Sandy in October 2012. Scientists from the U.S. Geological Survey (USGS) developed a multidisciplinary study of sediment transport and hydrodynamics to understand the mechanisms that govern estuarine and wetland responses to storm forcing. This report details the physical and chemical characteristics of surficial and downcore sediments from two areas within the bay. Eleven sites were sampled in both the central portion of the bay near Barnegat Inlet and in the southern portion of the bay in Little Egg Harbor. Laboratory analyses include Be-7, Pb-210, bulk density, porosity, x-radiographs, and grain-size distribution. These data will serve as a critical baseline dataset for understanding the current sedimentological regime and can be applied to future storms for understanding estuarine and wetland evolution. This report serves as an archive for sedimentological and radiochemical data derived from the surface sediments and box cores. Downloadable data are available as Excel spreadsheets, PDF files, and JPEG files, and includes sediment core data plots and x-radiographs, as well as, physical-properties, grain-size, alpha-spectoscopy, and gamma-spectroscopy data. Federal Geographic Data Committee metadata are available for analytical datasets in the data downloads page of this report.

Barnegat Bay, located along the eastern shore of New Jersey, was significantly impacted by Hurricane Sandy in October 2012. Scientists from the U.S. Geological Survey (USGS) developed a multidisciplinary study of sediment transport and hydrodynamics to understand the mechanisms that govern estuarine and wetland responses to storm forcing. This report details the physical and chemical characteristics of surficial and downcore sediments from two areas within the bay. Eleven sites were sampled in both the central portion of the bay near Barnegat Inlet and in the southern portion of the bay in Little Egg Harbor. Laboratory analyses include Be-7, Pb-210, bulk density, porosity, x-radiographs, and grain-size distribution. These data will serve as a critical baseline dataset for understanding the current sedimentological regime and can be applied to future storms for understanding estuarine and wetland evolution.This report serves as an archive for sedimentological and radiochemical data derived from the surface sediments and box cores. Downloadable data are available as Excel spreadsheets, PDF files, and JPEG files, and includes sediment core data plots and x-radiographs, as well as, physical-properties, grain-size, alpha-spectoscopy, and gamma-spectroscopy data. Federal Geographic Data Committee metadata are available for analytical datasets in the data downloads page of this report.

Barnegat Bay, located along the eastern shore of New Jersey, was significantly impacted by Hurricane Sandy in October 2012. Scientists from the U.S. Geological Survey (USGS) developed a multidisciplinary study of sediment transport and hydrodynamics to understand the mechanisms that govern estuarine and wetland responses to storm forcing. This report details the physical and chemical characteristics of surficial and downcore sediments from two areas within the bay. Eleven sites were sampled in both the central portion of the bay near Barnegat Inlet and in the southern portion of the bay in Little Egg Harbor. Laboratory analyses include Be-7, Pb-210, bulk density, porosity, x-radiographs, and grain-size distribution. These data will serve as a critical baseline dataset for understanding the current sedimentological regime and can be applied to future storms for understanding estuarine and wetland evolution.This report serves as an archive for sedimentological and radiochemical data derived from the surface sediments and box cores. Downloadable data are available as Excel spreadsheets, PDF files, and JPEG files, and includes sediment core data plots and x-radiographs, as well as, physical-properties, grain-size, alpha-spectoscopy, and gamma-spectroscopy data. Federal Geographic Data Committee metadata are available for analytical datasets in the data downloads page of this report.

The U.S. Geological Survey has a long history of responding to and documenting the impacts of storms along the Nation’s coasts and incorporating these data into storm impact and coastal change vulnerability assessments. Although physical changes caused by tropical and extratropical storms to the sandy beaches and dunes fronting barrier islands are generally well documented, the interaction between sandy shoreline erosion and overwash with the back-barrier wetland and estuarine environments is poorly constrained. The goal of the Barrier Island and Estuarine Wetland Physical Change Assessment project is to integrate a wetland-change assessment with existing coastal-change assessments for the adjacent sandy dunes and beaches, initially focusing on Assateague Island along the Maryland and Virginia coastline. Assateague Island was impacted by waves and storm surge associated with the passage of Hurricane Sandy in October 2012, causing erosion and overwash along the ocean-facing sandy shoreline as well as erosion and overwash deposition in the back-barrier and estuarine bay environments. Data Series 999 associated with this metadata record describes sediment data collected using sand augers in active overwash zones on Assateague Island in Maryland. Samples were collected by the U.S. Geological Survey (USGS) during two surveys in March/April and October 2014 (USGS Field Activity Numbers [FAN] 2014-301-FA and 2014-322-FA, respectively). The physical characteristics (for example, sediment texture or bedding structure) of and spatial differences among these deposits will provide information about overwash processes and sediment transport from the sandy barrier-island reaches to the back-barrier environments. Metrics derived from these data, such as mean grain size or deposit thicknesses, can be used to ground-truth remote sensing and geophysical data and can also be incorporated into sediment transport models. Data products, including sample location tables, descriptive core logs, core photographs and x-radiographs, the results of sediment grain-size analyses, and Geographic Information System (GIS) data files with accompanying formal Federal Geographic Data Committee (FGDC) metadata can be downloaded from https://pubs.usgs.gov/ds/0999/ds999_data.html.

The U.S. Geological Survey has a long history of responding to and documenting the impacts of storms along the Nation’s coasts and incorporating these data into storm impact and coastal change vulnerability assessments. Although physical changes caused by tropical and extratropical storms to the sandy beaches and dunes fronting barrier islands are generally well documented, the interaction between sandy shoreline erosion and overwash with the back-barrier wetland and estuarine environments is poorly constrained. The goal of the Barrier Island and Estuarine Wetland Physical Change Assessment project is to integrate a wetland-change assessment with existing coastal-change assessments for the adjacent sandy dunes and beaches, initially focusing on Assateague Island along the Maryland and Virginia coastline. Assateague Island was impacted by waves and storm surge associated with the passage of Hurricane Sandy in October 2012, causing erosion and overwash along the ocean-facing sandy shoreline as well as erosion and overwash deposition in the back-barrier and estuarine bay environments. Data Series 999 associated with this metadata record describes sediment data collected using sand augers in active overwash zones on Assateague Island in Maryland. Samples were collected by the U.S. Geological Survey (USGS) during two surveys in March/April and October 2014 (USGS Field Activity Numbers [FAN] 2014-301-FA and 2014-322-FA, respectively). The physical characteristics (for example, sediment texture or bedding structure) of and spatial differences among these deposits will provide information about overwash processes and sediment transport from the sandy barrier-island reaches to the back-barrier environments. Metrics derived from these data, such as mean grain size or deposit thicknesses, can be used to ground-truth remote sensing and geophysical data and can also be incorporated into sediment transport models. Data products, including sample location tables, descriptive core logs, core photographs and x-radiographs, the results of sediment grain-size analyses, and Geographic Information System (GIS) data files with accompanying formal Federal Geographic Data Committee (FGDC) metadata can be downloaded from http://pubs.usgs.gov/ds/999/ds999_data.html.

On October 29, 2012, Hurricane Sandy made landfall as a post-tropical storm near Brigantine, New Jersey, with sustained winds of 70 knots (80 miles per hour) and tropical-storm-force winds extending 870 nautical miles in diameter (Blake and others, 2013). The effects of Hurricane Sandy’s winds and storm surge included erosion of the beaches and dunes as well as breaching of barrier islands in both natural and heavily developed areas of the coast (Spokin et. al., 2014). On November 4-6, 2012, the U.S. Geological Survey (USGS) conducted an aerial survey of the coast from Cape Lookout, North Carolina, to Montauk Point, New York (Morgan and Krohn, 2014) collecting nearly 10,000 images during three days of surveying. In June 2014, the USGS developed a crowd-sourced online application, “iCoast – Did the Coast Change?” to enlist the help of citizen scientists (referred to as “users”) in the classification of coastal infrastructure, coastal processes, and storm impacts related to Hurricane Sandy. Hurricane Sandy was chosen as the inaugural project due to the broad and severe impact of the storm. By enlisting users in the analysis of these images, iCoast offers a chance to classify all the imagery from Hurricane Sandy into a form that scientists can use to analyze and verify predictive vulnerability models. This user audience spanned a wide range of expertise and enlisted anyone interested in coastal issues, including coastal researchers and emergency managers to coastal residents, students, and professors. The data provided in this data release represent the classification of imagery by iCoast users as of September 9, 2016. At that time all of the post-Hurricane Sandy images had at least one user classification. These datasets include user classifications of the coastal type, level of development, visible infrastructure, damage to visible infrastructure, and determination of the dominant coastal process in the image based on Sallenger’s (2000) coastal impact scale.

This U.S. Geological Survey (USGS) data release includes geospatial datasets that were created for the analysis of Virginia and Maryland Atlantic coastal wetland changes over time. Wetland change was determined by assessing two metrics: wetland persistence and land-cover switching. Because seasonal water levels, beach width, and vegetation differences can affect change analyses, only images acquired during the spring (March, April, and May) were included in the wetland-change metrics (N=10). Land-cover switching was evaluated using Landsat images for successive spring image-acquisition dates: 1985–1989, 1989–1994, 1994–1999, 1999–2004, 2004-2009, 2009-2011, 2011-2013, 2013-2014, and 2014-2015. To evaluate land-cover switching, land-cover types defined by Bernier and others (2015) were reclassified as 1 (water), 3 (wetland), or 7 (non-wetland). These values were chosen so the results of subtracting two dates will create unique values for each scenario. For example, if a cell in 1994 is classified as land and in 1989 was wetland, the result (1994-1989 or 7-3) is 4. If the cell in 1994 is wetland and in 1989 was water (3-1) the result is 2. With this analysis, each two-date combination results in a raster that identifies wetland-land-water conversions, such that water-to-land is -6, wetland-to-land is -4, water-to-wetland is -2, wetland-to-water equals 2, land-to-wetland is 4, and land-to-water is 6.

This U.S. Geological Survey (USGS) data release includes geospatial datasets that were created to analyze wetland changes along the Virginia and Maryland Atlantic coasts between 1984 and 2015. Wetland change was determined by assessing two metrics: wetland persistence and land-cover switching. Because seasonal water levels, beach width, and vegetation differences can affect change analyses, only images acquired during the spring (March, April, and May) were included in the wetland-change metrics (N=10). USGS Data Series 968 (Bernier and others, 2015) presented data that were derived from Landsat 5 and Landsat 8 imagery from 1984 to 2014, including wetland and terrestrial habitat extents; open-ocean, back-barrier, and estuarine mainland shoreline positions; and sand-line positions along the estuarine mainland and barrier shorelines from Assateague Island, Maryland to Metompkin Island, Virginia. As part of the wetland-change analyses, two additional satellite images (17-April-1985 and 05-May-2015) were processed and classified using the methods described by Bernier and others (2015) to provide a more complete time series dataset. One additional image (26-April-1994) was reprocessed to correct a classification error that was identified when comparing wetland and total analysis extents among all images.

A topographic lidar survey was conducted on July 12-14, 2013 over Dauphin Island, Alabama and Chandeleur, Stake, Grand Gosier and Breton Islands, Louisiana. The data were collected at a nominal pulse space of 1 meter (m) and processed to identify bare earth elevations. Bare earth Digital Elevation Models (DEMs) were generated based on these data. Photo Science, Inc., was contracted by the U.S. Geological Survey (USGS) to collect and process the lidar data. The bare earth DEMs are 32-bit floating point ERDAS Imagine (IMG) files with a horizontal spatial resolution of 1-m by 1-m. They are projected to Universal Transverse Mercator (UTM), Zone 16, North American Datum (NAD) 1983, meters (m) coordinates. Their vertical datum is NAVD88 (GEOID12A) meters. Eighty-five DEMs, based on a 2-kilometer (km) by 2-km tiling scheme, cover the entire survey area. These lidar data are available to Federal, State and local governments, emergency-response officials, resource managers, and the general public.

A topographic lidar survey was conducted July 12-14, 2013 over Dauphin Island, Alabama and Chandeleur, Stake, Grand Gosier and Breton Islands, Louisiana. Lidar data exchange format (LAS) 1.2 formatted classified point data files were generated based on these data. Photo Science, Inc. was contracted by the U.S. Geological Survey (USGS) to collect and process the lidar data. The lidar data were collected at a nominal pulse spacing (NPS) of 1.0 meter (m). The horizontal projection and datum of the data are Universe Transverse Mercator, zone 16N, North American Datum 1983 (UTM Zone 16N NAD83), meters. The vertical datum is North American Vertical Datum 1988, Geoid 2012a (NAVD88, GEOID12A), meters. Eighty-five LAS files, based on a 2-kilometer by 2-kilometer tiling scheme, cover the entire survey area. These lidar data are available to Federal, State and local governments, emergency-response officials, resource managers, and the general public. Lidar_Information Lidar_Collection_Information Lidar_Specification USGS-NGP Base Lidar Specification v1.0 Lidar_Sensor Leica ALS 70 Lidar_Maximum_Returns 4 Lidar_Pulse_Spacing 0.64 Lidar_Density 1.57 Lidar_Flight_Height 1524 Lidar_Flight_Speed 130 Lidar_Scan_Angle 20.0 Lidar_Scan_Frequency 29.6 Lidar_Pulse_Rate 178.4 Lidar_Pulse_Duration 4 Lidar_Pulse_Width 0.35 Lidar_Central_Wavelength 1064 Lidar_Multiple_Pulses_In_Air 0 Lidar_Beam_Divergence 0.22 Lidar_Swath_Width 1109.38 Lidar_Swath_Overlap 11.46% Lidar_Coordinate_Reference_System_Name NAD_1983_UTM_Zone_16N_Meters Lidar_Geoid National Geodetic Survey (NGS) Geoid03 Lidar_Accuracy_Information Lidar_Calculated_Horizontal_Accuracy 0.012 Lidar_Raw_Fundamental_Vertical_Accuracy 0.01 Lidar_LAS_Information Lidar_LAS_Version 1.2 Lidar_LAS_Point_Record_Format 1 Lidar_LAS_Witheld_Point_Identifier Withheld (ignore) points were identified in these files using the standard LAS Withheld bit. Lidar_LAS_Overage_Point_Identifier Swath "overage" points were identified in these files by adding 16 to the standard classification values. Lidar_LAS_Radiometric_Resolution 8 Lidar_LAS_Classification Lidar_LAS_Class_Code 1 Lidar_LAS_Class_Description Processed, but unclassified Lidar_LAS_Classification Lidar_LAS_Class_Code 2 Lidar_LAS_Class_Description Bare earth ground Lidar_LAS_Classification Lidar_LAS_Class_Code 7 Lidar_LAS_Class_Description Noise Lidar_LAS_Classification Lidar_LAS_Class_Code 9 Lidar_LAS_Class_Description Water Lidar_LAS_Classification Lidar_LAS_Class_Code 10 Lidar_LAS_Class_Description Ignored ground Lidar_LAS_Classification Lidar_LAS_Class_Code 17 Lidar_LAS_Class_Description Overlap default (unclassified) Lidar_LAS_Classification Lidar_LAS_Class_Code 18 Lidar_LAS_Class_Description Overlap bare-earth ground

A topographic lidar survey was conducted from September 5 to October 11, 2012, for the barrier islands of Alabama, Mississippi and southeast Louisiana, including the coast near Port Fourchon. Most of the data were collected September 5-10, 2012, with a reflight conducted on October 11, 2012, to increase point density in some areas. The data were collected at a nominal pulse space of 1-meter (m) and processed to identify bare earth elevations. Bare earth Digital Elevation Models(DEMs) were generated based on these data. Aero-Metric, Inc., was contracted by the U.S. Geological Survey (USGS) to collect and process the lidar data. The bare earth DEMs are 32-bit floating point ERDAS Imagine (IMG) files with a horizontal spatial resolution of 1-m by 1-m. They are projected to UTM zone 15N or 16N NAD83 meters. Their vertical datum is NAVD88 (GEOID12) meters. The DEMs are organized on a 2-kilometer (km) by 2-km tiling scheme that covers the entire survey area. These lidar data are available to Federal, State and local governments, emergency-response officials, resource managers, and the general public.

This Data Series Report contains lidar elevation data collected September 5 to October 11, 2012, for the barrier islands of Alabama, Mississippi and southeast Louisiana, including the coast near Port Fourchon. Most of the data were collected September 5-10, 2012, with a reflight conducted on October 11, 2012, to increase point density in some areas. Lidar data exchange format (LAS) 1.2 formatted point data files were generated based on these data. The point cloud data were processed to extract bare earth data; therefore, the point cloud data are organized into only four classes: 1-unclassified, 2-ground, 7-noise and 9-water. Aero-Metric, Inc., was contracted by the U.S. Geological Survey (USGS) to collect and process these data. The lidar data were collected at a nominal pulse spacing (NPS) of 1.0 meter (m). The horizontal projection and datum of the data are Universe Transverse Mercator, zones 15N and 16N, North American Datum 1983 (UTM Zone 15N or 16N NAD83), meters. The vertical datum is North American Vertical Datum 1988, Geoid 2012 (NAVD88, GEOID12), meters. These lidar data are available to Federal, State and local governments, emergency-response officials, resource managers, and the general public.

A topographic Lidar survey was conducted on February 6, 2012, over the Chandeleur Islands, Louisiana. The data were collected at a nominal pulse space of 0.5-meter (m) and processed to identify bare earth elevations. Bare earth digital elevation models (DEMs) were generated based on these data. Digital Aerial Solutions, LLC, was contracted by the U.S. Geological Survey (USGS) to collect and process the lidar data. The bare earth DEMs are 32-bit floating point ERDAS Imagine (IMG) files with a horizontal spatial resolution of 1-m by 1-m. They are in decimal degree geographic coordinates, North American Datum 1983, National Spatial Reference System 2007 (NAD83 NSRS2007)). Their vertical datum is North American Vertical Datum 1988, Geoid 2009, Geodetic Reference System 1980 (NAVD88 GEOID09 GRS80) in meters. Thirty-three DEMs, based on a 2-kilometer (km) by 2-km tiling scheme, cover the entire survey area. These lidar data are available to Federal, State and local governments, emergency-response officials, resource managers, and the general public.

This Data Series Report contains lidar elevation data collected February 6, 2012, over the Chandeleur Islands, Louisiana. LAS 1.2 formatted point data files were generated based on these data. The point cloud data were processed to extract bare earth data; therefore, the point cloud data are classified into only these classes: 1 and 17-unclassified, 2-ground, 9-water, and 10-breakline proximity. Digital Aerial Solutions, LLC, was contracted by the USGS to collect and process these data. The lidar data were collected at a nominal pulse spacing (NPS) of 0.5 meter (m). The data are in decimal degree geographic coordinates, North American Datum 1983, National Spatial Reference System 2007 (NAD83 NSRS2007)). The vertical datum is North American Vertical Datum 1988, Geoid 2009, Geodetic Reference System 1980 (NAVD88 GEOID09 GRS80) in meters. Thirty-three LAS files, based on a 2-kilometer by 2-kilometer tiling scheme, cover the entire survey area. These lidar data are available to Federal, State and local governments, emergency-response officials, resource managers, and the general public.

Shorelines Extracted from 1984-2015 Landsat Imagery: Cat Island, Mississippi (Polyline: Combined Dates) is a line shapefile representing shorelines generated from satellite imagery that was collected from 1984 to 2015. The sample frequency of satellite imagery is much higher, and the coverage much greater, than most routine high-resolution topographic surveys. Certain aspects of barrier island morphology, such as island size, shape and position, can be determined from these images and can indicate erosion, land loss, and island breakup. Studying how these characteristics evolve will help develop an understanding of how barrier islands will respond to climate change, sea level rise, and major storms in the future and that will serve to improve management of coastal resources.

Shorelines Extracted from 1984-2015 Landsat Imagery: Cat Island, Mississippi (Polyline: Individual Dates) is a line shapefile representing shorelines generated from satellite imagery that was collected from 1984 to 2015. The sample frequency of satellite imagery is much higher, and the coverage much greater, than most routine high-resolution topographic surveys. Certain aspects of barrier island morphology, such as island size, shape and position, can be determined from these images and can indicate erosion, land loss, and island breakup. Studying how these characteristics evolve will help develop an understanding of how barrier islands will respond to climate change, sea level rise, and major storms in the future and that will serve to improve management of coastal resources.

Shorelines Extracted from 1984-2015 Landsat Imagery: Cat Island, Mississippi (Polygon: Combined Dates) is a polygon shapefile representing shorelines generated from satellite imagery that was collected from 1984 to 2015. The sample frequency of satellite imagery is much higher, and the coverage much greater, than most routine high-resolution topographic surveys. Certain aspects of barrier island morphology, such as island size, shape and position, can be determined from these images and can indicate erosion, land loss, and island breakup. Studying how these characteristics evolve will help develop an understanding of how barrier islands will respond to climate change, sea level rise, and major storms in the future and that will serve to improve management of coastal resources.

Shorelines Extracted from 1984-2015 Landsat Imagery: Cat Island, Mississippi (Polygon: Individual Dates) is a dataset consisting of 268 polygon shapefiles representing shorelines generated from satellite imagery that was collected from 1984 to 2015. The sample frequency of satellite imagery is much higher, and the coverage much greater, than most routine high-resolution topographic surveys. Certain aspects of barrier island morphology, such as island size, shape and position, can be determined from these images and can indicate erosion, land loss, and island breakup. Studying how these characteristics evolve will help develop an understanding of how barrier islands will respond to climate change, sea level rise, and major storms in the future and that will serve to improve management of coastal resources.

Shorelines Extracted from 1984-2015 Landsat Imagery: Dauphin Island, Alabama (Polyline: Combined Dates) is a line shapefile representing shorelines generated from satellite imagery that was collected from 1984 to 2015. The sample frequency of satellite imagery is much higher, and the coverage much greater, than most routine high-resolution topographic surveys. Certain aspects of barrier island morphology, such as island size, shape and position, can be determined from these images and can indicate erosion, land loss, and island breakup. Studying how these characteristics evolve will help develop an understanding of how barrier islands will respond to climate change, sea level rise, and major storms in the future and that will serve to improve management of our coastal resources.

Shorelines Extracted from 1984-2015 Landsat Imagery: Dauphin Island, Alabama (Polyline: Individual Dates) is a line shapefile representing shorelines generated from satellite imagery that was collected from 1984 to 2015. The sample frequency of satellite imagery is much higher, and the coverage much greater, than most routine high-resolution topographic surveys. Certain aspects of barrier island morphology, such as island size, shape and position, can be determined from these images and can indicate erosion, land loss, and island breakup. Studying how these characteristics evolve will help develop an understanding of how barrier islands will respond to climate change, sea level rise, and major storms in the future and that will serve to improve management of our coastal resources.

Shorelines Extracted from 1984-2015 Landsat Imagery: Dauphin Island, Alabama (Polygon: Combined Dates) is a polygon shapefile representing shorelines generated from satellite imagery that was collected from 1984 to 2015. The sample frequency of satellite imagery is much higher, and the coverage much greater, than most routine high-resolution topographic surveys. Certain aspects of barrier island morphology, such as island size, shape and position, can be determined from these images and can indicate erosion, land loss, and island breakup. Studying how these characteristics evolve will help develop an understanding of how barrier islands will respond to climate change, sea level rise, and major storms in the future and that will serve to improve management of our coastal resources.

Shorelines Extracted from 1984-2015 Landsat Imagery: Dauphin Island, Alabama (Polygon: Individual Dates) is a dataset consisting of 223 polygon shapefiles representing shorelines generated from satellite imagery that was collected from 1984 to 2015. The sample frequency of satellite imagery is much higher, and the coverage much greater, than most routine high-resolution topographic surveys. Certain aspects of barrier island morphology, such as island size, shape and position, can be determined from these images and can indicate erosion, land loss, and island breakup. Studying how these characteristics evolve will help develop an understanding of how barrier islands will respond to climate change, sea level rise, and major storms in the future and that will serve to improve management of our coastal resources.

Shorelines Extracted from 1984-2015 Landsat Imagery: Horn Island, Mississippi (Polyline: Combined Dates) is a line shapefile representing shorelines generated from satellite imagery that was collected from 1984 to 2015. The sample frequency of satellite imagery is much higher, and the coverage much greater, than most routine high-resolution topographic surveys. Certain aspects of barrier island morphology, such as island size, shape and position, can be determined from these images and can indicate erosion, land loss, and island breakup. Studying how these characteristics evolve will help develop an understanding of how barrier islands will respond to climate change, sea level rise, and major storms in the future and that will serve to improve management of coastal resources.

Shorelines Extracted from 1984-2015 Landsat Imagery: Horn Island, Mississippi (Polyline: Individual Dates) is a line shapefile representing shorelines generated from satellite imagery that was collected from 1984 to 2015. The sample frequency of satellite imagery is much higher, and the coverage much greater, than most routine high-resolution topographic surveys. Certain aspects of barrier island morphology, such as island size, shape and position, can be determined from these images and can indicate erosion, land loss, and island breakup. Studying how these characteristics evolve will help develop an understanding of how barrier islands will respond to climate change, sea level rise, and major storms in the future and that will serve to improve management of coastal resources.

Shorelines Extracted from 1984-2015 Landsat Imagery: Horn Island, Mississippi (Polygon: Combined Dates) is a polygon shapefile representing shorelines generated from satellite imagery that was collected from 1984 to 2015. The sample frequency of satellite imagery is much higher, and the coverage much greater, than most routine high-resolution topographic surveys. Certain aspects of barrier island morphology, such as island size, shape and position, can be determined from these images and can indicate erosion, land loss, and island breakup. Studying how these characteristics evolve will help develop an understanding of how barrier islands will respond to climate change, sea level rise, and major storms in the future and that will serve to improve management of coastal resources.

Shorelines Extracted from 1984-2015 Landsat Imagery: Horn Island, Mississippi (Polygon: Individual Dates) is a dataset consisting of 254 polygon shapefiles representing shorelines generated from satellite imagery that was collected from 1984 to 2015. The sample frequency of satellite imagery is much higher, and the coverage much greater, than most routine high-resolution topographic surveys. Certain aspects of barrier island morphology, such as island size, shape and position, can be determined from these images and can indicate erosion, land loss, and island breakup. Studying how these characteristics evolve will help develop an understanding of how barrier islands will respond to climate change, sea level rise, and major storms in the future and that will serve to improve management of coastal resources.

Shorelines Extracted from 1984-2015 Landsat Imagery: Petit Bois Island, Mississippi (Polyline: Combined Dates) is a line shapefile representing shorelines generated from satellite imagery that was collected from 1984 to 2015. The sample frequency of satellite imagery is much higher, and the coverage much greater, than most routine high-resolution topographic surveys. Certain aspects of barrier island morphology, such as island size, shape and position, can be determined from these images and can indicate erosion, land loss, and island breakup. Studying how these characteristics evolve will help develop an understanding of how barrier islands will respond to climate change, sea level rise, and major storms in the future and that will serve to improve management of our coastal resources.

Shorelines Extracted from 1984-2015 Landsat Imagery: Petit Bois Island, Mississippi (Polyline: Individual Dates) is a line shapefile representing shorelines generated from satellite imagery that was collected from 1984 to 2015. The sample frequency of satellite imagery is much higher, and the coverage much greater, than most routine high-resolution topographic surveys. Certain aspects of barrier island morphology, such as island size, shape and position, can be determined from these images and can indicate erosion, land loss, and island breakup. Studying how these characteristics evolve will help develop an understanding of how barrier islands will respond to climate change, sea level rise, and major storms in the future and that will serve to improve management of our coastal resources.

Shorelines Extracted from 1984-2015 Landsat Imagery: Petit Bois Island, Mississippi (Polygon: Combined Dates) is a polygon shapefile representing shorelines generated from satellite imagery that was collected from 1984 to 2015. The sample frequency of satellite imagery is much higher, and the coverage much greater, than most routine high-resolution topographic surveys. Certain aspects of barrier island morphology, such as island size, shape and position, can be determined from these images and can indicate erosion, land loss, and island breakup. Studying how these characteristics evolve will help develop an understanding of how barrier islands will respond to climate change, sea level rise, and major storms in the future and that will serve to improve management of our coastal resources.

Shorelines Extracted from 1984-2015 Landsat Imagery: Petit Bois Island, Mississippi (Polygon: Individual Dates) is a dataset consisting of 271 polygon shapefiles representing shorelines generated from satellite imagery that was collected from 1984 to 2015. The sample frequency of satellite imagery is much higher, and the coverage much greater, than most routine high-resolution topographic surveys. Certain aspects of barrier island morphology, such as island size, shape and position, can be determined from these images and can indicate erosion, land loss, and island breakup. Studying how these characteristics evolve will help develop an understanding of how barrier islands will respond to climate change, sea level rise, and major storms in the future and that will serve to improve management of our coastal resources.

Shorelines Extracted from 1984-2015 Landsat Imagery: Ship Island, Mississippi (Polyline: Combined Dates) is a line shapefile representing shorelines generated from satellite imagery that was collected from 1984 to 2015. The sample frequency of satellite imagery is much higher, and the coverage much greater, than most routine high-resolution topographic surveys. Certain aspects of barrier island morphology, such as island size, shape and position, can be determined from these images and can indicate erosion, land loss, and island breakup. Studying how these characteristics evolve will help develop an understanding of how barrier islands will respond to climate change, sea level rise, and major storms in the future and that will serve to improve management of coastal resources.

Shorelines Extracted from 1984-2015 Landsat Imagery: Ship Island, Mississippi (Polyline: Individual Dates) is a line shapefile representing shorelines generated from satellite imagery that was collected from 1984 to 2015. The sample frequency of satellite imagery is much higher, and the coverage much greater, than most routine high-resolution topographic surveys. Certain aspects of barrier island morphology, such as island size, shape and position, can be determined from these images and can indicate erosion, land loss, and island breakup. Studying how these characteristics evolve will help develop an understanding of how barrier islands will respond to climate change, sea level rise, and major storms in the future and that will serve to improve management of coastal resources.

Shorelines Extracted from 1984-2015 Landsat Imagery: Ship Island, Mississippi (Polygon: Combined Dates) is a polygon shapefile representing shorelines generated from satellite imagery that was collected from 1984 to 2015. The sample frequency of satellite imagery is much higher, and the coverage much greater, than most routine high-resolution topographic surveys. Certain aspects of barrier island morphology, such as island size, shape and position, can be determined from these images and can indicate erosion, land loss, and island breakup. Studying how these characteristics evolve will help develop an understanding of how barrier islands will respond to climate change, sea level rise, and major storms in the future and that will serve to improve management of coastal resources.

Shorelines Extracted from 1984-2015 Landsat Imagery: Ship Island, Mississippi (Polygon: Individual Dates) is a dataset consisting of 280 polygon shapefiles representing shorelines generated from satellite imagery that was collected from 1984 to 2015. The sample frequency of satellite imagery is much higher, and the coverage much greater, than most routine high-resolution topographic surveys. Certain aspects of barrier island morphology, such as island size, shape and position, can be determined from these images and can indicate erosion, land loss, and island breakup. Studying how these characteristics evolve will help develop an understanding of how barrier islands will respond to climate change, sea level rise, and major storms in the future and that will serve to improve management of coastal resources.

The data release (Bernier, 2021) associated with this metadata record serves as an archive of coastal land-cover and feature datasets derived from Landsat satellite imagery at the northern Chandeleur Islands, Louisiana. To minimize the effects of tidal water-level variations, 75 cloud-free, low-water images acquired between 1984 and 2019 were analyzed. Water, bare earth (sand), vegetated, and intertidal land-cover classes were mapped from Hewes Point to Palos Island using successive thresholding and masking of the modified normalized difference water index (mNDWI), the normalized difference bare land index (NBLI), and the normalized difference vegetation index (NDVI). Vector shoreline, sand, and vegetated feature extents were extracted for each image by contouring the spectral indices using the calculated threshold values. The geographic information system (GIS) data files with accompanying formal Federal Geographic Data Committee (FGDC) metadata can be downloaded from https://doi.org/10.5066/P9HY3HOR.

The data release (Bernier, 2021) associated with this metadata record serves as an archive of coastal land-cover and feature datasets derived from Landsat satellite imagery at the northern Chandeleur Islands, Louisiana. To minimize the effects of tidal water-level variations, 75 cloud-free, low-water images acquired between 1984 and 2019 were analyzed. Water, bare earth (sand), vegetated, and intertidal land-cover classes were mapped from Hewes Point to Palos Island using successive thresholding and masking of the modified normalized difference water index (mNDWI), the normalized difference bare land index (NBLI), and the normalized difference vegetation index (NDVI). Vector shoreline, sand, and vegetated feature extents were extracted for each image by contouring the spectral indices using the calculated threshold values. The geographic information system (GIS) data files with accompanying formal Federal Geographic Data Committee (FGDC) metadata can be downloaded from https://doi.org/10.5066/P9HY3HOR.

This archive consists of two-dimensional marine seismic reflection profile data collected in the Barataria Basin of southern Louisiana. These data were acquired in May, June, and July of 2000 aboard the R/V G.K. Gilbert. Included here are data in a variety of formats including binary, American Standard Code for Information Interchange (ASCII), Hyper-Text Markup Language (HTML), shapefiles, and Graphics Interchange Format (GIF) and Joint Photographic Experts Group (JPEG) images. Binary data are in Society of Exploration Geophysicists (SEG) SEG-Y format and may be downloaded for further processing or display. Reference maps and GIF images of the profiles may be viewed with a web browser. The Geographic Information Systems (GIS) information provided here is compatible with Environmental Systems Research Institute (ESRI) GIS software. For more information on the seismic surveys see http://walrus.wr.usgs.gov/infobank/g/g100la/html/g-1-00-la.meta.html and http://walrus.wr.usgs.gov/infobank/g/g500la/html/g-5-00-la.meta.html These data are also available via GeoMapApp (http://www.geomapapp.org/) and Virtual Ocean ( http://www.virtualocean.org/) earth science exploration and visualization applications.

This archive consists of marine seismic reflection profile data collected in four survey areas from southeast of Charleston Harbor to the mouth of the North Edisto River of South Carolina. These data were acquired June 26 - July 1, 1996, aboard the R/V G.K. Gilbert. Included here are data in a variety of formats including binary, American Standard Code for Information Interchange (ASCII), Hyper Text Markup Language (HTML), Portable Document Format (PDF), Rich Text Format (RTF), Graphics Interchange Format (GIF) and Joint Photographic Experts Group (JPEG) images, and shapefiles. Binary data are in Society of Exploration Geophysicists (SEG) SEG-Y format and may be downloaded for further processing or display. Reference maps and GIF images of the profiles may be viewed with a web browser. The Geographic Information Systems (GIS) map documents provided were created with Environmental Systems Research Institute (ESRI) GIS software ArcView 3.2 and 8.1. For more information on the seismic surveys see http://walrus.wr.usgs.gov/infobank/g/g196sr/html/g-1-96-sr.meta.html These data are also available via GeoMapApp (http://www.geomapapp.org/) and Virtual Ocean ( http://www.virtualocean.org/) earth science exploration and visualization applications.

This archive consists of two-dimensional marine seismic reflection profile data collected in Timbalier Bay and in the Gulf of Mexico offshore East Timbalier Island, Louisiana. These data were acquired in June, July, and August of 2001 aboard the R/V G.K. Gilbert. Included here are data in a variety of formats including binary, American Standard Code for Information Interchange (ASCII), Hyper Text Markup Language (HTML), Portable Document Format (PDF), Rich Text Format (RTF), Graphics Interchange Format (GIF) and Joint Photographic Experts Group (JPEG) images, and shapefiles. Binary data are in Society of Exploration Geophysicists (SEG) SEG-Y format and may be downloaded for further processing or display. Reference maps and GIF images of the profiles may be viewed with a web browser. The Geographic Information Systems (GIS) information provided is compatible with Environmental Systems Research Institute (ESRI) GIS software. For more information on the seismic surveys see http://walrus.wr.usgs.gov/infobank/g/g401la/html/g-4-01-la.meta.html and http://walrus.wr.usgs.gov/infobank/g/g501la/html/g-5-01-la.meta.html These data are also available via GeoMapApp (http://www.geomapapp.org/) and Virtual Ocean (http://www.virtualocean.org/) earth science exploration and visualization applications.

This archive consists of two-dimensional marine seismic reflection profile data collected in Timbalier Bay and in the Gulf of Mexico offshore East Timbalier Island, Louisiana. These data were acquired June 30 - July 9 (01SCC01) and August 1 - 18 (01SCC02), 2001, aboard the R/V G.K. Gilbert and a University of New Orleans 21-foot Proline. Included here are data in a variety of formats including binary, American Standard Code for Information Interchange (ASCII), Hyper Text Markup Language (HTML), Portable Document Format (PDF), Rich Text Format (RTF), Graphics Interchange Format (GIF) and Joint Photographic Experts Group (JPEG) images, and shapefiles. Binary data are in Society of Exploration Geophysicists (SEG) SEG-Y format and may be downloaded for further processing or display. Reference maps and GIF images of the profiles may be viewed with a web browser. The Geographic Information Systems (GIS) map documents provided were created with Environmental Systems Research Institute (ESRI) GIS software ArcView 3.2 and 8.1. For more information on the seismic surveys see http://walrus.wr.usgs.gov/infobank/g/g401la/html/g-4-01-la.meta.html and http://walrus.wr.usgs.gov/infobank/g/g501la/html/g-5-01-la.meta.html These data are also available via GeoMapApp (http://www.geomapapp.org/) and Virtual Ocean ( http://www.virtualocean.org/) earth science exploration and visualization applications.

This archive consists of two-dimensional marine seismic reflection profile data collected in the Barataria Basin of southern Louisiana. These data were acquired in May, June, and July of 2000 aboard the R/V G.K. Gilbert. Included here are data in a variety of formats including binary, ASCII, HTML, PDF, RTF, shapefiles, and GIF and JPEG images. Binary data are in SEG-Y format and may be downloaded for further processing or display. Reference maps and GIF images of the profiles may be viewed with a web browser. The GIS information provided here is compatible with ESRI GIS software. For more information on the seismic surveys see http://walrus.wr.usgs.gov/infobank/g/g100la/html/g-1-00-la.meta.html and http://walrus.wr.usgs.gov/infobank/g/g500la/html/g-5-00-la.meta.html These data are also available via GeoMapApp (http://www.geomapapp.org/) and Virtual Ocean ( http://www.virtualocean.org/) earth science exploration and visualization applications.

The U.S. Geological Survey, in cooperation with the University of New Orleans, the Lake Pontchartrain Basin Foundation, the National Oceanic and Atmospheric Administration, the Coalition to Restore Coastal Louisiana, the U.S. Army Corps of Engineers, the Environmental Protection Agency, and the University of Georgia, conducted five geophysical surveys of Lakes Pontchartrain, Borgne, and Maurepas in Louisiana from 1994 to 1998. This report serves as an archive of unprocessed digital boomer seismic reflection data, trackline maps, navigation files, observers' logbooks, GIS information, and formal FGDC metadata. In addition, a filtered and gained GIF image of each seismic profile is provided. The archived trace data are in standard Society of Exploration Geophysicists (SEG) SEG-Y format (Barry and others, 1975) and may be downloaded and processed with commercial or public domain software such as Seismic Unix (SU). Examples of SU processing scripts and in-house (USGS) software for viewing SEG-Y headers (Zihlman, 1992) are also provided. Processed profile images, trackline maps, navigation files, Field Activity Collection System (FACS) logs, and formal metadata may be viewed with a web browser, and scanned handwritten logbooks may be viewed with Adobe Reader. For more information on the seismic surveys see http://woodshole.er.usgs.gov/operations/ia/public_ds_info.php?fa=1994-030-FA , http://woodshole.er.usgs.gov/operations/ia/public_ds_info.php?fa=1995-031-FA , http://walrus.wr.usgs.gov/infobank/g/g196la/html/g-1-96-la.meta.html , http://walrus.wr.usgs.gov/infobank/g/g297la/html/g-2-97-la.meta.html , and http://walrus.wr.usgs.gov/infobank/g/g298la/html/g-2-98-la.meta.html These data are also available via GeoMapApp (http://www.geomapapp.org/) and Virtual Ocean ( http://www.virtualocean.org/) earth science exploration and visualization applications.

This archive consists of two-dimensional marine seismic reflection profile data collected in the Mississippi River Delta, Atchafalaya River Delta, and Shell Island Pass in southern Louisiana. These data were acquired in April and May of 2001 aboard the R/V G. K. Gilbert. The data are available in a variety of formats, including binary, ASCII, HTML, shapefiles, and GIF and JPEG images. Binary data are in Society of Exploration Geophysicists (SEG) SEG-Y format and may be downloaded for further processing or display. Reference maps and GIF images of the profiles may be viewed with your web browser. The GIS information provided is compatible with ESRI's GIS software. For more information on the seismic surveys see http://walrus.wr.usgs.gov/infobank/g/g201la/html/g-2-01-la.meta.html These data are also available via GeoMapApp (http://www.geomapapp.org/) and Virtual Ocean ( http://www.virtualocean.org/) earth science exploration and visualization applications.

The U.S. Geological Survey has developed a method for estimating the mobility and potential alongshore transport of heavier-than-water sand and oil agglomerates (tarballs or surface residual balls, SRBs). During the Deepwater Horizon spill, some oil that reached the surf zone of the northern Gulf of Mexico mixed with suspended sediment and sank to form sub-tidal mats. If not removed, these mats can break apart to form SRBs and subsequently re-oil the beach. A method was developed for estimating SRB mobilization and alongshore movement. A representative suite of wave conditions was identified from buoy data for April, 2010, until August, 2012, and used to drive a numerical model of the spatially-variant alongshore currents. Potential mobilization of SRBs was estimated by comparing combined wave- and current-induced shear stress from the model to critical stress values for several sized SRBs. Potential alongshore flux of SRBs was also estimated to identify regions more or less likely to have SRBs deposited under each scenario. This methodology was developed to explain SRB movement and redistribution in the alongshore, interpret observed re-oiling events, and thus inform re-oiling mitigation efforts.

The U.S. Geological Survey has developed a method for estimating the mobility and potential alongshore transport of heavier-than-water sand and oil agglomerates (tarballs or surface residual balls, SRBs). During the Deepwater Horizon spill, some oil that reached the surf zone of the northern Gulf of Mexico mixed with suspended sediment and sank to form sub-tidal mats. If not removed, these mats can break apart to form SRBs and subsequently re-oil the beach. A method was developed for estimating SRB mobilization and alongshore movement. A representative suite of wave conditions was identified from buoy data for April, 2010, until August, 2012, and used to drive a numerical model of the spatially-variant alongshore currents. Potential mobilization of SRBs was estimated by comparing combined wave- and current-induced shear stress from the model to critical stress values for several sized SRBs. Potential alongshore flux of SRBs was also estimated to identify regions more or less likely to have SRBs deposited under each scenario. This methodology was developed to explain SRB movement and redistribution in the alongshore, interpret observed re-oiling events, and thus inform re-oiling mitigation efforts.

The U.S. Geological Survey has developed a method for estimating the mobility and potential alongshore transport of heavier-than-water sand and oil agglomerates (tarballs or surface residual balls, SRBs). During the Deepwater Horizon spill, some oil that reached the surf zone of the northern Gulf of Mexico mixed with suspended sediment and sank to form sub-tidal mats. If not removed, these mats can break apart to form SRBs and subsequently re-oil the beach. A method was developed for estimating SRB mobilization and alongshore movement. A representative suite of wave conditions was identified from buoy data for April, 2010, until August, 2012, and used to drive a numerical model of the spatially-variant alongshore currents. Potential mobilization of SRBs was estimated by comparing combined wave- and current-induced shear stress from the model to critical stress values for several sized SRBs. Potential alongshore flux of SRBs was also estimated to identify regions more or less likely to have SRBs deposited under each scenario. This methodology was developed to explain SRB movement and redistribution in the alongshore, interpret observed re-oiling events, and thus inform re-oiling mitigation efforts.

The U.S. Geological Survey has developed a method for estimating the mobility and potential alongshore transport of heavier-than-water sand and oil agglomerates (tarballs or surface residual balls, SRBs). During the Deepwater Horizon spill, some oil that reached the surf zone of the northern Gulf of Mexico mixed with suspended sediment and sank to form sub-tidal mats. If not removed, these mats can break apart to form SRBs and subsequently re-oil the beach. A method was developed for estimating SRB mobilization and alongshore movement. A representative suite of wave conditions was identified from buoy data for April, 2010, until August, 2012, and used to drive a numerical model of the spatially-variant alongshore currents. Potential mobilization of SRBs was estimated by comparing combined wave- and current-induced shear stress from the model to critical stress values for several sized SRBs. Potential alongshore flux of SRBs was also estimated to identify regions more or less likely to have SRBs deposited under each scenario. This methodology was developed to explain SRB movement and redistribution in the alongshore, interpret observed re-oiling events, and thus inform re-oiling mitigation efforts.

The U.S. Geological Survey has developed a method for estimating the mobility and potential alongshore transport of heavier-than-water sand and oil agglomerates (tarballs or surface residual balls, SRBs). During the Deepwater Horizon spill, some oil that reached the surf zone of the northern Gulf of Mexico mixed with suspended sediment and sank to form sub-tidal mats. If not removed, these mats can break apart to form SRBs and subsequently re-oil the beach. A method was developed for estimating SRB mobilization and alongshore movement. A representative suite of wave conditions was identified from buoy data for April, 2010, until August, 2012, and used to drive a numerical model of the spatially-variant alongshore currents. Potential mobilization of SRBs was estimated by comparing combined wave- and current-induced shear stress from the model to critical stress values for several sized SRBs. Potential alongshore flux of SRBs was also estimated to identify regions more or less likely to have SRBs deposited under each scenario. This methodology was developed to explain SRB movement and redistribution in the alongshore, interpret observed re-oiling events, and thus inform re-oiling mitigation efforts.

The U.S. Geological Survey has developed a method for estimating the mobility and potential alongshore transport of heavier-than-water sand and oil agglomerates (tarballs or surface residual balls, SRBs). During the Deepwater Horizon spill, some oil that reached the surf zone of the northern Gulf of Mexico mixed with suspended sediment and sank to form sub-tidal mats. If not removed, these mats can break apart to form SRBs and subsequently re-oil the beach. A method was developed for estimating SRB mobilization and alongshore movement. A representative suite of wave conditions was identified from buoy data for April, 2010, until August, 2012, and used to drive a numerical model of the spatially-variant alongshore currents. Potential mobilization of SRBs was estimated by comparing combined wave- and current-induced shear stress from the model to critical stress values for several sized SRBs. Potential alongshore flux of SRBs was also estimated to identify regions more or less likely to have SRBs deposited under each scenario. This methodology was developed to explain SRB movement and redistribution in the alongshore, interpret observed re-oiling events, and thus inform re-oiling mitigation efforts.

The U.S. Geological Survey has developed a method for estimating the mobility and potential alongshore transport of heavier-than-water sand and oil agglomerates (tarballs or surface residual balls, SRBs). During the Deepwater Horizon spill, some oil that reached the surf zone of the northern Gulf of Mexico mixed with suspended sediment and sank to form sub-tidal mats. If not removed, these mats can break apart to form SRBs and subsequently re-oil the beach. A method was developed for estimating SRB mobilization and alongshore movement. A representative suite of wave conditions was identified from buoy data for April, 2010, until August, 2012, and used to drive a numerical model of the spatially-variant alongshore currents. Potential mobilization of SRBs was estimated by comparing combined wave- and current-induced shear stress from the model to critical stress values for several sized SRBs. Potential alongshore flux of SRBs was also estimated to identify regions more or less likely to have SRBs deposited under each scenario. This methodology was developed to explain SRB movement and redistribution in the alongshore, interpret observed re-oiling events, and thus inform re-oiling mitigation efforts.

The U.S. Geological Survey has developed a method for estimating the mobility and potential alongshore transport of heavier-than-water sand and oil agglomerates (tarballs or surface residual balls, SRBs). During the Deepwater Horizon spill, some oil that reached the surf zone of the northern Gulf of Mexico mixed with suspended sediment and sank to form sub-tidal mats. If not removed, these mats can break apart to form SRBs and subsequently re-oil the beach. A method was developed for estimating SRB mobilization and alongshore movement. A representative suite of wave conditions was identified from buoy data for April, 2010, until August, 2012, and used to drive a numerical model of the spatially-variant alongshore currents. Potential mobilization of SRBs was estimated by comparing combined wave- and current-induced shear stress from the model to critical stress values for several sized SRBs. Potential alongshore flux of SRBs was also estimated to identify regions more or less likely to have SRBs deposited under each scenario. This methodology was developed to explain SRB movement and redistribution in the alongshore, interpret observed re-oiling events, and thus inform re-oiling mitigation efforts.

The U.S. Geological Survey has developed a method for estimating the mobility and potential alongshore transport of heavier-than-water sand and oil agglomerates (tarballs or surface residual balls, SRBs). During the Deepwater Horizon spill, some oil that reached the surf zone of the northern Gulf of Mexico mixed with suspended sediment and sank to form sub-tidal mats. If not removed, these mats can break apart to form SRBs and subsequently re-oil the beach. A method was developed for estimating SRB mobilization and alongshore movement. A representative suite of wave conditions was identified from buoy data for April, 2010, until August, 2012, and used to drive a numerical model of the spatially-variant alongshore currents. Potential mobilization of SRBs was estimated by comparing combined wave- and current-induced shear stress from the model to critical stress values for several sized SRBs. Potential alongshore flux of SRBs was also estimated to identify regions more or less likely to have SRBs deposited under each scenario. This methodology was developed to explain SRB movement and redistribution in the alongshore, interpret observed re-oiling events, and thus inform re-oiling mitigation efforts.

The U.S. Geological Survey has developed a method for estimating the mobility and potential alongshore transport of heavier-than-water sand and oil agglomerates (tarballs or surface residual balls, SRBs). During the Deepwater Horizon spill, some oil that reached the surf zone of the northern Gulf of Mexico mixed with suspended sediment and sank to form sub-tidal mats. If not removed, these mats can break apart to form SRBs and subsequently re-oil the beach. A method was developed for estimating SRB mobilization and alongshore movement. A representative suite of wave conditions was identified from buoy data for April, 2010, until August, 2012, and used to drive a numerical model of the spatially-variant alongshore currents. Potential mobilization of SRBs was estimated by comparing combined wave- and current-induced shear stress from the model to critical stress values for several sized SRBs. Potential alongshore flux of SRBs was also estimated to identify regions more or less likely to have SRBs deposited under each scenario. This methodology was developed to explain SRB movement and redistribution in the alongshore, interpret observed re-oiling events, and thus inform re-oiling mitigation efforts.

The U.S. Geological Survey has developed a method for estimating the mobility and potential alongshore transport of heavier-than-water sand and oil agglomerates (tarballs or surface residual balls, SRBs). During the Deepwater Horizon spill, some oil that reached the surf zone of the northern Gulf of Mexico mixed with suspended sediment and sank to form sub-tidal mats. If not removed, these mats can break apart to form SRBs and subsequently re-oil the beach. A method was developed for estimating SRB mobilization and alongshore movement. A representative suite of wave conditions was identified from buoy data for April, 2010, until August, 2012, and used to drive a numerical model of the spatially-variant alongshore currents. Potential mobilization of SRBs was estimated by comparing combined wave- and current-induced shear stress from the model to critical stress values for several sized SRBs. Potential alongshore flux of SRBs was also estimated to identify regions more or less likely to have SRBs deposited under each scenario. This methodology was developed to explain SRB movement and redistribution in the alongshore, interpret observed re-oiling events, and thus inform re-oiling mitigation efforts.

The U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center conducted research to quantify the combined effect of all constructive and destructive processes on modern coral reef ecosystems by measuring regional-scale changes in seafloor elevation in the Upper Florida Keys, Florida, including both coral-dominated habitats and adjacent, non-coral-dominated habitats. USGS staff used historical bathymetric data from the 1930’s and light detection and ranging (lidar)-derived data acquired in 2002 (Brock and others, 2006, 2007) to calculate changes in seafloor elevation (Yates and others, 2017). Using these changes in seafloor elevation, further analyses were conducted that calculated annual erosion rates and utilized those results to project seafloor elevation changes 25, 50, 75, and 100 years from 2002.

Data release doi:10.5066/F7BR8QBH associated with this metadata record serves as an archive of elevation data collected in August 2010 from Sabine National Wildlife Refuge (SNWR), Louisiana (U.S. Geological Survey [USGS] Field Activity Number [FAN] 10SWL01). Point (xyz) elevations were collected from historically formed open-water bodies and the surrounding emergent marsh using a combination of stop-and-go (semi-kinematic) and kinematic differential Global Positioning System (DGPS) surveying techniques. These data were collected as part of the U.S. Geological Survey’s Gulf Coast Subsidence project https://coastal.er.usgs.gov/gc-subsidence/) and provide more extensive spatial coverage than water depths and marsh-surface elevations collected along coring transects in 2008 (Bernier and others, 2011). All elevation data use the projected coordinate system North American Datum of 1983 (NAD83), Universal Transverse Mercator (UTM) Zone 15 North (15N) and all elevations are North American Vertical Datum of 1988 (NAVD88) orthometric heights, derived using the GEOID09 geoid model.

The U.S. Geological Survey (USGS) Coastal and Marine Geology Program has actively collected geophysical and sedimentological data in the northern Gulf of Mexico for several decades, including shallow subsurface data in the form of high-resolution seismic reflection profiles (HRSP). Prior to the mid-1990s most HRSP data were collected in analog format as paper rolls of continuous profiles up to 25 meters long. As part of the National Geological and Geophysical Data Preservation Program (NGGDPP, https://datapreservation.usgs.gov/), and in collaboration with the Bureau of Ocean Energy Management, Marine Minerals Program, scientists at the USGS St. Petersburg Coastal and Marine Science Center began converting the analog paper records to digital format using a large-format continuous scanner. The scanned image files were subsequently processed to fix distortions and crop out blank spaces prior to exporting as industry standard Society of Exploration Geophysicists date exchange (SEG-Y) formatted files. This data release serves as an archive of HRSP profiles annotated with header information, converted SEG-Y files, navigation data, cruise trackline files, logbooks, as well as annotated/core location maps. The HRSP data were collected using a Huntec boomer seismic system onboard the Research Vessel (R/V) Ecoli. Geophysical surveys were conducted in collaboration with University of South Florida (USF) between September 13 and 19, 1989. Data were acquired within Bay St. Louis and the adjacent Mississippi Sound offshore of Hancock County, Mississippi. Data collection and processing methods are described in USGS Data Series 1047 (https://doi.org/10.3133/ds1047).

This U.S. Geological Survey (USGS) data release includes geospatial datasets that were created for the analysis of Virginia and Maryland Atlantic coastal wetland changes over time. Wetland change was determined by assessing two metrics: wetland persistence and land-cover switching. Because seasonal water levels, beach width, and vegetation differences can affect change analyses, only images acquired during the spring (March, April, and May) were included in the wetland-change metrics (N=10). To assess wetland-area trends, including wetland persistence, the total marsh and mixed vegetation classes land-cover types defined by Bernier and others (2015) were reclassified as 1 (wetland presence) and all other classes were reclassified as 0 (wetland absence). When the baseline data (1985) is subtracted from a later dataset, the outcome results in cells with three possible values: 0, 1, or -1, where -1 is wetland loss, 0 is no change (persistence), and 1 is wetland gain.

This data release contains coastal wetland synthesis products for the geographic region of Blackwater, Chesapeake Bay, Maryland. Metrics for resiliency, including unvegetated to vegetated ratio (UVVR), marsh elevation, and others, are calculated for smaller units delineated from a digital elevation model, providing the spatial variability of physical factors that influence wetland health. The U.S. Geological Survey has been expanding national assessment of coastal change hazards and forecast products to coastal wetlands with the intent of providing Federal, State, and local managers with tools to estimate the vulnerability and ecosystem service potential of these wetlands. For this purpose, the response and resilience of coastal wetlands to physical factors need to be assessed in terms of the ensuing change to their vulnerability and ecosystem services.

This data release contains coastal wetland synthesis products for the geographic region of Blackwater salt marsh complex, Chesapeake Bay, Maryland. Metrics for resiliency, including unvegetated to vegetated ratio (UVVR), marsh elevation, and others, are calculated for smaller units delineated from a digital elevation model, providing the spatial variability of physical factors that influence wetland health. The U.S. Geological Survey has been expanding national assessment of coastal change hazards and forecast products to coastal wetlands with the intent of providing Federal, State, and local managers with tools to estimate the vulnerability and ecosystem service potential of these wetlands. For this purpose, the response and resilience of coastal wetlands to physical factors need to be assessed in terms of the ensuing change to their vulnerability and ecosystem services.

This data release contains coastal wetland synthesis products for the geographic region of Blackwater salt marsh complex, Chesapeake Bay, Maryland. Metrics for resiliency, including unvegetated to vegetated ratio (UVVR), marsh elevation, and others, are calculated for smaller units delineated from a digital elevation model, providing the spatial variability of physical factors that influence wetland health. The U.S. Geological Survey has been expanding national assessment of coastal change hazards and forecast products to coastal wetlands with the intent of providing Federal, State, and local managers with tools to estimate the vulnerability and ecosystem service potential of these wetlands. For this purpose, the response and resilience of coastal wetlands to physical factors need to be assessed in terms of the ensuing change to their vulnerability and ecosystem services.

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.

Low-altitude (80-100 meters above ground level) digital images were obtained from a camera mounted on a 3DR Solo quadcopter, a small unmanned aerial system (UAS), in three locations along the Lake Ontario shoreline in New York during July 2017. These data were collected to document and monitor effects of high lake levels, including shoreline erosion, inundation, and property damage in the vicinities of Braddock Bay, Sodus Bay, and Chimney Bluffs State Park, New York. This data release includes images tagged with locations determined from the UAS GPS; tables with updated estimates of camera positions and attitudes based on the photogrammetric reconstruction; tables listing locations of the base stations, ground control points, and transect points; geolocated, RGB-colored point clouds; orthomosaic images; and digital elevation models for each of the survey regions. Collection of these data was supported by the Federal Emergency Management Agency, the State of New York Departments of State and Environmental Conservation, and the USGS Coastal and Marine Geology Program and was conducted under USGS field activity number 2017-042-FA.

Low-altitude (80-100 meters above ground level) digital images were obtained from a camera mounted on a 3DR Solo quadcopter, a small unmanned aerial system (UAS), in three locations along the Lake Ontario shoreline in New York during July 2017. These data were collected to document and monitor effects of high lake levels, including shoreline erosion, inundation, and property damage in the vicinities of Braddock Bay, Sodus Bay, and Chimney Bluffs State Park, New York. This data release includes images tagged with locations determined from the UAS GPS; tables with updated estimates of camera positions and attitudes based on the photogrammetric reconstruction; tables listing locations of the base stations, ground control points, and transect points; geolocated, RGB-colored point clouds; orthomosaic images; and digital elevation models for each of the survey regions. Collection of these data was supported by the Federal Emergency Management Agency, the State of New York Departments of State and Environmental Conservation, and the USGS Coastal and Marine Geology Program and was conducted under USGS field activity number 2017-042-FA.

Low-altitude (80-100 meters above ground level) digital images were obtained from a camera mounted on a 3DR Solo quadcopter, a small unmanned aerial system (UAS), in three locations along the Lake Ontario shoreline in New York during July 2017. These data were collected to document and monitor effects of high lake levels, including shoreline erosion, inundation, and property damage in the vicinities of Braddock Bay, Sodus Bay, and Chimney Bluffs State Park, New York. This data release includes images tagged with locations determined from the UAS GPS; tables with updated estimates of camera positions and attitudes based on the photogrammetric reconstruction; tables listing locations of the base stations, ground control points, and transect points; geolocated, RGB-colored point clouds; orthomosaic images; and digital elevation models for each of the survey regions. Collection of these data was supported by the Federal Emergency Management Agency, the State of New York Departments of State and Environmental Conservation, and the USGS Coastal and Marine Geology Program and was conducted under USGS field activity number 2017-042-FA.

Low-altitude (80-100 meters above ground level) digital images were obtained from a camera mounted on a 3DR Solo quadcopter, a small unmanned aerial system (UAS), in three locations along the Lake Ontario shoreline in New York during July 2017. These data were collected to document and monitor effects of high lake levels, including shoreline erosion, inundation, and property damage in the vicinities of Braddock Bay, Sodus Bay, and Chimney Bluffs State Park, New York. This data release includes images tagged with locations determined from the UAS GPS; tables with updated estimates of camera positions and attitudes based on the photogrammetric reconstruction; tables listing locations of the base stations, ground control points, and transect points; geolocated, RGB-colored point clouds; orthomosaic images; and digital elevation models for each of the survey regions. Collection of these data was supported by the Federal Emergency Management Agency, the State of New York Departments of State and Environmental Conservation, and the USGS Coastal and Marine Geology Program and was conducted under USGS field activity number 2017-042-FA.

Low-altitude (80-100 meters above ground level) digital images were obtained from a camera mounted on a 3DR Solo quadcopter, a small unmanned aerial system (UAS), in three locations along the Lake Ontario shoreline in New York during July 2017. These data were collected to document and monitor effects of high lake levels, including shoreline erosion, inundation, and property damage in the vicinities of Braddock Bay, Sodus Bay, and Chimney Bluffs State Park, New York. This data release includes images tagged with locations determined from the UAS GPS; tables with updated estimates of camera positions and attitudes based on the photogrammetric reconstruction; tables listing locations of the base stations, ground control points, and transect points; geolocated, RGB-colored point clouds; orthomosaic images; and digital elevation models for each of the survey regions. Collection of these data was supported by the Federal Emergency Management Agency, the State of New York Departments of State and Environmental Conservation, and the USGS Coastal and Marine Geology Program and was conducted under USGS field activity number 2017-042-FA.

Low-altitude (80-100 meters above ground level) digital images were obtained from a camera mounted on a 3DR Solo quadcopter, a small unmanned aerial system (UAS), in three locations along the Lake Ontario shoreline in New York during July 2017. These data were collected to document and monitor effects of high lake levels, including shoreline erosion, inundation, and property damage in the vicinities of Braddock Bay, Sodus Bay, and Chimney Bluffs State Park, New York. This data release includes images tagged with locations determined from the UAS GPS; tables with updated estimates of camera positions and attitudes based on the photogrammetric reconstruction; tables listing locations of the base stations, ground control points, and transect points; geolocated, RGB-colored point clouds; orthomosaic images; and digital elevation models for each of the survey regions. Collection of these data was supported by the Federal Emergency Management Agency, the State of New York Departments of State and Environmental Conservation, and the USGS Coastal and Marine Geology Program and was conducted under USGS field activity number 2017-042-FA.

Low-altitude (80-100 meters above ground level) digital images were obtained from a camera mounted on a 3DR Solo quadcopter, a small unmanned aerial system (UAS), in three locations along the Lake Ontario shoreline in New York during July 2017. These data were collected to document and monitor effects of high lake levels, including shoreline erosion, inundation, and property damage in the vicinities of Braddock Bay, Sodus Bay, and Chimney Bluffs State Park, New York. This data release includes images tagged with locations determined from the UAS GPS; tables with updated estimates of camera positions and attitudes based on the photogrammetric reconstruction; tables listing locations of the base stations, ground control points, and transect points; geolocated, RGB-colored point clouds; orthomosaic images; and digital elevation models for each of the survey regions. Collection of these data was supported by the Federal Emergency Management Agency, the State of New York Departments of State and Environmental Conservation, and the USGS Coastal and Marine Geology Program and was conducted under USGS field activity number 2017-042-FA.

Low-altitude (80-100 meters above ground level) digital images were obtained from a camera mounted on a 3DR Solo quadcopter, a small unmanned aerial system (UAS), in three locations along the Lake Ontario shoreline in New York during July 2017. These data were collected to document and monitor effects of high lake levels, including shoreline erosion, inundation, and property damage in the vicinities of Braddock Bay, Sodus Bay, and Chimney Bluffs State Park, New York. This data release includes images tagged with locations determined from the UAS GPS; tables with updated estimates of camera positions and attitudes based on the photogrammetric reconstruction; tables listing locations of the base stations, ground control points, and transect points; geolocated, RGB-colored point clouds; orthomosaic images; and digital elevation models for each of the survey regions. Collection of these data was supported by the Federal Emergency Management Agency, the State of New York Departments of State and Environmental Conservation, and the USGS Coastal and Marine Geology Program and was conducted under USGS field activity number 2017-042-FA.

Low-altitude (80-100 meters above ground level) digital images were obtained from a camera mounted on a 3DR Solo quadcopter, a small unmanned aerial system (UAS), in three locations along the Lake Ontario shoreline in New York during July 2017. These data were collected to document and monitor effects of high lake levels, including shoreline erosion, inundation, and property damage in the vicinities of Braddock Bay, Sodus Bay, and Chimney Bluffs State Park, New York. This data release includes images tagged with locations determined from the UAS GPS; tables with updated estimates of camera positions and attitudes based on the photogrammetric reconstruction; tables listing locations of the base stations, ground control points, and transect points; geolocated, RGB-colored point clouds; orthomosaic images; and digital elevation models for each of the survey regions. Collection of these data was supported by the Federal Emergency Management Agency, the State of New York Departments of State and Environmental Conservation, and the USGS Coastal and Marine Geology Program and was conducted under USGS field activity number 2017-042-FA.

Low-altitude (80-100 meters above ground level) digital images were obtained from a camera mounted on a 3DR Solo quadcopter, a small unmanned aerial system (UAS), in three locations along the Lake Ontario shoreline in New York during July 2017. These data were collected to document and monitor effects of high lake levels, including shoreline erosion, inundation, and property damage in the vicinities of Braddock Bay, Sodus Bay, and Chimney Bluffs State Park, New York. This data release includes images tagged with locations determined from the UAS GPS; tables with updated estimates of camera positions and attitudes based on the photogrammetric reconstruction; tables listing locations of the base stations, ground control points, and transect points; geolocated, RGB-colored point clouds; orthomosaic images; and digital elevation models for each of the survey regions. Collection of these data was supported by the Federal Emergency Management Agency, the State of New York Departments of State and Environmental Conservation, and the USGS Coastal and Marine Geology Program and was conducted under USGS field activity number 2017-042-FA.

Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. 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 open-ocean 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.

Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. 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 open-ocean 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.

Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. 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 open-ocean 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.

Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. 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 open-ocean 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.

Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. 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 open-ocean 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.

Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. 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 open-ocean 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.

Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. 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 open-ocean 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.

Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. 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 open-ocean 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.

Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. 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 open-ocean 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.

Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. 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 open-ocean 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.

Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. 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 open-ocean 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.

Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. 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 open-ocean 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.

Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. 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 open-ocean 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.

Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. 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 open-ocean 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.

Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. 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 open-ocean 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.

Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. 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 open-ocean 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.

Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. 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 open-ocean 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.

Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. 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 open-ocean 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.

Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. 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 open-ocean 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.

Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. 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 open-ocean 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.

Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. 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 open-ocean 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.

Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. 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 open-ocean 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.

Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. 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 open-ocean 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.

Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. 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 open-ocean 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.

Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. 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 open-ocean 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.

Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. 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 open-ocean 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.

Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. 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 open-ocean 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.

Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. 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 open-ocean 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.

Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. 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 open-ocean 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.

Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. 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 open-ocean 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.

Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. 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 open-ocean 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.

Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. 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 open-ocean 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.

Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. 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 open-ocean 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.

Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. 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 open-ocean 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.

Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. 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 open-ocean 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.

Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. 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 open-ocean 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.

Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. 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 open-ocean 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.

Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. 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 open-ocean 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.

Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. 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 open-ocean 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.

Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. 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 open-ocean 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.

Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. 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 open-ocean 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.

Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. 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 open-ocean 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.

Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. 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 open-ocean 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.

Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. 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 open-ocean 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.

Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. 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 open-ocean 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.

Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. 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 open-ocean 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.

Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. 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 open-ocean 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.

This point cloud was derived from low-altitude aerial images collected from an unmanned aerial system (UAS) flown in the Cape Cod National Seashore on 1 March, 2016. The objective of the project was to evaluate the quality and cost of mapping from UAS images. The point cloud contains 434,096,824 unclassifed and unedited geolocated points. The points have horizontal coordinates in NAD83(2011) UTM Zone 19 North meters, vertical coordinates in NAVD88 meters, and colors in the red-green-blue (RGB) schema. The points were generated in photogrammetric software (Agisoft Photoscan Professional v. 1.2.6) from 1122 digital images taken approximately 120 m above the ground with a Canon Powershot SX280 12-mexapixel digital camera mounted in a Skywalker X8 flying wing operated by Raptor Maps, Inc., contractors to the U.S. Geological Survey. The photogrammetric processing incorporated 30 ground control points. The entire, un-edited unclassified point cloud is provided in standard LAZ format. All activities were conducted according to Federal Aviation Administration regulations and under a National Park Service Scientific Research and Collecting Permit, study number CACO-00285, permit number CACO-2016-SCI-003. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.

Natural and anthropogenic contaminants, pathogens, and viruses are found in soils and sediments throughout the United States. Enhanced dispersion and concentration of these environmental health stressors in coastal regions can result from sea level rise and storm-derived disturbances. The combination of existing environmental health stressors and those mobilized by natural or anthropogenic disasters could adversely impact the health and resilience of coastal communities and ecosystems. This dataset displays the exposure potential to environmental health stressors in the Edwin B. Forsythe National Wildlife Refuge (EBFNWR), which spans over Great Bay, Little Egg Harbor, and Barnegat Bay in New Jersey, USA. Exposure potential is calculated with the Sediment-bound Contaminant Resiliency and Response (SCoRR) ranking system (Reilly and others, 2015) designed to define baseline and post-event sediment-bound environmental health stressors. Facilities obtained from the Environmental Protection Agency’s (EPA) Toxic Release Inventory (TRI) and Facility Registry Service (FRS) databases were ranked based on their potential contaminant hazard. Ranks were based in part on previous work by Olsen and others (2013), literature reviews, and an expert review panel. A 2000 meter search radius was used to identify nearby ranked facility locations. As part of the Hurricane Sandy Science Plan, the U.S. Geological Survey has started a Wetland Synthesis Project to expand National Assessment of Coastal Change Hazards and forecast products to coastal wetlands. The intent is to provide federal, state, and local managers with tools to estimate their vulnerability and ecosystem service potential. For this purpose, the response and resilience of coastal wetlands to physical factors need to be assessed in terms of the ensuing change to their vulnerability and ecosystem services. EBFNWR was selected as a pilot study area.

Natural and anthropogenic contaminants, pathogens, and viruses are found in soils and sediments throughout the United States. Enhanced dispersion and concentration of these environmental health stressors in coastal regions can result from sea level rise and storm-derived disturbances. The combination of existing environmental health stressors and those mobilized by natural or anthropogenic disasters could adversely impact the health and resilience of coastal communities and ecosystems. This dataset displays the exposure potential to environmental health stressors in the Edwin B. Forsythe National Wildlife Refuge (EBFNWR), which spans over Great Bay, Little Egg Harbor, and Barnegat Bay in New Jersey, USA. Exposure potential is calculated with the Sediment-bound Contaminant Resiliency and Response (SCoRR) ranking system (Reilly and others, 2015) designed to define baseline and post-event sediment-bound environmental health stressors. Facilities obtained from the Environmental Protection Agency’s (EPA) Toxic Release Inventory (TRI) and Facility Registry Service (FRS) databases were ranked based on their potential contaminant hazard. Ranks were based in part on previous work by Olsen and others (2013), literature reviews, and an expert review panel. A 2000 meter search radius was used to identify nearby ranked facility locations. As part of the Hurricane Sandy Science Plan, the U.S. Geological Survey has started a Wetland Synthesis Project to expand National Assessment of Coastal Change Hazards and forecast products to coastal wetlands. The intent is to provide federal, state, and local managers with tools to estimate their vulnerability and ecosystem service potential. For this purpose, the response and resilience of coastal wetlands to physical factors need to be assessed in terms of the ensuing change to their vulnerability and ecosystem services. EBFNWR was selected as a pilot study area.

The safety, effectiveness and longevity of many construction and geotechnical engineering projects rely on correctly accounting for the evolution of soil properties over time. Critical sediment properties, such as compressibility, can change in response to pore-fluid chemistry changes, particularly if the sediment contains appreciable concentrations of fine-grained materials. Pore-fluid changes act at the micro scale, altering interactions between sediment particles, or between sediment particles and the pore fluid. These micro-scale alterations change how sediment fabrics and void ratios develop, which directly impacts macro-scale properties such as sediment compressibility. The goal of this study is to correlate sediment compressibility, a macro-scale property, to the micro-scale pore-fluid chemistry effects and ultimately to the electrical sensitivity for each sediment. Such a correlation would allow compressibility behavior to be estimated from knowledge of the index properties and mineralogy profile for each sediment. The data in this release support the correlation effort by providing: 1) sedimentation results that provide insight into micro-scale sediment fabric and void ratio dependence on sediment/fluid interactions, and 2) consolidation results that quantify the macro-scale compressibility and recompressibility parameters for a suite of fine-grained sediments and differing pore fluids. The related journal publication (Jang and others, 2018) demonstrates how the macro-scale compressibility and recompressibility results from the consolidation tests are linked back, through the sediment fabric and void ratio data from the sedimentation tests, to the micro-scale impact of pore-fluid chemistry and sediment electrical sensitivity.

The safety, effectiveness and longevity of many construction and geotechnical engineering projects rely on correctly accounting for the evolution of soil properties over time. Critical sediment properties, such as compressibility, can change in response to pore-fluid chemistry changes, particularly if the sediment contains appreciable concentrations of fine-grained materials. Pore-fluid changes act at the micro scale, altering interactions between sediment particles, or between sediment particles and the pore fluid. These micro-scale alterations change how sediment fabrics and void ratios develop, which directly impacts macro-scale properties such as sediment compressibility. The goal of this study is to correlate sediment compressibility, a macro-scale property, to the micro-scale pore-fluid chemistry effects and ultimately to the electrical sensitivity for each sediment. Such a correlation would allow compressibility behavior to be estimated from knowledge of the index properties and mineralogy profile for each sediment. The data in this release support the correlation effort by providing: 1) sedimentation results that provide insight into micro-scale sediment fabric and void ratio dependence on sediment/fluid interactions, and 2) consolidation results that quantify the macro-scale compressibility and recompressibility parameters for a suite of fine-grained sediments and differing pore fluids. The related journal publication (Jang and others, 2018) demonstrates how the macro-scale compressibility and recompressibility results from the consolidation tests are linked back, through the sediment fabric and void ratio data from the sedimentation tests, to the micro-scale impact of pore-fluid chemistry and sediment electrical sensitivity.

These data are a qualitatively derived interpretive polygon shapefile defining surficial sediment type and distribution, and geomorphology, for nearly 1,400 square kilometers of sea floor on the inner-continental shelf from Fenwick Island, Maryland to Fisherman’s Island, Virginia, USA. These data are classified according to Barnhardt and others (1998) bottom-type classification system, which was modified to highlight changes in secondary sediment-types such as mud and gravel across this primarily sandy shelf. Most of the geophysical and sample data used to create this interpretive layer were collected as part of the Linking Coastal Processes and Vulnerability: Assateague Island Regional Study project (GS2-2C), supported by the U.S. Department of the Interior Hurricane Sandy Recovery program. Additional sample data were provided by the Maryland Geological Survey and the Virginia Division of Geology and Mineral Resources. Additional hydrographic data were available through the National Oceanographic and Atmospheric Administration’s National Ocean Service surveys collected between 2006 and 2014. The primary objective of the Hurricane Sandy Recovery program is to provide science for coastal resilience, and these interpretive data support the program goal by supplying regional geologic framework information for the management of coastal and marine resources. Accurate data and maps of seafloor geology are important first steps toward protecting fish habitat, delineating marine resources on the inner-shelf, understanding sediment transport pathways, and assessing environmental changes because of natural or human effects. The Assateague Island Regional Study project is focused on the inner-continental shelf of Maryland and Virginia, north of Chesapeake Bay entrance. Data collected during the mapping portion of this study have been released in a series of USGS data releases (https://woodshole.er.usgs.gov/project-pages/delmarva/). A combination of geophysical and sample data including high resolution bathymetry, acoustic-backscatter intensity, bottom photographs, and sediment samples are used to create this seafloor interpretation.

Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. 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 open-ocean 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.

Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. 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 open-ocean 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.

Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. 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 open-ocean 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.

Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. 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 open-ocean 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.

Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. 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 open-ocean 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.

Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. 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 open-ocean 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.

Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. 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 open-ocean 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.

Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. 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 open-ocean 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.

Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. 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 open-ocean 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.

Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. 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 open-ocean 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.

Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. 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 open-ocean 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.

Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. 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 open-ocean 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.

Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. 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 open-ocean 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.

Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. 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 open-ocean 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.

Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. 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 open-ocean 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.

Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. 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 open-ocean 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.

Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. 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 open-ocean 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.

Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. 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 open-ocean 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.

Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. 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 open-ocean 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.

Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. 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 open-ocean 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.

Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. 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 open-ocean 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.

Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. 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 open-ocean 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.

Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. 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 open-ocean 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.

Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. 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 open-ocean 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.

Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. 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 open-ocean 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.

Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. 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 open-ocean 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.

Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. 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 open-ocean 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.

Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. 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 open-ocean 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.

Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. 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 open-ocean 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.

Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. 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 open-ocean 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.

Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. 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 open-ocean 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.

Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. 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 open-ocean 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.

Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. 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 open-ocean 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.

Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. 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 open-ocean 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.

Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. 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 open-ocean 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.

Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. 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 open-ocean 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.

Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. 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 open-ocean 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.

Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. 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 open-ocean 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.

Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. 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 open-ocean 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.

Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. 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 open-ocean 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.

Groundwater data were collected in the spring and fall of 2008 from three sites representing different geological settings and biogeochemical conditions within the surficial glacial aquifer of Long Island, NY. Investigations were designed to examine the extent to which average vadose zone thickness in contributing watersheds controlled biogeochemical conditions and processes, including dissolved oxygen concentration (DO), oxidation-reduction potential (Eh), dissolved organic carbon concentration (DOC), and microbial dinitrogen (N2) production. Greatest N2 production was observed at the south shore of Long Island, which is characterized by a thin vadose zone, low DO and Eh, and relatively high DOC. Limited N2 production occurred at the north shore of Long Island, which is characterized by a thick vadose zone, higher DO, higher Eh, and lower DOC. Our results show that vadose zone thickness exerts an important control on the extent of microbial N2 production in aquifers that lack a significant supply of sediment-bound reducing potential. We interpret heterotrophic denitrification to be the primary driver of N2 production in the present study, while acknowledging that anaerobic ammonium oxidation (anammox) likely plays an unquantified role as well.

Elevation distribution in the Edwin B. Forsythe National Wildlife Refuge (EBFNWR), which spans over Great Bay, Little Egg Harbor, and Barnegat Bay in New Jersey, USA is given in terms of mean elevation of conceptual marsh units defined by Defne and Ganju (2016). The elevation data is based on the 1-meter resampled 1/9 arc-second resolution USGS National Elevation Data. As part of the Hurricane Sandy Science Plan, the U.S. Geological Survey is expanding National Assessment of Coastal Change Hazards and forecast products to coastal wetlands. The intent is to provide federal, state, and local managers with tools to estimate their vulnerability and ecosystem service potential. For this purpose, the response and resilience of coastal wetlands to physical factors need to be assessed in terms of the ensuing change to their vulnerability and ecosystem services. EBFNWR was selected as a pilot study area. References: Defne, Zafer, and Ganju, N.K. 2016, Conceptual salt marsh units for wetland synthesis: Edwin B. Forsythe National Wildlife Refuge, New Jersey: U.S. Geological Survey data release, https://doi.org/10.5066/F7QV3JPG.

Biomass production is positively correlated with mean tidal range in salt marshes along the Atlantic coast of the United States of America. Recent studies support the idea that enhanced stability of the marshes can be attributed to increased vegetative growth due to increased tidal range. This dataset displays the spatial variation mean tidal range (i.e. Mean Range of Tides, MN) in the Edwin B. Forsythe National Wildlife Refuge (EBFNWR), which spans over Great Bay, Little Egg Harbor, and Barnegat Bay in New Jersey, USA. MN was based on the calculated difference in height between mean high water (MHW) and mean low water (MLW) using the VDatum (v3.5) software (http://vdatum.noaa.gov/). The input elevation was set to zero in VDatum to calculate the relative difference between the two datums. As part of the Hurricane Sandy Science Plan, the U.S. Geological Survey has started a Wetland Synthesis Project to expand National Assessment of Coastal Change Hazards and forecast products to coastal wetlands. The intent is to provide federal, state, and local managers with tools to estimate their vulnerability and ecosystem service potential. For this purpose, the response and resilience of coastal wetlands to physical factors need to be assessed in terms of the ensuing change to their vulnerability and ecosystem services. EBFNWR was selected as a pilot study area.

Biomass production is positively correlated with mean tidal range in salt marshes along the Atlantic coast of the United States of America. Recent studies support the idea that enhanced stability of the marshes can be attributed to increased vegetative growth due to increased tidal range. This dataset displays the spatial variation mean tidal range (i.e. Mean Range of Tides, MN) in the Edwin B. Forsythe National Wildlife Refuge (EBFNWR), which spans over Great Bay, Little Egg Harbor, and Barnegat Bay in New Jersey, USA. MN was based on the calculated difference in height between mean high water (MHW) and mean low water (MLW) using the VDatum (v3.5) software (http://vdatum.noaa.gov/). The input elevation was set to zero in VDatum to calculate the relative difference between the two datums. As part of the Hurricane Sandy Science Plan, the U.S. Geological Survey has started a Wetland Synthesis Project to expand National Assessment of Coastal Change Hazards and forecast products to coastal wetlands. The intent is to provide federal, state, and local managers with tools to estimate their vulnerability and ecosystem service potential. For this purpose, the response and resilience of coastal wetlands to physical factors need to be assessed in terms of the ensuing change to their vulnerability and ecosystem services. EBFNWR was selected as a pilot study area.

These text files contain tables of the file names, times, and locations of images obtained from an unmanned aerial systems (UAS) flown in the Cape Cod National Seashore. The objective of the fieldwork was to evaluate the quality and cost of mapping from UAS images. Low-altitude (approximately 120 meters above ground level) digital images were obtained from cameras in a fixed-wing unmanned aerial vehicle (UAV) flown from the lawn adjacent to the Coast Guard Beach parking lot on 1 March, 2016. The UAV was a Skywalker X8 flying wing operated by Raptor Maps, Inc., contractors to the U.S. Geological Survey. U.S. Geological Survey technicians deployed and mapped 28 targets that appear in some of the images for use as ground control points. All activities were conducted according to Federal Aviation Administration regulations and under a National Park Service Scientific Research and Collecting Permit, study number CACO-00285, permit number CACO-2016-SCI-003. Two consecutive UAS missions were flown, each with two cameras, autopilot computer, radios, and a global navigation satellite system (GNSS) positioning system as payload. The first flight (f1) was launched at approximately 1112 EST, and followed north-south flight lines, landing at about 1226 EST. Two Canon Powershot SX280 12-mexapixel digital cameras, designated rgb1 and rgb2 made images during this flight. The second flight (f2) was launched at 1320 EST and followed east-west flight lines, landing at 1450 Eastern Standard Time (EST). Prior to f2, rgb2 was replaced with a Canon SX280 modified with a Schott BG 3 filter to emphasize light at near-infrared wavelengths, designated nir1. Rgb1 and nir1 made images during this second flight. The four files are tables of images obtained from the two cameras during the two flights. These tables, which are text files of comma-separated values, contain the image file name, date and time (Universal Time; UT), longitude and latitude (WGS84 decimal degrees), easting and northing (NAD83(2011) UTM Zone 19 North meters, obtained by conversion of the latitude and longitude), and elevation (approximate meters above mean sea level) determined from the UAS GNSS system. Note that this location information was only used to determine proximity of images, and was replaced with calculated camera locations in photogrammetric processing.

This dataset documents the locations of ground control points associated with images obtained from unmanned aerial systems (UAS) flown in the Cape Cod National Seashore. Most of the ground control points were temporary targets placed by the U.S. Geological Survey field crew, but four were man-made features already in place, and two were points selected a posteriori from preliminary orthophotomosaics. Photographs of the four in-place features are included in this dataset, as are images showing the location of the two a posteriori points at two zoom levels. The locations of these ground control points can be used to constrain photogrammetric reconstructions based on the aerial imagery. The overall objective of the fieldwork was to evaluate the quality and cost of mapping from UAS images. Low-altitude (approximately 120 meters above ground level) digital images were obtained from cameras in a fixed-wing UAS flown from the lawn adjacent to the Coast Guard Beach parking lot on 1 March, 2016. All activities were conducted according to Federal Aviation Administration regulations and under a National Park Service Scientific Research and Collecting Permit, study number CACO-00285, permit number CACO-2016-SCI-003.

This dataset contains images obtained from unmanned aerial systems (UAS) flown in the Cape Cod National Seashore. The objective of the field work was to evaluate the quality and cost of mapping from UAS images. Low-altitude (approximately 120 meters above ground level) digital images were obtained from cameras in a fixed-wing unmanned aerial vehicle (UAV) flown from the lawn adjacent to the Coast Guard Beach parking lot on 1 March, 2016. The UAV was a Skywalker X8 flying wing operated by Raptor Maps, Inc., contractors to the U.S. Geological Survey. U.S. Geological Survey technicians deployed and mapped 28 targets that appear in some of the images for use as ground control points. All activities were conducted according to Federal Aviation Administration regulations and under a National Park Service Scientific Research and Collecting Permit, study number CACO-00285, permit number CACO-2016-SCI-003. Two consecutive UAS missions were flown, each with two cameras, autopilot computer, radios, and a global navigation satellite system (GNSS) positioning system as payload. The first flight (f1) was launched at approximately 1112 EST, and followed north-south flight lines, landing at about 1226 EST. Two Canon Powershot SX280 12-mexapixel digital cameras, designated rgb1 and rgb2 made images during this flight. The second flight (f2) was launched at 1320 EST and followed east-west flight lines, landing at 1450 EST. Prior to f2, rgb2 was replaced with a Canon SX280 modified with a Schott BG 3 filter to emphasize light at near-infrared wavelengths, designated nir1. Rgb1 and nir1 made images during this second flight. In addition to the images, this dataset also contains locations of both in-situ and placed targets that may be used as ground control to constrain photogrammetric reconstructions.

This dataset contains the locations of independent survey points acquired on the same day that images were obtained from unmanned aerial systems (UAS) flown in the Cape Cod National Seashore. The overall objective of the field work was to evaluate the quality and cost of mapping from UAS images. Low-altitude (approximately 120 meters above ground level) digital images were obtained from cameras in a fixed-wing unmanned aerial vehicle (UAV) flown from the lawn adjacent to the Coast Guard Beach parking lot on 1 March, 2016. U.S. Geological Survey technicians deployed and mapped 28 targets that appear in some of the images for use as ground control points. All activities were conducted according to Federal Aviation Administration regulations and under a National Park Service Scientific Research and Collecting Permit, study number CACO-00285, permit number CACO-2016-SCI-003. This dataset contains locations of both in place and placed targets that may be used as ground control to constrain photogrammetric reconstructions. One hundred and forty-four (144) points were measured along several cross-shore transects on the beach. These points were measured with real-time differential global positioning system (GPS) and have horizontal and vertical uncertainties of approximately +/ 0.03 m. These points were not used in photogrammetric processing, so they can be used for independent evaluation of photogrammetric products. The independent survey points are listed in file CACO_transect_Points_20160301.csv.

Subterranean estuaries extend inland into density-stratified coastal carbonate aquifers that contain a surprising diversity of endemic animals (mostly crustaceans) within a highly oligotrophic environment. How complex ecosystems thrive in this globally-distributed, cryptic habitat (termed anchialine) is poorly understood. The northeastern margin of the Yucatan Peninsula contains over 250 km of mapped, diver-accessible caves passages where previous studies have suggested chemoautotrophic processes are the source of carbon and energy sustaining the anchialine food web. This dataset, collected during four field events during U.S. Geological Survey (USGS) Coastal and Marine Geology Program Field Activities 2015-013-FA and 2016-003-FA in conjunction with Texas A&M University reports geochemical properties of the water column from Cenote Bang, a component of the Ox Bel Ha cave network that is located 5 km inland from the coast.

Subterranean estuaries extend inland into density-stratified coastal carbonate aquifers that contain a surprising diversity of endemic animals (mostly crustaceans) within a highly oligotrophic environment. How complex ecosystems thrive in this globally-distributed, cryptic habitat (termed anchialine) is poorly understood. The northeastern margin of the Yucatan Peninsula contains over 250 km of mapped, diver-accessible caves passages where previous studies have suggested chemoautotrophic processes are the source of carbon and energy sustaining the anchialine food web. This dataset, collected during four field events during U.S. Geological Survey (USGS) Coastal and Marine Geology Program Field Activities 2015-013-FA and 2016-003-FA in conjunction with Texas A&M University reports physical and chemical properties of the water column from Cenote Bang, a component of the Ox Bel Ha cave network that is located 5 km inland from the coast.

The accretion history of fringing microtidal salt marshes located on the south shore of Cape Cod, Massachusetts, was reconstructed from sediment cores collected in low and high marsh vegetation zones. The location of these marshes within protected embayments and the absence of large rivers on Cape Cod result in minimal sediment supply and a dominance of organic matter contribution to sediment peat. Age models based on 210-lead and 137-cesium were constructed to evaluate how vertical accretion and carbon burial rates have changed over the past century. The continuous rate of supply age model was used to age date 11 cores (10 low marsh and 1 high marsh) across four salt marshes. Both vertical accretion rates and carbon burial increased from 1900 to the years of collection, 2013 and 2014. Elevation of the marsh surface was measured to evaluate where the marsh falls within the current tidal frame. The historic marsh surface elevation was then reconstructed from the calculated age of each depth interval and its elevation, assuming that elevations within this shallow zone (less than 30 centimeters) have been preserved for the past century.

As part of the Hurricane Sandy Science Plan, the U.S. Geological Survey is expanding National Assessment of Coastal Change Hazards and forecast products to coastal wetlands. The intent is to provide federal, state, and local managers with tools to estimate the vulnerability of coastal wetlands to various factors and to evaluate their ecosystem service potential. For this purpose, the response and resilience of coastal wetlands to physical factors need to be assessed in terms of the ensuing change to their vulnerability and ecosystem services. Edwin B. Forsythe National Wildlife Refuge (EBFNWR), New Jersey, was selected as a pilot study area. As part of this data synthesis effort, hydrodynamic and sediment transport modeling of Barnegat Bay Little Egg Harbor (BBLEH) has been used to create the following wetland data layers in Edwin B. Forsythe National Wildlife Refuge (EBFNWR), New Jersey: 1) Hydrodynamic residence time , 2) salinity change and 3) salinity exposure change in wetlands, and 4) sediment supply to wetlands. The residence time layer was based on the hydrodynamic and particle tracking modeling of the period 3/1/2012 to 5/1/2012 by Defne and Ganju (2015). For this data layer, the residence time map of estuarine water has been projected over the EBFNWR salt marshes. The rest of the layers were derived from the BBLEH hydrodynamic modeling for the Hurricane Sandy period that spans from 10/27/2012 to 11/04/2012 (Defne and Ganju, 2016a). The model estimated changes in salinity and sediment concentrations over the salt marshes caused by storm-induced coastal flooding. The results are summarized over the previously determined conceptual salt marsh unit polygons (Defne and Ganju, 2016b).

As part of the Hurricane Sandy Science Plan, the U.S. Geological Survey is expanding National Assessment of Coastal Change Hazards and forecast products to coastal wetlands. The intent is to provide federal, state, and local managers with tools to estimate the vulnerability of coastal wetlands to various factors and to evaluate their ecosystem service potential. For this purpose, the response and resilience of coastal wetlands to physical factors need to be assessed in terms of the ensuing change to their vulnerability and ecosystem services. Edwin B. Forsythe National Wildlife Refuge (EBFNWR), New Jersey, was selected as a pilot study area. As part of this data synthesis effort, hydrodynamic and sediment transport modeling of Barnegat Bay Little Egg Harbor (BBLEH) has been used to create the following wetland data layers in Edwin B. Forsythe National Wildlife Refuge (EBFNWR), New Jersey: 1) Hydrodynamic residence time , 2) salinity change and 3) salinity exposure change in wetlands, and 4) sediment supply to wetlands. The residence time layer was based on the hydrodynamic and particle tracking modeling of the period 3/1/2012 to 5/1/2012 by Defne and Ganju (2015). For this data layer, the residence time map of estuarine water has been projected over the EBFNWR salt marshes. The rest of the layers were derived from the BBLEH hydrodynamic modeling for the Hurricane Sandy period that spans from 10/27/2012 to 11/04/2012 (Defne and Ganju, 2016a). The model estimated changes in salinity and sediment concentrations over the salt marshes caused by storm-induced coastal flooding. The results are summarized over the previously determined conceptual salt marsh unit polygons (Defne and Ganju, 2016b).

As part of the Hurricane Sandy Science Plan, the U.S. Geological Survey is expanding National Assessment of Coastal Change Hazards and forecast products to coastal wetlands. The intent is to provide federal, state, and local managers with tools to estimate the vulnerability of coastal wetlands to various factors and to evaluate their ecosystem service potential. For this purpose, the response and resilience of coastal wetlands to physical factors need to be assessed in terms of the ensuing change to their vulnerability and ecosystem services. Edwin B. Forsythe National Wildlife Refuge (EBFNWR), New Jersey, was selected as a pilot study area. As part of this data synthesis effort, hydrodynamic and sediment transport modeling of Barnegat Bay Little Egg Harbor (BBLEH) has been used to create the following wetland data layers in Edwin B. Forsythe National Wildlife Refuge (EBFNWR), New Jersey: 1) Hydrodynamic residence time , 2) salinity change and 3) salinity exposure change in wetlands, and 4) sediment supply to wetlands. The residence time layer was based on the hydrodynamic and particle tracking modeling of the period 3/1/2012 to 5/1/2012 by Defne and Ganju (2015). For this data layer, the residence time map of estuarine water has been projected over the EBFNWR salt marshes. The rest of the layers were derived from the BBLEH hydrodynamic modeling for the Hurricane Sandy period that spans from 10/27/2012 to 11/04/2012 (Defne and Ganju, 2016a). The model estimated changes in salinity and sediment concentrations over the salt marshes caused by storm-induced coastal flooding. The results are summarized over the previously determined conceptual salt marsh unit polygons (Defne and Ganju, 2016b).

As part of the Hurricane Sandy Science Plan, the U.S. Geological Survey is expanding National Assessment of Coastal Change Hazards and forecast products to coastal wetlands. The intent is to provide federal, state, and local managers with tools to estimate the vulnerability of coastal wetlands to various factors and to evaluate their ecosystem service potential. For this purpose, the response and resilience of coastal wetlands to physical factors need to be assessed in terms of the ensuing change to their vulnerability and ecosystem services. Edwin B. Forsythe National Wildlife Refuge (EBFNWR), New Jersey, was selected as a pilot study area. As part of this data synthesis effort, hydrodynamic and sediment transport modeling of Barnegat Bay Little Egg Harbor (BBLEH) has been used to create the following wetland data layers in Edwin B. Forsythe National Wildlife Refuge (EBFNWR), New Jersey: 1) Hydrodynamic residence time , 2) salinity change and 3) salinity exposure change in wetlands, and 4) sediment supply to wetlands. The residence time layer was based on the hydrodynamic and particle tracking modeling of the period 3/1/2012 to 5/1/2012 by Defne and Ganju (2015). For this data layer, the residence time map of estuarine water has been projected over the EBFNWR salt marshes. The rest of the layers were derived from the BBLEH hydrodynamic modeling for the Hurricane Sandy period that spans from 10/27/2012 to 11/04/2012 (Defne and Ganju, 2016a). The model estimated changes in salinity and sediment concentrations over the salt marshes caused by storm-induced coastal flooding. The results are summarized over the previously determined conceptual salt marsh unit polygons (Defne and Ganju, 2016b).

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.

Monitoring shoreline change is of interest in many coastal areas because it enables quantification of land loss over time. Evolution of shoreline position is determined by the balance between erosion and accretion along the coast. In the case of salt marshes, erosion along the water boundary causes a loss of ecosystem services, such as habitat provision, carbon storage, and wave attenuation. In terms of vulnerability, higher shoreline erosion rates indicate higher vulnerability. This dataset displays shoreline change rates at the Edwin B. Forsythe National Wildlife Refuge (EBFNWR), which spans over Great Bay, Little Egg Harbor, and Barnegat Bay in New Jersey, USA. Shoreline change rates are based on Smith and Terrano (2017) analysis of digital vector shorelines acquired from historic topographic sheets, aerial photography, and/or lidar using the AMBUR package (Jackson, 2010). Linear Regression Rates (LRR) of shoreline change were averaged along the shoreline of each salt marsh unit to generate this dataset. Positive and negative values indicate accretion and erosion respectively. As part of the Hurricane Sandy Science Plan, the U.S. Geological Survey is expanding National Assessment of Coastal Change Hazards and forecast products to coastal wetlands. The intent is to provide federal, state, and local managers with tools to estimate their vulnerability and ecosystem service potential. For this purpose, the response and resilience of coastal wetlands to physical factors need to be assessed in terms of the ensuing change to their vulnerability and ecosystem services. EBFNWR was selected as a pilot study area.

The salt marsh complex of the Edwin B. Forsythe National Wildlife Refuge (EBFNWR), which spans over Great Bay, Little Egg Harbor, and Barnegat Bay (New Jersey, USA), was delineated to smaller, conceptual marsh units by geoprocessing of surface elevation data. Flow accumulation based on the relative elevation of each location is used to determine the ridge lines that separate each marsh unit while the surface slope is used to automatically assign each unit a drainage point, where water is expected to drain through. Through scientific efforts associated with the Hurricane Sandy Science Plan, the U.S. Geological Survey has started to expand national assessment of coastal change hazards and forecast products to coastal wetlands. The intent is to provide federal, state, and local managers with tools to estimate their vulnerability and ecosystem service potential. For this purpose, the response and resilience of coastal wetlands to physical factors need to be assessed in terms of the ensuing change to their vulnerability and ecosystem services. EBFNWR was selected as a pilot study. Recent research shows that sediment budgets of microtidal marsh complexes on the Atlantic and Pacific coasts of the United States consistently scale with areal unvegetated/vegetated marsh ratio (UVVR) despite differences in sea-level rise, tidal range, elevation, vegetation, and stressors. This highlights UVVR as a broadly applicable indicator of microtidal marsh stability. It is also relatively quicker and less labor intensive compared to quantifying integrative sediment budgets and the associated transport mechanisms that requires extended tidal-timescale observations of sediment transport. UVVR indicates the link between open-water conversion processes and sediment transport, providing consistent results across a geomorphic and climatic spectrum of microtidal marshes, hence can be an independent measure of marsh health. Potentially, tracking future changes to UVVR may allow for widespread mapping of spatially variable vulnerability across microtidal marshes worldwide.

High resolution bathymetric, sea-floor backscatter, and seismic-reflection data were collected offshore of southeastern Louisiana aboard the research vessel Point Sur on May 19-26, 2017, in an effort to characterize mudflow hazards on the Mississippi River Delta front. As the initial field program of a research cooperative between the U.S. Geological Survey, the Bureau of Ocean Energy Management, and other Federal and academic partners, the primary objective of this cruise was to assess the suitability of sea-floor mapping and shallow subsurface imaging tools in the challenging environmental conditions found across delta fronts (for example, variably distributed water column stratification and widespread biogenic gas in the shallow subsurface). Approximately 675 kilometers (km) of multibeam bathymetry and backscatter data, 420 km of towed chirp data, and 550 km of multichannel seismic data were collected. Varied mudflow (gully, lobe), prodelta morphologies, and structural features were imaged in selected survey areas from Pass a Loutre to Southwest Pass.

High resolution bathymetric, sea-floor backscatter, and seismic-reflection data were collected offshore of southeastern Louisiana aboard the research vessel Point Sur on May 19-26, 2017, in an effort to characterize mudflow hazards on the Mississippi River Delta front. As the initial field program of a research cooperative between the U.S. Geological Survey, the Bureau of Ocean Energy Management, and other Federal and academic partners, the primary objective of this cruise was to assess the suitability of sea-floor mapping and shallow subsurface imaging tools in the challenging environmental conditions found across delta fronts (for example, variably distributed water column stratification and widespread biogenic gas in the shallow subsurface). Approximately 675 kilometers (km) of multibeam bathymetry and backscatter data, 420 km of towed chirp data, and 550 km of multichannel seismic data were collected. Varied mudflow (gully, lobe), prodelta morphologies, and structural features were imaged in selected survey areas from Pass a Loutre to Southwest Pass.

High resolution bathymetric, sea-floor backscatter, and seismic-reflection data were collected offshore of southeastern Louisiana aboard the research vessel Point Sur on May 19-26, 2017, in an effort to characterize mudflow hazards on the Mississippi River Delta front. As the initial field program of a research cooperative between the U.S. Geological Survey, the Bureau of Ocean Energy Management, and other Federal and academic partners, the primary objective of this cruise was to assess the suitability of sea-floor mapping and shallow subsurface imaging tools in the challenging environmental conditions found across delta fronts (for example, variably distributed water column stratification and widespread biogenic gas in the shallow subsurface). Approximately 675 kilometers (km) of multibeam bathymetry and backscatter data, 420 km of towed chirp data, and 550 km of multichannel seismic data were collected. Varied mudflow (gully, lobe), prodelta morphologies, and structural features were imaged in selected survey areas from Pass a Loutre to Southwest Pass.

High resolution bathymetric, sea-floor backscatter, and seismic-reflection data were collected offshore of southeastern Louisiana aboard the research vessel Point Sur on May 19-26, 2017, in an effort to characterize mudflow hazards on the Mississippi River Delta front. As the initial field program of a research cooperative between the U.S. Geological Survey, the Bureau of Ocean Energy Management, and other Federal and academic partners, the primary objective of this cruise was to assess the suitability of sea-floor mapping and shallow subsurface imaging tools in the challenging environmental conditions found across delta fronts (for example, variably distributed water column stratification and widespread biogenic gas in the shallow subsurface). Approximately 675 kilometers (km) of multibeam bathymetry and backscatter data, 420 km of towed chirp data, and 550 km of multichannel seismic data were collected. Varied mudflow (gully, lobe), prodelta morphologies, and structural features were imaged in selected survey areas from Pass a Loutre to Southwest Pass.

High resolution bathymetric, sea-floor backscatter, and seismic-reflection data were collected offshore of southeastern Louisiana aboard the research vessel Point Sur on May 19-26, 2017, in an effort to characterize mudflow hazards on the Mississippi River Delta front. As the initial field program of a research cooperative between the U.S. Geological Survey, the Bureau of Ocean Energy Management, and other Federal and academic partners, the primary objective of this cruise was to assess the suitability of sea-floor mapping and shallow subsurface imaging tools in the challenging environmental conditions found across delta fronts (for example, variably distributed water column stratification and widespread biogenic gas in the shallow subsurface). Approximately 675 kilometers (km) of multibeam bathymetry and backscatter data, 420 km of towed chirp data, and 550 km of multichannel seismic data were collected. Varied mudflow (gully, lobe), prodelta morphologies, and structural features were imaged in selected survey areas from Pass a Loutre to Southwest Pass.

High resolution bathymetric, sea-floor backscatter, and seismic-reflection data were collected offshore of southeastern Louisiana aboard the research vessel Point Sur on May 19-26, 2017, in an effort to characterize mudflow hazards on the Mississippi River Delta front. As the initial field program of a research cooperative between the U.S. Geological Survey, the Bureau of Ocean Energy Management, and other Federal and academic partners, the primary objective of this cruise was to assess the suitability of sea-floor mapping and shallow subsurface imaging tools in the challenging environmental conditions found across delta fronts (for example, variably distributed water column stratification and widespread biogenic gas in the shallow subsurface). Approximately 675 kilometers (km) of multibeam bathymetry and backscatter data, 420 km of towed chirp data, and 550 km of multichannel seismic data were collected. Varied mudflow (gully, lobe), prodelta morphologies, and structural features were imaged in selected survey areas from Pass a Loutre to Southwest Pass.

High resolution bathymetric, sea-floor backscatter, and seismic-reflection data were collected offshore of southeastern Louisiana aboard the research vessel Point Sur on May 19-26, 2017, in an effort to characterize mudflow hazards on the Mississippi River Delta front. As the initial field program of a research cooperative between the U.S. Geological Survey, the Bureau of Ocean Energy Management, and other Federal and academic partners, the primary objective of this cruise was to assess the suitability of sea-floor mapping and shallow subsurface imaging tools in the challenging environmental conditions found across delta fronts (for example, variably distributed water column stratification and widespread biogenic gas in the shallow subsurface). Approximately 675 kilometers (km) of multibeam bathymetry and backscatter data, 420 km of towed chirp data, and 550 km of multichannel seismic data were collected. Varied mudflow (gully, lobe), prodelta morphologies, and structural features were imaged in selected survey areas from Pass a Loutre to Southwest Pass.

High resolution bathymetric, sea-floor backscatter, and seismic-reflection data were collected offshore of southeastern Louisiana aboard the research vessel Point Sur on May 19-26, 2017, in an effort to characterize mudflow hazards on the Mississippi River Delta front. As the initial field program of a research cooperative between the U.S. Geological Survey, the Bureau of Ocean Energy Management, and other Federal and academic partners, the primary objective of this cruise was to assess the suitability of sea-floor mapping and shallow subsurface imaging tools in the challenging environmental conditions found across delta fronts (for example, variably distributed water column stratification and widespread biogenic gas in the shallow subsurface). Approximately 675 kilometers (km) of multibeam bathymetry and backscatter data, 420 km of towed chirp data, and 550 km of multichannel seismic data were collected. Varied mudflow (gully, lobe), prodelta morphologies, and structural features were imaged in selected survey areas from Pass a Loutre to Southwest Pass.

High resolution bathymetric, sea-floor backscatter, and seismic-reflection data were collected offshore of southeastern Louisiana aboard the research vessel Point Sur on May 19-26, 2017, in an effort to characterize mudflow hazards on the Mississippi River Delta front. As the initial field program of a research cooperative between the U.S. Geological Survey, the Bureau of Ocean Energy Management, and other Federal and academic partners, the primary objective of this cruise was to assess the suitability of sea-floor mapping and shallow subsurface imaging tools in the challenging environmental conditions found across delta fronts (for example, variably distributed water column stratification and widespread biogenic gas in the shallow subsurface). Approximately 675 kilometers (km) of multibeam bathymetry and backscatter data, 420 km of towed chirp data, and 550 km of multichannel seismic data were collected. Varied mudflow (gully, lobe), prodelta morphologies, and structural features were imaged in selected survey areas from Pass a Loutre to Southwest Pass.

High resolution bathymetric, sea-floor backscatter, and seismic-reflection data were collected offshore of southeastern Louisiana aboard the research vessel Point Sur on May 19-26, 2017, in an effort to characterize mudflow hazards on the Mississippi River Delta front. As the initial field program of a research cooperative between the U.S. Geological Survey, the Bureau of Ocean Energy Management, and other Federal and academic partners, the primary objective of this cruise was to assess the suitability of sea-floor mapping and shallow subsurface imaging tools in the challenging environmental conditions found across delta fronts (for example, variably distributed water column stratification and widespread biogenic gas in the shallow subsurface). Approximately 675 kilometers (km) of multibeam bathymetry and backscatter data, 420 km of towed chirp data, and 550 km of multichannel seismic data were collected. Varied mudflow (gully, lobe), prodelta morphologies, and structural features were imaged in selected survey areas from Pass a Loutre to Southwest Pass.

High resolution bathymetric, sea-floor backscatter, and seismic-reflection data were collected offshore of southeastern Louisiana aboard the research vessel Point Sur on May 19-26, 2017, in an effort to characterize mudflow hazards on the Mississippi River Delta front. As the initial field program of a research cooperative between the U.S. Geological Survey, the Bureau of Ocean Energy Management, and other Federal and academic partners, the primary objective of this cruise was to assess the suitability of sea-floor mapping and shallow subsurface imaging tools in the challenging environmental conditions found across delta fronts (for example, variably distributed water column stratification and widespread biogenic gas in the shallow subsurface). Approximately 675 kilometers (km) of multibeam bathymetry and backscatter data, 420 km of towed chirp data, and 550 km of multichannel seismic data were collected. Varied mudflow (gully, lobe), prodelta morphologies, and structural features were imaged in selected survey areas from Pass a Loutre to Southwest Pass.

High resolution bathymetric, sea-floor backscatter, and seismic-reflection data were collected offshore of southeastern Louisiana aboard the research vessel Point Sur on May 19-26, 2017, in an effort to characterize mudflow hazards on the Mississippi River Delta front. As the initial field program of a research cooperative between the U.S. Geological Survey, the Bureau of Ocean Energy Management, and other Federal and academic partners, the primary objective of this cruise was to assess the suitability of sea-floor mapping and shallow subsurface imaging tools in the challenging environmental conditions found across delta fronts (for example, variably distributed water column stratification and widespread biogenic gas in the shallow subsurface). Approximately 675 kilometers (km) of multibeam bathymetry and backscatter data, 420 km of towed chirp data, and 550 km of multichannel seismic data were collected. Varied mudflow (gully, lobe), prodelta morphologies, and structural features were imaged in selected survey areas from Pass a Loutre to Southwest Pass.

High resolution bathymetric, sea-floor backscatter, and seismic-reflection data were collected offshore of southeastern Louisiana aboard the research vessel Point Sur on May 19-26, 2017, in an effort to characterize mudflow hazards on the Mississippi River Delta front. As the initial field program of a research cooperative between the U.S. Geological Survey, the Bureau of Ocean Energy Management, and other Federal and academic partners, the primary objective of this cruise was to assess the suitability of sea-floor mapping and shallow subsurface imaging tools in the challenging environmental conditions found across delta fronts (for example, variably distributed water column stratification and widespread biogenic gas in the shallow subsurface). Approximately 675 kilometers (km) of multibeam bathymetry and backscatter data, 420 km of towed chirp data, and 550 km of multichannel seismic data were collected. Varied mudflow (gully, lobe), prodelta morphologies, and structural features were imaged in selected survey areas from Pass a Loutre to Southwest Pass.

High resolution bathymetric, sea-floor backscatter, and seismic-reflection data were collected offshore of southeastern Louisiana aboard the research vessel Point Sur on May 19-26, 2017, in an effort to characterize mudflow hazards on the Mississippi River Delta front. As the initial field program of a research cooperative between the U.S. Geological Survey, the Bureau of Ocean Energy Management, and other Federal and academic partners, the primary objective of this cruise was to assess the suitability of sea-floor mapping and shallow subsurface imaging tools in the challenging environmental conditions found across delta fronts (for example, variably distributed water column stratification and widespread biogenic gas in the shallow subsurface). Approximately 675 kilometers (km) of multibeam bathymetry and backscatter data, 420 km of towed chirp data, and 550 km of multichannel seismic data were collected. Varied mudflow (gully, lobe), prodelta morphologies, and structural features were imaged in selected survey areas from Pass a Loutre to Southwest Pass.

High resolution bathymetric, sea-floor backscatter, and seismic-reflection data were collected offshore of southeastern Louisiana aboard the research vessel Point Sur on May 19-26, 2017, in an effort to characterize mudflow hazards on the Mississippi River Delta front. As the initial field program of a research cooperative between the U.S. Geological Survey, the Bureau of Ocean Energy Management, and other Federal and academic partners, the primary objective of this cruise was to assess the suitability of sea-floor mapping and shallow subsurface imaging tools in the challenging environmental conditions found across delta fronts (for example, variably distributed water column stratification and widespread biogenic gas in the shallow subsurface). Approximately 675 kilometers (km) of multibeam bathymetry and backscatter data, 420 km of towed chirp data, and 550 km of multichannel seismic data were collected. Varied mudflow (gully, lobe), prodelta morphologies, and structural features were imaged in selected survey areas from Pass a Loutre to Southwest Pass.

High-resolution geophysical mapping of Lake Powell in the Glen Canyon National Recreation Area in Utah and Arizona was conducted between October 8 and November 15, 2017, as part of a collaborative effort between the U.S. Geological Survey and the Bureau of Reclamation to provide high-quality data needed to reassess the area-capacity tables for the Lake Powell reservoir. Seismic data collected during this survey can help to define the rates of deposition within the San Juan and Colorado Rivers, which are the main inflows to Lake Powell. These new data are intended to improve water budget management decisions that affect the natural and recreational resources of the reservoir. Multibeam echosounder bathymetry and backscatter data were collected along 2,312 kilometers of tracklines (331 square kilometers) of the lake floor to regionally define its depth and morphology, as well as the character and distribution of lake-floor sediments. Ninety-two kilometers of seismic-reflection profile data were also collected to define the thickness and structure of sediment deposits near the confluences of the San Juan and Colorado Rivers.

High-resolution geophysical mapping of Lake Powell in the Glen Canyon National Recreation Area in Utah and Arizona was conducted between October 8 and November 15, 2017, as part of a collaborative effort between the U.S. Geological Survey and the Bureau of Reclamation to provide high-quality data needed to reassess the area-capacity tables for the Lake Powell reservoir. Seismic data collected during this survey can help to define the rates of deposition within the San Juan and Colorado Rivers, which are the main inflows to Lake Powell. These new data are intended to improve water budget management decisions that affect the natural and recreational resources of the reservoir. Multibeam echosounder bathymetry and backscatter data were collected along 2,312 kilometers of tracklines (331 square kilometers) of the lake floor to regionally define its depth and morphology, as well as the character and distribution of lake-floor sediments. Ninety-two kilometers of seismic-reflection profile data were also collected to define the thickness and structure of sediment deposits near the confluences of the San Juan and Colorado Rivers.

High-resolution geophysical mapping of Lake Powell in the Glen Canyon National Recreation Area in Utah and Arizona was conducted between October 8 and November 15, 2017, as part of a collaborative effort between the U.S. Geological Survey and the Bureau of Reclamation to provide high-quality data needed to reassess the area-capacity tables for the Lake Powell reservoir. Seismic data collected during this survey can help to define the rates of deposition within the San Juan and Colorado Rivers, which are the main inflows to Lake Powell. These new data are intended to improve water budget management decisions that affect the natural and recreational resources of the reservoir. Multibeam echosounder bathymetry and backscatter data were collected along 2,312 kilometers of tracklines (331 square kilometers) of the lake floor to regionally define its depth and morphology, as well as the character and distribution of lake-floor sediments. Ninety-two kilometers of seismic-reflection profile data were also collected to define the thickness and structure of sediment deposits near the confluences of the San Juan and Colorado Rivers.

High-resolution geophysical mapping of Lake Powell in the Glen Canyon National Recreation Area in Utah and Arizona was conducted between October 8 and November 15, 2017, as part of a collaborative effort between the U.S. Geological Survey and the Bureau of Reclamation to provide high-quality data needed to reassess the area-capacity tables for the Lake Powell reservoir. Seismic data collected during this survey can help to define the rates of deposition within the San Juan and Colorado Rivers, which are the main inflows to Lake Powell. These new data are intended to improve water budget management decisions that affect the natural and recreational resources of the reservoir. Multibeam echosounder bathymetry and backscatter data were collected along 2,312 kilometers of tracklines (331 square kilometers) of the lake floor to regionally define its depth and morphology, as well as the character and distribution of lake-floor sediments. Ninety-two kilometers of seismic-reflection profile data were also collected to define the thickness and structure of sediment deposits near the confluences of the San Juan and Colorado Rivers.

High-resolution geophysical mapping of Lake Powell in the Glen Canyon National Recreation Area in Utah and Arizona was conducted between October 8 and November 15, 2017, as part of a collaborative effort between the U.S. Geological Survey and the Bureau of Reclamation to provide high-quality data needed to reassess the area-capacity tables for the Lake Powell reservoir. Seismic data collected during this survey can help to define the rates of deposition within the San Juan and Colorado Rivers, which are the main inflows to Lake Powell. These new data are intended to improve water budget management decisions that affect the natural and recreational resources of the reservoir. Multibeam echosounder bathymetry and backscatter data were collected along 2,312 kilometers of tracklines (331 square kilometers) of the lake floor to regionally define its depth and morphology, as well as the character and distribution of lake-floor sediments. Ninety-two kilometers of seismic-reflection profile data were also collected to define the thickness and structure of sediment deposits near the confluences of the San Juan and Colorado Rivers.

High-resolution geophysical mapping of Lake Powell in the Glen Canyon National Recreation Area in Utah and Arizona was conducted between October 8 and November 15, 2017, as part of a collaborative effort between the U.S. Geological Survey and the Bureau of Reclamation to provide high-quality data needed to reassess the area-capacity tables for the Lake Powell reservoir. Seismic data collected during this survey can help to define the rates of deposition within the San Juan and Colorado Rivers, which are the main inflows to Lake Powell. These new data are intended to improve water budget management decisions that affect the natural and recreational resources of the reservoir. Multibeam echosounder bathymetry and backscatter data were collected along 2,312 kilometers of tracklines (331 square kilometers) of the lake floor to regionally define its depth and morphology, as well as the character and distribution of lake-floor sediments. Ninety-two kilometers of seismic-reflection profile data were also collected to define the thickness and structure of sediment deposits near the confluences of the San Juan and Colorado Rivers.

High-resolution geophysical mapping of Lake Powell in the Glen Canyon National Recreation Area in Utah and Arizona was conducted between October 8 and November 15, 2017, as part of a collaborative effort between the U.S. Geological Survey and the Bureau of Reclamation to provide high-quality data needed to reassess the area-capacity tables for the Lake Powell reservoir. Seismic data collected during this survey can help to define the rates of deposition within the San Juan and Colorado Rivers, which are the main inflows to Lake Powell. These new data are intended to improve water budget management decisions that affect the natural and recreational resources of the reservoir. Multibeam echosounder bathymetry and backscatter data were collected along 2,312 kilometers of tracklines (331 square kilometers) of the lake floor to regionally define its depth and morphology, as well as the character and distribution of lake-floor sediments. Ninety-two kilometers of seismic-reflection profile data were also collected to define the thickness and structure of sediment deposits near the confluences of the San Juan and Colorado Rivers.

Elevation distribution in the Cape Cod National Seashore (CACO) salt marsh complex and approximal wetlands is given in terms of mean elevation of conceptual marsh units defined by Defne and Ganju (2019). The elevation data is based on the 1-meter resolution Coastal National Elevation Database (CoNED), where data gaps exist. Through scientific efforts initiated with the Hurricane Sandy Science Plan, the U.S. Geological Survey has been expanding national assessment of coastal change hazards and forecast products to coastal wetlands. The intent is to provide federal, state, and local managers with tools to estimate their vulnerability and ecosystem service potential. For this purpose, the response and resilience of coastal wetlands to physical factors need to be assessed in terms of the ensuing change to their vulnerability and ecosystem services. CACO is one of the selected domains to expand this study. References: Defne, Z., and Ganju, N.K., 2019, Conceptual marsh units for Cape Cod National Seashore salt marsh complex, Massachusetts: U.S. Geological Survey data release, https://doi.org/10.5066/P955K1Y2

The U.S. Geological Survey, Woods Hole Coastal and Marine Science Center in cooperation with the University of Maine mapped approximately 50 square kilometers of the seafloor within Belfast Bay, Maine. Three geophysical surveys conducted in 2006, 2008 and 2009 collected swath bathymetric (2006 and 2008) and chirp seismic reflection profile data (2006 and 2009). The project characterized the spatial, morphological and subsurface variability of the Belfast Bay, Maine pockmark field. Pockmarks are large seafloor depressions that are associated with seabed fluid escape.

The U.S. Geological Survey, Woods Hole Coastal and Marine Science Center in cooperation with the University of Maine mapped approximately 50 square kilometers of the seafloor within Belfast Bay, Maine. Three geophysical surveys conducted in 2006, 2008 and 2009 collected swath bathymetric (2006 and 2008) and chirp seismic reflection profile data (2006 and 2009). The project characterized the spatial, morphological and subsurface variability of the Belfast Bay, Maine pockmark field. Pockmarks are large seafloor depressions that are associated with seabed fluid escape.

The U.S. Geological Survey, Woods Hole Coastal and Marine Science Center in cooperation with the University of Maine mapped approximately 50 square kilometers of the seafloor within Belfast Bay, Maine. Three geophysical surveys conducted in 2006, 2008 and 2009 collected swath bathymetric (2006 and 2008) and chirp seismic reflection profile data (2006 and 2009). The project characterized the spatial, morphological and subsurface variability of the Belfast Bay, Maine pockmark field. Pockmarks are large seafloor depressions that are associated with seabed fluid escape.

Elevation distribution in the Fire Island National Seashore and central Great South Bay salt marsh complex is given in terms of mean elevation of conceptual marsh units defined by Defne and Ganju (2018). The elevation data is based on the 1-meter resolution Coastal National Elevation Database (CoNED). Through scientific efforts initiated with the Hurricane Sandy Science Plan, the U.S. Geological Survey has been expanding national assessment of coastal change hazards and forecast products to coastal wetlands, including the Fire Island National Seashore and central Great South Bay salt marshes, with the intent of providing Federal, State, and local managers with tools to estimate the vulnerability and ecosystem service potential of these wetlands. For this purpose, the response and resilience of coastal wetlands to physical factors need to be assessed in terms of the ensuing change to their vulnerability and ecosystem services. References: Defne, Z., and Ganju, N.K., 2018, Conceptual marsh units for Fire Island National Seashore and central Great South Bay salt marsh complex, New York: U.S. Geological Survey data release, https://doi.org/10.5066/P95U2MQ7.

In summer 2018, the U.S. Geological Survey partnered with the U.S Department of Energy and the Bureau of Ocean Energy Management to conduct the Mid-Atlantic Resources Imaging Experiment (MATRIX) as part of the U.S. Geological Survey Gas Hydrates Project. The field program objectives were to acquire high-resolution 2-dimensional multichannel seismic-reflection and split-beam echosounder data along the U.S Atlantic margin between North Carolina and New Jersey to determine the distribution of methane gas hydrates in below-sea floor sediments and investigate potential connections between gas hydrate dynamics and sea floor methane seepage. MATRIX field work was carried out between August 8 and August 28, 2018 on the research vessel Hugh R. Sharp and resulted in acquisition of more than 2,000 track-line kilometers of multichannel seismic-reflection and colocated split-beam echosounder data, along with wide-angle seismic reflection and refraction data from 63 expendable sonobuoy deployments.

Understanding how effectively methane can be extracted from a gas hydrate reservoir requires knowing how compressible, permeable, and strong the overlying seal sediment is. This data release provides results for flow-through permeability, consolidation, and direct shear measurements made on fine-grained seal sediment from Site NGHP-02-08 offshore eastern India. The sediment was collected in a pressure core from the Krishna-Godavari Basin during the 2015 Indian National Gas Hydrate Program Expedition 2 (NGHP-02). Gas hydrate is a crystalline solid that forms naturally in the sediment of certain marine and permafrost environments where pressure is relatively high (equivalent to the pressure measured at ~300 meters water depth or more) and temperature is relatively low (but generally above freezing). The concentration of methane can be high enough to make certain gas hydrate occurrences potentially relevant as energy resources. To extract methane from gas hydrate, the in situ formation (generally a coarse-grained, gas-hydrate-bearing sediment interval) can be depressurized by drawing pore water out through a production well. As the pore pressure falls below the gas hydrate stability limit, the solid gas hydrate breaks down, releasing gas and water that migrate toward the production well for collection. How effectively the production well can depressurize the gas-hydrate-bearing interval depends on how permeable the overlying seal sediment is. If the seal is permeable, depressurizing the reservoir to extract methane causes water to flow out of the seal and into the reservoir. This can limit the ability of the production well to maintain the low reservoir pressure required to break down gas.

Understanding how effectively methane can be extracted from a gas hydrate reservoir requires knowing how compressible, permeable, and strong the overlying seal sediment is. This data release provides results for flow-through permeability, consolidation, and direct shear measurements made on fine-grained seal sediment from Site NGHP-02-08 offshore eastern India. The sediment was collected in a pressure core from the Krishna-Godavari Basin during the 2015 Indian National Gas Hydrate Program Expedition 2 (NGHP-02). Gas hydrate is a crystalline solid that forms naturally in the sediment of certain marine and permafrost environments where pressure is relatively high (equivalent to the pressure measured at ~300 meters water depth or more) and temperature is relatively low (but generally above freezing). The concentration of methane can be high enough to make certain gas hydrate occurrences potentially relevant as energy resources. To extract methane from gas hydrate, the in situ formation (generally a coarse-grained, gas-hydrate-bearing sediment interval) can be depressurized by drawing pore water out through a production well. As the pore pressure falls below the gas hydrate stability limit, the solid gas hydrate breaks down, releasing gas and water that migrate toward the production well for collection. How effectively the production well can depressurize the gas-hydrate-bearing interval depends on how permeable the overlying seal sediment is. If the seal is permeable, depressurizing the reservoir to extract methane causes water to flow out of the seal and into the reservoir. This can limit the ability of the production well to maintain the low reservoir pressure required to break down gas.

Understanding how effectively methane can be extracted from a gas hydrate reservoir requires knowing how compressible, permeable, and strong the overlying seal sediment is. This data release provides results for flow-through permeability, consolidation, and direct shear measurements made on fine-grained seal sediment from Site NGHP-02-08 offshore eastern India. The sediment was collected in a pressure core from the Krishna-Godavari Basin during the 2015 Indian National Gas Hydrate Program Expedition 2 (NGHP-02). Gas hydrate is a crystalline solid that forms naturally in the sediment of certain marine and permafrost environments where pressure is relatively high (equivalent to the pressure measured at ~300 meters water depth or more) and temperature is relatively low (but generally above freezing). The concentration of methane can be high enough to make certain gas hydrate occurrences potentially relevant as energy resources. To extract methane from gas hydrate, the in situ formation (generally a coarse-grained, gas-hydrate-bearing sediment interval) can be depressurized by drawing pore water out through a production well. As the pore pressure falls below the gas hydrate stability limit, the solid gas hydrate breaks down, releasing gas and water that migrate toward the production well for collection. How effectively the production well can depressurize the gas-hydrate-bearing interval depends on how permeable the overlying seal sediment is. If the seal is permeable, depressurizing the reservoir to extract methane causes water to flow out of the seal and into the reservoir. This can limit the ability of the production well to maintain the low reservoir pressure required to break down gas.

The salt marsh complex of Assateague Island National Seashore (ASIS) and Chincoteague Bay was delineated to smaller, conceptual marsh units by geoprocessing of surface elevation data. Flow accumulation based on the relative elevation of each location is used to determine the ridge lines that separate each marsh unit while the surface slope is used to automatically assign each unit a drainage point, where water is expected to drain through. Through scientific efforts initiated with the Hurricane Sandy Science Plan, the U.S. Geological Survey has been expanding national assessment of coastal change hazards and forecast products to coastal wetlands, including the Assateague Island National Seashore and Chincoteague Bay salt marshes, with the intent of providing Federal, State, and local managers with tools to estimate the vulnerability and ecosystem service potential of these wetlands. For this purpose, the response and resilience of coastal wetlands to physical factors need to be assessed in terms of the ensuing change to their vulnerability and ecosystem services.

Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated into predictive models and the training data used to parameterize those models. This data release contains the extracted metrics of barrier island geomorphology and spatial data layers of habitat characteristics that are input to Bayesian networks for piping plover habitat availability and barrier island geomorphology. These datasets and models are being developed for sites along the northeastern coast of the United States. This work is one component of a larger research and management program that seeks to understand and sustain the ecological value, ecosystem services, and habitat suitability of beaches in the face of storm impacts, climate change, and sea-level rise.

Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated into predictive models and the training data used to parameterize those models. This data release contains the extracted metrics of barrier island geomorphology and spatial data layers of habitat characteristics that are input to Bayesian networks for piping plover habitat availability and barrier island geomorphology. These datasets and models are being developed for sites along the northeastern coast of the United States. This work is one component of a larger research and management program that seeks to understand and sustain the ecological value, ecosystem services, and habitat suitability of beaches in the face of storm impacts, climate change, and sea-level rise.

Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated into predictive models and the training data used to parameterize those models. This data release contains the extracted metrics of barrier island geomorphology and spatial data layers of habitat characteristics that are input to Bayesian networks for piping plover habitat availability and barrier island geomorphology. These datasets and models are being developed for sites along the northeastern coast of the United States. This work is one component of a larger research and management program that seeks to understand and sustain the ecological value, ecosystem services, and habitat suitability of beaches in the face of storm impacts, climate change, and sea-level rise.

Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated into predictive models and the training data used to parameterize those models. This data release contains the extracted metrics of barrier island geomorphology and spatial data layers of habitat characteristics that are input to Bayesian networks for piping plover habitat availability and barrier island geomorphology. These datasets and models are being developed for sites along the northeastern coast of the United States. This work is one component of a larger research and management program that seeks to understand and sustain the ecological value, ecosystem services, and habitat suitability of beaches in the face of storm impacts, climate change, and sea-level rise.

Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated into predictive models and the training data used to parameterize those models. This data release contains the extracted metrics of barrier island geomorphology and spatial data layers of habitat characteristics that are input to Bayesian networks for piping plover habitat availability and barrier island geomorphology. These datasets and models are being developed for sites along the northeastern coast of the United States. This work is one component of a larger research and management program that seeks to understand and sustain the ecological value, ecosystem services, and habitat suitability of beaches in the face of storm impacts, climate change, and sea-level rise.

Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated into predictive models and the training data used to parameterize those models. This data release contains the extracted metrics of barrier island geomorphology and spatial data layers of habitat characteristics that are input to Bayesian networks for piping plover habitat availability and barrier island geomorphology. These datasets and models are being developed for sites along the northeastern coast of the United States. This work is one component of a larger research and management program that seeks to understand and sustain the ecological value, ecosystem services, and habitat suitability of beaches in the face of storm impacts, climate change, and sea-level rise.

Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated into predictive models and the training data used to parameterize those models. This data release contains the extracted metrics of barrier island geomorphology and spatial data layers of habitat characteristics that are input to Bayesian networks for piping plover habitat availability and barrier island geomorphology. These datasets and models are being developed for sites along the northeastern coast of the United States. This work is one component of a larger research and management program that seeks to understand and sustain the ecological value, ecosystem services, and habitat suitability of beaches in the face of storm impacts, climate change, and sea-level rise.

Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated into predictive models and the training data used to parameterize those models. This data release contains the extracted metrics of barrier island geomorphology and spatial data layers of habitat characteristics that are input to Bayesian networks for piping plover habitat availability and barrier island geomorphology. These datasets and models are being developed for sites along the northeastern coast of the United States. This work is one component of a larger research and management program that seeks to understand and sustain the ecological value, ecosystem services, and habitat suitability of beaches in the face of storm impacts, climate change, and sea-level rise.

Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated into predictive models and the training data used to parameterize those models. This data release contains the extracted metrics of barrier island geomorphology and spatial data layers of habitat characteristics that are input to Bayesian networks for piping plover habitat availability and barrier island geomorphology. These datasets and models are being developed for sites along the northeastern coast of the United States. This work is one component of a larger research and management program that seeks to understand and sustain the ecological value, ecosystem services, and habitat suitability of beaches in the face of storm impacts, climate change, and sea-level rise.

Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated into predictive models and the training data used to parameterize those models. This data release contains the extracted metrics of barrier island geomorphology and spatial data layers of habitat characteristics that are input to Bayesian networks for piping plover habitat availability and barrier island geomorphology. These datasets and models are being developed for sites along the northeastern coast of the United States. This work is one component of a larger research and management program that seeks to understand and sustain the ecological value, ecosystem services, and habitat suitability of beaches in the face of storm impacts, climate change, and sea-level rise.

Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated into predictive models and the training data used to parameterize those models. This data release contains the extracted metrics of barrier island geomorphology and spatial data layers of habitat characteristics that are input to Bayesian networks for piping plover habitat availability and barrier island geomorphology. These datasets and models are being developed for sites along the northeastern coast of the United States. This work is one component of a larger research and management program that seeks to understand and sustain the ecological value, ecosystem services, and habitat suitability of beaches in the face of storm impacts, climate change, and sea-level rise.

Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated into predictive models and the training data used to parameterize those models. This data release contains the extracted metrics of barrier island geomorphology and spatial data layers of habitat characteristics that are input to Bayesian networks for piping plover habitat availability and barrier island geomorphology. These datasets and models are being developed for sites along the northeastern coast of the United States. This work is one component of a larger research and management program that seeks to understand and sustain the ecological value, ecosystem services, and habitat suitability of beaches in the face of storm impacts, climate change, and sea-level rise.

Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated into predictive models and the training data used to parameterize those models. This data release contains the extracted metrics of barrier island geomorphology and spatial data layers of habitat characteristics that are input to Bayesian networks for piping plover habitat availability and barrier island geomorphology. These datasets and models are being developed for sites along the northeastern coast of the United States. This work is one component of a larger research and management program that seeks to understand and sustain the ecological value, ecosystem services, and habitat suitability of beaches in the face of storm impacts, climate change, and sea-level rise.

Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated into predictive models and the training data used to parameterize those models. This data release contains the extracted metrics of barrier island geomorphology and spatial data layers of habitat characteristics that are input to Bayesian networks for piping plover habitat availability and barrier island geomorphology. These datasets and models are being developed for sites along the northeastern coast of the United States. This work is one component of a larger research and management program that seeks to understand and sustain the ecological value, ecosystem services, and habitat suitability of beaches in the face of storm impacts, climate change, and sea-level rise.

Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated into predictive models and the training data used to parameterize those models. This data release contains the extracted metrics of barrier island geomorphology and spatial data layers of habitat characteristics that are input to Bayesian networks for piping plover habitat availability and barrier island geomorphology. These datasets and models are being developed for sites along the northeastern coast of the United States. This work is one component of a larger research and management program that seeks to understand and sustain the ecological value, ecosystem services, and habitat suitability of beaches in the face of storm impacts, climate change, and sea-level rise.

Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated into predictive models and the training data used to parameterize those models. This data release contains the extracted metrics of barrier island geomorphology and spatial data layers of habitat characteristics that are input to Bayesian networks for piping plover habitat availability and barrier island geomorphology. These datasets and models are being developed for sites along the northeastern coast of the United States. This work is one component of a larger research and management program that seeks to understand and sustain the ecological value, ecosystem services, and habitat suitability of beaches in the face of storm impacts, climate change, and sea-level rise.

Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated into predictive models and the training data used to parameterize those models. This data release contains the extracted metrics of barrier island geomorphology and spatial data layers of habitat characteristics that are input to Bayesian networks for piping plover habitat availability and barrier island geomorphology. These datasets and models are being developed for sites along the northeastern coast of the United States. This work is one component of a larger research and management program that seeks to understand and sustain the ecological value, ecosystem services, and habitat suitability of beaches in the face of storm impacts, climate change, and sea-level rise.

Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated into predictive models and the training data used to parameterize those models. This data release contains the extracted metrics of barrier island geomorphology and spatial data layers of habitat characteristics that are input to Bayesian networks for piping plover habitat availability and barrier island geomorphology. These datasets and models are being developed for sites along the northeastern coast of the United States. This work is one component of a larger research and management program that seeks to understand and sustain the ecological value, ecosystem services, and habitat suitability of beaches in the face of storm impacts, climate change, and sea-level rise.

Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated into predictive models and the training data used to parameterize those models. This data release contains the extracted metrics of barrier island geomorphology and spatial data layers of habitat characteristics that are input to Bayesian networks for piping plover habitat availability and barrier island geomorphology. These datasets and models are being developed for sites along the northeastern coast of the United States. This work is one component of a larger research and management program that seeks to understand and sustain the ecological value, ecosystem services, and habitat suitability of beaches in the face of storm impacts, climate change, and sea-level rise.

Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated into predictive models and the training data used to parameterize those models. This data release contains the extracted metrics of barrier island geomorphology and spatial data layers of habitat characteristics that are input to Bayesian networks for piping plover habitat availability and barrier island geomorphology. These datasets and models are being developed for sites along the northeastern coast of the United States. This work is one component of a larger research and management program that seeks to understand and sustain the ecological value, ecosystem services, and habitat suitability of beaches in the face of storm impacts, climate change, and sea-level rise.

Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated into predictive models and the training data used to parameterize those models. This data release contains the extracted metrics of barrier island geomorphology and spatial data layers of habitat characteristics that are input to Bayesian networks for piping plover habitat availability and barrier island geomorphology. These datasets and models are being developed for sites along the northeastern coast of the United States. This work is one component of a larger research and management program that seeks to understand and sustain the ecological value, ecosystem services, and habitat suitability of beaches in the face of storm impacts, climate change, and sea-level rise.

Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated into predictive models and the training data used to parameterize those models. This data release contains the extracted metrics of barrier island geomorphology and spatial data layers of habitat characteristics that are input to Bayesian networks for piping plover habitat availability and barrier island geomorphology. These datasets and models are being developed for sites along the northeastern coast of the United States. This work is one component of a larger research and management program that seeks to understand and sustain the ecological value, ecosystem services, and habitat suitability of beaches in the face of storm impacts, climate change, and sea-level rise.

Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated into predictive models and the training data used to parameterize those models. This data release contains the extracted metrics of barrier island geomorphology and spatial data layers of habitat characteristics that are input to Bayesian networks for piping plover habitat availability and barrier island geomorphology. These datasets and models are being developed for sites along the northeastern coast of the United States. This work is one component of a larger research and management program that seeks to understand and sustain the ecological value, ecosystem services, and habitat suitability of beaches in the face of storm impacts, climate change, and sea-level rise.

Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated into predictive models and the training data used to parameterize those models. This data release contains the extracted metrics of barrier island geomorphology and spatial data layers of habitat characteristics that are input to Bayesian networks for piping plover habitat availability and barrier island geomorphology. These datasets and models are being developed for sites along the northeastern coast of the United States. This work is one component of a larger research and management program that seeks to understand and sustain the ecological value, ecosystem services, and habitat suitability of beaches in the face of storm impacts, climate change, and sea-level rise.

Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated into predictive models and the training data used to parameterize those models. This data release contains the extracted metrics of barrier island geomorphology and spatial data layers of habitat characteristics that are input to Bayesian networks for piping plover habitat availability and barrier island geomorphology. These datasets and models are being developed for sites along the northeastern coast of the United States. This work is one component of a larger research and management program that seeks to understand and sustain the ecological value, ecosystem services, and habitat suitability of beaches in the face of storm impacts, climate change, and sea-level rise.

Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated into predictive models and the training data used to parameterize those models. This data release contains the extracted metrics of barrier island geomorphology and spatial data layers of habitat characteristics that are input to Bayesian networks for piping plover habitat availability and barrier island geomorphology. These datasets and models are being developed for sites along the northeastern coast of the United States. This work is one component of a larger research and management program that seeks to understand and sustain the ecological value, ecosystem services, and habitat suitability of beaches in the face of storm impacts, climate change, and sea-level rise.

Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated into predictive models and the training data used to parameterize those models. This data release contains the extracted metrics of barrier island geomorphology and spatial data layers of habitat characteristics that are input to Bayesian networks for piping plover habitat availability and barrier island geomorphology. These datasets and models are being developed for sites along the northeastern coast of the United States. This work is one component of a larger research and management program that seeks to understand and sustain the ecological value, ecosystem services, and habitat suitability of beaches in the face of storm impacts, climate change, and sea-level rise.

Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated into predictive models and the training data used to parameterize those models. This data release contains the extracted metrics of barrier island geomorphology and spatial data layers of habitat characteristics that are input to Bayesian networks for piping plover habitat availability and barrier island geomorphology. These datasets and models are being developed for sites along the northeastern coast of the United States. This work is one component of a larger research and management program that seeks to understand and sustain the ecological value, ecosystem services, and habitat suitability of beaches in the face of storm impacts, climate change, and sea-level rise.

Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated into predictive models and the training data used to parameterize those models. This data release contains the extracted metrics of barrier island geomorphology and spatial data layers of habitat characteristics that are input to Bayesian networks for piping plover habitat availability and barrier island geomorphology. These datasets and models are being developed for sites along the northeastern coast of the United States. This work is one component of a larger research and management program that seeks to understand and sustain the ecological value, ecosystem services, and habitat suitability of beaches in the face of storm impacts, climate change, and sea-level rise.

Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated into predictive models and the training data used to parameterize those models. This data release contains the extracted metrics of barrier island geomorphology and spatial data layers of habitat characteristics that are input to Bayesian networks for piping plover habitat availability and barrier island geomorphology. These datasets and models are being developed for sites along the northeastern coast of the United States. This work is one component of a larger research and management program that seeks to understand and sustain the ecological value, ecosystem services, and habitat suitability of beaches in the face of storm impacts, climate change, and sea-level rise.

Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated into predictive models and the training data used to parameterize those models. This data release contains the extracted metrics of barrier island geomorphology and spatial data layers of habitat characteristics that are input to Bayesian networks for piping plover habitat availability and barrier island geomorphology. These datasets and models are being developed for sites along the northeastern coast of the United States. This work is one component of a larger research and management program that seeks to understand and sustain the ecological value, ecosystem services, and habitat suitability of beaches in the face of storm impacts, climate change, and sea-level rise.

Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated into predictive models and the training data used to parameterize those models. This data release contains the extracted metrics of barrier island geomorphology and spatial data layers of habitat characteristics that are input to Bayesian networks for piping plover habitat availability and barrier island geomorphology. These datasets and models are being developed for sites along the northeastern coast of the United States. This work is one component of a larger research and management program that seeks to understand and sustain the ecological value, ecosystem services, and habitat suitability of beaches in the face of storm impacts, climate change, and sea-level rise.

Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated into predictive models and the training data used to parameterize those models. This data release contains the extracted metrics of barrier island geomorphology and spatial data layers of habitat characteristics that are input to Bayesian networks for piping plover habitat availability and barrier island geomorphology. These datasets and models are being developed for sites along the northeastern coast of the United States. This work is one component of a larger research and management program that seeks to understand and sustain the ecological value, ecosystem services, and habitat suitability of beaches in the face of storm impacts, climate change, and sea-level rise.

Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated into predictive models and the training data used to parameterize those models. This data release contains the extracted metrics of barrier island geomorphology and spatial data layers of habitat characteristics that are input to Bayesian networks for piping plover habitat availability and barrier island geomorphology. These datasets and models are being developed for sites along the northeastern coast of the United States. This work is one component of a larger research and management program that seeks to understand and sustain the ecological value, ecosystem services, and habitat suitability of beaches in the face of storm impacts, climate change, and sea-level rise.

Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated into predictive models and the training data used to parameterize those models. This data release contains the extracted metrics of barrier island geomorphology and spatial data layers of habitat characteristics that are input to Bayesian networks for piping plover habitat availability and barrier island geomorphology. These datasets and models are being developed for sites along the northeastern coast of the United States. This work is one component of a larger research and management program that seeks to understand and sustain the ecological value, ecosystem services, and habitat suitability of beaches in the face of storm impacts, climate change, and sea-level rise.

Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated into predictive models and the training data used to parameterize those models. This data release contains the extracted metrics of barrier island geomorphology and spatial data layers of habitat characteristics that are input to Bayesian networks for piping plover habitat availability and barrier island geomorphology. These datasets and models are being developed for sites along the northeastern coast of the United States. This work is one component of a larger research and management program that seeks to understand and sustain the ecological value, ecosystem services, and habitat suitability of beaches in the face of storm impacts, climate change, and sea-level rise.

Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated into predictive models and the training data used to parameterize those models. This data release contains the extracted metrics of barrier island geomorphology and spatial data layers of habitat characteristics that are input to Bayesian networks for piping plover habitat availability and barrier island geomorphology. These datasets and models are being developed for sites along the northeastern coast of the United States. This work is one component of a larger research and management program that seeks to understand and sustain the ecological value, ecosystem services, and habitat suitability of beaches in the face of storm impacts, climate change, and sea-level rise.

Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated into predictive models and the training data used to parameterize those models. This data release contains the extracted metrics of barrier island geomorphology and spatial data layers of habitat characteristics that are input to Bayesian networks for piping plover habitat availability and barrier island geomorphology. These datasets and models are being developed for sites along the northeastern coast of the United States. This work is one component of a larger research and management program that seeks to understand and sustain the ecological value, ecosystem services, and habitat suitability of beaches in the face of storm impacts, climate change, and sea-level rise.

Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated into predictive models and the training data used to parameterize those models. This data release contains the extracted metrics of barrier island geomorphology and spatial data layers of habitat characteristics that are input to Bayesian networks for piping plover habitat availability and barrier island geomorphology. These datasets and models are being developed for sites along the northeastern coast of the United States. This work is one component of a larger research and management program that seeks to understand and sustain the ecological value, ecosystem services, and habitat suitability of beaches in the face of storm impacts, climate change, and sea-level rise.

Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated into predictive models and the training data used to parameterize those models. This data release contains the extracted metrics of barrier island geomorphology and spatial data layers of habitat characteristics that are input to Bayesian networks for piping plover habitat availability and barrier island geomorphology. These datasets and models are being developed for sites along the northeastern coast of the United States. This work is one component of a larger research and management program that seeks to understand and sustain the ecological value, ecosystem services, and habitat suitability of beaches in the face of storm impacts, climate change, and sea-level rise.

Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated into predictive models and the training data used to parameterize those models. This data release contains the extracted metrics of barrier island geomorphology and spatial data layers of habitat characteristics that are input to Bayesian networks for piping plover habitat availability and barrier island geomorphology. These datasets and models are being developed for sites along the northeastern coast of the United States. This work is one component of a larger research and management program that seeks to understand and sustain the ecological value, ecosystem services, and habitat suitability of beaches in the face of storm impacts, climate change, and sea-level rise.

Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated into predictive models and the training data used to parameterize those models. This data release contains the extracted metrics of barrier island geomorphology and spatial data layers of habitat characteristics that are input to Bayesian networks for piping plover habitat availability and barrier island geomorphology. These datasets and models are being developed for sites along the northeastern coast of the United States. This work is one component of a larger research and management program that seeks to understand and sustain the ecological value, ecosystem services, and habitat suitability of beaches in the face of storm impacts, climate change, and sea-level rise.

Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated into predictive models and the training data used to parameterize those models. This data release contains the extracted metrics of barrier island geomorphology and spatial data layers of habitat characteristics that are input to Bayesian networks for piping plover habitat availability and barrier island geomorphology. These datasets and models are being developed for sites along the northeastern coast of the United States. This work is one component of a larger research and management program that seeks to understand and sustain the ecological value, ecosystem services, and habitat suitability of beaches in the face of storm impacts, climate change, and sea-level rise.

Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated into predictive models and the training data used to parameterize those models. This data release contains the extracted metrics of barrier island geomorphology and spatial data layers of habitat characteristics that are input to Bayesian networks for piping plover habitat availability and barrier island geomorphology. These datasets and models are being developed for sites along the northeastern coast of the United States. This work is one component of a larger research and management program that seeks to understand and sustain the ecological value, ecosystem services, and habitat suitability of beaches in the face of storm impacts, climate change, and sea-level rise.

Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated into predictive models and the training data used to parameterize those models. This data release contains the extracted metrics of barrier island geomorphology and spatial data layers of habitat characteristics that are input to Bayesian networks for piping plover habitat availability and barrier island geomorphology. These datasets and models are being developed for sites along the northeastern coast of the United States. This work is one component of a larger research and management program that seeks to understand and sustain the ecological value, ecosystem services, and habitat suitability of beaches in the face of storm impacts, climate change, and sea-level rise.

Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated into predictive models and the training data used to parameterize those models. This data release contains the extracted metrics of barrier island geomorphology and spatial data layers of habitat characteristics that are input to Bayesian networks for piping plover habitat availability and barrier island geomorphology. These datasets and models are being developed for sites along the northeastern coast of the United States. This work is one component of a larger research and management program that seeks to understand and sustain the ecological value, ecosystem services, and habitat suitability of beaches in the face of storm impacts, climate change, and sea-level rise.

Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated into predictive models and the training data used to parameterize those models. This data release contains the extracted metrics of barrier island geomorphology and spatial data layers of habitat characteristics that are input to Bayesian networks for piping plover habitat availability and barrier island geomorphology. These datasets and models are being developed for sites along the northeastern coast of the United States. This work is one component of a larger research and management program that seeks to understand and sustain the ecological value, ecosystem services, and habitat suitability of beaches in the face of storm impacts, climate change, and sea-level rise.

Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated into predictive models and the training data used to parameterize those models. This data release contains the extracted metrics of barrier island geomorphology and spatial data layers of habitat characteristics that are input to Bayesian networks for piping plover habitat availability and barrier island geomorphology. These datasets and models are being developed for sites along the northeastern coast of the United States. This work is one component of a larger research and management program that seeks to understand and sustain the ecological value, ecosystem services, and habitat suitability of beaches in the face of storm impacts, climate change, and sea-level rise.

Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated into predictive models and the training data used to parameterize those models. This data release contains the extracted metrics of barrier island geomorphology and spatial data layers of habitat characteristics that are input to Bayesian networks for piping plover habitat availability and barrier island geomorphology. These datasets and models are being developed for sites along the northeastern coast of the United States. This work is one component of a larger research and management program that seeks to understand and sustain the ecological value, ecosystem services, and habitat suitability of beaches in the face of storm impacts, climate change, and sea-level rise.

Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated into predictive models and the training data used to parameterize those models. This data release contains the extracted metrics of barrier island geomorphology and spatial data layers of habitat characteristics that are input to Bayesian networks for piping plover habitat availability and barrier island geomorphology. These datasets and models are being developed for sites along the northeastern coast of the United States. This work is one component of a larger research and management program that seeks to understand and sustain the ecological value, ecosystem services, and habitat suitability of beaches in the face of storm impacts, climate change, and sea-level rise.