Polygon attributes Data_type = type of data (bathymetry, lidar, etc.) Date = date of data collection Agency = agency that collected the data or commissioned its collection Native_res = native horizontal resolution of the source data Survey_ID = survey ID used in website or data report
California State University, Monterey Bay (CSUMB) and Fugro Pelagos, Inc., for California State Waters Mapping Project (CSWMP) from Pat Iampietro; Pillar Point Harbor from Anne Sturm (U.S. Army Corps of Engineers); National Geophysical Data Center (NGDC); National Oceanic and Atmospheric Administration (NOAA); Marin County from Brian Quinn (County of Marin GIS Division of Community Development Agency); Bolinas Lagoon from Matt Brennan (ESA-PWA); Carignan, K.S., Taylor, L.A., Eakins, B.W., Caldwell, R.J., Friday, D.Z., Grothe, P.R., and Lim, E., 2011, Digital elevation models of central California and San Francisco Bay: Procedures, data sources and analysis: NOAA Technical Memorandum NESDIS NGDC-52, 49 p. and datasets, available at http://www.ngdc.noaa.gov/dem/squareCellGrid/download/741
This DEM data set was constructed to provide critical model boundary conditions (bathymetry and topography) necessary to predict the impacts of severe winter storms and sea level rise along this stretch of coast, using the Coastal Storm Modeling System (CoSMoS). This process based modeling system was first applied along the coast of Southern California (Barnard and others, 2009). CoSMoS can be run in real-time or with prescribed scenarios, incorporating atmospheric forcing information (wind and pressure fields) with a suite of state-of-the-art physical process models (WaveWatch3, SWAN, XBeach, Delft3D) to enable detailed prediction of water levels, run-up, wave heights, and currents, ultimately predicting the spatial distribution of coastal flooding, inundation, erosion, and cliff failure. The DEM was constructed to define the general shape of the nearshore, beach and cliff surfaces as accurately as possible, with less emphasis on the detailed variations in elevation inland of the coast and on bathymetry inside harbors. As a result this DEM should not be used for navigation purposes.
(Detailed DEM Construction Procedures) Performed by Amy Foxgrover and Patrick Barnard: 1. Divide study area into ~10 km alongshore segments A. Define DEM coverage area/polygon that extends ~10 km alongshore, from 3 nautical miles offshore inland beyond the +20 m topographic contour B. Ensure that adjacent DEM coverage areas overlap by ~250 m 2. Acquire most recent or highest resolution data sets in DEM coverage areas A. Lidar B. Multibeam bathymetry C. Local high-resolution beach topography (usually ATV-acquired) and nearshore bathymetry (usually PWC-acquired). 3. Fill gaps with older/lower resolution datasets A. Lower resolution DEMs - for example, NGDC's 10-m resolution tsunami inundation DEM, (Carignan and others, 2011) in Bodega Harbor B. Bathymetric data derived from single beam bathymetry - for example, 1980s survey in Drakes Estero and 1998 bathymetry in Bolinas Lagoon 4. Convert all datasets into identical horizontal coordinate system, vertical datum, and grid resolution A. Horizontal coordinate system: UTM NAD83 Zone 10 North B. Vertical Datum: NAVD88 I. If different [usually Mean Lower Low Water (MLLW)], convert using local NOAA tide station information [http://tidesandcurrents.noaa.gov/ (last accessed December 12, 2011)] based on survey metadata C. Grid resolution: 2 m I. If already gridded at higher resolution(<2 m), resample to 2 m using bilinear interpolation II. If already gridded at a lower resolution (> 2m), export as xyz file, reimport as xyz points, create TIN (triangular irregular network), create 2-m grid from TIN using linear interpolation of the TIN triangles, and clip to survey extent III. Ungridded: a) Lower resolution surveys (for example, PWC-collected bathymetry): create TIN from points then convert to 2-m grid using linear interpolation of the TIN triangles 5. Clip datasets to DEM/coverage needs, if necessary A. Useful for data management and processing efficiency B. Necessary for very large data sets, such as county-wide lidar datasets (for example, Golden Gate Lidar Project data) C. Remove ocean water surfaces and offshore rocky outcrops/islands I. Aerial topographic lidar from 2010 was provided as bare-earth hydro-flattened DEMs. The breakline polygons provided with aerial lidar data were used to generate 2-m resolution grids of water surfaces over the ocean or tidal embayments where bathymetric data was to be inserted. Use this grid to mask out water surfaces in the topographic DEM using the Set Null tool in Arc Toolbox. II. Hydro-flattened surfaces of small inland water bodies were retained in the final DEM. Since these areas are of less importance for this research, no attempt was made to obtain bathymetric depths for these inland ponds or lakes (for example, Lake Merced in San Francisco). Hydro-flattened features that were retained in the final DEM are provided in shapefile format at the end of this report. III. Extract small islands and rocky outcrops from topographic lidar datasets using breaklines provided. These features are not included in the nearshore interpolation, but are incorporated into the final DEM in step 8. 6. Manage overlapping datasets A. Data sets were allowed to overlap extensively only if they are from the same time period, of comparable quality, and not within the dynamic nearshore region, otherwise allow only minimal (~10-30 m) overlap to ensure smooth DEM transitions B. Clip low-resolution data sets pushed to 2-m resolution, such as Personal Watercraft data and regional DEMs, to minimal overlap with adjacent high-resolution data sets (usually multibeam and topographic lidar) C. Clip topographic lidar so that only a single dataset is used for the coastal zone. Where it exists, the USGS lidar is given highest preference in the nearshore zone because it was collected in the summer and fall of 2010 when the beach morphology was less likely to influenced by winter storm events. The Golden Gate Lidar Project data are used for all reaches landward of the USGS lidar coverage (roughly 10-m elevation and higher) and along the coastline where USGS lidar was not collected. The OPC lidar is present only in two small sections that are not covered by USGS or GGLP lidar (within DEM sections 1 and 14). 7. Fill in data gaps between high-resolution datasets A. If no high-resolution data are available between the offshore multibeam bathymetry and coastal topographic lidar in protected harbors/embayments, or in other areas where interpolation from surrounding data sets will create a surface unlikely to reflect actual bathymetry/topography accurately, fill in gaps with regional DEMs or other low-resolution data sets. Otherwise, interpolate across gaps. I. Filling in harbors or embayments using regional DEMs/other low-resolution data: a) Clip best available regional DEM or bathymetry to gap area, allowing only minimal overlap (~20 m) with adjacent high-resolution data sets b) Export clipped grid as xyz, reimport as points, create TIN, create 2-m grid from TIN, clip to gap extent II. Interpolation across nearshore gaps: a) Create preliminary DEM using Mosaic tool with the following settings: Coordinate System: UTM Zone 10 North Pixel Type: 32 Bit Float Cell Size: 2 Mosaic Method: Blend Mosaic Color Map: Last b) Create polygon of data gap(s) to fill within the preliminary DEM surface c) Buffer the data gap polygon with a linear distance of 20 m using the Buffer tool in Arc Toolbox d) Clip preliminary DEM using the buffered polygon, export clipped grid as xyz, reimport as points (fig. 3B), create TIN, create 2-m grid from TIN, clip to buffered gap extent III. Interpolation around perimeter of Bolinas Lagoon and Drakes Estero: a) Fill any narrow gaps between bathymetry grids of Bolinas Lagoon and Drakes Estero and the nearest high resolution topography using the same procedure as used above for interpolating across nearshore gaps. 8. Compile final DEMs A. Load all datasets for DEM B. Verify all significant data gaps filled (few missing cells OK) in DEM coverage area C. Build interim DEM using Mosaic to New Raster tool in ArcGIS with same setting as noted above in Step 7 D. Build final DEM using Mosaic to New Raster tool in ArcGIS. Input rasters are the above interim DEM and a grid of small islands or rocky outcrops obtained from lidar. The islands are given top priority in the mosaicking algorithm so that island elevations from the lidar overwrite elevations from the nearshore interpolation. E. Clip output to DEM coverage area F. Create contours and plot cross-shore profiles to verify data quality and consistency
Are there legal restrictions on access or use of the data?
Use_Constraints:Majority of data (that is, lidar, multibeam bathymetry) derived from topographic/bathymetry data collected at 1-2 m horizontal resolution, with vertical uncertainty at time of data collection ranging from 10 cm to 1 m. Use at greater scales not advised. See full Data Series report for more information: Foxgrover, A.C., and Barnard, P.L., 2012, A seamless, high-resolution digital elevation model (DEM) of the north-central California coast: U.S. Geological Survey Data Series 684, 11 p. and datasets, available at https://doi.org/10.3133/ds684/.
Unless otherwise stated, all data, metadata and related materials are considered to satisfy the quality standards relative to the purpose for which the data were collected. Although these data and associated metadata have been reviewed for accuracy and completeness and approved for release by the U.S. Geological Survey (USGS), no warranty expressed or implied is made regarding the display or utility of the data on any other system or for general or scientific purposes, nor shall the act of distribution constitute any such warranty.
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