Shorelines of the Southern California coastal region (1852-2016) used in shoreline change analysis

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What does this data set describe?

1. How might this data set be cited?
   U.S. Geological Survey, 20240112, Shorelines of the Southern California coastal region (1852-2016) used in shoreline change analysis: data release DOI:10.5066/P94J0K7Z, U.S. Geological Survey, Coastal and Marine Hazards and Resources Program, Woods Hole Coastal and Marine Science Center, Woods Hole, MA.

   Online Links:

   • https://doi.org/10.5066/P94J0K7Z
   • https://www.sciencebase.gov/catalog/item/64adda5fd34e70357a293280

   This is part of the following larger work.
   Kratzmann, Meredith G., Farris, Amy S., and Himmelstoss, Emily A., 2024, National Shoreline Change—A GIS compilation of vector shorelines and associated shoreline change data from the 1800s to 2010s for the coast of California: data release DOI:10.5066/P94J0K7Z, U.S. Geological Survey, Reston, VA.

   Online Links:

   • https://doi.org/10.5066/P94J0K7Z
   • https://www.sciencebase.gov/catalog/item/619668d0d34eb622f69155a7

   Other_Citation_Details:

   suggested citation: Kratzmann, M.G., Farris, A.S., and Himmelstoss, E.A., 2024, National Shoreline Change—A GIS compilation of vector shorelines and associated shoreline change data from the 1800s to 2010s for the coast of California: U.S. Geological Survey data release, https://doi.org/10.5066/P94J0K7Z.

2. What geographic area does the data set cover?

   West_Bounding_Coordinate: -120.000895
   East_Bounding_Coordinate: -117.123896
   North_Bounding_Coordinate: 34.457484
   South_Bounding_Coordinate: 32.534372
   

3. What does it look like?

   https://www.sciencebase.gov/catalog/file/get/64adda5fd34e70357a293280?name=BG_Socal_shorelines.jpg&allowOpen=true (JPEG)

   Map view of data

4. Does the data set describe conditions during a particular time period?

   Beginning_Date:

   Ending_Date: 2016

   Currentness_Reference:

   ground condition

5. What is the general form of this data set?

   Geospatial_Data_Presentation_Form: vector digital data
   

6. How does the data set represent geographic features?
   1. How are geographic features stored in the data set?
      This is a Vector data set. It contains the following vector data types (SDTS terminology):
      • String (1382)
   2. What coordinate system is used to represent geographic features?
      Horizontal positions are specified in geographic coordinates, that is, latitude and longitude. Latitudes are given to the nearest 0.0197395052. Longitudes are given to the nearest 0.0264490611. Latitude and longitude values are specified in Decimal seconds. The horizontal datum used is WGS_1984.
      The ellipsoid used is WGS_84.
      The semi-major axis of the ellipsoid used is 6378137.0.
      The flattening of the ellipsoid used is 1/298.257223563.
      
7. How does the data set describe geographic features?

   Socal_shorelines

   Shorelines for Southern California (SoCal) used in shoreline change analysis. (Source: U.S. Geological Survey)

   FID

   Internal feature number. (Source: Esri) Sequential unique whole numbers that are automatically generated.

   Shape

   Feature geometry. (Source: Esri) Coordinates defining the features.

   Route_ID

   Route identification value assigned to individual lidar shoreline line segments. A unique cross-shore profile identification value is stored at each vertex of the lidar route and serves as a common attribute to the shoreline uncertainty table. (Source: U.S. Geological Survey)

   Value	Definition
   0	Short integer field where zeros are "no data" and automatically filled in for the remaining shoreline polylines not derived from lidar.

   DATE_

   Date of shoreline position; date of survey as indicated on source material. A default date of 07/01 was assigned to shorelines where only the year was known (month and day unknown). Using July, the mid-point month of the calendar year, minimizes the potential offset to the actual shoreline date by a maximum of six months. (Source: U.S. Geological Survey) Date of the shoreline in mm/dd/yyyy

   Year_

   Four digit year of shoreline (Source: U.S. Geological Survey)

   Range of values
   Minimum:	1852
   Maximum:	2016

   Uncy

   Estimate of shoreline position uncertainty. Actual shoreline position is within the range of this value (plus or minus, meters). The historic shoreline uncertainty values incorporate measurement uncertainties associated with mapping methods and materials for historical shorelines, the geographic registration of shoreline position, and shoreline digitizing. The lidar shoreline position uncertainty values for the 2009 lidar are stored in the associated _uncertainty.dbf file. Uncertainty for the other lidar shorelines is described in the process steps of this metadata file. (Source: U.S. Geological Survey)

   Range of values
   Minimum:	0.45
   Maximum:	10.8

   Source

   Agency that provided shoreline feature or the data source used to digitize shoreline feature. (Source: U.S. Geological Survey) Character string of length 25

   Source_b

   Type of data used to create shoreline. (Source: U.S. Geological Survey)

   Value	Definition
   lidar	Light detection and ranging (lidar).
   T or TP with number	NOAA/NOS topographic survey sheet (T- or TP-sheet) with associated registry number.

   Default_D

   Differentiates between shorelines that have known month and day attributes and those that use the default value of 07/01 when only the year is known. (Source: U.S. Geological Survey)

   Value	Definition
   0	Shoreline month and day are known.
   1	Shoreline month and day are unknown and default value of 07/01 was used.

   DSAS_type

   Shoreline type field used to specify the datum to which the shoreline is referenced. It is a required field when proxy-based and datum-based shorelines are combined to compute rates in DSAS. (Source: U.S. Geological Survey)

   Value	Definition
   MHW	Mean High Water (datum-based shoreline).
   HWL	High Water Line (proxy-based shoreline).

   Shape_Leng

   Length of shoreline in meter units (UTM zone 11N WGS84). (Source: U.S. Geological Survey, Woods Hole Science Center)

   Range of values
   Minimum:	8.077058
   Maximum:	420184.583166

   Entity_and_Attribute_Overview:

   The entity and attribute information provided here describes the tabular data associated with the dataset. Please review the individual attribute descriptions for detailed information.

   Entity_and_Attribute_Detail_Citation: U.S. Geological Survey
   

Who produced the data set?

1. Who are the originators of the data set? (may include formal authors, digital compilers, and editors)
   • U.S. Geological Survey
2. Who also contributed to the data set?
3. To whom should users address questions about the data?
   U.S. Geological Survey

   Attn: Meredith G. Kratzmann

   384 Woods Hole Road

   Woods Hole, MA
   

   USA

   508-548-8700 (voice)

   508-457-2310 (FAX)

   mkratzmann@contractor.usgs.gov

Why was the data set created?

How was the data set created?

1. From what previous works were the data drawn?

   T-sheet shorelines (source 1 of 5)

   National Oceanic and Atmospheric Administration (NOAA), National Ocean Service (NOS), Unknown, Scanned National Ocean Service (NOS) Coastal Survey Maps (also known as Topographic Survey sheets, or T-sheets).

   Online Links:

   • https://shoreline.noaa.gov/data/datasheets/t-sheets.html

   Type_of_Source_Media: digital data
   Source_Contribution:

   T-sheets used for QA/QC of NOAA-digitized 1800s-1980s shorelines and/or used as data source to digitize shorelines.

   2009-2011 lidar (source 2 of 5)

   Department of Commerce (DOC), National Oceanic and Atmospheric Administration (NOAA), National Ocean Service (NOS), Office for Coastal Management, 201201, 2009 - 2011 CA Coastal Conservancy Coastal Lidar Project.

   Online Links:

   • https://www.coast.noaa.gov/
   • https://coast.noaa.gov/digitalcoast/data/
   • https://www.fisheries.noaa.gov/inport/item/48166

   Type_of_Source_Media: digital data
   Source_Contribution: Lidar data that were used to extract a shoreline.
   

   2015 lidar (source 3 of 5)

   National Oceanic and Atmospheric Administration (NOAA) Office for Coastal Management (OCM), U.S. Army Corps of Engineers (USACE), Joint Airborne Lidar Bathymetry Technical Center of eXpertise (JALBTCX), 2018, 2015 USACE NCMP Topobathy Lidar DEM: California.

   Online Links:

   • https://inport.nmfs.noaa.gov/inport/item/51862
   • https://coast.noaa.gov/htdata/raster2/elevation/USACE_CA_Topobathy_DEM_2015_8482/

   Type_of_Source_Media: digital data
   Source_Contribution: Lidar data that were used to extract a shoreline.
   

   2015 lidar UCSD (source 4 of 5)

   Flick, Reinhard E., Gallien, Timu W., Giddings, Sarah N., Guza, R. T., Harvey, M., Lenain, Luc, Ludka, B.C., Melville, W. Kendall, O'Reilly, W.C., and Young, Adam P., 20190716, Data from: Southern California Coastal Response to the 2015-16 El Niño: 2015-10-06 Survey - Research Data Curation Program, UC San Diego.

   Online Links:

   • https://doi.org/10.6075/J0F18WXQ

   Type_of_Source_Media: digital data
   Source_Contribution: Lidar data that were used to extract a shoreline.
   

   2016 lidar (source 5 of 5)

   National Oceanic and Atmospheric Administration (NOAA) Office for Coastal Management (OCM), United States Geological Survey (USGS), U.S. Army Corps of Engineers (USACE), 20170412, 2016 USGS Lidar DEM: West Coast El Niño (WA, OR, CA).

   Online Links:

   • https://inport.nmfs.noaa.gov/inport/item/48383
   • https://coast.noaa.gov/htdata/raster2/elevation/West_Coast_El_Nino_DEM_2016_6260/
   • https://coast.noaa.gov/htdata/lidar2_z/geoid12b/data/6259/supplemental/west_coast_2016_el_nino_m6259_lidar_report.pdf

   Type_of_Source_Media: digital data
   Source_Contribution: Lidar data that were used to extract a shoreline.
   

2. How were the data generated, processed, and modified?

   Date: 2013 (process 1 of 15)

   Data from the previously-published National Assessment of Shoreline Change study for the sandy shorelines of the California Coast (USGS Open-File Report 2006-1219 [Hapke et al., 2006] and USGS Open-File Report 2006-1251 [Hapke and Reid, 2006]) were used as the starting point for this update. Any additionally available shorelines derived from T-sheets were downloaded from NOAA's shoreline survey website: https://shoreline.noaa.gov/data/datasheets/t-sheets.html
   This process step and all other process steps were performed by the same person - Meredith Kratzmann, unless otherwise stated in the contact information associated with the step. Person who carried out this activity:
   

   U.S. Geological Survey

   Attn: Meredith Kratzmann

   384 Woods Hole Road

   Woods Hole, MA
   

   508-548-8700 (voice)

   508-457-2310 (FAX)

   mkratzmann@contractor.usgs.gov

   Data sources used in this process:

   • T-sheet shorelines

   Data sources produced in this process:

   • Historical shorelines derived from T-sheets

   Date: 2014 (process 2 of 15)

   A quality control assessment of Digital Raster Graphic (DRG) data for Northern California and Central California was completed. DRG shorelines were included due to the lack of T-sheet data for the 1940s-1970s time period in NorCal and CenCal. No DRG shorelines are included in the SoCal dataset. DRG shorelines were edited to meet project standards (e.g., digitized headlands were not included in the dataset). These data are from the original USGS report for California shoreline change (USGS Open-File Report 2006-1219 and USGS Open-File Report 2006-1251).

   Date: 2016 (process 3 of 15)

   An extensive quality control assessment was conducted for all historical shoreline data to determine which of the originally published shorelines (USGS OFR 2006-1251) would be retained and which areas of California would be supplemented with the newly acquired NOAA T-sheet data. The most accurate shoreline for any given area was included in the dataset resulting in a comprehensive collection of historical shorelines from both originally compiled data (USGS OFR 2006-1251) and T-sheet derived data from NOAA. The source information is included in the attributes of the shorelines file for each subregion of California.

   Date: 2013 (process 4 of 15)

   The following information applies to the 2009/2010/2011 shoreline extracted from lidar data.
   The reference line that was used for the first National Assessments shoreline was used for this work (except for a couple of areas where there were gaps in the reference line which were filled). The reference line is made of straight segments and is roughly coast-following with points every 20 meters. At each point a profile line (or transect) is defined that is perpendicular to the reference line. A program written by Amy Farris using Matlab version 2012b loads the cloud of (x,y,z) lidar points and finds all the data points within 2 meters of each profile line. The shoreline was extrapolated from data on each of these profiles.
   The Matlab program described by Stockdon et al. (2002) was further developed by Laura Fauver, Jeff List and Kathy Weber at USGS. It was run on the profile data using Matlab version 2012b. This code works with one profile at a time. The code identified points located on the foreshore and fit a linear regression through them. The slope of the regression is an estimate of the slope of the foreshore. The intersection of the regression line with Mean High Water (MHW) is the calculated shoreline position. If the MHW elevation was obscured by water points, or if a data gap was present at MHW, the linear regression was simply extrapolated to the MHW elevation.
   The MHW values used for the three regions of California are: Northern California: Oregon/California border to Cape Mendocino 1.81 meters, Central California: Cape Mendocino to Point Buchon 1.46 meters, and Southern California: Point Buchon to U.S./Mexico Border 1.33 meters (Weber et al., 2005 and Hapke et al., 2006).
   The shoreline code has a graphical user interface (GUI) which plots all the data for a given profile, indicates which points were determined to be on the foreshore, plots the regression line and the calculated shoreline position. The information was visually checked and then the solution was either accepted or rejected. This manual verification process was repeated for each profile.
   Each lidar shoreline point has an error associated with it. This error has three components: the error due to the linear regression, the error associated with the lidar data collection system, and the error due to extrapolation (if the shoreline point was determined by extrapolation). The error due to the linear regression is simply the 95% confidence interval about the regression estimate.
   Sallenger et al. (2003) determined that the vertical accuracy of NASA's Airborne Topographic Mapper lidar system is about 15 centimeters. This vertical error is converted to a horizontal error using the beach slope as determined by the linear regression. The final part of the total shoreline error is the error due to extrapolation. If the shoreline point was determined by extrapolation, this error term is calculated as the amount of uncertainty in horizontal shoreline position due to the variability of the beach slope between the last point on the linear regression and the MHW elevation. These three error terms are then added in quadrature, yielding a total error for each shoreline point.
   Stockdon, H.F., Sallenger, A.H., List, J.H., and Holman, R.A., 2002. Estimation of Shoreline Position and Change using Airborne Topographic Lidar Data: Journal of Coastal Research, v.18, n.3, pp.502-513.
   Sallenger, A.H., Krabill, W., Swift, R., Brock, J., List, J., Hansen, M., Holman, R. A., Manizade, S., Sonntag, J., Meredith, A., Morgan, K., Yunkel, J.K., Frederick, E., and Stockdon, H., 2003. Evaluation of airborne scanning lidar for coastal change applications: Journal of Coastal Research, v. 19, pp. 125-133.
   After the shoreline code was run, the resultant shoreline was fed into another GUI for further quality checking. This GUI created a map view plot of the profile data color-coded by z values with a color jump at z = MHW so the approximate location of MHW is easily visible. The shoreline solutions were added to this plot. The shoreline data were visually scanned to look for incorrect solutions. For example the shoreline point was occasionally on the back of a barrier island or on a groin. These solutions were flagged and later removed. Small gaps on straight beaches were identified and later filled using linear interpolation but gaps at inlets or large gaps on curved beaches were not interpolated over. A visual quality check using imagery was conducted at a later stage.
   This datum-based shoreline is often used in conjunction with proxy-based shoreline positions. There is a recognized offset between datum-based and proxy-based shorelines, therefore the proxy-datum bias as defined by Ruggiero and List (2009) was calculated for these shorelines. The formula for the bias is based on an equation for wave run-up which depends on beach slope and the recent wave climate (specifically, wavelength and wave height). The beach slope was calculated by the shoreline code (described in a previous process step). It was averaged alongshore in 1-kilometer non-overlapping blocks. The wave climate was estimated from averages of historical data. Historical wave lengths were obtained from offshore buoys. The buoy data were downloaded from the National Buoy Data Center (https://www.ndbc.noaa.gov/). Buoys were chosen that had at least 10 years of data in at least 100 meters of water. Historical wave heights were obtained from wave information studies (WIS) stations (http://wis.usace.army.mil/). At least 10 years of data were averaged. The formula for the proxy-datum bias also needs MHW and Mean Higher High Water, which were taken from the OFR mentioned in a previous Process Step (USGS OFR 2005-1027).
   Ruggiero, P., and List, J.H., 2009. Improving Accuracy and Statistical Reliability of Shoreline Position and Change Rate Estimates: Journal of Coastal Research v.25, n.5, pp. 1069-1081.
   The output file from the shoreline code, the map-view checker and the bias code were merged and saved as an American Standard Code for Information Interchange (ASCII) text file to be loaded by DSAS. Person who carried out this activity:
   

   U.S. Geological Survey

   Attn: Amy Farris

   384 Woods Hole Road

   Woods Hole, MA
   

   508-548-8700 x2344 (voice)

   508-457-2310 (FAX)

   afarris@usgs.gov

   Data sources used in this process:

   • 2009-2011 lidar

   Data sources produced in this process:

   • ASCII text file

   Date: 2014 (process 5 of 15)

   The following information applies to the 2009/2010/2011 shoreline extracted from lidar data.
   The ASCII file was converted into a calibrated route shapefile for use in ArcGIS by using a Python script. The script generates a point shapefile, converts it to a polyline-M file, saves the uncertainty information in an accessory dBase (.dbf) file and finally generates a calibrated route for the newly-created polyline-M file. Calibration is based on the unique and sequential profile ID value provided with the point data and stored as the M-value. This value is also stored as an attribute in the uncertainty .dbf file and is used as the common field linking the two files. The lidar data were collected in projected coordinates (WGS 84 UTM zone 10N). During the rate calculation process DSAS uses linear referencing to retrieve the uncertainty values stored in the associated table.
   For a detailed explanation of the method used to convert the lidar shoreline to a route, please refer to the DSAS user guide:
   Himmelstoss, E.A., Henderson, R.E., Kratzmann, M.G., and Farris, A.S., 2018, Digital Shoreline Analysis System (DSAS) version 5.0 user guide: U.S. Geological Survey Open-File Report 2018–1179, 110 p., https://doi.org/10.3133/ofr20181179 Person who carried out this activity:
   

   U.S. Geological Survey

   Attn: Emily Himmelstoss

   384 Woods Hole Road

   Woods Hole, MA
   

   508-548-8700 x2262 (voice)

   508-457-2310 (FAX)

   ehimmelstoss@usgs.gov

   Data sources used in this process:

   • ASCII text file from 2009-2011 lidar

   Data sources produced in this process:

   • calibrated route shapefile
   • accessory dBase (.dbf) file

   Date: 2014 (process 6 of 15)

   The 2009/2010/2011 lidar shoreline shapefile was then visually checked against imagery contained in Esri's World_Imagery GIS server to make sure the lidar shoreline was not interpolated through a headland or structure, for example. Attribute fields were added (based on DSAS requirements) to the attribute table and those fields were populated with the relevant information.

   Date: 2017 (process 7 of 15)

   The 2016 USGS Lidar DEM: West Coast El Niño (WA, OR, CA) was downloaded through NOAA's Data Access Viewer. The DEM was then imported into Esri ArcMap v10.6. The operational MHW elevation is defined as the average of the mean high tides of local tidal gauges observed over the National Tidal Datum Epoch (Weber et al., 2005). The operational MHW elevation in each analysis region (Northern, Central, and Southern California) maintained consistency with the MHW elevations defined by the National Assessment of Shoreline Change (Hapke et al., 2006).
   The MHW values used for the three regions of California are: Northern California: Oregon/California border to Cape Mendocino 1.81 meters, Central California: Cape Mendocino to Point Buchon 1.46 meters, and Southern California: Point Buchon to U.S./Mexico Border 1.33 meters (Weber et al., 2005 and Hapke et al., 2006).
   The operational MHW line was extracted from the DEM using the smoothed contour method (Farris et al., 2018) using the contour tool: Esri ArcToolbox v10.6 > 3D Analyst > Raster Surface > Contour. Tool Settings: Contour Interval= 50, Base Contour= Operational MHW elevation, Z factor= default. Next, the contour line was smoothed using ArcToolbox v10.6 > Contour Cartography Tools > Generalization > Smooth Line. The smoothing algorithm was set to PAEK, the smoothing tolerance was set to 30 meters, and all other settings within the tool were left as default.
   A basemap of satellite imagery was added to the ArcMap v10.6 map document, by selecting the add data icon > basemap > imagery. 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). The attribute table of the shapefile was formatted according to the requirements of the DSAS version 5.0 user guide (Himmelstoss et al., 2018).
   Uncertainty: The uncertainty associated with each lidar-derived shoreline is comprised of the DEM vertical and horizontal uncertainty found in the metadata document of the DEM, accessible through the NOAA Data Access Viewer. It is assumed that uncertainty associated with the extraction of a MHW line from a DEM is negligible (Ruggiero et al., 2003). Using uncertainty values reported in NOAA metadata, the DEM vertical uncertainty was converted to horizontal uncertainty using the slope of the beach at the operational MHW line contour. To do this, slope maps were created from the DEMs within ArcMap v10.6 using ArcToolbox v10.6 > 3D Analyst > Raster Surface > Slope. The input raster was the 2016 USGS Lidar DEM: West Coast El Niño (WA, OR, CA).
   The MHW lines were converted to 50m-spaced points using XTools PRO > Feature Conversions > Convert Features to Points, in which the input feature is the final operational MHW contour line that was generated, and all of the tool parameters are left as default. The slope value of the DEM was extracted at each point, using ArcToolbox v10.6 > 3D Analyst > Functional Surface > Add Surface Information, in which the input feature class is the previously generated point file, the input surface is the previously generated slope map, ‘Z’ is selected, and all other tool parameters are left as default. The shoreline uncertainty was calculated for each region by using the point slope values to convert the vertical uncertainty of the DEM to a horizontal uncertainty. This was done using the formula 0.20/Tan([Z]*3.14159/180), in which Z is the slope at each point. The resulting values (the vertical uncertainty at each shoreline point, now in the format of horizontal uncertainty) were summed in quadrature with the original horizontal uncertainty to calculate a final uncertainty value associated with each shoreline. These uncertainty values were stored in the attribute tables of the shapefiles in meters.
   Citations: Barnard, P.L., Smith, S.A., and Foxgrover, A.C., 2020, California shorelines and shoreline change data, 1998-2016: U.S. Geological Survey data release, https://doi.org/10.5066/P91QSGXF
   Farris, A.S., Weber, K.M., Doran, K.S., and List, J.H., 2018. Comparing methods used by the U.S. Geological Survey Coastal and Marine Geology Program for deriving shoreline position from lidar data: U.S. Geological Survey Open-File Report 2018–1121, 13 p., https://doi.org/10.3133/ofr20181121
   Hapke, C.J., Reid, D., Richmond, B.M., Ruggiero, P., and List, J., 2006. National assessment of shoreline change: Part 3: Historical shoreline changes and associated coastal land loss along the sandy shorelines of the California coast: U.S. Geological Survey Open-file Report 2006-1219, 72 p., https://pubs.usgs.gov/of/2006/1219/
   Himmelstoss, E.A., Henderson, R.E., Kratzmann, M.G., and Farris, A.S., 2018. Digital Shoreline Analysis System (DSAS) version 5.0 user guide: U.S. Geological Survey Open-File Report 2018–1179, 110 p., https://doi.org/10.3133/ofr20181179
   Ruggiero, P., Kaminsky, G., and Gelfenbaum, G., 2003. Linking Proxy-Based and Datum-Based Shorelines on a High-Energy Coastline: Implications for Shoreline Change Analyses. Journal of Coastal Research 38: 57-82.
   Weber, K. M., List, J. H., and Morgan, K. L. M., 2005. An Operational Mean High Water Datum for Determination of Shoreline Position from Topographic Lidar Data. U.S. Geological Survey Open-File Report 2005-1027, https://pubs.usgs.gov/of/2005/1027/index.html Person who carried out this activity:
   

   U.S. Geological Survey

   Attn: Schuyler Smith

   2885 Mission St

   Santa Cruz, CA
   

   831-460-7588 (voice)

   n/a (FAX)

   schuylersmith@contractor.usgs.gov

   Data sources used in this process:

   • 2016 lidar

   Data sources produced in this process:

   • Shoreline derived from 2016 lidar

   Date: 2018 (process 8 of 15)

   The 2015 USACE National Coastal Mapping Program (NCMP) Topobathy Lidar Digital Elevation Model (DEM): California was downloaded through NOAA's Data Access Viewer. The DEM was then imported into ArcGIS ArcMap v10.6. The operational MHW elevation was defined as the average of the mean high tides of local tidal gauges observed over the National Tidal Datum Epoch (Weber et al., 2005). The operational MHW elevation in each analysis region (Northern, Central, and Southern California) maintained consistency with the MHW elevations defined by the National Assessment of Shoreline Change (Hapke et al., 2006).
   The MHW values used for the three regions of California are: Northern California: Oregon/California border to Cape Mendocino 1.81 meters, Central California: Cape Mendocino to Point Buchon 1.46 meters, and Southern California: Point Buchon to U.S./Mexico Border 1.33 meters (Weber et al., 2005 and Hapke et al., 2006).
   The operational MHW line was extracted from the DEM using the smoothed contour method (Farris et al. 2018) using the contour tool: Esri ArcToolbox v10.6 > 3D Analyst > Raster Surface > Contour. Tool Settings: Contour Interval= 50, Base Contour= Operational MHW elevation, Z factor= default. Next, the contour line was smoothed using: ArcToolbox v10.6 > Contour Cartography Tools > Generalization > Smooth Line. The smoothing algorithm was set to “PAEK”, the smoothing tolerance was set to 30 meters, and all other settings within the tool were left as default.
   A basemap of satellite imagery was added to the ArcMap v10.6 map document, by selecting the add data icon > basemap > imagery. 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). The attribute table of the shapefile was formatted according to the requirements of the Digital Shoreline Analysis System (DSAS) version 5.0 user guide (Himmelstoss et al., 2018).
   Uncertainty: The uncertainty associated with each lidar-derived shoreline is comprised of the DEM vertical and horizontal uncertainty found on the metadata document of the DEM, accessible through the NOAA Data Access Viewer. It is assumed that uncertainty associated with the extraction of a MHW line from a DEM is negligible (Ruggiero et al., 2003). Using uncertainty values reported in NOAA metadata, the DEM vertical uncertainty was converted to horizontal uncertainty using the slope of the beach at the operational MHW line contour. To do this, slope maps were created from the DEMs within ArcMap v10.6 using ArcToolbox v10.6 > 3D Analyst > Raster Surface > Slope. The input raster was the 2015 USACE NCMP Topobathy Lidar Digital Elevation Model.
   The MHW lines were converted to 50 meter-spaced points using XTools PRO > Feature Conversions > Convert Features to Points, in which the input feature is the final operational MHW contour line that was generated, and all of the tool parameters are left as default. The slope value of the DEM was extracted at each point, using ArcToolbox v10.6 > 3D Analyst > Functional Surface > Add Surface Information, in which the input feature class is the previously generated point file, the input surface is the previously generated slope map, ‘Z’ is selected, and all other tool parameters are left as default. The shoreline uncertainty was calculated for each region by using the point slope values to convert the vertical uncertainty of the DEM to a horizontal uncertainty. This was done using the formula 0.20/Tan([Z]*3.14159/180), in which Z is the slope at each point. The resulting values (the vertical uncertainty at each shoreline point, now in the format of horizontal uncertainty) were summed in quadrature with the original horizontal uncertainty to calculate a final uncertainty value associated with each shoreline. These uncertainty values were stored in the attribute tables of the shapefiles in meters.
   Citations: Barnard, P.L., Smith, S.A., and Foxgrover, A.C., 2020, California shorelines and shoreline change data, 1998-2016: U.S. Geological Survey data release, https://doi.org/10.5066/P91QSGXF
   Farris, A.S., Weber, K.M., Doran, K.S., and List, J.H., 2018. Comparing methods used by the U.S. Geological Survey Coastal and Marine Geology Program for deriving shoreline position from lidar data: U.S. Geological Survey Open-File Report 2018–1121, 13 p., https://doi.org/10.3133/ofr20181121
   Hapke, C.J., Reid, D., Richmond, B.M., Ruggiero, P., and List, J., 2006. National assessment of shoreline change: Part 3: Historical shoreline changes and associated coastal land loss along the sandy shorelines of the California coast: U.S. Geological Survey Open-file Report 2006-1219, 72 p., https://pubs.usgs.gov/of/2006/1219/
   Himmelstoss, E.A., Henderson, R.E., Kratzmann, M.G., and Farris, A.S., 2018. Digital Shoreline Analysis System (DSAS) version 5.0 user guide: U.S. Geological Survey Open-File Report 2018–1179, 110 p., https://doi.org/10.3133/ofr20181179
   Ruggiero, P., Kaminsky, G., and Gelfenbaum, G., 2003. Linking Proxy-Based and Datum-Based Shorelines on a High-Energy Coastline: Implications for Shoreline Change Analyses. Journal of Coastal Research 38: 57-82.
   Weber, K. M., List, J. H., and Morgan, K. L. M., 2005. An Operational Mean High Water Datum for Determination of Shoreline Position from Topographic Lidar Data. U.S. Geological Survey Open-File Report 2005-1027, https://pubs.usgs.gov/of/2005/1027/index.html Person who carried out this activity:
   

   U.S. Geological Survey

   Attn: Schuyler Smith

   2885 Mission St

   Santa Cruz, CA
   

   831-460-7588 (voice)

   n/a (FAX)

   schuylersmith@contractor.usgs.gov

   Data sources used in this process:

   • 2015 lidar

   Data sources produced in this process:

   • Shoreline derived from 2015 lidar

   Date: 2019 (process 9 of 15)

   The Southern California Coastal Response to the 2015-16 El Niño: 2015-10-06 Survey Digital Elevation Model (DEM) was downloaded through the U.C. San Diego Research Data Curation Program. The DEM was then imported into ArcGIS ArcMap v10.6. The operational MHW elevation is defined as the average of the mean high tides of local tidal gauges observed over the National Tidal Datum Epoch (Weber et al., 2005). The operational MHW elevation in each analysis region (Northern, Central, and Southern California) maintained consistency with the MHW elevations defined by the National Assessment of Shoreline Change (Hapke et al., 2006).
   The MHW values used for the three regions of California are: Northern California: Oregon/California border to Cape Mendocino 1.81 meters, Central California: Cape Mendocino to Point Buchon 1.46 meters, and Southern California: Point Buchon to U.S./Mexico Border 1.33 meters (Weber et al., 2005 and Hapke et al., 2006).
   The operational MHW line was extracted from the DEM using the smoothed contour method (Farris et al., 2018) using the contour tool: Esri ArcToolbox v10.6 > 3D Analyst > Raster Surface > Contour. Tool Settings: Contour Interval= 50, Base Contour= Operational MHW elevation, Z factor= default. Next, the contour line was smoothed using: ArcToolbox v10.6 > Contour Cartography Tools > Generalization > Smooth Line. The smoothing algorithm was set to “PAEK”, the smoothing tolerance was set to 30 meters, and all other settings within the tool were left as default.
   A basemap of satellite imagery was added to the ArcMap v10.6 map document, by selecting the add data icon > basemap > imagery. 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). The attribute table of the shapefile was formatted according to the requirements of the Digital Shoreline Analysis System (DSAS) version 5.0 user guide (Himmelstoss et al., 2018).
   Uncertainty: The uncertainty associated with each lidar-derived shoreline is comprised of the DEM vertical and horizontal uncertainty found on the metadata document of the DEM, accessible through the NOAA Data Access Viewer. It is assumed that uncertainty associated with the extraction of a MHW line from a DEM is negligible (Ruggiero et al., 2003). Using uncertainty values reported in NOAA metadata, the DEM vertical uncertainty was converted to horizontal uncertainty using the slope of the beach at the operational MHW line contour. To do this, slope maps were created from the DEMs within ArcMap v10.6 using ArcToolbox v10.6 > 3D Analyst > Raster Surface > Slope. The input raster was the Southern California Coastal Response to the 2015-16 El Niño: 2015-10-06 Survey Digital Elevation Model.
   The MHW lines were converted to 50 meter-spaced points using XTools PRO > Feature Conversions > Convert Features to Points, in which the input feature is the final operational MHW contour line that was generated, and all of the tool parameters are left as default. The slope value of the DEM was extracted at each point, using ArcToolbox v. 10.6 > 3D Analyst > Functional Surface > Add Surface Information, in which the input feature class is the previously generated point file, the input surface is the previously generated slope map, ‘Z’ is selected, and all other tool parameters are left as default. The shoreline uncertainty was calculated for each region by using the point slope values to convert the vertical uncertainty of the DEM to a horizontal uncertainty. This was done using the formula 0.20/Tan([Z]*3.14159/180), in which Z is the slope at each point. The resulting values (the vertical uncertainty at each shoreline point, now in the format of horizontal uncertainty) were summed in quadrature with the original horizontal uncertainty to calculate a final uncertainty value associated with each shoreline. These uncertainty values were stored in the attribute tables of the shapefiles in meters.
   Citations: Barnard, P.L., Smith, S.A., and Foxgrover, A.C., 2020, California shorelines and shoreline change data, 1998-2016: U.S. Geological Survey data release, https://doi.org/10.5066/P91QSGXF
   Farris, A.S., Weber, K.M., Doran, K.S., and List, J.H., 2018. Comparing methods used by the U.S. Geological Survey Coastal and Marine Geology Program for deriving shoreline position from lidar data: U.S. Geological Survey Open-File Report 2018–1121, 13 p., https://doi.org/10.3133/ofr20181121
   Hapke, C.J., Reid, D., Richmond, B.M., Ruggiero, P., and List, J., 2006. National assessment of shoreline change: Part 3: Historical shoreline changes and associated coastal land loss along the sandy shorelines of the California coast: U.S. Geological Survey Open-file Report 2006-1219, 72 p., https://pubs.usgs.gov/of/2006/1219/
   Himmelstoss, E.A., Henderson, R.E., Kratzmann, M.G., and Farris, A.S., 2018. Digital Shoreline Analysis System (DSAS) version 5.0 user guide: U.S. Geological Survey Open-File Report 2018–1179, 110 p., https://doi.org/10.3133/ofr20181179
   Ruggiero, P., Kaminsky, G., and Gelfenbaum, G., 2003. Linking Proxy-Based and Datum-Based Shorelines on a High-Energy Coastline: Implications for Shoreline Change Analyses. Journal of Coastal Research 38: 57-82.
   Weber, K. M., List, J. H., and Morgan, K. L. M., 2005. An Operational Mean High Water Datum for Determination of Shoreline Position from Topographic Lidar Data. U.S. Geological Survey Open-File Report 2005-1027, https://pubs.usgs.gov/of/2005/1027/index.html Person who carried out this activity:
   

   U.S. Geological Survey

   Attn: Schuyler Smith

   2885 Mission St

   Santa Cruz, CA
   

   831-460-7588 (voice)

   n/a (FAX)

   schuylersmith@contractor.usgs.gov

   Data sources used in this process:

   • 2015 lidar UCSD

   Data sources produced in this process:

   • Shoreline derived from 2015 lidar UCSD

   Date: 2019 (process 10 of 15)

   The 2015 and 2016 lidar shorelines contained a very large number of vertices that caused DSAS v5.0 to run out of memory before completing casting transects. An experiment was conducted to test whether using a tool to reduce the number of vertices altered the position of the line beyond the lidar uncertainty value. The tool chosen was Simplify Line: Cartography Tools > Generalization > Simplify Line. Simplification algorithm= point remove, simplification tolerance= 0.25. DSAS (Net Shoreline Movement, NSM) was run on a sample area of the original 2016 shoreline and the reduced vertex 2016 shoreline. The difference in values between the original shoreline and the simplified shoreline clearly did not exceed the uncertainty value and therefore was deemed a reasonable solution to the problem. DSAS was able to cast transects successfully.
   Douglas, D. and Peucker, T., 1973. Algorithms for the reduction of the number of points required to represent a digitized line or its caricature, The Canadian Cartographer 10(2), 112–122.

   Date: 2019 (process 11 of 15)

   Historical shorelines were merged in Esri's ArcToolbox v10.6, Data Management Tools > General > Merge. The merged file was projected in ArcToolbox v10.6 > Data Management Tools > Projections and Transformations > Project. Parameters: input coordinate system = geographic (NAD 83); output coordinate system = UTM zone 10N (NorCal and CenCal) or UTM zone 11N (SoCal) (WGS 84); geographic transformation = NAD_1983_To_WGS_1984_1.

   Date: 2019 (process 12 of 15)

   Historical shorelines were merged with the lidar shorelines in ArcToolbox v10.6 > Data Management Tools > General > Merge to produce a single shoreline file for each region. The final shoreline dataset was coded with attribute fields (DATE_, Uncertainty (Uncy), Source, Source_b, Year_, Default_D, Location, DSAS_type). These fields are required by DSAS, which was used to calculate shoreline change rates.

   Date: 2020 (process 13 of 15)

   The shorelines file and the uncertainty table (.dbf) were imported into a personal geodatabase in ArcCatalog v10.7 by right-clicking on the geodatabase > Import (feature class for shoreline file and table for uncertainty table) for use with the DSAS v5.0 software to perform rate calculations.

   Date: 2020 (process 14 of 15)

   The shoreline feature class was exported from the personal geodatabase back to a shapefile in ArcCatalog v10.7 by right-clicking on the shoreline file > Export > To Shapefile (single) for publication purposes.

   Date: 2020 (process 15 of 15)

   The data were projected in ArcToolbox v10.7 > Data Management Tools > Projections and Transformations > Project. Parameters: input projection = UTM zone 10N WGS84 (NorCal and CenCal) or UTM zone 11N WGS84 (SoCal); output projection = geographic coordinates (WGS84); transformation = none.

3. What similar or related data should the user be aware of?
   Kratzmann, Meredith G., 2024, National Shoreline Change—Summary Statistics of Shoreline Change From the 1800s To the 2010s for the Coast of California: data report 1187, U.S. Geological Survey, Reston, VA.

   Online Links:

   • https://doi.org/10.3133/dr1187

   Other_Citation_Details: Data report associated with data release DOI:10.5066/P94J0K7Z.
   

   Himmelstoss, Emily A., Farris, Amy S., Henderson, Rachel E., Kratzmann, Meredith G., Ergul, Ayhan, Zhang, Ouya, and Zichichi, Jessica L., 2018, Digital Shoreline Analysis System (version 5.0): U.S. Geological Survey Software: software release version 5.0, U.S. Geological Survey, Reston, VA.

   Online Links:

   • https://code.usgs.gov/cch/dsas/
   • https://doi.org/10.5066/P9VW42I0
   • https://www.usgs.gov/centers/whcmsc/science/digital-shoreline-analysis-system-dsas

   Himmelstoss, Emily A., Henderson, Rachel E., Kratzmann, Meredith G., and Farris, Amy S., 2018, Digital Shoreline Analysis System (DSAS) Version 5.0 User Guide: Open-File Report 20181179, U.S. Geological Survey, Reston, VA.

   Online Links:

   • https://doi.org/10.3133/ofr20181179
   • https://www.usgs.gov/centers/whcmsc/science/digital-shoreline-analysis-system-dsas

   Hapke, Cheryl J., Reid, David, Richmond, Bruce M., Ruggiero, Peter, and List, Jeff, 2006, National Assessment of Shoreline Change Part 3: Historical Shoreline Change and Associated Coastal Land Loss Along Sandy Shorelines of the California Coast: Open-File Report 2006-1219, U.S. Geological Survey, Reston, VA.

   Online Links:

   • https://doi.org/10.3133/ofr20061219

   Hapke, Cheryl J., and Reid, David, 2006, National Assessment of Shoreline Change: A GIS Compilation of Vector Shorelines and Associated Shoreline Change Data for the Sandy Shorelines of the California Coast: Open-File Report 2006-1251, U.S. Geological Survey, Reston, VA.

   Online Links:

   • https://doi.org/10.3133/ofr20061251

   Ruggiero, Peter, and List, Jeffrey H., 200909, Improving Accuracy and Statistical Reliability of Shoreline Position and Change Rate Estimates: Journal of Coastal Research vol. 255, Coastal Education and Research Foundation, n/a.

   Online Links:

   • https://doi.org/10.2112/08-1051.1

   Other_Citation_Details: pp. 1069-1081
   

   National Oceanic and Atmospheric Administration (NOAA), National Ocean Service (NOS), Unknown, Scanned National Ocean Service (NOS) Coastal Survey Maps (also known as Topographic Survey sheets, or T-sheets): NOAA shoreline manuscripts (T-sheets) n/a, National Oceanic and Atmospheric Administration, Washington, D.C..

   Online Links:

   • https://shoreline.noaa.gov/data/datasheets/t-sheets.html

   National Oceanic and Atmospheric Administration (NOAA) Office for Coastal Management, Unknown, 1998 Spring West Coast (post El Niño) NOAA/USGS/NASA Airborne LiDAR Assessment of Coastal Erosion (ALACE) Project for the US Coastline: NOAA Office for Coastal Management, Charleston, SC.

   Online Links:

   • https://coast.noaa.gov/
   • https://coast.noaa.gov/digitalcoast/data/
   • https://www.fisheries.noaa.gov/inport/item/48155

   Department of Commerce (DOC), National Oceanic and Atmospheric Administration (NOAA), National Ocean Service (NOS), Coastal Services Center (CSC), Unknown, 2002 NASA/USGS Airborne LiDAR Assessment of Coastal Erosion (ALACE) Project for California, Oregon, and Washington Coastlines: NOAA's Ocean Service, Coastal Services Center (CSC), Charleston, SC.

   Online Links:

   • https://coast.noaa.gov/
   • https://coast.noaa.gov/digitalcoast/data/
   • https://www.fisheries.noaa.gov/inport/item/49634

   Weber, Kathryn M., List, Jeffrey H., and Morgan, Karen L.M., 2005, An operational mean high water datum for determination of shoreline position from topographic lidar data: Open-File Report 2005-1027, U.S. Geological Survey, Reston, VA.

   Online Links:

   • https://doi.org/10.3133/ofr20051027

   Barnard, Patrick L., Smith, Schuyler A., and Foxgrover, Amy C., 2020, Mean high water (MHW) shorelines along the coast of California used to calculated shoreline change from 1998 to 2016: data release DOI:10.5066/P91QSGXF, U.S. Geological Survey, Reston, VA.

   Online Links:

   • https://doi.org/10.5066/P91QSGXF

   Young, Adam P., Flick, Reinhard E., Gallien, Timu W., Giddings, Sarah N., Guza, R. T., Harvey, M., Lenain, Luc, Ludka, B.C., Melville, W. Kendall, and O'Reilly, W.C., 20181105, Southern California Coastal Response to the 2015–2016 El Niño: Journal of Geophysical Research Earth Surface 123, Journal of Geophysical Research, n/a.

   Online Links:

   • https://doi.org/10.1029/2018JF004771

   Other_Citation_Details: pp. 3069–3083
   

How reliable are the data; what problems remain in the data set?

1. How well have the observations been checked?
   The data provided here are a compilation of shorelines from multiple sources, spanning >150 years. The attributes are based on the requirements of the Digital Shoreline Analysis System (DSAS) software and have gone through a series of quality assurance procedures.
2. How accurate are the geographic locations?
   The horizontal accuracy of the shoreline data varies with respect to the data source from which the shorelines were digitized, the lidar data from which the shorelines were extracted, and the time period. Shorelines prior to 1960 (T-sheets) have an estimated positional uncertainty of plus or minus 10.8 meters. DRG shorelines (1940s-1970s) have an estimated positional uncertainty of plus or minus 15 meters. Shorelines from the 1960s-1980s (T-sheets) have an estimated positional uncertainty of plus or minus 5.1 meters. The lidar shoreline from 1998/2002 has an estimated positional uncertainty of plus or minus 1.5 meters (Hapke et al., 2006).
   The lidar shoreline from 2009/2010/2011 has an estimated positional uncertainty of plus or minus 1.9 meters (NorCal), plus or minus 2.2 meters (CenCal), plus or minus 2.3 meters (SoCal). The lidar shoreline from 2015 (NOAA/USACE/JALBTCX) has an estimated positional uncertainty of plus or minus 4.06 meters (NorCal), plus or minus 4.06-4.35 meters (CenCal), plus or minus 4.13 meters (SoCal). The lidar shoreline from 2015 (UC San Diego) has an estimated positional uncertainty of plus or minus 0.45 meters (SoCal). The lidar shoreline from 2016 has an estimated positional uncertainty of plus or minus 0.74 meters (NorCal), plus or minus 0.72-0.74 meters (CenCal), plus or minus 0.70 meters (SoCal). More information regarding the uncertainty of these lidar shorelines is contained in the process steps of this metadata file.
   Crowell, M., Leatherman, S.P., and Buckley, M.K., 1991. Historical Shoreline Change: Error Analysis and Mapping Accuracy. Journal of Coastal Research: v.7, n.3, pp.839-852.
3. How accurate are the heights or depths?
   
4. Where are the gaps in the data? What is missing?
   This shoreline file is complete and contains all shoreline segments used to calculate shoreline change rates along sections of the California coastal region where shoreline position data were available. These data adequately represented the shoreline position at the time of the survey. Gaps in these data, if applicable, are a consequence of non-existing data or existing data that did not meet quality assurance standards.
   The digitized shoreline vectors downloaded from NOAA included attributes defining the shoreline type (attribute field name varies by file). For the open-ocean coasts, only shoreline features (Natural.Mean High Water; SPOR; 20) were retained. Other shoreline features (such as seawalls, bulkheads, manmade objects) were deleted.
5. How consistent are the relationships among the observations, including topology?
   Adjacent shoreline segments do not overlap and are not necessarily continuous. Shorelines were quality checked for accuracy. Any slight offsets between adjacent segments due to georeferencing and digitizing error are taken into account in the uncertainty of the shoreline position, as reported in the horizontal accuracy section of this metadata file.

How can someone get a copy of the data set?

1. Who distributes the data set? (Distributor 1 of 1)
   
   U.S. Geological Survey - ScienceBase

   Federal Center, Building 810, MS 302

   Denver, CO
   

   USA

   1-888-275-8747 (voice)

   sciencebase@usgs.gov
2. What's the catalog number I need to order this data set? The dataset contains polyline shorelines (SHP and other shapefile components), browse graphic, and the FGDC CSDGM metadata.
3. What legal disclaimers am I supposed to read?
   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 for other purposes, nor on all computer systems, nor shall the act of distribution constitute any such warranty. Although these data have been processed successfully on a computer system at the U.S. Geological Survey (USGS), no warranty expressed or implied is made regarding the display or utility of the data for other purposes, nor on all computer systems, nor shall the act of distribution constitute any such warranty. The USGS or the U.S. Government shall not be held liable for improper or incorrect use of the data described and/or contained herein.
4. How can I download or order the data?
   • Availability in digital form:

     Data format:	These files (.cpg, .dbf, .prj, .sbn, .sbx, .shp, .html, and .shx) are a collection of files with a common filename prefix and must be downloaded and stored in the same directory. Together they are the components of the shapefile and include FGDC CSDGM-compliant metadata in HTML format. in format Shapefile (version ArcGIS 10.7) Esri polyline shapefile Size: 1.54
     Network links:	https://doi.org/10.5066/P94J0K7Z https://www.sciencebase.gov/catalog/file/get/64adda5fd34e70357a293280 https://www.sciencebase.gov/catalog/item/64adda5fd34e70357a293280

   • Cost to order the data: None

5. What hardware or software do I need in order to use the data set?
   These data are available in a polyline shapefile format. The user must have software to read and process the data components of a shapefile.

Who wrote the metadata?

Dates:

Last modified: 12-Jan-2024

Metadata author:

Meredith G. Kratzmann

U.S. Geological Survey

384 Woods Hole Road

Woods Hole, MA

USA

508-548-8700 (voice)

508-457-2310 (FAX)

whsc_data_contact@usgs.gov

Contact_Instructions:

The metadata contact email address is a generic address in the event the person is no longer with USGS.

Metadata standard:

FGDC Content Standards for Digital Geospatial Metadata (FGDC-STD-001-1998)