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

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.

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).

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.

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.

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

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.

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

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

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

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.

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.

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.

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.

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.

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.