U.S. Geological Survey 2017 1 vector digital data data release DOI:10.5066/F74X55X7 Woods Hole Coastal and Marine Science Center, Woods Hole, MA U.S. Geological Survey, Coastal and Marine Geology Program https://doi.org/10.5066/F74X55X7 https://www.sciencebase.gov/catalog/item/58b89084e4b01ccd5500c2ac https://www.sciencebase.gov/catalog/file/get/58b89084e4b01ccd5500c2ac M.G. Kratzmann E.A. Himmelstoss E.R. Thieler 2017 1 data release DOI:10.5066/F74X55X7 Reston, VA U.S. Geological Survey https://doi.org/10.5066/F74X55X7 https://www.sciencebase.gov/catalog/item/58055f50e4b0824b2d1c1ee7 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 dataset includes shorelines from 148 years ranging from 1852 to 2000 in South Carolina's coastal region. Shorelines were compiled from topographic survey sheets (T-sheets; National Oceanic and Atmospheric Administration (NOAA)), Coastal Engineering Research Center (CERC) maps (NOAA/National Ocean Service (NOS)), and lidar (U.S. Geological Survey (USGS)/NOAA/National Aeronautics and Space Administration (NASA)). 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. Cross-referenced citations are applicable to the dataset as a whole. Additional citations are located within individual process steps that pertain specifically to the method described in that step. 1852 2000 ground condition Complete None planned -80.877131 -78.544042 33.880254 32.055557 USGS Metadata Identifier USGS:58b89084e4b01ccd5500c2ac None Shoreline Shoreline Change Digital Shoreline Analysis System DSAS U.S. Geological Survey USGS Coastal and Marine Geology Program CMGP Woods Hole Coastal and Marine Science Center WHCMSC National Assessment of Shoreline Change Project MHW Mean High Water HWL High Water Line U.S. Army Corps of Engineers USACE National Oceanic and Atmospheric Administration NOAA National Ocean Service NOS Coastal Engineering Research Center CERC CERC map ISO 19115 Topic Category oceans environment geoscientificInformation Marine Realms Information Bank (MRIB) Keywords effects of coastal change coastal processes shoreline accretion shoreline erosion USGS Thesaurus coastal processes erosion shoreline accretion None South Carolina SC Little River Inlet Myrtle Beach St Helena Sound Magnolia Beach Cape Romain Santee Point Bull Island Capers Island James Island Kiawah Island Edisto Island Hilton Head Atlantic Coast United States North America None Public domain data from the U.S. Government are freely redistributable with proper metadata and source attribution. Please recognize the U.S. Geological Survey as the originator of the dataset. U.S. Geological Survey E.A. Himmelstoss mailing and physical address

384 Woods Hole Road

Woods Hole MA 02543-1598 USA 508-548-8700 508-547-2310 ehimmelstoss@usgs.gov Microsoft Windows 7 Version 6.1 (Build 7601) Service Pack 1; Esri ArcGIS 10.2.2.3552 Robert A. Morton Tara L. Miller 2005 Open-File Report 2005-1401 Reston, VA U.S. Geological Survey https://pubs.usgs.gov/of/2005/1401/ Tara L. Miller Robert A. Morton Asbury H. Sallenger 2005 Open-File Report 2005-1326 Reston, VA U.S. Geological Survey https://pubs.usgs.gov/of/2005/1326/index.html E.R. Thieler E.A. Himmelstoss J.L. Zichichi A. Ergul 2009 Open-File Report 2008-1278 Reston, VA U.S. Geological Survey Current version of software at time of use was 4.3 https://woodshole.er.usgs.gov/project-pages/DSAS/version4/ https://woodshole.er.usgs.gov/project-pages/DSAS/ Department of Commerce (DOC), National Oceanic and Atmospheric Administration (NOAA), National Ocean Service (NOS), Office for Coastal Management United States Geological Survey (USGS) National Aeronautics and Space Administration (NASA) 20140121 Charleston, SC NOAA's Ocean Service, Office for Coastal Management Lidar data were obtained prior to the publication date listed in this citation. https://coast.noaa.gov/digitalcoast/data/coastallidar/ https://www.coast.noaa.gov National Oceanic and Atmospheric Administration (NOAA), National Ocean Service (NOS) Unknown Washington, D.C. National Oceanic and Atmospheric Administration NOAA shoreline manuscripts (T-sheets) https://nosimagery.noaa.gov/images/shoreline_surveys/noaa_shoreline_surveys.kmz https://oceanservice.noaa.gov/news/features/feb13/historical-shoreline.html https://nosimagery.noaa.gov/images/shoreline_surveys/survey_scans/NOAA_Shoreline_Survey_Scans.html National Oceanic and Atmospheric Administration (NOAA), National Ocean Service (NOS) / U.S. Army Corps of Engineers (USACE), Coastal Engineering Research Center (CERC) 1988 Washington, D.C. National Ocean Service Report 3 of a series containing 32 maps at 1:24,000 scale E.A. Himmelstoss M.G. Kratzmann E.R. Thieler 2017 Open-File Report 2017-1015 Reston, VA U.S. Geological Survey https://doi.org/10.3133/ofr20171015 The data provided here are a compilation of shorelines from multiple sources, spanning 148 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. 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. In some cases, a CERC shoreline was used in place of the NOAA T-sheet/shoreline if NOAA data were not available. This shoreline file is complete and contains all shoreline segments used to calculate shoreline change rates along sections of the South Carolina coastal region where shoreline position data were available. These data adequately represented the shoreline position at the time of the survey. Remaining 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 facing shorelines, only Mean High Water shoreline features (Natural.Mean High Water; SPOR; 20) were retained. Other shoreline features (such as seawalls, bulkheads, manmade objects) were deleted. NOAA has made non-georeferenced NOAA Shoreline Survey Scans available for download, and additional data for the region may be available (https://nosimagery.noaa.gov/images/shoreline_surveys/survey_scans/NOAA_Shoreline_Survey_Scans.html). Shoreline data have been acquired from 1852 to 2000, the horizontal accuracy of which varies with respect to 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, CERC maps) have an estimated positional uncertainty of plus or minus 10.8 meters. Shorelines from the 1960s-1980s (T-sheets, CERC maps) have an estimated positional uncertainty of plus or minus 5.1 meters. The lidar shoreline from 2000 has an estimated positional uncertainty of plus or minus 3.2 meters. 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. The historic shorelines are proxy-based and estimate the HWL. The lidar shoreline is tidal-based and estimates MHW. While the proxy-based HWL shorelines are not shifted to the MHW datum, the distance measurements established by the transects and used by DSAS to compute rate-of-change statistics are adjusted to account for the offset between HWL and MHW. Therefore, all distance measurements along DSAS transects are referenced to the MHW datum before rates are calculated. An operational Mean High Water (MHW) shoreline was extracted from the lidar surveys within MATLAB v7.6 using a method similar to the one developed by Stockdon et al. (2002). Shorelines were extracted from cross-shore profiles which consist of bands of lidar data 2 m wide in the alongshore direction and spaced every 20 m along the coast. For each profile, the seaward sloping foreshore points were identified and a linear regression was fit through them. The regression was evaluated at the operational MHW elevation to yield the cross-shore position of the MHW shoreline. 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 operational MHW elevation. A lidar positional uncertainty associated with this point was also computed. The horizontal offset between the datum-based lidar MHW shoreline and the proxy-based historical shorelines nearly always acts in one direction and the "bias" value was computed at each profile (Ruggiero and List, 2009). In addition an uncertainty associated with the bias was also computed, which can also be thought of as the uncertainty of the HWL shorelines due to water level fluctuations. Repeating this procedure at successive profiles generated a series of X,Y points that contain a lidar positional uncertainty, a bias, and a bias uncertainty value. 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. 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. 20081006 Amy Farris U.S. Geological Survey mailing and physical address

384 Woods Hole Road

Woods Hole MA 02543-1598 USA 508-548-8700 508-457-2310 afarris@usgs.gov The lidar data were collected in projected coordinates (WGS 84 UTM zone 17N). The series of operational MHW points extracted from the cross-shore lidar profiles were converted into a calibrated route shapefile for use in ArcGIS 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 attribute field linking the shoreline route file (shoreline.shp) to the uncertainty table (shorelines_uncertainty.dbf) storing the lidar positional uncertainty, the bias correction value, and the uncertainty of the bias correction for each point of the original lidar data. During the rate calculation process DSAS uses linear referencing to retrieve the uncertainty and bias values stored in the associated table. Visually identified HWL-type proxy shorelines are virtually never coincident with datum-based MHW-type shorelines. In fact, HWL shorelines are almost universally estimated to be higher (landward) on the beach profile than MHW shorelines. Not accounting for this offset will cause shoreline change rates to be biased toward slower shoreline retreat, progradation rather than retreat, or faster progradation than in reality (for the typical case where datum-based shorelines are more recent data than the proxy-based shoreline dates), depending on actual changes at a given site. Ruggiero, Peter, and List, J.H., 2009, Improving accuracy and statistical reliability of shoreline position and change rate estimates: Journal of Coastal Research, v. 25, no. 5, p. 1069–1081. Ruggiero, Peter, Kaminsky, G.M., and Gelfenbaum, Guy, 2003, Linking proxy-based and datum-based shorelines on high-energy coastlines—Implications for shoreline change analyses: Journal of Coastal Research, special issue 38, p. 57–82. For a detailed explanation of the method used to convert the lidar shoreline to a route, please refer to "Appendix 2- A case study of complex shoreline data" in the DSAS user guide: Himmelstoss, E.A. 2009. "DSAS 4.0 Installation Instructions and User Guide" in: Thieler, E.R., Himmelstoss, E.A., Zichichi, J.L., and Ergul, Ayhan. 2009. Digital Shoreline Analysis System (DSAS) version 4.0 - An ArcGIS extension for calculating shoreline change: U.S. Geological Survey Open-File Report 2008-1278. https://woodshole.er.usgs.gov/project-pages/DSAS/version4/images/pdf/DSASv4_3.pdf 20100326 E.A. Himmelstoss U.S. Geological Survey mailing address

384 Woods Hole Road

Woods Hole MA 02543 USA 508-548-8700 x2262 508-457-2310 ehimmelstoss@usgs.gov Data from the previously-published National Assessment of Shoreline Change study for the Southeast Atlantic (USGS Open-File Report 2005-1401 and USGS Open-File Report 2005-1326) were used as the starting point for the project's continued efforts to compile as many quality shorelines as possible for the region. Digital shorelines, if available from another agency, were acquired. This process step and all subsequent process steps were performed by the same person - M.G. Kratzmann. 2012 M.G. Kratzmann U.S. Geological Survey mailing address

384 Woods Hole Road

Woods Hole MA 02543 USA 508-548-8700 508-457-2310 mkratzmann@usgs.gov Digitized shorelines were downloaded in vector format from the NOAA Historical Shoreline Survey Viewer, currently available as a Google Earth KMZ (https://nosimagery.noaa.gov/images/shoreline_surveys/noaa_shoreline_surveys.kmz). The extracted shorelines are digitized vectors of the mean high water (MHW) position derived from the scanned and georeferenced T-sheet raster imagery that NOAA also manages. Although NOAA labels historical shorelines as MHW, they are proxy-based and therefore considered HWL for the purposes of shoreline change rate calculation with the proxy-datum bias correction. The modern lidar shoreline used in the analysis is datum-based, not proxy-based, and designated as operational MHW which reflects this difference. NOAA topographic survey sheets (T-sheets) and shorelines were quality checked and edited where necessary. Edgematching was corrected when discovered. If no digital shoreline vectors were available, the original T-sheet scan and world file were downloaded from the NOAA Google Earth viewer. Vector shorelines were digitized from the georeferenced T-sheets using standard editing tools in ArcMap v10. Quality assessments were performed and shorelines were edited to remove any overlap between adjacent t-sheets from the same time period. No edge-matching between neighboring shorelines was attempted. The DSAS-required attribute fields were added to the shapefiles and populated. 2013 Historical shorelines were merged in Esri's ArcToolbox (v10), Data Management Tools > General > Merge. Then the merged file was projected in ArcToolbox (v10) > Data Management Tools > Projections and Transformations > Feature > Project. Parameters: input projection = geographic (NAD 83); output projection = UTM zone 17N (WGS 84); transformation = NAD_1983_To_WGS_1984_1. 2013 Historical shorelines were appended to the lidar route data in ArcToolbox (v10) > Data Management Tools > General > Append to produce a single shoreline file for the region. The final shoreline dataset was coded with attribute fields (Date, Uncertainty (Uncy), Source, Source_b, Year_, Default_D, Location). These fields are required for the Digital Shoreline Analysis System (DSAS), which was used to calculate shoreline change rates. 2013 The appended shoreline file and the shoreline uncertainty table (.dbf) were imported into a personal geodatabase in ArcCatalog v10 by right-clicking on the geodatabase > Import (feature class for shoreline file and table for uncertainty table) for use with the Digital Shoreline Analysis System (DSAS) v4.3 software to perform rate calculations. 2013 The shoreline feature class was exported from the personal geodatabase back to a shapefile in ArcCatalog v10 by right-clicking on the shoreline file > Export > To Shapefile (single) for publication purposes. 2013 The data were projected in ArcToolbox v10.2 > Data Management Tools > Projections and Transformations > Project. Parameters: input projection = UTM zone 17N (WGS84); output projection = geographic coordinates (WGS84); transformation = none. 2015 Keywords section of metadata optimized for discovery in USGS Coastal and Marine Geology Data Catalog. 20170825 U.S. Geological Survey Alan O. Allwardt Contractor -- Information Specialist mailing and physical address

2885 Mission Street

Santa Cruz CA 95060 831-460-7551 831-427-4748 aallwardt@usgs.gov Added keywords section with USGS persistent identifier as theme keyword. Process performed on 20200810. The metadata title was modified to move the location name to the beginning of the title to account for the alphabetical listing of the individual records when ScienceBase migrates the data release to a new platform and "flattens" the data release structure (20260302). 20260302 U.S. Geological Survey VeeAnn A. Cross Marine Geologist Mailing and Physical

384 Woods Hole Road

Woods Hole MA 02543-1598 508-548-8700 x2251 508-457-2310 vatnipp@usgs.gov Vector String 590 0.000001 0.000001 Decimal degrees D_WGS_1984 WGS_1984 6378137.000000 298.257224 SC_shorelines Vector shorelines U.S. Geological Survey FID Internal feature number. Esri Sequential unique whole numbers that are automatically generated. Shape Feature geometry. Esri Coordinates defining the features. RouteID 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 (SC_shorelines_uncertainty.dbf). U.S. Geological Survey 0 Short integer field where zeros are "no data" and automatically filled in for the remaining shoreline polylines not derived from lidar. U.S. Geological Survey 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. U.S. Geological Survey Character string of length 10 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 uncertainty of the High Water Line (HWL) at an individual transect was determined by using positional uncertainty information stored along the shoreline route. The HWL uncertainty was added to the historic shoreline uncertainty stored in the shoreline attribute table during the DSAS rate calculation process to better account for uncertainty at individual transects alongshore as opposed to using a regionally averaged value. This was done using linear referencing that interpolates a value based on data stored in the SC_shorelines_uncertainty dBase file (.dbf) at a specific transect/shoreline intersect alongshore. The lidar shoreline position uncertainty is also stored in the associated shorelines_uncertainty.dbf file and values are interpolated at specific transect/shoreline intersections using the same linear referencing method. The lidar uncertainty attribute field was filled with null values while residing in the geodatabase. Upon exporting the shoreline feature class to a shapefile for publication, the null values in the lidar uncertainty field were automatically converted to zero values. U.S. Geological Survey 0 Zeros are "no data" and serve as place fillers for the lidar shorelines. Actual uncertainty values for lidar are stored in shoreline uncertainty dBase (.dbf) file. U.S. Geological Survey Source Agency that provided shoreline feature or the data source used (e.g. T-sheet) to digitize shoreline feature. U.S. Geological Survey USGS U.S. Geological Survey U.S. Geological Survey NOAA National Oceanic and Atmospheric Administration U.S. Geological Survey NOAA/NOS National Oceanic and Atmospheric Administration/National Ocean Service U.S. Geological Survey Source_b Type of data used to create shoreline. U.S. Geological Survey lidar Light detection and ranging (lidar). U.S. Geological Survey CERC map Coastal Engineering Research Center (CERC) map U.S. Geological Survey T or TP with number NOAA/NOS topographic survey sheet (T- or TP-sheet) with associated registry number U.S. Geological Survey Year_ Four digit year of shoreline U.S. Geological Survey 1852 2000 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. U.S. Geological Survey 0 Shoreline month and day are known. U.S. Geological Survey 1 Shoreline month and day are unknown and default value of 07/01 was used. U.S. Geological Survey Location Location of shoreline with respect to wave energy exposure. An open ocean coast is directly exposed to ocean waves and is typically characterized by higher wave energy. A sheltered coast is not directly exposed to ocean waves and is characterized by lower wave energy. This shoreline dataset only includes open ocean locations. U.S. Geological Survey open ocean Shoreline on a coast with open ocean wave exposure. U.S. Geological Survey Shape_Leng Length of feature in meter units (UTM zone 17N, WGS 84) Esri 20.85 19367.96 meters The entity and attribute information provided here describes the tabular data associated with the dataset. Please review the individual attribute descriptions for detailed information. U.S. Geological Survey U.S. Geological Survey mailing and physical address

Denver Federal Center

Building 810

MS 302

Denver CO 80225 USA 1-888-275-8747 sciencebase@usgs.gov Neither the U.S. Government, the Department of the Interior, nor the USGS, nor any of their employees, contractors, or subcontractors, make any warranty, express or implied, nor assume any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, nor represent that its use would not infringe on privately owned rights. The act of distribution shall not constitute any such warranty, and no responsibility is assumed by the USGS in the use of these data or related materials. Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the U.S. Government. Shapefile ArcGIS 10.2 Esri polyline shapefile These files (.cpg, .dbf, .prj, .sbn, .sbx, .shp, .shp.xml, 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 compliant metadata. no compression applied 1.98 https://www.sciencebase.gov/catalog/file/get/58b89084e4b01ccd5500c2ac https://www.sciencebase.gov/catalog/item/58b89084e4b01ccd5500c2ac https://www.sciencebase.gov/catalog/item/58055f50e4b0824b2d1c1ee7 https://doi.org/10.5066/F74X55X7 The first link downloads the contents of the data page as a zip file, the second link is to the landing page of the data, the third and fourth links are to the main landing page of the data release. None These data are available in a polyline shapefile format. The user must have software to read and process the data components of a shapefile. 20260302 M.G. Kratzmann U.S. Geological Survey mailing address

384 Woods Hole Road

Woods Hole MA 02543-1598 USA 508-548-8700 508-547-2310 whsc_data_contact@usgs.gov The metadata contact email address is a generic address in the event the person is no longer with USGS. (updated on 20240319) FGDC Content Standards for Digital Geospatial Metadata FGDC-STD-001-1998 local time