Justin J. Birchler Kara S. Doran 20251117 vector digital data Kara S. Doran Justin J. Birchler Heather A. Schreppel Hilary F. Stockdon David M. Thompson 20190619 2.0 U.S. Geological Survey data release doi:10.5066/P9Z362BC St. Petersburg, Florida U.S. Geological Survey https://doi.org/10.5066/P9Z362BC This dataset defines storm-induced coastal erosion hazards for the New York, Rhode Island, Massachusetts, New Hampshire and Maine coastline. The analysis was based on a storm-impact scaling model that used observations of beach morphology combined with sophisticated hydrodynamic models to predict how the coast would respond to the direct landfall of Hurricane Lee in September 2023. Storm-induced water levels, due to both surge and waves, were compared to beach and dune elevations to determine the probabilities of three types of coastal change: collision (dune erosion), overwash, and inundation. To provide data on the probability of storm-induced coastal erosion hazards for the New York, Rhode Island, Massachusetts, New Hampshire and Maine coast for Hurricane Lee. 20230915 ground condition Complete None planned, however future updates and post-storm analyses are anticipated. -74.1002 -69.9291 43.5630 40.5437 USGS Metadata Identifier USGS:40b6e708-1e9b-4034-b29a-c81a7c6e039d None U.S. Geological Survey USGS St. Petersburg Coastal and Marine Science Center SPCMSC Coastal/Marine Hazards and Resources Program CMHRP coastal Hurricane Lee ISO 19115 Topic Category oceans elevation environment geoscientificInformation USGS Thesaurus hazards marine geology ocean sciences coastal processes erosion Data Categories for Marine Planning distributions bathymetry and elevation physical habitats and geomorphology Marine Realms Information Bank (MRIB) Keywords effects of coastal change shoreline accretion shoreline erosion storm erosion topographic mapping hurricanes and typhoons Geographic Names Information System United States of America New York Rhode Island Massachusetts New Hampshire Maine No access constraints. Please see 'Distribution Information' for details. These data are marked with a Creative Commons CC0 1.0 Universal License. These data are in the public domain and do not have any use constraints. Users are advised to read the dataset's metadata thoroughly to understand appropriate use and data limitations. Justin J. Birchler Physical Scientist mailing and physical

600 4th Street South

Saint Petersburg FL 33701 UNITED STATES 727-502-8000 jbirchler@usgs.gov The predicted elevations of storm surge were extracted from the National Oceanic and Atmospheric Administration's (NOAA) Probabilistic Hurricane Storm Surge (P-Surge) model; an ensemble model based on the Sea, Lake, and Overland Surges from Hurricanes (SLOSH) model, which has been employed by NOAA in inundation risk studies and operational storm surge forecasting. Wave runup and setup conditions were generated using NOAA's Nearshore Wave Prediction System model. Microsoft Windows 10; Esri ArcGIS 10.8.1 Stockdon, H.F. Doran, K.J. Thompson, D.M. Sopkin, K.L. Plant, N.G. Sallenger, A.H. 2012 U.S. Geological Survey Open-File Report doi:10.3133/ofr20121084 Reston, VA U.S. Geological Survey https://doi.org/10.3133/ofr20121084 No additional checks for consistency were performed on this data. This dataset includes dune morphology and hurricane hydrodynamic data used to generate probabilities of hurricane-induced erosion, elevation data from lidar surveys are not included. Measurements were collected approximately every 10-meters (m) and summarized to 500 m segments. Horizontal accuracy was not estimated. Vertical accuracy for hydrodynamic measurements (surge, setup, and runup) is dependent on input data. SLOSH model accuracy is estimated to be +/- 20 percent of the calculated value. No other accuracy checks were performed. Vertical accuracy for dune morphology (dune crest and toe elevation) data is dependent on the positional accuracy of the lidar data. Estimated accuracy of lidar surveys are +/- 15 centimeters. However, vertical accuracies may vary based on the type of terrain (for example, inaccuracies may increase as slope increases or with the presence of extremely dense vegetation), the accuracy of the global positioning system (GPS), and aircraft-attitude measurements. National Hurricane Center, National Oceanic and Atmospheric Administration 20230915 https://slosh.nws.noaa.gov/psurge2.0/ ftp://ftp.ncep.noaa.gov/pub/data/nccf/com/psurge/prod/ Online digital data 20230915 The date when web page was last modified. P-Surge Data provides water levels that have a 1 in 10 chance of being exceeded within the next 102 hours. NOAA National Weather Service Environmental Modeling Center 20230915 https://polar.ncep.noaa.gov/nwps/para/viewer.shtml Online digital data 20230915 The date when web page was last modified. NWPS Wave model that was used to estimate wave setup and runup conditions at the shoreline within the next 145 hours. U.S. Army Corps of Engineers (USACE), Joint Airborne Lidar Bathymetry Technical Center of eXpertise (JALBTCX) 20181108 Geographic Coverage: NJ and NY https://noaa-nos-coastal-lidar-pds.s3.amazonaws.com/laz/geoid18/9000/index.html https://coast.noaa.gov/dataviewer/#/lidar/search/where:ID=9000 Online digital data 20200120 20200127 The date when lidar surveys were collected. USACE NAN NJ NY 2020 A lidar survey that was used to estimate dune morphology variables. National Oceanic Atmospheric Administration (NOAA) National Geodetic Survey (NGS) Remote Sensing Division 20151220 Geographic Coverage: RI https://noaa-nos-coastal-lidar-pds.s3.amazonaws.com/laz/geoid18/4976/index.html https://coast.noaa.gov/dataviewer/#/lidar/search/where:ID=4976 Online digital data 20140717 20140809 The date when lidar surveys were collected. NOAA NGS RI 2014 A lidar survey that was used to estimate dune morphology variables. United States Army Corps of Engineers (USACE) National Coastal Mapping Program (NCMP), Joint Airborne Lidar Bathymetry Technical Center of eXpertise (JALBTCX) 20181108 Geographic Coverage: MA https://noaa-nos-coastal-lidar-pds.s3.amazonaws.com/laz/geoid18/8688/index.html https://coast.noaa.gov/dataviewer/#/lidar/search/where:ID=8688 Online digital data 20180509 20180827 The date when lidar surveys were collected. USACE NCMP MA 2018 A lidar survey that was used to estimate dune morphology variables. Department of Commerce (DOC), National Oceanic and Atmospheric Administration (NOAA), National Ocean Service (NOS), Office for Coastal Management (OCM), United States Geological Survey (USGS), Coastal and Marine Geology Program (CMGP) 20160523 Geographic Coverage: NH https://noaa-nos-coastal-lidar-pds.s3.amazonaws.com/laz/geoid18/4914/index.html https://coast.noaa.gov/dataviewer/#/lidar/search/where:ID=4914 Online digital data 20131116 20141227 The date when lidar surveys were collected. USACE NH 2013 A lidar survey that was used to estimate dune morphology variables. United States Army Corps of Engineers (USACE) 201408 Geographic Coverage: ME https://noaa-nos-coastal-lidar-pds.s3.amazonaws.com/laz/geoid18/1174/index.html https://coast.noaa.gov/dataviewer/#/lidar/search/where:ID=1174 Online digital data 20100524 20100710 The date when lidar surveys were collected. USACE ME 2010 A lidar survey that was used to estimate dune morphology variables. U.S. Army Corps of Engineers (USACE), Joint Airborne Lidar Bathymetry Technical Center of eXpertise (JALBTCX) 20150813 Geographic Coverage: ME https://noaa-nos-coastal-lidar-pds.s3.amazonaws.com/laz/geoid18/4930/index.html https://coast.noaa.gov/dataviewer/#/lidar/search/where:ID=4930 Online digital data 20130620 20130620 The date when lidar surveys were collected. USACE ME 2013 A lidar survey that was used to estimate dune morphology variables. U.S. Army Corps of Engineers (USACE), Joint Airborne Lidar Bathymetry Technical Center of eXpertise (JALBTCX) 20150626 Geographic Coverage: ME https://noaa-nos-coastal-lidar-pds.s3.amazonaws.com/laz/geoid18/4911/index.html https://coast.noaa.gov/dataviewer/#/lidar/search/where:ID=4911 Online digital data 20141109 20141115 The date when lidar surveys were collected. USACE ME 2014 A lidar survey that was used to estimate dune morphology variables. For dune morphology data: Elevation data from lidar surveys were interpolated in MATLAB (version R2021a) to a gridded domain that was rotated parallel to the shoreline and had a resolution of 10 m in the alongshore direction and 2.5 m in the cross-shore direction. The interpolation method applied spatial filtering with a Hanning window that was twice as wide as the grid resolution. Dune morphology data were extracted from the elevation grid in MATLAB. Dune morphology data were then summarized to 500 m sections. Sections with greater than 75 percent of data missing were flagged with the invalid number of -999. The 1-kilometer smoothed dune crest (DHIGH), toe (DLOW) and root mean square (RMS) error for each (DHIrms and DLOrms) were written to line shapefiles using MATLAB's shapewrite.m script. USACE NAN NJ NY 2020 NOAA NGS RI 2014 USACE NCMP MA 2018 USACE NH 2013 USACE ME 2010 USACE ME 2013 USACE ME 2014 2023 Dune morphology (DHIGH, DLOW, DHIrms, DLOrms) Justin J. Birchler Physical Scientist mailing and physical

600 4th Street South

Saint Petersburg FL 33701 UNITED STATES 727-502-8000 jbirchler@usgs.gov For hydrodynamic data: Water level was computed in MATLAB by adding storm surge from NOAA's Probabilistic Tropical Storm Surge (P-Surge) model (https://slosh.nws.noaa.gov/psurge2.0/) to wave setup and runup. The wave height and period used for calculating wave runup and setup came from the Nearshore Wave Prediction System model (https://polar.ncep.noaa.gov/nwps/). Hydrodynamic parameters were calculated in MATLAB and exported into ArcGIS shapefile format. For details on modeling parameterization, see Stockdon and others (2012). P-Surge NWPS 20230915 Hydrodynamics (SURGE, SETUP, RUNUP) Justin J. Birchler Physical Scientist mailing and physical

600 4th Street South

Saint Petersburg FL 33701 UNITED STATES 727-502-8000 jbirchler@usgs.gov Probabilities of coastal erosion hazards were based on estimating the likelihood that the beach system would experience erosion and deposition patterns consistent with collision (PCOL), overwash (POVW), or inundation (PIND) regimes. The regimes were calculated by using values of dune morphology and mean and extreme water levels for each 500 m section, such that the probability of collision (PCOL) occurs when extreme water levels reach the dune toe; overwash (POVW) when extreme water levels reach the dune crest; and inundation (PIND) when mean water levels reach the dune crest. In cases where DLOW is null (-999) PCOL is also null (-999) due to the inability to compute water level elevation reaching dune toe. Probabilities were calculated in MATLAB and exported using MATLAB's shapewrite.m script. For details on modeling parameterization, see Stockdon and others (2012). Dune morphology Hydrodynamics 20230915 Probabilities (PCOL, POVW, PIND) Justin J. Birchler Physical Scientist mailing and physical

600 4th Street South

Saint Petersburg FL 33701 UNITED STATES 727-502-8000 jbirchler@usgs.gov Vector String 2013 0.01975663976 0.0249972312 Decimal Degrees D WGS 1984 WGS 1984 6378137.0 298.257223563 Lee_PCOI_line Probabilities of hurricane-induced coastal erosion, dune morphology, and hurricane hydrodynamic data USGS FID Internal feature number. Esri Sequential unique whole numbers that are automatically generated. Shape Feature geometry. Esri Coordinates defining the features. DHIGH Elevation of dune crest, in meters, using the North American Vertical Datum of 1988 (NAVD88). Extracted from lidar surveys collected from May 2010 to January 2020. USGS 1.4657 48.0191 meters -999 Null value USGS DLOW Elevation of the dune toe, in meters NAVD88. Extracted from lidar surveys collected from May 2010 to January 2020. USGS 1.0938 5.4136 meters -999 Null value USGS DHIrms Root mean square error of dune crest elevation measurements. USGS 0.1714 4.0561 meters -999 Null value USGS DLOrms Root mean square error of dune toe elevation measurements. USGS 0.1670 1.2108 meters -999 Null value USGS SURGE Storm surge water level. NOAA 0.549 1.9131 meters -999 Null value NOAA RUNUP Wave runup water level. USGS 0.2679 6.2448 meters -999 Null value USGS SETUP Wave setup water level. USGS 0.047515 2.6049 meters -999 Null value USGS PCOL Probability of collision. USGS 1.1205 100 percent -999 Null value USGS POVW Probability of overwash. USGS 0 99.9991 percent -999 Null value USGS PIND Probability of inundation. USGS 0 94.3049 percent -999 Null value USGS MEAN Mean water level (surge + setup). USGS 0.90788 4.2809 meters -999 Null value USGS EXTREME Extreme water level (surge + runup). USGS 1.1297 7.9208 meters -999 Null value USGS TIDE Predicted tide water level. USGS 0 0 meters U.S. Geological Survey, St. Petersburg Coastal and Marine Science Center USGS SPCMSC Data Management mailing and physical

600 4th Street South

Saint Petersburg FL 33701 727-502-8000 gs-g-spcmsc_data_inquiries@usgs.gov 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. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government. shapefile zip https://coastal.er.usgs.gov/data-release/doi-P9Z362BC/data/Lee_2023.zip None, if obtained online. 20251117 U.S. Geological Survey, St. Petersburg Coastal and Marine Science Center USGS SPCMSC Data Management mailing and physical

600 4th Street South

Saint Petersburg FL 33701 727-502-8000 gs-g-spcmsc_data_inquiries@usgs.gov Content Standard for Digital Geospatial Metadata FGDC-STD-001-1998