Legna M. Torres-Garcia David M. Thompson Kara S. Doran Priscila Vargas-Babilonia 20220815 vector digital data Kara S. Doran Legna M. Torres-Garcia David M. Thompson Justin J. Birchler Kirsten J. Bendik Alex C. Seymour Priscila Vargas-Babilonia Curt D. Storlazzi Mark Buckley Margaret L. Palmsten 20220815 U.S. Geological Survey data release doi:10.5066/P9N01XLQ St. Petersburg, Florida U.S. Geological Survey - St. Petersburg Coastal and Marine Science Center https://doi.org/10.5066/P9N01XLQ This dataset contains information on the probabilities of hurricane-induced erosion (collision, inundation and overwash) for each 100-meter (m) section of the Puerto Rico coast for category 1-5 hurricanes. The analysis is based on a storm-impact scaling model that uses observations of beach morphology combined with sophisticated hydrodynamic models to predict how the coast will respond to the direct landfall of category 1-5 hurricanes. Hurricane-induced water levels, due to both surge and waves, are compared to beach and dune elevations to determine the probabilities of three types of coastal change: collision (dune erosion), overwash, and inundation. Data on dune and cliff morphology (dune crest and toe elevation, cliff top and toe elevation) and hydrodynamics (storm surge, wave setup and runup) are also included in this data set. As new morphology observations and storm predictions become available, this analysis will be updated to describe how coastal vulnerability to storms will vary in the future. The data presented here include the dune and cliff morphology observations, as derived from light detection and ranging (lidar) surveys. To provide data on the probability of hurricane-induced coastal erosion hazards for the coast of Puerto Rico. For more information about scenario-based assessments for coastal change hazard forecasts, please visit the U.S. Geological Survey St. Petersburg Coastal and Marine Science Center s (USGS SPCMSC) science page, https://www.usgs.gov/centers/spcmsc/science/scenario-based-assessments-coastal-change-hazard-forecasts. The data included in this data release (Doran and others, 2022) is also available in the National Assessment of Coastal Change Hazards (CCH) Portal, https://marine.usgs.gov/coastalchangehazardsportal/. The CCH Portal allows online access to data and tools that enables users to apply coastal change hazard assessments to their specific needs. 20220815 Publication date Complete None planned. However, future updates and post-storm analyses are anticipated. -67.2710 -65.5896 18.5160 17.9269 USGS Metadata Identifier USGS:d84db9e3-daed-4a49-86c4-58b85cd94b8f USGS Thesaurus coastal processes hurricanes geographic information systems erosion ISO 19115 Topic Category elevation environment geoscientificInformation oceans None U.S. Geological Survey USGS St. Petersburg Coastal and Marine Science Center SPCMSC Coastal and Marine Geology Program CMGP Open-File Report 2012-1084 OFR 2012-1084 coastal None United States Caribbean Sea Puerto Rico None The U.S. Geological Survey requests to be acknowledged as originators of the data in future products or derivative research. U.S. Geological Survey Legna M. Torres-Garcia mailing and physical

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Saint Petersburg FL 33701 UNITED STATES 727-502-8105 727-502-8182 ltorresgarcia@usgs.gov The predicted elevations of combined high tide and storm surge for category 1-5 hurricanes were obtained from the Caribbean Coastal Ocean Observing System (CARICOOS) Storm Surge Atlas for Puerto Rico, a tightly-coupled hydrodynamic and wind wave model developed with the Advance Circulation Model (ADCIRC) and Simulating Waves Nearshore (SWAN) by Benitez-Menendez and Mercado-Irizarry (2015). The algorithm estimates the maximum storm surge elevation during hypothetical hurricane scenarios at each computational node of an unstructured mesh that covers the Caribbean Sea, extending between latitudes 9 degrees North to 24 degrees North and longitudes 72 degrees West to 58 degrees West, with a spacing of 14 m nearshore and 13 kilometers (km) in deep water. The data used represents the Maximum of the Maximum (MOM) storm surge elevations modeled, providing the expected worst-case surge during an extreme storm, assuming no sea level rise. Microsoft Windows 10; MATLAB R2021a; ESRI ArcGIS 10.8.1. Hilary F. Stockdon Kara J. Doran David M. Thompson Kristin L. Sopkin Nathaniel G. Plant Asbury H. Sallenger 2012 U.S. Geological Survey Open-File Report doi:10.3133/ofr20121084 Reston, Virginia U.S. Geological Survey https://doi.org/10.3133/ofr20121084 Data gaps exist for coastlines in Puerto Rico without sandy beaches or rocky cliffs (such as mangroves or river deltas) where elevation metrics do not exist. In addition, gaps may be present where the modeled total water level was determined to be invalid by the analyst. No additional checks for consistency were performed on this data. These data include dune and cliff morphology, along with hurricane hydrodynamic data used to generate probabilities of hurricane-induced erosion. Elevation data from lidar surveys were not included. Measurements were collected approximately every 10-m and summarized to 100-m segments. Horizontal accuracy of the lidar digital elevation model (DEM) is 1 meter. The nearshore ADCIRC model grid resolution is 14 m, and each Xbeach transect with accompanying morphology is approximately 100 m apart in the alongshore direction. Data are referenced to the World Geodetic System of 1984 (WGS84) coordinate system. Vertical accuracy for hydrodynamic measurements (surge, setup, and runup) is dependent on input data. No quantitative comparisons have been made, but qualitative storm surge model predictions match well with the observations. The significant wave height predictions, although in phase with the observations in that the time occurrence of the peaks match, the model predictions tend to be much larger than the measurements. No other accuracy checks were performed. Vertical accuracy for dune and cliff morphology (dune and cliff crest and toe elevation) data is dependent on the positional accuracy of the lidar data. Estimated accuracy of lidar surveys are +/- 15 centimeters (cm). 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) and the accuracy of the Global Positioning System (GPS) and aircraft-attitude measurements. Data are referenced to the North American Vertical Datum of 1988 (NAVD88) coordinate system. Kirsten J. Bendik Alexander C. Seymour Kara S. Doran 20210121 https://doi.org/10.5066/F7610XCX vector digital data 20180703 20180817 ground condition Cliff top and toe Cliff top and toe delineation data derived from lidar for Puerto Rico in 2018 were summarized into 100-meter sections and used as inputs to generate cliff morphology (DHIGH, DLOW, DHIrms, DLOrms). Jose L. Benitez-Menendez Aurelio Mercado-Irizarry 2015 https://www.caricoos.org/map/storm-surge?locale=en digital data 2022 Date last modified MOM Maximum of Maximum (MOM) data provide a worst-case snapshot for a particular hurricane category under "perfect" storm conditions and was used to estimate water level under hurricane categories 1-5. Deltares 20201019 https://oss.deltares.nl/web/xbeach Computer program 20201019 Date downloaded XBeach Model used to estimate wave setup and runup conditions for different hurricane categories. US Army Corps of Engineers (USACE) Joint Airborne Lidar Bathymetry Technical Center for Expertise (JALBTCX) 20180717 https://noaa-nos-coastal-lidar-pds.s3.amazonaws.com/laz/geoid18/8560/index.html digital data 20180701 20180830 Ground condition 2018 USACE PR A lidar survey of Puerto Rico that was used to estimate dune morphology variables (DHIGH, DLOW, DHIrms, DLOrms). For dune morphology data: Elevation data from lidar surveys (2018 USACE PR) were interpolated in MATLAB (R2021a) to a gridded domain that was rotated parallel to the shoreline and had a resolution of 10 meters in the long-shore 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 (dune crest and dune toe) were extracted from the elevation grid in MATLAB using methods described in Stockdon and others (2012). Dune morphology data were then summarized to 100-meter sections. Sections with greater than 75 percent of the data missing were flagged with the invalid number of 999. The 100-m smoothed dune crest (DHIGH), toe (DLOW) and root mean square (DHIrms, DLOrms) error for each section are then used to compute probabilities. 2018 USACE PR 2022 Dune morphology (DHIGH, DLOW, DHIrms, DLOrms) U.S. Geological Survey Kara S. Doran mailing and physical

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Saint Petersburg FL 33701 UNITED STATES 727-502-8117 727-502-8182 kdoran@usgs.gov The coastal cliff top and toe delineation data of Puerto Rico in 2018 from Bendik and others (2021) were summarized to 100-meter sections. Sections with greater than 75 percent of the data missing were flagged with the invalid number of 999. The 100-m smoothed cliff top (DHIGH), toe (DLOW) and root mean square (DHIrms, DLOrms) error for each section are then used to compute probabilities Areas with cliffs were interpolated separate from dunes to avoid averaging over multiple morphologies. 2018 USACE PR Cliff top and toe 2022 Cliff morphology (DHIGH, DLOW, DHIrms, DLOrms) U.S. Geological Survey Kara S. Doran mailing and physical

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Saint Petersburg FL 33701 UNITED STATES 727-502-8117 727-502-8182 kdoran@usgs.gov For hydrodynamic data: One dimensional XBeach models were set up around the island, spaced approximately 100 m apart. Inputs included the maximum of the maximum (MOM) significant wave heights and MOM storm surge values, along with variable bottom friction due to coral coverage. For each hurricane category, the XBeach models were run for 60 minutes. The last thirty-four minutes of each sixty-minute time series of runup computed by XBeach were analyzed to calculate wave setup and the 2% exceedance value of runup. Reading of the netCDF XBeach output files and subsequent calculations were done in MATLAB (R2021a). This resulted in the hydrodynamics (SURGE, SETUP, RUNUP), which was used an input for the next step. MOM XBeach 2022 Hydrodynamics (SURGE, SETUP, RUNUP) U.S. Geological Survey Kara S. Doran mailing and physical

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Saint Petersburg FL 33701 UNITED STATES 727-502-8117 727-502-8182 kdoran@usgs.gov Probabilities of coastal erosion hazards are based on estimating the likelihood that the beach system will experience erosion and deposition patterns consistent with collision, overwash, or inundation regimes. The regimes were calculated by using values of dune and cliff morphology and mean and extreme water levels (hydrodynamics) for each 100-m section, such that probability of collision occurs when extreme water levels reach the dune or cliff toe; overwash when extreme water levels reach the dune crest or cliff top; and inundation when mean water levels reach the dune crest or cliff top. Probabilities were calculated in MATLAB (R2021a) and exported in ArcGIS shapefile (.shp) format using MATLAB shapewrite.m. This produced the probabilities of collision, overwash, inundation, mean water level and extreme water level data for category 1 through 5 hurricanes, found in the final shapefile (PR_PCOI_line.shp). For details on modeling parameterization, refer to the methods in Stockdon and others (2012). Dune morphology (DHIGH, DLOW, DHIrms, DLOrms) Cliff morphology (DHIGH, DLOW, DHIrms, DLOrms) Hydrodynamics (SURGE, SETUP, RUNUP) 2011 PR_PCOI_line.shp U.S. Geological Survey Kara S. Doran mailing and physical

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Saint Petersburg FL 33701 UNITED STATES 727-502-8117 727-502-8182 kdoran@usgs.gov Vector String 3325 0.0197699442 0.0239719265 Decimal Degrees D WGS 1984 WGS 1984 6378137.0 298.257223563 North American Vertical Datum 1988 0.15 meters Attribute values PR_PCOI_line.shp Shapefile (.shp) containing the probabilities of hurricane-induced coastal erosion, dune morphology, and hurricane hydrodynamic data of Puerto Rico for category 1 through 5 storm scenarios. 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 or cliff top in meters, referenced to NAVD88. USGS 0.405438 100.565053 meters DLOW Elevation of the dune or cliff toe in meters, referenced to NAVD88. USGS 0 7.71334 meters 999 Null value USGS DHIrms Root mean squared error of dune crest or cliff top elevation measurements (square meters). USGS 0.021528 27.465203 square meters DLOrms Root mean square error of dune or cliff toe elevation measurements (square meters). USGS 0 2.776537 square meters 999 Null value USGS PCOL1 Probability of collision from a category 1 storm USGS 0 100 percent 999 Null value USGS PCOL2 Probability of collision from a category 2 storm USGS 0 100 percent 999 Null value USGS PCOL3 Probability of collision from a category 3 storm USGS 0 100 percent 999 Null value USGS PCOL4 Probability of collision from a category 4 storm USGS 0.043041 100 percent 999 Null value USGS PCOL5 Probability of collision from a category 5 storm USGS 54.723087 100 percent 999 Null value USGS POVW1 Probability of overwash from a category 1 storm USGS 0 100 percent POVW2 Probability of overwash from a category 2 storm USGS 0 100 percent POVW3 Probability of overwash from a category 3 storm USGS 0 100 percent POVW4 Probability of overwash from a category 4 storm USGS 0 100 percent POVW5 Probability of overwash from a category 5 storm USGS 0 100 percent PIND1 Probability of inundation from a category 1 storm USGS 0 100 percent PIND2 Probability of inundation from a category 2 storm USGS 0 100 percent PIND3 Probability of inundation from a category 3 storm USGS 0 100 percent PIND4 Probability of inundation from a category 4 storm USGS 0 100 percent PIND5 Probability of inundation from a category 5 storm USGS 0.00 100.00 percent MEAN1 Mean water level (Setup + Surge) for a category 1 storm USGS 0.373387 3.747773 meters MEAN2 Mean water level (Setup + Surge) for a category 2 storm USGS 0.41675 4.201769 meters MEAN3 Mean water level (Setup + Surge) for a category 3 storm USGS 0.64477 4.996417 meters MEAN4 Mean water level (Setup + Surge) for a category 4 storm USGS 0.870202 5.57431 meters MEAN5 Mean water level (Setup + Surge) for a category 5 storm USGS 1.139342 6.069454 meters EXTREME1 Extreme water level (Runup + Surge) for a category 1 storm USGS 0.433844 5.960094 meters EXTREME2 Extreme water level (Runup + Surge) for a category 2 storm USGS 0.614942 9.370895 meters EXTREME3 Extreme water level (Runup + Surge) for a category 3 storm USGS 0.727493 11.174338 meters EXTREME4 Extreme water level (Runup + Surge) for a category 4 storm USGS 1.015742 13.113772 meters EXTREME5 Extreme water level (Runup + Surge) for a category 5 storm USGS 1.232767 13.053943 meters U.S. Geological Survey Kara S. Doran mailing and physical

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Saint Petersburg FL 33701 727-502-8117 727-502-8182 kdoran@usgs.gov All of this report is available online. Although these data have been processed successfully on a computer system at the USGS, no warranty expressed or implied is made regarding the display or utility of the data on any other system, or for general or scientific purposes, nor shall the act of distribution constitute any such warranty. The USGS shall not be held liable for improper or incorrect use of the data described and/or contained herein. 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-P9N01XLQ/data/PR_PCOI_line.zip None, if obtained online. 20250612 U.S. Geological Survey Kara S. Doran mailing and physical

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Saint Petersburg FL 33701 UNITED STATES 727-502-8117 727-502-8182 kdoran@usgs.gov Content Standard for Digital Geospatial Metadata FGDC-STD-001-1998 local time