Marie K. Bartlett Julia L. Heslin Kathryn M. Weber Erika E. Lentz 20250717 1.0 raster digital data data release DOI:10.5066/P13JKJUT Woods Hole Coastal and Marine Science Center, Woods Hole, MA U.S. Geological Survey, Coastal and Marine Hazards and Resources Program https://doi.org/10.5066/P13JKJUT https://www.sciencebase.gov/catalog/item/6811383bd4be0276ecc8495b Marie K. Bartlett Julia L. Heslin Kathryn M. Weber Erika E. Lentz 2025 1.0 raster digital data data release DOI:10.5066/P13JKJUT Reston, VA U.S. Geological Survey, Coastal and Marine Hazards and Resources Program Suggested Citation: Bartlett, M.K., Heslin, J.L., Weber, K.M., and Lentz, E.E., 2025, Coastal landscape response to sea-level rise assessment for the northeastern United States: U.S. Geological Survey data release, https://doi.org/10.5066/P13JKJUT. https://doi.org/10.5066/P13JKJUT https://www.sciencebase.gov/catalog/item/681134d7d4be0276ecc84941 This data release presents an update to the Coastal Response Likelihood (CRL) model (Lentz and others 2015); a spatially explicit, probabilistic model that evaluates coastal response for the Northeastern U.S. under various sea-level scenarios. The model considers the variable nature of the coast and provides outputs at spatial and temporal scales suitable for decision support. Updated model results provide higher spatial resolution predictions (from 30 meters (m) to 10 m) of adjusted land elevation ranges (AE) with respect to projected relative sea-level scenarios, a likelihood estimate of this outcome (PAE), and a probability of coastal response (CR) characterized as either static (inundated) or dynamic (maintaining or changing state). The predictions span the coastal zone vertically from 10 m below to 10 m above mean high water (MHW). Results are produced at a horizontal resolution of 10 meters for four decades (2030, 2050, 2080 and 2100) and two possible sea-level change scenarios (Intermediate Low (IL), Intermediate High (IH)) as defined by Sweet and others 2022. Adjusted elevations and their respective probabilities are generated using regional geospatial datasets of relative sea-level scenarios and current elevation data. Coastal response outcomes are determined by combining adjusted elevation outputs with land cover data and expert judgment (Lentz and others 2015) to assess whether an area is likely to maintain its existing land class, or transition to a new one (dynamic), or become submerged (static). The intended users of these data include scientific researchers, coastal planners, and natural resource managers. These GIS layers provide a forecast of the adjusted land elevation (AE) with respect to Intermediate High (IH) and Intermediate Low (IL) predicted sea-level rise (SLR) scenarios for the Northeastern U.S. for the 2030s, 2050s, 2080s and 2100s. Outcomes are based on relative SLR and elevation input data. The output displays the most likely of one of five adjusted elevation ranges (-10 to -1, -1 to 0, 0 to 1, 1 to 5, and 5 to 10 m) to be observed for the projection year as defined by a probabilistic framework (a Bayesian network), and should be used concurrently with the likelihood layer (PAE), also available from https://www.sciencebase.gov/catalog/item/6811368ad4be0276ecc84953, which provides users with an estimation of the forecast elevation range occurring when compared with the four other elevation ranges. These data layers primarily show the distribution of adjusted elevation ranges over a large spatial scale and should therefore be used qualitatively (see Horizontal Positional Accuracy Report). These data layers are a model output produced as part of the U.S. Geological Survey Future Landscape Adaptation and Coastal Change (FLACC) project. 2025 Ground condition as represented by the 2023 Coastal Change Likelihood Fabric dataset, as cited in the Sourch Citation section of this metadata record Complete None planned -77.5278 -66.9432 45.1918 36.5437 ISO 19115 Topic Category geoscientificInformation oceans None U.S. Geological Survey USGS Coastal and Marine Hazards and Resources Program Natural Hazards Mission Area Elevation Interpretation Landcover Land Cover Sea Level Rise ArcPy USGS Thesaurus coastal processes sea-level change mathematical modeling geospatial datasets scientific interpretation land use and land cover bathymetry topography USGS Metadata Identifier USGS:6811383bd4be0276ecc8495b None Northeast US Maine New Hampshire Massachusetts Rhode Island Connecticut New York New Jersey Delaware Maryland Virginia United States None. Please see 'Distribution Info' for details. Not to be used for navigation. Public domain data from the U.S. Government are freely redistributable with proper metadata and source attribution. Please recognize the U.S. Geological Survey (USGS) as the source of this information. Additionally, there are limitations associated with coastal change hazard assessments. U.S. Geological Survey Marie K. Bartlett Mailing and Physical

384 Woods Hole Rd

Woods Hole MA 02543 508-548-8700 x2306 mbartlett@usgs.gov https://www.sciencebase.gov/catalog/file/get/6811383bd4be0276ecc8495b?name=AE_NE_Graphic.jpg&allowOpen=true Example of AE output at Plum Island, Massachusetts JPEG Windows 11 build 22631.5039; ESRI ArcGIS Pro v3.3.0 Erika E. Lentz Sawyer R. Stippa E. Robert Thieler Nathaniel G. Plant Dean B. Gesch Radley M. Horton 2015 Version 1.0: Originally posted February 13, 2015; Version 2.0: December 21, 2015 publication Open-File Report 2014-1252 Reston, VA U.S. Geological Survey https://doi.org/10.3133/ofr20141252 Julia L. Heslin Kathryn M. Weber Erika E. Lentz Donya P. Frank-Gilchrist Jason J. Mercer 2024 software release DOI:10.5066/P1SQIVEW Reston, VA U.S. Geological Survey Only available to internal users within U.S. Geological Survey https://doi.org/10.5066/p1sqivew Travis K. Sterne Elizabeth A. Pendleton Erika E. Lentz Rachel E. Henderson 2023 raster digital data data release DOI:10.5066/P96A2Q5X Woods Hole Coastal and Marine Science Center, Woods Hole, MA U.S. Geological Survey, Coastal and Marine Geology Program https://doi.org/10.5066/P96A2Q5X https://www.sciencebase.gov/catalog/item/61781f88d34e4c6b7fe2a444 Elizabeth A. Pendleton Erika E. Lentz Travis K. Sterne Rachel E. Henderson 2023 publication Data Report 1169 Reston, VA U.S. Geological Survey Suggested citation: Pendleton, E.A., Lentz, E.E., Sterne, T.K., and Henderson, R.E., 2023, Development and application of a coastal change likelihood assessment for the northeast region, Maine to Virginia: U.S. Geological Survey Data Report 1169, 56 p., https://doi.org/10.3133/dr1169. The CCL data release (https://doi.org/10.5066/P96A2Q5X) is associated with the CCL Data Report (https://doi.org/10.3133/dr1169) https://doi.org/10.3133/dr1169 https://pubs.er.usgs.gov/publication/dr1169 These GeoTIFFs were generated from attributed point data that underwent quality assurance and quality control (QA/QC) procedures to ensure consistency and accuracy. As a result, the GeoTIFFs are considered to accurately represent the results of the modeling process and the original source data used for attribution. The raster dataset representing adjusted elevation values (1-5) is spatially consistent, with a uniform 10-meter grid resolution across the entire extent. QA/QC checks confirmed that all cells contain valid data within the expected range or designated NoData values. No anomalies or formatting errors were found. The dataset conforms to standard raster formatting and alignment protocols. Data from approximately 777,740,279 coastal grid points throughout the Northeast from Maine to Virginia were used to make coastal response predictions. Model inputs (raster format) were either upscaled or downscaled to provide inputs at the 10 m horizontal resolution of the land cover data. Each cell in this data layer displays the most probable adjusted elevation range of five possible outcomes: -10 to -1, -1 to 0, 0 to 1, 1 to 5, and 5 to 10 meters. Forecast values were calculated for select decades between 2030 to 2100, and will vary if performed on a different time period if different models are used or if different model inputs (such as updated elevation data, revised relative sea-level estimates, updated land cover information), discretization of such inputs, or parameterizations were chosen. A probabilistic model (Bayesian Network) is used to generate the forecast of coastal response type shown in this data layer. Because the overall horizontal accuracy of the dataset depends on the accuracy of the model, the forcing values used, expert knowledge, the underlying inputs (i.e., relative sea-level scenarios, elevation, land cover), the spatial accuracy of this dataset cannot be meaningfully quantified. These maps are intended to provide a qualitative and relative regional assessment of sea-level impacts to the landscape at the 10 m horizontal resolution displayed. Users are advised not to use the dataset to determine specific values quantitatively at any particular geographic location. For more information regarding the horizontal accuracy of source data, see Sterne and others 2023 listed in the Source Citation section. This dataset is derived from topographic and bathymetric data compiled by Sterne and others (2023), who merged USGS and NOAA source elevation data (listed below) into a seamless elevation surface with an overall expected vertical accuracy of less than 0.5 meters. This merged elevation dataset was combined with SLR scenarios from Sweet and others (2022) for selected future decades (2030, 2050, 2080, and 2100). The resulting values were binned into the following adjusted elevation (flooded surface) classes: -10 to -1 m, -1 to 0 m, 0 to 1 m, 1 to 5 m, and 5 to 10 m. The integration of projected SLR data with elevation, along with the binning process used for Bayesian analysis, introduces additional uncertainty relative to the original depth values. Users are encouraged to consult the original elevation datasets from Sterne and others (2023) for detailed, unbinned depth values. This dataset is referenced to the Mean High Water (MHW) tidal datum. Source elevation data were originally referenced to the North American Vertical Datum of 1988 (NAVD 88) and were transformed to MHW using NOAA’s VDatum tool (https://vdatum.noaa.gov/). Travis K. Sterne Elizabeth A. Pendleton Erika E. Lentz Rachel E. Henderson 2023 raster digital data data release DOI:10.5066/P96A2Q5X Woods Hole Coastal and Marine Science Center, Woods Hole, MA U.S. Geological Survey, Coastal and Marine Geology Program https://doi.org/10.5066/P96A2Q5X https://www.sciencebase.gov/catalog/item/61781f88d34e4c6b7fe2a444 Digital 2010 2021 ground condition Fabric Contains source elevation and landcover domain used to create point data which was ingested into the CRL code William V. Sweet Benjamin D. Hamlington Robert E. Kopp Christopher P. Weaver Patrick L. Barnard David Bekaert William Brooks Michael Craghan Gregory Dusek Thomas Frederikse Gregory Garner Ayesha S. Genz John P. Krasting Eric Larour Doug Marcy John J. Marra Jayantha Obeysekera Mark Osler Matthew Pendleton Daniel Roman Lauren Schmied Will Veatch Kathleen D. White Casey Zuzak 202202 CSV NOAA Technical Report NOS 01 Silver Springs National Oceanic and Atmospheric Administration Sweet, W.V., Hamlington, B.D., Kopp, R.E., Weaver, C.P., Barnard, P.L., Bekaert, D., Brooks, W., Craghan, M., Dusek, G., Frederikse, T., Garner, G., Genz, A.S., Krasting, J.P., Larour, E., Marcy, D., Marra, J.J., Obeysekera, J., Osler, M., Pendleton, M., Roman, D., Schmied, L., Veatch, W., White, K.D., and Zuzak, C., 2022, Global and regional sea level rise scenarios for the United States—Updated mean projections and extreme water level probabilities along U.S. coastlines: NOAA Technical Report NOS 01, National Oceanic and Atmospheric Administration, National Ocean Service, Silver Spring, MD, 111 pp. https://earth.gov/sealevel/us/internal_resources/756/noaa-nos-techrpt01-global-regional-SLR-scenarios-US.pdf https://earth.gov/sealevel/us/internal_resources/756/noaa-nos-techrpt01-global-regional-SLR-scenarios-US.pdf Digital 2030 2050 2080 2100 ground condition of source data SLR Data Contains sea-level change data used to create point data ingested into the CRL code. Outcomes are based on the 2030, 2050, 2080, and 2100 scenarios. Jeffrey Danielson Dean Tyler 2018 raster digital data https://www.usgs.gov/core-science-systems/eros/coned Digital 2018 publication date CONED Elevation B.D. Andrews W.E. Baldwin D.W. Sampson W.C. Schwab 20191227 Version 3 raster digital data data release DOI:10.5066/F72806T7 Reston, VA U.S. Geological Survey https://doi.org/10.5066/F72806T7 Digital 20191227 publication date CZM Topobathy Elevation Office for Coastal Management 2016 raster digital data https://www.fisheries.noaa.gov/inport/item/48106 Digital 2016 publication date NOAA SLR Topo Elevation Three input data are required for the Coastal Response Likelihood (CRL) model: SLR scenarios, digital elevation model (DEM) data used to generate the Coastal Change Likelihood (CCL) Fabric dataset and land cover data used in the CCL Fabric dataset. Each SLR scenario from Sweet and others (2022) used for this data release was converted into a one-degree raster grid representing the corresponding SLR value. For detailed processing steps used to generate the CCL Fabric dataset, refer to the metadata provided by Pendleton and others (2023) and Sterne and others (2023). This step and the subsequent step were completed by Julia Heslin. Any further steps that mention the use of “tools” or “functions” refer to geoprocessing tools utilized in ArcGIS Pro. SLR Data Fabric CZM Topobathy CONED NOAA SLR Topo 2024 Julia L. Heslin Mailing and Physical

384 Woods Hole Rd

Woods Hole MA 02543 508-548-8700 x2230 jheslin@usgs.gov To maximize processing efficiency, input data were split into smaller sections and converted to CSV format. 1. First, an extent polygon of the study area is required to split the data into sections. The input land cover raster was converted to a polygon by reclassifying the raster to a single value with the Reclassify tool then inputting that raster in the Raster to Polygon tool. 2. The study area extent polygon was split into 20 sections using the Subdivide Polygon tool in ArcGIS Pro. For each section, the land cover raster was converted to points using the Raster to Point tool. There were approximately 38-39 million points per section. 3. The Extract Multi Value to Point tool was used to extract the elevation and SLR scenarios to the attribute table of the land cover point file. 4. Easting and northing fields are added to the attribute table using the Calculate Geometry tool to determine the X and Y coordinates for each of the points. The attribute tables were then exported as CSV files. 2024 The exported CSV files containing landcover, elevation, and SLR scenarios were run through the newly published CRL code, listed in the cross-reference section of this metadata (https://doi.org/10.5066/P1SQIVEW). For each section, the code outputs shapefile point files in WGS 84 Web Mercator (auxiliary sphere). Code runs were completed by Marie Bartlett and Kathy Weber. 2024 Marie K. Bartlett physical

384 Woods Hole Rd

Woods Hole MA 02540 508-548-8700 x2306 mbartlett@usgs.gov Remaining post-processing steps were completed by Marie Bartlett using ESRI ArcGIS Pro Version 3.3.0 geospatial software. Steps were automated when possible using the ArcPy package for python programming. 1. Shapefile points for each section were converted to Esri GRIDs using the Point to Raster Conversion tool, and aggregated based on type of output (CR, AE, PAE), year (2030, 2050, 2080, 2100) and scenario (IH, IL) using the Mosaic to New Raster tool. 2. GRIDS were exported to TIFF format using the Export Raster tool and selecting LZW compression to reduce file size while maintaining precision. 3. Probability output values (CR, PAE) were rounded to reflect the accuracy of the source data using the Integer function within an ArcPy script: Int(raster * 100 + 0.5) / 100 2025 Raster Grid Cell 127607 117827 1 WGS 1984 Web Mercator (auxiliary sphere) 0.0 0.0 0.0 0.0 0.0 0.0 row and column 10.0 10.0 meters WGS_1984 WGS 84 6378137.0 298.257223563 North American Vertical Datum of 1988 0.01 meters Explicit elevation coordinate included with horizontal coordinates AE_year_scenario Raster geospatial data file Producer Defined OID Internal object identifier. Esri Sequential unique whole numbers that are automatically generated. VALUE Values correspond to the most likely of one of five adjusted elevation ranges (-10 to -1, -1 to 0, 0 to 1, 1 to 5, and 5 to 10 m) designated by the probabilistic model U.S. Geological Survey 1 Predicted levation range of -10 to -1 m U.S. Geological Survey 2 Predicted elevation range of -1 to 0 m U.S. Geological Survey 3 Predicted elevation range of 0 to 1 m U.S. Geological Survey 4 Predicted elevation range of 1 to 5 m U.S. Geological Survey 5 Predicted elevation range of 5 to 10 m U.S. Geological Survey COUNT The number of pixels that fall within the elevation range Esri N/A See entity and attribute overview section for minimum and maximum count values Producer defined A companion ArcGIS Pro LayerFile (AE_symbology.lyrx) is intended to be used when viewing each of the adjusted elevation (AE) raster outputs for ease of interpretation. A color ramp has been selected to clearly distinguish between elevation ranges: each color corresponds to one of the five elevation bins (-10 to -1 m, -1 to 0 m, 0 to 1 m, 1 to 5 m, and 5 to 10 m). Minimum and maximum pixel count values are listed below AE_2030_IL: 33480993, 359330324; AE_2030_IH: 33633887, 359839922; AE_2050_IL: 28095340, 382040638; AE_2050_IH: 31601324, 389027173; AE_2080_IL: 10611721, 393026899; AE_2080_IH: 62906076, 401495637; AE_2100_IL: 4712519, 393696794; AE_2100_IH: 48894719, 415816639 U.S. Geological Survey - ScienceBase U.S. Geological Survey - ScienceBase mailing and physical address

Denver Federal Center, Building 810, Mail Stop 302

Denver CO 80225 US 1-888-275-8747 sciencebase@usgs.gov This dataset contains the raster data layer (.tif) and associated files (.tfw, .ovr, .cpg, and .dbf) needed to view and edit the information it contains, as well as the FGDC CSDGM metadata in XML format. The .lyrx is an ArcGIS Pro LayerFile provided to display the data, the .tfw world file is a text file used to georeference the GeoTIFF, the .ovr file contains the pyramids used by a GIS to display the data at different scales, the .cpg file is for charactersets, and the .dbf is a dBASE table file used to store data attributes. 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 on any other system or for general or scientific purposes, nor shall the act of distribution constitute any such warranty. GeoTIFF ESRI ArcGIS Pro v3.3.0 1000 https://doi.org/10.5066/P13JKJUT https://www.sciencebase.gov/catalog/item/6811383bd4be0276ecc8495b https://www.sciencebase.gov/catalog/item/681134d7d4be0276ecc84941 The first link is to the USGS publication page, the second link is to the AE child page, and the third link is to the larger work landing page. Because the GeoTiffs are large (over 1 GB), it is recommended to download each file individually None 20250717 U.S. Geological Survey Marie K. Bartlett Physical Scientist Mailing and Physical

384 Woods Hole Rd

Woods Hole MA 02543 508-548-8700 x2306 whsc_data_contact@usgs.gov The metadata contact email address is a generic address in the event the metadata author is no longer with the USGS. Content Standard for Digital Geospatial Metadata, FGDC-STD-001-1998 FGDC-STD-001.1-1998