10-meter rasters of coastal response type probabilities with respect to projected sea-level change for the Northeastern U.S. for the 2030s, 2050s, 2080s and 2100s

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Frequently anticipated questions:

What does this data set describe?

Title:
10-meter rasters of coastal response type probabilities with respect to projected sea-level change for the Northeastern U.S. for the 2030s, 2050s, 2080s and 2100s
Abstract:
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.
Supplemental_Information:
These data layers are a model output produced as part of the U.S. Geological Survey Future Landscape Adaptation and Coastal Change (FLACC) project.
  1. How might this data set be cited?
    Bartlett, Marie K., Heslin, Julia L., Weber, Kathryn M., and Lentz, Erika E., 20250717, 10-meter rasters of coastal response type probabilities with respect to projected sea-level change for the Northeastern U.S. for the 2030s, 2050s, 2080s and 2100s: data release DOI:10.5066/P13JKJUT, U.S. Geological Survey, Coastal and Marine Hazards and Resources Program, Woods Hole Coastal and Marine Science Center, Woods Hole, MA.

    Online Links:

    This is part of the following larger work.

    Bartlett, Marie K., Heslin, Julia L., Weber, Kathryn M., and Lentz, Erika E., 2025, Coastal landscape response to sea-level change for the northeastern United States: data release DOI:10.5066/P13JKJUT, U.S. Geological Survey, Coastal and Marine Hazards and Resources Program, Reston, VA.

    Online Links:

    Other_Citation_Details:
    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.
  2. What geographic area does the data set cover?
    West_Bounding_Coordinate: -77.5278
    East_Bounding_Coordinate: -66.9432
    North_Bounding_Coordinate: 45.1918
    South_Bounding_Coordinate: 36.5437
  3. What does it look like?
    https://www.sciencebase.gov/catalog/file/get/6811381bd4be0276ecc84958?name=CR_NE_Graphic.jpg&allowOpen=true (JPEG)
    Example of CR output at Plum Island, Massachusetts
  4. Does the data set describe conditions during a particular time period?
    Calendar_Date: 2025
    Currentness_Reference:
    Ground condition as represented by the 2023 Coastal Change Likelihood Fabric dataset, as cited in the Sourch Citation section of this metadata record
  5. What is the general form of this data set?
    Geospatial_Data_Presentation_Form: raster digital data
  6. How does the data set represent geographic features?
    1. How are geographic features stored in the data set?
      This is a Raster data set. It contains the following raster data types:
      • Dimensions 127607 x 117827 x 1, type Grid Cell
    2. What coordinate system is used to represent geographic features?
      The map projection used is WGS 1984 Web Mercator (auxiliary sphere).
      Projection parameters:
      False_Easting: 0.0
      False_Northing: 0.0
      Latitude_of_Projection_Origin: 0.0
      Longitude_of_Central_Meridian: 0.0
      Standard_Parallel: 0.0
      Standard_Parallel: 0.0
      Planar coordinates are encoded using row and column
      Abscissae (x-coordinates) are specified to the nearest 10.0
      Ordinates (y-coordinates) are specified to the nearest 10.0
      Planar coordinates are specified in meters
      The horizontal datum used is WGS_1984.
      The ellipsoid used is WGS 84.
      The semi-major axis of the ellipsoid used is 6378137.0.
      The flattening of the ellipsoid used is 1/298.257223563.
      Vertical_Coordinate_System_Definition:
      Altitude_System_Definition:
      Altitude_Datum_Name: North American Vertical Datum of 1988
      Altitude_Resolution: 0.01
      Altitude_Distance_Units: meters
      Altitude_Encoding_Method:
      Explicit elevation coordinate included with horizontal coordinates
  7. How does the data set describe geographic features?
    Entity_and_Attribute_Overview:
    A companion ArcGIS Pro LayerFile (CR_symbology.lyrx) is intended to be used when viewing each of the Coastal Response (CR) raster outputs for ease of interpretation. A color ramp has been selected to clearly distinguish between response types: orange represents higher probabilities of a dynamic response (values closer to 1), while blue represents higher probabilities of an inundated (static) response (values closer to 0). Colors in the middle of the ramp represent probabilities around 0.5 and indicate areas of greater uncertainty in the model's prediction of coastal response. A specific value of 0.78 has been turned off (not rendered) in the layer file, as this value typically represents deeper water areas below -1 meter in elevation. These areas are often less relevant for end users and have higher associated uncertainty. This decision was made to improve visual clarity and focus on nearshore dynamics. Users may choose to turn this value back on in the symbology settings if desired.
    Entity_and_Attribute_Detail_Citation:
    This raster dataset does not contain a traditional attribute table. Pixel values represent either probabilistic outcomes or classified elevation bins, as described in the accompanying documentation and metadata. Symbology (.lyrx) files are included to define the display of value ranges and categories. See Lentz and others (2015), and Heslin and others (2024) for more information on the model framework and output descriptions.

Who produced the data set?

  1. Who are the originators of the data set? (may include formal authors, digital compilers, and editors)
    • Marie K. Bartlett
    • Julia L. Heslin
    • Kathryn M. Weber
    • Erika E. Lentz
  2. Who also contributed to the data set?
  3. To whom should users address questions about the data?
    U.S. Geological Survey
    Attn: Marie K. Bartlett
    384 Woods Hole Rd
    Woods Hole, MA

    508-548-8700 x2306 (voice)
    mbartlett@usgs.gov

Why was the data set created?

These raster layers represent the probability of observing a static versus dynamic coastal response (CR) for the Northeastern U.S., with respect to Intermediate High (IH) and Intermediate Low (IL) predicted sea-level rise (SLR) scenarios—for the 2030s, 2050s, 2080s, and 2100s. The data are derived from a probabilistic framework (Bayesian network) that integrates relative SLR scenarios, elevation data, and land cover information. The outputs provide a probability value for a binary outcome (static or dynamic response) for each projection year. Because these responses are mutually exclusive, the probability of a dynamic response can be calculated by subtracting the static response probability from 1 (and vice versa). These layers are intended to show the spatial distribution of likely coastal response types over a broad area and should be interpreted qualitatively (see Horizontal Positional Accuracy Report for limitations).

How was the data set created?

  1. From what previous works were the data drawn?
    Fabric (source 1 of 5)
    Sterne, Travis K., Pendleton, Elizabeth A., Lentz, Erika E., and Henderson, Rachel E., 2023, Coastal Change Likelihood in the U.S. Northeast Region: Maine to Virginia - Fabric Dataset: data release DOI:10.5066/P96A2Q5X, U.S. Geological Survey, Coastal and Marine Geology Program, Woods Hole Coastal and Marine Science Center, Woods Hole, MA.

    Online Links:

    Type_of_Source_Media: Digital
    Source_Contribution:
    Contains source elevation and landcover data used to create point data which was ingested into the CRL code
    SLR Data (source 2 of 5)
    Sweet, William V., Hamlington, Benjamin D., Kopp, Robert E., Weaver, Christopher P., Barnard, Patrick L., Bekaert, David, Brooks, William, Craghan, Michael, Dusek, Gregory, Frederikse, Thomas, Garner, Gregory, Genz, Ayesha S., Krasting, John P., Larour, Eric, Marcy, Doug, Marra, John J., Obeysekera, Jayantha, Osler, Mark, Pendleton, Matthew, Roman, Daniel, Schmied, Lauren, Veatch, Will, White, Kathleen D., and Zuzak, Casey, 202202, 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, Silver Springs.

    Online Links:

    Other_Citation_Details:
    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
    Type_of_Source_Media: Digital
    Source_Contribution:
    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.
    CONED (source 3 of 5)
    Danielson, Jeffrey, and Tyler, Dean, 2018, Coastal National Elevation Database.

    Online Links:

    Type_of_Source_Media: Digital
    Source_Contribution: Elevation
    CZM Topobathy (source 4 of 5)
    Andrews, B.D., Baldwin, W.E., Sampson, D.W., and Schwab, W.C., 20191227, Continuous bathymetry and elevation models of the Massachusetts coastal zone and continental shelf: data release DOI:10.5066/F72806T7, U.S. Geological Survey, Reston, VA.

    Online Links:

    Type_of_Source_Media: Digital
    Source_Contribution: Elevation
    NOAA SLR Topo (source 5 of 5)
    Office for Coastal Management, 2016, NOAA Office for Coastal Management Sea Level Rise Data: 1-10ft Seal Level Rise Inundation Extent.

    Online Links:

    Type_of_Source_Media: Digital
    Source_Contribution: Elevation
  2. How were the data generated, processed, and modified?
    Date: 2024 (process 1 of 4)
    Three input data are required for the 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. Person who carried out this activity:
    Julia L. Heslin
    384 Woods Hole Rd
    Woods Hole, MA

    508-548-8700 x2230 (voice)
    jheslin@usgs.gov
    Data sources used in this process:
    • SLR Data
    • Fabric
    • CZM Topobathy
    • CONED
    • NOAA SLR Topo
    Date: 2024 (process 2 of 4)
    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.
    Date: 2024 (process 3 of 4)
    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. Person who carried out this activity:
    Marie K. Bartlett
    384 Woods Hole Rd
    Woods Hole, MA

    508-548-8700 x2306 (voice)
    mbartlett@usgs.gov
    Date: 2025 (process 4 of 4)
    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
  3. What similar or related data should the user be aware of?
    Lentz, Erika E., Stippa, Sawyer R., Thieler, E. Robert, Plant, Nathaniel G., Gesch, Dean B., and Horton, Radley M., 2015, Evaluating coastal landscape response to sea-level rise in the northeastern United States: approach and methods: Open-File Report 2014-1252, U.S. Geological Survey, Reston, VA.

    Online Links:

    Heslin, Julia L., Weber, Kathryn M., Lentz, Erika E., Frank-Gilchrist, Donya P., and Mercer, Jason J., 2024, Coastal Response Likelihood: software release DOI:10.5066/P1SQIVEW, U.S. Geological Survey, Reston, VA.

    Online Links:

    Other_Citation_Details: Only available to internal users within U.S. Geological Survey
    Pendleton, Elizabeth A., Lentz, Erika E., Sterne, Travis K., and Henderson, Rachel E., 2023, Development and application of a coastal change likelihood assessment for the northeast region, Maine to Virginia: Data Report 1169, U.S. Geological Survey, Reston, VA.

    Online Links:

    Other_Citation_Details:
    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)

How reliable are the data; what problems remain in the data set?

  1. How well have the observations been checked?
    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.
  2. How accurate are the geographic locations?
    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.
  3. How accurate are the heights or depths?
    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 sea level rise (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/).
  4. Where are the gaps in the data? What is missing?
    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 probability of a static or dynamic response on a scale of 0 to 1, respectively. Values greater than 0.5 are dynamic whereas values less than 0.5 are static (values equal to 0.5 highlight greatest uncertainty). 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) were chosen.
  5. How consistent are the relationships among the observations, including topology?
    The raster dataset representing coastal response probabilities (values from 0 to 1) 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.

How can someone get a copy of the data set?

Are there legal restrictions on access or use of the data?
Access_Constraints None. Please see 'Distribution Info' for details.
Use_Constraints 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.
  1. Who distributes the data set? (Distributor 1 of 1)
    U.S. Geological Survey - ScienceBase
    Denver Federal Center, Building 810, Mail Stop 302
    Denver, CO
    US

    1-888-275-8747 (voice)
    sciencebase@usgs.gov
  2. What's the catalog number I need to order this data set? This dataset contains the raster data layer (.tif) and associated files (.tfw, .ovr) 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, and the .ovr file contains the pyramids used by a GIS to display the data at different scales.
  3. What legal disclaimers am I supposed to read?
    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.
  4. How can I download or order the data?

Who wrote the metadata?

Dates:
Last modified: 17-Jul-2025
Metadata author:
U.S. Geological Survey
Attn: Marie K. Bartlett
Physical Scientist
384 Woods Hole Rd
Woods Hole, MA

508-548-8700 x2306 (voice)
whsc_data_contact@usgs.gov
Contact_Instructions:
The metadata contact email address is a generic address in the event the metadata author is no longer with the USGS.
Metadata standard:
Content Standard for Digital Geospatial Metadata, FGDC-STD-001-1998 (FGDC-STD-001.1-1998)
This page is <https://cmgds.marine.usgs.gov/catalog/whcmsc/SB_data_release/DR_P13JKJUT/CR_NE_Metadata.faq.html>
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