Coastal Change Likelihood in the U.S. Northeast Region: Maine to Virginia - Perpetual Hazards

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Frequently anticipated questions:

What does this data set describe?

Title:
Coastal Change Likelihood in the U.S. Northeast Region: Maine to Virginia - Perpetual Hazards
Abstract:
Coastal resources are increasingly impacted by erosion, extreme weather events, sea-level rise, tidal flooding, and other potential hazards related to climate change. These hazards have varying impacts on coastal landscapes due to the numerous geologic, oceanographic, ecological, and socioeconomic factors that exist at a given location. Here, an assessment framework is introduced that synthesizes existing datasets describing the variability of the landscape and hazards that may act on it to evaluate the likelihood of coastal change along the U.S coastline within the coming decade. The pilot study, conducted in the Northeastern U.S. (Maine to Virginia), is comprised of datasets derived from a variety of federal, state, and local sources. First, a decision tree-based dataset is built that describes the fabric or integrity of the coastal landscape and includes landcover, elevation, slope, long-term (>150 years) shoreline change trends, dune height, and marsh stability data. A second database was generated from coastal hazards, which are divided into event hazards (e.g., flooding, wave power, and probability of storm overwash) and persistent hazards (e.g., relative sea-level rise rate, short-term (about 30 years) shoreline erosion rate, and storm recurrence interval). The fabric dataset is then merged with the coastal hazards databases and a training dataset made up of hundreds of polygons is generated from the merged dataset to support a supervised learning classification. Results from this pilot study are location-specific at 10-meter resolution and are made up of four raster datasets that include (1) quantitative and qualitative information used to determine the resistance of the landscape to change, (2 & 3) the potential coastal hazards that act on it, (4) the machine learning output, or Coastal Change Likelihood (CCL), based on the cumulative effects of both fabric and hazards, and (5) an estimate of the hazard type (event or persistent) that is the likely to influence coastal change. Final outcomes are intended to be used as a first order planning tool to determine which areas of the coast may be more likely to change in response to future potential coastal hazards, and to examine elements and drivers that make change in a location more likely.
  1. How might this data set be cited?
    Sterne, Travis K., Pendleton, Elizabeth A., Lentz, Erika E., and Henderson, Rachel E., 20230228, Coastal Change Likelihood in the U.S. Northeast Region: Maine to Virginia - Perpetual Hazards: data release DOI:10.5066/P96A2Q5X, 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.

    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: data release DOI:10.5066/P96A2Q5X, U.S. Geological Survey, Reston, VA.

    Online Links:

    Other_Citation_Details:
    Suggested citation: Sterne, T.K., Pendleton, E.A., Lentz, E.E., and Henderson, R.E., 2023, Coastal Change Likelihood in the U.S. Northeast Region — Maine to Virginia: U.S. Geological Survey data release, https://doi.org/10.5066/P96A2Q5X.
  2. What geographic area does the data set cover?
    West_Bounding_Coordinate: -77.4828
    East_Bounding_Coordinate: -66.5998
    North_Bounding_Coordinate: 45.3000
    South_Bounding_Coordinate: 36.5148
  3. What does it look like?
    https://www.sciencebase.gov/catalog/file/get/6178323ad34e4c6b7fe2a4a0?name=PerpHaz_Graphic.jpg (JPEG)
    Outer Cape Cod with Perpetual Hazards data layer.
  4. Does the data set describe conditions during a particular time period?
    Beginning_Date: 2010
    Ending_Date: 2021
    Currentness_Reference:
    ground condition of source data
  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 128024 x 117988 x 1, type Grid Cell
    2. What coordinate system is used to represent geographic features?
      The map projection used is Mercator_1SP.
      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.
  7. How does the data set describe geographic features?
    USGS_CCL_PerpetualHazards_2022.tif
    Raster geospatial data file. (Source: U.S. Geological Survey)
    OID
    Internal object identifier. (Source: Esri) Sequential unique whole numbers that are automatically generated.
    Value
    Unique numeric values contained in each raster cell. (Source: U.S. Geological Survey)
    ValueDefinition
    40No relative sea-level rise, low storm frequency, no erosion data
    41Relative sea-level rise, low storm frequency, no erosion data
    50No relative sea-level rise, moderate storm frequency, no erosion data
    51Relative sea-level rise, moderate storm frequency, no erosion data
    140No relative sea-level rise, low storm frequency, low erosion rate
    141Relative sea-level rise, low storm frequency, low erosion rate
    150No relative sea-level rise, moderate storm frequency, low erosion rate
    151Relative sea-level rise, moderate storm frequency, low erosion rate
    240No relative sea-level rise, low storm frequency, moderate erosion rate
    241Relative sea-level rise, low storm frequency, moderate erosion rate
    250No relative sea-level rise, moderate storm frequency, moderate erosion rate
    251Relative sea-level rise, moderate storm frequency, moderate erosion rate
    340No relative sea-level rise, low storm frequency, high erosion rate
    341Relative sea-level rise, low storm frequency, high erosion rate
    350No relative sea-level rise, moderate storm frequency, high erosion rate
    351Relative sea-level rise, moderate storm frequency, high erosion rate
    Count
    Number of raster cells with this value (Source: Esri)
    Range of values
    Minimum:63153
    Maximum:276569584
    RSLR
    Relative Sea-level Rise Presence/Absence (Source: U.S. Geological Survey)
    ValueDefinition
    NIndicates that specific area is not likely to be affected by relative seal-level rise in the coming decade
    YIndicates that specific area is likely to be affected by relative seal-level rise in the coming decade
    Storm_Freq
    Storm frequency interval per 10 years. (Source: U.S. Geological Survey)
    ValueDefinition
    LowLess than or equal to 3 tropical storms per 10 years have passed within 100 nautical miles of this location since records began in the 1840s.
    ModerateGreater than or equal to 4 tropical storms per 10 years have passed within 100 nautical miles of this location since records began in the 1840s.
    Erosion
    Erosion in meters based on short-term (<30 years) shoreline change records (Source: U.S. Geological Survey)
    ValueDefinition
    NoDataNo Data
    Low0 to 0.75 meters erosion based on calculated short-term regression rates.
    moderate0.75 to 3 meters erosion based on calculated short-term regression rates.
    highGreater than 3 meters erosion based on calculated short-term regression rates.

Who produced the data set?

  1. Who are the originators of the data set? (may include formal authors, digital compilers, and editors)
    • Travis K. Sterne
    • Elizabeth A. Pendleton
    • Erika E. Lentz
    • Rachel E. Henderson
  2. Who also contributed to the data set?
  3. To whom should users address questions about the data?
    U.S. Geological Survey
    Attn: Travis K Sterne
    384 Woods Hole Rd
    Woods Hole, MA

    (508) 548 8700 x2219 (voice)
    tsterne@usgs.gov

Why was the data set created?

CCL is a first order planning tool that estimates the likelihood that an area of coast will experience change based on its inherit resistance to change, metrics associated with specific land cover types, and the hazards that impact a coast. The CCL Perpetual Hazards dataset is a 10 mpp compilation of three perpetual coastal hazards (relative sea-level rise, storm recurrence interval, and short-term shoreline erosion rate) used in building the final CCL product in geotiff format. Each raster cell is assigned a unique value based on the potential hazard scenario expected to occur in a given location. All relevant information pertaining to each grid cell is stored in the associated attribute table. This dataset covers the Northeast US coastline between +/- 10 meters elevation relative to mean high water (MHW) from Maine to Virginia.

How was the data set created?

  1. From what previous works were the data drawn?
    Shoreline Change - MA (source 1 of 6)
    Himmelstoss, E.A., Farris, A.S., and Weber, K.M., 2019, Massachusetts Shoreline Change Project, 2018 Update: A GIS Compilation of Shoreline Change Rates Calculated Using Digital Shoreline Analysis System Version 5.0, With Supplementary Intersects and Baselines for Massachusetts.

    Online Links:

    Other_Citation_Details:
    Suggested citation: Himmelstoss, E.A., Farris, A.S., Weber, K.M., and Henderson, R.E., 2019, Massachusetts shoreline change project, 2018 update–A GIS compilation of shoreline change rates calculated using Digital Shoreline Analysis System version 5.0, with supplementary intersects and baselines for Massachusetts (ver. 2.0, August 2019): U.S. Geological Survey data release, https://doi.org/10.5066/P9RRBEYK.
    Type_of_Source_Media: Digital and/or Hardcopy
    Source_Contribution: Shoreline Change Rates
    Shoreline Change - Not MA (source 2 of 6)
    Himmelstoss, E.A., Kratzmann, M., Hapke, C., Thieler, E.R., and List, J., 2010, The national assessment of shoreline change: A GIS compilation of vector shorelines and associated shoreline change data for the New England and Mid-Atlantic Coasts: U.S. Geological Survey Open-File Report 2010–1119.

    Online Links:

    Other_Citation_Details:
    Suggested citation: Himmelstoss, E.A., Kratzmann, M., Hapke, C., Thieler, E.R., and List, J., 2010, The national assessment of shoreline change: A GIS compilation of vector shorelines and associated shoreline change data for the New England and Mid-Atlantic Coasts: U.S. Geological Survey Open-File Report 2010–1119, available only online at https://doi.org/10.3133/ofr20101119.
    Type_of_Source_Media: Digital and/or Hardcopy
    Source_Contribution: Shoreline Change Rates
    RSLR (source 3 of 6)
    Sweet, W.V., Kopp, R.E., Weaver, C.P., Obeysekera, R.M., Horton, R.M., Thieler, E.R., and Zervas, C., 20170101, Global and Regional Sea Level Rise Scenarios for the United States.

    Online Links:

    Type_of_Source_Media: Digital and/or Hardcopy
    Source_Contribution: Sea Level Rise Rates
    NOAA Storm Counts (source 4 of 6)
    Knapp, K.R., Diamond, H.J., Kossin, J.P., Kruk, M.C., and Schreck, C.J. (III), 2018, International Best Track Archive for Climate Stewardship (IBTrACS) Project.

    Online Links:

    Type_of_Source_Media: Digital and/or Hardcopy
    Source_Contribution: Storm Frequency for the North Atlantic
    HTF (source 5 of 6)
    Sweet, W.V., Marcy, D., Dusek, G., Marra, J.J., and Pendleton, M., 2017, State of U.S. High Tide Flooding with a 2018 Outlook, National Oceanic and Atmospheric Administration (NOAA) Center for Operational Oceanographic Products and Services.

    Online Links:

    Type_of_Source_Media: Digital and/or Hardcopy
    Source_Contribution: High tide flooding potential
    NHC Forecast Cone (source 6 of 6)
    National Oceanic and Atmospheric Administration, 2021, Definition of the NHC Track Forecast Cone.

    Online Links:

    Type_of_Source_Media: Digital and/or Hardcopy
    Source_Contribution: Radius of storm influence
  2. How were the data generated, processed, and modified?
    Date: 2020 (process 1 of 5)
    This step and all the subsequent steps were completed by Elizabeth A. Pendleton or Travis K. Sterne using ESRI ArcGIS Pro geospatial software. Any steps that mention the use of “tools” or “functions” refer to geoprocessing tools utilized in ArcGIS Pro. The steps described in detail below are computed on the domain defined by the fabric dataset found in this data release. Each hazard dataset processed and included in this dataset has been clipped or modified to fit within the domain of the Northeast CCL study area. The final hazards raster (at end of step 4) presented here is a combination of three hazards that can be classified as perpetual hazards because they occur near constantly. Perpetual hazards include relative sea-level rise projections for 2030, storm recurrence interval , and short-term shoreline erosion rate. Perpetual hazard magnitude are stored according to place holder values, such that the ones place is occupied by relative sea-level rise rate, the tens place is occupied by storm recurrence interval, and the hundreds place is occupied by shoreline erosion rate. Each hazard has between 2 and 4 classes associated with presence, thresholds, or magnitude defined either in this study or the source data. Details about the source data and processing for each hazard layer are described below
    Date: 2021 (process 2 of 5)
    Step 1: Relative Sea-Level Rise: Sea-level rise projections were derived from estimates in Sweet et al. (2017) for 2030 (1-meter rise by 2100, intermediate projection). Estimated relative sea-level rise for 2030 was subtracted from the adjusted MHW Elevation Mosaic produced in the Fabric dataset using the Raster Calculator tool. This created an adjusted elevation raster, where values are relative to predicted relative sea-level rise in 2030. This layer was then reclassified to a value of 1 for pixels equal to or less than 0 meters MHW for the adjusted elevation, and 0 for values greater than 0 meters MHW. Person who carried out this activity:
    Travis K Sterne
    U.S. Geological Survey, NORTHEAST REGION
    Geographer
    384 Woods Hole Road
    Woods Hole, MA
    US

    (508) 548 8700 x2219 (voice)
    tsterne@usgs.gov
    Data sources used in this process:
    • RSLR
    Data sources produced in this process:
    • PerpetualHazards1
    Date: 2021 (process 3 of 5)
    Step 2: Storm frequency: Storm tracks classified as tropical storm or hurricanes according to NOAA’s International Best Track Archive for Climate Stewardship (IBTrACS) Project were used to compile an estimate of storm frequency since 1842 for the Northeast region of the US. The vector data provided by the data source was first buffered using the Buffer tool to a radius of 100 nautical miles in order simulate the potential for variations in landfall consistent with the methods used by NOAA’s National Hurricane Center (NHC) in estimating 5-day forecasts for tropical cyclones. Overlapping buffered storm tracks were then counted using the Count Overlapping Features tool, resulting in a new vector polygon layer in which each individual polygon represented the number of buffered storm tracks overlapping in each location. This vector polygon layer was then clipped to the elevation domain and converted to raster format using the Clip and Feature to Raster tools. The resulting raster was then normalized to represent number of storms per 10 years and reclassified using Raster Calculator. The thresholds are (threshold range, reclassified value): Three storms or less, 40; four or more storms, 50 Person who carried out this activity:
    Elizabeth A. Pendleton
    U.S. Geological Survey, NORTHEAST REGION
    Geologist
    384 Woods Hole Road
    Woods Hole, MA
    US

    (508) 548 8700 x2259 (voice)
    ependleton@usgs.gov
    Data sources used in this process:
    • NHC Forecast Cone
    • NOAA Storm Counts
    Data sources produced in this process:
    • PerpetualHazards2
    Date: 2021 (process 4 of 5)
    Step 3: Erosion: Short-term shoreline change transects from Himmelstoss et al. (2010) and Himmelstoss, Farris, and Weber (2018) were clipped to eliminate spatial overhang into inapplicable portions of the landscape such as back-bay areas of barrier islands using the Clip tool. Transects were then converted to raster format using the Feature to Raster tool and interpolated using two low-pass smoothing filters (Filter tool) order to fill in data gaps between the 50-meter-spaced transects. Only negative (erosion) shoreline change rates were used in this hazards layer. Thresholds were defined and the raster layer was reclassified as follows (erosion in meters, reclassified value): 0 to 0.75m, 100; 0.75 to 3m, 200; greater than 3m, 300. Raster Calculator was used to reclassify the landscape according to these thresholds. Data sources used in this process:
    • Shoreline Change - MA
    • Shoreline Change - Not MA
    Data sources produced in this process:
    • PerpetualHazards3
    Date: 2021 (process 5 of 5)
    Step 4: The individual perpetual hazards layers were combined into a single geotiff using Raster Calculator by adding all previously compiled raster layers together (PerpetualHazards1 + PerpetualHazards2 + PerpetualHazards3). The resultant grid had values between 40 and 351, representing the combination of hazards and their magnitudes that exist for a given area. The attribute table for this integer grid was updated to include text descriptor fields to help users identify the perpetual hazards present for each grid value. See entity and attribute definitions for grid value definitions. Data sources used in this process:
    • PerpetualHazards1
    • PerpetualHazards2
    • PerpetualHazards3
    Data sources produced in this process:
    • PerpetualHazardsComposite
  3. What similar or related data should the user be aware of?
    Thieler, E.R., and Hammar-Klose, E.S., 1999, National assessment of coastal vulnerability to sea-level rise; U.S. Atlantic Coast: Open-File Report 1999-593, U.S. Geological Survey, Reston, VA.

    Online Links:

    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?
    All data values represent a compilation of coastal hazards likely to be present in the coming decade based on previous empirical research and expert opinion. The final output generated is the expected outcome based on this information.
  2. How accurate are the geographic locations?
    Horizontal coordinate information is referenced to the World Geodetic System of 1984 (WGS 1984) in a Geographic Coordinate System or WGS 1984 Web Mercator (auxiliary sphere) in a Projected Coordinate System. Source data were resampled to 10 mpp for use. There may be resampling errors associated with coarsening (e.g. elevation data were resampled from 1 mpp to 10 mpp) or fining (UVVR were resampled from 30 mpp to 10 mpp) of the data. Likewise some data, like NOAA’s ESI and the shoreline change data were rasterized from a source vector, and there can be spatial inconsistencies associated with the rasterization of vector data. The horizontal accuracy of this dataset is assumed to be better than +/– 30 meters, but dynamic coastal areas may experience much higher rates of change during storms, and horizontal offset at the shoreline maybe much higher (+/– 100 meters) in certain areas.
  3. How accurate are the heights or depths?
    This dataset’s domain is defined by the z-values (elevation) domain of the Fabric dataset (of this publication), and as such has a horizontal positional uncertainty of up to 50 cm along the edge of the domain, which corresponds to +/- 10 meters MHW . However, this dataset has no explicit vertical depth values itself, and therefore there is no vertical position accuracy estimate except along the boundary of this dataset domain.
  4. Where are the gaps in the data? What is missing?
    CCL is a model for coastal landscapes in the Northeast United States. All output is "clipped" to an elevation domain; this dataset represents coastal hazards likely to be present in the coming decade from -10 to +10 meters MHW elevation, where data exists. Existing gaps in coverage for this dataset within this domain are a result of data gaps in source information.
  5. How consistent are the relationships among the observations, including topology?
    All data were checked for accuracy during processing. Any inconsistencies in the final data product are artifacts of source data.

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. Although these data are published at a resolution of 10 mpp and are considered high resolution, the intended scale for use is around 1:24,000. Please read the associated data release (https://doi.org/10.3133/dr1169) for a list of caveats, applications, and use recommendations for these data.
  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 (.sld, .ovr, .cpg, and .dbf) needed to view and edit the information it contains, as well as the FGDC CSDGM metadata in XML format. The .sld is a Service Layer Definition file used by ScienceBase to display the data, 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.
  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: 28-Feb-2023
Metadata author:
U.S. Geological Survey
Attn: Elizabeth A. Pendleton
Geologist
384 Woods Hole Rd
Woods Hole, MA

(508) 457 2259 (voice)
ependleton@usgs.gov
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_P96A2Q5X/USGS_CCL_PerpetualHazards_2022.faq.html>
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