Coastal Change Likelihood in the U.S. Northeast Region: Maine to Virginia - Hazard Impact Type
Dates
Publication Date
2023-02-28
Start Date
2010
End Date
2021
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.
Summary
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. [...]
Summary
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.
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HazImpType_Graphic.jpg “Map showing the hazards type layer, which highlights influential hazard class”
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USGS_CCL_HazardImpactType_2022.zip
USGS_CCL_HazardImpactType_2022.xml Original FGDC Metadata
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USGS_CCL_HazardImpactType_2022.tfw
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USGS_CCL_HazardImpactType_2022.tif
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USGS_CCL_HazardImpactType_2022.tif.ovr
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USGS_CCL_HazardImpactType_2022.tif.vat.CPG
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USGS_CCL_HazardImpactType_2022.tif.vat.dbf
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USGS_CCL_HazardImpactType_2022.sld
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Related External Resources
Type: Related Primary Publication
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.
Coastal Change Likelihood (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 Hazard Impact Type dataset is a comparison of the Event and Perpetual Hazard supervised learning outcomes. Each 10 mpp raster cell is assigned a value between 1 and 4, where 1 is low expected hazard impact and 4 is high expected hazard impact. 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.
Preview Image
Map showing the hazards type layer, which highlights influential hazard class