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

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.  

Contacts

Point of Contact :: Travis K Sterne
Originator :: Travis K Sterne, Elizabeth P Pendleton, Erika Lentz, Rachel E Henderson
Data Owner :: Woods Hole Coastal and Marine Science Center
Mission Area :: Natural Hazards
USGS Program :: Coastal and Marine Hazards and Resources Program
Distributor :: U.S. Geological Survey - ScienceBase

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MaxCCL_Graphic.jpg “Map showing the maximum coastal change likelihood value ”		477.97 KB	image/jpeg

Extension: USGS_CCL_MaximumCCL_2022.zip
USGS_CCL_MaximumCCL_2022.xml Original FGDC Metadata	View	33.02 KB
USGS_CCL_MaximumCCL_2022.tif		208.45 MB
USGS_CCL_MaximumCCL_2022.tif.ovr		44.26 MB
USGS_CCL_MaximumCCL_2022.tif.vat.CPG		5 Bytes
USGS_CCL_MaximumCCL_2022.tif.vat.dbf		428 Bytes
USGS_CCL_MaximumCCL_2022.sld		1.7 KB

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.	https://doi.org/10.3133/dr1169

Purpose

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 Maximum Change Likelihood is the combination of supervised learning outcomes from the Fabric, Perpetual, and Event hazards. Each 10 mpp raster cell is assigned a value between 1 and 10 that is an estimate of change likelihood, where 1 is low and 10 is high, based on an ordinal scale. 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.

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

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