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.
Purpose:
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 Event Hazards dataset is a 10-meter-per-pixel (mpp) compilation of three event-driven coastal hazards (high-tide flooding, wave power, and overwash potential) 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.
Theme:
Theme_Keyword_Thesaurus: ISO 19115 Topic Category
Theme_Keyword: geoscientificInformation
Theme_Keyword: oceans
Theme:
Theme_Keyword_Thesaurus: None
Theme_Keyword: U.S. Geological Survey
Theme_Keyword: USGS
Theme_Keyword: Coastal and Marine Hazards Mission Area
Theme_Keyword: Woods Hole Coastal and Marine Science Center
Theme_Keyword: Coastal Fabric
Theme_Keyword: Elevation
Theme_Keyword: Interpretation
Theme_Keyword: Bathymetry
Theme_Keyword: Landcover
Theme_Keyword: Land Cover
Theme_Keyword: Topography
Theme_Keyword: UVVR
Theme_Keyword: Unvegetated-Vegetated Ratio
Theme_Keyword: Shoreline Change
Theme_Keyword: Coastal Hazards
Theme_Keyword: High Tide Flooding
Theme_Keyword: Storm Recurrence
Theme_Keyword: Wave Power
Theme_Keyword: Storm Overwash
Theme_Keyword: Sea Level Rise
Theme_Keyword: Coastal Change Hazard Assessment
Theme_Keyword: Coastal Vulnerability Index
Theme_Keyword: Machine Learning
Theme_Keyword: Autoclassification
Theme_Keyword: Automation
Theme_Keyword: Arcpy
Theme_Keyword: ArcGIS Pro
Theme_Keyword: Support Vector Machine
Theme_Keyword: Training Samples
Theme_Keyword: Supervised Classification
Theme_Keyword: Decision Tree Framework
Theme_Keyword: scientific interpretation
Theme_Keyword: land use and land cover
Theme:
Theme_Keyword_Thesaurus: USGS Thesaurus
Theme_Keyword: marine geology
Theme_Keyword: coastal processes
Theme_Keyword: topography
Theme_Keyword: sea-level change
Theme_Keyword: hazards
Theme:
Theme_Keyword_Thesaurus: USGS Metadata Identifier
Theme_Keyword: USGS:61783250d34e4c6b7fe2a4a2
Place:
Place_Keyword_Thesaurus: None
Place_Keyword: Northeast US
Place_Keyword: Maine
Place_Keyword: New Hampshire
Place_Keyword: Massachusetts
Place_Keyword: Rhode Island
Place_Keyword: Connecticut
Place_Keyword: New York
Place_Keyword: New Jersey
Place_Keyword: Delaware
Place_Keyword: Maryland
Place_Keyword: Virginia
Place_Keyword: St. Croix Island International Historic Site
Place_Keyword: Acadia National Park
Place_Keyword: Gateway National Recreation Area
Place_Keyword: George Washington Birthplace National Monument
Place_Keyword: Cape Cod National Seashore
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.
