Coastal Change Likelihood Assessment
A synthesis of factors that determine future coastal change
Coastal Change Likelihood Assessment
A synthesis of factors that determine future coastal change
Geonarrative
The U.S. Geological Survey, in cooperation with the National Park Service, developed the Coastal Change Likelihood assessment to determine the future likelihood of coastal change along the U.S. northeast coast in the next decade.
This story is part of the Earth Day 2023 Special Issue of the Sound Waves Newsletter.
Coastal communities, important habitats, and cultural and natural resources can be threatened by hazards associated with coastal change. These hazards, such as storms, sea level rise, and erosion, have varying effects on different types of coastal landscapes at different locations. Understanding where coastal change is most likely to occur and which types of hazards, event-driven (e.g., storms) and/or perpetual (e.g., sea level rise), are more likely to affect a specific location is essential to planning for future vulnerabilities to people and resources.
To help provide this critical information, the U.S. Geological Survey (USGS), in partnership with the National Park Service through the Natural Resource Preservation Program, developed the Coastal Change Likelihood (CCL) assessment.
The CCL is a machine-learning framework that combines over 20 existing datasets from a variety of federal, state, and private organizations, including USGS and the National Oceanic and Atmosphere Administration (NOAA), to describe the landscape and six common hazards—erosion, storm frequency, relative sea level rise, tidal flooding, storm overwash probability, and wave power—that may affect the landscape to determine the likelihood of coastal change on a decadal scale.
The assessment integrates data describing coastal characteristics, landscape composition, and the level of resistance to change, with data defining the drivers of change that impact the coast, such as waves and flooding. These data types are known as fabric and hazards, respectively. Together, they produce the final CCL outcomes—the Maximum Coastal Change Likelihood (Max CCL) and the Hazard Impact Type. The Max CCL is the combination of all the fabric and hazard data values and identifies the likelihood of coastal change, ranging from extremely unlikely to change to extremely likely to change. The Hazard Impact Type refines the outcome by indicating which hazard type (event or perpetual) is most likely to cause coastal change in a given area.
These outcomes, produced across the coastal zone up to 10-meters above the average high-water level, can be used to support near-term decision making in conjunction with other data and expertise to explore vulnerabilities and potential landscape changes. Additionally, the CCL can be used as a base layer for coastal landscape assessments, a synthesis of common coastal hazards, and a geospatial inventory of the source datasets.
The National Park Service will specifically use the CCL to support archeological resource management in national parks.
“I see CCL as a tool that could support a wide variety of cultural resource management needs,” said Amanda Babson, Coastal Climate Adaptation Coordinator for the National Park Service in the North Atlantic-Appalachian Region. “If NPS cultural resources managers can demonstrate broad applicability, there are NPS programs beyond parks that could benefit, such as coastal National Historic Landmarks, Network to Freedom Underground Railroad sites or National Heritage Areas.”
The CCL is an updated version of the older Coastal Vulnerability Index, first published in 1999. While the original product was focused on change in the next 50-100 years based solely on sea level rise, the new CCL is more near-term, focusing on change over the next decade as a result of multiple coastal hazards. The CCL incorporates significant improvements to the Coastal Vulnerability Index thanks to technological updates and improvements in coastal data source quality and resolution. Specific improvements include (1) expanded coverage of the coastal zone, allowing this assessment to cover inland coastal areas, as well as the coastline; (2) higher resolution predictive decision support datasets and maps that are area-based as opposed to line-based; and (3) using a machine learning approach as opposed to the spatial analysis methods utilized in the Coastal Vulnerability Index. Overall, this means the data in the CCL is more accurate, includes more complex information, better accounts for geological and ecological variability and human development, and the data layers can be used together or independently to evaluate where different types of hazards are likely to have the greatest impacts on the coast.
“It is enormously satisfying to find a way to bring together this sophisticated array of coastal datasets with data processing improvements to tell a more wholistic story about the future of the coast,” said lead author Elizabeth Pendleton, USGS Woods Hole Coastal and Marine Science Center.
This initial study was conducted in the Northeast region of the U.S., from Maine to Virginia, as a proof-of-concept pilot for updating the original Coastal Vulnerability Index to the new Coastal Change Likelihood assessment. To provide focus on archaeological and cultural resource applications of interest to the National Park Service, five coastal national parks, including Cape Cod National Seashore, St. Croix Island International Historic Site, George Washington Birthplace National Monument, Gateway National Recreation Area, and Acadia National Park, were used as case study examples of the data, methodology, and utility of the CCL assessment.
“Providing comprehensive change information at the regional scale allows coastal managers and planners to evaluate and prioritize where assets—whether these be archeological sites, infrastructure, or habitat—may be most vulnerable across an entire region,” said co-author Erika Lentz, USGS Woods Hole Coastal and Marine Science Center.
Read the data report, “The Workflow of the Coastal Change Likelihood Assessment for the Northeast Region, Maine to Virginia,” published on February 28, 2023, to learn more.
The USGS is committed to bringing the CCL assessment to the Great Lakes coastline next through a collaboration with the Great Lakes Restoration Initiative, which includes numerous state and federal partners including NOAA, the Environmental Protection Agency, and the U.S. Army Corps of Engineers. Expansion beyond the Northeast and Great Lakes is currently being explored.
Fundamental parts of the CCL assessment, including the fabric and hazards datasets, as well as coastal change likelihood outcomes, are available as part of a companion data release. Download the data now!
Visit the Coastal Change Likelihood webpage for more information and stay tuned for an upcoming geonarrative that allows you to explore all the data maps!
Data Report:
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.
Data Release:
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.
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