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The goal of this project is to quantify, at the National scale, the relative susceptibility of the Nation's coast to sea- level rise through the use of a coastal vulnerability index (CVI). This initial classification is based upon the variables coastal geomorphology, regional coastal slope, tidal range, wave height, historical rates of relative sea-level rise and shoreline erosion and accretion rates. The combination of these variables furnishes a broad overview of regions where physical changes are likely to occur due to sea-level rise. Data downloads available at http://pubs.usgs.gov/dds/dds68/
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Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline data along open-ocean sandy shores of the conterminous United States and parts of Alaska and Hawaii under the National Assessment of Shoreline Change project.There is no widely accepted standard for analyzing shoreline...
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This dataset consists of short-term (~32 years) shoreline change rates for the north coast of Alaska between the U.S. Canadian Border and the Hulahula River. Rate calculations were computed within a GIS using the Digital Shoreline Analysis System (DSAS) version 4.3, an ArcGIS extension developed by the U.S. Geological Survey. Short-term rates of shoreline change were calculated using a linear regression rate-of-change method based on available shoreline data between 1978 and 2010. A reference baseline was used as the originating point for the orthogonal transects cast by the DSAS software. The transects intersect each shoreline establishing measurement points, which are then used to calculate short-term rates.
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This dataset consists of short-term (~31 years) shoreline change rates for the north coast of Alaska between the Point Barrow and Icy Cape. Rate calculations were computed within a GIS using the Digital Shoreline Analysis System (DSAS) version 4.3, an ArcGIS extension developed by the U.S. Geological Survey. Short-term rates of shoreline change were calculated using a linear regression rate-of-change method based on available shoreline data between 1979 and 2010. A reference baseline was used as the originating point for the orthogonal transects cast by the DSAS software. The transects intersect each shoreline establishing measurement points, which are then used to calculate short-term rates.
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This dataset is part of an extensive analysis of sea-level rise impacts on coastal habitats along the Chesapeake Bay, Delaware Bay, and the ocean beaches of southern New Jersey, Delaware, Maryland, and Virginia. The National Wildlife Federation commissioned Jonathan S. Clough of Warren Pinnacle Consulting, Inc., to apply the Sea Level Affecting Marshes Model (SLAMM, Version 5.0) to the Chesapeake Bay region. The SLAMM model is widely regarded as the premier research tool for simulating the dominant processes involved in wetland conversions and shoreline modifications during long-term sea-level rise. Our analysis looked at a range of sea-level rise scenarios from the 2001 Intergovernmental Panel on Climate Change...
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The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. The shoreline position and change rate are used to inform management decisions regarding the erosion of coastal resources. In 2001, a shoreline from 1994 was added to calculate both long- and short-term shoreline change rates along ocean-facing sections of the Massachusetts coast. In 2013, two oceanfront shorelines for Massachusetts were added using 2008-9 color aerial orthoimagery and 2007 topographic lidar datasets obtained from the National Oceanic and Atmospheric Administration's Ocean Service, Coastal Services Center. This 2018 data release includes rates that incorporate...
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Attempts to stabilize the shore can greatly influence rates of shoreline change. Beach nourishment in particular will bias rates of observed shoreline change toward accretion or stability, even though the natural beach, in the absence of nourishment, would be eroding. Trembanis and Pilkey (1998) prepared a summary of identifiable beach nourishment projects in the Gulf Coast region that had been conducted before 1996. Those records were used to identify shoreline segments that had been influenced by beach nourishment. Supplemental information regarding beach nourishment was collected from agencies familiar with nourishment projects in the State. All records were compiled to create a GIS layer depicting the spatial...
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The goal of this project is to provide a preliminary overview, at a National scale, the relative susceptibility of the Nation's coast to sea- level rise through the use of a coastal vulnerability index (CVI). This initial classification is based upon the variables geomorphology, regional coastal slope, tide range, wave height, relative sea-level rise and shoreline erosion and accretion rates. The combination of these variables and the association of these variables to each other furnish a broad overview of regions where physical changes are likely to occur due to sea-level rise.
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Sandy ocean beaches in the United States are popular tourist and recreational destinations and constitute some of the most valuable real estate in the country. The boundary between land and water along the coastline is often the location of concentrated residential and commercial development and is frequently exposed to a range of natural hazards, which include flooding, storm effects, and coastal erosion. In response, the U.S. Geological Survey (USGS) is conducting a national assessment of coastal change hazards. One component of this research effort, the National Assessment of Shoreline Change Project, documents changes in shoreline position as a proxy for coastal change. Shoreline position is an easily understood...
Categories: Data; Types: Citation, Downloadable, Map Service, OGC WFS Layer, OGC WMS Layer, Shapefile; Tags: Bald Point State Park, CMGP, Coastal and Marine Geology Program, DSAS, Digital Shoreline Analysis System, All tags...
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Sandy ocean beaches in the United States are popular tourist and recreational destinations and constitute some of the most valuable real estate in the country. The boundary between land and water along the coastline is often the location of concentrated residential and commercial development and is frequently exposed to a range of natural hazards, which include flooding, storm effects, and coastal erosion. In response, the U.S. Geological Survey (USGS) is conducting a national assessment of coastal change hazards. One component of this research effort, the National Assessment of Shoreline Change Project (http://coastal.er.usgs.gov/shoreline-change/), documents changes in shoreline position as a proxy for coastal...
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Sandy ocean beaches in the United States are popular tourist and recreational destinations and constitute some of the most valuable real estate in the country. The boundary between land and water along the coastline is often the location of concentrated residential and commercial development and is frequently exposed to a range of natural hazards, which include flooding, storm effects, and coastal erosion. In response, the U.S. Geological Survey (USGS) is conducting a national assessment of coastal change hazards. One component of this research effort, the National Assessment of Shoreline Change Project, documents changes in shoreline position as a proxy for coastal change. Shoreline position is an easily understood...
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Sandy ocean beaches in the United States are popular tourist and recreational destinations and constitute some of the most valuable real estate in the country. The boundary between land and water along the coastline is often the location of concentrated residential and commercial development and is frequently exposed to a range of natural hazards, which include flooding, storm effects, and coastal erosion. In response, the U.S. Geological Survey (USGS) is conducting a national assessment of coastal change hazards. One component of this research effort, the National Assessment of Shoreline Change Project, documents changes in shoreline position as a proxy for coastal change. Shoreline position is an easily understood...
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The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. The shoreline position and change rate are used to inform management decisions regarding the erosion of coastal resources. In 2001, a shoreline from 1994 was added to calculate both long- and short-term shoreline change rates along ocean-facing sections of the Massachusetts coast. In 2013, two oceanfront shorelines for Massachusetts were added using 2008-9 color aerial orthoimagery and 2007 topographic lidar datasets obtained from the National Oceanic and Atmospheric Administration's Ocean Service, Coastal Services Center. This 2018 data release includes rates that incorporate...
thumbnail
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. The shoreline position and change rate are used to inform management decisions regarding the erosion of coastal resources. In 2001, a shoreline from 1994 was added to calculate both long- and short-term shoreline change rates along ocean-facing sections of the Massachusetts coast. In 2013, two oceanfront shorelines for Massachusetts were added using 2008-9 color aerial orthoimagery and 2007 topographic lidar datasets obtained from the National Oceanic and Atmospheric Administration's Ocean Service, Coastal Services Center. This 2018 data release includes rates that incorporate...
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The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. The shoreline position and change rate are used to inform management decisions regarding the erosion of coastal resources. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. In 2013 two oceanfront shorelines for Massachusetts were added using 2008-2009 color aerial orthoimagery and 2007 topographic lidar datasets obtained from NOAA's Ocean Service, Coastal Services Center. This 2018 update includes two new mean high water (MHW) shorelines for the Massachusetts...
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This dataset is part of an extensive analysis of sea-level rise impacts on coastal habitats along the Chesapeake Bay, Delaware Bay, and the ocean beaches of southern New Jersey, Delaware, Maryland, and Virginia. The National Wildlife Federation commissioned Jonathan S. Clough of Warren Pinnacle Consulting, Inc., to apply the Sea Level Affecting Marshes Model (SLAMM, Version 5.0) to the Chesapeake Bay region. The SLAMM model is widely regarded as the premier research tool for simulating the dominant processes involved in wetland conversions and shoreline modifications during long-term sea-level rise. Our analysis looked at a range of sea-level rise scenarios from the 2001 Intergovernmental Panel on Climate Change...
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This dataset is part of an extensive analysis of sea-level rise impacts on coastal habitats along the Chesapeake Bay, Delaware Bay, and the ocean beaches of southern New Jersey, Delaware, Maryland, and Virginia. The National Wildlife Federation commissioned Jonathan S. Clough of Warren Pinnacle Consulting, Inc., to apply the Sea Level Affecting Marshes Model (SLAMM, Version 5.0) to the Chesapeake Bay region. The SLAMM model is widely regarded as the premier research tool for simulating the dominant processes involved in wetland conversions and shoreline modifications during long-term sea-level rise. Our analysis looked at a range of sea-level rise scenarios from the 2001 Intergovernmental Panel on Climate Change...
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The goal of this project is to provide a preliminary overview, at a National scale, the relative susceptibility of the Nation's coast to sea- level rise through the use of a coastal vulnerability index (CVI). This initial classification is based upon the variables geomorphology, regional coastal slope, tide range, wave height, relative sea-level rise and shoreline erosion and accretion rates. The combination of these variables and the association of these variables to each other furnish a broad overview of regions where physical changes are likely to occur due to sea-level rise. Digital Data Series - 68
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This dataset consists of long-term (~63 years) shoreline change rates for the north coast of Alaska between the Hulahula River and the Colville River. Rate calculations were computed within a GIS using the Digital Shoreline Analysis System (DSAS) version 4.3, an ArcGIS extension developed by the U.S. Geological Survey. Long-term rates of shoreline change were calculated using a linear regression rate-of-change method based on available shoreline data between 1947 and 2010. A reference baseline was used as the originating point for the orthogonal transects cast by the DSAS software. The transects intersect each shoreline establishing measurement points, which are then used to calculate long-term rates.
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This dataset includes a reference baseline used by the Digital Shoreline Analysis System (DSAS) to calculate rate-of-change statistics for the exposed north coast of Alaska coastal region between the Hulahula River and the Colville River for the time period 1947 to 2010. This baseline layer serves as the starting point for all transects cast by the DSAS application and can be used to establish measurement points used to calculate shoreline-change rates.


map background search result map search result map WA Short Term Shoreline Change Coastal Vulnerability USGS National Index of Coastal Vulnerability to Sea-Level Rise Chesapeake Bay region sea-level rise modelling - Habitat classification, 2050 (A1B mean scenario) Chesapeake Bay region sea-level rise modelling - Habitat classification, 2025 (B1 mean scenario) Chesapeake Bay region sea-level rise modelling - Habitat classification, 1996 Wave Height Data for the Gulf of Mexico Digital Shoreline Analysis System version 4.3 Transects with Short-Term Linear Regression Rate Calculations for Louisiana Digital Shoreline Analysis System version 4.3 Transects with Short-Term End Point Rate Calculations for Louisiana Shorelines of the Florida north (FLnorth) coastal region used in shoreline change analysis Digital Shoreline Analysis System version 4.3 Transects with Short-Term End Point Rate Calculations for central North Carolina (NCcentral) Offshore baseline for the exposed Central Beaufort Sea, Alaska coastal region (Hulahula River to the Colville River) generated to calculate shoreline change rates Beach Nourishment in the Gulf of Mexico Digital Shoreline Analysis System (DSAS) version 4.3 Transects with Long-Term Linear Regression Rate Calculations for the Exposed Central Beaufort Sea coast of Alaska between the Hulahula River and the Colville River Digital Shoreline Analysis System (DSAS) version 4.3 Transects with Short-Term Linear Regression Rate Calculations for the Sheltered East Beaufort Sea coast of Alaska between the U.S. Canadian Border and the Hulahula River Digital Shoreline Analysis System (DSAS) version 4.3 Transects with Short-Term Linear Regression Rate Calculations for the Exposed East Chukchi Sea coast of Alaska between the Point Barrow and Icy Cape Martha's Vineyard Intersects for Martha's Vineyard, Massachusetts, generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.0 Intersects for the Buzzards Bay coastal region in Massachusetts, generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.0 Uncertainty table for lidar-derived shorelines used when calculating rates in the Digital Shoreline Analysis System software for the South Shore of MA Digital Shoreline Analysis System version 4.3 Transects with Short-Term End Point Rate Calculations for central North Carolina (NCcentral) Intersects for the Buzzards Bay coastal region in Massachusetts, generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.0 Uncertainty table for lidar-derived shorelines used when calculating rates in the Digital Shoreline Analysis System software for the South Shore of MA Digital Shoreline Analysis System (DSAS) version 4.3 Transects with Short-Term Linear Regression Rate Calculations for the Exposed East Chukchi Sea coast of Alaska between the Point Barrow and Icy Cape Digital Shoreline Analysis System (DSAS) version 4.3 Transects with Short-Term Linear Regression Rate Calculations for the Sheltered East Beaufort Sea coast of Alaska between the U.S. Canadian Border and the Hulahula River WA Short Term Shoreline Change Digital Shoreline Analysis System version 4.3 Transects with Short-Term End Point Rate Calculations for Louisiana Digital Shoreline Analysis System version 4.3 Transects with Short-Term Linear Regression Rate Calculations for Louisiana Shorelines of the Florida north (FLnorth) coastal region used in shoreline change analysis Digital Shoreline Analysis System (DSAS) version 4.3 Transects with Long-Term Linear Regression Rate Calculations for the Exposed Central Beaufort Sea coast of Alaska between the Hulahula River and the Colville River Offshore baseline for the exposed Central Beaufort Sea, Alaska coastal region (Hulahula River to the Colville River) generated to calculate shoreline change rates Chesapeake Bay region sea-level rise modelling - Habitat classification, 2050 (A1B mean scenario) Chesapeake Bay region sea-level rise modelling - Habitat classification, 2025 (B1 mean scenario) Chesapeake Bay region sea-level rise modelling - Habitat classification, 1996 Beach Nourishment in the Gulf of Mexico Wave Height Data for the Gulf of Mexico USGS National Index of Coastal Vulnerability to Sea-Level Rise Coastal Vulnerability