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This data contains maximum depth of flooding (cm) in the region landward of the present-day shoreline for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate bridge. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average...
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This data contains maximum depth of flooding (cm) in the region landward of the present-day shoreline for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate bridge. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average...
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This data contains maximum model-derived ocean currents (in meters per second) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate bridge. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions)...
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This data contains model-derived total water levels (in meters) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate bridge. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated...
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Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for developing approaches that balance the needs of humans and native species. Given the magnitude of the threat posed by sea-level rise, and the urgency to better understand it, there is an increasing need to forecast sea-level rise effects on barrier islands. To address this problem, scientists in the U.S. Geological Survey (USGS) Coastal and Marine Geology program are developing Bayesian networks as a tool to evaluate and to forecast the effects of sea-level rise on shoreline change, barrier island geomorphology, and habitat availability for species such as the piping plover (Charadrius melodus)...
Categories: Data; Types: Downloadable, Map Service, OGC WFS Layer, OGC WMS Layer, Shapefile; Tags: Assateague Island, Assateague Island, Assateague Island National Seashore, Assateague Island National Seashore, Atlantic Ocean, All tags...
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The U.S. Geological Survey (USGS) has compiled national shoreline data for more than 20 years to document coastal change and serve the needs of research, management, and the public. Maintaining a record of historical shoreline positions is an effective method to monitor national shoreline evolution over time, enabling scientists to identify areas most susceptible to erosion or accretion. These data can help coastal managers and planners understand which areas of the coast are vulnerable to change. This data release includes one new mean high water (MHW) shoreline extracted from lidar data collected in 2017 for the entire coastal region of North Carolina which is divided into four subregions: northern North Carolina...
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The U.S. Geological Survey (USGS) has compiled national shoreline data for more than 20 years to document coastal change and serve the needs of research, management, and the public. Maintaining a record of historical shoreline positions is an effective method to monitor national shoreline evolution over time, enabling scientists to identify areas most susceptible to erosion or accretion. These data can help coastal managers and planners understand which areas of the coast are vulnerable to change. This data release includes one new mean high water (MHW) shoreline extracted from lidar data collected in 2017 for the entire coastal region of North Carolina which is divided into four subregions: northern North Carolina...
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The U.S. Geological Survey (USGS) has compiled national shoreline data for more than 20 years to document coastal change and serve the needs of research, management, and the public. Maintaining a record of historical shoreline positions is an effective method to monitor national shoreline evolution over time, enabling scientists to identify areas most susceptible to erosion or accretion. These data can help coastal managers and planners understand which areas of the coast are vulnerable to change. This data release includes two new mean high water (MHW) shorelines extracted from lidar data collected in 2010 and 2017-2018. Previously published historical shorelines for South Carolina (Kratzmann and others, 2017)...
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The Coupled Ocean-Atmosphere-Wave-Sediment Transport (COAWST Warner and others, 2019; Warner and others, 2010) model was used to simulate three-dimensional hydrodynamics and waves to study salinity intrusion in the Delaware Bay estuary for 2016, 2018, 2021. Salinity intrusion in coastal systems is due in part to extreme events like drought or low-pressure storms and longer-term sea level rise, threatening economic infrastructure and ecological health. Along the eastern seaboard of the United States, approximately 13 million people rely on the water resources of the Delaware River basin, which is actively managed to suppress the salt front (or ~0.52 daily averaged psu line) through river discharge targets. However,...
Categories: Data; Types: Map Service, NetCDF OPeNDAP Service, OGC WMS Layer; Tags: Earth Science > Oceans > Ocean Circulation > Ocean Currents, Earth Science > Oceans > Ocean Temperature > Potential Temperature, Earth Science > Oceans > Salinity/Density > Salinity, Earth Science > Oceans > Sea Surface Topography > Sea Surface Height, Earth Science Services > Models > Weather Research/Forecast Models, All tags...
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This data contains model-derived total water levels (in meters) for the sea-level rise (SLR) and storm condition indicated. The Coastal Storm Modeling System (CoSMoS) makes detailed predictions (meter-scale) over large geographic scales (100s of kilometers) of storm-induced coastal flooding and erosion for both current and future sea-level rise (SLR) scenarios. Projections for CoSMoS v3.1 in Central California include flood-hazard information for the coast from Pt. Conception to the Golden Gate bridge. Outputs include SLR scenarios of 0.0, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 3.0, and 5.0 meters; storm scenarios include background conditions (astronomic spring tide and average atmospheric conditions) and simulated...
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This data release contains reference baselines for primarily open-ocean sandy beaches along the west coast of the United States (California, Oregon and Washington). The slopes were calculated while extracting shoreline position from lidar point cloud data collected between 2002 and 2011. The shoreline positions have been previously published, but the slopes have not. A reference baseline was defined and then evenly-spaced cross-shore beach transects were created. Then all data points within 1 meter of each transect were associated with each transect. Next, it was determined which points were one the foreshore, and then a linear regression was fit through the foreshore points. Beach slope was defined as the slope...
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The U.S. Geological Survey (USGS) has compiled national shoreline data for more than 20 years to document coastal change and serve the needs of research, management, and the public. Maintaining a record of historical shoreline positions is an effective method to monitor national shoreline evolution over time, enabling scientists to identify areas most susceptible to erosion or accretion. These data can help coastal managers and planners understand which areas of the coast are vulnerable to change. This data release includes one new mean high water (MHW) shoreline extracted from lidar data collected in 2017 for the entire coastal region of North Carolina which is divided into four subregions: northern North Carolina...
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The U.S. Geological Survey (USGS) has compiled national shoreline data for more than 20 years to document coastal change and serve the needs of research, management, and the public. Maintaining a record of historical shoreline positions is an effective method to monitor national shoreline evolution over time, enabling scientists to identify areas most susceptible to erosion or accretion. These data can help coastal managers and planners understand which areas of the coast are vulnerable to change. This data release includes one new mean high water (MHW) shoreline extracted from lidar data collected in 2017 for the entire coastal region of North Carolina which is divided into four subregions: northern North Carolina...
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Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for developing approaches that balance the needs of humans and native species. Given the magnitude of the threat posed by sea-level rise, and the urgency to better understand it, there is an increasing need to forecast sea-level rise effects on barrier islands. To address this problem, scientists in the U.S. Geological Survey (USGS) Coastal and Marine Geology program are developing Bayesian networks as a tool to evaluate and to forecast the effects of sea-level rise on shoreline change, barrier island geomorphology, and habitat availability for species such as the piping plover (Charadrius melodus)...
Categories: Data; Types: Downloadable, Map Service, OGC WFS Layer, OGC WMS Layer, Shapefile; Tags: Assateague Island, Assateague Island, Assateague Island National Seashore, Assateague Island National Seashore, Atlantic Ocean, All tags...
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Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for developing approaches that balance the needs of humans and native species. Given the magnitude of the threat posed by sea-level rise, and the urgency to better understand it, there is an increasing need to forecast sea-level rise effects on barrier islands. To address this problem, scientists in the U.S. Geological Survey (USGS) Coastal and Marine Geology program are developing Bayesian networks as a tool to evaluate and to forecast the effects of sea-level rise on shoreline change, barrier island geomorphology, and habitat availability for species such as the piping plover (Charadrius melodus)...
Categories: Data; Types: Downloadable, Map Service, OGC WFS Layer, OGC WMS Layer, Shapefile; Tags: Assateague Island, Assateague Island, Assateague Island National Seashore, Assateague Island National Seashore, Atlantic Ocean, All tags...
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Projected wave climate trends from WAVEWATCH3 model output were used as input for nearshore wave models (for example, SWAN) for the main Hawaiian Islands to derive data and statistical measures (mean and top 5 percent values) of wave height, wave period, and wave direction for the recent past (1996-2005) and future projections (2026-2045 and 2085-2100). Three-hourly global climate model (GCM) wind speed and wind direction output from four different GCMs provided by the Coupled Model Inter-Comparison Project, phase 5 (CMIP5), were used as boundary conditions to the physics-based WAVEWATCH3 numerical wave model for the area encompassing the main Hawaiian islands. Two climate change scenarios for each of the four GCMs...
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The U.S. Geological Survey (USGS) has compiled national shoreline data for more than 20 years to document coastal change and serve the needs of research, management, and the public. Maintaining a record of historical shoreline positions is an effective method to monitor national shoreline evolution over time, enabling scientists to identify areas most susceptible to erosion or accretion. These data can help coastal managers and planners understand which areas of the coast are vulnerable to change. This data release includes one new mean high water (MHW) shoreline extracted from lidar data collected in 2017 for the entire coastal region of North Carolina which is divided into four subregions: northern North Carolina...
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The U.S. Geological Survey (USGS) has compiled national shoreline data for more than 20 years to document coastal change and serve the needs of research, management, and the public. Maintaining a record of historical shoreline positions is an effective method to monitor national shoreline evolution over time, enabling scientists to identify areas most susceptible to erosion or accretion. These data can help coastal managers and planners understand which areas of the coast are vulnerable to change. This data release includes one new mean high water (MHW) shoreline extracted from lidar data collected in 2017 for the entire coastal region of North Carolina which is divided into four subregions: northern North Carolina...
The Coupled Ocean-Atmosphere-Wave-Sediment Transport (COAWST; Warner and others, 2019; Warner and others, 2010) model was used to simulate ocean circulation, waves, and sediment transport in Cape Cod Bay, MA. Larger scale simulations of the US East Coast (Warner and Kalra, 2022) were used to drive numerical grids covering the Gulf of Maine (~1000m resolution) with a two-way nested downscaled region into Cape Cod Bay (~250m resolution). Results were analyzed to investigate bay-scale dynamics of net transport, seafloor elevation changes, and net sediment fluxes. Those results were further used to drive a coastal scale grid that stretched along ~17km of the coast from the Cape Cod Canal to Sandy Neck Beach. This nearshore...
Categories: Data; Types: Map Service, NetCDF OPeNDAP Service, OGC WMS Layer; Tags: CMG_Portal, Earth Science > Human Dimensions > Natural Hazards > Floods, Earth Science > Oceans > Marine Sediments >Sediment Transport, Earth Science > Oceans > Ocean Circulation > Ocean Currents, Earth Science > Oceans > Ocean Temperature > Potential Temperature, All tags...
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The U.S. Geological Survey (USGS) has compiled national shoreline data for more than 20 years to document coastal change and serve the needs of research, management, and the public. Maintaining a record of historical shoreline positions is an effective method to monitor national shoreline evolution over time, enabling scientists to identify areas most susceptible to erosion or accretion. These data can help coastal managers and planners understand which areas of the coast are vulnerable to change. This data release includes two new mean high water (MHW) shorelines extracted from lidar data collected in 2010 and 2017-2018. Previously published historical shorelines for South Carolina (Kratzmann and others, 2017)...


map background search result map search result map Dynamically downscaled future wave projections from SWAN model results for the main Hawaiian Islands CoSMoS v3.1 water level projections: 1-year storm in Santa Cruz County CoSMoS v3.1 flood depth and duration projections: 100-year storm in Santa Cruz County CoSMoS v3.1 water level projections: 20-year storm in Santa Cruz County CoSMoS v3.1 ocean-currents hazards: average conditions in Santa Cruz County CoSMoS v3.1 flood depth and duration projections: average conditions in Santa Cruz County Seabeach Amaranth Presence-Absence Data, Assateague Island National Seashore, 2008 Seabeach Amaranth Presence-Absence Data, Assateague Island National Seashore, 2010 Seabeach Amaranth Presence-Absence Data, Assateague Island National Seashore, 2014 U.S. Geological Survey simulations of hydrodynamics and morphodynamics in Cape Cod Bay, MA: Sandwich Jan - April 2021 U.S. Geological Survey simulations of 3D-hydrodynamics in Delaware Bay (2021) Long-term shoreline change rate transects for the South Carolina coastal region, calculated with and without the proxy-datum bias using the Digital Shoreline Analysis System version 5.1 Intersects for the coastal region of South Carolina generated to calculate long-term shoreline change rates using the Digital Shoreline Analysis System version 5.1 Long and short-term shoreline intersect points for the western coast of North Carolina (NCwest), calculated using the Digital Shoreline Analysis System version 5.1 2017 lidar-derived mean high water shoreline for the coast of North Carolina from Cape Hatteras to Cape Lookout (NCcentral) Baseline for the North Carolina coastal region from Cape Hatteras to Cape Lookout (NCcentral) 2017 lidar-derived mean high water shoreline for the southern coast of North Carolina from Cape Lookout to Cape Fear (NCsouth) Bias feature containing proxy-datum bias information to be used in the Digital Shoreline Analysis System for the southern coast of North Carolina from Cape Lookout to Cape Fear (NCsouth) Long and short-term shoreline change rate transects for the northern North Carolina coastal region (NCnorth), calculated with and without the proxy-datum bias using the Digital Shoreline Analysis System version 5.1 Reference baselines used to extract shorelines for the West Coast of the United States Seabeach Amaranth Presence-Absence Data, Assateague Island National Seashore, 2008 Seabeach Amaranth Presence-Absence Data, Assateague Island National Seashore, 2010 Seabeach Amaranth Presence-Absence Data, Assateague Island National Seashore, 2014 CoSMoS v3.1 water level projections: 1-year storm in Santa Cruz County CoSMoS v3.1 flood depth and duration projections: 100-year storm in Santa Cruz County CoSMoS v3.1 water level projections: 20-year storm in Santa Cruz County CoSMoS v3.1 ocean-currents hazards: average conditions in Santa Cruz County CoSMoS v3.1 flood depth and duration projections: average conditions in Santa Cruz County Long and short-term shoreline change rate transects for the northern North Carolina coastal region (NCnorth), calculated with and without the proxy-datum bias using the Digital Shoreline Analysis System version 5.1 2017 lidar-derived mean high water shoreline for the coast of North Carolina from Cape Hatteras to Cape Lookout (NCcentral) Baseline for the North Carolina coastal region from Cape Hatteras to Cape Lookout (NCcentral) 2017 lidar-derived mean high water shoreline for the southern coast of North Carolina from Cape Lookout to Cape Fear (NCsouth) Bias feature containing proxy-datum bias information to be used in the Digital Shoreline Analysis System for the southern coast of North Carolina from Cape Lookout to Cape Fear (NCsouth) U.S. Geological Survey simulations of 3D-hydrodynamics in Delaware Bay (2021) Intersects for the coastal region of South Carolina generated to calculate long-term shoreline change rates using the Digital Shoreline Analysis System version 5.1 Long-term shoreline change rate transects for the South Carolina coastal region, calculated with and without the proxy-datum bias using the Digital Shoreline Analysis System version 5.1 Dynamically downscaled future wave projections from SWAN model results for the main Hawaiian Islands Reference baselines used to extract shorelines for the West Coast of the United States