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Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated...
Categories: Data; Types: Downloadable, GeoTIFF, Map Service, OGC WFS Layer, OGC WMS Layer, Raster, Shapefile; Tags: Assawoman Island, Assawoman Island, Atlantic Ocean, Barrier Island, Bayesian Network, All tags...
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This data contains geographic extents of projected coastal flooding, low-lying vulnerable areas, and maximum/minimum flood potential (flood uncertainty) associated with 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...
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This data contains geographic extents of projected coastal flooding, low-lying vulnerable areas, and maximum/minimum flood potential (flood uncertainty) associated with 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...
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This dataset contains projections for Santa Cruz County. 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. CoSMoS v3.1 for Central California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal planners with critical storm-hazards information that can be used to increase public safety, mitigate physical damages, and more effectively manage and allocate resources within complex coastal settings. Data for Central California covers the coastline from Pt. Conception to Golden Gate Bridge. Methods and...
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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 dataset contains projections of shoreline positions and uncertainty bands for future scenarios of sea-level rise. Projections were made using the Coastal Storm Modeling System - Coastal One-line Assimilated Simulation Tool (CoSMoS-COAST), a numerical model forced with global-to-local nested wave models and assimilated with lidar-derived shoreline vectors. Read metadata carefully.
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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 significant wave height (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)...
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This data contains maximum model-derived significant wave height (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)...
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Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated...
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Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated...
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Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated...
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Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated...
Categories: Data; Types: Downloadable, GeoTIFF, Map Service, OGC WFS Layer, OGC WMS Layer, Raster, Shapefile; Tags: Atlantic Ocean, Barrier Island, Bayesian Network, CMHRP, Coastal Erosion, 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 crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated...
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Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated...
Categories: Data; Types: Downloadable, GeoTIFF, Map Service, OGC WFS Layer, OGC WMS Layer, Raster, Shapefile; Tags: Atlantic Ocean, Barrier Island, Bayesian Network, CMHRP, Coastal Erosion, All tags...
thumbnail
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated...
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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 dataset contains projections for San Luis Obispo County. 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. CoSMoS v3.1 for Central California shows projections for future climate scenarios (sea-level rise and storms) to provide emergency responders and coastal planners with critical storm-hazards information that can be used to increase public safety, mitigate physical damages, and more effectively manage and allocate resources within complex coastal settings. Data for Central California covers the coastline from Pt. Conception to Golden Gate Bridge. Methods...
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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 geographic extents of projected coastal flooding, low-lying vulnerable areas, and maximum/minimum flood potential (flood uncertainty) associated with 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...


map background search result map search result map CoSMoS Central California v3.1 projections of shoreline change due to 21st century sea level rise CoSMoS v3.1 - San Luis Obispo County CoSMoS v3.1 water level projections: 1-year storm in Santa Barbara County CoSMoS v3.1 flood depth and duration projections: average conditions in Santa Barbara County CoSMoS v3.1 wave-hazard projections: 1-year storm in San Mateo County CoSMoS v3.1 ocean-currents hazards: 100-year storm in San Mateo County CoSMoS v3.1 flood hazard projections: average conditions in San Mateo County CoSMoS v3.1 flood depth and duration projections: 100-year storm in San Francisco County SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters of landcover, geomorphic setting, substrate type, vegetation density, and vegetation type: Monomoy Island, MA, 2013-2014 SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters of landcover, geomorphic setting, substrate type, vegetation density, and vegetation type: Parker River, MA, 2014 SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters of landcover, geomorphic setting, substrate type, vegetation density, and vegetation type: Cape Hatteras, NC, 2014 SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters of landcover, geomorphic setting, substrate type, vegetation density, and vegetation type: Rhode Island National Wildlife Refuge, RI, 2014 points, transects, beach width: Barrier island geomorphology and shorebird habitat metrics at 50-m alongshore transects and 5-m cross-shore points: Assawoman Island, VA, 2014 points, transects, beach width: Barrier island geomorphology and shorebird habitat metrics at 50-m alongshore transects and 5-m cross-shore points: Myrtle Island, VA, 2014 SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters of landcover, geomorphic setting, substrate type, vegetation density, and vegetation type: Myrtle Island, VA, 2014 points, transects, beach width: Barrier island geomorphology and shorebird habitat metrics at 50-m alongshore transects and 5-m cross-shore points: Ship Shoal Island, VA, 2014 CoSMoS v3.1 - Santa Cruz County CoSMoS v3.1 wave-hazard projections: 100-year storm in Santa Cruz County CoSMoS v3.1 flood hazard projections: 100-year storm in Santa Cruz County CoSMoS v3.1 flood hazard projections: average conditions in Santa Cruz County points, transects, beach width: Barrier island geomorphology and shorebird habitat metrics at 50-m alongshore transects and 5-m cross-shore points: Ship Shoal Island, VA, 2014 points, transects, beach width: Barrier island geomorphology and shorebird habitat metrics at 50-m alongshore transects and 5-m cross-shore points: Myrtle Island, VA, 2014 SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters of landcover, geomorphic setting, substrate type, vegetation density, and vegetation type: Myrtle Island, VA, 2014 points, transects, beach width: Barrier island geomorphology and shorebird habitat metrics at 50-m alongshore transects and 5-m cross-shore points: Assawoman Island, VA, 2014 CoSMoS v3.1 wave-hazard projections: 1-year storm in San Mateo County CoSMoS v3.1 ocean-currents hazards: 100-year storm in San Mateo County CoSMoS v3.1 flood hazard projections: average conditions in San Mateo County SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters of landcover, geomorphic setting, substrate type, vegetation density, and vegetation type: Rhode Island National Wildlife Refuge, RI, 2014 CoSMoS v3.1 water level projections: 1-year storm in Santa Barbara County CoSMoS v3.1 flood depth and duration projections: average conditions in Santa Barbara County CoSMoS v3.1 - Santa Cruz County CoSMoS v3.1 wave-hazard projections: 100-year storm in Santa Cruz County CoSMoS v3.1 flood hazard projections: 100-year storm in Santa Cruz County CoSMoS v3.1 flood hazard projections: average conditions in Santa Cruz County SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters of landcover, geomorphic setting, substrate type, vegetation density, and vegetation type: Cape Hatteras, NC, 2014 CoSMoS v3.1 - San Luis Obispo County CoSMoS Central California v3.1 projections of shoreline change due to 21st century sea level rise