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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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Habitat projections from the WARMER-2 model for four tidal wetland sites in San Francisco Bay estuary under the constant sediment scenario, plus 0.2 ppt per decade salinity scenario, and the community transition organic productivity function under a 99 cm by 2100 sea-level rise scenario. Results are the average from one hundred Monte Carlo simulations.
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The Herring River estuary in Wellfleet, Cape Cod, Massachusetts, has been tidally restricted for more than a century by a dike constructed near the mouth of the river. Upstream from the dike, the tidal restriction has caused the conversion of salt marsh wetlands to various other ecosystems including impounded freshwater marshes, flooded shrub land, drained forested upland, and brackish wetlands dominated by Phragmites australis. This estuary is now managed by the National Park Service, which plans to replace the aging dike and restore tidal flow to the estuary. To assist National Park Service land managers with restoration planning, the U.S. Geological Survey collected fourteen sediment cores from different ecosystems...
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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; Tags: Assateague Island, Assateague Island, Assateague Island National Seashore, Assateague Island National Seashore, Atlantic Ocean, All tags...
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This data release contains coastal wetland synthesis products for the state of Maine. Metrics for resiliency, including the unvegetated to vegetated ratio (UVVR), marsh elevation, tidal range, and lifespan, are calculated for smaller units delineated from a digital elevation model, providing the spatial variability of physical factors that influence wetland health. The U.S. Geological Survey has been expanding national assessment of coastal change hazards and forecast products to coastal wetlands with the intent of providing federal, state, and local managers with tools to estimate the vulnerability and ecosystem service potential of these wetlands. For this purpose, the response and resilience of coastal wetlands...
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This data release consists of two ESRI geodatabases that store inundation areas for various future scenarios of sea-level rise (SLR), groundwater rise, and storm waves for Laysan Island and Midway Atoll in the Northwestern Hawaiian Islands. Five types of inundation scenarios are considered: (1) passive SLR, (2) passive SLR including groundwater rise, (3) wave-driven inundation during storm events, (4) wave-driven inundation during storm events including groundwater rise, and (5) wave-driven inundation during storm events, assuming unlimited seawater volumes and no infiltration (i.e., theoretical maximum). This scenario applies only to Laysan Island which has topographic depressions that fill with water during flood...
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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 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 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 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 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 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 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)...


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 flood hazard projections: 20-year storm in San Luis Obispo County CoSMoS v3.1 flood depth and duration projections: 1-year storm in San Luis Obispo County CoSMoS v3.1 water level projections: 1-year storm in Santa Barbara County CoSMoS v3.1 ocean-currents hazards: average conditions in San Luis Obispo County CoSMoS v3.1 wave-hazard projections: 1-year storm 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: 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: 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 CoSMoS v3.1 - 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 Collection, analysis, and age-dating of sediment cores from Herring River wetlands and other nearby wetlands in Wellfleet, Massachusetts, 2015–17 Tidal Wetland Habitat Projections with Sea-level Rise Across Sites in the San Francisco Bay Estuary (2020-2100) Barrier island geomorphology and seabeach amaranth metrics at 50-m alongshore transects, and 5-m cross-shore points for 2008 — Assateague Island, MD and VA. Lifespan of marsh units in Maine salt marshes Collection, analysis, and age-dating of sediment cores from Herring River wetlands and other nearby wetlands in Wellfleet, Massachusetts, 2015–17 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 Barrier island geomorphology and seabeach amaranth metrics at 50-m alongshore transects, and 5-m cross-shore points for 2008 — Assateague Island, MD and VA. CoSMoS v3.1 wave-hazard projections: 1-year storm in San Mateo County Tidal Wetland Habitat Projections with Sea-level Rise Across Sites in the San Francisco Bay Estuary (2020-2100) 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 - 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 flood hazard projections: 20-year storm in San Luis Obispo County CoSMoS v3.1 flood depth and duration projections: 1-year storm in San Luis Obispo County CoSMoS v3.1 ocean-currents hazards: average conditions in San Luis Obispo County CoSMoS Central California v3.1 projections of shoreline change due to 21st century sea level rise Lifespan of marsh units in Maine salt marshes