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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 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...
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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 portion of the data release presents a digital surface model (DSM) and hillshade of Whiskeytown Lake and the surrounding area derived from Structure from Motion (SfM) processing of aerial imagery acquired on 2018-12-02. Unlike a digital elevation model (DEM), the DSM represents the elevation of the highest object within the bounds of a cell. Vegetation, buildings and other objects have not been removed from the data. In addition, data artifacts resulting from noise and vegetation in the original imagery have not been removed. However, in unvegetated areas such as reservoir shorelines and deltas, the DSM is equivalent to a DEM because it represents the ground surface elevation. The raw imagery used to create...
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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, Cape Cod, 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...
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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...
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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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This part of DS 781 presents data for the transgressive contours of the Punta Gorda to Point Arena, California, region. The vector data file is included in the "TransgressiveContours_PuntaGordaToPointArena.zip," which is accessible from https://doi.org/10.5066/P9PNNI9H. As part of the USGS's California State Waters Mapping Project, a 50-m grid of sediment thickness for the seafloor within the 3-nautical mile limit between Punta Gorda and Point Arena was generated from seismic-reflection data collected between 2010 and 2012, and supplemented with geologic structure (fault) information following the methodology of Wong (2012). Water depths determined from bathymetry data were added to the sediment thickness data to...
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In spring and summer 2017, the U.S. Geological Survey’s Gas Hydrates Project conducted two cruises aboard the research vessel Hugh R. Sharp to explore the geology, chemistry, ecology, physics, and oceanography of sea-floor methane seeps and water column gas plumes on the northern U.S. Atlantic margin between the Baltimore and Keller Canyons. Split-beam and multibeam echo sounders and a chirp subbottom profiler were deployed during the cruises to map water column backscatter, sea-floor bathymetry and backscatter, and subsurface stratigraphy associated with known and undiscovered sea-floor methane seeps. The first cruise, known as the Interagency Mission for Methane Research on Seafloor Seeps and designated as field...
Categories: Data; Types: Downloadable, Map Service, OGC WFS Layer, OGC WMS Layer, Shapefile; Tags: Accomac Canyon, Atlantic Ocean, CMHRP, Chincoteague Ridge, Coastal and Marine Hazards and Resources Program, All tags...
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Vegetation type and density data were collected by the U.S. Geological Survey (USGS) Pacific Coastal and Marine Science Center at three locations in the Sacramento-San Joaquin Delta. Data were collected in Lindsey Slough in April 2017, and Middle River and the Mokelumne River in March 2018. Vegetation samples were collected by divers, and used to determine dry biomass density. These data were collected as part of a cooperative project, with the USGS California Water Science Center and the California Department of Fish and Wildlife, on the effects of invasive aquatic vegetation on sediment transport in the Sacramento-San Joaquin Delta.
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Time-series data of water level, water temperature, and salinity were collected at 10 locations along west Hawaii Island between 2010 and 2011 in nearshore coral reef settings. Conductivity-temperature-depth sensors were attached to fossil limestone, rock, or dead coral within otherwise healthy coral reef settings spanning water depths of 8 to 23 ft. Continuous measurements were made every 10 or 20 minutes.
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Spatial measurements of water temperature, specific conductance, salinity, pH, and dissolved oxygen between 0.25 and 0.50 m water depth were collected every 10-seconds along and across shore at 12 principal study areas along west Hawaii Island. Measurements were made between 2010 and 2013 during different seasons and tide states over the course of 1.0 to 2.5 hours to evaluate the spatial and temporal extent of water properties that influence coral reef health and coral reef habitat availability.
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Conductivity-Temperature-Depth (CTD) profile data were collected along transects and across study areas of west Hawaii Island between 2010 and 2014. Measurements were made over a range of tide and weather conditions and help characterize the spatial extent and variability in estuarine conditions across the reef when grouped by 1 to 2-hour survey period or by season.


map background search result map search result map Surface-water temperature, salinity, Ph, and dissolved oxygen data from nearshore coral reef locations along the west coast of Hawaii Island (2010-2013) Water level, temperature, and salinity time-series data from nearshore coral reef locations along the west coast of Hawaii Island (2010-2011) Vegetation biomass and density from three locations in the Sacramento-San Joaquin Delta, California, 2017 to 2018 Conductivity, temperature, and depth (CTD) data from nearshore coral reef locations along the west coast of Hawaii Island (2010-2014) CoSMoS v3.1 flood hazard projections: 100-year storm in San Luis Obispo County CoSMoS v3.1 wave-hazard projections: 1-year storm in Santa Barbara County Transgressive Contours--Punta Gorda to Point Arena, California CoSMoS v3.1 water level projections: 20-year storm in San Luis Obispo County CoSMoS v3.1 ocean-currents hazards: average conditions in San Mateo County points, transects, beach width: Barrier island geomorphology and shorebird habitat metrics at 50-m alongshore transects and 5-m cross-shore points: Monomoy Island, MA, 2013-2014 Development: Development delineation: Parker River, MA, 2014 DCpts, DTpts, SLpts: Dune crest, dune toe, and mean high water shoreline positions: Cape Lookout, NC, 2014 Development: Development delineation: Cape Lookout, NC, 2014 ElevMHW: Elevation adjusted to local mean high water: 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: Rhode Island National Wildlife Refuge, RI, 2014 shoreline, inletLines: Shoreline polygons and tidal inlet delineations: Assateague Island, MD & VA, 2014 SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters of landcover, geomorphic setting, substrate type, vegetation density, and vegetation type: Metompkin Island, VA, 2014 DisOcean: Distance to the ocean: Wreck Island, VA, 2014 Sound velocity profiles - locations, images, and text files for sound velocity profiles calculated from XBT and CTD casts conducted during USGS field activities 2017-001-FA and 2017-002 FA Topographic digital surface model (DSM) for Whiskeytown Lake and surrounding area, 2018-12-02 DisOcean: Distance to the ocean: Wreck Island, VA, 2014 Development: Development delineation: Cape Lookout, NC, 2014 SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters of landcover, geomorphic setting, substrate type, vegetation density, and vegetation type: Metompkin Island, VA, 2014 Topographic digital surface model (DSM) for Whiskeytown Lake and surrounding area, 2018-12-02 points, transects, beach width: Barrier island geomorphology and shorebird habitat metrics at 50-m alongshore transects and 5-m cross-shore points: Monomoy Island, MA, 2013-2014 points, transects, beach width: Barrier island geomorphology and shorebird habitat metrics at 50-m alongshore transects and 5-m cross-shore points: Rhode Island National Wildlife Refuge, RI, 2014 shoreline, inletLines: Shoreline polygons and tidal inlet delineations: Assateague Island, MD & VA, 2014 CoSMoS v3.1 ocean-currents hazards: average conditions in San Mateo County ElevMHW: Elevation adjusted to local mean high water: Rhode Island National Wildlife Refuge, RI, 2014 CoSMoS v3.1 wave-hazard projections: 1-year storm in Santa Barbara County DCpts, DTpts, SLpts: Dune crest, dune toe, and mean high water shoreline positions: Cape Lookout, NC, 2014 Conductivity, temperature, and depth (CTD) data from nearshore coral reef locations along the west coast of Hawaii Island (2010-2014) CoSMoS v3.1 flood hazard projections: 100-year storm in San Luis Obispo County CoSMoS v3.1 water level projections: 20-year storm in San Luis Obispo County Surface-water temperature, salinity, Ph, and dissolved oxygen data from nearshore coral reef locations along the west coast of Hawaii Island (2010-2013) Transgressive Contours--Punta Gorda to Point Arena, California Sound velocity profiles - locations, images, and text files for sound velocity profiles calculated from XBT and CTD casts conducted during USGS field activities 2017-001-FA and 2017-002 FA