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This portion of the USGS data release presents bathymetry data collected during surveys performed in the Columbia River littoral cell, Washington and Oregon in 2014 (USGS Field Activity Number 2014-631-FA). Bathymetry data were collected using four personal watercraft (PWCs) equipped with single-beam sonar systems and global navigation satellite system (GNSS) receivers. The sonar systems consisted of an Odom Echotrac CV-100 single-beam echosounder and 200 kHz transducer with a 9 degree beam angle. Raw acoustic backscatter returns were digitized by the echosounder with a vertical resolution of 1.25 cm. Depths from the echosounders were computed using sound velocity profiles measured using a YSI CastAway CTD during...
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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 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 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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We used the Coupled Ocean-Atmosphere-Wave-Sediment Transport (COAWST; Warner and others, 2010) model to simulate ocean circulation, waves, and sediment transport in Barnegat Bay, New Jersey, during Hurricane Sandy. The simulation period was from October 27 to November 4, 2012. Initial conditions for the salinity and temperature fields in the domain were acquired from a 7-month simulation of the same domain (Defne and Ganju, 2018). We used a 2012 digital terrain model (Andrews and others, 2015) to prescribe the prestorm bathymetry. Wetting and drying was enabled, wave-current interaction was modeled with a boundary-layer formulation accounting for the apparent roughness of waves, and the vortex force formulation...
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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...
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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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We monitored displacement of the Slumgullion landslide located in Hinsdale County, Colorado. We measured displacement at the ground surface between 12 August 2011 and 10 October 2018, and in the subsurface between 4 September 2016 and 7 December 2016. Both types of data were acquired at irregular time intervals. Displacement at the ground surface was measured at locations within the upper, middle, and lower parts of the landslide using electronic cable extension transducers (extensometers) with stated ±0.7 mm accuracy (Extensometer_data.csv). Subsurface displacement was measured near the middle of the landslide using a 16-sensor array of 30.48-cm-long tilt sensors (inclinometer) installed within a PVC-cased borehole....
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This dataset contains projections from the Coastal Storm Modeling System (CoSMoS) for Santa Barbara County, north of Pt. Conception. 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...
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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 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 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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Time series data of water surface elevation and wave height were acquired at ten locations for 517 days (in three separate deployments) off the north coast of Roi-Namur Island, Kwajalein Atoll, Marshall Islands, in support of a study on the coastal circulation patterns and the transformation of surface waves over the coral reefs. The relative placement of sensors on the reefs were as follows: ROI13W1 and ROI13E1 – fore reef ROI13W2 and ROI13E2 – outer reef flat ROI13W1 and ROI13E1 – middle reef flat ROI13W1 and ROI13E1 – inner reef flat
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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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This data release contains coastal wetland synthesis products for the geographic region from Jamaica Bay to western Great South Bay, located in southeastern New York State. Metrics for resiliency, including unvegetated to vegetated ratio (UVVR), marsh elevation, and mean tidal range, are calculated for smaller units delineated from a Digital Elevation Model, providing the spatial variability of physical factors that influence wetland health. Through scientific efforts initiated with the Hurricane Sandy Science Plan, 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...


map background search result map search result map Nearshore bathymetry of the Columbia River littoral cell, Washington and Oregon, 2014 U.S. Geological Survey hydrodynamic model simulations for Barnegat Bay, New Jersey, during Hurricane Sandy, 2012 CoSMoS v3.1 - Santa Barbara County Coastal wetlands from Jamaica Bay to western Great South Bay, New York CoSMoS v3.1 ocean-currents hazards: 1-year storm in Santa Barbara County CoSMoS v3.1 wave-hazard projections: 1-year storm in Santa Barbara County CoSMoS v3.1 wave-hazard projections: 100-year storm in Santa Barbara County Transgressive Contours--Punta Gorda to Point Arena, California CoSMoS v3.1 flood depth and duration projections: 100-year storm in San Mateo County Roi-Namur Island, Marshall Islands, wave and water level data, 2013-2015 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 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 Data from in-situ displacement monitoring, Slumgullion landslide, Hinsdale County, Colorado 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 Roi-Namur Island, Marshall Islands, wave and water level data, 2013-2015 Development: Development delineation: Cape Lookout, NC, 2014 Development: Development delineation: Parker River, MA, 2014 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 Coastal wetlands from Jamaica Bay to western Great South Bay, New York shoreline, inletLines: Shoreline polygons and tidal inlet delineations: Assateague Island, MD & VA, 2014 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 DCpts, DTpts, SLpts: Dune crest, dune toe, and mean high water shoreline positions: Cape Lookout, NC, 2014 Transgressive Contours--Punta Gorda to Point Arena, California