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This portion of the data release contains information on cores that were collected by the U.S. Geological Survey in Kahana Valley, O'ahu, Hawaii in 2015 and 2017. Sites were cored in order to describe wetland stratigraphy and to identify potential tsunami deposits. These cores contain mud, peat, fluvial sands, and marine carbonate sands, reflecting deposition in a variety of coastal environments. PDF files describe twenty-four (24) gouge and ‘Russian’ cores (hand held, side-filling peat augers) that were collected and described in the field. Cores collected in 2017 were described using the Troels-Smith sediment classification scheme (Troels-Smith, 1955; Nelson, 2015). Another pdf file (Kahana_cores_legend.pdf) contains...
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This part of the data release presents projected flooding extent polygon (flood masks) and flooding depth points (flood points) shapefiles based on wave-driven total water levels for the Territory of Puerto Rico (the islands of Culebra, Puerto Rico, and Vieques). For each island there are 8 associated flood mask and flood depth shapefiles: one for each four nearshore wave energy return periods (rp; 10-, 50-, 100-, and 500-years) and both with (wrf) and without (worf) the presence of coral reefs. Flooding depth point data are also presented as a comma-separated value (.csv) text file.
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This part of DS 781 presents data for the transgressive contours of the Point Sur to Point Arguello, California, region. The vector data file is included in the “TransgressiveContours_PointSurToPointArguello.zip,” which is accessible from https://doi.org/10.5066/P97CZ0T7. 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 Point Sur and Point Arguello was generated from seismic-reflection data collected between 2008 and 2014, and supplemented with geologic structure (fault and fold) information following the methodology of Wong (2012). Water depths determined from bathymetry data were added to the sediment thickness...
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In September 2018, the U.S. Geological Survey, in collaboration with the U.S. Army Corps of Engineers, conducted high-resolution geophysical mapping and sediment sampling to determine the distribution of historical mine tailings on the floor of Lake Superior. Large amounts of waste material from copper mining, locally known as “stamp sands,” were dumped into the lake in the early 20th century, with wide-reaching consequences that have continued into the present. Mapping was focused offshore of the town of Gay on the Keweenaw Peninsula of Michigan, where ongoing erosion and re-deposition of the stamp sands has buried miles of native, white-sand beaches. Stamp sands are also encroaching onto Buffalo Reef, a large...
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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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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...
This dataset includes one vector shapefile delineating the position of the top edge of the coastal permafrost bluffs at Barter Island, Alaska spanning seven decades, between the years of 1950 and 2020. Bluff-edge positions delineated from a combination of aerial photography, declassified satellite photography, and very-high resolution satellite imagery can be used to quantify the movement of the bluff edge through time. These data were used to calculate rates of change every 10 meters alongshore using the Digital Shoreline Analysis System (DSAS) version 5.0. DSAS uses a measurement baseline method to calculate rate-of-change statistics. Transects are cast from the reference baseline to intersect each bluff edge...
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In summer 2018, the U.S. Geological Survey partnered with the U.S Department of Energy and the Bureau of Ocean Energy Management to conduct the Mid-Atlantic Resources Imaging Experiment (MATRIX) as part of the U.S. Geological Survey Gas Hydrates Project. The field program objectives were to acquire high-resolution 2-dimensional multichannel seismic-reflection and split-beam echosounder data along the U.S Atlantic margin between North Carolina and New Jersey to determine the distribution of methane gas hydrates in below-sea floor sediments and investigate potential connections between gas hydrate dynamics and sea floor methane seepage. MATRIX field work was carried out between August 8 and August 28, 2018 on the...
Categories: Data, Data Release - In Progress; Types: Downloadable, Map Service, OGC WFS Layer, OGC WMS Layer, Shapefile; Tags: Atlantic Ocean, BOEM, Bureau of Ocean Energy Management, CMHRP, Cape Hatteras, All tags...
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In summer 2018, the U.S. Geological Survey partnered with the U.S Department of Energy and the Bureau of Ocean Energy Management to conduct the Mid-Atlantic Resources Imaging Experiment (MATRIX) as part of the U.S. Geological Survey Gas Hydrates Project. The field program objectives were to acquire high-resolution 2-dimensional multichannel seismic-reflection and split-beam echosounder data along the U.S Atlantic margin between North Carolina and New Jersey to determine the distribution of methane gas hydrates in below-sea floor sediments and investigate potential connections between gas hydrate dynamics and sea floor methane seepage. MATRIX field work was carried out between August 8 and August 28, 2018 on the...
Categories: Data; Types: Downloadable, Map Service, OGC WFS Layer, OGC WMS Layer, Shapefile; Tags: Atlantic Ocean, BOEM, Bureau of Ocean Energy Management, CMHRP, Cape Hatteras, All tags...
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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 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 USGS data release presents topography data collected during surveys performed in the Columbia River littoral cell, Washington and Oregon, in 2015 (USGS Field Activity Number 2015-647-FA). Topographic profiles were collected by walking along survey lines with global navigation satellite system (GNSS) receivers mounted on backpacks. Prior to data collection, vertical distances between the GNSS antennas and the ground were measured using a tape measure. Hand-held data collectors were used to log raw data and display navigational information allowing surveyors to navigate survey lines spaced at 100- to 1000-m intervals along the beach. Profiles were surveyed from the landward edge of the study area...
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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 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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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...
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...


map background search result map search result map Core descriptions and sand bed thickness data from Kahana Valley, O'ahu, Hawai'i Projected flood extent polygons and flood depth points based on 10-, 50-, 100-, and 500-year wave-energy return periods, with and without coral reefs, for the Territory of Puerto Rico (the islands of Culebra, Puerto Rico, and Vieques) Transgressive Contours—Point Sur to Point Arguello, California Beach topography of the Columbia River littoral cell, Washington and Oregon, 2015 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 DCpts, DTpts, SLpts: Dune crest, dune toe, and mean high water shoreline positions: Monomoy Island, MA, 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: Fisherman Island, VA, 2014 shoreline, inletLines: Shoreline polygons and tidal inlet delineations: Metompkin 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 shoreline, inletLines: Shoreline polygons and tidal inlet delineations: 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 Seismic Reflection, EdgeTech SB-424 Chirp tracklines collected in the vicinity of Buffalo Reef, Michigan, within Lake Superior during USGS field activity 2018-043-FA, (Esri polyline shapefile, GCS WGS 84) Multichannel Seismic-Reflection and Navigation Data Collected Using Sercel GI Guns and Geometrics GeoEel Digital Streamers During the Mid-Atlantic Resource Imaging Experiment (MATRIX), USGS Field Activity 2018-002-FA Split-beam Echo Sounder and Navigation Data Collected Using a Simrad EK80 Wide Band Tranceiver and ES38-10 Transducer During the Mid-Atlantic Resource Imaging Experiment (MATRIX), USGS Field Activity 2018-002-FA. Historical coastal bluff edge positions at Barter Island, Alaska for the years spanning 1950 to 2020 Core descriptions and sand bed thickness data from Kahana Valley, O'ahu, Hawai'i shoreline, inletLines: Shoreline polygons and tidal inlet delineations: 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: Fisherman 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 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 shoreline, inletLines: Shoreline polygons and tidal inlet delineations: Metompkin Island, VA, 2014 Seismic Reflection, EdgeTech SB-424 Chirp tracklines collected in the vicinity of Buffalo Reef, Michigan, within Lake Superior during USGS field activity 2018-043-FA, (Esri polyline shapefile, GCS WGS 84) DCpts, DTpts, SLpts: Dune crest, dune toe, and mean high water shoreline positions: Monomoy Island, MA, 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 water level projections: 1-year storm in Santa Barbara County 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 Projected flood extent polygons and flood depth points based on 10-, 50-, 100-, and 500-year wave-energy return periods, with and without coral reefs, for the Territory of Puerto Rico (the islands of Culebra, Puerto Rico, and Vieques) Transgressive Contours—Point Sur to Point Arguello, California Multichannel Seismic-Reflection and Navigation Data Collected Using Sercel GI Guns and Geometrics GeoEel Digital Streamers During the Mid-Atlantic Resource Imaging Experiment (MATRIX), USGS Field Activity 2018-002-FA Split-beam Echo Sounder and Navigation Data Collected Using a Simrad EK80 Wide Band Tranceiver and ES38-10 Transducer During the Mid-Atlantic Resource Imaging Experiment (MATRIX), USGS Field Activity 2018-002-FA.