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Two dams on the Elwha River, Washington State, USA trapped over 20 million m3 of sediment, reducing downstream sediment fluxes and contributing to erosion of the river's coastal delta. The removal of the Elwha and Glines Canyon dams between 2011 and 2014 induced massive increases in river sediment supply and provided an unprecedented opportunity to examine the response of a delta system to changes in sediment supply. The U.S. Geological Survey (USGS) developed an integrated research program aimed at understanding the ecosystem responses following dam removal. The research program included repeated surveys of beach topography, nearshore bathymetry, and surface sediment grain size to quantify changes in delta morphology...
Categories: Data; Tags: Geomorphology, Sedimentology
We mosaicked twelve LandSat-8 OLI satellite images taken during the summer of 2014, which were used in an object based image analysis (OBIA) to classify the landscape. We mapped seventeen of the most dominant geomorphic land cover classes on the Alaskan Coastal Plain (ACP): **value** | **class name** 1 | Coastal saline waters 2 | Large lakes 3 | Medium lakes 4 | Small lakes 5 | Ponds 6 | Rivers 7 | Nonpatterned Drained Thaw Lake Basins 8 | Coalescent low-center polygons 9 | Low-center polygons 10 | Flat-center polygons 11 | High-center polygons 12 | Drained slope 13 | Sandy barrens 14 | Sand dunes 15 | Riparian corridors 16 | Ice 17 | Urban (i.e. towns and roads)
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On February 14th, 2019, a strong atmospheric river storm (AR4 on the Atmospheric River scale of Ralph et al., 2019) struck California. The heavy rainfall caused landslides in both northern and southern California (Hatchett et al., 2020). This data release includes two subsets of mapped shallow landslide source locations in the vicinity of western Riverside County, California, where sufficient post-event imagery was available within Google Earth (image date: August 15, 2019). The data release includes: 1) .csv files containing the point locations of shallow hillslope landslides, 2) .zip files containing shapfiles (.shp) of the mapped study areas. Ralph, F., Rutz, J. J., Cordeira, J. M., Dettinger, M., Anderson,...
This release is an update to the online "Quaternary fault and fold database" for Washington State. The online database was last updated for Washington in 2014 – this 2020 update includes newly identified and modified traces and geometries for on-shore faults gleaned from new peer-reviewed studies and mapping of active faults within the state of Washington. These data contain lines representing the location of faults with known or suspected Quaternary (<1,600,000 yrs) activity in the state of Washington. This data was compiled in conjunction with the Washington State Geological Survey. Faults are attributed following the Quaternary fault and fold database attributes, including information such as age, slip sense,...
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Data in this release record ground-surface positions obtained during post-disaster emergency response following the 2014 catastrophic Oso (SR 530) landslide, Snohomish County, Washington. Global Positioning System (GPS) data were collected using three USGS GPS-seismometer spider units deployed adjacent to (OSO1), upslope of (OSO2), and on (OSO3) the landslide (see image for locations) for about five weeks. Details of the post-disaster response as well as the spider units are described in the accompanying publication. Positions were determined in near real-time relative to a base-station GPS receiver (OSO0) located on stable ground less than 2 km from the landslide using static, differential GPS processing techniques....
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Note: this data release has been deprecated. Please see new data release here: https://doi.org/10.5066/P9RZ8GHO. On November 4-7, 2019, bathymetric data were collected on the Sandusky River between Tiffin and Fremont, Ohio. Wading measurements were made at cross-sections shallower than about 1 foot using a survey pole with a Trimble R10 Global Navigation Satellite System (GNSS) receiver connected to the Ohio Department of Transportation (ODOT) real-time virtual reference station (VRS) network. Cross-sections that were deeper than about 1 foot were measured with a CEE-ECHO single-beam echosounder mounted to a canoe. A Trimble R10 GNSS receiver connected to the ODOT real-time VRS network was mounted directly above...
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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...
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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 data release contains luminescence data from the manuscript "Application of a luminescence-based sediment transport model" by Gray et al. It contains data from the luminescence measurements and experiments in the paper.
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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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From June to September 2017, the United States Geological Survey (USGS) collected a total of 116 surficial sediment and bedrock samples from abandoned mine wastepiles, ephemeral channels below wastepiles, nearby outcrops, and background areas representative of the undisturbed lithology on the western slope of the northern half of the Oquirrh Mountain Range, approximately 20 miles southwest of Salt Lake City, Utah. The sample locations can be spatially clustered into four groups: the Bates Canyon group in the foothills below Bates Canyon; the Middle Canyon group in Middle Canyon; the Ridgeline group within the Bingham Mining District located at or near the Tooele-Salt Lake County border on the Oquirrh Mountain ridge;...
Categories: Data; Tags: Bates Canyon, Bingham Mining District, Economic Geology, Environmental Health, Geochemistry, 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...


map background search result map search result map Fish/Judy Creek Watershed map Data release for application of a luminescence-based sediment transport model ElevMHW: Elevation adjusted to local mean high water: Cedar Island, VA, 2014 DCpts, DTpts, SLpts: Dune crest, dune toe, and mean high water shoreline positions: Edwin B. Forsythe NWR, NJ, 2012 shoreline, inletLines: Shoreline polygons and tidal inlet delineations: Fire Island, NY, 2010 DisOcean: Distance to the ocean: Monomoy Island, MA, 2014 DCpts, DTpts, SLpts: Dune crest, dune toe, and mean high water shoreline positions: Assawoman Island, VA, 2014 DCpts, DTpts, SLpts: Dune crest, dune toe, and mean high water shoreline positions: Fisherman Island, VA, 2014 ElevMHW: Elevation adjusted to local mean high water: Parramore 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: Parramore Island, VA, 2014 DisOcean: Distance to the ocean: Smith Island, VA, 2014 DisMOSH, Cost, MOSH_Shoreline: Distance to foraging areas for piping plovers including foraging shoreline, cost mask, and least-cost path distance: Wreck Island, VA, 2014 SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters of landcover, geomorphic setting, substrate type, vegetation density, and vegetation type: Wreck Island, VA, 2014 Abandoned Mine Land (AML) Geochemical Data: Western Slope of the Oquirrh Mountains, Tooele County, Utah Bathymetric and water quality data on the Sandusky River between Tiffin and Fremont, Ohio, November 4-7, 2019 2020 Update to the Quaternary Fault and Fold Database for Washington State GPS monitoring data from spider units on the post-disaster 2014 Oso landslide, Snohomish County, Washington Bathymetry, topography, and sediment grain-size data from the Elwha River delta, Washington, August 2022 Landslides triggered by the February 2019 atmospheric river storm, western Riverside County, California, USA Bathymetry, topography, and sediment grain-size data from the Elwha River delta, Washington, August 2022 GPS monitoring data from spider units on the post-disaster 2014 Oso landslide, Snohomish County, Washington DisMOSH, Cost, MOSH_Shoreline: Distance to foraging areas for piping plovers including foraging shoreline, cost mask, and least-cost path distance: Wreck Island, VA, 2014 SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters of landcover, geomorphic setting, substrate type, vegetation density, and vegetation type: Wreck Island, VA, 2014 DCpts, DTpts, SLpts: Dune crest, dune toe, and mean high water shoreline positions: Fisherman Island, VA, 2014 ElevMHW: Elevation adjusted to local mean high water: Cedar Island, VA, 2014 DisOcean: Distance to the ocean: Smith Island, VA, 2014 ElevMHW: Elevation adjusted to local mean high water: Parramore Island, VA, 2014 DisOcean: Distance to the ocean: Monomoy Island, MA, 2014 DCpts, DTpts, SLpts: Dune crest, dune toe, and mean high water shoreline positions: 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: Parramore Island, VA, 2014 Abandoned Mine Land (AML) Geochemical Data: Western Slope of the Oquirrh Mountains, Tooele County, Utah Data release for application of a luminescence-based sediment transport model 2020 Update to the Quaternary Fault and Fold Database for Washington State Fish/Judy Creek Watershed map