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This dataset is a digital elevation model (DEM) of the beach topography of Lake Superior at Minnesota Point, Duluth, Minnesota. The DEM has a 1-meter (m; 3.28084 foot [ft]) cell size and was created from a LAS (industry-standard binary format for storing large point clouds) dataset of terrestrial light detection and ranging (LiDAR) data with an average point spacing of 0.137 m (0.45 ft). LiDAR data were collected August 10, 2019 using a boat-mounted Optech ILRIS scanner and methodology similar to that described by Huizinga and Wagner (2019). References: Huizinga, R.J. and Wagner, D.M., 2019, Erosion monitoring along selected bank locations of the Coosa River in Alabama using terrestrial light detection and ranging...
This data set includes the relative production scenarios for bufflaograss [0.72(Temp) - 0.12(Precip) - 0.04(Sand) + 3.08]; this is the model from Epstein, et al. (1998). Soil texture (percent by weight) came from the Earth Systems Science Center (2008) which provided processed soils data from NRCS (gSSURGO), mean annual temperature (Celsius) and/or mean annual precipitation (millimeters) came from contemporary (1981 - 2010) estimates (Maurer et al. 2002) or a GCM. Global Climate Models (GCM) providing scenarios included: warmer-wetter scenario (CESM1-BGC, RCP4.5, Neale et al., 2010), warmer drier scenario (GISS-E2-R, RCP4.5, Schmidt, 2014), hotter-wetter scenario (Miroc-ESM, RCP8.5, Watanabe et al., 2011), and hotter-drier...
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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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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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A digital model of the sedimentary Northern Atlantic Coastal Plain aquifer system is composed of 20 rasters and hydrogeologic unit extent polygons. Rasters describe the top elevations of regional aquifers and confining units at a resolution of 2640 feet (1/2 mile). The rasters are clipped to the extent polygons, which represent the spatial extents of the hydrogeologic units onshore and several miles offshore. This three-dimensional hydrogeologic model was constructed as part of a U.S. Geological Survey Groundwater Resources Program study of groundwater availability in the Northern Atlantic Coastal Plain (NACP) aquifer system, including parts of New York, New Jersey, Delaware, Maryland, Virginia, and North Carolina....
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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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Shallow subsurface electrical conductivity was mapped at Stateline National Wildlife Refuge (NWR) in northeast Montana using the DUALEM421 electromagnetic sensor (Dualem, Inc., ON, Canada) in the winter of 2017. Data were acquired by towing the DUALEM421 sensor on a sled behind an all-terrain vehicle or snow machine, with the sensor at a nominal height of 0.3 meters (m) above ground surface. Approximately 3 line-kilometers (km) of data were acquired over an area of approximately .2 square-kilometers. Data were manually edited to remove sensor dropouts, lag corrected for apparent offsets between recorded GPS location and data locations for each coil pair, and averaged to a sounding distance of 1m along the survey...
These data were collected during a travel time and dissolved oxygen study along a 1-mile reach of the Blue River downstream from 58th Street in Kansas City, Missouri. The study was a cooperative effort between the U.S. Geological Survey (USGS), the U.S. Environmental Protection Agency Urban Waters Federal Partnership program, and the Kansas City, Missouri, Water Services Department. This is part of a larger USGS effort supporting the Middle Blue River Basin Urban Waters Federal Partnership. This data release contains continuous water-quality measurements (Blue_River_CQWM.csv), rhodamine dye sensor data (Blue_River_dye_profiles.csv), analytical data from synoptic water-quality samples (Blue_River_QWDATA.csv), manual...
Exotic annual grasses are one of the most damaging biological stressors in western North America and increase the susceptibility of landscapes to wildfire occurrence. Here we couple estimates of long-term rangeland component fractions (e.g. exotic annual grasses) with remote sensing, climate data, and machine learning techniques to estimate the long-term (1985 to 2019) probability of wildfire occurrence (30-m spatial resolution) in sagebrush-dominated landscapes of the western United States.
Exotic annual grasses [EAG] are one of the most damaging biological stressors in western North America. Despite numerous environmental and societal impacts associated with EAG there remains a need to enhance regional monitoring capabilities to better guide management and conservation efforts. Here we provide estimates of historic and potential future trends in EAG abundance that were developed using linear trend analysis and machine learning techniques at a 30-m spatial resolution. Specifically, these data represent historic (1985 to 2019) and potential future (2025-2040) rates of exotic annual grass change as estimated using Theil-Sen regression and a process-constrained, random forest model assuming only changes...
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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, CMGP, 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...


map background search result map search result map Digital elevations and extents of regional hydrogeologic units in the Northern Atlantic Coastal Plain aquifer system Nearshore bathymetry of the Columbia River littoral cell, Washington and Oregon, 2014 SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters (landcover, geomorphic setting, substrate type, vegetation density, and vegetation type): Cedar Island, VA, 2012–2013 Development: Development delineation: Edwin B. Forsythe NWR, NJ, 2010 points, transects, beach width: Barrier island geomorphology and shorebird habitat metrics at 50-m alongshore transects and 5-m cross-shore points: Edwin B. Forsythe NWR, NJ, 2010 DisOcean: Distance to the ocean: Edwin B. Forsythe NWR, NJ, 2012 DisMOSH, Cost, MOSHShoreline: Distance to foraging areas for piping plovers (foraging shoreline, cost mask, and least-cost path distance): Edwin B. Forsythe NWR, NJ, 2013–2014 DisOcean: Distance to the ocean: Edwin B. Forsythe NWR, NJ, 2014 ElevMHW: Elevation adjusted to local mean high water: Fire Island, NY, 2014 SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters (landcover, geomorphic setting, substrate type, vegetation density, and vegetation type): Rockaway Peninsula, NY, 2010–2011 SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters (landcover, geomorphic setting, substrate type, vegetation density, and vegetation type): Rockaway Peninsula, NY, 2012 Digital elevation model (DEM) of beach topography of Lake Superior at Minnesota Point, Duluth, MN, August 2019 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 Stateline NWR, Montana, 2017 Modelled long-term wildfire occurrence probabilities in sagebrush-dominated ecosystems in the western US (1985 to 2019) Historic and future trends in exotic annual grass (%) cover in the western US (1985 to 2019 and 2025 to 2040) Travel Time and Dissolved Oxygen Data from August 2020 Along a 1-Mile Reach of the Blue River near 58th Street, Kansas City, Missouri Stateline NWR, Montana, 2017 Travel Time and Dissolved Oxygen Data from August 2020 Along a 1-Mile Reach of the Blue River near 58th Street, Kansas City, Missouri Digital elevation model (DEM) of beach topography of Lake Superior at Minnesota Point, Duluth, MN, August 2019 Development: Development delineation: Edwin B. Forsythe NWR, NJ, 2010 DisOcean: Distance to the ocean: Edwin B. Forsythe NWR, NJ, 2012 DisMOSH, Cost, MOSHShoreline: Distance to foraging areas for piping plovers (foraging shoreline, cost mask, and least-cost path distance): Edwin B. Forsythe NWR, NJ, 2013–2014 DisOcean: Distance to the ocean: Edwin B. Forsythe NWR, NJ, 2014 points, transects, beach width: Barrier island geomorphology and shorebird habitat metrics at 50-m alongshore transects and 5-m cross-shore points: Edwin B. Forsythe NWR, NJ, 2010 ElevMHW: Elevation adjusted to local mean high water: Fire Island, NY, 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 Nearshore bathymetry of the Columbia River littoral cell, Washington and Oregon, 2014 Digital elevations and extents of regional hydrogeologic units in the Northern Atlantic Coastal Plain aquifer system Historic and future trends in exotic annual grass (%) cover in the western US (1985 to 2019 and 2025 to 2040) Modelled long-term wildfire occurrence probabilities in sagebrush-dominated ecosystems in the western US (1985 to 2019)