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This data release consist of the annual sediment depositional volume at five floodplain and five point bar sites on Powder River in southeastern Montana from 1979 through 2017. These 10 sites are a subgroup of a larger group of cross-sections established in 1975 and 1977 to monitor the channel changes along a 90-kilometer reach of Powder River. In addition to the sediment deposition data, characteristic of the annual peak flood are listed. The data are in 1 Excel files containing worksheets (10) corresponding to each channel cross-section .
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Denitrification measurements and ecosystem attributes in hummock-hollow microtopography of tidal freshwater forested wetlands along longitudinal riverine positions (upper, middle, and lower tidal river sites, and nearby upstream nontidal forested floodplains) of the adjoining Pamunkey and Mattaponi Rivers, Virginia.
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Water surface elevations within seven Willamette River off-channel features (OCF; alcoves and side channels) were measured using submerged pressure transducers. Transducers were installed from late May through mid-October, 2016, when discharge of the Willamette River was between approximately 5,500 and 45,000 cubic feet per second at Salem, Oregon (USGS gage 14191000) and 3,500 to 17,500 cubic feet per second at Harrisburg, Oregon (USGS gage 14166000). Pressure transducer sensor depth was measured at all seven sites. For five of the sites, pressure transducer sensor depths were converted to water surface elevations by surveying the water surface at each transducer with a real-time kinematic global positioning system...
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The files consist of two types: tabulated data files and graphical map files. Data files consist of six .csv files, representing six experiment dates (2016_06_14, 2016_16_15, 2016_18_15, 2016_16_21, 2016_16_22, 2016_16_23). Each of these files contains multiple columns of data, with each column representing either a time measurement or the value of a physical quantity measured at that time (e.g., flow depth, pore pressure, normal stress, etc.). Map files consist of six .pdf files, each representing an experiment date listed above. The maps show the thickness of the sediment deposited onto the runout pad after each experiment. Sediment thickness was determined using photogrammetery software from Adam Technology.
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These data are bathymetry (river bottom elevation) in XYZ format, generated from the March 29-30, 2017 and April 13, 2017, bathymetric survey of the East Fork White River at Columbus, Indiana. The bathymetry was collected from approximately the confluence of Driftwood and Flatrock rivers, downstream to the confluence of Haw Creek. Hydrographic data were collected using an acoustic Doppler current profiler (ADCP) with integrated Differential Global Positioning System (DGPS). Data were collected as the surveying vessel traversed the river, approximately perpendicular to the velocity vectors at 55 cross sections which were spaced 200 feet apart along the river. Additional cross sections were collected upstream and...
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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...
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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High-resolution acoustic backscatter data, bathymetry data, single channel minisparker seismic-reflection data were collected by the U.S. Geological Survey (USGS) and the Alaska Department of Fish and Game in May of 2014 southwest of Chenega Island and southwest of Montague Island, Alaska. Data were collected aboard the Alaska Department of Fish and Game vessel, R/V Solstice, during USGS field activity 2014-622-FA, using a pole mounted 100-kHz Reson 7111 multibeam echosounder, a 500 Joule SIG 2-mille minisparker sound source and a single channel streamer.
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We used the 1981 historical imagery of the Escalante River, Utah in ArcGIS to quantify channel area and average width and quantify woody riparian vegetation cover in two reaches of the river. Reach 1 was approximately 15 river kilometers (rkms) long and located between Sand and Boulder creeks within Grand Staircase Escalante National Monument. Reach 2 was approximately 16 rkms in length, extending from the Glen Canyon National Recreation Area boundary to just upstream of Choprock Canyon. We delineated the extent of active channel. Active channel was defined as the portion of the channel free of vegetation. We also delineated fluvial geomorphic features such as point bars, mid-channel bars, lateral bars and floodplain....
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Scientists from the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center investigated the sedimentary and geochemical properties of the lower reaches of the Pascagoula River along the Mississippi coast of the Gulf of Mexico by collecting estuarine, riverine and marsh sediments. This was done in order to increase understanding of the region's environmental history, describe the long-term (millennial-scale) depositional history, and identify sedimentary intervals associated with extreme marine intrusions. To this end, the group obtained long sediment cores, shovel-dug sediment slabs and marsh and riverine channel/estuarine surface samples from a north-south transect along the river edge from...
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This digital elevation model provides a tool for calibrating tsunami risk to observations of the 1945 Makran tsunami in Karachi Harbour. The DEM bathymetry is derived from soundings made mainly during the first eight years after the tsunami. Although deficient in portraying intertidal backwaters and upland topography, the DEM accurately depicts the sheltered setting of one of the two tide gauges that recorded the 1945 tsunami.
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These data were compiled for investigating the relationship between acoustic backscattering by riverbeds composed of various riverbed substrates (bed sediment), and for developing and testing a probabilistic model for substrate classification based on high-frequency multibeam acoustic backscatter. The model is described in Buscombe et al. (2017). The data consist of various quantities on coincident grids, from various sites along the Colorado River in Grand Canyon, including water depth, bed roughness, the area (or footprint) of the acoustic beam, unfiltered and filtered backscatter magnitude, sediment classification (for each location, 1 of 5 sediment classes in a categorical scheme), and the probabilities for...


map background search result map search result map Sensor data from debris-flow experiments conducted in June, 2016, at the USGS debris-flow flume, HJ Andrews Experimental Forest, Blue River, Oregon Water surface elevations recorded by submerged water level loggers in off-channel features of the middle and upper Willamette River, Oregon, Summer, 2016 Bathymetry on the East Fork White River at Columbus, Indiana, March 29-30 and April 13, 2017 Fish/Judy Creek Watershed map Acoustic backscatter - Data and Python Code A polygon shapefile of bottomland vegetation cover and geomorphic features of the Escalante River, Utah mapped from 1981 aerial imagery Sedimentary data from the lower Pascagoula River, Mississippi, USA Sediment Deposition on Floodplains and Point Bars of Powder River in Southeastern Montana from 1979 through 2017 Bathymetric and topographic grid intended for simulations of the 1945 Makran tsunami in Karachi Harbour Data on soil denitrification potential and physico-chemical characteristics of tidal freshwater forested wetlands in Virginia SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters (landcover, geomorphic setting, substrate type, vegetation density, and vegetation type): Cedar Island, VA, 2012–2013 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 Sensor data from debris-flow experiments conducted in June, 2016, at the USGS debris-flow flume, HJ Andrews Experimental Forest, Blue River, Oregon Bathymetry on the East Fork White River at Columbus, Indiana, March 29-30 and April 13, 2017 Sedimentary data from the lower Pascagoula River, Mississippi, USA SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters (landcover, geomorphic setting, substrate type, vegetation density, and vegetation type): Cedar Island, VA, 2012–2013 Bathymetric and topographic grid intended for simulations of the 1945 Makran tsunami in Karachi Harbour SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters (landcover, geomorphic setting, substrate type, vegetation density, and vegetation type): Rockaway Peninsula, NY, 2012 SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters (landcover, geomorphic setting, substrate type, vegetation density, and vegetation type): Rockaway Peninsula, NY, 2010–2011 A polygon shapefile of bottomland vegetation cover and geomorphic features of the Escalante River, Utah mapped from 1981 aerial imagery Data on soil denitrification potential and physico-chemical characteristics of tidal freshwater forested wetlands in Virginia 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 Sediment Deposition on Floodplains and Point Bars of Powder River in Southeastern Montana from 1979 through 2017 Acoustic backscatter - Data and Python Code Fish/Judy Creek Watershed map