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This bathymetric dataset provides an update to the stage-storage relation for Quail Lake (reservoir) located in the El Dorado National Forest, Calif. Bathymetric data was collected using a multibeam echo sounder to provide near-complete coverage and was merged with USGS 3DEP lidar to compute a DEM of the lake and near shore. The DEM was used to computed storage and surface area for a range of stage elevations. Results show that the spillway elevation was 6799.3 feet (NAVD88) and the crest elevation was 6802.5 feet (NAVD88). At the spillway elevation the storage was 141.74 ac-ft with a surface area of 14.20 ac. At the crest elevation the storage was 190.05 ac-ft with a surface area of 15.89 ac.
From May 2017 to November 2019, the U.S. Geological Survey conducted bathymetric surveys of New York City's East of Hudson Reservoirs. Bathymetry data were collected at Kirk Lake during June 2017. Depth data were collected primarily with a multibeam echosounder. Quality assurance points were measured with a single-beam echosounder. Water surface elevations were established using real-time kinematic (RTK) and static global navigation satellite system (GNSS) surveys and submersible pressure transducers. Measured sound velocity profiles were used to correct echosounder depth measurements for thermal stratification. Digital elevation models were created by combining the measured bathymetry data with lidar elevation...
From May 2017 to November 2019, the U.S. Geological Survey conducted bathymetric surveys of New York City's East of Hudson Reservoirs. Bathymetry data were collected at Lake Gleneida during May 2017. Depth data were collected primarily with a multibeam echosounder. Quality assurance points were measured with a single-beam echosounder. Water surface elevations were established using real-time kinematic (RTK) and static global navigation satellite system (GNSS) surveys and submersible pressure transducers. Measured sound velocity profiles were used to correct echosounder depth measurements for thermal stratification. Digital elevation models were created by combining the measured bathymetry data with lidar elevation...
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This imagery dataset consists of 3-meter resolution, lidar-derived imagery of the Carlisle 30 x 60 minute quadrangle in Pennsylvania. The source data used to construct this imagery consists of 1-meter resolution lidar-derived digital elevation models (DEMs). The lidar source data were compiled from different acquisitions published between 2019 and 2020 and downloaded from the USGS National Map TNM Download. The data were processed using geographic information systems (GIS) software. The data is projected in WGS 1984 Web Mercator. This representation illustrates the terrain as a hillshade with contrast adjusted to highlight local relief according to a topographic position index (TPI) calculation.
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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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Marine geophysical mapping of the Queen Charlotte Fault in the eastern Gulf of Alaska was conducted in 2016 as part of a collaborative effort between the U.S. Geological Survey and the Alaska Department of Fish and Game to understand the morphology and subsurface geology of the entire Queen Charlotte system. The Queen Charlotte fault is the offshore portion of the Queen Charlotte-Fairweather Fault: a major structural feature that extends more than 1,200 kilometers from the Fairweather Range of southern Alaska to northern Vancouver Island, Canada. The data published in this data release were collected along the Queen Charlotte Fault between Cross Sound and Noyes Canyon, offshore southeastern Alaska from May 18 to...
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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...
From May 2017 to November 2019, the U.S. Geological Survey conducted bathymetric surveys of New York City's East of Hudson Reservoirs. Bathymetry data were collected at Middle Branch Reservoir during July and August, 2017. Depth data were collected primarily with a multibeam echosounder. Quality assurance points were measured with a single-beam echosounder. Water surface elevations were established using real-time kinematic (RTK) and static global navigation satellite system (GNSS) surveys and submersible pressure transducers. Measured sound velocity profiles were used to correct echosounder depth measurements for thermal stratification. Digital elevation models were created by combining the measured bathymetry...
From May 2017 to November 2019, the U.S. Geological Survey conducted bathymetric surveys of New York City's East of Hudson Reservoirs. Bathymetry data were collected at West Branch Reservoir during September 2017, October 2017, and October 2019. Depth data were collected primarily with a multibeam echosounder; additional bathymetry points were measured using an acoustic Doppler current profiler (ADCP). Quality assurance points were measured with a single-beam echosounder. Water surface elevations were established using real-time kinematic (RTK) and static global navigation satellite system (GNSS) surveys and submersible pressure transducers. Measured sound velocity profiles were used to correct echosounder depth...
From May 2017 to November 2019, the U.S. Geological Survey conducted bathymetric surveys of New York City's East of Hudson Reservoirs. Bathymetry data were collected at Boyd Corners Reservoir during September 2017. Depth data were collected primarily with a multibeam echosounder. Quality assurance points were measured with a single-beam echosounder. Water surface elevations were established using real-time kinematic (RTK) and static global navigation satellite system (GNSS) surveys and submersible pressure transducers. Measured sound velocity profiles were used to correct echosounder depth measurements for thermal stratification. Digital elevation models were created by combining the measured bathymetry data with...
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This bathymetric dataset provides an update to the stage-storage relation for Little Rock Reservoir located in the Angeles National Forest, California. Bathymetric data was collected using a multibeam echo sounder to provide near-complete coverage and was merged with U.S. Geological Survey 3D Elevation Project lidar to compute a digital elevation model (DEM) of the reservoir and surrounding watershed. The DEM was used to computed storage and surface area for a range of stage elevations. Results show that the mean cross-spillway elevation was 3273 feet above the North American Vertical Datum 1988 (NAVD88) and the mean dam crest elevation was 3277 feet (NAVD88). At the spillway elevation the storage was 3335.8 acre-feet...
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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 Bathymetric and topographic grid intended for simulations of the 1945 Makran tsunami in Karachi Harbour ElevMHW: Elevation adjusted to local mean high water: Cedar Island, VA, 2014 ElevMHW: Elevation adjusted to local mean high water: Fire Island, NY, 2014 Multibeam bathymetric data collected in the eastern Gulf of Alaska during USGS Field Activity 2016-625-FA using a Reson 7160 multibeam echosounder (10 meter resolution, 32-bit GeoTIFF, UTM 8 WGS 84, WGS 84 Ellipsoid) ElevMHW: Elevation adjusted to local mean high water: Rhode Island National Wildlife Refuge, RI, 2014 Geospatial bathymetry datasets for Boyd Corners Reservoir, New York, 2017 Geospatial bathymetry datasets for Kirk Lake, New York, 2017 Geospatial bathymetry datasets for Lake Gleneida, New York, 2017 Geospatial bathymetry datasets for Middle Branch Reservoir, New York, 2017 Geospatial bathymetry datasets for West Branch Reservoir, New York, 2017 to 2019 Final surface model (SRF) for basin A1 Vegetation classification model (Veg) for basin A1 Orthoimagery for basin A2 Digital elevation model (DEM) for basin A2 Digital terrain model (DTM) for basin B1 Final surface model (SRF) for basin B1 Digital terrain model (DTM) for basin B2 Bathymetric survey and stage-storage assessment of Quail Lake, Calif., collected in 2022 Little Rock Reservoir, California, 2022 bathymetric survey and stage-storage computations Enhanced Terrain Imagery of the Carlisle 30 x 60 Minute Quadrangle from Lidar-Derived Elevation Models at 3-Meter Resolution Geospatial bathymetry datasets for Lake Gleneida, New York, 2017 Bathymetric survey and stage-storage assessment of Quail Lake, Calif., collected in 2022 Final surface model (SRF) for basin A1 Vegetation classification model (Veg) for basin A1 Geospatial bathymetry datasets for Boyd Corners Reservoir, New York, 2017 Geospatial bathymetry datasets for Kirk Lake, New York, 2017 Digital terrain model (DTM) for basin B2 Orthoimagery for basin A2 Digital elevation model (DEM) for basin A2 Geospatial bathymetry datasets for Middle Branch Reservoir, New York, 2017 Digital terrain model (DTM) for basin B1 Final surface model (SRF) for basin B1 Little Rock Reservoir, California, 2022 bathymetric survey and stage-storage computations Geospatial bathymetry datasets for West Branch Reservoir, New York, 2017 to 2019 ElevMHW: Elevation adjusted to local mean high water: Cedar Island, VA, 2014 Bathymetric and topographic grid intended for simulations of the 1945 Makran tsunami in Karachi Harbour Enhanced Terrain Imagery of the Carlisle 30 x 60 Minute Quadrangle from Lidar-Derived Elevation Models at 3-Meter Resolution Multibeam bathymetric data collected in the eastern Gulf of Alaska during USGS Field Activity 2016-625-FA using a Reson 7160 multibeam echosounder (10 meter resolution, 32-bit GeoTIFF, UTM 8 WGS 84, WGS 84 Ellipsoid)