Filters: Tags: {"scheme":"USGS Thesaurus"} (X) > partyWithName: U.S. Geological Survey (X) > partyWithName: Phillip J Goodling (X)
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This dataset includes raw hydrologic data (streamflow data, groundwater level data, precipitation data) and geochemical data (geochemical results, fluorometric monitoring results) collected from January 1st, 2000 to December 31st, 2020 This dataset also includes interpreted results described in a U.S. Geological Survey Scientific Investigation Report (Goodling and others, 2023). This report describes how the hydrologic data were analyzed to calculate a water budget and how the geochemical data were interpreted. Goodling, P.J., Fleming, B.J., Solder, J., Soroka, A., and Raffensperger, J., 2023, Hydrogeologic characterization of Area B, Fort Detrick, Maryland: U.S. Geological Survey Scientific Investigations Report...
Categories: Data;
Tags: Frederick County,
Geophysics,
Hydrology,
Maryland,
USGS Science Data Catalog (SDC),
This dataset describes groundwater quality monitoring wells in the vicinity of the Badger Army Ammunition Plant in Sauk County, Wisconsin, and information associated with each unique well. This includes the values used for mapping plume boundaries, concentration trends for contaminants in each individual well, and monitoring optimization results from the Monitoring and Redediation Optimization System (MAROS) analysis.
Categories: Data;
Types: ArcGIS REST Map Service,
ArcGIS Service Definition,
Downloadable,
Map Service,
OGC WFS Layer,
OGC WMS Layer,
Shapefile;
Tags: Sauk,
Wisconsin,
groundwater,
groundwater quality,
water quality
This dataset describes concentrations of select contaminants in groundwater, collected at monitoring wells in the vicinity of the Badger Army Ammunition Plant in Sauk County, Wisconsin, between 2000 - 2018. The data were used to assess trends in contaminant concentrations over time, delineate plume boundaries at various time intervals, and assess the overall monitoring network at the Badger Army Ammunition Plant. The original water quality data were compiled by the U.S. Army Environmental Command (AEC) and SpecPro Professional Services and provided to the U.S. Geological Survey (USGS). A subset of the data were analyzed by the USGS. This subset of data and additional products derived from these data are provided...
In July 2016, July 2019, and March 2020, 318 seismic recordings were acquired at locations within Shenandoah National Park, Virginia, using MOHO Tromino Model TEP-3C three-component seismometers to assess depth to bedrock using the HVSR method. This method requires a measurement of estimate of shear wave velocity, which depends on the regolith sediment composition and density, for the conversion of measured resonance frequency to a depth to bedrock. Shear wave velocities were calculated for sediment in Shenandoah NP at locations where regolith thickness is known (e.g. at documented boreholes). The locations in this study were generally selected to characterize the depths to bedrock adjacent to streams monitored...
Categories: Data;
Types: Map Service,
OGC WFS Layer,
OGC WMS Layer,
OGC WMS Service;
Tags: HVSR,
Headwater Streams,
Hydrogeology,
Regolith Thickness,
Shenandoah National Park,
This data release contains the output of the National Hydrologic Hydrologic Model (NHM) version 1.0 aggregated to twelve-digit and ten-digit Hydrologic Unit Code (HUC) boundaries contained in the NHDPlus v2.1 dataset. The data are intended to provide "local" water budgets for each HUC boundary as total aggregated streamflow across HUC boundaries is not included. The HUC boundaries are periodically updated; this data release uses HUC boundaries downloaded on 10-26-2020. The NHM outputs aggregated in this release are calibrated using a step-wise calibration procedure to determine optimal parameter set and utilize the Muskingum routing (referred to as byHRU Musk-Obs). See Hay and LaFontaine (2020) for additional information...
Categories: Data;
Tags: Hydrologic processes,
Hydrology,
Hydrology,
USGS Science Data Catalog (SDC),
United States,
This metadata record describes model outputs and supporting model code for the Data-Driven Drought Prediction project of the Water Resources Mission Area Drought Program. The data listed here include outputs of multiple machine learning model types for predicting hydrological drought at select locations within the conterminous United States. The child items referenced below correspond to different models and spatial extents (Colorado River Basin region or conterminous United States). See the list below or metadata files in each sub-folder for more details. Daily streamflow percentile predictions for the Colorado River Basin region — Outputs from long short-term memory (LSTM) deep learning models corresponding to...
This metadata record describes outputs from 12 configurations of long short-term memory (LSTM) models which were used to predict streamflow drought occurrence at 384 stream gage locations in the Colorado River Basin region. The models were trained on data from 01-Oct-1981 to 31-Mar-2005 and validated over the period of record spanning 01-Apr-2005 to 31-Mar- 2014. The models use explanatory variable inputs described in Wieczorek (2023) (doi.org/10.5066/P98IG8LO) to predict daily streamflow and streamflow percentiles as described in Simeone (2022) (doi.org/10.5066/P92FAASD). Separate models were trained to predict daily streamflow and streamflow percentiles. Two types of percentiles were modeled: (1) fixed-threshold...
Categories: Data;
Tags: Colorado River Basin,
climatologyMeteorologyAtmosphere,
deep learning,
drought prediction,
droughts,
The U.S. Geological Survey (USGS) Water Mission Area (WMA) is working to address a need to understand where the Nation is experiencing water shortages or surpluses relative to the demand for water need by delivering routine assessments of water supply and demand. It is also improving understanding of the natural and human factors affecting the balance between supply and demand. A key part of these national assessments is identifying long-term trends in water availability, including groundwater and surface water quantity, quality, and use. To describe the long-term trends in the surface water quality component of water availability, data from the USGS and other Federal, State, and local agencies were accessed primarily...
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