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Federal
3 Model Forcings: Deep learning approaches for improving prediction of daily stream temperature in data-scarce, unmonitored, and dammed basins
Department of the Interior —
This data release component contains model inputs including river basin attributes, weather forcing data, and simulated and observed river discharge. -
Federal
Exploring the exceptional performance of a deep learning stream temperature model and the value of streamflow data
Department of the Interior —
This data release provides all data and code used in Rahmani et al. (2020) to model stream temperature and assess results. Briefly, we used a subset of the USGS... -
Federal
Great Smoky Mountains National Park Vital Signs Watersheds
Department of the Interior —
The natural resource monitoring component of the parks I&M Program provides park managers, planners, and other key audiences with scientifically-credible data and... -
Federal
Airborne electromagnetic, magnetic, and radiometric survey of the Mississippi Alluvial Plain, November 2019 - March 2020: AEM processed survey data
Department of the Interior —
Airborne electromagnetic (AEM), magnetic, and radiometric data were acquired November 2019 to March 2020 along 24,030 line-kilometers (line-km) over the Mississippi... -
Federal
Supply of and demand for water purification of nonpoint source pollutants in the Southeast United States
Department of the Interior —
Natural land cover can remove pollutants from runoff water by slowing water flow and physically trapping suspended particles. We identified natural land cover in the... -
Federal
1 Site Information: Deep learning approaches for improving prediction of daily stream temperature in data-scarce, unmonitored, and dammed basins
Department of the Interior —
This data release component contains shapefiles of river basin polygons and monitoring site locations coincident with the outlets of those basins. A table of basin... -
Federal
Exploring the exceptional performance of a deep learning stream temperature model and the value of streamflow data: 2 Observations
Department of the Interior —
This data release component contains mean daily stream water temperature observations, retrieved from the USGS National Water Information System (NWIS) and used to... -
Federal
AEM processed survey data of the Mississippi Alluvial Plain, November 2018 - February 2019
Department of the Interior —
Airborne electromagnetic (AEM), magnetic, and radiometric data were acquired November 2018 to February 2019 along 16,816 line-kilometers (line-km) over the... -
Federal
Airborne electromagnetic, magnetic, and radiometric survey of the Mississippi Alluvial Plain, November 2019 - March 2020: AEM inverted resistivity models
Department of the Interior —
Airborne electromagnetic (AEM), magnetic, and radiometric data were acquired November 2019 to March 2020 along 24,030 line-kilometers (line-km) over the Mississippi... -
Federal
Airborne electromagnetic, magnetic, and radiometric survey of the Mississippi Alluvial Plain, Mississippi Embayment, and Gulf Coastal Plain, September 2021 - January 2022
Department of the Interior —
Airborne electromagnetic (AEM), magnetic, and radiometric data were acquired September 2021 to January 2022 along 27,204 line-kilometers (line-km) over the... -
Federal
Mississippi Alluvial Plain (MAP): Electrical Resistivity & Facies Classification Grids
Department of the Interior —
Electrical resistivity results from two regional airborne electromagnetic (AEM) surveys (Minsley et al. 2021 and Burton et al. 2021) over the Mississippi Alluvial... -
Federal
Mean seasonal time-step estimates of daily streamflow and daily baseflow, and loads of total nitrogen, total phosphorus, and total suspended solids at surface-water stations in the southeastern United States, 2001-14
Department of the Interior —
This metadata record describes mean seasonal time-step estimates of daily streamflow and daily baseflow, and total and baseflow estimates of loads of total nitrogen,... -
Federal
Supply of and demand for water purification of nonpoint source pollutants in the Southeast United States - 2022 Updates (version 2.0, February 2023)
Department of the Interior —
Natural land cover can remove pollutants from runoff water by slowing water flow and physically trapping suspended particles. We identified natural land cover in the... -
Federal
Historical (1940–2006) and recent (2019–20) aquifer slug test datasets used to model transmissivity and hydraulic conductivity of the Mississippi River Valley alluvial aquifer from recent (2018–20) airborne electromagnetic (AEM) survey data.
Department of the Interior —
The Mississippi River Valley alluvial aquifer (“alluvial aquifer”) is one of the most extensively developed aquifers in the United States. The alluvial aquifer is... -
Federal
Exploring the exceptional performance of a deep learning stream temperature model and the value of streamflow data: 4 Models
Department of the Interior —
This data release component contains model code and configurations for the LSTM and linear regression models used to predict stream temperature. -
Federal
Combined results and derivative products of hydrogeologic structure and properties from airborne electromagnetic surveys in the Mississippi Alluvial Plain
Department of the Interior —
Electrical resistivity results from two regional airborne electromagnetic (AEM) surveys (Minley et al. 2021 and Burton et al. 2021) over the Mississippi Alluvial... -
Federal
Airborne EM, magnetic, and radiometric survey data of the Mississippi Alluvial Plain, November 2018 - February 2019
Department of the Interior —
Airborne electromagnetic (AEM), magnetic, and radiometric data were acquired November 2018 to February 2019 along 16,816 line-kilometers (line-km) over the... -
Federal
Processed airborne magnetic and radiometric grids of the Mississippi Alluvial Plain, November 2018 - February 2019
Department of the Interior —
Airborne electromagnetic (AEM), magnetic, and radiometric data were acquired November 2018 to February 2019 along 16,816 line-kilometers (line-km) over the... -
Federal
4 Model Code: Deep learning approaches for improving prediction of daily stream temperature in data-scarce, unmonitored, and dammed basins
Department of the Interior —
This data release component contains model code and configurations for the LSTM models used to predict stream temperature. -
Federal
5 Model Predictions: Deep learning approaches for improving prediction of daily stream temperature in data-scarce, unmonitored, and dammed basins
Department of the Interior —
This data release item contains water temperature predictions for 455 river sites across the U.S. Predictions are from the models described by Rahmani et al. (2021b).