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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
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
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
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
Predicting water temperature in the Delaware River Basin
Department of the Interior —
Daily temperature predictions in the Delaware River Basin (DRB) can inform decision makers who can use cold-water reservoir releases to maintain thermal habitat for... -
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). -
Federal
2 Observations: 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 mean daily stream water temperature observations, retrieved from the USGS National Water Information System (NWIS) and used to... -
Federal
Daily surface temperature predictions for 185,549 U.S. lakes with associated observations and meteorological conditions (1980-2020)
Department of the Interior —
Daily lake surface temperatures estimates for 185,549 lakes across the contiguous United States from 1980 to 2020 generated using an entity-aware long short-term... -
Federal
Predicting water temperature in the Delaware River Basin: 1 Waterbody information for 456 river reaches and 2 reservoirs
Department of the Interior —
This dataset provides one shapefile of polylines for the 456 river segments in this study, and one shapefile of reservoir polygons for the Pepacton and Cannonsville... -
Federal
Exploring the exceptional performance of a deep learning stream temperature model and the value of streamflow data: 1 Spatial information
Department of the Interior —
This data release component contains a shapefile of monitoring site locations coincident with the outlets of the 118 river basins modeled by Rahmani et al. (2020). -
Federal
Deep learning approaches for improving prediction of daily stream temperature in data-scarce, unmonitored, and dammed basins
Department of the Interior —
This data release provides all data and code used in Rahmani et al. (2021b) to model stream temperature and assess results. Briefly, we modeled stream temperature at... -
Federal
Exploring the exceptional performance of a deep learning stream temperature model and the value of streamflow data: 3 Model inputs
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: 6 Model evaluation
Department of the Interior —
This data release component contains evaluation metrics used to assess the predictive performance of each stream temperature model. For further description, see the... -
Federal
Exploring the exceptional performance of a deep learning stream temperature model and the value of streamflow data: 5 Model predictions
Department of the Interior —
This data release component contains water temperature predictions in 118 river catchments across the U.S. Predictions are from the four models described by Rahmani... -
Federal
Predicting water temperature in the Delaware River Basin: 4 Model inputs
Department of the Interior —
This dataset includes model inputs including gridded weather data, a stream network distance matrix, stream reach attributes and metadata, and reservoir characteristics. -
Federal
Predicting water temperature in the Delaware River Basin: 2 Water temperature and flow observations
Department of the Interior —
Observations related to water and thermal budgets in the Delaware River Basin. Data from reservoirs in the basin include reservoir characteristics (e.g., bathymetry),... -
Federal
Predicting water temperature in the Delaware River Basin: 5 Model prediction data
Department of the Interior —
Several models were used to improve water temperature prediction in the Delaware River Basin. PRMS-SNTemp was used to predict daily temperatures at 456 stream reaches... -
Federal
Model code, outputs, and supporting data for approaches to process-guided deep learning for groundwater-influenced stream temperature predictions
Department of the Interior —
This model archive provides all data, code, and modeling results used in Barclay and others (2023) to assess the ability of process-guided deep learning stream...