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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. Model code for model archive: Identifying structural priors in a hybrid differentiable model for stream water temperature modeling
Department of the Interior —
This section provides model code described by Rahmani et al. (2023b). This code accepts basin attributes and forcings and predicts stream temperatures using a... -
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
Identifying structural priors in a hybrid differentiable model for stream water temperature modeling at 415 U.S. basin outlets, 2010-2016
Department of the Interior —
This model archive (Rahmani et al. 2023a) provides all data, code, and model outputs used in Rahmani et al. (2023b) to improve model representations toward improved... -
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
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. Inputs for model archive: Identifying structural priors in a hybrid differentiable model for stream water temperature modeling
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. Three file formats... -
Federal
4. Figure code for model archive: Identifying structural priors in a hybrid differentiable model for stream water temperature modeling
Department of the Interior —
This section provides code for reproducing the figures in Rahmani et al. (2023b). The full model archive is organized into these four child items: 1. Model code -... -
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
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
3. Simulations for model archive: Identifying structural priors in a hybrid differentiable model for stream water temperature modeling at 415 U.S. basin outlets, 2010-2016
Department of the Interior —
This section provides model simulation outputs from the models described by Rahmani et al. (2023b), as well as a subset of model outputs produced by Rahmani et al.... -
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...