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Predictions and supporting data for network-wide 7-day ahead forecasts of water temperature in the Delaware River Basin: 2) model driver data
This data release contains the forcings and outputs of 7-day ahead maximum water temperature forecasting models that makes predictions at 70 river reaches in the upper Delaware River Basin. This section contains forcing data for water temperature forecasting models reported in Zwart et al. (2023), including a process-based pre-trainer, gridded weather and forecasted weather data, and flow and temperature for reservoir inlets and outlets.
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Predictions and supporting data for network-wide 7-day ahead forecasts of water temperature in the Delaware River Basin: 2) model driver data
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This data release contains the forcings and outputs of 7-day ahead maximum water temperature forecasting models that makes predictions at 70 river reaches in the upper Delaware River Basin. This section contains forcing data for water temperature forecasting models reported in Zwart et al. (2023), including a process-based pre-trainer, gridded weather and forecasted weather data, and flow and temperature for reservoir inlets and outlets.
Predictions and supporting data for network-wide 7-day ahead forecasts of water temperature in the Delaware River Basin: 5) model code
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This data release contains the forcings and outputs of 7-day ahead maximum water temperature forecasting models that makes predictions at 70 river reaches in the upper Delaware River Basin. This section includes code that prepares data for model training and forecasts maximum stream temperature using neural network models. Finally, the code evaluates the models using various accuracy and uncertainty metrics.
Predictions and supporting data for network-wide 7-day ahead forecasts of water temperature in the Delaware River Basin: 5) model code
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This data release contains the forcings and outputs of 7-day ahead maximum water temperature forecasting models that makes predictions at 70 river reaches in the upper Delaware River Basin. This section includes code that prepares data for model training and forecasts maximum stream temperature using neural network models. Finally, the code evaluates the models using various accuracy and uncertainty metrics.
Predictions and supporting data for network-wide 7-day ahead forecasts of water temperature in the Delaware River Basin: 1) Waterbody information for 70 river reaches and 2 reservoirs
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Daily maximum water temperature predictions in the Delaware River Basin (DRB) can inform decision makers who can use cold-water reservoir releases to maintain thermal habitat for sensitive fish species. This data release contains the forcings and outputs of 7-day ahead maximum water temperature forecasting models that makes predictions at 70 river reaches in the upper DRB. The modeling approach includes process-guided deep learning and data assimilation (Zwart et al., 2023). The model is driven by weather forecasts and observed reservoir releases and produces maximum water temperature forecasts for the issue day (day 0) and 7 days into the future (days 1-7). In combination with data provided in Oliver et al. (2022), this release contains all data used to train and validate the water temperature forecast models. This includes a process-based model pre-trainer, forecasted gridded weather data, reservoir releases, and water temperature data. Additionally, this release contains predictions from five models: a long-short term memory network (LSTM), a recurrent graph convolution network (RGCN), LSTM with data assimilation, a RGCN with data assimilation, and a persistence model. The release contains a tidy version of the model predictions with paired observations for easier reuse.
The data are organized into 4 child folders: 1) waterbody information, 2) model driver data, 3) model configurations, 4) model predictions, 5) model code.

  1. Waterbody Information - One shapefile of polylines for 70 river segments in this study, and one shapefile of reservoir polygons for the Pepacton and Cannonsville reservoirs
  2. Model Driver Data - Data used to drive predictive models (daily meteorology for river reaches and reservoirs, observed reservoir diversions and releases)
  3. Model Configurations - Model parameters and metadata used to configure GLM 3.1 reservoir models
  4. Model Predictions - Temperature predictions data files, including GLM 3.1 predictions of outflow and water temperature for reservoir outflow reaches, stream temperature predictions from the distance-weighted-average lotic-lentic input network, and 7-day ahead deep learning water temperature forecasts at 5 priority sites
  5. Model Code - Model code repository used to prepare data for training, validation, testing, and evaluation of model output

    6. This research was funded by the USGS.

Predictions and supporting data for network-wide 7-day ahead forecasts of water temperature in the Delaware River Basin: 1) Waterbody information for 70 river reaches and 2 reservoirs
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This section provides spatial data files that describe the river and reservoirs in the Delaware River Basin included in this release. One shapefile of polylines describes the 70 river reaches that define the modeling network, and another shapefile of polygons includes the two reservoirs (Pepacton, Cannonsville) for which data are included in this release.
Predictions and supporting data for network-wide 7-day ahead forecasts of water temperature in the Delaware River Basin: 3) model configurations
공공데이터포털
This data release contains the forcings and outputs of 7-day ahead maximum water temperature forecasting models that makes predictions at 70 river reaches in the upper Delaware River Basin. This section includes model parameters and metadata used to configure reservoir models.
Predictions and supporting data for network-wide 7-day ahead forecasts of water temperature in the Delaware River Basin: 3) model configurations
공공데이터포털
This data release contains the forcings and outputs of 7-day ahead maximum water temperature forecasting models that makes predictions at 70 river reaches in the upper Delaware River Basin. This section includes model parameters and metadata used to configure reservoir models.
Data to support network-wide 7-day ahead forecasting of water temperature in the Delaware River Basin: 4) model predictions
공공데이터포털
This data release contains the forcings and outputs of 7-day ahead maximum water temperature forecasting models that makes predictions at 70 river reaches in the upper Delaware River Basin. This section includes predictions from several models, including a model pre-trainer that is predictions from a distance-weighted-average lotic-lentic input network (DWALLIN) model, reservoir outlet temperature predictions from a process-based model, forecasts from a persistence stream water temperature model, and stream water temperature forecasts from two deep learning models, a long-short term memory network and recurrent convolutional graph network model.
Predictions and supporting data for network-wide 7-day ahead forecasts of water temperature in the Delaware River Basin
공공데이터포털

Daily maximum water temperature predictions in the Delaware River Basin (DRB) can inform decision makers who can use cold-water reservoir releases to maintain thermal habitat for sensitive fish species. This data release contains the forcings and outputs of 7-day ahead maximum water temperature forecasting models that makes predictions at 70 river reaches in the upper DRB. The modeling approach includes process-guided deep learning and data assimilation (Zwart et al., 2023). The model is driven by weather forecasts and observed reservoir releases and produces maximum water temperature forecasts for the issue day (day 0) and 7 days into the future (days 1-7). In combination with data provided in Oliver et al. (2022), this release contains all data used to train and validate the water temperature forecast models. This includes a process-based model pre-trainer, forecasted gridded weather data, reservoir releases, and water temperature data. Additionally, this release contains predictions from five models: a long-short term memory network (LSTM), a recurrent graph convolution network (RGCN), LSTM with data assimilation, a RGCN with data assimilation, and a persistence model. The release contains a tidy version of the model predictions with paired observations for easier reuse.
The data are organized into 4 child folders: 1) waterbody information, 2) model driver data, 3) model configurations, 4) model predictions, 5) model code.

  1. Waterbody Information - One shapefile of polylines for 70 river segments in this study, and one shapefile of reservoir polygons for the Pepacton and Cannonsville reservoirs
  2. Model Driver Data - Data used to drive predictive models (daily meteorology for river reaches and reservoirs, observed reservoir diversions and releases)
  3. Model Configurations - Model parameters and metadata used to configure GLM 3.1 reservoir models
  4. Model Predictions - Temperature predictions data files, including GLM 3.1 predictions of outflow and water temperature for reservoir outflow reaches, stream temperature predictions from the distance-weighted-average lotic-lentic input network, and 7-day ahead deep learning water temperature forecasts at 5 priority sites
  5. Model Code - Model code repository used to prepare data for training, validation, testing, and evaluation of model output

    6. This research was funded by the USGS.

Predicting water temperature in the Delaware River Basin: 5 Model prediction data
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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 in the Delaware River Basin. Daily stream temperature predictions for inflow and outflow reaches for Cannonsville and Pepacton reservoirs were pulled aside into a separate csv to be used as inputs to the General Lake Model (GLM). Reservoir outflow predictions and in-reservoir temperature predictions were generated with calibrated models built using GLM v3.1. We calculated a decay rate based on the modeled reservoir outflow temperatures and observed downstream river temperature to estimate the decay of the reservoir influence on stream temperature as a function of distance downstream of a reservoir. These decay rates from Pepacton and Cannonsville were used to weight the predictions from GLM and PRMS-SNTemp on the East and West Branch of the Delaware River, respectively, to represent the mix of stream and reservoir processes that affect temperature dynamics. These weighted values were then used to pre-train the deep learning models that were used to forecast temperature. More details on these methods can be found in Zwart and others 2021.

Code that generated these results can be found in Zwart and others 2021 (https://doi.org/10.5281/zenodo.5164910).

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