Predictions and supporting data for network-wide 7-day ahead forecasts of water temperature in the Delaware River Basin

&lt;p&gt;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.
&lt;br/&gt;The data are organized into 4 child folders: 1) waterbody information, 2) model driver data, 3) model configurations, 4) model predictions, 5) model code. &lt;/p&gt; &lt;ol&gt; &lt;li&gt;&lt;a href="https://www.sciencebase.gov/catalog/item/624f211ed34e21f82769a7a4"&gt;Waterbody Information&lt;/a&gt; - One shapefile of polylines for 70 river segments in this study, and one shapefile of reservoir polygons for the Pepacton and Cannonsville reservoirs&lt;/li&gt; &lt;li&gt;&lt;a href="https://www.sciencebase.gov/catalog/item/624f21b9d34e21f82769a7ab"&gt;Model Driver Data &lt;/a&gt; - Data used to drive predictive models (daily meteorology for river reaches and reservoirs, observed reservoir diversions and releases)&lt;/li&gt; &lt;li&gt;&lt;a href="https://www.sciencebase.gov/catalog/item/624f21ddd34e21f82769a7af"&gt;Model Configurations &lt;/a&gt; - Model parameters and metadata used to configure GLM 3.1 reservoir models &lt;/li&gt; &lt;li&gt;&lt;a href="https://www.sciencebase.gov/catalog/item/624f21fad34e21f82769a7b1"&gt;Model Predictions &lt;/a&gt; - 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 &lt;/li&gt; &lt;li&gt;&lt;a href="https://www.sciencebase.gov/catalog/item/6487548dd34ef77fcafe1753"&gt;Model Code &lt;/a&gt; - Model code repository used to prepare data for training, validation, testing, and evaluation of model output &lt;/li&gt; &lt;br/&gt; &lt;p&gt;This research was funded by the USGS.&lt;/p&gt;

Tags: deep-learning, environment, forecast, hybrid-modeling, inlandwaters, machine-learning, modeling, new-york, ny, pa, pennsylvania, reservoirs, streams, temperature, united-states, us, usgs-6238fcead34e915b67cc4856, water, water-resources
Organization: Department of the Interior