<p>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 prediction of stream temperature and groundwater/subsurface flow contributions to stream temperature. Briefly, we modeled stream temperature at sites across the continental United States using a hybrid differentiable model that combines neural network components with differentiable implementations of several structural priors, i.e., process-based equations. The differentiable framework permits estimation of parameters and comparison of structural priors as well as prediction of stream temperature.</p> <p>The data are organized into these child items: <li><a href="https://www.sciencebase.gov/catalog/item/648f9bbdd34ef77fcb001ffc"> 1. Model code </a>- Python files and README for reproducing model training and evaluation </li> <li><a href="https://www.sciencebase.gov/catalog/item/648f9c49d34ef77fcb001fff"> 2. Inputs </a>- Basin attributes and shapefiles, forcing data, and stream temperature observations </li> <li><a href="https://www.sciencebase.gov/catalog/item/648f9caed34ef77fcb002001"> 3. Simulations </a>- Simulation descriptions, configurations, and outputs </li> <li><a href="https://www.sciencebase.gov/catalog/item/6495df90d34ef77fcb01e285"> 4. Figure code </a>- Jupyter notebook to recreate the figures in Rahmani et al. (2023b) </li> </p> <p>The publication associated with this model archive is: Rahmani, F., Appling, A.P., Feng, D., Lawson, K., and Shen, C. 2023b. Identifying structural priors in a hybrid differentiable model for stream water temperature modeling. Water Resources Research. <a href=https://doi.org/10.1029/2023WR034420>https://doi.org/10.1029/2023WR034420</a>.</p>; <p>This data compilation was funded by the Integrated Water Prediction Program at the U.S. Geological Survey.</p>