Exploring the exceptional performance of a deep learning stream temperature model and the value of streamflow data

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 GAGES-II dataset as a test case for temperature prediction using deep learning methods. The associated manuscript explores the value of including stream discharge as a predictor in the temperature models, including the value of predicted discharge from a separate model when no discharge measurements are available. The data are organized into these items: Spatial Information - Locations of the 118 monitoring sites used in this study Observations - Water temperature observations for the 118 sites used in this study Model Inputs - Model inputs, including basin attributes, weather drivers, and discharge Models - Code and configurations for the stream temperature models Model Predictions - Predictions of stream water temperature Model Evaluation - Performance metrics for each stream temperature model This research was funded by the Integrated Water Prediction Program at the US Geological Survey.