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.
|Title||Exploring the exceptional performance of a deep learning stream temperature model and the value of streamflow data|
|Authors||Farshid Rahmani, Kathryn Lawson, Wenyu Ouyang, Alison Appling, Samantha Oliver, Chaopeng Shen|
|Product Type||Data Release|
|Record Source||USGS Digital Object Identifier Catalog|
|USGS Organization||Integrated Information Dissemination Division|