Model Code, Outputs, and Supporting Data for Approaches to Process-Guided Deep Learning for Groundwater-Influenced Stream Temperature Predictions
This model archive provides all data, code, and modeling results used in Barclay and others (2023) to assess the ability of process-guided deep learning stream temperature models to accurately incorporate groundwater-discharge processes. We assessed the performance of an existing process-guided deep learning stream temperature model of the Delaware River Basin (USA) and explored four approaches for improving groundwater process representation: 1) a custom loss function that leverages the unique patterns of air and water temperature coupling resulting from different temperature drivers, 2) inclusion of additional groundwater-relevant catchment attributes, 3) incorporation of additional process model outputs, and 4) a composite model. The associated manuscript examines changes in the predictive accuracy, feature importance, and predictive ability in un-seen reaches resulting from each of the four approaches. This model archive includes four zipped folders for 1) Data Preparation, 2) Model Code, 3) Model Predictions, and 4) the catchment attributes that were compiled for reaches in the study area. Instructions for running data preparation and modeling code can be found in the README.md files in 01_Data_Prep and 02_Model_Code respectively. File dictionaries have also been included and serve as metadata documentation for the files and datasets within the four zipped folders.
|Model Code, Outputs, and Supporting Data for Approaches to Process-Guided Deep Learning for Groundwater-Influenced Stream Temperature Predictions
|Janet R Barclay, Simon N Topp, Lauren E Koenig Snyder, Margaux J Sleckman, Jeffrey M Sadler, Alison P Appling
|USGS Digital Object Identifier Catalog
|New England Water Science Center