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  3. Model Code, Outputs, and Supporting Data for Approaches to Process-Guided Deep Learning for Groundwater-Influenced Stream Temperature Predictions

Model Code, Outputs, and Supporting Data for Approaches to Process-Guided Deep Learning for Groundwater-Influenced Stream Temperature Predictions

Dates

Publication Date
Start Date
1979-10-01
End Date
2021-09-30

Citation

Barclay, J.R., Topp, S.N., Koenig, L.E., Sleckman, M.J., Sadler, J.M., and Appling, A.P., 2023, Model code, outputs, and supporting data for approaches to process-guided deep learning for groundwater-influenced stream temperature predictions: U.S. Geological Survey data release, https://doi.org/10.5066/P9KO49OT.

Summary

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. [...]

Contacts

Point of Contact :
Margaux J Sleckman, U.S. Geological Survey
Originator :
Janet R Barclay, Simon N Topp, Lauren E Koenig Snyder, Margaux J Sleckman, Jeffrey M Sadler, Alison P Appling
Metadata Contact :
Margaux J Sleckman, U.S. Geological Survey
Publisher :
U.S. Geological Survey
Distributor :
U.S. Geological Survey - ScienceBase
SDC Data Owner :
New England Water Science Center
USGS Mission Area :
Water Resources

Attached Files

Click on title to download individual files attached to this item.

fgdc_metadata.xml
Original FGDC Metadata
View 		28.5 KB application/fgdc+xml
nhm_attributes.zip 167.36 KB application/zip
03_Model_Predictions.zip 191.92 MB application/zip
01_Data_Prep.zip 6.93 MB application/zip
02_Model_Code.zip 561.54 KB application/zip

Purpose

This archive is relevant to better incorporation of groundwater discharge into stream temperature predictions and exploration of approaches to adding knowledge guidance to deep learning models.

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Type Scheme Key
DOI https://www.sciencebase.gov/vocab/category/item/identifier doi:10.5066/P9KO49OT

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