Hydrology and Earth System Sciences
Hydrology and Earth System Sciences
HESS
Articles | Volume 29, issue 12
Articles | Volume 29, issue 12
https://doi.org/10.5194/hess-29-2521-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-29-2521-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 29, issue 12
Review article
 | 
17 Jun 2025
Review article |  | 17 Jun 2025

Machine learning in stream and river water temperature modeling: a review and metrics for evaluation

Claudia Rebecca Corona and Terri Sue Hogue

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Latest update: 17 Jun 2025
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Short summary
Stream water temperature (SWT) is a key indicator of water quality with implications for public use and health, ecosystem function, and aquatic life survival. Advances in modeling have helped improve our understanding of SWT dynamics, but challenges remain. Recently, machine learning (ML) has been used in SWT modeling, but questions remain about how the use of ML improves insight into SWT causes and effects. This work reviews ML-SWT modeling studies (and metrics) and considers future directions.
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