Evaluation of daily stream temperature predictions (1979-2021) across the contiguous United States using a spatiotemporal aware machine learning algorithm
August 19, 2025
Stream temperature controls a variety of physical and biological processes that affect ecosystems, human health, and economic activities. We used 42 years (1979–2021) of data to predict daily summary statistics of stream temperature across >50,000 stream reaches in the contiguous United States using a recurrent graph convolution network. We comprehensively documented the performance – both across all reaches and by stream type (e.g., reservoir or groundwater influence) – as a baseline for future improvement. The model showed reach-level RMSE of
Citation Information
| Publication Year | 2025 |
|---|---|
| Title | Evaluation of daily stream temperature predictions (1979-2021) across the contiguous United States using a spatiotemporal aware machine learning algorithm |
| DOI | 10.1016/j.envsoft.2025.106655 |
| Authors | Jeremy Alejandro Diaz, Samantha K. Oliver, Galen Gorski |
| Publication Type | Article |
| Publication Subtype | Journal Article |
| Series Title | Environmental Modelling & Software |
| Index ID | 70271353 |
| Record Source | USGS Publications Warehouse |
| USGS Organization | WMA - Integrated Information Dissemination Division |
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Jeremy Diaz
Machine Learning Specialist
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Galen Gorski
Machine Learning Specialist
Machine Learning Specialist
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Related
Jeremy Diaz
Machine Learning Specialist
Machine Learning Specialist
Email
Galen Gorski
Machine Learning Specialist
Machine Learning Specialist
Email