Evaluation of daily stream temperature predictions (1979-2021) across the contiguous United States using a spatiotemporal aware machine learning algorithm
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Abstract
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 <2 °C with 90 % prediction intervals that contain 90.7 % of observations. We also assessed how the model captured variability in ecologically relevant metrics (e.g., R2 for annual 7-day maximum = 0.76; R2 for days exceeding 25 °C = 0.75). This model does not outperform state-of-the-art machine learning efforts (e.g., RMSE ≤1.5 °C) due to a limited input set but does provide the most spatially complete modeling to date to support water availability assessments.
Study Area
| Publication type | Article |
|---|---|
| Publication Subtype | Journal Article |
| Title | Evaluation of daily stream temperature predictions (1979-2021) across the contiguous United States using a spatiotemporal aware machine learning algorithm |
| Series title | Environmental Modelling & Software |
| DOI | 10.1016/j.envsoft.2025.106655 |
| Volume | 193 |
| Year Published | 2025 |
| Language | English |
| Publisher | Elsevier |
| Contributing office(s) | WMA - Integrated Information Dissemination Division |
| Description | 106655, 16 p. |
| Country | United States |
| Other Geospatial | contiguous United States |