Climate-Driven Shifts in Stream Thermal Regimes of the Laurentian Great Lakes Basin: The Role of Rain-on-Snow Events
Bibliographic record
Abstract
Stream temperature influences aquatic ecosystem health, species distribution, and water quality. In snow-dominated watersheds, rain-on-snow (ROS) events regulate stream temperature by introducing cold meltwater, yet their role under future climate is uncertain. We investigate spatial variability of stream thermal regimes in the Great Lakes Basin (GLB) under historical (1960–2014) and future (2047–2074) scenarios using a basin-scale Soil and Water Assessment Tool (SWAT) enhanced with ROS and stream temperature modules. The model was calibrated with daily streamflow and snowpack water equivalent and driven with downscaled CMIP6 climate data. ROS events strongly cool streams in winter and spring, especially in colder northwest and northeast subbasins. However, under future warming, as ROS intensity declines, cooling weakens and stream temperatures increase across all seasons. Summer temperatures are projected to rise by 3–5°C in the southeast and northeast, with cumulative degree days (above 6°C) increasing and warming occurring earlier. These changes are linked to ROS-driven cooling and diminished summer flows. Thermal homogenization is projected across the basin, as historically cooler northern streams warm more rapidly, reducing spatial variability. Hot day duration shows strong negative correlations (−0.4 to −0.6) with ROS timing and intensity, particularly in the Lake Michigan and Ontario basins. The projected loss of ROS-induced cooling may elevate thermal stress for cold-water species, alter ecosystem metabolism, and increase invasive species risk. Adaptive strategies such as riparian shading and forest management will be critical to sustain thermal resilience.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".