Modeling the Streamflow Response to Heatwaves Across Glacierized Basins in Southwestern Canada
Bibliographic record
Abstract
Abstract In addition to having far‐reaching impacts on human health, agriculture, wildfires, ecosystems, and infrastructure, heatwaves control streamflow through the melting of seasonal snow and glacier ice. Despite their importance, there is limited understanding of how heatwaves modify streamflow at regional scales, how these impacts vary by heatwave timing and duration, and how glaciers control the streamflow response. Here, we use a deep learning hydrological model, which has previously been trained, evaluated, and interpreted in southwestern Canada, to simulate the streamflow response to heatwaves at 111 basins in the region. The model, driven by gridded ERA5 reanalysis temperature and precipitation data from 1979 to 2015, is forced by synthetic heatwave conditions that vary in their duration and onset throughout the year. We consider how the streamflow response to heatwaves is sensitive to annual temperatures by adding spatially and temporally uniform warming of 2°C across the study region, under the assumption that the underlying hydrological system behavior remains unchanged. We find that heatwaves, particularly in spring and summer, induce an initial streamflow surplus followed by a streamflow deficit, relative to the non‐heatwave case. In summer, glacier contributions to streamflow partially compensate for streamflow deficits that arise from heatwaves earlier in the melt season. In the scenario with 2°C warmer annual temperatures, heatwaves induce a lesser streamflow response in spring when the seasonal streamflow is most increased due to the advancing freshet. Our findings demonstrate how glaciers buffer the impacts of heatwaves on streamflow, but this buffering effect is expected to diminish as glaciers retreat.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".