A century of variability of heatwave-driven streamflow in melt-driven basins and implications under climate change
Bibliographic record
Abstract
Abstract Meltwater runoff from snow and glaciers in high mountain regions supports the water needs of hundreds of millions of people, but extreme events such as heatwaves modify the timing and magnitude of water available for downstream communities and ecosystems. The streamflow response to heatwaves depends strongly on heatwave timing and temperature, and the amount of snow and glacier ice available to melt. However, as ongoing climate change continues to alter both seasonal melt patterns and the frequency and intensity of heatwaves, it is not well understood how such streamflow responses will evolve relative to the seasonal cycle of streamflow. We address this knowledge gap by using long-term meteorological and hydrological datasets to characterize spatial and temporal heterogeneity in the streamflow sensitivity to heatwaves at six basins with >80 years of observations in Western Canada. We use years with earlier freshets and less snowfall as proxies of anticipated climate change, and apply a metric to describe how the streamflow sensitivity to heatwaves varies across years with different hydro-climatological characteristics. We find that in future proxy years relative to baseline years, nival streamflow is more sensitive to early spring heatwaves but less sensitive to late spring and summer heatwaves. Relative to baseline years, late spring heatwaves in future proxy years generate a smaller fraction of peak streamflow that is diminished as the freshet progresses. Our findings imply that future heatwave-driven peak flows in spring may be lessened by the diminished streamflow sensitivity to heatwaves in late spring, but this may be partially offset by excess melt during future heatwaves that are longer and hotter.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".