Spatiotemporal variability of heatwave-driven streamflow in nival and glacial basins: Future insights from a century of observations
Bibliographic record
Abstract
Heatwaves – short-term periods of anomalous warmth – can play an outsized role in shaping downstream water resources as they impact the timing and magnitude of snow and glacier melt. Melt-driven runoff enhanced during spring heatwaves is particularly important as it can cause downstream flooding and damage. It is well understood that heatwaves will become more frequent and more severe in the future due to climate change; however, it is not well understood how the hydrological response to heatwaves will change in the future.Here, we quantify the streamflow response to heatwaves over the past century across Western Canada. We investigate how such streamflow responses vary in space, time, by streamflow regime, and by hydroclimate conditions, and we present a simple mathematical framework to partition the responses into seasonal and heatwave-driven components. We use freshet timing and winter snowfall as metrics that are expected to change under climate change, and we compare how streamflow responses to heatwaves differ between baseline historical years (later freshet and more snowfall) and future proxy years (earlier freshet and less snowfall). We find that in future proxy years, the streamflow response to spring heatwaves is diminished when seasonal streamflow is enhanced, indicating that peak streamflow during heatwaves does not necessarily increase under climate warming. We also find that the proportion of spring streamflow generated by heatwaves is lessened relative to seasonal streamflow, and this proportion is diminished as the freshet progresses.Our results contextualize how the streamflow responses to heatwaves have varied over the past century, to better understand how they may change in the future. Importantly, our findings have implications for future heatwave-driven flooding in nival and glacial basins at both regional and global scales, and we present novel observational signals of change in heatwave-driven streamflow that can be further investigated by future modelling studies.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".