Rising temperatures drive lower summer minimum flows across hydrologically diverse catchments
Bibliographic record
Abstract
Excessively low stream flows harm ecosystems and societies, so two key goals of low-flow hydrology are to understand their drivers and to predict their severity and frequency. We show that simple linear regressions can accomplish both goals across diverse catchments. We analyse 230 unregulated moderate to high relief catchments across rainfall-dominated, hybrid, snowmelt-dominated, and glacial regimes in British Columbia, Canada, with drainage areas spanning 5 orders of magnitude from 0.5 to 55,000 km2. We find that summer low flows are decreasing in rainfall-dominated and hybrid catchments and are dominantly driven by summer precipitation and temperature, and only weakly influenced by winter storage. We apply this understanding of drivers to create regression models to predict the minimum summer flow using monthly temperature and precipitation data. These models outperform distributed process-based models for every common goodness-of-fit metric; the performance improvement is mostly a result of abandoning the requirement to simulate all parts of the annual hydrograph. We use these regression models to reconstruct streamflow droughts, environmental flow threshold transgressions, and low flow anomalies from 1901-2022. We reproduce recent drying trends in rainfall-dominated and hybrid catchments, but also show that present conditions are comparable to those seen almost one hundred years ago. However, anomalously low flows last century were caused by severe precipitation deficits while current declines are being driven by rising summer temperatures during a period of above-average precipitation.
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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 | 0.001 | 0.001 |
| 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".