Storylines of summer streamflow droughts in western Canadian watersheds: historical attribution and future projections
Bibliographic record
Abstract
<title>Abstract</title> Southern British Columbia, Canada experienced successive summer streamflow droughts in 2023 and 2024, with flows approximately 23%–43% below the 1955–2024 means and substantial impacts on water and energy supplies. Here, we characterize these events in a storyline framework by driving a large-scale hydrological model with meteorological forcings from factual, counterfactual and future climates. The results showed that the 2023 drought was primarily driven by anomalously high May–June temperatures, whereas the 2024 drought was primarily caused by an exceptionally low snowpack. Long-term climate change has reduced summer flows by approximately 8%–31% and intensified the severity of both droughts. Future projections suggest increased frequency and severity of summer streamflow droughts, with events exceeding the historically extreme 2023 drought becoming more common, and the compounding effects of meteorological and snow droughts becoming more prevalent. Overall, these results underscore the need to enhance resiliency to summer streamflow droughts.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".