Human influence on the 2021 British Columbia floods
Bibliographic record
Abstract
A strong atmospheric river made landfall in southwestern British Columbia, Canada on November 14th, 2021, bringing two days of intense precipitation to the region. The resulting floods and landslides led to the loss of at least five lives, cut Vancouver off entirely from the rest of Canada by road and rail, and made this the costliest natural disaster in the province's history. Here we show that when characterised in terms of storm-averaged water vapour transport, the variable typically used to characterise the intensity of atmospheric rivers, westerly atmospheric river events of this magnitude are approximately one in ten year events in the current climate of this region, and that such events have been made at least 60% more likely by the effects of human-induced climate change. Characterised in terms of the associated two-day precipitation, the event is substantially more extreme, approximately a one in fifty to one in a hundred year event, and the probability of events at least this large has been increased by a best estimate of 45% by human-induced climate change. The effects of this precipitation on streamflow were exacerbated by already wet conditions preceding the event, and by rising temperatures during the event that led to significant snowmelt, which led to streamflow maxima exceeding estimated one in a hundred year events in several basins in the region. Based on a large ensemble of simulations with a hydrological model which integrates the effects of multiple climatic drivers, we find that the probability of such extreme streamflow events in October to December has been increased by human-induced climate change by a best estimate of 120–330%. Together these results demonstrate the substantial human influence on this compound extreme event, and help motivate efforts to increase resiliency in the face of more frequent events of this kind in the future.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".