Storyline analytical framework for understanding future severe low-water episodes and their consequences
Bibliographic record
Abstract
The 2021 drought highlighted the vulnerability of Quebec's water resources and the potential for widespread consequences in a region that is generally perceived as having abundant water. This study uses a storyline approach to explore plausible future drought conditions for an event similar to what occurred in 2021 but under two different warming scenarios corresponding to increases of 2 and 3 °C in global surface temperatures compared to preindustrial levels. The storyline is constructed using a scenario-based approach, where analogues of the 2021 drought are derived from a large ensemble of regional climate simulations and combined with simulations produced by a hydrological model to offer a comprehensive understanding of both climate and hydrological conditions during and leading up to these potential future events. Results indicate a further deterioration in river conditions, particularly if a drought similar to that of 2021 were to be accompanied by a 3 °C rise in global temperature. In the hardest-hit areas of the province, low water levels could persist for a month longer, and river streamflow could drop by an additional 50 %, thus falling short of the threshold required to maintain the health of ecosystems for an extended period of time and resulting in significant impacts on ecosystems and human activities. By linking future river conditions to a real event that affected southern Quebec, storyline approaches have the potential to facilitate discussions on the impacts of climate change on water deficits in the region, particularly in cases where those impact-inducing thresholds are not well understood.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".