Storyline Analytical Framework for Understanding Future Severe Low-Water Episodes and Their Consequences
Bibliographic record
Abstract
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 the plausible impacts of future drought conditions for an event similar to what occurred in 2021, but under two different warming scenarios corresponding to increases of 2 °C and 3 °C in global surface temperatures compared to preindustrial levels. The approach employs analogues derived from a large ensemble of regional climate simulations combined with simulations generated by a hydrological model to offer a comprehensive understanding of both climate and hydrological conditions during, and leading up to, these potential future events. This approach allowed for enhanced collaboration with water management experts and other stakeholders to project the possible impacts of climate change on serious water deficits in Quebec. Results indicate a further deterioration in river conditions, particularly under a +3 °C global temperature rise. In the hardest-hit areas of the province under that scenario, future low-water levels persist for a month longer and river streamflows 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 suggesting significant impacts on ecosystems and human activities. This study also highlights the need for improved systematic data collection during meteorological and hydrological droughts in Quebec, particularly with respect to their impacts on human activities and ecosystems.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".