Vulnerability of Water Demand and Aquatic Habitat in the Context of Climate Change and Analysis of a No-Regrets Adaptation Strategy: Study of the Yamaska River Basin, Canada
Bibliographic record
Abstract
Climate change will have a significant impact on the hydrological cycle. This paper presents the results of a pilot project for the Yamaska River in Québec. The objective of this project is to evaluate the river’s vulnerability to low flows attributable to climate change and to analyze a no-regrets adaptation strategy at locations identified as vulnerable. The vulnerability was evaluated using statistical indicators (low flow indices) based on long-term observations at four locations in the basin. A distributed physically-based hydrological model in use in Québec was calibrated and validated against observed streamflow data to properly represent low flows. Hydrological simulations used seven climate projections provided by the north american regional climate change assessment program (NARCCAP) s project. Also, five members of the canadian regional climate model (CRCM), nested with the coupled global climate model (CGCM) under the special report on emission scenarios (SRES) A2 emission scenario, were run for a reference (1971–2000) period and a future (2041–2070) period. Streamflow simulations indicate a degradation of future low flow conditions, particularly in June and August, when compared to the reference period. In addition, the 7-day low flow value with a 2-year return period (7Q2) and the 7-day low flow value with a 10-year return period (7Q10) decrease by 16–64% and 18–45% respectively. A no-regrets adaptation strategy allowing stakeholders to reduce withdrawal according to alert levels was implemented. Simulations of the application of the no-regrets adaptation strategy reduced the number of days where streamflows are below the Crisis level in the future period by at least 20%.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".