Assessing the Effect of Climate Change on River Flow Using General Circulation Models and Hydrological Modelling – Application to the Chaudière River, Québec, Canada
Bibliographic record
Abstract
s part of a wider study on the adaptation of agricultural land use to climate change (CC), this paper presents an assessment of possible future hydrological regimes of the Chaudière River watershed, Québec, Canada. We first present a review of the various methods used to integrate outputs of General Circulation Models (GCMs) into hydrological models that are applied at a local scale. Following this review, the delta method, statistical downscaling, and a combination of both methods were selected for this investigation. Data from different GCMs (in the case of the delta method) corresponding to different gas emission scenarios and simulation members were also considered to provide a range of possible future conditions. We used the integrated modelling system GIBSI, which is based on the distributed hydrological model HYDROTEL, to simulate streamflows for a reference period (1970-1999) and a short-term future period (2010-2039). For all three methods, results show a slight decrease in annual runoff (-5% on average). On a monthly scale, the effect is more heterogeneous depending on the method used, showing, in most cases, an increase in water discharge in the winter due to higher temperature and a decrease during the summer and fall. When using statistical downscaling, spring peak flow decreased slightly (-6.7% on average) while summer base flow remained unchanged. This study highlights the importance of using different methods and different sources of data in the assessment of potential CC effects on watershed hydrology.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".