Can high-resolution convection-permitting climate models improve flood simulation in southern Quebec watersheds?
Bibliographic record
Abstract
Abstract. In August 2024, Montreal and its surroundings, located in the south of the Quebec province, experienced one of its most destructive meteorological events in history, associated to the remnants of the tropical storm Debby, according to the Insurance Bureau of Canada (Published on 2024, September 13). With climate change, the frequency and intensity of extreme weather events are expected to increase, explaining why government and private sectors, particularly insurance companies, requires enhancing their preparedness. Recent studies highlighted the potential of high-resolution climate models (with grid sizes smaller than 4 km) to improve precipitation extremes at sub-daily timescales. This study focuses on heavy rainfall events during the warm season, comparing outputs from the latest Canadian Regional Climate Model (CRCM6/GEM5) at 12 km and 2.5 km resolutions. For the first time, we estimated that the CRCM6/GEM5-2.5km better captured the intensity of extreme hourly rainfall events compared to the CRCM6/GEM5-12km, aligning more closely with weather station data. To assess whether this added value extends to hydrological modeling, we used a lumped hydrological model to simulated water flows at an hourly time step for 11 basins located over southern Quebec for the period 2001–2018. For most basins, summer-fall peak flows simulated using the CRCM6/GEM5-2.5km had lower biases compared to those simulated with the CRCM6/GEM5-12km. These findings emphasize the importance of high-resolution climate models in improving extreme event simulations, which is essential for better risk assessment and adaptation strategies in a warming climate.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".