Reconstruction of the Spring 2011 Richelieu River Flood by Two Regional Climate Models and a Hydrological Model
Bibliographic record
Abstract
Abstract Climate simulations made with two regional climate models (RCMs), the French Aire Limitée Adaptation Dynamique Développement International (ALADIN) and the Canadian Regional Climate Model, version 5 (CRCM5), operating on 10-km meshes for the period 1989–2011, and the Hydro-Québec hydrological model (HSAMI), are used to reconstruct the spring 2011 Richelieu River flood in the southern region of the province of Québec, Canada. The analysis shows that the simulated fields of 2-m air temperature, precipitation, and snow water equivalent by the RCMs closely match the observations with similar multiyear means and a high correlation of the monthly anomalies. The climatic conditions responsible for the 2011 flood are generally well simulated by the RCMs. The use of multidecadal RCM simulations facilitates the identification of anomalies that contributed to the flood. The flood was linked to a combination of factors: the 2010/11 winter was cold and snowy, the snowmelt in spring was fast, and there was a record amount of precipitation in April and May. Driven by outputs from the RCMs, HSAMI was able to reproduce the mean hydrograph of the Richelieu River, but it underestimated the peak of the 2011 flood. HSAMI adequately computes the water transport from the mountains to the river mouth and the storage effect of Lake Champlain, which dampens the flood over a long period. Overall, the results suggest that RCM simulations can be useful for reconstructing high-resolution climate information and providing new variables that can help better understand the causes of extreme climatic events.
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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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".