Does a convection-permitting climate model improve the simulation of flash floods ? A case study over a Mediterranean watershed
Bibliographic record
Abstract
Extreme rainfall and associated river floodings are important concerns in modern human societies. Despite a recent increase of extreme rainfall, there is no evidence of an increase of the intensity and frequency of flash floods in Southern Europe, and future projections of flash-floods are quite uncertain in part due to the coarse resolution of available climate model simulations.The recent development of convection-permitting climate models allow a better representation of precipitation extremes. This new generation of climate models have been little employed in combination with hydrological models up to now, and their added value for flash flood modeling remains to be identified. In this work, a 2.5-km convection-permitting climate model (CNRM-AROME) simulation is used to force two hydrological models (CREST and GR5H). This new modeling chain is tested in a French mediterranean catchment, the Gardon at Anduze, that experienced severe flash flooding episodes over the last decades. Hydrological models are calibrated using the COMEPHORE 1 km observed precipitation dataset merging radar and rain gauge rainfall at the hourly time step. We compare the CNRM-AROME-based hydrological simulation to a benchmark run driven by a conventional CORDEX 12-km CNRM-ALADIN simulation. The analysis of the peak discharges simulated by both hydrological models driven by the different meteorological inputs allows to determine how higher resolution precipitation could improve the simulation of flash floods.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".