Climate scenarios of extreme precipitation using a combination of parametric and non-parametric bias correction methods in the province of Québec
Bibliographic record
Abstract
Realistic simulation of heavy precipitation in climate simulations is a major challenge for adaptation, as the grid resolution of most climate models is too coarse to explicitly resolve convective processes. When proper future extreme precipitation events are required, such as for adaptation to future flooding, users therefore rely on bias-corrected precipitation data. However, the commonly used quantile–quantile mapping procedure is not well suited to post-process distribution tails. As a response to a need expressed by the province of Québec for the purpose of the government’s INFO-Crue project, which aims in part to provide a better understanding of future floods and incorporate this information in flood mapping, a new, mixed method was proposed for post-processing the entire range of precipitation, including heavy precipitation. The method uses a non-parametric quantile–quantile mapping procedure for the bulk distribution and a parametric procedure based on extreme value theory for the right tail. The method’s performance is illustrated on a watershed in Québec (Canada), using external Generalized Extreme Value (GEV) parameters. Results show that the proposed method is able to keep the important characteristics of simulated distribution tails, such as the initial ranking and scaling between values, keep spatial coherence and provide robust estimates of high return levels. The proposed method represents a flexible framework that relies on the quantile–quantile mapping procedure that is trusted by the end users, while incorporating information from the statistical community where necessary to ensure that heavy precipitation that might drive flooding, such as the 20or 100-year 24h precipitation, is bias-corrected in a more robust manner. The method is available in the open-source package ClimateTools.jl written in Julia and Python’s xclim package.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".