Changement climatique et risque hydrologique : évaluation de la méthode SCHADEX en contexte non-stationnaire
Bibliographic record
Abstract
Since 2006, Électricité de France (EDF) applies a new hydro-climatological approach of extreme rainfall and flood predetermination - the SCHADEX method - for the design of dam spillways. In a context of potential increase of extreme event intensity and frequency due to climate change, the use of the SCHADEX method in non-stationary conditions is a main interest topic for EDF hydrologists. Thus, the scientific goal of this Ph.D. thesis work has been to evaluate the ability of the SCHADEX method to take into account future climate simulations for the estimation of future extreme floods. The recognized inabilities of climate models and downscaling methods to simulate (extreme) rainfall distribution at the catchment-scale have been avoided, by developing and testing new methodological approaches. Moreover, the decomposition of the flood-producing factors proposed by the SCHADEX method has been used for considering different simulated climatic evolutions and for quantifying the relative impact of these factors on the extreme flood estimation. First, the SCHADEX method has been applied in present time over different climatic contexts (France, Austria, Canada and Norway), thanks to several colorations with academic and industrial partners. A sensitivity analysis allowed to quantify the extreme flood estimation sensitivity to rainfall hazard, catchment saturation hazard and rainfall-runoff transformation, independently. The results showed a large sensitivity of SCHADEX flood estimations to the rainfall hazard and to the rainfall-runoff transformation. Using the sensitivity analysis results, tests have been done in order to estimate the future evolution of "key" variables previously identified. New climate model outputs (done within the CMIP5 project) have been analyzed and used for determining future frequency of rainfall events and future catchment saturation conditions. Considering these simulated evolutions within the SCHADEX method lead to a significant decrease of simulated extreme floods. In order to predict the future intensity of rainfall events with air temperature simulated series, tests have been performed on several hundred of French catchment rainfall series, trying to link observed air temperature with observed rainfall event intensity. The results obtained show a mean heavy rainfall intensity increase of 6% per degree of air temperature rise, at the daily timescale. Nevertheless, supplementary tests are needed in order to estimate the timestep and the temperature range at which this rainfall event intensity increase is valid. Finally, these different future evolutions have all been used as inputs of the SCHADEX method. This last test showed the difficulty to quantify the impact of climate change on extreme floods due to a combination of extreme flood decrease factors (extreme flood decrease due both to future rainfall event frequency and catchment saturation condition evolution) and extreme flood increase factor (extreme flood increase due to an increase of future rainfall event intensity ). Even if the extreme flood estimation sensitivity analysis has raised numerous questions about the rainfall-runoff model calibration within the SCHADEX method, quantifying the future rainfall event intensity is the key question for the extreme rainfall and flood predetermination in a climate change context.
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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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".