Assessing the impact of climate change on high return levels of peak flows in Bavaria applying the CRCM5 large ensemble
Bibliographic record
Abstract
Severe floods with extreme return periods of 100 years and beyond have been observed in several large rivers in Bavaria in the last 3 decades. Flood protection structures are typically designed based on a 100-year event, relying on statistical extrapolations of relatively short observation time series while ignoring potential temporal non-stationarity. However, future precipitation projections indicate an increase in the frequency and intensity of extreme rainfall events, as well as a shift in seasonality. This study aims to examine the impact of climate change on the 100-year flood (HF 100 ) events of 98 hydrometric gauges within hydrological Bavaria. A hydrological climate change impact (CCI) modeling chain consisting of a regional Single Model Initial-condition Large Ensemble (SMILE) and a single hydrological model was created. The 50 equally probable members of the Canadian Regional Climate Model version 5 large ensemble (CRCM5-LE) were used to drive the hydrological model WaSiM (Water balance Simulation Model) to create a hydro-SMILE. As a result, a database of 1500 model years (50 members × 30 years) per investigated time period was established for extreme value analysis (EVA) to illustrate the benefit of the hydro-SMILE approach for a robust estimation of HF 100 based on annual maxima (AM) and to examine the CCI on the frequency and magnitude of HF 100 in different discharge regimes under a strong-emission scenario (RCP8.5). The results demonstrate that the hydro-SMILE approach provides a clear advantage for a robust estimation of HF 100 using the empirical probability of 1500 AM compared to its estimation using the generalized extreme value (GEV) distribution of 1000 samples of typically available time series sizes of 30, 100, and 200 years. Thereby, by applying the hydro-SMILE framework, the uncertainty from statistical estimation can be reduced. The study highlights the added value of using hydrological SMILEs to project future flood return levels. The CCI of HF 100 varies for different flow regimes, with snowmelt-driven catchments experiencing severe increases in frequency and magnitude, leading to unseen extremes that impact the distribution. Pluvial regimes show a lower intensification or even decline. The dynamics of HF 100 driving mechanisms depict a decline in snowmelt-driven events in favor of rainfall-driven events, an increase in events driven by convective rainfall, and almost no change in the ratio between single-driver and compound events towards the end of the century.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".