Reducing the computational cost of process-based flood frequency estimation by extracting precipitation events from a large-ensemble climate dataset
Bibliographic record
Abstract
• Proposed a method to reduce computations while using large climate ensembles. • Estimated discharge quantiles for every 150-m river grid. • Achieved high accuracy in discharge peaks and quantiles. • Contributed to large-scale high-resolution future flood hazard assessments. The generation of flood projection ensembles for large areas and at a high resolution has been a long-standing computational challenge. Previous studies focused on improving model efficiency and hardware acceleration. An intriguing question arises; is it possible to reduce the computational demand for obtaining specific flood characteristics (e.g., flood frequency curves, FFCs) through precipitation data preprocessing, while maintaining high accuracy? In this study, we developed an aggregating grid event (AGE) method based on hydrological concepts to extract essential precipitation events from large-ensemble climate-change dataset. A total of 2,966 events were extracted from dynamically downscaled 720-year precipitation data covering all over Japan with the 5-km spatial resolution. By inputting the precipitation data into the Rainfall–Runoff–Inundation model at a 150 m resolution, we computed hourly discharges across all river grids in the study area, Shikoku Island, Japan. Based on the simulated peak discharges, we computed FFCs for return periods exceeding 10 years at each river grid using the peak-over-threshold method. The results demonstrated the effectiveness of the AGE method, with a relative bias (BIAS) of −1.38 % and a root mean square error (RMSE) of 36.45 m 3 /s for flood peaks across locations. Furthermore, the BIAS of quantiles at 100-year return period was −1.04 % compared to the references, which were estimated from all the valid 25,700 precipitation events. By using 2,966 events instead of 25,700 events, the AGE method significantly reduces the computational burden in estimating FFCs at all river grids while maintaining accuracy. This approach is applicable for any grid-based precipitation dataset, marking a crucial advancement in regional hyper-resolution flood studies based on climate projection ensembles.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".