The use of flood regime information in regional flood frequency analysis
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Understanding the hydro-climatological controls on floods is fundamental for estimating flood frequency. The river flood regime is a reflection of a complex catchment hydrological response to flood producing processes. Hence, the catchment similarity in a flood regime is a feasible basis for identifying flood frequency pooling groups used in regional estimation of design events. This study describes a focused pooling approach that is based on flood regime information. A flood regime descriptor that is sensitive to the modality of the underlying temporal distribution of flood occurrences, and depicts both flood seasonal pattern and flood regularity, was developed and tested. The approach was applied to peaks-over-threshold data from a number of essentially rural sites using a site-focused pooling framework. The relative performance of this approach was evaluated and compared with the performance of a pooling approach based on a previously used flood seasonality measure, using a regional bootstrap resampling technique. The regional bootstrap model was further used for quantifying the sensitivity of the proposed flood regime descriptor to the record length and the length of overlapping period. The results demonstrate that pooling based on the regime index proposed in this study out-performed the pooling based on the previously used seasonality measure in terms of both bias and RMSE of estimated flow quantiles. A detailed description of flood regime captured in the proposed index provides sufficient information for effective regional estimation of extreme flow quantiles for the study area.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 it