Development of a new rainfall‐triggering index of flash flood warning‐case study in Yunnan province, China
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Flash floods, characterized by rapid, short‐duration, and high‐velocity flows, are the major causes of property damage and casualties worldwide. Flash flood warning is one of the key measures to prevent flash floods. Relying upon rain gage data and official statistics of flash flood events with casualties, this study proposes a new rainfall triggering index, β , defined as the ratio of accumulated rainfall to intraday rainfall, which effectively divides floods into events triggered by heavy intraday rainfall (0 < β ≤ 5) and those triggered by high cumulative rainfall ( β > 5). Then, historical disaster events were used to evaluate the performance of the proposed index. Results reveal that: (1) when 0 < β ≤ 5, the intensity–duration ( I – D ) curve method is more desirable, and the simulation result has a high correlation coefficient ( r = 0.95) with the measured result; (2) when β > 5, the rainfall triggering index (RTI) method is more suitable ( r = 0.8); (3) the cumulative critical rainfall using the RTI method ranges from 50 to 400 mm. This paper stretches the thought of flash flood warning method and provides the reference for flood‐prone regions.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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