Greater flood risks in response to decreasing tropical cyclone translation speed over the coast of China
Bibliographic record
Abstract
Torrential rains induced by tropical cyclones (TCs) are a major trigger of flood hazards in many coastal regions of the world. Devastating TCs causing unprecedented floods in recent years were usually characterized by low translation speeds. For example, Hurricane Harvey in 2017 lingered over Texas for 4 days, leading to the unprecedented flood and enormous socio-economic losses. The total amount of rainfall associated with TCs over a given region is proportional to rainfall intensity and the inverse of TC translation speed. Although the contributions of increase in rainfall intensity to higher total rainfall amounts have been extensively examined, observational evidence on impacts of the long-term slowdown of TCs on local total rainfall amounts is limited. This study, based on observations and Global Climate Models, found a significant decreasing trend in TCs translation speed (11% in observations and 10% in simulations, respectively) during 1961-2017 over the coast of China. The analyses of long-term observations showed a significant increase in the 90th percentile of TC-induced local rainfall totals and significant negative correlations between TC translation speeds and local rainfall totals over the study period. This study also showed that TCs with lower translation speed and higher rainfall totals occurred more frequently in recent years in the Pearl River Delta in southern China. That is, 10 out of 14 recorded TCs with translation speed ≤ 15 km/h and rainfall intensity ≥ 30 mm/d occurred after 1990, and 3 of them produced rainfall totals of more than 200 mm in the Pearl River Delta. The probability analysis indicated that slow-moving TCs (translation speed ≤ 15 km/h) are more likely to generate higher total rainfall amounts than fast-moving TCs (translation speed ≥ 25 km/h). On average, the local rainfall total of slow-moving TCs is 99.1 mm, which is 20% higher than that of the fast-moving TCs (i.e., 80.5 mm). This study provided observational evidence that the slowdown of TCs tends to elevate local rainfall totals and thus impose greater flood risks at the regional scale.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".