Intensifying Extreme Rainfall Will Threaten the Socioeconomic Development of the Yangtze River Basin
Bibliographic record
Abstract
ABSTRACT The Yangtze River Basin (YRB) has always been characterised by the abundant rainfall. The rainfall in YRB will become more extreme in the future. An extreme rainfall projection and risk analysis (ERPR) approach is developed to identify the future extreme heavy rainfall (ER), decompose the spatio‐temporal features for ER indices (ERI), explore the role of abnormal sea level pressure (SLP) on ER and quantify the ER risks (ERR) to society and economics. Results suggest that the ERI will increase in the future. The highest ERI is projected under SSP585, which is 1.2 times greater than the baseline. High ERIs are more likely to be concentrated in the southern and eastern YRB. Based on empirical orthogonal function (EOF) decomposition, the spatial heterogeneity of ERI is projected to decline in the future. The homogenous distribution pattern accounts for an average of 11.76%. SLP anomaly is supposed to be a pre‐signal for ER. The earliest signal is detected in SSP126 and SSP585. The ERR is higher in the long‐term future; it is 1.63 times higher than the short‐term ERR. The Yangtze River Delta is projected to undertake the highest risk caused by ER in the future. The ERPR provides a comprehensive analysis for regional climate extremes. It is applicable to other climate extremes in other regions. It is expected to help improve the climate risk management system.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".