Spatial-temporal variations of extreme precipitation indices and their linkage with atmospheric circulation in the middle and lower reaches of the Yangtze River
Bibliographic record
Abstract
Abstract Under the background of climate change, increasing frequency and intensity of extreme precipitation causes serious impacts and huge losses to society and economy. It is significant to analyze the spatial and temporal variation of extreme precipitation for regional risk assessment. Nine extreme precipitation indices were calculated using the daily precipitation data of the middle and lower reaches of the Yangtze River (MLYR) from 1979 to 2015. Linear trend analysis and the Mann-Kendall trend test were carried out to determine the variation trend of the nine extreme precipitation indices. In addition, the potential influences of anomalous atmospheric circulation factors on extreme precipitation were explored using the wavelet coherence analysis technique. The results show that: (1) the overall wetting trend is detected in the MLYR, and the PRCPTOT and RX1day increased significantly; (2) Extreme precipitation decreased from southeast to northwest in the MLYR, indicating that extreme precipitation events are more prone to occur in the southeast of the MLYR; (3) the Western Pacific Subtropical High (WPSH), the South China Sea high (SCSH) and the East Asian westerly jet (EAJ) all strongly impact on the changes of precipitation in the MLYR, among which the WPSH has the most significant impact, followed by the SCSH, and the EAJ is weaker.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".