Copulas‐based risk analysis for inter‐seasonal combinations of wet and dry conditions under a changing climate
Bibliographic record
Abstract
Abrupt transitions between droughts and floods present greater challenges to water resource management than independent drought or flood events. It is therefore of significant importance to further include drought–flood transitions in the risk analysis of water‐related hazards under a changing climate. This study more generally evaluates the risks of combinations of dry and wet conditions between adjacent seasons. First, dry and wet conditions are monitored by the standardized precipitation index (SPI). Then, a copula‐based framework is proposed for the deviation of joint return periods of dryness–wetness combinations at different severity levels. In addition, SPI series trend detection is conducted using the Mann–Kendall test to analyse the temporal–spatial changes in dry and wet conditions. Wavelet analysis is applied to investigating correlations of dry and wet conditions with climate variability signals, which may provide predictive signals for dryness–wetness combinations. The results of a case study in the Pearl River basin (PRB), China over the period of 1960–2015 indicate that (a) the flood season (from July to October) tends towards dryness and there are wetting trends in the late autumn and winter; (b) as the joint return period is considered the proxy for the risk of dryness–wetness combination, shorter joint return periods remind a higher risk of suffering from abrupt dryness–wetness transitions in the spring–summer and summer–autumn, as well as the more frequent occurrence of continued dryness/wetness in the autumn–winter and winter–spring; (c) the western and eastern PRB are separately characterized by intensified and reduced risks of the most frequent combinations under a changing climate; and (d) El Niño–Southern Oscillation events, the Pacific Decadal Oscillation and sunspot activities have a close association with dry and wet conditions in the PRB. The study provides a supplement for the current risk map and may benefit the early warning and mitigation of water‐related hazards.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".