Drought Occurring With Hot Extremes: Changes Under Future Climate Change on Loess Plateau, China
Bibliographic record
Abstract
Abstract Drought is one of the most widespread and destructive hazards over the Loess Plateau (LP) of China. Due to climate change, extremely high temperature accompanied with drought (expressed as hot drought) may lead to intensive losses of both properties and human deaths in future. A hot drought probabilistic recognition system is developed to investigate how potential future climate changes will impact the simultaneous occurrence of drought and hot extremes (hot days exceeding certain values) on the LP. Two regional climate models, coupled with multiple bias‐correction techniques and multivariate probabilistic inference, are innovative integrated into the hot drought probabilistic recognition system to reveal the concurrence risk of droughts and hot extremes under different Representative Concentration Pathway (RCP) scenarios. The hot‐day index, TX90p, indicating the number of days with daily maximum temperature (Tmax) exceeding the 90th percentile threshold, and the Standardized Precipitation Index are applied to identify the joint risks on the LP using copula‐based methods. The results show that precipitation will increase throughout most of the LP under both RCP4.5 and RCP8.5 scenarios of 2036–2095, while Tmax may increase significantly all over the LP (1.8–2.7 °C for RCP4.5 and 2.7–3.6 °C for RCP8.5). The joint return periods of Standardized Precipitation Index and TX90p show that fewer stations will experience severe drought with long‐term hot extremes in two future scenarios. However, some stations may experience hot droughts that are more frequent and extreme, particularly certain stations in the southwest and south‐central regions of the LP with recurrence period less than 10 years.
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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 | 0.000 | 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) | 0.001 | 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".