Stepwise cluster ensemble downscaling for drought projection under climate change
Bibliographic record
Abstract
Abstract Drought is one of the most serious natural disasters exacerbated by climate change. Changes in precipitation and temperature in the future increase the likelihood of drought in China. In this study, a stepwise cluster ensemble downscaling (SCED) model was developed to bias‐correct projections of temperature and precipitation from multiple RCM outputs, and further characterized the drought hazards. The developed SCED model was used to aggregate and correct the results of multiple regional climate models, and its performance was proved to be reliable by comparing with the observed results. The proposed SCED method has been applied for drought projections over the Fujian province, China. The results showed that the changes of precipitation and temperature in Fujian would have obvious spatial heterogeneous characteristics. The temperature in the southeast coastal areas will increase by up to 4°C and the precipitation will decrease by 3.1% in the late 21st century, while the temperature rises and precipitation increases in the southwest. Temperature in inland areas will be lower and precipitation will be less. The drought hazards were also characterized by both SPI and SPEI based on biased‐corrected projections from SCED model. According to the SPI and SPEI indices, although the number of dry months in Fujian province will not change significantly in future, the spatial and temporal heterogeneity may become more explicit. Moreover, the moderate drought (from SPI) may increase while the general drought may decrease (from both SPI and SPEI). For extreme droughts, there would not be visible changes detected by SPI, but an increasing trend characterized since the impact of temperature was included in SPEI. In addition, there would be an increasing trend on drought when increasing temperature and precipitation occurred simultaneously.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".