Seasonal drought classification and its characteristics in the red soil region of southern China
Bibliographic record
Abstract
Seasonal droughts in humid regions often inflict severe damage to crop yields due to their abrupt onset and hidden nature. However, the identification and characterization of seasonal drought events in the Chinese red soil region, an area of strong economic and social importance, remain unexplored. To address this gap, we employed a novel approach combining the k-means clustering algorithm with the daily Standardized Precipitation Evapotranspiration Index (SPEI) to distinguish the spatial and temporal dynamics of droughts using meteorological data from 444 stations spanning five decades (1962-2019). Temporal analysis revealed a climatic pattern in the red soil region characterized by cyclic fluctuations of "dry-wet-moderate" conditions. From 1962 to 2019, regional SPEI-365 trends fluctuated between -0.05 to 0.2 per decade, while drought duration trends ranged from -12 to 2 days per decade. Similarly, regional trends in drought intensity fluctuated between 0.01 and -0.02 per decade. However, the intensification of drought duration and intensity observed in the central region over the past two decades was unparalleled. We classified a total of 190,201 drought events into three types within the red soil region, with average occurrence frequencies of 5.6, 1.5, and 0.3 times per year for types I, II, and III, respectively. Notably, the frequency of type I drought events peaked in the 2000s, while the 1960s witnessed the highest frequency of type II and III events. These findings significantly contribute to our scientific understanding of drought event classification and offer valuable insights for drought risk mitigation strategies in the red soil region. By elucidating the spatial and temporal patterns of droughts, our study lays a foundation for informed decision-making and adaptive agricultural practices to enhance resilience against future drought events.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.000 | 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".