Seasonal drought classification and its characteristics in the red soil region of southern China
Bibliographic record
Abstract
The red soil region of southern China The red soil region of southern China faces serious seasonal drought, which poses a great threat to the sustainable development of local agriculture. Yet the characterization of drought types remains unexplored due to lacking systematic classification methods. The present study addressed this knowledge gap by analyzing daily meteorological data of 444 stations from 1961 to 2019 using the Standardized Precipitation Evapotranspiration Index (SPEI). A total of 15,854 drought events were classified into three distinct types (I, II, and III) employing run theory and k-means clustering. The spatiotemporal patterns of three types’ events were further elucidated. Trend analysis revealed a pronounced “warming and drying” trend, with temperature rising by 0.02°C and relative humidity declining by 0.05 % per year. Temporal analysis identified three distinct stages: dry (1961–1979), wet (1980–1999), and dry-wet alternation (2000–2019). Seasonal drought in this region has intensified since the 1990s, characterized by increased frequency, duration, severity, and intensity of the drought events. Identified three drought event types exhibited annual frequencies of 0.39, 0.17, and 0.04 time, respectively. Mild and moderate drought (Type I and II) dominated across the region, while severe drought (Type III) concentrated in northern and southeastern coastal areas. These findings offer valuable insights for drought risk management strategies in the red soil region. • Daily SPEI effectively captures seasonal drought dynamics in China's red soil region. • The red soil region of southern China exhibits a pronounced “warming and drying” trend. • Drought trends show a "dry–wet–alternating dry and wet" pattern over six decades. • Novel approach integrates run theory and machine learning for drought classification.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 teacher head, 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".