Spatial-Temporal Change Analysis for Multivariate Drought Risk Based on Bayesian Copula: Application to the Balkhash Lake Basin
Bibliographic record
Abstract
Abstract In this study, a spatial-temporal Bayesian copula (SBC) method is developed through integrating spatial-temporal analysis and Bayesian copula into a general framework. SBC method can help model dependence structures of variable pairs and handle the uncertainty caused by parameter in copulas, and SBC can reveal the spatial and temporal changes of drought events. SBC is applied to the Balkhash Lake Basin (in Central Asia) to analyze spatial-temporal characteristic and drought risk in 1901-2020. Several findings can be summarized: (1) Balkhash Lake Basin suffered 53 drought events in 1901-2020, and five typical severe drought events occurred in 1916-1920, 1943-1945, 1973-1977, 1995-1998 and 2007-2009; (2) the most severe drought event lasted for 40 months (1973.10-1977.1), affecting 335,800 km2 of the study basin; (3) drought usually develops from east to west, and Ili River delta and alluvial plain has the highest frequency of drought (47.2%), following by plateau desert (28.3%) and arid grassland in north Balkhash Lake (24.5%); (4) drought shows significant seasonality in the study basin, which usually begins in spring and summer (64.2%) and ends in summer and autumn (66.0%); and drought risk of middle and lower reaches of Ili River is highest in spring and summer; (5) in Balkhash Lake Basin, multivariate characteristics (duration, severity and affected area) significantly affect drought risk; (6) the range of drought risk is [1.9%, 18.1%], [3.7%, 33.1%], [8.7%, 46.0%], [16.0%, 55.1%] and [27.6%, 59.8%] when guarantee rate is 0.99, 0.98, 0.95, 0.90 and 0.80, respectively.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".