A meta-analytic review on the spatial and climatic distribution of meteorological drought indices
Bibliographic record
Abstract
Abnormalities in climate in recent years have been attributed to climate change. In particular, an increase in the frequency of droughts has started to influence the way we live. There is an urgent need to study and monitor droughts to prevent severe damage. Drought monitoring research is conducted using the meteorological drought index, a compilation of weather data. Although weather data are highly dependent on climatic zones, existing studies have not considered the drought index by climatic zones. This study aims to identify the spatial and climatic distribution of meteorological drought index studies and provide a well-matched combination of the drought index and climatic classification through a meta-analytic review. Data were used in the form of research papers covering the Palmer Drought Severity Index (PDSI), Standardized Precipitation Index (SPI), and Standardized Precipitation Evapotranspiration Index (SPEI) published from 2011 to 2021. The trends showed that SPEI has become a leading index for the study of drought monitoring among the three drought indices since 2015. Additionally, Asia was the focus of much of this drought research due to climate events in China. Each continent and climate classification was assigned to a different dominant drought index. For example, PDSI was commonly used in North America and continental climates, SPI in Africa, Europe, South America, and tropical and dry climates, and SPEI in Asia and temperate climates. This evaluates countries, in which drought monitoring studies are rare, using a meteorological index based on the same climatic zones. Monitoring and predicting drought patterns would improve the basic needs of humankind.
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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 | 0.012 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.018 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".