Spatiotemporal characterization of meteorological drought: a global approach using the Drought Exceedance Probability Index (DEPI)
Why this work is in the frame
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Bibliographic record
Abstract
We present a global spatiotemporal characterization of meteorological droughts using historical precipitation data through the Drought Exceedance Probability Index (DEPI). The relationship between meteorological drought characteristics and monthly precipitation is explored at a global level. This study contributes to our understanding of the drought features observed in different areas of the planet, which can help predict the behavior of future droughts. The DEPI was applied to the Climate Research Unit global gridded high-resolution rainfall data set covering the period 1901-2019. Monthly drought index series were examined to extract the number of droughts experienced in each pixel (0.50° × 0.50°) of the globe, as well as their durations, intensities and severities. Results show agreement with other global drought characterization efforts, revealing areas with a greater drought occurrence. This paper demonstrates that regions with less seasonality and less intra- and inter-annual rainfall variability report fewer drought episodes. Duration and severity of droughts are also related to these rainfall features. The last part of the study describes the temporal distribution of droughts throughout the world. We conclude that regions with many events show stable, even distributions over time, but many pixels in the intertropical regions, the Middle East and smaller patches in Mongolia, China, Siberia and Canada currently show higher-intensity and longer-duration drought events than at the beginning of the twentieth century, while the opposite occurs in parts of Scandinavia, Russia, Argentina and Tanzania. The analysis demonstrates that DEPI is easy to use, is applicable to different climates and is effective in detecting the onset, end and intensity of droughts.
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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 | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it