Assessing Spatiotemporal Variability of Drought in Tihama Plain, Yemen, Using the Standardized Precipitation Index (SPI) with GIS
Bibliographic record
Abstract
Drought remains the most frequent and serious environmental threat in the Middle East region. In Yemen, drought has negatively affected both livelihood and sustainable development. This study aims to assess spatiotemporal variability of drought severity in the Tihama Plain—one of Yemen's most important agricultural areas, which contributes about 42 % of the country's total agricultural production. In recent years, the Tihama Plain has seen changes in the rainy season that have had a negative impact on agriculture production and water security in the area. This study uses the Standardized Precipitation Index (SPI) to conduct a temporal evaluation of the drought situation, as well as using geographic information systems (GIS) to show the spatial distribution of drought in the study area. The SPI-6 analysis results show that the years 1984, 1991, 2002–6, and 2008 were the most affected by drought during the 30-year study period (1980–2010), and show that 1991 was the worst drought year experienced by the study area during this period. Because the study area is the most important agricultural area in Yemen, a study of the drought and its impacts on agricultural crops in this region is recommended.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".