Trend and periodicity of drought over Ethiopia
Bibliographic record
Abstract
ABSTRACT We analyse rainfall extreme events in Ethiopia from 1979 to 2014 using the standardized precipitation index (SPI) and the Palmer drought severity index (PDSI) derived from both station and satellite‐based observation data sets. Causal mechanisms of extreme events are also discussed. Trend principal component (TPC), regression, wavelet and composite analyses are used to investigate the trend, frequency and inter/intra‐annual variability of extreme events (dryness/wetness of rainfall) over Ethiopia. All methods of analysis, applied to monthly mean data, show that the north and northwest regions of Ethiopia experienced frequent and more severe drought conditions centred at the year 1983/1984, a recovery in the middle of the study period and a return to moderate dry events in recent years. For the southern and southwestern regions, drought conditions have become more frequent and intense during the study period, particularly since ∼1997. Analysis at the seasonal scale shows that the observed drying trend over the south and southwestern regions of the country is dominated by the spring season, which corresponds to the season of maximum precipitation. No observed long‐term trend is found in the north, northwestern and central mountainous regions of the country. This contrast reflects differing climate sensitivities of these different portions of the country: the observed periodicity of dryness/wetness over the northern regions corresponds largely to ENSO variability in both the spring and summer rainy seasons, while the drying trend in the south and southwest is associated with Atlantic Ocean warming and sea surface temperature gradients across the western Pacific Ocean.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".