Analyzing meteorological drought under historical and future climate scenarios in East Bale zone, South-eastern Ethiopia
Bibliographic record
Abstract
Droughts are recurrent climatic events with widespread geographic impacts, particularly pronounced in arid and semi-arid regions. Severe drought events have been occurring in the low-lying agro-pastoral lands spanning from Wello in the north to Bale in the South-eastern Ethiopia. This study was aimed to assess the potential of the Climate Hazard group InfraRed Precipitation with Station (CHIRPS) rainfall product, characterize meteorological droughts from 1993 to 2022 during the growing season (March–May), and project the influence of climate scenarios on drought patterns in future (2023–2052) utilizing the three month Standardized Precipitation Index (SPI-3) in the lowlands of the East Bale Zone. Datasets from the CHIRP), observed station rainfall, Canadian earth system model version5 (CanESM5) and national Center for environmental prediction department of energy predictors were used. The monthly near-future rainfall was downscaled using a statistical downscaling model (SDSM4.2) under Shared Socio-economic Pathways SSP2-4.5 and SSP5-8.5 scenarios. CHIRPS rainfall estimates was evaluated against observed rainfall using statistical indicators like Pearson correlation (r), R-squared (R2), root mean square error (RMSE), mean absolute error, mean bias and was improved using GeoCLIM software version 1.2.1. The evaluation reveals that Pearson correlation (r), R-squared (R2), root mean square error (RMSE), mean absolute error, and mean bias was found to be 0.744, 0.55, 41.68, 31.55, and 24.76. The study identified several meteorological drought years including 2004, 2008, 2009, 2011, 2014, 2015, 2017, 2019, and 2022. Severe and extreme droughts were appeared in 2.04%–57.2% of the study area, concentrated in the northern, central, southern lowlands and midlands of the study area. Comparing the historical period (1993–2022) to the near future (2023–2052), the study predicts a decrease in the frequency of moderate drought by 19.55% and 7.45% under SSP2-4.5 and SSP5-8.5 scenarios respectively, while an increase of about 14.71% and 1.07% in severe drought and 4.84% and 6.38% in extreme drought frequency is expected. This study suggests that effective environmental monitoring and pre-awareness about future drought patterns can be considered the best steps to reduce the increasing frequency and severity of drought in the east Bale zone. Permanent magnet synchronous motors are highly coupled, multivariable, and nonlinear, and sliding mode control has gradually gained traction in motor control systems due to its robustness. However, conventional sliding mode control suffers from issues such as severe chattering and insufficient convergence speed. To address these limitations, we propose an integral sliding mode control strategy, enhanced by an improved power exponential reaching law (IPERL). This strategy integrates an IPERL based on state error and an integral sliding mode surface, enabling the system to respond rapidly to changes in operating conditions while effectively mitigating oscillatory behavior. The proposed control strategy is validated through simulations, demonstrating its superior performance compared to traditional methods in terms of both stability and convergence speed.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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".