Analyzing meteorological drought under historical and future climate scenarios in East Bale zone, South-eastern Ethiopia
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».