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Mapping drought evolution in Ethiopia: trends, clustering, and Bayesian estimation of abrupt changes

Author

Listed:
  • Fabio Di Nunno

    (University of Cassino and Southern Lazio)

  • Mehmet Berkant Yıldız

    (University of Cassino and Southern Lazio)

  • Yordanos Gebru Afework

    (University of Cassino and Southern Lazio)

  • Giovanni de Marinis

    (University of Cassino and Southern Lazio)

  • Francesco Granata

    (University of Cassino and Southern Lazio)

Abstract

Recent climate change has significantly impacted Ethiopian regions, leading to substantial alterations in precipitation patterns, with profound implications for local ecosystems and water resources. This study comprehensively analyzes the Standardized Precipitation-Evapotranspiration Index (SPEI) at 6, 12, and 24-month time scales across Ethiopia to explore drought trends. The Seasonal Kendall (SK) test, applied to SPEI data, reveals significant trends indicating increasing drought conditions in several regions. Specifically, 71.8% of data points for SPEI-24 show significant negative trends, which indicate more frequent and severe droughts, particularly in northwestern, western, and southern Ethiopia. In contrast, 9.8% of data points exhibit significant positive trends, suggesting a reduction in drought severity, mostly in northern and mid-western areas. The remaining 18.4% exhibit non-significant trends. Two clustering algorithms, K-means and Hierarchical, were used to identify homogeneous regions in terms of drought trends. Both algorithms divided Ethiopia into four clusters: Cluster C1 (central and northern Ethiopia) showed significant increasing drought trends, while C2 and C4, covering both southern and western Ethiopia, exhibited decreasing trends in drought intensity. Cluster C3 varied between algorithms: in K-means, it acted as a transitional zone, whereas in Hierarchical clustering, it was the westernmost cluster with a distinct decreasing trend. To assess abrupt changes in SPEI, the Bayesian Estimator of Abrupt Change, Seasonal Change, and Trend (BEAST) algorithm was used, identifying abrupt drought events such as those in 1984, 1999, and 2002/2003. These changes highlight historical drought variability across different regions of Ethiopia, offering insights into long-term climate impacts.

Suggested Citation

  • Fabio Di Nunno & Mehmet Berkant Yıldız & Yordanos Gebru Afework & Giovanni de Marinis & Francesco Granata, 2025. "Mapping drought evolution in Ethiopia: trends, clustering, and Bayesian estimation of abrupt changes," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 121(4), pages 3775-3803, March.
    • Handle: RePEc:spr:nathaz:v:121:y:2025:i:4:d:10.1007_s11069-024-06935-0
    DOI: 10.1007/s11069-024-06935-0
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    References listed on IDEAS

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    1. Belay Simane & Benjamin Zaitchik & Jeremy Foltz, 2016. "Agroecosystem specific climate vulnerability analysis: application of the livelihood vulnerability index to a tropical highland region," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 21(1), pages 39-65, January.
    2. Misganaw Teshager Abeje & Atsushi Tsunekawa & Nigussie Haregeweyn & Zerihun Nigussie & Enyew Adgo & Zemen Ayalew & Mitsuru Tsubo & Asres Elias & Daregot Berihun & Amy Quandt & Mulatu Liyew Berihun & T, 2019. "Communities’ Livelihood Vulnerability to Climate Variability in Ethiopia," Sustainability, MDPI, vol. 11(22), pages 1-22, November.
    3. Fabio Di Nunno & Marco De Matteo & Giovanni Izzo & Francesco Granata, 2023. "A Combined Clustering and Trends Analysis Approach for Characterizing Reference Evapotranspiration in Veneto," Sustainability, MDPI, vol. 15(14), pages 1-23, July.
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