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<p>Flooding remains a major environmental hazard in the Lower Awash River Basin (ARB), disproportionately affecting agropastoral households. This study integrates GIS‑based flood hazard mapping with the IPCC‑aligned Livelihood Vulnerability Index (LVI‑IPCC) to identify exposure hotspots and socioeconomic susceptibility in Afambo, Aysaita, and Dubti districts. Flood hazard was mapped using multi‑criteria decision analysis (MCDA‑AHP) with seven biophysical variables, while 294 households were selected using probability proportional to size and systematic sampling. Model validation using the 2021 Global Flood Monitoring event showed that 52% of observed flooded areas overlapped with very‑high hazard zones and 26% with high zones. About 57% of the basin faces high (34%) or very high (23%) flood susceptibility, concentrated along the Awash River floodplain. Notably, this hazard prediction found valid terms of observed flood events of 2020, where 78% of actual flood extents were aligned with the high to very high level of susceptibility areas. LVI‑IPCC results showed Dubti as the most vulnerable district (IPCC‑VI = − 0.083), driven by high exposure and sensitivity and comparatively lower adaptive capacity. Aysaita exhibited severe physical flood exposure, while Afambo’s vulnerability stemmed from limited adaptive resources. Key drivers include proximity to rivers, low elevation, and gentle slopes (0–0.5°). Socioeconomically, financial inclusion, irrigation access, and diversified incomes significantly reduced vulnerability. We recommend district-specific flood management: Dubti requires financial empowerment, Aysaita needs expanded irrigation infrastructure, and Afambo warrants enhanced early-warning systems. The study concludes that integrating spatial hazard information with livelihood dimensions provides a robust basis for building resilience in flood‑prone dryland ecosystems.</p>

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Flood hazard assessment and livelihoods vulnerability of agropastoral households in lower Awash river Basin, Ethiopia

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月29日

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  • Setiye Abebaw Tefera,
  • Vijaya Lakshmi Thatiparthi,
  • Teshome Betru Tadesse,
  • Getachew Wollie Asmare

摘要

Flooding remains a major environmental hazard in the Lower Awash River Basin (ARB), disproportionately affecting agropastoral households. This study integrates GIS‑based flood hazard mapping with the IPCC‑aligned Livelihood Vulnerability Index (LVI‑IPCC) to identify exposure hotspots and socioeconomic susceptibility in Afambo, Aysaita, and Dubti districts. Flood hazard was mapped using multi‑criteria decision analysis (MCDA‑AHP) with seven biophysical variables, while 294 households were selected using probability proportional to size and systematic sampling. Model validation using the 2021 Global Flood Monitoring event showed that 52% of observed flooded areas overlapped with very‑high hazard zones and 26% with high zones. About 57% of the basin faces high (34%) or very high (23%) flood susceptibility, concentrated along the Awash River floodplain. Notably, this hazard prediction found valid terms of observed flood events of 2020, where 78% of actual flood extents were aligned with the high to very high level of susceptibility areas. LVI‑IPCC results showed Dubti as the most vulnerable district (IPCC‑VI = − 0.083), driven by high exposure and sensitivity and comparatively lower adaptive capacity. Aysaita exhibited severe physical flood exposure, while Afambo’s vulnerability stemmed from limited adaptive resources. Key drivers include proximity to rivers, low elevation, and gentle slopes (0–0.5°). Socioeconomically, financial inclusion, irrigation access, and diversified incomes significantly reduced vulnerability. We recommend district-specific flood management: Dubti requires financial empowerment, Aysaita needs expanded irrigation infrastructure, and Afambo warrants enhanced early-warning systems. The study concludes that integrating spatial hazard information with livelihood dimensions provides a robust basis for building resilience in flood‑prone dryland ecosystems.