Understanding physical climate risks and their implication for community adaptation in the borana zone of southern Ethiopia using mixed-methods research

Pastoralists in the Borana zone of southern Ethiopia are grappling with climatic risks and impacts, and their livelihoods appear to be precarious. The long-term changes in precipitation, temperature and extreme events are under-researched aspects of climate risk in the study area. Climate observations, local people’s experiences, and memories of weather and drought patterns were analysed in this work to better understand the climate risks in the study region and provide actionable knowledge to facilitate adaptations. In southern Ethiopia, the primary drivers of climate risks are rainfall variabilities, rising temperatures, and drought. The annual rainfall variability observed in the study area (20–35%) is greater than the overall estimate for Eastern Africa (15–25%), indicative of a heightened climate risk in this area. Furthermore, seasonal and intra-seasonal rainfall variabilities are being aggravated by rising temperatures, leading to increased frequency and magnitude of extreme events, particularly in the lowland area. These changes, specifically droughts of different intensities occurring every 2–3 years, erode the livelihood of the pastoralists. The lack of consensus among stakeholders as to the causes and aggravating factors of these climate changes impedes adaptation actions. The authors recommend the importance of initiating a participatory platform that will facilitate discussion on climate change to create a common understanding of the problem and relate it to public policy. The use of gridded precipitation and temperature data is recommended in future research to improve the availability of climate information for climate risk management. In addition, the use of mixed methods and local knowledge, as demonstrated in this study, is highly recommended to better understand climate risks, particularly when there is a limited availability and quality of long-time-series climate data.