Assessing resource stress under irrigation expansion and climate change in Ethiopia
Abstract
This study evaluates the implications of Ethiopia’s irrigation expansion targets under climate change scenarios using an enhanced Climate, Land, Energy, and Water systems (CLEWs) model. By incorporating refined spatial and temporal resolution and modelling seasonal crop stages, the research quantifies trade-offs across resource systems. Indicators of water stress and biodiversity impact are analysed. Results aim to inform integrated climate adaptation planning, aligning Ethiopia’s low-emission development strategy with sustainable resource use.
Full text
ASSESSING RESOURCE STRESS UNDER IRRIGATION EXPANSION AND CLIMATE CHANGE IN ETHIOPIA Camilla Lo Giudice¹, Francesco Gardumi¹, and Daniel Adshead¹ ➢AR6 IPCC calls for a progress in adaptation planning. ➢Rainfall variability and climate uncertainty are forecasted to increase, leading to erratic run off and variation in evapotranspiration. 1. RESEARCH CONTEXT Figure 1 – Agro-ecological clustering based on potential yield Land Precipitation Energy Growing Harvesting Crop land ➢Low mechanized ➢High mechanized ➢Rainfed ➢Irrigated Evapotranspiration Surface water Groundwater Water •Municipal •Industrial Crops Resources Seasons Land system Water system Demand Climate 𝑓 𝑇𝑖𝑚𝑒, 𝑆𝑝𝑎𝑐𝑒, 𝐶𝑟𝑜𝑝 Indicator Formula Biodiversity habitat index BHI = 𝐹𝑜𝑟𝑒𝑠𝑡 𝐴𝑟𝑒𝑎 𝑟𝑒𝑡𝑎𝑖𝑛𝑒𝑑 𝐹𝑜𝑟𝑒𝑠𝑡 𝐴𝑟𝑒𝑎 𝑜𝑟𝑖𝑔𝑖𝑛𝑎𝑙 𝑧 , z= 0.25 Water seasonal stress ws = min(1, max(0, 𝑊𝑎𝑡𝑒𝑟 𝐷𝑒𝑚𝑎𝑛𝑑 𝐴𝑣𝑎𝑖𝑙𝑎𝑏𝑙𝑒 𝑤𝑎𝑡𝑒𝑟)) Seasonal score 𝑆 = max(0, min 5, ln 𝑤𝑠 −ln 0.1 ln 2+ 1 ) 4. KEY FINDINGS Seasonal water stress score ¹ KTH Royal Institute of Technology, Stockholm, Sweden Biodiversity habitat index Camilla Lo Giudice PhD candidate Tel: +46 – 721486341 Email: [email protected] IAMC – 18th Annual Meeting Buzios, November 11-13, 2025 2. OBJECTIVE To assess potential increase in resource stress due to erratic precipitation patterns. 3. METHOD ➢Non-linear crop yield responses under climate change alter water stress and biodiversity patterns across space. ➢Incorporating seasonal variability is essential to accurately capture deviations in water output from annual averages ➢Higher spatio-temporal resolution enables identification of where and when resource stress is most likely to occur. Figure 2 – Climate, Land, Energy, and Water system (CLEWs) framework modelled in OSeMOSYS The authors acknowledge funding from the European Union’s Horizon program IAM COMPACT grant agreement No 101056306, and the the support of the Climate Compatible Growth Program (CCG) of the UK's Foreign Development and Commonwealth Office (FCDO).