Analysis of Future Drought Characterization Under Multiple Drought Indices and Climate Change Impact Conditions for Developing Best Strategic Measures with CMIP-6 Climate Model in Mille Watershed, Lower Awash Basin, Ethiopia

Abstract

Abstract The agricultural sector is vulnerable to extreme phenomena such as droughts, particularly in arid and semi-arid environments and in regions where water infrastructure is limited. The objective of this study is to analyze future drought characteristics in meteorological, hydrology, and agricultural droughts under climate change in Mille watershed using three drought indices; Reconnaissance Drought Index (RDI), Streamflow Drought Index (SDI) and Agricultural Standardized Precipitation Index (aSPI). The study used baseline data (1985–2014) and future (2041–2100) downscaled from Coupled Model Intercomparison Project 6 (CMIP-6) based on the three Global Climate Models; (MIROC-6, CMCC and MRI) under two scenarios of the Shared Socioeconomic Pathways (SSP2-4.5, SSP5-8.5) with Soil and Water Assessment Tool (SWAT) model to simulate future streamflow for two future time horizons 2050s (2041–2070) and 2080s (2071–2100). as a result, in the future most hazardous Reconnaissance Drought Index (RDI) event occurred at Kombolcha, Mille, Batie and Sirinka with magnitude and frequency of 2050s (-1.51, 4.76%), 2058 (-1.49, 6.16%), 2063s (-1.52, 5.65%) and 2066 (-1.5, 4.48%) respectively, but in the annual time scale (SDI-12) extreme and severe hydrological drought events occurred 2088 (-2.00) and 2091 (-1.51) in SSP2-4.5 for study area, and also compared to other Mille and Batie stations experienced more frequently agricultural drought in terms of location. Generally, the RDI confirmed a significant drought trend in the past, while the frequency and severity of drought will increase in the future, however relatively less frequent hydrological drought events are observed in the study area and in the watershed both precipitation and streamflow will decline in the wet seasons and increase in the dry seasons. These findings may be useful for preparedness plans, including means for efficient monitoring and timely identification of drought events, is essential for informed decision making on drought mitigation and water management, especially for the water-dependent agricultural sector.