Abdulkerim Bedewi SERUR
2022, 19(08): 2260-2271.
The Bale mountains ecoregion in Ethiopia provides a number of benefits for the local communities mainly in terms of water supply, power generation, tourism activity, and irrigation development. Notwithstanding, the ecoregion has been characterized primarily by recurring floods and droughts, as well as crop failure due to a variety of natural and human-activity-driven change factors. As a matter of fact, the purpose of this study would be to examine long-term changes and fluctuation in precipitation (PCP), maximum temperature (T_(max)),and minimum temperature (T_(min)) in the Bale mountains ecoregion using ensembles of three climate models with three representative concentration pathways (RCPs) scenarios from the coupled model inter-comparison project phase five(CMIP5) dataset. Statistical downscaling model(SDSM) was applied to project PCP, T_(max), and T_(min) in the forthcoming period considering three RCPs: low emission (RCP2.6), intermediate (RCP4.5), and high emission (RCP8.5). SDSM's performance in capturing historical daily PCP, T_(max), and T_(min) has been validated using standard statistical metrics such as coefficient of determination (R~2), Nash Sutcliff efficiency (NSE),and root mean square error (RMSE). SDSM has the potential to generate a statistical transfer function between large-scale variables and local climate,allowing PCP, T_(max), and T_(min) to be downscaled to a point scale for the ecoregion. The magnitude of mean yearly changes in PCP, T_(max), and T_(min) were investigated throughout three thirty-year time slices,corresponding to the 2020s, 2050s, and 2080s. The Mann-Kendall non-parametric test was used to analyse trends in PCP, T_(max), and T_(min) from 2011 to2100. Inter-annual variability in PCP, T_(max), and T_(min )were investigated for the aforementioned period,taking standard deviation into account under each RCP scenarios. The results reveal that mean annual PCP, T_(max), and T_(min) are rising in all three time slices and in all three CMIP5 RCP scenarios as compared to the baseline scenario. Mean annual PCP is projected to increase within the uncertainty range of 6.68% to17.93% (RCP2.6), 7.45% to 21.94% (RCP4.5), and19.70% to 33.69% (RCP4.5) (RCP8.5). T_(max) increases from 0.04°C to 0.24°C (RCP2.6), 0.05°C to 0.31°C(RCP4.5), and 0.04°C to 0.42°C (RCP8.5), whereas T_(min) increases from 0.22°C to 0.52°C (RCP2.6),0.23°C to 0.67°C (RCP4.5), and 0.26°C to 1.14°C(RCP8.5) (RCP8.5). For future projections at the end of the 21~(st) century, the mean annual PCP, T_(max), and T_(min) for all three analysed climate models and RCPs have shown a positive trend. The inter-annual variability of PCP, T_(max), and T_(min) is higher in the RCP8.5 than RCP4.5 and RCP2.6 in all climate models. The findings clearly implied that prior understanding of long-term climate change and variability need to be addressed to plan effective and efficient mitigation strategies, as well as to maintain adequate quantity and quality of water supplies to the communities residing in the ecoregion.
