Climate change is a phenomenon that makes the climate system of a given region to be more unpredictable and increases the risk of water-related problems. GCMs under the new CMIP6 framework holds several climate models with many improvements as compared to past similar efforts. The improvements are mainly in the number of scenarios formulated, setup, parametrization, and resolution. In this study, 10 downscaled climate models from CMIP6 are evaluated by applying statistical and data mining tools and are ranked based on their capability to describe the historical observed series. The result of the analysis showed that the outputs of the MPI-ESM1-2-HR model have a good overall ranking among those 10 models. The output of this top-ranked model is used to understand future climate over UASB after properly bias-corrected using the QM method. Results of the bias correction step show that average annual precipitation has shown an increment of 6.5\% in the middle (SSP2-4.5) and 10.3\% in the worst (SSP5-8.5) case scenarios for the mid-century (2040 - 2069). Similarly, for the end of the century (2070 - 2099) an increment of 4.7\% and 17.5\% was predicted for the two scenarios respectively. Whereas average annual maximum temperature series showed an increment of 1.5 $\degree C$ for middle and 2.6 $\degree C$ for the worst case in the mid-century. At the same time, an increment of 2.2 $\degree C$ and 3.5 $\degree C$ were predicted for the end of the century similarly for those scenarios. Furthermore, it was predicted that the average annual minimum temperature series will have an increment of 2.6 $\degree C$ and 3.1 $\degree C$ for mid-century and 3.1 $\degree C$ and 4.7 $\degree C$ for the end century for the two scenarios respectively. An increase in precipitation with increased land degradation problems in the sub-basin increases the risk of flood events in the future.

Any change in the amount and annual distribution of rainfall causes a major socioeconomic and environmental problem where rainfed agriculture is predominant. For that reason, the objective of this study was to determine the long-term variability and trends of precipitation in the Wabi Shebele River Basin (WSRB), Ethiopia. The basin was discretized into 7 local agroclimatic zones (ACZ) based on annual rainfall and elevation. In this study, the coefficient of variation (CV) was used to check the variability of rainfall and modified Mann-Kendall (MK) and Innovative Trend Analysis (ITA) methods were used to detect rainfall trends. For each ACZ, stations with long-term records and less than 10 % missing data were selected for further analysis. The mean annual rainfall in the basin ranges from 227.2 mm to 1047.4 mm. The study revealed most of the ACZs showed a very high variation in Belg/Spring season rainfall (CV % > 30) than Kiremt/Summer and annual rainfall. Trend analysis revealed that no uniform trend was detected among ACZs at each temporal scale. But, most ACZ in the arid and semi-arid areas showed a non-significant decreasing trend. In comparison, a similar result was observed using MK and ITA methods.

Soil erosion and sediment transport are quite complex processes as they depend on physical, biological, mechanical, and chemical processes within a particular catchment. Therefore, it is highly essential to better explain engaged physical processes and means of accounting for site-specific conditions, for soil loss and sediment yield estimation. This paper mainly focuses on physical explanations behind soil erosion and common soil erosion models like Universal or Revised Universal Soil Loss Equation(USLE/RUSLE) and Modified Universal Soil Loss Equation(MUSLE). Based on the physical explanations and overall limitations, the MUSLE is selected for the application of sediment yield estimation. The main objective of this paper is to estimate the best exponent of the MUSLE, and to estimate the best combination of the exponent and topographic factor of the MUSLE under hydro-climatic conditions of Ethiopia. For the sake of calibration procedure, the main parameters of the MUSLE which directly affect soil erosion process such as cover, conservation practice, soil erodibility, and topographic factors are estimated based on the past experiences from literature and comparative approaches, whereas the other parameters which do not directly affect the erosion process or which have no any physical meaning (i.e coefficient a and exponent b) are estimated through calibration. It is verified that the best exponent of the MUSLE is 1 irrespective of the topographic factor, which results in the maximum performance of the MUSLE (i.e approximately 100\%). For the best combination of the exponent and topographic factor, the performance of the MUSLE is greater than or equal to 80\% for all four watersheds under our consideration, we expect the same for other watersheds of Ethiopia.