Soil erosion is currently a global problem that causes land degradation and long-lasting challenges in Ethiopia. Sediment yield is influenced by the watershed characteristics such as land cover, soil class, and slope, which are considered the drivers of soil erosion in the basin. The middle Awata watershed is highly susceptible to soil erosion due to its topographical features. This study is therefore aimed at estimating sediment yield, examining its spatial distribution, and evaluating the selected Best Management Practices (BMPs) to reduce soil erosion-prone areas at downstream. The model simulation was done by dividing the total watershed area of 1912 km2 into 37 subbasins and 294 hydrologic response units (HRUs) for 31 years (1988–2018). The model’s uncertainty evaluation was carried out on monthly basis using the Sequential Uncertainty Fitting (SUFI-2) algorithm. The performance of the model was evaluated by statistical parameters that gave R2 = 0.76, NSE = 0.75, RSR = 0.51, and PBIAS = 5.6% for calibration and R2 = 0.75, NSE = 0.74, RSR = 0.51, and PBIAS = 2.7% for validation of streamflow. Meanwhile, sediment yield in the watershed was also simulated with R2 = 0.69, NES = 0.66, RSR = 0.58, and PBIAS = 3.7% for calibration and R2 = 0.67, NES = 0.65, RSR = 0.61, and PBIAS = 5.6% for validation of sediment distribution. The simulated annual average sediment yield was 34.543 × 103 ton/year at the outlet of the middle Awata watershed. The developed spatial distribution of sediment yield identified the first twelve upstream subwatersheds as being soil erosion-prone areas. Following an evaluation of the four selected BMPs, it was determined that parallel terracing is the most recommended method for soil erosion reduction option in all critical subbasins found in the watershed.

Selecting a suitable bias correction method is important to provide reliable inputs for evaluation of climate change impact. Their influence was studied by comparing three discharge outputs from the SWAT model. The result after calibration with original RCM indicates that the raw RCM are heavily biased, and lead to streamflow simulation with large biases (NSE = 0.1, R2 = 0.53, MAE = 5.91 mm/°C, and PBIAS = 0.51). Power transformation and linear scaling methods performed best in correcting the frequency-based indices, while the LS method performed best in terms of the time series-based indices (NSE = 0.87, R2 = 0.78, MAE = 3.14 mm/°C, PBIAS = 0.24) during calibration. Meanwhile, daily translation was underestimating simulated streamflow compared with observed and was considered as the least performing method. The precipitation correction method has higher visual influence than temperature, and its performance in streamflow simulations was consistent and considerable. Power transformation and variance scaling showed highly qualified performance compared to others with indicated time series values (NSE = 0.92, R2 = 0.88, MAE = 1.58 mm/°C and PBIAS = 0.12) during calibration and validation of streamflow. Hence, PT and VARI were the dominant methods to remove bias from RCM models at Akaki River basin.

The main focus of this study was to investigate and evaluate the Performance of Four Regional Climate Models irrespective of their capability in simulating mean precipitation and Temperature. In this fact and concern, the evaluation of those climate models was basically on how they simulate mean annual climatology, annual cycle and interannual variability of precipitation, maximum and minimum temperature over the entire catchment. All observed data used for the baseline period of 1980-2006 was obtained from Ethiopian National Meteorological Agency and RCM data was extracted from CORDEX-Africa-44 using grid points. RCM shows significant bias and almost all of them simulate those climate variables' at different levels. In the analysis of the annual cycle of precipitation during the summer season, all RCM was underestimated. However, RACMO22T and RCA4 show better adjustment at the simulation of both precipitation and Temperatures despite their significant bias. The bias was deliberately associated with the higher error in simulating maximum and minimum temperature at the highest topography found at sebeta and Addis Ababa catchments. The inter-annual variability of precipitations and temperature was shown as great evidence where the region is under the impact of climate change specifically when the trend of annual projected temperature shown incremental modality. As far as concern the mean climatology analysis by statistical parameters, almost all models perform nearly equal excluding the seasonal point of view in which RCMs performed quite differently during season analysis. In all aspects and evidence by statistically evaluated output realize that RACMO22T and RCA4 were better performed at upper awash catchments although some of their bias and uncertainty were available. Generally, the performance of Regional climate models was different at different catchments along with the specified locations and topographies. Furthermore, the seasonal analysis over Akaki catchment indicates that climate models were more capable of simulating wet season than dry.

Land use and land cover (LULC) changes in many parts of river basins have caused water shortages, flood risks, land degradation, soil loss, biodiversity loss, and ecosystem deterioration. LULC change and topography are the main factors that cause land degradation and soil erosion in the Ethiopian highlands. The aim was to evaluate the rate of the LULC change and its effects on runoff and sediment yield in the semihumid subtropical Awash watershed using the SWAT + model. The land use maps of 2000, 2010, and 2020, along with constant climate data from 1992 to 2020, were used to investigate the effects of LULC dynamics on runoff and sediment yields. Agriculture and urbanization both increased at 7.1% and 7.95%, respectively. In contrast, the forest area decreased by 8.8% and shrubland by 3.25% from 2000 to 2020. Bare soil and urban areas covered the majority of the landscape units that were labeled as potential runoff generators. The majority of the soil erosion-prone areas that were classified as severe in the second and third scenarios covered a sizable area of urban, agricultural, and shrubland. These soil erosion hotspots covered an area of 3,777.3 ha (3.18%) and 13,413.1 ha (11.3%), with a total annual sediment yield of 361.93 m/ton and 1239.24 m/ton, respectively. In general, the change in LULC results in the annual sediment yield, with mean annual amounts of 241.8 tons/ha, 408.7 tons/ha, and 732.4 tons/ha for each scenario in the sequence. The model performance was tested using R2 = 0.88, NSE = 0.9, and PBIAS = −2.36, which indicate good agreement between simulated and observed flow, and R2 = 0.82, NSE = 0.86, and PBIAS = 4.38 for the simulated against recorded sediment yield. The increases in sediment yields have serious implications for reservoir siltation downstream of the watershed and warn land use managers to take action.

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