Prioritization of Susceptible Watershed to Sediment Yield and Evaluation of Best Management Practice: A Case Study of Awata River, Southern Ethiopia

Abstract

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.

Erosion Risk Assessment for Prioritization of Conservation Measures in the Watershed of Genale Dawa-3 Hydropower Dam, Ethiopia

“…The corresponding mean annual sediment yield contribution of each subbasin is shown in Table 9. The estimated sediment yield in the Genale Dawa-3 dam watershed was consistent with that of other studies done in other basins in Ethiopia, including the studies by [30] in the Finchaa catchment [33], in the Awata watershed [36], in the Bilate watershed [37], in the Megech reservoir catchment [38], in the Welmel watershed, and [39] in the Toba watershed in Ethiopia.…”

“…The PBIAS result shows the model had a slight overestimation (negative) during the calibration and an underestimation (positive) during the validation. Hence, the statistical model performance indicators at the Genale Dawa-3 dam watershed were slightly high in comparison to those estimated by various studies conducted in different parts of Ethiopia [28,[30][31][32][33]. The difference may be due to the soil, land use, and slope steepness in the study areas.…”

Evaluation of best management practices to reduce sediment yield in the upper Gilo watershed, Baro akobo basin, Ethiopia using SWAT

“…Contouring is a type of tillage that helps mitigate soil erosion by slowing surface runoff and allowing it to penetrate by impounding it [ 41 ]. This scenario assessed contouring in agricultural lands by modifying the soil conservation service curve number (CN2) and USLE Practice factor (USLE_P) in SWAT based on the land slope, literature reports, and SWAT user manual recommendations [ 44 ].…”

“…Furthermore, Kefay et al [ 44 ] confirmed that the SWAT could quantify sediment with its spatial distribution as well as identify hotspot areas for prioritization management approaches in Ethiopia. However, without hydrological models, it is difficult to quantify the intricate interactions between hydrological variables and evaluate the anthropogenic impacts on changes in sediment.…”

Effectiveness of physical soil and water conservation measures under simulated and observed dataset in Ethiopia: Meta‐analysis

“…Thus, it lies within the acceptable limit, which is verified through statistical tools for model performance evaluation. The simulation assumes that erosion is conducted through a natural process by considering well‐known erosion factors, such as soil, rainfall, slope length, slope steepness, land use, and management characteristics of the watershed (Amdihun et al, 2014; Kefay et al, 2022). It is used to gain insight into the system operation and interaction impact of all factors on erosion in a way that allows us to see the impact of each factor by identifying sensitive factors that govern the gross output and answer “what if” questions by omitting and including each factor without disturbing the natural system, which is difficult to conduct in a real manner.…”