Estimation of soil loss using remote sensing and GIS-based universal soil loss equation in northern catchment of Lake Tana Sub-basin, Upper Blue Nile Basin, Northwest Ethiopia

Abstract

Background Soil erosion is one of the major environmental challenges and has a significant impact on potential land productivity and food security in many highland regions of Ethiopia. Quantifying and identifying the spatial patterns of soil erosion is important for management. The present study aims to estimate soil erosion by water in the Northern catchment of Lake Tana basin in the NW highlands of Ethiopia. The estimations are based on available data through the application of the Universal Soil Loss Equation integrated with Geographic Information System and remote sensing technologies. The study further explored the effects of land use and land cover, topography, soil erodibility, and drainage density on soil erosion rate in the catchment. Results The total estimated soil loss in the catchment was 1,705,370 tons per year and the mean erosion rate was 37.89 t ha−1 year−1, with a standard deviation of 59.2 t ha−1 year−1. The average annual soil erosion rare for the sub-catchments Derma, Megech, Gumara, Garno, and Gabi Kura were estimated at 46.8, 40.9, 30.9, 30.0, and 29.7 t ha−1 year−1, respectively. Based on estimated erosion rates in the catchment, the grid cells were divided into five different erosion severity classes: very low, low, moderate, high and extreme. The soil erosion severity map showed about 58.9% of the area was in very low erosion potential (0–1 t ha−1 year−1) that contributes only 1.1% of the total soil loss, while 12.4% of the areas (36,617 ha) were in high and extreme erosion potential with erosion rates of 10 t ha−1 year−1 or more that contributed about 82.1% of the total soil loss in the catchment which should be a high priority. Areas with high to extreme erosion severity classes were mostly found in Megech, Gumero and Garno sub-catchments. Results of Multiple linear regression analysis showed a relationship between soil erosion rate (A) and USLE factors that soil erosion rate was most sensitive to the topographic factor (LS) followed by the support practice (P), soil erodibility (K), crop management (C) and rainfall erosivity factor (R). Barenland showed the most severe erosion, followed by croplands and plantation forests in the catchment. Conclusions Use of the erosion severity classes coupled with various individual factors can help to understand the primary processes affecting erosion and spatial patterns in the catchment. This could be used for the site-specific implementation of effective soil conservation practices and land use plans targeted in erosion-prone locations to control soil erosion.

Regionalization of the SWAT+ model for projecting climate change impacts on sediment yield: An application in the Nile basin

“…It is important to point out that some few studies such as; Girmay et al (2020) reported a maximum factor range of 100–311 occupying 1.5 % in Agewmariayam watershed, northern Ethiopia and Balabathina et al (2020) reported a maximum factor of 96.47 occupying 1.6 % of the Lake Tana basin in Ethiopia which further shows the diverse sensitivity of the topographic factor in the region. Additionally, Claessens et al (2008) also found out that the topographic factor was the main factor defining the large potential erosion patterns in East Africa.…”

“…The response of each MUSLE parameter was measured using a simple deterministic sensitivity analysis approach developed by ( Benaman, 2002 ). Maximum and minimum possible values of each MUSLE factor for this region selected from literature ( Kassam et al, 1991 , Nill et al, 1996 , Balabathina et al, 2020 , Girmay et al, 2020 , Kebede et al, 2021 ) were used in the sensitivity analysis. Initially, the model was run using the basin factor values used as baseline values.…”

Land Use and Land Cover Changes and Its Impact on Soil Erosion in Stung Sangkae Catchment of Cambodia

“…The application of the RUSLE model is simple and applicable in limited data conditions. Because of its suitable capacity and relatively simple computational inputs, RUSLE has been widely used around the world [22,[24][25][26][27][28] including in Ethiopia [71]; Kenya [15]; Zimbabwe [9]; China [29,32]; Japan [6]; India [30,31]; Nepal [33,34]; Sri Lanka [72]; South-East Asian countries (Philippines [73][74][75]; Thailand [76,77]; and Mekong River Basin [3,35,37]. Furthermore, the RUSLE also provides international applicability and comparability for the results and methods, because the model can be adjusted and applied in many parts of the world.…”

“…Some recent studies that have applied the RUSLE model to investigate the soil loss from the land use and land cover (LULC) changes include those by Gashaw et al [22]; Mustefa et al [23]; Kidane et al [24]; Gelagay and Minale [25]; Tadesse et al [26]; Balabathina et al [27]; Ayele et al [28] in Ethiopia; Kogo et al [15] in Kenya; Marondedze and Schütt [9] in Zimbabwe; Hui et al [29] in China; Prasannakumar et al [30]; Javed et al [31]; Kolli et al [32] in India; Talchabhadel et al [33]; Koirala et al [34] in Nepal; Thuy and Lee [35] in Vietnam; Plangoen et al [36] in Thailand; and Chuenchum et al [3,37] in Mekong River Basin.…”

Estimating the Best Exponent of the Modified Universal Soil Loss Equation and Regionalizing the Modified Universal Soil Loss Equation under Hydro-Climatic Condition of Ethiopia

“…128 However, the USLE/RUSLE was applied at different parts of Ethiopia following similar 129 or different approaches to estimate any of its factors (e.g, [28,[33][34][35][36][37][39][40][41][42][43][44]46,48]).…”