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Soil erosion assessment using RUSLE model and GIS in Huluka watershed, Central Ethiopia

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Soil erosion is the loss of soil by physical movement of particles from a given site leading to land degradation. The present study was carried out in Huluka watershed of the Oromia regional state of Ethiopia. Land-use/land-cover (LULC) changes and their potential impacts on soil erosion during 1998–2018 were studied. Landsat 5 thematic mapper (TM) and sentinel images of 1998, 2008 and 2018 were acquired and classified using maximum likelihood classification method. Soil erosion maps were generated with RUSLE model. Soil loss in the study area was 400 t ha−1 year−1, which was the highest rate of erosion. Severe erosion class covered 1115 ha (6% of the total watershed), while high to very high erosion-risk class covered 4032 ha (21%) and low to moderate erosion risk class covered 13,424 ha (73%), respectively. Critical sub-watersheds were identified and prioritized based on their average annual soil loss for future intervention and soil and water conservation measures. Average annual soil loss from these sub-watersheds ranged from 14.4 to 27 t ha−1 year−1. Based on the findings, it is recommended that the study area having extreme erosion should be given priority during integrated management interventions for soil conservation. Spatial erosion inputs generated with RUSLE model can serve as effective inputs in deriving strategies for land planning and management in the environmentally sensitive watershed areas.

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We are thankful to the School of Earth Sciences, College of Natural and Computational Sciences, Addis, Ababa University for providing funds and facilities for this research. We are also thankful to the anonymous reviewer for comments for the improvement of the manuscript.

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Correspondence to K. V. Suryabhagavan.

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Atoma, H., Suryabhagavan, K.V. & Balakrishnan, M. Soil erosion assessment using RUSLE model and GIS in Huluka watershed, Central Ethiopia. Sustain. Water Resour. Manag. 6, 12 (2020).

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  • GIS
  • Land-use changes
  • Soil loss
  • Sentinel data