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Soil Erosion Assessment in a Hilly Catchment of North Eastern India Using USLE, GIS and Remote Sensing

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Abstract

In the present study, soil erosion assessment of Dikrong river basin of Arunachal Pradesh (India) was carried out. The river basin was divided into 200 × 200 m grid cells. The Arc Info 7.2 GIS software and RS (ERDAS IMAGINE 8.4 image processing software) provided spatial input data and the USLE was used to predict the spatial distribution of the average annual soil loss on grid basis. The average rainfall erositivity factor (R) for Dikrong river basin was found to be 1,894.6 MJ mm ha−1 h−1 year−1. The soil erodibility factor (K) with a magnitude of 0.055 t ha h ha−1 MJ−1 mm−1 is the highest, with 0.039 t ha h ha−1 MJ−1 mm−1 is the least for the watershed. The highest and lowest value of slope length factor (LS) is 53.5 and 5.39 respectively for the watershed. The highest and lowest values of crop management factor (C) were found out to be 0.004 and 1.0 respectively for the watershed. The highest and lowest value of conservation factor (P) were found to be 1 and 0.28 respectively for the watershed. The average annual soil loss of the Dikrong river basin is 51 t ha−1 year−1. About 25.61% of the watershed area is found out to be under slight erosion class. Areas covered by moderate, high, very high, severe and very severe erosion potential zones are 26.51%, 17.87%, 13.74%, 2.39% and 13.88% respectively. Therefore, these areas need immediate attention from soil conservation point of view.

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Authors and Affiliations

  1. Department of Agricultural Engineering, NERIST, Nirjuli, Itanagar, Arunachal Pradesh, 791109, India

    P. P. Dabral & Neelakshi Baithuri

  2. Department of Water Resources Development and Management, IIT Roorkee, Roorkee, Uttarakhand, 247 667, India

    Ashish Pandey

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  1. P. P. Dabral
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  2. Neelakshi Baithuri
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  3. Ashish Pandey
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Correspondence to P. P. Dabral.

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Dabral, P.P., Baithuri, N. & Pandey, A. Soil Erosion Assessment in a Hilly Catchment of North Eastern India Using USLE, GIS and Remote Sensing. Water Resour Manage 22, 1783–1798 (2008). https://doi.org/10.1007/s11269-008-9253-9

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