Soil erosion risk assessment in Sanjal watershed, Jharkhand (India) using geo-informatics, RUSLE model and TRMM data

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• 作者：Dipanwita Dutta ; Subhasish Das ; Arnab Kundu…
• 关键词：RUSLE ; DEM ; TRMM ; Landsat ; Watershed ; Potential soil erosion risk
• 刊名：Modeling Earth Systems and Environment
• 出版年：2015
• 出版时间：December 2015
• 年：2015
• 卷：1
• 期：4
• 全文大小：1,333 KB
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• 作者单位：Dipanwita Dutta (1)
  Subhasish Das (2)
  Arnab Kundu (3)
  Afrin Taj (1)
  
  1. Department of Remote Sensing and GIS, Vidyasagar University, West Bengal, India
  2. Department of Geology and Geophysics, Indian Institute of Technology, Kharagpur, West Bengal, India
  3. Centre of Atmosphere, Ocean and Space Studies, Institute of Interdisciplinary Studies, University of Allahabad, Uttar Pradesh, India
  
• 刊物类别：Earth System Sciences; Math. Appl. in Environmental Science; Statistics for Engineering, Physics, Co
• 刊物主题：Earth System Sciences; Math. Appl. in Environmental Science; Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences; Mathematical Applications in the Physical Sciences; Ec
• 出版者：Springer International Publishing
• ISSN：2363-6211

文摘

Soil erosion is a serious environmental issue in many parts of India. As the removal of fertile top soil has direct impact upon productivity of crops, there is a need to assess the potential risk of soil loss and take preventive measures. In order to study this issue, a remarkable number of soil erosion models have come up in the last few decades. Among them, RUSLE has adopted by many researchers for estimating the areas of potential soil erosion risk. Unfortunately, these researches have long been used sparsely distributed meteorological station based data for calculating rainfall erosivity factor. Since, rainfall erosivity is one of the major driving forces of soil loss, spatial variation of rainfall data should be considered during estimation of potential rainfall. In this context, the present study aims to predict potential risk of soil erosion in Sanjal watershed of Jharkhand using TRMM data. The study reveals a close agreement between spatial patterns of potential soil erosion and slope in the watershed. The predicted amount of soil loss as estimated by RUSLE is ranging between 0.2 and 61.4 t ha−1 year−1. Although about half of the total area having very low risk of soil erosion, few portions specially areas with steep slope has high risk of soil loss. The incorporation of TRMM data has enhanced the model through adding more accurate spatial information of rainfall erosivity.