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Evaluation of soil loss change after Grain for Green Project in the Loss Plateau: a case study of Yulin, China

  • Bo Yang
  • Quanjiu Wang
  • Xiaoting Xu
Original Article
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Abstract

Soil erosion is one of the serious and urgent issues in the Loss Plateau of China. Chinese government has implemented Grain for Green Project to restore the ecological environment since 1999. In order to explore the spatiotemporal evolution of erosion and sediment yield before and after Grain for Green Project in the Loss Plateau, annual soil loss of Yulin from 2000 to 2013 is estimated by Chinese Water Erosion on Hillslope Prediction Model in conjunction with Remote Sensing and Geographic Information Systems. This model has the characteristics of a simple algorithm and can be applied to predict erosion in the Loss Plateau. The result shows that vegetation cover increased significantly after Grain for Green Project, and the annual average value of NDVI increased from 0.20 to 0.33. The spatiotemporal variations of soil erosion are largely related to rainfall erosion distribution, slope, and land use type. The overall soil erosion categories in the south region are higher than those of the northwest. Mid slopes and valleys are the major topographic contributors to soil erosion. With the growth of slope gradient, soil erosion significantly increased. The soil loss has a decreasing tendency after Grain for Green Project. Although the rainfall of 2002 and 2013 is similar, the soil loss decreased from 5192.86 to 3598.94 t/(km2 a), decreasing by 30.33%. It is also expressed that soil loss appears a reducing trend in the same degree of slope and elevation in 2002, 2007, and 2013. Under the simulation of the maximum and the minimum rainfall, soil erosion amount in 2013 decreased by 29.16 and 30.88%. The study proved that GFG has already achieved conservation of water and soil. The results indicate that the vegetation restoration as part of the Grain for Green Project on the Loss Plateau is effective.

Keywords

Soil loss Chinese water erosion on hillslope prediction model Remote sensing Yulin 
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Notes

Acknowledgements

This study is supported by the National Natural Science Foundation of China (Nos. 51239009, 51179150), the Science Foundation of Shaanxi Province Education Department (Nos. 14JZ063, 16JZ089), and Distinctive Discipline Construction Project of Common Higher Education in Shaanxi Province (The Historical Geography: No. 0602).

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© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Institute of Water Resources and Hydro-electric EngineeringXi’an University of TechnologyXi’anChina
  2. 2.Institute of Natural Resources Environment and Historical CultureXian Yang Normal UniversityXian YangChina

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