Quantification of river bank erosion by RTK GPS monitoring: case studies along the Ningxia-Inner Mongolia reaches of the Yellow River, China

  • Zhuodong Zhang
  • Anping Shu
  • Keli ZhangEmail author
  • Hongyuan Liu
  • Jing Wang
  • Jiabing Dai


The Ningxia-Inner Mongolia reaches of the Yellow River suffer from bank erosion problems; in order to identify the bank erosion dynamics, Real Time Kinematic Global Positioning System (RTK GPS) was applied to monitor bank morphology at three sites: Taole Cropland (TC), Maobula Shrubland (MS), and Maobula Cropland (MC). The measured data were analyzed using the Geographical Information System (GIS) to quantify the volume and amount of bank erosion. To verify the feasibility of other means quantifying bank erosion including remote sensing image interpretation and Bank-Stability and Toe-Erosion Model (BSTEM) simulation, their results were compared with the directly monitored results by RTK GPS. Results show that the bank erosion moduli at the TC, MS, and MC sites are 12,762, 6681 and 44,142 t km−1 a−1 respectively based on RTK GPS measurements from 2011 to 2014, with the bank erosion amount varying between flood and non-flood seasons and among different years. The bank erosion quantified by remote sensing interpretation and BSTEM simulation agreed well with results from RTK GPS measurement. The main factors that influence bank erosion on the upper reaches of the Yellow River include land use in the bank area, bank height, and bank curvature. More rational land use along the Yellow River and stabilization of the river bank are required for this area. This study shows that RTK GPS monitoring is reliable and useful for bank erosion research, which has not yet been fully exploited. There is potential of applying remote sensing and model simulation to determine bank erosion of large rivers, while they should be combined and supported by field investigated data.


Quantification River bank erosion RTK GPS Ningxia-Inner Mongolia reaches Yellow River 



The authors would like to thank Hailun Dai, Jianzhi Dong, Liang Liu, Jicheng Guo, Li Lian, and Xiaoyan Liu for their assistance in field investigation and preparing figures.

Funding information

This study is a part of the Key Project (41730748) funded by the National Natural Science Foundation of China and the National Basic Research Program (2011CB403304) funded by the Ministry of Science and Technology of China.


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Zhuodong Zhang
    • 1
  • Anping Shu
    • 2
  • Keli Zhang
    • 1
    Email author
  • Hongyuan Liu
    • 1
  • Jing Wang
    • 1
  • Jiabing Dai
    • 1
  1. 1.State Key Laboratory of Earth Surface Processes and Resource Ecology, School of Geography, Faculty of Geographical ScienceBeijing Normal UniversityBeijingChina
  2. 2.School of EnvironmentBeijing Normal UniversityBeijingChina

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