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Estimating the mass change of mountain glacier using a novel method based on InSAR observations

  • Jianmin ZhouEmail author
  • Zhen Li
  • Xiyou Fu
  • Bangsen Tian
  • Lei Huang
  • Quan Chen
  • Ping Zhang
  • Dejing Qiao
Original Article
  • 52 Downloads

Abstract

Melting glaciers have a direct contribution to sea level rise, runoff and glacier lake outburst flood disasters. Therefore, accurate estimations of glacier mass balances with high spatial (large region) and temporal (annual, seasonal) resolutions are very important. However, the estimation of glacial mass balances in mountainous regions is usually hampered by remoteness and the lack of high-precision topographic data for most mountain ranges. This study presents a novel method for estimating mass changes in mountain glaciers using InSAR data. We utilised observations of glacier surface deformation to derive changes in thickness, and then calculated the mass change. This method can accurately estimate short-term mass changes. We apply this method to the Koxkar glacier in the Tien Shan Mountain Range in China and successfully estimate the seasonal mass change. Using theoretical error and statistical error analysis, we determined that the accuracy of this method is much better than other geodetic methods. We analyse the spatial characteristics of the mass changes in the ablation zone for the first time. The results show considerable spatial and seasonal variability, which were mainly from ablation in the summer, with the greatest amount of glacial ablation reaching up to − 3872 mm water equivalent (w.e.), and from accumulation in the winter.

Keywords

Mountain glacier Mass balance InSAR observation Koxkar glacier 

Notes

Acknowledgements

The authors gratefully acknowledge the European Space Agency and the USGS for providing the SAR data. Special appreciation is given to W. Yang for his helpful suggestions on this work. This work was supported in part by the National Natural Science Foundation of China (Grant number 41471066); National Key Research and Development Program of China (Grant numbers 2016YFB0501501, 2016YFA0600304); State Key Laboratory of Remote Sensing, Institute of Remote Sensing and Digital Earth, CAS and Beijing Normal University, China (Grant number OFSLRSS201617); Key Laboratory of Geo-Informatics of State Bureau of Surveying and Mapping (Grant number 201415); and International Partnership Program of Chinese Academy of Sciences (Grant number 131C11KYSB20160061).

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital EarthChinese Academy of SciencesBeijingChina
  2. 2.State Key Lab Remote Sensing Science, Institute of Remote Sensing and Digital EarthChinese Academy of SciencesBeijingChina
  3. 3.Airborne Remote Sensing Center, Institute of Remote Sensing and Digital EarthChinese Academy of SciencesBeijingChina
  4. 4.College of Geology and EnvironmentXi’an University of Science and TechnologyXi’anChina

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