Remote Sensing (Nov 2022)

Deformation and Volumetric Change in a Typical Retrogressive Thaw Slump in Permafrost Regions of the Central Tibetan Plateau, China

  • Chenglong Jiao,
  • Fujun Niu,
  • Peifeng He,
  • Lu Ren,
  • Jing Luo,
  • Yi Shan

DOI
https://doi.org/10.3390/rs14215592
Journal volume & issue
Vol. 14, no. 21
p. 5592

Abstract

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Ice-rich permafrost in the Qinghai–Tibet Plateau (QTP), China, is becoming susceptible to thermokarst landforms, and the most dramatic among these terrain-altering landforms is retrogressive thaw slump (RTS). Concurrently, RTS development can in turn affect the eco-environment, and especially soil erosion and carbon emission, during their evolution. However, there are still a lack of quantitative methods and comprehensive studies on the deformation and volumetric change in RTS. The purpose of this study is to quantitatively assess the RTS evolution through a novel and feasible simulation framework of the GPU-based discrete element method (DEM) coupled with the finite difference method (FDM). Additionally, the simulation results were calibrated using the time series observation results from September 2021 to August 2022, using the combined methods of terrestrial laser scanning (TLS) and unmanned aerial vehicle (UAV). The results reveal that, over this time, thaw slump mobilized a total volume of 1335 m3 and approximately 1050 m3 moved to a displaced area. Additionally, the estimated soil erosion was about 211 m3. Meanwhile, the corresponding maximum ground subsidence and headwall retrogression were 1.9 m and 3.2 m, respectively. We also found that the amount of mass wasting in RTS development is highly related to the ground ice content. When the volumetric ice content exceeds 10%, there will be obvious mass wasting in the thaw slump development area. Furthermore, this work proposed that the coupled DEM-FDM method and field survey method of TLS-UAV can provide an effective pathway to simulate thaw-induced slope failure problems and complement the research limitations of small-scale RTSs using remote sensing methods. The results are meaningful for assessing the eco-environmental impacts on the QTP.

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