EGU23-13421
https://doi.org/10.5194/egusphere-egu23-13421
EGU General Assembly 2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Susceptibility of Glacier Debris Flow Based on Remote Sensing: Case Study in the Tien Shan

Ruoshen Lin1,2 and Gang Mei2
Ruoshen Lin and Gang Mei
  • 1University the Lausanne, Geoscience and Environment, Risk Group, Lanusanne, Switzerland (ruoshen.lin@unil.ch)
  • 2School of Engineering and Technology, China University of Geosciences (Beijing), Beijing 100083, China

Glacier is sensitive to climate warming, and changes in mountainous areas can lead to serious hazards to human society. Glacial debris flow is a type of geological hazards characterized by suddenness and high mobility in high-mountain regions due to deglaciation. The study of susceptibility analysis for glacial debris flow can effectively reduce its potential negative effects. However, when evaluating susceptibility of glacial mudflow, most research work takes the existing glacier area into consideration and ignores the effect of glacier ablation volume. The improved glacial geomorphological information entropy theory based on glacial correction coefficients can be used to evaluate the susceptibility. The correction coefficients can be calculated by investigating the changes in glacier ablation and distribution based on remote sensing applications. In addition, a deep learning-based approach for extracting glacier boundaries is proposed. We present a case study evaluating the susceptibility of along the Duku Highway in Tien Shan area. The results show that the improved method based on glacier ablation can effectively increase the accuracy of the susceptibility analysis. Based on the theory of glaciology and geomorphology, the changes of glacier can be used in the susceptibility of glacial debris flow. In the future, we will explore a new prediction method of geo-hazards based on glacier dynamics.

How to cite: Lin, R. and Mei, G.: Susceptibility of Glacier Debris Flow Based on Remote Sensing: Case Study in the Tien Shan, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-13421, https://doi.org/10.5194/egusphere-egu23-13421, 2023.