Preprints
https://doi.org/10.5194/egusphere-2022-772
https://doi.org/10.5194/egusphere-2022-772
 
16 Sep 2022
16 Sep 2022
Status: this preprint is open for discussion.

Hazard assessment modeling and software development of earthquake-triggered landslides in the Sichuan-Yunnan area, China

Xaioyi Shao1,2, Siyuan Ma3,4, and Chong Xu1,2 Xaioyi Shao et al.
  • 1National Institute of Natural Hazards, Ministry of Emergency Management of China, Beijing 100085, China
  • 2Key Laboratory of Compound and Chained Natural Hazards Dynamics, Ministry of Emergency Management of China, Beijing 100085, China
  • 3Institute of Geology, China Earthquake Administration, Beijing, 100029, China
  • 4Key Laboratory of Seismic and Volcanic Hazards, Institute of Geology, China Earthquake Administration, Beijing, 100029, China

Abstract. To enhance the timeliness and accuracy of spatial prediction of co-seismic landslides, we propose an improved three-stage spatial prediction strategy and developed a corresponding hazard assessment software named Mat.LShazard V1.0. Based on this software, we evaluate the applicability of this improved spatial prediction strategy in six earthquake events that have occurred near the Sichuan Yunnan region including the Wenchuan, Ludian, Lushan, Jiuzhaigou, Minxian and Yushu earthquakes. The results indicate that in the first stage (within a half-hour of the earthquake), except for the 2013 Minxian earthquake, the AUC values of the modelling performance in other five events are above 0.8. Among them, the AUC value of the Wenchuan earthquake is the highest, reaching 0.947. The prediction results in the first stage can meet the requirements of emergency rescue with immediately obtaining the overall predicted information of the possible coseismic landslide locations in the quake-affected area. In the second and third stages (Within 12 hours of the quake), with the improvement of landslide data quality, the prediction ability of the model based on the entire landslide database is gradually improved. Based on the entire landslide database, the AUC value of the six events exceeds 0.9, indicating a very high prediction accuracy. Whether in the second or third stage (After 3 days of the seismic event), the predicted landslide area (Ap) is in good agreement with the observed landslide area (Ao). However, based on incomplete landslide data in the meizoseismal area, Ap is much smaller than Ao. When the prediction model based on complete landslide data is built, Ap is nearly identical to Ao. This study provides a new application tool for coseismic landslide disaster prevention and mitigation in different stages of emergency rescue, temporary resettlement, and latereconstruction after a major earthquake.

Xaioyi Shao et al.

Status: open (until 11 Nov 2022)

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Xaioyi Shao et al.

Xaioyi Shao et al.

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Short summary
Scientific understandings of the distribution of coseismic landslides, followed by emergency and medium and long-term risk assessment can reduce the landslide risk. The aim of this study is to propose an improved three-stage spatial prediction strategy and develop a corresponding Hazard assessment software called Mat.LShazard V1.0, which provides a new application tool for coseismic landslide disaster prevention and mitigation in different stages .