Optimizing Earthquake-Induced Landslide Hazard: A Multi-Phase Assessment Framework for Case Study of Jiuzhaigou Earthquake

Earthquake-induced landslides can cause extensive damage, often surpassing the earthquake's direct impact. Effective emergency response and post-disaster planning rely on rapid, accurate landslide hazard assessments. However, in mountainous regions with frequent cloud cover, obtaining accurate remote sensing data can be challenging. This study addresses these limitations through a three-phase landslide hazard assessment approach, focusing on emergency response, mid-term resettlement, and reconstruction phases. We applied two rapid assessment models, Xu2019 and Shao2023 to the first-phase evaluation of the 2017 Jiuzhaigou. In the second and third phases, assessment models were developed based on incomplete and complete landslide data, respectively. The results shows that in the first phase, the Shao2023 model outperformed the Xu2019 model. For the Jiuzhaigou earthquake, the Shao2023 model achieved a prediction accuracy of 87%compared to 66.7% for the Xu2019 model. As coseismic landslide data became available, the prediction accuracy of the model improved, though persistent cloud cover reduced reliability in some regions. In the third phase, the development of comprehensive landslide databases significantly enhanced model performance. Predicted landslide areas closely aligned with observed distributions, confirming the robust predictive capabilities of these models when supported by complete landslides data.

Keywords: Cosesimic landslides, Three-phase strategy, Landslide hazard assessment, Jiuzhaigou earthquake.