Research on the Evolution Network Model of the Landslide Disaster Chain: A Case Study of the Baige Landslide

Abstract

In the context of Western China’s unique geography, recurrent landslide disasters pose substantial threats to both resident safety and economic stability. The escalating frequency of these incidents emphasizes the critical need for innovative disaster research, particularly focused on the concept of a disaster chain. This research aims to enhance disaster preparedness and management strategies with the ultimate goal of minimizing losses. On the basis of predecessors, this study changes the previous analysis forms of single or partial disaster events, innovatively collects all secondary disaster events derived from the landslide disaster chain, and builds an evolutionary network model. In concrete terms, our study concentrates on the Baige landslide within the Qinghai-Tibet Plateau, pinpointing sub-hazard events as crucial disaster nodes within the landslide. By establishing directed connections, we have developed a comprehensive landslide disaster chain evolution network model firmly grounded in the principles of disaster chain dynamics and complex network theory. This model encompasses 31 distinct disaster nodes and 77 connecting edges. To assess the inherent risks in the landslide catastrophe chain, we conducted a thorough analysis considering node access degree and clustering coefficients. Critical nodes driving economic losses, such as floods, debris flows, secondary landslides, and downstream water damage, were identified. Additionally, we isolated vulnerable connections within the evolving network, evaluating the susceptibility of each edge. Our research underscores the significance of proactive measures, including pre-disaster monitoring, early warning systems, and timely post-disaster information dissemination. Implementing these actions can play a pivotal role in mitigating the impact of landslide disasters, preserving lives and sustaining regional prosperity.

Research on a Multi-Dimensional Indicator Assessment Model for Evaluating Landslide Risk near Large Alpine Reservoirs

“…The authors of [15,16] demonstrated that continuously monitoring soil moisture can significantly improve the accuracy of landslide risk assessment and the lead time for early warnings. In this study, we mainly focused on the in-depth monitoring of soil moisture through six indicators: soil bulk density, the angle of internal soil friction, soil shear strength, soil moisture content, the porosity ratio, and the Topographic Wetness Index.…”