Unveiling the long-term cascading effects of the 2018 Baige landslide and subsequent outburst flood with satellite radar observations

Abstract

Landslide-dammed lakes (LDL) and landslide lake outburst flood (LLOF) can significantly alter the kinematic behavior of upstream and downstream landslides, posing severe threats to human life and infrastructure. However, the long-term impacts of LDL and LLOF on surrounding landslide stability remain poorly understood. In this study, we systematically examine the cascading effects triggered by the 2018 Baige LDL and LLOF on adjacent landslides, based on time series interferometric synthetic aperture radar (InSAR) analysis of 1437 satellite radar images. Unlike previous studies that focused on individual landslides or localized areas, we developed an automated method to detect the onset of landslide acceleration, leading to the establishment of an inventory of 65 accelerated landslides (ALs) and a quantitative evaluation of their controlling factors. Our results show that approximately 30 % of the flood-affected active landslides changed their deformation mechanisms, which can be categorized into five distinct types. Among the landslides accelerated by the Baige event, 43 % exhibited persistent acceleration, whereas 57 % showed signs of self-recovery. For the latter, deformation velocity typically decayed by 90 % within an average of 9.3 years after the outburst, returning to near pre-event levels. Furthermore, compared to 378 flood-involved but non-ALs, ALs preferentially occur on gentler slopes and in areas with lower vegetation cover. More notably, those ALs generally experienced greater flood depth, higher flow velocity, and stronger flood power. This study is the first to assess the long-lasting cascading effects of LDL and LLOF on creep landslides. These findings advance our understanding of LDL and LLOF-induced landslide mechanisms and offer valuable insights for the long-term risk assessment and geohazard mitigation of landslide-prone regions affected by similar cascading processes.This research was funded by the National Natural Science Foundation of China (Ref. 41941019), the Shaanxi Province Geoscience Big Data and Geohazard Prevention Innovation Team (2022), the Generic Technical Development Platform of Shaanxi Province for Imaging Geodesy (2024ZG-GXPT-07), the Conselleria de Innovación, Universidades, Ciencia y Sociedad Digital in the framework of Project CIAICO/2021/335, the ESA-MOST China DRAGON-6 project (Grant No. 95355) and by the funding scheme of the European Commission, Marie Skłodowska-Curie Actions Staff Exchanges in the frame of the project UPGRADE – GA 101131146