Coupling Remote Sensing and Hydrological-Geotechnical Modeling for Rapid Assessment of Cascading Flood-Landslide Risks

Rainfall-induced floods and landslides are globally prevalent natural hazards. Moreover, floods and landslides often occur in a cascading manner, posing significant risks and amplifying losses beyond each individual hazard event. Effective disaster preparedness and hazard management heavily rely on sufficient knowledge of flood-landslide cascading processes and accurate assessment of potential consequences. However, existing methods predominately analyse individual hazard event, and there is a notable lack of rapid, physically-based modeling approaches, particularly for regions where observations are limited. To address this challenge, we propose a novel framework to quantify flood and landslide risks by integrating remote sensing data with a high-performance hydrological-geotechnical model. The model is driven exclusively by remote sensing data (including meteorological forcings and ground properties) and forecasts flood and landslide processes based on physical principles. Moreover, this framework quantifies risk by synthesizing hazard intensity, population exposure, and regional socioeconomic conditions, while explicitly accounting for the compound interactions between these hazards. We evaluate this framework utilizing a heavy rainfall event of July 3–4, 2012 in the Yuehe River Basin, which triggered widespread floods, landslides, and debris flows. Our results demonstrate that the model effectively reproduces meteorological forcings and disaster processes, offering a new perspective for disaster risk assessment in data-scarce regions. The proposed framework could contribute to the development of effective mitigation strategies, enhancing regional resilience against cascading natural hazards.