Landslides are one of the most common types of geological disasters. However, the waves generated by landslides often cause more severe damage than the landslides themselves. This paper proposes an improved numerical model for landslide-induced waves. The model’s governing equations are derived from the equilibrium of forces acting on columns and principles of fluid dynamics. It can rapidly and effectively simulate the entire process of landslide-induced wave generation. Moreover, the model improves computational efficiency through the use of fixed computational cells, particularly in mountainous regions. The model was validated using a case study by Fernández-Nieto and then applied to simulate the Huangtian landslide-induced waves in the XW reservoir, China. The simulated landslide deposit closely matches the digital surface model obtained from unmanned aerial vehicle surveys. Simulated results showed a maximum wave height of 40.85 m, a maximum run-up height on the opposite bank of 34.1 m, and a wave height of 3.75 m near the dam. The fixed computational cells, which accounted for approximately 22% of the total cells, led to a four-fold improvement in computational efficiency over a full grid approach. Even with the additional cost of the preliminary simulation, the overall method still achieved a 3.7-fold improvement in computational efficiency over a full grid approach.

The process of reservoir impoundment poses a significant threat to the stability of reservoir bank slopes, potentially triggering new landslides or reactivating ancient ones. Consequently, long-term and stable monitoring of surface deformation in reservoir areas is essential for ensuring safe reservoir operation. SBAS-InSAR technology—characterized by its high precision, multi-temporal capability, and wide spatial coverage—offers an effective means of comprehensively characterizing landslide deformation in such environments. In this study, SBAS-InSAR is applied to monitor landslides in the Xiluodu Reservoir area using Sentinel-1A imagery. Ascending and descending orbit data are jointly inverted to reconstruct the two-dimensional (2D) surface deformation time series. The deformation patterns and their spatiotemporal evolution are analyzed in conjunction with remote sensing imagery, topographic and geological data, and reservoir water level fluctuations. The integrated analysis identifies 10 and 12 significant deformation zones in the vertical and east–west directions, respectively—demonstrating improved detection accuracy compared to single-orbit approaches. Two representative landslides, the Mixiluo and Huanghua landslides, are selected for detailed investigation. Their toe deformation exhibits a pronounced response to both rainfall and reservoir water level variations. These findings provide valuable reference data and technical support for the early identification of reservoir bank landslides and the safe operation of reservoirs in this and similar engineering contexts.