High-speed and accurate simulations of landslide-generated tsunamis are of great importance for the understanding of generation and propagation of water waves and for prediction of these natural disasters. A three-dimensional numerical model, based on Reynolds-averaged Navier–Stokes equations, is developed to simulate the landslide-generated tsunami. Available experiment data is used to validate the numerical model and to investigate the scale effect of numerical model according to the Froude similarity criterion. Based on grid convergence index (GCI) analysis, fourteen cases are arranged to study the sensitivity of numerical results to mesh resolution. Results show that numerical results are more sensitive to mesh resolution in near field than that in the propagation field. Nonuniform meshes can be used to balance the computational efficiency and accuracy. A mesh generation strategy is proposed and validated, achieving an accurate prediction and nearly 22 times reduction of computational cost. Further, this strategy of mesh generation is applied to simulate the Laxiwa Reservoir landslide tsunami. The results of this study provide an important guide for the establishment of a numerical model of the real-world problem of landslide tsunami.

Rainfall-triggered flood and landslide hazards pose significant threats to human lives and infrastructure worldwide. This study aims to evaluate the applicability of three satellite rainfall data sets—namely, CMORPH, GPM, and TRMM—for the prediction of flood and landslide hazards using a coupled hydrological-slope stability model. The spatial distribution of annual rainfall from the three satellite data sets was similar to that of gauge rainfall, with an increasing trend from the north to the south of Shaanxi Province. The average annual rainfall of CMORPH was the lowest, while that of TRMM was the highest. The modeled discharges forcing by satellite rainfall generally matched the observed discharges at four hydrological stations for the period 2010–2012, with average correlation coefficients of 0.51, 0.61, and 0.57 for the CMORPH, GPM, and TRMM rainfall, respectively. The exceedance probabilities of modeled discharges for the three satellite rainfall data sets were close to those of the observations, particularly when the discharges were low. Moreover, the landslide prediction results demonstrated that the three satellite rainfall data sets could simulate the spatial distribution of landslide events well; these simulations were consistent with the information in the landslide inventory map. Furthermore, when compared to the classical Intensity-Duration (ID) rainfall threshold method, the physically based slope stability model presented higher global accuracy under all three satellite rainfall data sets. The global accuracy of GPM rainfall was the highest among the three data sets (0.973 for GPM vs. 0.951 for CMORPH and 0.965 for TRMM), indicating that GPM rainfall provides the highest quality compared to CMORPH and TRMM rainfall. These findings provide a crucial basis for the application of satellite rainfall data in the context of flood and landslide prediction.