A prototype adaptive mesh generator for enhancing computational efficiency and accuracy in physically-based modeling of flood-landslide hazards

Abstract

Predicting landslides across large regions using physically-based models requires balancing computational accuracy and efficiency. Current methods often use limited resolutions, underutilizing available data. We present a prototype mesh generator that manages multiple resolutions in grid-based modeling frameworks, focusing on identifying likely landslide initiation points and conditionally stable pixels as meshing criteria. The generator refines critical locations using finer grids based on set parameters and is integrated into a coupled hydrological-geotechnical framework that combines one-dimensional (1D) and three-dimensional (3D) slope stability models. This framework operates on non-uniform grids and adaptively applies 1D or 3D models according to local accuracy requirements. Testing in the upper Han River basin, China (∼ ${10}^5$ ${\text{km}}^2$), during the July 2010 floods and landslides demonstrated that the mesh generator enhances landslide prediction, and the combined 1D-3D approach outperforms the standalone 1D model. This prototype shows promise for large-scale flood-landslide forecast systems.