The role of grid cell size, flow routing algolithm and spatial variability of soil depth on shallow landslide prediction

Abstract

To assess the spatial pattern of landslide susceptibility, we linked a simple hydrological model and an infinite slope stability model to predict the spatial pattern of critical steady-state rainfall required to cause slope instability. We studied a headwater in the Aratani River basin, western Japan. To clarify soil depth spatial patterns, we measured soil depth on the hillslope by using knocking pole tests. We compared two widely used procedures for determining local slope angle and upslope contributing area: a single-flow-direction procedure and an algorithm based on proportioning flow into two downslope pixels. Further, we examined the role of the analysis grid cell size on the precision of landslide prediction. We showed that by choosing an optimal grid cell size and using an optimal procedure for calculation of the upslope contributing area and by performing a detailed field survey to determine soil depth, the precision of landslide susceptibility assessment could be remarkably improved