A distributed hydrological-geotechnical model using satellite-derived rainfall estimates for shallow landslide prediction system at a catchment scale

This paper describes the potential applicability of a hydrological-geotechnical modeling system using satellite-based rainfall estimates for a shallow landslide prediction system. The physically based distributed model has been developed by integrating a grid-based distributed kinematic wave rainfall-runoff model with an infinite slope stability approach. The model was forced by the satellite-based near real-time half-hourly CMORPH global rainfall product prepared by NOAA-CPC. The method combines the following two model outputs necessary for identifying where and when shallow landslides may potentially occur in the catchment: (1) the time-invariant spatial distribution of areas susceptible to slope instability map, for which the river catchment is divided into stability classes according to the critical relative soil saturation; this output is designed to portray the effect of quasi-static land surface variables and soil strength properties on slope instability and (2) a produced map linked with spatiotemporally varying hydrologic properties to provide a time-varying estimate of susceptibility to slope movement in response to rainfall. The proposed hydrological model predicts the dynamic of soil saturation in each grid element. The stored water in each grid element is then used for updating the relative soil saturation and analyzing the slope stability. A grid of slope is defined to be unstable when the relative soil saturation becomes higher than the critical level and is the basis for issuing a shallow landslide warning. The method was applied to past landslides in the upper Citarum River catchment (2,310 km(2)), Indonesia; the resulting time-invariant landslide susceptibility map shows good agreement with the spatial patterns of documented historical landslides (1985-2008). Application of the model to two recent shallow landslides shows that the model can successfully predict the effect of rainfall movement and intensity on the spatiotemporal dynamic of hydrological variables that trigger shallow landslides. Several hours before the landslides, the model predicted unstable conditions in some grids over and near the grids at which the actual shallow landslides occurred. Overall, the results demonstrate the potential applicability of the modeling system for shallow landslide disaster predictions and warnings.