Abstract. Rainfall-induced landslides number among the most devastating natural hazards in the world and early warning models are urgently needed to reduce losses and fatalities. Most landslide early warning systems are based on rainfall thresholds defined on the regional scale, regardless of the different landslide susceptibilities of various slopes. Here we divided slope units in southern Taiwan into three categories (high, moderate and low) according to their susceptibility. For each category, we established separate rainfall thresholds so as to provide differentiated thresholds for different degrees of susceptibility. Logistic regression (LR) analysis was performed to evaluate landslide susceptibility by using event-based landslide inventories and predisposing factors. Analysis of rainfall patterns of 941 landslide cases gathered from field investigation led to the recognition that 3 h mean rainfall intensity (I3) is a key rainfall index for rainfall of short duration but high intensity; in contrast, 24 h accumulated rainfall (R24) was recognized as a key rainfall index for rainfall of long duration but low intensity. Thus, the I3–R24 rainfall index was used to establish rainfall thresholds in this study. Finally, an early warning model is proposed by setting alert levels including yellow (advisory), orange (watch) and red (warning) according to a hazard matrix. These differentiated thresholds and alert levels can provide essential information for local governments to use in deciding whether to evacuate residents.

The implementation of landslide probability analysis was undertaken to evaluate the effect of landsliding on closures of major mountain road networks at Guoshin Township in central Taiwan. To achieve this objective, an event-based landslide probability analysis method was adopted to establish a landslide prediction model by using a set of training data from the landslides triggered by Typhoon Mindulle in July 2004. Landslide causative factors and triggering factors were selected in a logistic regression scheme so that the criteria for successfully distinguishing landslides from nonlandslides were established. Landslide occurrence probability was mapped in the whole study area and along the road route. Locations of high potential for landslide occurrence were, thus, highlighted along the road route and were proposed for road closure during typhoon events. At last, the proposed locations for road closure were validated by historical road closures caused by subsequent typhoons after Typhoon Mindulle. Validation results show that the proposed model could be used in predicting road closure resulting from storm-induced landslides.