Adopting the I3–R24 rainfall index and landslide susceptibility for the establishment of an early warning model for rainfall-induced shallow landslides
- 1Department of Geosciences, National Taiwan University, Taipei City, Taiwan
- 2Institute of Applied Geology, National Central University, Taoyuan City, Taiwan
- 3Disaster Prevention Technology Research Center, Sinotech Engineering Consultants, INC., Taipei City, Taiwan
- 4National Science and Technology Center for Disaster Reduction, New Taipei City, Taiwan
- 5Central Geological Survey, MOEA, New Taipei City, Taiwan
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.