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W2779892372 startingPage "1717" @default.
W2779892372 abstract "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." @default.
W2779892372 created "2018-01-05" @default.
W2779892372 creator A5004167859 @default.
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W2779892372 creator A5066845262 @default.
W2779892372 creator A5089171202 @default.
W2779892372 date "2018-06-20" @default.
W2779892372 modified "2023-10-16" @default.
W2779892372 title "Adopting the I3–R24 rainfall index and landslide susceptibility for the establishment of an early warning model for rainfall-induced shallow landslides" @default.
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W2779892372 doi "https://doi.org/10.5194/nhess-18-1717-2018" @default.
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