Abstract
Abstract. One purpose of landslide research is to establish early warning thresholds for rainfall-induced landslides. Insufficient observations of past events have inhibited the analysis of critical rainfall conditions triggering landslides. This difficulty may be resolved by extracting the timing of landslide occurrences through analysis of seismic signals. In this study, seismic records of the Broadband Array in Taiwan for Seismology were examined to identify ground motion triggered by large landslides that occurred in the years 2005 to 2014. A total of 62 landslide-induced seismic signals were identified. The seismic signals were analyzed to determine the timing of landslide occurrences, and the rainfall conditions at those times – including rainfall intensity (I), duration (D), and effective rainfall (Rt) – were assessed. Three common rainfall threshold models (I–D, I–Rt, and Rt–D) were compared, and the crucial factors of a forecast warning model were found to be duration and effective rainfall. In addition, rainfall information related to the 62 landslides was analyzed to establish a critical height of water model, (I-1.5)⋅D=430.2. The critical height of water model was applied to data from Typhoon Soudelor of 2015, and the model issued a large landslide warning for southern Taiwan.
Highlights
In recent years, the frequency of extreme rainfall events and the number of large-scale natural disasters have increased globally (Tu and Chou, 2013; Saito et al, 2014)
Even under the same rainfall duration, the rainfall intensities of many small landslides were higher than those of large landslides. These results sufficiently demonstrated that rainfall intensity could not be used to distinguish small landslides from large landslides
The relationship between average rainfall intensity (I ) and total effective rainfall (Rt) was analyzed, and the results indicated that the product values of both factors for 5 % cumulative probability were 5640 mm2 h−1 (Fig. 6c)
Summary
The frequency of extreme rainfall events and the number of large-scale natural disasters have increased globally (Tu and Chou, 2013; Saito et al, 2014). These largescale natural disasters (e.g., landslides, floods, etc.) cause huge economic losses and human casualties. Discriminating large landslides from small landslides still presents challenges Both the velocity of mass movement and depth of excavation are difficult to measure, so the landslide area is commonly regarded as an indicator of the scale of a landslide.
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