Application and verification of a multivariate real-time early warning method for rainfall-induced landslides: implicati
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Zongji Yang I Liyong Wang I Jianping Qiao I Taro Uchimura I Lin Wang
Application and verification of a multivariate real-time early warning method for rainfall-induced landslides: implication for evolution of landslide-generated debris flows
Abstract Rainfall-induced landslides are a frequent and often catastrophic geological disaster, and the development of accurate early warning systems for such events is a primary challenge in the field of risk reduction. Understanding of the physical mechanisms of rainfall-induced landslides is key for early warning and prediction. In this study, a real-time multivariate early warning method based on hydro-mechanical analysis and a long-term sequence of real-time monitoring data was proposed and verified by applying the method to predict successive debris flow events that occurred in 2017 and 2018 in Yindongzi Gully, which is in Wenchuan earthquake region, China. Specifically, long-term sequence slope stability analysis of the in situ datasets for the landslide deposit as a benchmark was conducted, and a multivariate indicator early warning method that included the rainfall intensity-probability (I-P), saturation (Si), and inclination (Ir) was then proposed. The measurements and analysis in the two early warning scenarios not only verified the reliability and practicality of the multivariate early warning method but also revealed the evolution processes and mechanism of the landslide-generated debris flow in response to rainfall. Thus, these findings provide a new strategy and guideline for accurately producing early warnings of rainfall-induced landslides. Keywords Early warning . Multivariate . Rainfall-induced landslide . Debris flows Introduction Rainfall-induced landslides and the subsequent debris flows pose a great threat to the safety of people and their properties in mountainous areas and are one of the most common types of geological disasters worldwide (Iverson 2000; Hong et al. 2006). After the Wenchuan earthquake (7.9 MW) struck China on May 12, 2008, the widespread earthquake landslide deposits posed a high disaster risk to the earthquake-stricken regions. A significant outbreak of sudden landslides triggered by heavy rainfall and their frequent conversion into debris flows greatly affected the restoration and reconstruction of these disaster-stricken areas (Guzzetti et al. 2008; Feng et al. 2016). Many studies have demonstrated that landslide early warning system (LEWS) is among the most effective non-structural mitigation measures (Damiano et al. 2012; Thiebes et al. 2014; Pumo et al. 2016; Uhlemann et al. 2016). The rainfall intensity-duration model (I-D) is the main rainfallinduced landslide early warning model currently in use, and it directly applies real-time rainfall data to forecasting landslide initiations. A reliable rainfall threshold is crucial for establishing an accurate early warning system for landslide disasters. Numerous studies have been conducted worldwide utilizing rainfall intensity and duration data to correlate with the slope failures in
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