**1. Introduction**

In the past two decades, the frequency of occurrence of extreme rainfall events and large-scale natural hazards has increased significantly worldwide [1–6], causing substantial economic losses and human casualties. In past studies [7–9], the characteristics of a large-scale landslide have been reported, including (1) extremely high movement velocity, (2) a large collapse volume, and (3) deep excavations into bedrock. Nevertheless, discriminating large-scale landslides (LSLs) from small-scale landslides (SSLs) requires many in situ observations, which remain difficult to accomplish extensively. In practical terms, the mass movement velocity and excavation depth are both difficult to observe, so the disturbed area or volume is mainly treated as a scale indicator of a landslide [10]. In the study, large-scale landslides (LSLs) are defined as landslides with a disturbed area more massive than 0.1 km2. Although

the occurrence frequency of LSLs is much lower than that of SSLs, LSLs induce rapid alterations of the topography, causing calamities on a far greater scale than do SSLs. Moreover, the Earth's surface processes in mountainous areas are significantly affected by LSLs.

For better evaluation of landslide hazards induced by rainfall-triggered LSLs, it is essential to comprehend the circumstances that induce failure and the mass movement following collapse [11,12]. Accurate landslide information on occurrence time, size, and location are beneficial for comprehending when, where, and how slopes may collapse following heavy rainfall [13]. Rainfall is well known as one of the significant factors in landslide occurrence, so in-depth knowledge of the effects of rainfall conditions is required. At present, Taiwan has an early-warning system for debris flows based on the relationship between rainfall intensity and effective rainfall [14]. The effective rainfall contributing to debris-flow occurrence includes the cumulative rainfall during the considered rainfall event and its 7-day antecedent rainfall before the rainfall event. However, there is no early-warning system for massive landslides in Taiwan. To control damage, the rainfall conditions that induce LSLs must be determined and used to define a rainfall threshold as a criterion of early-warning for the prevention and mitigation of disasters.

Rainfall parameters, including duration, intensity, cumulative rainfall, and antecedent rainfall, have been utilized in many previous studies to identify the essential rainfall conditions for shallow landslide occurrence [15–19]. Among the characteristics of rainfall, the cumulative rainfall represents the total height of precipitation on the ground surface, but it may not reflect the intratelluric water content, which involves the processes of infiltration, drainage, and even evapotranspiration. Kuo, et al. [20] adopted the traditional dual-factor analysis, i.e., rainfall intensity versus rainfall duration (*I-D*), cumulative rainfall versus rainfall duration (*R-D*), and rainfall intensity versus cumulative rainfall (*I-R*), to investigate in preliminary terms the rainfall thresholds for triggering LSLs. They reported that the cumulative rainfall might be the deterministic factor in triggering LSLs. However, the complicated relationship between the meteorological trigger and the hydrological cause was not considered in the study of Kuo, et al. [20]. Bogaard and Greco [21] have proposed analyzing the precipitation thresholds for landslides and debris flows from a hydro-meteorological point of view. In their study, the soil water index (SWI) is treated as a proxy for both meteorological trigger and hydrological cause.

The SWI proposed by Sugawara, et al. [22] is derived from a three-layer tank model. The value of the SWI is estimated to represent the depth of the remaining water in the three-layer tank. Similarly, Segoni, et al. [23] discovered that the performance of a regional scale landslide warning system could be improved by using soil moisture data instead of antecedent rainfall. The influences of infiltration and drainage on water content within slopes are considered when calculating the SWI. Currently, the Japan Meteorological Agency (JMA) adopts the SWI as the conceptual soil water content affected by antecedent rainfall as well as event rainfall [24]. Furthermore, the tank model has been successfully applied to discuss the influence of water infiltration on deep-seated landslides [25,26]. The estimation of groundwater supply caused by infiltration using the tank model can be considered as an indicator of pore water pressure changes in the deep layer of a slope. Chen, et al. [27] first proposed the SWI–*D* curve as an empirical rainfall threshold for shallow landslides in Taiwan. They noted that the SWI can be used as the indicator of the antecedent rainfall condition and recommended establishing a suitable warning system in Taiwan.

For landslide early warning systems, the thresholds for LSLs would be different from those for SSLs. Thus, different disaster alerts and evacuation strategies would be produced [21,28,29]. It would be worthwhile to create a regional warning threshold for landslides of different scales for Taiwan. However, the rainfall thresholds for landslides having a sizable disturbed area (i.e., exceeding 0.1 km2) have rarely been determined for Taiwan in the past due to the limited number of cases. In this study, an LSL dataset containing information on landslide and rainfall parameters is created to carry out statistical analysis of multiple rainfall parameters. Then SWI and rainfall duration can be used to determine the critical threshold for triggering LSLs. Moreover, this study attempts to construct a multi-threshold model different from the single threshold for shallow landslides constructed in the

past to provide a new landslide warning model, which can be used at different stages or for landslides of different scales. The threshold will provide invaluable information for helping disaster management authorities to alert the general public and prepare for prevention and disaster mitigation.
