**1. Introduction**

Rainfall triggered landslides are one of the most devastating naturally occurring disasters across the world [1]. The global dataset of landslide hazards in the 2004–2016 period extracted from Reference [2] showed that almost 75% of the world's fatal landslides occurred in the Himalayan region. Bhutan is no exception to this, and is a part of one of the world's highly landslide-prone regions in the world [3]. The damage caused by landslides in this country has led to casualties and loss of land, affecting people's livelihoods and disrupting the transportation network, which is key to the country's economy. Most of the landslides in the Bhutan Himalayas are triggered by rainfall, especially during the monsoon period [4,5]. Therefore, it is imperative to identify the areas that could be affected by landslides, in order to reduce the probability of damage in the future. The key to achieving this is

through a detailed landslide hazard assessment that will help civic authorities to curtail landslide damage through effective land use management.

Landslide hazard may be defined as the probability of a damaging landslide in a spatial ("where") and temporal ("when") context, along with the magnitude ("how large") of the event [6,7]. Landslide susceptibility is defined as the likelihood of landslide occurrence ("where") in an area depending on local terrain conditions [8]. It may be regarded as the first step towards analyzing hazard and risk. Various spatial assessment models for landslide susceptibility have been developed [9–11]. However, compared to spatial assessment, there have been fewer attempts to carry out temporal probability assessment (studies have been conducted in Nilgiris, India [12], Hoa Binh, Vietnam [13], and Cameroon [14]). The two main techniques used to assess temporal probability for future landslide occurrences are (i) analysis of potential slope failure and (ii) statistical analysis of past landslide events [13,15]. The first technique involves evaluation of the current slope conditions and determination of the probability for future slope instability, which may be difficult to apply in large study areas [16]. Statistical analysis of past landslide events may be done directly using records of the landslides identified in the study area or, alternatively, it may be performed indirectly by using information related to recurrence of the landslide-triggering events [17]. Direct analysis requires a long time span of historical landslide data which is extremely difficult to obtain, especially in underdeveloped countries. Therefore, an indirect approach analyzing the frequency of occurrence of rainfall was used in this study to determine temporal probability. Even though this approach did not require complete multi-temporal landslide inventory data, it required determination of the relationship between rainfall and landslide incidences. After the calculation of rainfall thresholds, the landslide temporal probability was computed based on the number of times precipitation exceeded the threshold value [16]. As the frequency of rainfall-induced landslides only evaluates how often landslides might occur, it therefore needs to be integrated with spatial probability (susceptibility) and temporal probability to develop a landslide hazard map [17,18].

The prediction of landslide incidences using rainfall thresholds has been successfully carried out for various regions, including Italy [19–21], New Zealand [22], Malaysia [23], and the Himalayan arc [24–26]. The calculation of rainfall thresholds for landslide triggering can be determined using three main approaches: (i) physically based models [27], (ii) empirical rainfall threshold models [28], and (iii) statistically based models [29]. The physically based models are linked to the physical attributes of the study region and can be difficult to apply in cases of unavailability of an extensive dataset. Empirical models calculate rainfall thresholds based on past rainfall events which led to landslide incidences. The threshold is usually obtained by drawing lower-bound lines to the rainfall conditions that resulted in landslides plotted in Cartesian, semi-logarithmic, or logarithmic coordinates [30]. Statistical models use statistical tools like Bayesian inference or logistic regression to calculate thresholds [31].

In the case of Bhutan, studies to date have focused on the southwest region covering the Phuentsholing–Thimphu Highway (known as the Asian Highway), which connects the national capital Thimphu with neighboring countries. These studies have primarily focused on rainfall estimation and spatial assessment, using various techniques such as a probabilistic approach [5,32], a semi-automatic algorithm approach [26], an empirical approach [4], and machine learning models [33]. The other major highway, Samdrup Jongkhar–Trashigang (S-T), situated in the eastern part of the country, has been neglected, and a landslide study in this region is yet to be conducted. The main aim of this study was to assess landslide susceptibility utilizing temporal rainfall for the S-T highway. The two objectives in this study were (i) to estimate the temporal probability, and (ii) to estimate the landslide susceptibility using a multi-criterion decision-making approach. We addressed three major research themes in the current study: (i) determination of rainfall threshold, probability estimation of the threshold being exceeded, and landslide probability after the threshold has exceeded; (ii) susceptibility of the region with respect to landslides; and (iii) validation of the thresholds and susceptibility map. For this, the rainfall thresholds were determined based on the relation between daily rainfall and past landslide events that occurred between 2014 and 2017. The thresholds were validated using the rainfall records of 2018. Thereafter, the exceedance probability of the threshold was calculated and the temporal

probability of landslides was determined using a Poisson model. Finally, a landslide susceptibility map was developed using the analytic hierarchy process (AHP), utilizing the determined threshold values. This study was the first attempt to conduct such an elaborate study for the eastern region of Bhutan. The results from the present work can be understood as a preliminary step towards setting up an operational landslide early warning system so that damage to the transportation corridor can be reduced and human lives can be saved.
