A Regional-Scale Landslide Warning System Based on 20 Years of Operational Experience

Consistently over the years, particularly during monsoon seasons, landslides and related geohazards in Bhutan are causing enormous damage to human lives, property, and road networks. The determination of thresholds for rainfall triggered landslides is one of the most effective methods to develop an early warning system. Such thresholds are determined using a variety of rainfall parameters and have been successfully calculated for various regions of the world at different scales. Such thresholds can be used to forecast landslide events which could help in issuing an alert to civic authorities. A comprehensive study on the determination of rainfall thresholds characterizing landslide events for Bhutan is lacking. This paper focuses on defining event rainfall-duration thresholds for Chukha Dzongkhag, situated in south-west Bhutan. The study area is chosen due to the increase in frequency of landslides during monsoon along Phuentsholing-Thimphu highway, which passes through it and this highway is a major trade route of the country with the rest of the world. The present threshold method revolves around the use of a power law equation to determine event rainfall-duration thresholds. The thresholds have been established using available rainfall and landslide data for 2004-2014. The calculated threshold relationship is fitted to the lower boundary of the rainfall conditions leading to landslides and plotted in logarithmic coordinates. The results show that a rainfall event of 24 h with a cumulated rainfall of 53 mm can cause landslides. Later on, the outcome of antecedent rainfall varying from 3-30 days was also analysed to understand its effect on landslide incidences based on cumulative event rainfall. It is also observed that a minimum 10-day antecedent rainfall of 88 mm and a 20-day antecedent rainfall of 142 mm is required for landslide occurrence in the area. The thresholds presented can be improved with the availability of hourly rainfall data and the addition of more landslide data. These can also be used as an early warning system especially along the Phuentsholing-Thimphu Highway to prevent any disruptions of trade. initiated due to heavy monsoon precipitation and aggravated due to the increase in human activities. The increasing number of landslide events in Bhutan can be attributed to complex geological conditions, steep slopes, climate change, type of soil, and tectonic activity. Landslides in Bhutan mostly occur during the monsoons during which the torrential rainfall leads to several flash floods and landslide triggering, cutting off many parts of south Bhutan from the rest.The relationship between the amount of rainfall associated with landslide occurrences is generally studied using either an empirical or physical based approach [4][5][6][7]. Physical process models are based on numerical models which study the relationship between rainfall, pore water pressure, soil type, and volumetric water content that can lead to slope instability. Such a study is usually site specific due to variation in soil prop...

Section: Resultsmentioning

confidence: 98%

Estimating Rainfall Thresholds for Landslide Occurrence in the Bhutan Himalayas

Dikshit

Sarkar

Pradhan

et al. 2019

“…The main reason for the better performance of SIGMA seems to be the effectiveness in predicting the slow movements occurred in 2017: a technique based on detecting antecedent rainfall anomalies. SIGMA is more suited than ID and ED thresholds to forecast slope movements with a complex hydrological response [26]. ID and ED thresholds correctly predicted seven out of eight shallow landslides happened in 2016 but failed to forecast the slow movements in 2017 except for one day.…”

Section: Discussionmentioning

confidence: 96%

“…It is well accepted that shallow landslides and debris flows are triggered by high intensity-short rainfalls and deep-seated landslides occur as a result of less intense rain over a long time [16,23,24] When an area is prone to both shallow and rapid and deep-seated and slow moving landslides, a threshold model which can accommodate the effect of both the cases should be defined. A model that holds this characteristic is SIGMA (sistema integrato gestione monitoraggio allerta-integrated system for management, monitoring and alerting), which was developed for managing the risk associated with landslides triggered by rainfall in the Emilia-Romagna Region, Italy [23,25,26]. The model takes cumulative rainfall as input, and it considers the long-term and short-term behavior in order to account for shallow and deep landslides, respectively.…”

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confidence: 99%

“…Another important advantage of this method is the indication of warning level. The model can be calibrated with respect to the severity of landslides and can be used for developing regional site specific thresholds.An ongoing research continuously produced new upgrades and optimization of the model SIGMA [19,26,27], making it possible to be used across the world for landslide hazard warning. However, to our knowledge, the model has never been applied outside Italy, thus leaving the claimed flexibility of application only theoretical.…”

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confidence: 99%

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Rainfall Threshold Estimation and Landslide Forecasting for Kalimpong, India Using SIGMA Model

Abraham

Satyam

Kushal

et al. 2020

Self Cite

Rainfall-induced landslides are among the most devastating natural disasters in hilly terrains and the reduction of the related risk has become paramount for public authorities. Between the several possible approaches, one of the most used is the development of early warning systems, so as the population can be rapidly warned, and the loss related to landslide can be reduced. Early warning systems which can forecast such disasters must hence be developed for zones which are susceptible to landslides, and have to be based on reliable scientific bases such as the SIGMA (sistema integrato gestione monitoraggio allerta—integrated system for management, monitoring and alerting) model, which is used in the regional landslide warning system developed for Emilia Romagna in Italy. The model uses statistical distribution of cumulative rainfall values as input and rainfall thresholds are defined as multiples of standard deviation. In this paper, the SIGMA model has been applied to the Kalimpong town in the Darjeeling Himalayas, which is among the regions most affected by landslides. The objectives of the study is twofold: (i) the definition of local rainfall thresholds for landslide occurrences in the Kalimpong region; (ii) testing the applicability of the SIGMA model in a physical setting completely different from one of the areas where it was first conceived and developed. To achieve these purposes, a calibration dataset of daily rainfall and landslides from 2010 to 2015 has been used; the results have then been validated using 2016 and 2017 data, which represent an independent dataset from the calibration one. The validation showed that the model correctly predicted all the reported landslide events in the region. Statistically, the SIGMA model for Kalimpong town is found to have 92% efficiency with a likelihood ratio of 11.28. This performance was deemed satisfactory, thus SIGMA can be integrated with rainfall forecasting and can be used to develop a landslide early warning system.

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confidence: 99%

Overview of Landslide Hydrology

Sidle

Greco

Bogaard

2019

Most landslides and debris flows worldwide occur during or following periods of rainfall, and many of these have been associated with major disasters causing extensive property damage and loss of life [1][2][3][4][5][6][7][8][9]. Given concerns about the effects of climate change on precipitation regime, in the future, some mountainous areas may likely experience more landslides with a faster response to rainfall; however, most such projections are weakly based and remain untested [10,11].Subsurface hydrology is usually the main triggering mechanism of these landslides and associated debris flows. While the effects of hillslope hydrology on runoff generation have been thoroughly studied, much less attention has been paid to these effects on landslide and debris flow initiation. Recent syntheses demonstrate that it is no longer appropriate to view the subsurface as a static media which facilitates the transit of subsurface water, rather a variety of factors affecting the dynamics of subsurface hydrology need to be considered [12,13]. This dynamic nature of subsurface hydrology depends on the complex interactions among precipitation inputs, physical properties and heterogeneity of soils and bedrock, local geomorphology, and vegetation and associated biomass. These factors influence the timing of landslides with respect to precipitation inputs and antecedent soil moisture [14][15][16][17], the mass and mode of failure [18], and the extent of runout or transformation of landslides into debris flows [19].Both the infiltration of rainwater and snowmelt and bedrock exfiltration provide the local trigger of these landslides, while drainage and evapotranspiration tend to stabilize hillslopes by rerouting and removing subsurface water. Subsurface hydrology is strongly affected by preferential flow within the soil, substrate topography, and exfiltration from fractures in bedrock [2,13,18,[20][21][22]; the overall regolith moisture regime and recharge rates are influenced by evapotranspiration, soil development processes, soil water-groundwater interactions, and landform aspect and shape [14,16,[23][24][25]. The dynamic behavior amongst these interacting hydro-eco-geomorphic components evolves across spatial and temporal domains creating the conditions for landslide initiation. As such, the resulting hydrologic dynamics that induce changes in soil moisture and pore water pressure remain an important focal area of investigation and are addressed in this special issue along with hydro-meteorological thresholds for landslide assessment and early warning and modelling [15,17].Incorporation of complex hydrological processes into landslide simulations is still lacking and has significantly lagged the development of reliable predictive hydrodynamic models for landslide occurrence. This conundrum is largely due to the complexities of both the regolith properties and the different modes of failure. In addition to the complications of simulating subsurface flow, monitoring groundwater levels and soil moisture in unstable terrain is ch...