Rainfall-induced Landslides Research Articles

Potential impacts in soil slope of deformation and water content on elastic wave amplitude

Early warning during rainfall-induced landslides employs widely to save economic losses and casualties. Using elastic wave velocity for early warning benefits from the relationship between water content, shear deformation, and elastic wave velocity of unsaturated soil slope. Amplitude changes by means of the elastic wave in the soil can also reflect the physical properties. In this work, we proposed an idea to determine the impacts in the soil of water content and deformation on elastic wave amplitude to realize early warning. Model box tests were designed to study the action of one factor, volumetric water content, on the elastic wave amplitude. Slope model tests were aimed to consider the effects of volumetric water content and deformation on elastic wave amplitude during rainfall-induced landslides. The results show that the elastic wave amplitude non-linear decreased with the volumetric water content, which could be mutually verified from the data of the model box tests and the slope model tests. The deformation caused the wave amplitude to increase, and the range of the increase could reflect the range of the deformation. The statistical correlation coefficient quantified that the increase in wave amplitude was not directly related to the water content. The wave amplitude change can be represented more visibly by eliminating the residual after EMD decomposition, and it further verifies the above results. This work provides a new idea and a reliable basis for landslide prevention and mitigation and prediction.

The large deep-seated landslide induced by the March 12th, 2012 rainfall event in the city of Azazga, Northern Algeria: deformation characteristics and failure mechanisms

A rainfall-induced deep large landslide occurred on March 12, 2012, in the city of Azazga (Northern Algeria), causing heavy damages to residential buildings and public infrastructures. In this work, we investigate the inventory mapping, the deformation characteristics, the controlling factors, and the failure mechanisms of this landslide based on the field investigation, aerial photographs and satellite images interpretation, deep boreholes equipped with piezometers and inclinometers, as well as subsurface geophysical imaging by electric resistivity tomography (ERT). The obtained landslide inventory map shows that the landslide of March 2012 affected an area of 0.40 km2. This landslide is considered as a partial reactivation of a large pre-existing one (0.606 km2), which represents 6.65% of the total urban area. Moreover, the analyses identified two types of causative factors: (1) the triggering factor related to the high intensity and antecedent rainfall as well as human activity through slope excavations and embankments; and (2) the susceptibility factors related to the lithological nature and the internal structure of the flysch deposits, their weak mechanical resistance characteristics, the presence of shallow aquifers, and basal undercutting erosion of the Iazoughen and Aboud river torrents. The inclinometer measurements and ERT imaging reveal a complex, deep-seated and rapidly moving landslide whose failure surfaces are located at a depth of 11–29 m with an average velocity of 1–29 cm year−1. The entire slip surface is located along the geotechnical interface between the flysch bedrock and the overlying scree. This comprehensive study provides useful information on rainfall-induced landslides and may constitute guidance for landslide hazard mitigation and prevention.

Developmental characteristics of rainfall-induced landslides from 1999 to 2016 in Wenzhou City of China

Many landslides are triggered by excess precipitation. In the eastern part of China, landslides caused by extreme rainfall from typhoons in the monsoon season are the main geomorphological process with catastrophic impacts on society and the environment. In this study, Wenzhou City in eastern China was taken as the study area, and we compiled a detailed inventory of rainfall-triggered landslides between 1999 and 2016. The developmental characteristics of these landslides were determined with an emphasis on temporo-spatial distribution. The results showed that most of the landslides were located in the mountainous area of the western part of Wenzhou City. Landslides triggered by typhoon rainfall were commonly concentrated in a short period from July to September, when more than 70% of the landslides occurred. The landslides in this region were mainly of the debris-flow type, most of which were on a small scale, but had severe effects because of large elevation differences and long runout distances. Because the precipitation in typhoon events was commonly extreme, the initiation area of most landslides coincided with the region of highest hourly precipitation. Our results can provide reference data and guidelines for developing an early warning system for landslides and risk reduction in the study area.

The Use of Soil Moisture and Pore-Water Pressure Sensors for the Interpretation of Landslide Behavior in Small-Scale Physical Models.

This paper presents some of the results and experiences in monitoring the hydraulic response of downscaled slope models under simulated rainfall in 1 g. The downscaled slope model platform was developed as part of a four-year research project, “Physical modeling of landslide remediation constructions’ behavior under static and seismic actions”, and its main components are briefly described with the particular focus on the sensor network that allows monitoring changes in soil moisture and pore-water pressure (pwp). The technical characteristics of the sensors and the measurement methods used to provide the metrics are described in detail. Some data on the hydraulic and mechanical responses obtained from the conducted tests on slope models built from different soil types under different test conditions are presented and interpreted in the context of rainfall-induced landslides. The results show that the sensor network used is suitable for monitoring changes in the soil moisture and pwp in the model, both in terms of the transient rainfall infiltration through partially saturated soil and in terms of the rise in the water table and pwp build-up under fully saturated conditions. It is shown how simultaneous monitoring of soil moisture and pwp can be used to reconstruct stress paths that the monitored points undergo during different test phases. Finally, some peculiarities related to hydraulic hysteresis and surface erosion that were observed in some of tests are discussed, as well as possible difficulties in achieving and maintaining the targeted initial moisture distribution in slope models.

Displacement prediction method of rainfall-induced landslide considering multiple influencing factors

Displacement prediction method of rainfall-induced landslide considering multiple influencing factors

Monitoring of rainfall-induced landslides at Songmao and Lushan, Taiwan, using IoT and big data-based monitoring system

Monitoring of rainfall-induced landslides at Songmao and Lushan, Taiwan, using IoT and big data-based monitoring system

Impact Analysis of a Building Collapse Caused by a Rainfall-Induced Landslide in Kerala, India

This study presents the experimental, numerical analysis, and dynamic impact analysis of a building collapse caused by a rainfall-induced landslide (vertical cut slope failure) on 15 August 2018, in Peringavu, Kerala, India, which resulted in the death of nine people. The volume of 1500 m3 soil-applied lateral thrust force on the building’s rear side led to its demolition. The study includes extensive geotechnical characterization. General limit equilibrium and finite element methods were used in the numerical analysis. The infiltration analysis involved a rainfall pattern of low, moderate, and higher intensities on the slope. The study involved a two-stage analysis. Firstly, the analysis of the vertical cut slope with the application rainfall intensities, and second, the analysis of the building under the dynamic impact of the landslide. As a result of the study, the failure mechanism of the vertical cut during intense rainfall and triggering factors were evaluated. The dynamic impact analysis was carried out to examine the effects of the impact of the landslide debris on the building and the performance of the building under the impact load. The load-bearing walls experienced high-intensity impact force developed by the landslide, resulting in the lateral displacement of 170 mm and differential settlement of 92 mm, which led to the building’s demolition. The flexural failures, excessive deflections, bending moments, foundation settlements, and displacement of structural elements were studied.

Assessing rainfall-induced landslide risk in a humanitarian context: The Kutupalong Rohingya Camp in Cox's Bazar, Bangladesh

We have proposed a novel method for assessing landslide risk by applying cutting-edge geospatial technologies and combining hazard, exposure, and vulnerability components. The Kutupalong Rohingya Camp (KRC) in Cox's Bazar, Bangladesh, was taken as a case study area. The district was hosting over one million Rohingya refugees from Myanmar, fleeing genocide and crimes against humanity. Extensive vegetation removal, hill cutting, exceptionally dense camps, fragile shelters, unstable soils, and excess monsoon rainfall were linked to yearly landslides. At first, ten landslide factor maps were produced, and a detailed fieldwork-based landslide inventory was conducted. Next, we applied various statistical and knowledge-based approaches for landslide susceptibility modelling. The most accurate susceptibility map was utilised to produce landslide hazard maps for different rainfall return periods. Next, we used population and shelter density data for exposure mapping and integrated age and gender-based factors for social vulnerability analysis. The hazard, exposure, and vulnerability maps were combined to produce the landslide risk map. Finally, we calculated shelters and refugee populations at risk for different scenarios. Camp 10 had the highest risk value, and historical landslide events validated the results. This study is replicable in a similar context and can be scaled up. It provides authentic information on landslide disaster scenarios that should be adopted in the decision-making process to reduce future impacts of catastrophic hydrometeorological disasters in the KRC and surrounding host community areas.

Rainfall-induced landslide (RFIL) disaster in Dima Hasao, Assam, Northeast India

Rainfall-induced landslide (RFIL) disaster in Dima Hasao, Assam, Northeast India

Characterizing the Distribution Pattern and a Physically Based Susceptibility Assessment of Shallow Landslides Triggered by the 2019 Heavy Rainfall Event in Longchuan County, Guangdong Province, China

Rainfall-induced landslides pose a significant threat to the lives and property of residents in the southeast mountainous and hilly area; hence, characterizing the distribution pattern and effective susceptibility mapping for rainfall-induced landslides are regarded as important and necessary measures to remediate the damage and loss resulting from landslides. From 10 June 2019 to 13 June 2019, continuous heavy rainfall occurred in Longchuan County, Guangdong Province; this event triggered extensive landslide disasters in the villages of Longchuan County. Based on high-resolution satellite images, a landslide inventory of the affected area was compiled, comprising a total of 667 rainfall-induced landslides over an area of 108 km2. These landslides consisted of a large number of shallow landslides with a few flowslides, rockfalls, and debris flows, and the majority of them occurred in Mibei and Yanhua villages. The inventory was used to analyze the distribution pattern of the landslides and their relationship with topographical, geological, and hydrological factors. The results showed that landslide abundance was closely related to slope angle, TWI, and road density. The landslide area density (LAD) increased with the increase in the above three influencing factors and was described by an exponential or linear relationship. In addition, southeast and south aspect hillslopes were more prone to collapse than the northwest­–north aspect ones because of the influence of the summer southeast monsoon. A new open-source tool named MAT.TRIGRS(V1.0) was adopted to establish the landslide susceptibility map in landslide abundance areas and to back-analyze the response of the rainfall process to the change in landslide stability. The prediction results were roughly consistent with the actual landslide distribution, and most areas with high susceptibility were located on both sides of the river valley; that is, the areas with relatively steep slopes. The slope stability changes in different periods revealed that the onset of heavy rain on 10 June 2019 was the main triggering factor of these group‑occurring landslides, and the subsequent rainfall with low intensity had little impact on slope stability.

Threshold assessment of rainfall-induced landslides in Sangzhi County: statistical analysis and physical model

Threshold assessment of rainfall-induced landslides in Sangzhi County: statistical analysis and physical model

Machine learning for the definition of landslide alert models: a case study in Campania region, Italy

Landslide early warning systems at regional scale are typically based on correlations between landslides that occurred in the past and rainfall monitoring data in order to identify trigger rainfall thresholds. Recently, the availability of large datasets of atmospheric measurements allows including additional variables, increasing the reliability of the models. However, conducting the analyses with traditional techniques can be quite complex and time-consuming. The purpose of this preliminary study is to demonstrate that machine learning techniques can be used to analyze monitoring data in order to select the most relevant variables for the triggering of shallow rainfall-induced landslides at regional scale. The models developed herein were tested in one of the alert zones defined by civil protection for the management of geo-hydrological risk in Campania region, Italy. Two data sources were used in the analysis. The atmospheric variables are derived from the ERA5-Land atmospheric reanalysis. The data on landslide events are retrieved from “FraneItalia”, a georeferenced catalog of landslides occurred in Italy developed by consulting online sources from 2010 onwards. The models developed were calibrated and validated in order to define combinations of rainfall variables and soil water content for the prediction of the occurrence of landslides. Finally, the performance of the models was assessed using statistical indicators derived from contingency matrices.

Capture and Prediction of Rainfall-Induced Landslide Warning Signals Using an Attention-Based Temporal Convolutional Neural Network and Entropy Weight Methods.

The capture and prediction of rainfall-induced landslide warning signals is the premise for the implementation of landslide warning measures. An attention-fusion entropy weight method (En-Attn) for capturing warning features is proposed. An attention-based temporal convolutional neural network (ATCN) is used to predict the warning signals. Specifically, the sensor data are analyzed using Pearson correlation analysis after obtaining data from the sensors on rainfall, moisture content, displacement, and soil stress. The comprehensive evaluation score is obtained offline using multiple entropy weight methods. Then, the attention mechanism is used to weight and sum different entropy values to obtain the final landslide hazard degree (LHD). The LHD realizes the warning signal capture of the sensor data. The prediction process adopts a model built by ATCN and uses a sliding window for online dynamic prediction. The input is the landslide sensor data at the last moment, and the output is the LHD at the future moment. The effectiveness of the method is verified by two datasets obtained from the rainfall-induced landslide simulation experiment.

Evaluation of potential changes in landslide susceptibility and landslide occurrence frequency in China under climate change

Climate change can alter the frequency and intensity of extreme rainfall across the globe, leading to changes in hazards posed by rainfall-induced landslides. In recent decades, China suffered great human and economic losses due to rainfall-induced landslides. However, how the landslide hazard situation will evolve in the future is still unclear, also because of sparse comprehensive evaluations of potential changes in landslide susceptibility and landslide occurrence frequency under climate change. This study builds upon observed and modelled rainfall data from 24 bias-corrected Coupled Model Intercomparison Project Phase 6 (CMIP6) Global Climate Models (GCMs), a statistical landslide susceptibility model, and empirical rainfall thresholds for landslide initiation, to evaluate changes in landslide susceptibility and landslide occurrence frequency at national-scale. Based on four Shared Socioeconomic Pathways (SSP) scenarios, changes in the rainfall regime are projected and used to evaluate subsequent alterations in landslide susceptibility and in the frequency of rainfall events exceeding empirical rainfall thresholds. In general, the results indicate that the extend of landslide susceptible terrain and the frequency of landslide-triggering rainfall will increase under climate change. Nevertheless, a closer inspection provides a spatially heterogeneous picture on how these landslide occurrence indicators may evolve across China. Until the late 21st century (2080–2099) and depending on the SSP scenarios, the mean annual precipitation is projected to increase by 13.4 % to 28.6 %, inducing an 1.3 % to 2.7 % increase in the modelled areal extent of moderately to very highly susceptible terrain. Different SSP scenarios were associated with an increase in the frequency of landslide-triggering rainfall events by 10.3 % to 19.8 % with respect to historical baseline. Spatially, the southeastern Tibetan Plateau and the Tianshan Mountains in Northwestern Basins are projected to experience the largest increase in landslide susceptibility and frequency of landslide-triggering rainfall, especially under the high emission scenarios. Adaptation and mitigation methods should be prioritized for these future landslide hotspots. This work provides a better understanding of potential impacts of climate change on landslide hazard across China and represents a first step towards national-scale quantitative landslide exposure and risk assessment under climate change.

Effect of the Water-Air Coupling on the Stability of Rainfall-Induced Landslides Using a Coupled Infiltration and Hydromechanical Model

The water and air flow in soil pores in response to rainfall infiltration is an important factor affecting the stability of rainfall-induced landslides. The objective of this paper is to investigate the effect of water-air flow on the deformation and stability of rainfall-induced landslides based on a coupled infiltration and hydromechanical model. The model is formulated by the water and air flow equation, the mechanical equilibrium, and porosity equations. The rainfall infiltration is introduced as a flux boundary, which is determined according to the comparison of the infiltration capacity and rainfall intensity. The numerical model and solution approach are validated by simulating the Liakopoulos drainage test and a rainfall infiltration experiment with satisfactory results. Taking the Tanjiawan landslide as a case study, the water and air flow in response to rainfall infiltration and its effect on deformation and stability are examined. The results show that the pore air is gradually trapped and compressed due to rainwater infiltration. The entrapped air has a slowing effect on the rainfall infiltration and a pushing-out effect on the front sliding body, which is an important driving force for the evolution of the slope deformation and stability due to rainfall infiltration.

Analyzing the 27 July 2021 rainfall-induced catastrophic landslide event in the Kutupalong Rohingya Camp in Cox’s Bazar, Bangladesh

This article critically investigates a catastrophic rainfall-induced landslide event that occurred on 27 July 2021 in the Kutupalong Rohingya Camp (KRC) in Cox’s Bazar, Bangladesh, from geological and geomorphological perspectives. Large-scale anthropogenic interventions mainly caused the disastrous landslide event in the KRC in addition to intense rainfall. Before the landslide occurrence, about 300 mm of cumulative rainfall was recorded in the previous seven days and 120 mm of rainfall during the landslide event. A preliminary investigation was conducted to understand the extent, causative factors, and landslide characteristics. The landslide is of mud-flow type, but on the nearby slope, slumping was also visible. The landslide length was about 33 m, width 31 m, and area 612 m2. The approximate volume of slope materials displaced during the landslide event was about 2450 m3. The displaced slope materials mainly were silt and sand. The landslide event caused five fatalities and damaged nearly 5000 shelters in the KRC area. The devastation from such a small landslide event was attributed to dense households on the slope’s hilltop, slope, and toe. The camp areas and host communities are subjected to frequent and fatal landslides in the years to come due to intense human interventions and climatic conditions. The modifications of the slopes have been reducing the cohesion and the shear strength of the slope materials. Therefore, it is recommended to undertake proper mitigation and preparedness measures, including developing and implementing a landslide early warning system to address the emerging humanitarian crisis in the KRC and its surroundings.

Using Electrical Resistivity Tomography to Monitor the Evolution of Landslides’ Safety Factors under Rainfall: A Feasibility Study Based on Numerical Simulation

Although electrical resistivity tomography (ERT) may gather the internal resistivity information from a landslide area in a large-scale, low-cost, and non-invasive manner compared to point-based sensor monitoring technology, the indirect resistivity information obtained cannot directly evaluate the landslide’s current mechanical status, such as stress, strength, etc. Based on ERT monitoring data, a framework for quantitatively and directly evaluating the evolution of the factor of safety (FOS) of landslides during rainfall is proposed. The framework first inverts ERT observation data using the inexact Gauss–Newton method based on multiple constraints to obtain a more realistic resistivity distribution, then calculates the saturation distribution using Archie’s equation, and finally calculates the FOS of landslides using the finite element strength reduction method. Twelve sets of numerical experiments were designed and carried out based on the synthetic data of a theoretical model. The experimental results show that the proposed framework is valid and reliable under various arrays, apparent resistivity noise, and uncertainty in the water-electric correlation curve, with the Dipole-Dipole array outperforming the others in terms of accuracy, sensitivity, and anti-noise capability. The proposed framework is significant in improving ERT monitoring and early warning capabilities for rainfall-induced landslides.

An investigation of the combined effect of rainfall and road cut on landsliding

The reduction of soil suction and consequent loss of shear strength due to infiltration is known to trigger shallow landslides during periods of concentrated rainfall. In the mountainous terrain of Nepal, the risk of shallow rainfall-induced landsliding is further exacerbated by non-engineered hillslope excavation for local roads construction. To better understand the combined effect of rainfall and road cutting on landsliding, a detailed investigation of a shallow landslide was conducted, on a site with a steep road cut that failed due to rainfall infiltration in July 2018. An integrated investigation approach was adopted, combining field and laboratory testing and field monitoring with a series of coupled hydro-mechanical analyses with the finite element code PLAXIS 2D. The field and laboratory tests were performed to characterise the subsoil condition and determine the soil parameters for the hydro-mechanical analyses. The field monitoring program was set up to obtain real-time measurements of rainfall and volumetric water content of the soil. The monitored data was used to calibrate the numerical model and assess the reliability of its predictions. Results of the numerical back-analysis suggest that the investigated landslide was triggered by rainfall infiltration causing a gradual reduction of soil suction at shallow depths of ≤1.7 m and the presence of the steep road cut promoted slope failure by allowing larger displacements to occur in the hillslope. Without the road cut, the slope was found to have ~35% greater initial factor of safety and under the landslide-triggering rainfall, the undisturbed slope was found to remain stable with ~170% greater factor of safety than that in the slope with the road cut. This indicates that the presence of a road cut increases the likelihood of landslide during rainfall and lowers the minimum level of rainfall needed for landslide initiation. Hence, rainfall-induced roadside slope failures could become more frequent and extensive if roads continue to be built by informal slope excavation, without adopting suitable interventions, some examples of which are presented in this study.

Engineering geomorphological reconnaissance of the December 2018 Waimata Valley mud volcano eruption, Gisborne, New Zealand

Gisborne (North Island, NZ) is affected by rainfall-induced landslides, earthquakes and tsunami, as well as mud volcanoes (MVs). The latter form via upward mobilization of Eocene–age sediments, and have not been studied from an engineering geological standpoint, so the 15 December 2018 Waimata Valley MV eruption provided a unique opportunity. The event erupted c. 16 900 m 3 of mud, forming an elevated vent area, and three mudflows propagating north, east and south. Scanning electron microscopy (SEM) identified smectite as the dominant clay in the Sr-rich mud, and Atterberg limits indicate high plasticity. In-situ testing using dynamic cone penetrometer and shear vane (3–168 kPa) revealed wide variability in strength properties with depth, while ring shear values are 11.3–13.5°. A fault extends NW beneath the Waimata Valley MV, coinciding with the pre-existing Arakihi Road MV. The Waimata Valley MV area was subject to uplift and cracking during the September 2016 Te Araroa earthquake ( M w 7.1), which caused increased activity at pre-existing mud volcanoes at that time. Geodetic data for the Gisborne district shows an uplift phase culminating around the December 15 2018 MV eruption, followed by the commencement of a Hikurangi subduction zone ‘slow slip event’. Nevertheless, relationships between tectonics and MV eruptions remain equivocal.

Quantifying annual occurrence probability of rainfall-induced landslide at a specific slope

Although many countries and regions (e.g., Australia, the Netherlands, Hong Kong) have adopted risk assessment and management methods for landslide risk mitigation, a conventional method based on factor of safety (FS), instead of annual failure probability, is widely used in existing practice of slope engineering. It remains challenging for geotechnical practitioners to assess quantitatively annual failure probability and landslide risk of a specific slope, hindering implementation of landside risk assessment and mitigation in practice. This study proposes an innovative yet practical method to assess quantitatively annual failure probability of a site-specific slope caused by rainfall infiltration. The proposed method integrates slope stability analysis currently used in geotechnical practice with rainfall infiltration into the slope and annual exceedance probability of rainfall to obtain explicit relationships for estimation of the annual failure probability. The quantified annual failure probability comprehensively reflects possible rainfall patterns to the slope, and model settings and soil properties used in slope seepage and stability analyses. It can be implemented conveniently in geotechnical practice through a repeated execution of conventional slope seepage and stability analyses. The proposed method is illustrated by a real slope example in Hong Kong for calculating the annual failure probability of the slope before and after slope retrofitting.