Landslide Reactivation Research Articles

Deformation characteristics and reactivation mechanism of an old landslide induced by combined action of excavation and heavy rainfall

The reactivation mechanism of old landslides has been extensively studied from building load, erosion of the slope toe, heavy rainfall, and slope cutting for existing research. However, previous research on the reactivation of old landslides pays little attention to the combined action of engineering disturbance and heavy rainfall is rarely studied. This paper describes an old landslide in Wushan County, Chongqing, China, that was reactivated in August 2019 due to engineering disturbance and heavy rainfall. The deformation of the old landslide was first observed in 2007 and 2008, resulting from excavation and rainfall, respectively, and remained stable for about 11 years after treatment. In August 2019, the landslide was reactivated by slope cutting and damaged anti-sliding piles at the toe, and entered a state of imminent sliding due to the concentrated heavy rainfall events that occurred from October 4 to 22, 2019. In order to reveal the deformation features and reactivation mechanism of the landslide, field investigations, drilling activities and monitoring were performed. The results showed that tectonic effects and the stratigraphic lithology were the main reasons for the formation of the old Dashuitian landslide. The cut slope and damaged anti-sliding piles at the toe of the landslide provided the sliding space and reduced the anti-sliding force, and therefore resulted in the reactivation of the landslide. Continuous intense rainfall increased the weight of the landslide, decreased the mechanical properties and increased the pore water pressure of the weak interlayer, which accelerated the deformation rate. Therefore, 1.5 million m3 of rock and soil masses slid along the weak interlayer under the action of gravity, threatening the safety of Wuliu Road, Ring Road, National Road G42 and the Wu-Da Expressway. Our research provides a theoretical basis for reducing the hazard of similar engineering projects involving slopes.

A quantitative enhanced assessment for ancient landslide reactivation risk considering cross-time scale joint response mechanism

Ancient landslide has strong concealment and disturbance sensitivity due to its special geotechnical mechanical characteristics, and it is the potential hazard that cannot be ignored in human activities and major engineering planning. The quantitative assessment of ancient landslide reactivation risk has become more necessary for pre-disaster scientific warning. However, because the mechanisms of deformation and damage during the evolution of ancient landslides are quite complex, traditional landslide risk assessment methods only select the single-time scale and relatively stable environmental factors for analysis, lacking consideration of dynamic triggering factors such as rainfall. Focusing on the complexity, a quantitative enhanced assessment for ancient landslide reactivation risk considering cross-time scale joint response mechanism is proposed. First, on the basis of systematic analysis of the implicit genesis mechanism and explicit characterization, an evaluation system of the cross-time scale joint characteristics of ancient landslide reactivation is constructed. Then, XGBoost algorithm and SBAS-InSAR are used to establish the long-time scale developmental evolution mechanism model and the short-time scale dynamical trigger model, respectively. Subsequently, we propose a cross-time scale joint response mechanism. The information entropy weight method is applied to calculate the contribution degree of long-short time scale assessment models for ancient landslide reactivation based on the constraints of quantitative interval thresholds, and the assessment processes of different time scales are dynamically and quantitatively correlated. Finally, the updated optimization of the assessment of ancient landslide reactivation risk is achieved. In this research, experimental analysis was carried out for ancient landslide groups in a geological hazard-prone area in Fengjie County, Chongqing, a typical mountainous region of China. The results of the comparative analysis validate the superiority of the method in this paper. It helps to accurately assess the ancient landslide potential hazard in advance, providing scientific basis and technical support for the risk assessment of mountainous watershed geological hazards and major engineering projects.

Multi-Level Data Analyses in the Gajevo Landslide Research, Croatia

The Gajevo landslide is located in a hilly area of northern Croatia, where numerous landslides endanger and damage houses, roads, water systems, and power lines. Nevertheless, available landslide data are relatively scarce. Therefore, the Gajevo landslide location was chosen for detailed research and the development of a typical landslide model for this area. During initial research, the geographical and geological settings were reviewed and historical orthophotos were analysed. Due to the complexity and vulnerability of the area, the location required detailed investigations and the integration of multi-level data: remote (based on high-resolution LiDAR data) and field landslide mapping were performed and a map of the landslide area was developed. Precipitation data were reviewed, while shallow boreholes with material sampling and geophysical measurements provided information on material characteristics and 3D (depth) insight. As a result, knowledge was gained about material resistivity and composition along with the depth of sliding surfaces, and an engineering geological map of the Gajevo landslide area with the landslide and directly endangered areas marked was developed to be used by the local community in landslide risk assessment. As it is reasonable to expect that an extreme rainfall event will occur in combination with snowmelt in the coming years, resulting in the reactivation of Gajevo landslide, further research and continuous landslide monitoring are recommended.

Study of an ancient landslide reactivation mechanism based on centrifuge model testing: an example of the Jiangdingya ancient landslide reactivation in 2018, Gansu Province, China

Study of an ancient landslide reactivation mechanism based on centrifuge model testing: an example of the Jiangdingya ancient landslide reactivation in 2018, Gansu Province, China

Site response of ancient landslides to initial impoundment of Baihetan Reservoir (China) based on ambient noise investigation

When large reservoirs are impounded, the reactivation of ancient landslides significantly contributes to erosion. In this study, we investigated the site response of ancient landslides during the initial impoundment of the Baihetan Reservoir. Ambient noise recordings collected at various reservoir water levels were analysed using the microtremor horizontal-to-vertical spectral ratio technique and single-station RayDec method. Our results indicate that, with an increase in the early reservoir water level, the resonance frequencies and spectral ratio amplitude of landslides changed. However, the direction of site resonance did not change. Furthermore, to explain the site response changes, we simulated the pore water pressure and stability of the S2# landslide using Geo-Studio software. We observed that the subsurface shear wave velocity of the slope decreased with an increase in reservoir water. Our study provides a new method for analysing the site response of ancient landslides and a new perspective for studying landslide stability in reservoir areas.

Guidelines from direct shear modeling in centrifuge for TDR landslide monitoring

The suitability of using time-domain reflectometry (TDR) for landslide monitoring and assessment has been validated by field studies. This method can be used for detecting shear surfaces and monitoring shear displacements at depth, which can provide warning of potential slope failures or landslide reactivations. Although laboratory tests have suggested using the TDR reflection coefficient to estimate shear displacement, quantifying the relationship between these parameters is challenging because it is affected by various cable–grout–ground interactions. Investigating these interactions is challenging because of the limited artificial overburden pressure that can be applied in a laboratory. Therefore, this study improved the TDR applicability through high-gravity centrifuge modeling to simulate sliding at depth. Furthermore, a flexible coaxial cable was modified to improve its sensitivity, which was evaluated using the simple direct shear test. The modified coaxial cable with stiff grout had sufficient sensitivity to detect a small-scale shear displacement of 0.5 mm. Reversed-fault centrifuge modeling was conducted for sandy soil to simulate the shear plane under a gravity level of 55 g, and the shear plane was realistically generated and detected by a cable–grout–ground composite and cables located adjacent to an inclinometer casing. Notably, only the model-scale fault movements directly correlated with TDR reflection peaks and integration areas. Finally, guidelines for conducting TDR-based slope monitoring and directions for further research regarding TDR-based landslide centrifuge modeling are suggested.

Interpretation of the reactivation of slow-moving landslides based on ring shear tests and monitoring

Interpretation of the reactivation of slow-moving landslides based on ring shear tests and monitoring

The influence of the rainfall data temporal resolution on the results of numerical modelling of landslide reactivation in flysch slope

The influence of the rainfall data temporal resolution on the results of numerical modelling of landslide reactivation in flysch slope

Kinematic and Geometric Characterization of the Vögelsberg Rockslide (Tyrol, Austria) by Means of MT-InSAR Data

This paper focuses on the study of the Vögelsberg landslide located in the municipality of Wattens (Tyrol, Austria), which reactivated in 2016, causing damages to nearby buildings and infrastructures. Since the date of reactivation, a modern monitoring system has been implemented with the installation of in-situ geodetic automated tracking total stations (ATTS), an inclinometer and two piezometers. Here, we describe two distinctive methods, the Breaks for Additive Seasonal and Trend (BFAST) and the Vector Inclination Method (VIM) used to characterize the landslide from the kinematic and geometrical point of view. The main input data, used for both methods, derive from processing a stack of several Sentinel-1 differential interferograms with the Multiple Small Baseline Subset (MSBAS) 2D and 3D algorithms. BFAST allowed highlighting the seasonality of the phenomenon from the analysis of the time series as well as the trend and the breakpoints that identify the landslide reactivation phases. These latter were then correlated with the main triggering factors such as rain and snow melting. The application of the VIM through the exploitation of the MSBAS displacement vectors allowed the reconstruction of the depth of the landslide slip surface along both the longitudinal and transversal direction and, in turn, the evaluation of the volumes of material mobilized by the landslide. The results obtained further prove that procedures for the in-depth analysis of Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) data can contribute to slow-moving landslide characterization, which represents a fundamental step for landslide hazard assessment within quantitative risk analyses.

The role of snow and spring moisture conditions in terms of geomorphic activity inferred from tree-ring based chronologies in Central European mid-mountains

The role of snow is underrated in the dendrogeomorphic research in terms of the interpretation of the climate factors responsible for the geomorphic activity. We analysed snow parameters and the combined effect of spring and summer climate variables to interpret their role in debris flow/flood and flow-like landslide initiation in two Central European mid-mountain regions. We revisited the tree-ring based chronologies based on a total of 1043 trees for four debris flow/flood catchments and four flow-like landslide bodies. Three approaches were used to determine the event year, including a floating event-response index and different weighted index thresholds. In addition, data from precipitation and streamflow gauges were used to identify the best indicators of rapid snow melting and find the best explanatory climate factors during event years using logistic regression. We identified 24–40 event years with hydrogeomorphic activity and 10–29 years with flow-like landslide reactivations during 1961–2017. The amount of melted snowpack and rain-on-snow during spring were considered the best rapid snowmelt parameters obtained from the precipitation gauges due to highest correlations with the stream gauge data (R = 0.69–0.70). We identified very likely rapid snowmelt in seven debris flow/flood event years and six landslide event years since 1981. Furthermore, high maximum snowpack in spring combined with extreme one-day rainfall in summer were the best explanatory factors for hydrogeomorphic activity, but probably not during the high-magnitude debris flows, which were more dependent on the extreme summer rainfall alone. Landslide reactivations were most likely to occur during years with extreme one-day rainfall events in May to September preceded by a wet period since the last day of continuous snow cover. This study defines a step-by-step procedure to reveal the role of snowmelt and antecedent precipitation in dendrogeomorphic research and shows likely scenarios of geomorphic activity typical of the study area.

Detection and characterization of slow-moving landslides in the 2017 Jiuzhaigou earthquake area by combining satellite SAR observations and airborne Lidar DSM

On 8th August 2017, a catastrophic Ms. 7.0 earthquake with a focal depth of 20 km struck the Jiuzhaigou County in Sichuan Province, China. It exerted a strong influence on the slope stability within the surrounding areas and triggered numerous secondary geohazards including rockfalls and other co-seismic landslides, which incurred drastic surface changes, and thus can be easily identified from cloud-free high-resolution optical imagery. Most of such landslides became stabilized shortly after the earthquake while others moving very slowly for years. In contrast, some slopes were destabilized without significant surface change into slow-moving landslides, which may pose long-term potential threats to people's life and property. Therefore, it is crucial to accurately identify these slow-moving landslides and regularly monitor their post-seismic activity. In this study, we employed the synthetic aperture radar interferometry (InSAR) techniques to detect and monitor slow-moving landslides after the earthquake in the Jiuzhaigou area, and analyzed the impacts of the earthquake on these landslides through integration of multi-source data (InSAR, Lidar, optical image, and field survey). As a result, 16 slow-moving landslides were detected by InSAR in the Jiuzhaigou area, including several historical landslides. The results of time-series InSAR analyses enabled identification of three kinds of landslide evolution modes affected by the earthquake, i.e. acceleration of deformation of pre-existing landslides, reactivation of dormant landslide, and remobilization of earthquake-triggered landslide. Each mode is supported by detailed analyses of multi-source data. The results demonstrated that satellite InSAR combined with high-resolution Lidar and optical data can provide a cost-effective approach of post-earthquake geohazards detection and monitoring.

UAV applications to assess short-term dynamics of slow-moving landslides under dense forest cover

Abstract The paper presents a methodology to rapidly assess and map the landslide kinematics in areas with dense vegetation cover. The method uses aerial imagery collected with UAVs (Unmanned Aerial Vehicles) and their derived products obtained from the structure from motion technique. The landslide analysed in the current paper occurred in the spring of 2021 and is located in Livadea village from Curvature Subcarpathians, Romania. This landslide affected the houses in the vicinity, and people were relocated because of the risk of landslide reactivation. To mitigate the landslide consequences, a preliminary investigation based on UAV imagery and geological-geomorphological field surveys was carried out to map the active parts of the landslide and establish evacuation measures. Three UAV flights were performed between 6 May and 10 June using DJI Phantom 4 and Phantom 4 RTK UAVs (Real-Time Kinematic Unmanned Aerial Vehicles). Because it is a densely forested area, semi-automated analyses of the landslide kinematics and change detection analysis were not possible. Instead, the landslide displacement rates and the changes in terrain morphology were assessed by manually interpolating the landmarks, mostly tilted trees, collected from all three UAV flights. The results showed an average displacement of approximately 20 m across the landslides, with maximum values reaching 45 m in the transport area and minimum values below 1 m in the toe area. This approach proved quick and efficient for rapid landslide investigations in a densely forested area when fast response and measures are necessary to reduce the landslide consequences.

Snowmelt-triggered reactivation of a loess landslide in Yili, Xinjiang, China: mode and mechanism

Snowmelt-triggered reactivation of a loess landslide in Yili, Xinjiang, China: mode and mechanism

Earthquake-induced reactivation of landslides under variable hydrostatic conditions: evaluation at regional scale and implications for risk assessment

Earthquake-induced reactivation of landslides under variable hydrostatic conditions: evaluation at regional scale and implications for risk assessment

Assessment of the Bureya River Landslide Reactivation Using the Persistent Scatterer Interferometry

Assessment of the Bureya River Landslide Reactivation Using the Persistent Scatterer Interferometry

Displacement Characterization and Spatial-Temporal Evolution of the 2020 Aniangzhai Landslide in Danba County Using Time-Series InSAR and Multi-Temporal Optical Dataset

On 17 June 2020, a large ancient landslide over the Aniangzhai (ANZ) slope, Danba County, Sichuan Province, China, was reactivated by a series of multiple phenomena, including debris flow triggered by heavy rainfall and flooding. In this study, Synthetic Aperture Radar (SAR) images acquired by the Sentinel-1A/B satellite and optical images captured by the PlanetScope satellites were jointly used to analyze and explore the deformation characteristics and the Spatial-Temporal evolution of the ANZ landslide before and after the multi-hazard chain. Several areas of pre-failure movements were found from the multi-temporal optical images analysis before the reactivation of the ANZ landslide. The large post-failure surface deformation over the ANZ slope was also retrieved by the optical pixel offset tracking (POT) technique. A major northwest movement with the maximum horizontal deformation of up to 14.4 m was found. A time-series InSAR technique was applied to analyze the descending and ascending Sentinel-1A/B datasets spanning from March 2018 to July 2020, showing that the maximum magnitudes of the Line of Sight (LoS) displacement velocities were −70 mm/year and 45 mm/year, respectively. The Spatial-Temporal evolution over the ANZ landslide was analyzed based on the time-series results. No obvious change in acceleration (precursory deformation) was detected before the multi-hazard chain, while clear accelerated deformation can be observed over the slope after the event. This suggested that heavy rainfall was the most significant triggering factor for the generation and reactivation of the ANZ landslide. Other preparatory factors, including the deformation behavior, the undercutting and erosion of the river and the outburst flood, the local terrain conditions, and earthquakes, might also have played an important role in the generation and reactivation of the landslide.

Fusion of SAR Interferometry and Polarimetry Methods for Landslide Reactivation Study, the Bureya River (Russia) Event Case Study

In this paper, we demonstrate the estimation capabilities of landslide reactivation based on various SAR (Synthetic Aperture Radar) methods: Cloude-Pottier decomposition of Sentinel-1 dual polarimetry data, MT-InSAR (Multi-temporal Interferometric Synthetic Aperture Radar) techniques, and cloud computing of backscattering time series. The object of the study is the landslide in the east of Russia that took place on 11 December 2018 on the Bureya River. H-α-A polarimetric decomposition of C-band radar images not detected significant transformations of scattering mechanisms for the surface of the rupture, whereas L-band radar data show changes in scattering mechanisms before and after the main landslide. The assessment of ground displacements along the surface of the rupture in the 2019–2021 snowless periods was carried out using MT-InSAR methods. These displacements were 40 mm/year along the line of sight. The SBAS-InSAR results have allowed us to reveal displacements of great area in 2020 and 2021 snowless periods that were 30–40 mm/year along the line-of-sight. In general, the results obtained by MT-InSAR methods showed, on the one hand, the continuation of displacements along the surface of the rupture and on the other hand, some stabilization of the rate of landslide processes.

The June 2020 Aniangzhai landslide in Sichuan Province, Southwest China: slope instability analysis from radar and optical satellite remote sensing data

A large, deep-seated ancient landslide was partially reactivated on 17 June 2020 close to the Aniangzhai village of Danba County in Sichuan Province of Southwest China. It was initiated by undercutting of the toe of this landslide resulting from increased discharge of the Xiaojinchuan River caused by the failure of a landslide dam, which had been created by the debris flow originating from the Meilong valley. As a result, 12 townships in the downstream area were endangered leading to the evacuation of more than 20000 people. This study investigated the Aniangzhai landslide area by optical and radar satellite remote sensing techniques. A horizontal displacement map produced using cross-correlation of high-resolution optical images from Planet shows a maximum horizontal motion of approximately 15 meters for the slope failure between the two acquisitions. The undercutting effects on the toe of the landslide are clearly revealed by exploiting optical data and field surveys, indicating the direct influence of the overflow from the landslide dam and water release from a nearby hydropower station on the toe erosion. Pre-disaster instability analysis using a stack of SAR data from Sentinel-1 between 2014 and 2020 suggests that the Aniangzhai landslide has long been active before the failure, with the largest annual LOS deformation rate more than 50 mm/yr. The 3-year wet period that followed a relative drought year in 2016 resulted in a 14% higher average velocity in 2018–2020, in comparison to the rate in 2014–2017. A detailed analysis of slope surface kinematics in different parts of the landslide indicates that temporal changes in precipitation are mainly correlated with kinematics of motion at the head part of the failure body, where an accelerated creep is observed since spring 2020 before the large failure. Overall, this study provides an example of how full exploitation of optical and radar satellite remote sensing data can be used for a comprehensive analysis of destabilization and reactivation of an ancient landslide in response to a complex cascading event chain in the transition zone between the Qinghai-Tibetan Plateau and the Sichuan Basin.

Study on the mechanism of Xiajiang Hydraulic Project landslide reactivation based on long-term in situ monitoring techniques

Landslide reactivation is a progressive process that takes a long time and is influenced by various factors. Studies that focus on short periods or limited factors may fall short of revealing the complicated reactivation mechanism. In this study, the mechanism of Xiajiang Hydraulic Project landslide reactivation is investigated using long-term in situ monitoring techniques. The slope’s long-term reactions were monitored using different sensors installed since the initial landslide. The reactivation process is analyzed based on the obtained monitoring data, revealing the reactivation mechanism. The results show the following: (1) Heavy rainfall and poor drainage conditions are direct trigger factors for landslide reactivation. (2) The reactivation process is promoted by multiple factors such as the slope deformation, rainfall infiltration, and the strength of geomaterials, which are mutually coupled. Further, suggestions are proposed to improve safety management based on the aforementioned results.

The reactivated mechanism of Aniangzhai ancient landslide in Danba County, China

This study presents a catastrophic landslide that occurred on June 17, 2020, on the left bank of the Xiaojinchuan River in Danba County, Sichuan Province of China. This landslide was a large-scale reactivation of the Aniangzhai ancient landslide. The failure event blocked the Xiaojinchuan River, induced a landslide-outburst flood disaster chain, destroying a 6.6 km-long section of national road G350 and causing significant economic losses. Based on field investigations, unmanned aerial vehicle surveys, and geophysical prospecting, this study unravels the causing factors and reactivates the landslide mechanism. The results suggest that the topography and unconsolidated rock-soil mass are predisposing factors for reactivation. The hourly precipitation within three hours reached 61.80 mm and induced the Meilong debris flow. The debris flow rushed into the Xiaojinchuan River and converted it to flow along the foot of the Aniangzhai ancient landslide. The intense scouring of slope toe caused by the rise of river level is the inducing factor of the landslide. Based on the geometry and geomorphic parameters, the landslide can be divided into four areas: leading edge collapse area, middle bulging area, upstream cracking area, and downstream cracking area. The reactivated Aniangzhai landslide could be determined as a compound landslide involving incipient retrogressive failure and latter progressive slide. Several cracks had developed in the upper part of the slope due to the movement of the landslide, and the four areas of the reactivated landslide mass were still creeping. A detailed monitoring and mitigation measure should be paid to this reactivated deposit.