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The Xieliupo landslide is a typical large-scale active landslide in Zhouqu, China, in the Pingding-Huama fault zone that has slid eight times since the beginning of the twentieth century. The landslide has caused catastrophic environmental, economic, and life losses in the area. In this study, combining the geomorphological field surveys, Unmanned Aerial Vehicle (UAV) mapping, interpretation of optical images, InSAR, and Cosi-Corr (Co-registration of Optically Sensed Images and Correlation) monitoring analysis, we conducted insights into the deformation kinematics of large Xieliupo landslide. The Xieliupo landslide has the characteristics of graded sub-activity with eight secondary landslides. The secondary landslides I and II in the upper parts are loess slide-flow with a moving pattern of creep–rip–push on the condition of steep gradient and the triggering of intense precipitation. The middle part was divided into two sides by a relatively stable ridge, where the loess rotational landslides III and collapse VI in the northern side are controlled by low-lying terrain in the valley, and the landslides IV and V are moderately active under the influence of steep gradient and compression from the landslides I and II. The secondary landslides VII and VIII in the toe are relatively slightly active with a gentle slope gradient influenced by road construction and Bailong River erosion. Time-series InSAR deformation from 2015 to 2020 revealed a close positive correlation between accelerating displacements and increasing precipitation. The deformation response characteristics of cascade collapse retreat and fluctuating acceleration of the Xieliupo landslide to heavy rainfall were revealed. The seismic activity is one probable factor contributing to the reactivated displacement of Xieliupo landslide. Notably, the intense rainfall on August 13, 2020, triggered the maximum displacement of 5.93 m in a sliding direction of 319° (NW) at secondary landslide II. This study significantly provides new insight into the deformation characteristics and kinematics of Xieliupo and reference for catastrophic landslide hazard mitigation. The proposed technique strategy can be used for analyzing similar large complex landslides in other mountainous regions. [ABSTRACT FROM AUTHOR]

Knowledge of kinematics is crucial for understanding landslide mechanisms and assessing hazards. However, characterizing landslide kinematics is often challenging because landslides are sudden events with spatial and temporal coverage. Combining field investigations and remote sensing, the present study reveals the kinematic features, evolution processes, and triggering mechanism of a translational debris slide (the Tonghua landslide) that occurred on August 8 of 2017 in Sichuan Province, China. Based on digital orthophoto maps and digital elevation models derived from unmanned aerial vehicle photogrammetry, 3 years of post-failure kinematics were surveyed in detail. The landslide body experienced moderate-velocity (1.19–3.98 m/d) to slow-velocity (33.2–205.5 mm/d) sliding, eventually entering a period of very-slow-velocity (0.1–1.0 mm/d) sliding. A long-term pre-failure creeping process in the source area of the landslide was detected by interpreting interferometric synthetic aperture radar data and satellite images. At the front part of the source area, the observed deformation velocity was 15–25 mm/year between November 7, 2014 and July 30, 2017. Additionally, the source area of the landslide was found to be located on a hollow slope within a cirque catchment of ~ 67,500 m2. This complicated topographic feature promoted groundwater infiltration and long-term slope creeping; indeed, the 2008 Wenchuan earthquake may have aggravated surface cracking and soil weakening. Long-term creep of the slope caused cracking, saturation, and deformation of the soil body. Finally, seismic effects caused by tunnel blasting triggered sliding of the deformed soils. [ABSTRACT FROM AUTHOR]

The present study provided a preliminary investigation on the kinetic and fragmentation characteristics of the Xuzha rock avalanche, a ridge-top rock avalanche located in the southeast of the Tibetan Plateau, China. Field investigation revealed that the Xuzha rock avalanche originated from a steep slope and caused the tributary of the Jinsha River dammed, showing an extreme high mobility. The vertical profile of avalanche debris showed that it experienced intensive fragmentation during transport. To back-analyze its kinetic and fragmentation processes, the discrete element modeling was conducted. Simulation results suggested that the Xuzha rock avalanche lasted about 80 s and had a maximum velocity of ~ 73 m/s, about 1.5 times the highest average velocity (48 m/s). The travel distance and the thickness of the deposits matched well with the field observations. Notable features including the preservation of the original positional order and the inverse vertical grading of avalanche debris obtained from the simulation were consistent with the field observations. The statistical results of fragments showed that the fragment size distributions could be described by Weibull distribution. In general, with the landslide propagation, the size of fragments gradually decreased and the fragment size distribution became narrower. Further, dynamic fragmentation continuously occurred with the landslide propagation, generating an abundance of fine-grain particles which behaved as interstitial granular fluids between coarse particles. Finally, we inferred that dynamic rock fragmentation and fine particle fluidization were the reasonable explanations for the high mobility of the Xuzha rock avalanche. [ABSTRACT FROM AUTHOR]

At 8:40 p.m. (Beijing time) on July 23, 2019, a large landslide occurred at Jichang, a village in Shuicheng, Guizhou Province, China, resulting in 42 deaths, 9 missing people, and 21 buildings buried. Field investigation and unmanned aerial vehicle (UAV) photography allow us to characterize this slope failure and analyze its process and causes. Initially, the materials detached from the mountainside and slid over from the slope. Then, the sliding materials partly accumulated in the middle and upper parts of the slope which were blocked by a convex terrain. The rest turned into a high-speed debris flow, divided by the stable block, and descended along two existing gullies at high velocity, and finally decelerating and accumulating. Based on the high-resolution post-sliding DEM data, the original topography of the study area was reconstructed to estimate the volume of the landslide that was about 1,500,000 m3. Based on a first-order evaluation of the landslide kinematics, the whole sliding process of this event lasted about 60 s, the maximum speed was about 25 m/s, and the average speed was 16.6 m/s, indicating a typical high-speed long-runout debris flow. Analysis suggests that continuous heavy rainfall was the dominant factor to trigger this landslide, and the loose weathered regolith and special local topography were the basic intrinsic factors for this event. [ABSTRACT FROM AUTHOR]

On July 23, 2019, a large-scale landslide occurred in Jichang town, Shuicheng County, Liupanshui City, Guizhou Province in China. The landslide, which moved along two gullies, resulted in strong punching-shear, induced scarping on vegetation and large destruction of houses, and finally formed a deposit with a volume of 2 × 106 m3. This research aims to understand the effect of topography on landslide kinematics. To achieve this aim, a detailed field investigation was first carried out with an unmanned aerial vehicle (UAV) aerial photography survey, resident interviews, and field sampling. The rainfall analysis indicates the effective rainfall within 7 days before landslides was 70.14 mm which exceeded the rainfall threshold of 54.3 mm in this region, which finally triggered the landslide. Traditional soil mechanic tests were then performed to identify the soil properties of the source material. Combined with numerical simulation using the nonlinear shallow water equation, the whole process of landslides was divided into four stages: instability stage, acceleration stage, transformation stage, and impact and accumulation stage. The simulations results show the landslide block slid with a low velocity of 8 m/s for about 100 m. Then, Froude number of landslide increases from 2 to 3 when passing the high and steep terrain, indicating that landslide change to inertial dominated with potential same Froude behavior of classic debris flow. The rupture mass slid with the peak velocity of 23 m/s and diverged in two gullies and ran out for about 600 m. The maximum velocity is 23 m/s in east gully while only 15 m/s in west gully. Compared with deep and incised valleys in the west, shallow and straight valley in the east decreases the deposit depth and further increases the velocity of landslide material with increased runout distance. This research may provide a fast flow path of back analyzing geo-hazards on complex terrain and serve as a basis for future research on long runout landslides. [ABSTRACT FROM AUTHOR]

Rock avalanches represent a serious risk for human lives, properties, and infrastructures. On June 24, 2017, a catastrophic landslide destroyed the village of Xinmo (Maoxian County, Sichuan, China) causing a large number of fatalities. Adjacent to the landslide source area, further potentially unstable masses were identified. Among them, a 4.5-million m3 body, displaced during the landslide event by about 40 m, raised serious concerns. Field monitoring and a reliable secondary risk assessment are fundamental to protect the infrastructure and the population still living in the valley. In this framework, the use of distinct element methods and continuum model methods to simulate the avalanche process was discussed. Various models (PFC, MatDEM, MassMov2D, Massflow) were used with the aim of reproducing the Xinmo landslide and, as predictive tools, simulating the kinematics and runout of the potentially unstable mass, which could cause a new catastrophic event. The models were all able to reproduce the first-order characteristics of the landslide kinematics and the morphology of the deposit, but with computational times differing by several orders of magnitude. More variability of the results was obtained from the simulations of the potential secondary failure. However, all models agreed that the new landslide could invest several still-inhabited buildings and block the course of the river again. Comparison and discussion of the performances and usability of the models could prove useful towards the enforcement of physically based (and multi-model) risk assessments and mitigation countermeasures. [ABSTRACT FROM AUTHOR]

Landslides are major threats to the construction and operation of electric towers of the Sichuan–Tibet Grid Interconnection Project (STGIP). The long, narrow project corridor is vulnerable to different types of landslides, making risk management a difficult task. In this study, a kinematic-based, landslide risk management framework that includes hazard analysis, risk estimation, risk evaluation, and risk control is proposed and implemented. The spatial density of historical landslides and the spatial distribution of landslide kinematics are used to analyze landslide hazard. Kinetic energy derived from the landslide physical process model is employed to analyze the vulnerability of the towers in the risk estimation step. Then, the landslide hazard and tower vulnerability are used to comprehensively evaluate the landslide risk level of the electric towers. Electric towers with high risk are mainly situated in areas near rivers and faults, with fragile steep slopes. Fragmented rock masses and a thick regolith cover provide abundant materials for landslides, and steep slopes supply the initial conditions and energy that make them a risk. Risk-control strategies are provided based on site-specific risk. The safe construction and operation of the STGIP in the past 10 years show the effectiveness and reliability of the proposed framework. We suggest that the workflow and guidelines for landslide risk management used in this study can be adopted for large engineering projects in complex environments. • Landslide risk management in the STGIP is challenged by complex conditions. • A kinematic-based landslide risk management framework for the STGIP is proposed. • This framework has supported the safe construction and operation of the STGIP. [ABSTRACT FROM AUTHOR]

On June 24, 2017, a catastrophic rock avalanche with a volume of 5.08 × 106 m occurred in Xinmo village, Maoxian County, Sichuan Province, SW China. Several studies focusing on the triggering factors, failure mechanism and dynamic process of the Xinmo landslide have been reported in the literature. However, there are few studies with detailed three-dimensional investigations based on numerical modeling that assess the post-failure stage of the landslide. In the present study, the three-dimensional material point method is used to reproduce the kinematics of the landslide to obtain better insight into the disaster by considering the effects of fragmentation and entrainment. Based on this framework, a velocity-weakening friction law is implemented to describe the basal friction between the moving mass and the substrate, and a strain–softening Drucker–Prager constitutive model is used to describe the internal deformation of the moving mass. The entrainment processes obtained with and without the proposed model are compared, and the simulated results generated with the presented models showing the distribution characteristics of the materials in the deposit area match the observed results fairly well. The implementation of a velocity-weakening friction law and a strain-softening Drucker–Prager constitutive model in the material point method may provide valuable insight for the enforcement of physically based risk assessments of "rockslide-debris avalanches". • The 3D material point method modeling of the Xinmo landslide overcomes some limitations of other numerical methods. • The model accurately reflected the effects of fragmentation and entrainment. • Kinematic energy curves were developed to examine the post-failure behavior of the Xinmo landslide. • Topography played a crucial role in kinetic energy growth and dissipation. [ABSTRACT FROM AUTHOR]

Purely empirical and numerical methods are widely used in landslide movement prediction because they can forecast the failure time and consider influence factors, respectively. However, the combination of these two methods for prediction is rare. This paper develops an integrated landslide movement prediction model that can fully consider landslide kinematics and external influence factors using the Verhulst inverse function (VIF) and the random forest (RF) algorithm. The VIF is applied to describe the kinematic behavior of landslides using the rationale of three-stage creep deformation. The RF algorithm is to quantify the response of landslide displacement to the influence of external factors such as reservoir water level and rainfall intensities. The novelty of the VIF-RF model is illustrated by applying to a reservoir landslide, Gapa Landslide, in Southwestern China. The results show that the VIF-RF model shows significant improvement in predicting landslide movement compared with the VIF or RF model. The error analysis confirms that the root mean square error of the VIF-RF decreases by more than 20% compared with the VIF and RF models. In addition, the mean absolute percentage error of the VIF-RF models is less than 5%, a decrease by 2.3% and 10.1% compared to the VIF and RF models, respectively. The feasibility of the VIF-RF model for predicting movement of other reservoir landslides was successfully verified by the Majiagou landslide in the TGRA. The developed VIF-RF model indicates the Gapa landslide deformation is at the primary stage over the monitoring period. The displacement at the G1 and G2 monitoring locations of the Gapa landslide is projected to increase to 2719.8 and 2438.8 mm in January 2021, respectively, and the average rate in the accelerated deformation periods is projected to be 67.8 mm/month. The presented VIF-RF model provides an effective approach for predicting the long-term landslide deformation and identifying its deformation stage. • A novel prediction approach incorporating Verhulst Inverse Function (VIF) and Random Forest (RF) algorithm is proposed. • The VIF-RF model can consider landslide kinematic characteristics and external influence factors. • Predictions can be used to identify the deformation stage of a landslide. • The model overcomes the limitation of machine learning models in predicting future displacements. [ABSTRACT FROM AUTHOR]