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Small-scale landslides that occur at unexpected locations and seasons can cause severe losses of life and property. Unusually, the Shidang landslide occurred prior to the rainy season in Jinkouhe County, Sichuan Province, China, resulting in 19 deaths and missing persons. In this paper, landslide characteristics and causes are revealed through a field investigation, laboratory experiments, and numerical calculations. The results show that the Shidang landslide was a small-scale landslide with a total volume of approximately 19500 m3, originating from hollow colluvial deposits. The geomorphological hollow above the landslide provided topographic conditions for the collection of subsurface runoff. Groundwater flow was transported downward along dolomite and accumulated in the area above shale, with a low conductivity. Groundwater table rise combined with the decrease in the soil strength led to landslide occurrence. Improper siting of residential areas and insufficient vigilance against post-rainfall landslides caused this tragedy. Considering the post-disaster lessons, it is strongly recommended that in the future, more attention should be given to the investigation of landslides triggered by groundwater, especially in determining landslide hydrological system boundaries. In mountainous areas where the developable space is limited, areas with hollow landforms are potentially at risk of geohazards. This paper could provide a reference for the prevention and management of similar landslides and new insights for updating landslide investigation systems and residential housing plans. [ABSTRACT FROM AUTHOR]

Slow-moving landslide-prone areas (SLAs) are unstable objects on the terrestrial surface that can collapse rapidly when provoked by earthquakes, leading to infrastructure damage. It is critical to identify SLAs prior to earthquake events and assess their hazard-causing probabilities when triggered. An assessment approach of earthquake-triggered geohazards is proposed in this paper by combining interferometric synthetic aperture radar (InSAR) derived SLAs and geological and geomorphological factors. Taking the Ms6.8 Luding earthquake, which occurred in the Sichuan Province of southwestern China on September 5, 2022, as an example, 1320 scenes of Sentinel-1 SAR data in western Sichuan were processed using the small baseline subset (SBAS) InSAR technique before the earthquake. After the earthquake, hazard probability assessment was performed in real-time by filtering the SLAs using a spatial analysis technique with geological and geomorphological factors, e.g., real-time peak ground acceleration (PGA), slope, distance to fault (DTF), and distance to the river (DTR) data. The results show that 11 SLAs were classified into high-risk zones. As verified by the Luding co-seismic landslide dataset from visual interpretation of optical remote sensing images, 142 coseismic landslides were triggered by the earthquake in these high-risk regions. In these areas, an ancient landslide, with high pre-earthquake displacement rates (−50 mm/year) on the scarp was reactivated under the Luding earthquake forces. This method can provide a scientific tool for disaster mitigation and rapid response emergency management. [ABSTRACT FROM AUTHOR]

Landslides which occur at unexpected locations and times, and which have unrecognizable risk areas, often cause mass casualties. Despite there being few preceding landslides in the same season, the catastrophic Qijiaba landslide occurred in the new government-selected settlement of Wufu village, Sichuan Province, China, on September 5, 2021, killing three people and destroying 13 houses. In this paper, the landslide features and causes are discussed based on field investigations, remote sensing interpretations, and laboratory-based soil experiments. The results showed that it was a typical soil landslide within a geomorphic hollow of the slope with a volume of 20,230 m3, recurrence interval of at least 69 years, and runout distance of 137.5 m. This type of landslide is difficult to predict. The soil strength in the sliding surface decreased suddenly as the water content increased, and the soil's rich clay mineral and high clay particle contents determined its high mobility (Heim's ratio of 0.44). The occurrence time of the Qijiaba landslide lagged 1.3 days behind the peak rainfall, and the 2-day cumulative rainfall was a crucial factor in the landslide occurrence. Finally, the large water confluence area behind the main scarp was determined to be 3.9 times the area of the landslide itself, thereby providing abundant water conditions to induce failure. The factors underlying the casualties included inappropriate site selection, insufficient awareness of potential landslides, and failure to evacuate in time. The results of this study provide a basis for future prevention, mitigation, and early warning of similar landslides. [ABSTRACT FROM AUTHOR]

Machine learning models have been widely used in landslide susceptibility prediction. However, landslide multidimensional feature extraction, model generalization ability, and prediction quantification of deep learning are still challenging. This paper proposes a new approach, the landslide density-based bidirectional long short-term memory (LD-BiLSTM) model with multichannel input and an optimized sampling strategy to predict and map landslide susceptibility in active seismic mountainous areas of Sichuan Province, China. First, to ensure the generalization ability of the LD-BiLSTM model, other regions in Sichuan were selected as the model training area independent of the prediction area (Luding County). Multichannel landslide datasets were constructed to extract high-dimensional geospatial features of landslides. Subsequently, the landslide density of each grid cell was utilized as the label for the corresponding input sample. The LD-BiLSTM model was improved by using transfer learning and sampling optimization strategies, which makes our method attenuate the impact of historical landslide inventory deviation on the spatial susceptibility mode compared with the existing DL model, which usually uses landslide objects (LO) as input sample labels. Model performance evaluation results show that the LD-BiLSTM model (precision = 0.903, recall = 0.899, F1-score = 0.901, Area under receiver operating characteristic curve (AUC) = 0.940) outperformed the LO-BiLSTM model (precision = 0.812, recall = 0.815. F1-score = 0.813, AUC = 0.910) in the case areas. Meanwhile, the performance of the LD-BiLSTM model (AUC = 0.9407) significantly outperformed both the information value (IV) (AUC = 0.7207) model and the random forest (RF) (AUC = 0.8116) models in the landslide prediction area (Luding), which confirms that the proposed LD-based method is superior to traditional LO-based methods. Significantly, our approach can effectively extract the spatial distribution of landslides and predict potential landslides in complex high-mountain environments. [ABSTRACT FROM AUTHOR]

On 24 June 2017, a rockslide-debris avalanche occurred in Xinmo Village, Maoxian County, Sichuan Province, China. The displaced materials traveled approximately 2.6 km and caused 83 casualties. Combined with the field investigation, seismic signal, and numerical simulation, the dynamic process and deposit characteristics of the Xinmo landslide were analyzed. Based on the Arias intensity and Hibert time–frequency analysis of the seismic signal, the Xinmo landslide is determined to have a total duration of 120 s and with three-stage dynamic processes, namely, the initial accelerating stage (50 s), erosion impact stage (50 s), and accumulation stage (20 s). Then, taking the field investigation results, the total duration, and kinetic parameters inverted from the seismic signal as criteria, the DAN3D (Dynamic Analysis of Landslides) was employed to reconstruct the dynamic process of the Xinmo landslide. The results show that the erosion and deposit depth distributions obtained from the simulation are in agreement with the pre- and post-landslide DEM (digital elevation model) data and field results. Further, the three-stage dynamic processes (40 s, 60 s, 20 s) obtained from the numerical simulation are also close to the seismic analysis results. In this study, the results of the field investigation and seismic analysis provide a more detailed set of inversion criteria for the numerical simulation work, which contributes to reconstructing the dynamic processes of the Xinmo landslide more accurately. Based on the approach of multi-source data collaboration in this paper, those landslides that lack direct observation data can be better understood and the numerical parameters used for landslide dynamics inversion are better calibrated, which can be applied to predict the runout behavior and disaster scope of potential landslides in the immediate area. [ABSTRACT FROM AUTHOR]

A rainfall-induced catastrophic landslide occurred at UTC + 8 03:50 local time on August 21, 2020, in Zhonghaicun village (N 29°20′30″, E 102°41′40″), Hanyuan County, Sichuan Province, China, resulting in damage to 6 buildings and 9 casualties (7 fatalities and 2 missing). In this paper, multitemporal remote sensing images, comprehensive field investigations, drilling holes, deformation monitoring, and numerical simulations are used to identify the main characteristics and the formation mechanism of the event. The results showed that long-term intensive rainfall in 2020 played an essential role in the failure of the Zhonghaicun landslide and that strong earthquakes in Wenchuan County (Ms 8.0, on 12 May 2008) and Lushan County (Ms 7.0, on 20 April 2013) also exerted a negative influence on the evolution process of this catastrophic landslide. Several traction zones appeared after the occurrence of the landslide. Fortunately, no evident deformation was observed and these slopes were in a stable state. In addition, a large-scale potential sliding slope, four small-scale landslides, and a debris flow were found in the study area. [ABSTRACT FROM AUTHOR]

Calculating the failure probability of a landslide is important in engineering related to geological process and geomorphological evolution. Strength parameters of a soil (i.e., cohesion and internal friction angle) are regarded as uncertain-but-bounded parameters. In this study, a new method is proposed for computing the landslide failure probability based on a convex set model and artificial neural network (ANN). In the new method, ANN is used to determine the limit state function of landslide stability, and the failure probability is determined using a simple iterative algorithm. The new method was applied to calculate the failure probability of the Gufenping landslide in Nanjiang, Sichuan, China. The results calculated using a Monte Carlo simulation (MCS) method confirmed that the new method accurately and quickly obtains the failure probability of a landslide. Additionally, compared with the two-dimensional calculation method, the one-dimensional analysis method overestimates the failure probability of the landslide. The results of single factor and global sensitivity analysis indicate that the average of internal friction angle is the main factor affecting the stability of the landslide. It is easy to calculate failure probability of landslides using the novel method than using the conventional methods. [ABSTRACT FROM AUTHOR]

On September 5, 2022, an Ms6.8 earthquake struck Luding County, Sichuan Province, China. Through creating a coseismic landslide prediction model, we obtained the spatial distribution of the triggered geological hazards immediately after the earthquake. Through collecting all available multi-source optical remote sensing images of the earthquake-affected area via UAV and satellite platforms, the exact information of coseismic landslide was achieved by pattern recognition and visual inspection. According to the current results, the Luding earthquake triggered 5336 landslides with a total area of 28.53km2. The spatial distribution of the coseismic landslides is correlated statistically to various seismic, terrain, and geological factors, to evaluate their susceptibility at regional scale and to identify the most typical characteristics of these failures. The results reveal that the coseismic landslides mainly occurred on the sides of the Xianshuihe fault (within 1.2 km) and Dadu River (within 0.5 km) in striped patterns. They are concentrated in the regions with an elevation range of 1000–1800 m, a slope range of 25–55°, and lithologies of acid plutonic rocks, mixed sedimentary rocks, and siliciclastic sedimentary rocks. Besides, the coseismic landslides of the Luding earthquake are smaller in size and shallower than those triggered by the 2008 Wenchuan earthquake and the 2017 Jiuzhaigou earthquake. Rapidly achieving the spatial locations and distribution patterns of the coseismic landslides enables to provide effective support and guidance to emergency rescue, risk mitigation, and reconstruction planning. [ABSTRACT FROM AUTHOR]

Landslide is regarded as one of the most prevalent and destroying geological hazards in natural terrain areas. Reliable landslide susceptibility analysis procedures are vital for policymakers to manage the regional-scale landslide risk. In the framework of physically based modelling analysis, the infinite slope model is commonly used to assess the surficial landslide susceptibility with deterministically defined geotechnical and geological parameters. This work aims to develop a user-friendly geographic information system (GIS) extension tool called the GIS-FORM landslide prediction toolbox using the Python programming language to consider the possible uncertainties in the physically based landslide susceptibility analysis in seismic areas. We implement the first-order reliability method (FORM) algorithm to calculate the probability of infinite slope failures. The proposed toolbox can produce some regional hazard distribution maps of different indexes, such as the factor of safety (FoS), reliability index (RI), and failure probability (Pf). Furthermore, the toolbox enables coseismic landslide displacement prediction using either the direct Newmark integration method and/or the empirical formula method. Outputs of the GIS-FORM landslide prediction analysis are verified using published data in the literature. Further, it is also successfully employed for landslide susceptibility analysis of the Ms 7.0 Jiuzhaigou earthquake in Sichuan Province, China. Without loss of generality, the GIS-FORM landslide prediction toolbox can serve for the rapid hazard mapping of earthquake-induced regional landslides where uncertainties in geological and geotechnical parameters should be considered. [ABSTRACT FROM AUTHOR]

In tectonically active steep mountain belts, such as those in west Sichuan in China, frequent landslide disasters can occur during the rainy season. High-locality landslides often initiate with the failure of fractured rock masses or regolith near the ridge of a high and steep slope. Often, these events occur suddenly in the sense that people downslope are caught by surprise. High-locality landslides can be destructive owing to the large momentum and entrainment potential of the moving mass. Increasing attention has been given to these events since the 2008 Wenchuan earthquake. Indeed, slope ridges are particularly susceptible of topographic amplification of the seismic shaking, resulting in mechanical weakening that predisposes them to enhanced weathering and rain-triggered failures years or decades after the mainshock. Risk reduction in such cases must begin with a systematic surveying of the ridges to identify criticalities. Then, quantitative (numerical) approaches must be used to evaluate volumes and describe the movement. Risk assessments can then be produced, and mitigations suggested. Here, we describe the Tonghua landslide in Li County, which is currently in a creeping stage but threatens critical infrastructures and dwellings. We monitored the site for 3 years and identified additional potentially unstable areas before performing physically based modeling and hazard zonation. Our results could be useful in decision-making for risk reduction. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Landslides following rainfall occurrence are a widespread phenomenon. The neglect of this phenomenon leads to serious loss of life when disasters occur. At 03:45 (GMT + 8) on August 21, 2020, a semi-diagenetic landslide occurred in Zhonghai Village, Hanyuan County, China, which occurred 42 h after earlier rainfall. Nine people privately returned to their homes after evacuation of the dangerous area. In this disaster, eight people were lost and one injured. This study explores the failure characteristics, inducement, and mechanisms of the landslide via field investigations, resident interviews, multi-temporal images, field drilling, and geotechnical tests. Hydrological numerical calculations were also performed to uncover the seepage and transfer processes of the groundwater in the slope. Finally, problems in the current community early warning system were analyzed and corresponding suggestions put forward. The results show that the maximum sliding depth of the landslide was 27.5 m, the total area was 80,000 m2, and the sliding volume was about 58,0000 m3, making it a medium-sized deep landslide. In addition to the vertical seepage of rainfall in the landslide area, the downward movement of rainfall in the back and upper catchment areas along the silt sand strata also affected the stability of the landslide. More needs to be done to make the population aware of this lag phenomenon to achieve scientific disaster reduction. This study not only provides a case study of a lagging semi-diagenetic landslide, but also provides insight into hydrological boundary determination and landslide early warning system construction. [ABSTRACT FROM AUTHOR]

The intrusion of debris avalanches into reservoirs frequently occurs in mountainous regions and is a complex solid-fluid interaction. A coupled discrete element method (DEM) and smooth particle hydrodynamics (SPH) method is developed to forecast the entire process of disaster chains including landslide motion, wave generation, and wave propagation on Kaiding slopes at the Houziyan Reservoir, Sichuan Province, China. The sliding masses are treated as granular assemblies with collisions instead of rigid solid. The simulation results indicate that the maximum velocity of debris avalanches when swarming into the water surface is beyond 30 m/s, and the maximum wave elevation on the watercourse and the maximum run-up wave elevation generated by landslides reach 1867.4 m and 1883.0 m, respectively, and the minimum wave trough is 1822.1 m occurring near opposite bank of landslide. Furthermore, the range of landslide-generated waves is estimated to be approximately a propagation distance of 800.0 m along the axis of the river. The coupled DEM-SPH simulation method provides important inspiration for hazard prevention on reservoir landslides. [ABSTRACT FROM AUTHOR]

On 25 August 1933, a 7.5-magnitude earthquake struck the eastern margin of the Tibetan Plateau in Sichuan, China. The Diexi earthquake is among the largest known geohazard events worldwide and is frequently cited by those studying the effects of large earthquakes. And yet, the attention focused on this event has failed to deliver a clear picture of landslides and their geomorphic impacts—key attributes of the Diexi earthquake and its aftermath remain obscure and debated. By integrating present-day LiDAR topographic data with existing records (including studies published in Chinese), and a series of unique archival photographs (from 1910, 1920, and 1934), we present the first inventory of coseismic landslides from the epicentral region of this catastrophic 1933 event. We find that the earthquake-triggered landslides were mainly of shallow to deep rock/debris fall/avalanche type, containing mass detached from steep slopes at the top of the mountain ridge source. We reinterpreted three major landslide dams on the Minjiang River based on their geomorphology and sedimentology, and we reconstructed the processes of impoundment and the maximum area of the dammed lake that breached 45 days after the earthquake. Since 1933, we estimate that 43.15–47.68 million m3 of post-earthquake sediment has been delivered to valley floors from the erosion of the three landslide dams, with a sediment yield of up to 131.71 t km−2 year−1. Retrospective studies of the effects of historical earthquakes are challenging, but long-term observations like these are the cornerstone of emerging knowledge of earthquake-induced landsliding and related landscape response. [ABSTRACT FROM AUTHOR]

Enhanced debris flow activity observed after the 2008 Wenchuan earthquake, Sichuan Province, SW China, is still intense more than a decade since the earthquake. A heavy rainstorm on 20 August 2019 caused catastrophic debris flows in the epicentral area of the earthquake. By remote sensing imagery interpretation and field investigation, 13 debris flows were identified. An empirical rainfall threshold I-D model (intensity–duration) of post-seismic debris flows is established. Changing rainfall thresholds, characteristics, and initiation mechanisms of the 2019 debris flows were analyzed, which are different from the events that occurred in previous years. We find that the 2019 debris flows were mainly initiated by concentrated runoff erosion of sediments stored in tributary channels of catchments, with little contribution from the coseismic landslides and new hillslope failures. Differently, the major sediment supply of debris flows in previous years was mainly from the reactivation of coseismic landslide deposits. By analyzing the triggering rainfall of all recorded debris flows after the Wenchuan earthquake, we find that the rainfall threshold in 2019 is about 1/2–4/5 of the pre-earthquake value, and the I-D model we built can predict the 2019 debris flows successfully. Considering that there are still a large number of coseismic landslide deposits in the mountain catchments and the rainfall thresholds are slowly recovering, the post-seismic debris flows may last for a long period. [ABSTRACT FROM AUTHOR]

At approximately 3:50 a.m. (UTC + 8) on August 21, 2020, a massive rainfall-induced landslide occurred in Zhonghaicun, Fuquan town, Hanyuan County, Sichuan Province, China, forming an approximately 40.85 × 104 m3 landslide accumulation, burying eight houses and approximately 100 m of roads, and causing long-term traffic interruptions. The landslide was comprehensively evaluated through field investigation, UAV photography, borehole drilling, and laboratory tests. According to movement and accumulation characteristics, the landslide is divided into the main sliding zones (the source area, impact sliding area, shoveling-accumulation area, and accumulation area) and landslide-affected zones. The deformation and failure of the Zhonghaicun landslide are related to the lithology (existence of a weak interlayer), geomorphology (microrelief changes), and antecedent rainfall. However, the main trigger of the landslide is continuous rainfall, which increases the landslide saturation and pore water pressure and reduces the mechanical strength of the weak layer. The landslide failure mode is complex. The upper slope is affected by rainfall and loses stability first. Under the impact of the sliding mass, sliding of the lower slope is triggered. This study of the Zhonghaicun landslide characterizes the evolution process of a complex rainfall-induced landslide and provides ways to mitigate landslide disasters. [ABSTRACT FROM AUTHOR]