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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]

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]

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]

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]

Critical acceleration is an inherent property of a slope and determines the slope stability under seismic action. The critical acceleration model is a core element of regional seismic landslide hazard assessment. Therefore, the purpose of this paper is to reveal the influence of different critical acceleration models on assessments of potential earthquake–induced landslide hazards. Traditionally, the Newmark critical acceleration model has commonly been used to evaluate the potential earthquake–induced landslide hazard. This method needs to assume the failure depth of the slope, which leads to an underestimation of the predicted displacement of the seismic landslide. Recently, the prediction equations of critical acceleration based on a parametric study of the limit equilibrium method overcomes the limitation of Newmark critical acceleration model and has been applied to assessments of co-seismic landslide hazards. In this study, we use Newmark critical acceleration model and prediction equations of critical acceleration to obtain the distribution maps of potential earthquake–induced landslide hazard in Shimian County, with peak ground acceleration of 10% and 2% exceeding the probability in 50 years. In addition, the nonlinear effect of site and topographic effects on peak ground acceleration were considered. The results show that Newmark critical acceleration model underestimates the area and value of the predicted displacement, while prediction equations of critical acceleration produces seismic landslides in a wider range of mountainous areas. This indicates that the critical acceleration model has a significant influence on assessments of potential earthquake–induced landslide hazards. In addition, the study not only provides valuable reference for assessment of potential earthquake–induced landslide hazard, emergency response of seismic landslides, and land planning in the study area, it also provides a useful demonstration for the selection of a critical acceleration model in seismic landslide hazard assessments for future researches. [ABSTRACT FROM AUTHOR]

Landslides with large displacements are more dangerous than landslides with small displacements, because the first category causes more serious damages. In this paper, a new prediction method is introduced for rock planar slides with large displacements. The prediction model is not limited to a certain region but limited to a certain failure mechanism of a landslide. This case study was carried out in the red bed area, Nanjiang County, Sichuan Province, China, and validated in the red bed area of Yunyang and Fengjie Counties, Chongqing, China. The topographic factor T was proposed as a topographical indicator. The influence of the normalized rainfall R which is a combination of the duration of rainfall (D) and the average intensity (I) of rainfall was analyzed. A threshold value for rock planar sliding with large displacement was obtained by establishing a relationship between the T factor and the R factor. The primary probability factor P gives a final indication of the probability of rock planar sliding with large displacement. The prediction model is applied to the existing rock planar slides or the slopes with the presence of a number of preconditions for sliding. The prediction model obtained from Nanjiang County was validated successfully in the red bed area of the Yunyang and Fengjie Counties. It is assumed that the prediction model is suitable for other regions with a red bed structure as well. [ABSTRACT FROM AUTHOR]

This paper reports on a typical slow-moving landslide that occurred in Shuitianba village in Wanyuan County, Sichuan Province, China, on September 16, 2017. The landslide in Wanyuan County is regionally outstanding for its features and well-developed landslide morphology. Hence, this event can provide a good case study for understanding the possible triggering conditions of the occurrences of all the landslides examined in Wanyuan County. The 225 residents of Shuitianba village did not flee the village because the deformation of the landslide was extremely slow. However, the primary emergency tasks are still necessary to reduce the landslide risk that may affect the residents. Detailed field investigations, boreholes, and exploratory trenches were performed to investigate the characteristics and triggering factors of this landslide. Rainfall and anthropogenic activities (such as informal, unplanned settlements and deforestation) were revealed to be the main triggering factors. The main engineering control measures, including anti-sliding piles, an anti-sliding retaining wall, and intercepting ditch, were applied to stabilize the landslide. However, landslide mitigation measures have not been finished, and hence, some interim measures are suggested to effectively reduce landslide risk. [ABSTRACT FROM AUTHOR]

This paper reports on a recent landslide event, which occurred in Yanyuan County at Xichang City, Sichuan Province, China, on 19 July 2018. Due to the continuous and heavy rainfall between 8 July 2018 and 18 July 2018, approximately 18 million m3 of soil detached from an ancient landslide and buried 65 buildings, the entire sliding process lasted for several minutes. Here, based on field investigations and unmanned aerial vehicle (UAV) photography, we describe the characteristics and failure mechanism of this landslide. The results showed that the landslide was triggered by the continuous and heavy rainfall. Additionally, during the first post-failure stage, the motion of this landslide involved typical successive movement, and over time, the style of movement progressively turned flow-like, which can be classified as debris slide by using the 1978 Varnes classification scheme. The landslide event described here is an outstanding case for landslide studies. In particular, this landslide represents a good illustration of the transform mechanism of landslide movement from sliding to flow-like. [ABSTRACT FROM AUTHOR]

Two successive landslides within a month started in October 11, 2018, and dammed twice the Jinsha River at the border between Sichuan Province and Tibet in China. Both events had potential to cause catastrophic flooding that would have disrupted lives of millions and induced significant economic losses. Fortunately, prompt action by local authorities supported by the deployment of a real-time landslide early warning system allowed for quick and safe construction of a spillway to drain the dammed lake. It averted the worst scenario without loss of life and property at least one order of magnitude less to what would have been observed without quick intervention. Particularly, the early warning system was able to predict the second large-scale slope failure 24 h in advance, along with minor rock falls during the spillway construction, avoiding false alerts. This paper presents the main characteristics of both slope collapses and damming processes, and introduces the successful landslide early warning system. Furthermore, we found that the slope endured cumulative creeping displacements of > 40 m in the past decade before the first event. Twenty-five meter displacement occurred in the year immediately before. The deformation was measured by the visual interpretation of multitemporal satellite images, which agrees with the interferometry synthetic aperture radar (InSAR) measurement. If these had been done before the emergency, economic losses could have been reduced further. Therefore, our findings strengthen the case for the deployment of systematic monitoring of potential landslide sites by integrating earth observation methods (i.e., multitemporal satellite or UAV images) and in situ monitoring system as a way to reduce risk. It is expected that this success story can be replicated worldwide, contributing to make our society more resilient to landslide events. [ABSTRACT FROM AUTHOR]

On April 20, 2013, a Mw 6.6 earthquake struck Lushan County of Sichuan Province, China, about 80 km southwest of the epicenter of the 12 May 2008 Wenchuan Mw 7.9 event. It triggered a large number of landslides in a broad area, causing causality and injuries and damages to roads, drains, dwellings, and infrastructures. Field investigations of coseismic landslides provide a basis for better understanding and illustrating the spatial distributions and hazards related to the Lushan earthquake-triggered landslides. Therefore, this paper presents results of field investigations of the Lushan earthquake-triggered landslides, which can be classified into two main categories of disrupted landslides and coherent landslides, and constitutes seven types. The disrupted landslides include five types: rock falls, rock slides, rock avalanches, soil falls, and soil slides; and the coherent landslides have two types: soil slumps and slow earth flows. All of the seven types of landslides can be observed in many field photos, aerial photographs, and/or satellite images. Among them, seven typical large-scale landslides, distributed in a northeast trending line consistent with the Shuangshi-Dachuan fault, which is presumably related with the earthquake, are described in detail. We prepared an original, emergency-based coseismic landslide inventory map based on visual interpretation of available post-earthquake aerial photographs and pre-earthquake satellite images, combined with results from emergency-based field investigations. The original inventory map registered 3884 landslides. Later, 11,761 more landslides were identified by much more careful and time-consuming visual interpretation based on the same aerial photographs and satellite images. Subsequently, all the 15,645 coseismic landslides were correlated with six common landslide impact factors, including elevation, slope angle, slope aspect, topographic position, stratum/lithology, and peak ground accumulation (PGA). These observational and compiled coseismic landslide data allow us to make subsequent compiling of a detailed and completed landslide inventory by visual interpretation of remote sensing images as well as further studies of landslides induced by the Lushan earthquake. [ABSTRACT FROM AUTHOR]

During the five rainy seasons following the 2008 Wenchuan earthquake, at least eight catastrophic events of multiple debris flows occurred in the Yinchanggou area of Baishui River basin, in southwestern China. The deposits had some serious impacts on the transportation, communication, and reconstruction activities. It indicates the start of a period with a high-frequency debris flow activities. In this paper, a detailed review is made of the latest 12 debris flow events, which occurred on 18th August 2012. Field reconnaissance and measurements, supported by a remote sensing interpretation, were conducted to reveal the locations and morphological characteristics of debris flow catchments. A total volume of 50.26 million m co-seismic landslides, triggered by the Wenchuan earthquake, served as source materials for these debris flows and/or shallow landslides. Bedrocks, which were on the hanging wall of the Yingxiu-Beichuan Fault with a mean slope gradient larger than 30°, were the most susceptible for these seism-triggered failures. The volume of debris flow deposits on individual fans vary by many orders of magnitude from 2,900 to 223,500 m. The huge amount of reserves of source material compared to debris flow volumes currently deposited and taking into account the relatively low morphology index values form an indication of the early stage in the development of debris flows and the immense potential for debris flow occurrence in the future. There are two patterns of debris flow initiation which can be distinguished in this study: (a) the channel initiation pattern of concentrated overland flow erosion in the channel beds and (b) the slope initiation pattern which shows the development of new landslides and a transition into debris flows during a heavy rainfall. A catastrophic debris flow in the Haihuiqiao watershed of the Yinchanggou area was selected to show in detail the mechanism of debris flow initiation as a result of intensive erosion in loose debris materials. The critical rainfall threshold in terms of daily precipitation decreased by 77 % compared to the pre-earthquake threshold. A primary rainfall intensity-duration ( ID) threshold curve was constructed for debris flows for an early-warning system and compared with other global rainfall thresholds. The discussion is focused on the initiation mechanisms of debris flows and the assessment of the critical meteorological threshold. [ABSTRACT FROM AUTHOR]

The Niumiangou Creek rock avalanche was triggered by an M 8.0 earthquake that happened on 12 May 2008 in the Sichuan Province, China. The rock avalanche traveled a horizontal distance of 3.0 km over a vertical elevation difference of 0.89 km, equivalent to a coefficient of friction of only 0.29. The travel path of the rock avalanche can be divided into three segments: (1) failing and disintegrating, (2) flying, (3) flowing. In the failing and disintegrating segment, the rock slope failed because of the coupled action of horizontal and vertical force of the earthquake, then smashed into the opposite mountain and disintegrated. In the flying segment, the disintegrating rock mass changed direction and flew into the Lianhuaxin Creek, which was different from the previous research results that concluded rock debris flowed in Lianhuaxin Creek. A great amount of air trapped and compressed under the rock debris acted as air cushion and supported the rock debris to fly a further distance. In the flowing segment, the rock debris flowed on the ground surface in Niumiangou Creek. The flowing velocity has been estimated from the maximum elevation and runup according to the damaged trimlines of the debris. The saturated fine material in Niumiangou Creek entrained by the failed debris mass is thought to have contributed to the long runout of the debris. The Niumiangou Creek rock avalanche is one of the three longest rock avalanches triggered by Wenchuan earthquake. The conclusions of the paper have implications for hazard assessment of potential rock avalanches in the earthquake area and the other similar mountainous area in west China. [ABSTRACT FROM AUTHOR]