Search

Toe rock bridge is a key block for controlling failure mode and overall stability of a rockslide. In this paper, 3D Fast Lagrangian Analysis Code (FLAC3D) is used to simulate the progressive failure process of a simple translational rockslide and a compound rockslide (an engineering case), both of which are assumed to have a toe rock bridge. A range of strain, stress, and plastic zone in the sliding block or on the failure surface were analyzed in the progressive failure process. Based on the concept of the Prandtl failure, this paper proposed a limit equilibrium method considering the Prandtl wedge damage (LEM-PW) for the rockslide with toe rock bridge. Moreover, the influence of the size and position of the Prandtl wedge on the overall stability of the rockslide was discussed. The results show that the combination of the 3D finite difference numerical simulation and the limit equilibrium method can interpret the displacement accumulation at the rear of the rockslide, the stress concentration characteristics of the toe rock bridge, and the variation trend of the landslide stability after the Prandtl wedge damage. [ABSTRACT FROM AUTHOR]

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]

The aim of this paper is to report on fatal landslides resulting from two extraordinary storm events in January and February 2023, in Auckland, New Zealand, including the characteristics of the terrain, the site geology, and the storms. The January 27 Auckland Anniversary storm was from an "atmospheric river," dumping Summer's worth of rain (265 mm) in one day. This was the highest 24-h total on record, estimated a 1 in 200-year event, with a peak 2-min rainfall of 4.2 mm, coinciding with Friday's evening "rush hour." This led to widespread landsliding throughout Auckland, including a fatal landslide in Parnell, central Auckland. Notably, this occurred only meters from a similarly destructive landslide in 1997. With January 2023 Auckland's wettest January on record, and with the region still in a state of emergency, Tropical Cyclone Gabrielle crossed the region on February 13–14. This also caused widespread landsliding, including a fatal landslide at Muriwai on the west Auckland coast. Fatal landslides from the same escarpment had occurred in 1965, close to the February 13, 2023, fatal landslide. Taken together, both storm events caused > 140,000 landslides across the North Island, and flooding, making hundreds of people homeless, with damage estimates of > US$8.6 billion (3.4% of NZ's GDP). For future disaster risk reduction, attention should be paid to revising legislation and local planning, which currently allows people to legally build (and re-build) houses in landslide-prone areas of New Zealand. [ABSTRACT FROM AUTHOR]

Impulse waves result from submarine or subaerial landslides colliding with water. To model this multiphase disaster, it is imperative to gain a comprehensive understanding of the complex processes and interactions involved. Achieving accurate simulations over a wide area is computationally demanding and presents a significant challenge. In this paper, we analyze the 1946 Aleutian tsunami to assess the effectiveness of various models: SNAKE (rigid landslide model), Ls-Rapid (progressive landslide model advocated by the International Consortium on Landslides), and the Cornell Multi-grid Coupled Tsunami model. We integrate these models using Fortran, presenting two approaches: RL-SWE (rigid landslide-shallow water equations) and PL-SWE (progressive landslide-shallow water equations). The RL-SWE model assumes a rigid semi-ellipsoid sliding mass, while the PL-SWE model considers progressive failure. We conducted computational simulations and validated the results using observed data. Significantly, the PL-SWE model outperformed LS-Tsunami (linked to Ls-Rapid) in simulating submarine landslide-generated waves. This demonstrates the practicality of using the PL-SWE model for large-scale landslide-generated impulse wave simulations. [ABSTRACT FROM AUTHOR]

Slip-buckling failure is a common occurrence in bedding rock slopes, and it can be triggered by long-term gravitational stress, earthquakes, top loading and intense rainfall. In this paper, an analytical solution is proposed to address the slip-buckling failure of bedding rock slopes under the influences of top loading and earthquakes based on mechanical analysis and energy equilibrium theory. The rock layers of bedding slopes are treated as inclined plate beams with hinge support at the slope top and fixed support at the slope toe. The equation for the deflection curve of each rock column of the bedding rock slope under earthquake and top loading can be derived by analysing the forces acting on each rock layer. According to the energy equilibrium theory, the deformation energy of the rock column equals the work done by external forces, from which the critical length of a slope rock layer for slip-buckling failure can be determined. The location of the toe of the rupture surface, where slip-buckling failure occurs, corresponds to the point of maximum deflection on the deflection curve of the rock layer. Clearly, the location of the toe of the rupture surface is where the first derivative of the deflection curve equation equals zero. This approach has been applied to the Hejia landslide and Tangjiashan earthquake-triggered landslide, and the calculation results are in good agreement with the field investigation results. [ABSTRACT FROM AUTHOR]

Landslide event inventories are one of the most critical datasets to increase knowledge on landslide occurrences. However, they are rarely available in various regions, especially in countries of the Global South. This study aims to generate rainfall-induced landslide event inventories and define the rainfall thresholds responsible for landslide occurrence at the national scale of Malawi, Africa. We mainly followed a three-step methodology to generate landslide inventories. First, we went through media reports to identify documented landslide events. Second, we used Sentinel-2 images to identify possible areas affected by landslides using automated change detection algorithms based on vegetation indices. Third, we manually went through optical images provided by Planet Lab and Google Earth and mapped landslides via visual image interpretation. Overall, we mapped 27 rainfall-induced landslide inventories between 2003 and 2022, with a total of 4709 individual landslides. We then analysed the Malawian terrain and identified two different landscape clusters (i.e. Cluster 1 and Cluster 2) showing similar morphometric and climatic conditions. Ultimately, we calculated the rainfall threshold for each landscape cluster. The minimum rainfall amounts responsible for landsliding correspond to 66 mm/two-day and 51 mm/day in Clusters 1 and 2, respectively. In this context, our paper not only presents and shares the first national-scale, digital rainfall-induced landslide event inventory database of Malawi but also suitable rainfall thresholds to be potentially exploited for a national scale landslide early warning system. A similar framework could be applied to generate landslide inventories for other data scarce regions. [ABSTRACT FROM AUTHOR]

This study proposed a modified lift-off test that enables evaluation of the anchoring condition (anchored capacity or creep ratio) during the investigation of the residual anchor load and facilitates the restressing of anchors. To verify the applicability of the proposed measure in practice, a case study of the rehabilitated landslide slope of the T16 support tower of the Taipei Maokong Gondola System was conducted, a two-stage procedure that simultaneously facilitated the investigation and rehabilitation of an anchored slope with load loss. Results of an investigation of the residual load acting on 619 anchors indicated that the average load loss of the anchors was approximately 36% compared with the design load. In conventional design, approximately 223 extra anchors must be installed in the study area. However, the corresponding number in the proposed measure is about one-fifth the number of anchors required in the conventional approach. This paper emphasizes the merit of using the modified lift-off test in reducing construction time and construction costs in slope rehabilitation. Furthermore, the proposed rehabilitation measure adopted in this study did not involve adding many concrete structures to the original anchored slope. Thus, these measures are environmentally friendly and contribute to sustainable development. [ABSTRACT FROM AUTHOR]

Landslide hazards have significant social, economic, and environmental impact. This work provides a critical review of the main existing literature using satellite data for mapping landslides. We created and examined an extensive bibliographic database from Web of Science (WoS) consisting in 291 outputs from > 1,000 authors who studied almost 700,000 landslides across all continents, for a total of 52 countries represented with China and Italy on top of the list with more authors. The outputs are equivalent to ~ 5% of the whole landslide-related production for the period 1996–2022, with a 600% increase in the number of papers after 2014 driven by the availability of Sentinel-1 and Sentinel-2 data. Analysis of the geographical location across the 66 different countries analysed shows that, within the total number of contributions, the satellite imagery was used to detect and map two main types of landslides: flows and slides. When specified in the manuscripts, the events have been triggered by rainfall (104 cases), earthquakes (32 cases), or both (17 cases). Slope instabilities in these areas were predominantly identified through manual detection (40%); but since 2020, the advent of artificial intelligence is suppressing all other techniques. Despite the undisputed progress of EO-based landslide mapping over the last 26 years, which makes it a consolidated tool for many landslide-related applications, challenges still remain for an effective and operational use of EO images for landslide detection and mapping, and we provide a perspective for future applications considering the existing and the planned SAR satellite missions. [ABSTRACT FROM AUTHOR]

The reactivation of ancient landslides, a persistent issue that hinders urban development and threatens human safety, is the central focus of this paper. Specifically, this paper offers a detailed description and analysis of an ancient landslide that reactivated on February 26, 2021, in Guoye town, Zhouqu County, Gansu Province, China, and has remained slow-moving since then. With a volume of approximately 21.2 million m3 of rock and soil masses involved, this landslide poses a significant risk to the safety of 1119 individuals from 282 households in Moli village and Guoye village. A combination of field investigations, remote sensing interpretation, in situ monitoring, and interferometric synthetic aperture radar (InSAR) was used to investigate the landslide characteristics and reactivation mechanisms. The results revealed that the presence of sliding-prone rock formations and tectonic movement were the primary contributors to the formation of the Moli landslide. Furthermore, the accumulation of bedrock blocks in the quarry and collapsed rock masses caused by fault activity at the rear parts, along with the additional load from buildings on the middle and rear sections, increased the sliding force. Simultaneously, the erosion of the Dashuiba River resulted in a reduction in the anti-slip force, thereby directly facilitating landslide reactivation. The notable increase in annual rainfall in recent years has played a crucial role in ancient landslide reactivation by elevating the groundwater level and softening the shear strength of the sliding zone soil. The findings of this study contribute to an enhanced understanding of the reactivation mechanisms in giant, ancient landslides situated within areas characterized by highly complex geological conditions. [ABSTRACT FROM AUTHOR]

Interferometric synthetic aperture radar (InSAR) has gained considerable attention as a landslide monitoring strategy owing to its high accuracy, large coverage, and relatively low associated costs. A crucial drawback of InSAR, however, has limited its further incorporation: one-dimensional estimations along the sensor's line-of-sight (LOS). This leads to an ambiguity in results and a less intuitive understanding of landslide kinematics. A frequently exercised approach to address this issue has been taking inspiration from the topography to establish compatibility assumptions between velocity components, yet little insight exists on the performance of these methods. The objective of this paper is to investigate the performance of four renowned topography-based methods—Surface Parallel Flow Model (SPFM), SPFM coupled with least-squares method (SPF-LSM), Aspect Parallel Flow Model (APFM), and Steepest Terrain Following Model (STFM)—in evaluating the magnitude and geometry of total velocity vectors. To this end, the analysis is performed on 202 Radarsat-2 and 243 Sentinel-1 scenes acquired over a section of the Thompson River valley, a critical railway corridor in Western Canada traversing 14 landslides. The results indicate that the APFM provides estimations with the lowest magnitude error (15–19 mm/yr or 18.75–23.75% of in situ measurements) compared to the other approaches. SPFM and SPF-LSM are highly sensitive to LOS variance and tend to bias the interpreted vectors toward the north orientation. However, APFM and STFM reflect more realistic aspect angles, with the former inclined to steeper travel angles and the latter suffering from erratic upward travel angles due to local topographies. [ABSTRACT FROM AUTHOR]

A set of four thematic articles dealing with "Community Participation for Landslide Disaster Risk Reduction" will be published in this issue of Landslides following a call for papers, promoted by the authors, aimed at gathering field experiences in implementing and adopting landslide risk reduction measures at the community level in different social and economic environments. This editorial article briefly introduces the topic, states the objectives and the main findings of the four research studies, and provides some concluding remarks. [ABSTRACT FROM AUTHOR]

On June 17, 2020, Aniangzhai landslide, an ancient landslide located in Danba County, southwest China, was reactivated by Meilonggou debris flow. The front edge of the slope collapsed, mobilizing a soil mass of about 2.35 × 106 m3. Evaluating the stability of the whole slope is of great importance to avoid further landslides and mitigate the damage for Aniangzhai villagers living on this slope. This paper focuses on the inaccessible upper slope of Aniangzhai landslide (no attention paid before) that exhibits a relative elevation difference of more than 1000 m. Multi-source remote sensing, including unmanned aerial vehicle (UAV) photogrammetry, light detection and ranging (LiDAR), and satellite-based interferometric synthetic aperture radar (InSAR) techniques, was used in this research to identify and evaluate this high mountain upper slope potential hazard in Aniangzhai landslide. Considering the huge height difference and the steep slope of Aniangzhai landslide, an iterative route planning method was proposed and adopted to obtain a 3D model with 0.02 m resolution and a DEM with 0.25 m resolution by using UAV and LiDAR close-in flight method, respectively. Meter-level huge cracks were clearly identified by the high-resolution UAV 3D model and LiDAR data, which confirm that the location of these cracks is related to the morphological structure of this ancient landslide. Time series InSAR analysis reveals the activity of this high-altitude area, with a maximum LOS displacement rate of 15 cm/a. The combination of the above remote sensing technologies confirms and reveals the high potential risk and the reactivated condition of the upper slope of Aniangzhai landslide. Through this finding, we show that the evolution of Aniangzhai landslide happened through four stages with a cascading effect. This paper proves the usefulness of an integrated method to successfully identify and evaluate the high-altitude upper slope potential hazard and compares the technical features of them, providing a reference for future works that aimed at mitigating the potential damage of the upper slope. [ABSTRACT FROM AUTHOR]

This paper discusses the role of plants in the prevention of shallow landslides induced by rain (soil slips); these phenomena, related to "hydrogeological instability," are among the most feared because their evolutionary processes can cause huge damages and losses of human lives when interacting with anthropized areas and infrastructures. The paper first highlights how the plants interact with the soil; then introduces the G-SLIP (Green – Shallow Landslides Instability Prediction) model, i.e., the simplified physically-based SLIP model, modified to predict soil slips at punctual and large scale taking into account the vegetation effects. The G-SLIP model is thus applied to a case study of the Parma Apennines (Northern Italy) by using the G-XSLIP platform. In this area, during the intense events of rain between the 4th and 5th of April 2013, numerous landslides occurred, provoking huge damages to structures and infrastructures, and consequent economic losses. The stability analyses carried out with G-XSLIP demonstrate that the presence of vegetation in the study area led to a significant reduction in the triggering of shallow landslides. Finally, an attempt at soil slip mitigation through naturalistic techniques (planting of specific vegetation) is presented. [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]

This paper presents a triggering factor causing landslide in stable gentle soil slope. The triggering factor is a small mass of pressurized pore gas in the soil slope and illustrated with a flume soil slope model. The pore gas has a small mass of oxygen and is generated via the injection of a small volume of H2O2 solution into the cement powder core of the soil slope. The core is covered by a layer of saturated soft clay that forms a trap for the new pore gas to build its pore gas pressure. The pore gas pressure is the driving force to cause the landslide in the stable gentle soil slope. The mass of the pore gas is smaller than 0.1% of the mass of either the gentle soil slope or the landslide body. The videos of the landslide are captured and used for the analysis of movement and displacement of the sliding soil mass with time. The pressure of the pore gas is estimated from the acceleration model of the sliding soil mass. A calibration test is further carried out for the generation of the oxygen gas mass and pressure via the decomposition of the H2O2 solution in cement powder. The results quantitatively demonstrate that the small mass of the oxygen gas in the voids of the cement powder core can have enough pressure to trigger the landslide in the stable gentle soil slope model. Such pressurized pore gas triggering factor is generally not noticeable since its mass can be very small and disappear rapidly without a trace. This triggering factor could be responsible for the huge and disastrous landslide that suddenly and dramatically occurred in a huge gentle fill soil slope on December 20, 2015, at Hengtaiyu Industrial Park, Shenzhen, China. [ABSTRACT FROM AUTHOR]

This paper provides a summary of the investigations carried out for the preliminary assessment of potential geohazards affecting the archaeological site of Dadan in the Kingdom of Saudi Arabia. The site is characterized by the presence of Cambro-Ordovician sandstone cliffs (Siq formation), which were quarried in ancient times (Dadan/Lihyan kingdom) for building materials. Both the steep quarried portion (Upper Siq) and the gentler underlying slope (Middle Siq) contain tombs of significant archaeological value. Landsliding and erosion are the main geomorphological processes affecting the site, posing risks to the safety of visitors, archaeological workers, and the preservation of the site. The primary processes affecting the rock cliffs, the underlying slope talus, and the tombs were identified using geological and geomechanical surveys, in addition to various geomatic acquisitions. The mechanical properties of the rock formations and discontinuity sets were identified through laboratory testing and in situ surveying, respectively. This information represents the first step in promoting further actions for risk mitigation and site management. [ABSTRACT FROM AUTHOR]

The article discusses a few points emerging from the paper "Expected Damage From Displacement of Slow-Moving Slides," by M. F. Mansour et al. The paper presents a list of published cases concerning slow-moving slides and their interaction with man-made works. It reports on two schematic diagrams that focused on the three basic phases of creep.

Integrated disaster risk management is crucial in reducing landslide risk. The International Consortium on Landslides has launched several initiatives to enhance research and practice in landslide risk management, including the Tokyo Action Plan 2006, the ISDR-ICL Sendai Partnerships 2015–2025, and the Kyoto Landslide Commitment 2020. This article presents a collection of papers covering various aspects of landslide research and disaster risk management across diverse scales and regions worldwide. To effectively manage landslide disaster risk, it is essential to have a solid understanding of disaster risk and foster a sustained collaboration between science and policy-making to strengthen disaster risk governance. The ICL is dedicated to this mission, and by working together, its members and partners can contribute to the comprehension, reduction, and mitigation of landslide disaster risk globally. [ABSTRACT FROM AUTHOR]

The measurement of global progress in implementing a Sendai Framework for Disaster Risk Reduction (SFDRR) targets should be able to report on a set of 38 indicators including those related to disaster damage. The ability of a hazard-specific database (e.g. landslide database in this case) in providing information that meets the SFDRR target is not well researched. This study aims to examine the coverage of information in the existing landslide databases that is relevant to the SFDRR indicators, to check the availability of damage data and to evaluate the current data collection practices according to SFDRR compatibility to local scale application in assessing the applicability of existing damage data to estimate the landslide costs. Content analysis was conducted to assess the availability of damage data and compile landslide events data from different sources to develop a local-level landslide database. Replacement cost and market price approaches were used to estimate landslide costs. Kuala Lumpur is chosen as a study area to represent the lowest spatial resolution at the municipal level. The results show that existing national landslide databases have significant shortages with regard to the availability of damage data necessary for SFDRR reporting. The landslide data in Kuala Lumpur show a high level of missing fundamental hazard information, such as the type (73%), cause (93%) and size (92%). Of the compiled events, 35.9% had no associated damage data, 64.1% had at least one accompanying recorded damage indicator and 58.6% had at least one accompanying reported monetizable damage indicator. This paper contributes to literature by identifying gaps in current landslide data management practices in Malaysia. [ABSTRACT FROM AUTHOR]

Accurate early warning systems for landslides are a reliable risk-reduction strategy that may significantly reduce fatalities and economic losses. Several machine learning methods have been examined for this purpose, underlying deep learning (DL) models' remarkable prediction capabilities. The long short-term memory (LSTM) and gated recurrent unit (GRU) algorithms are the sole DL model studied in the extant comparisons. However, several other DL algorithms are suitable for time series forecasting tasks. In this paper, we assess, compare, and describe seven DL methods for forecasting future landslide displacement: multi-layer perception (MLP), LSTM, GRU, 1D convolutional neural network (1D CNN), 2xLSTM, bidirectional LSTM (bi-LSTM), and an architecture composed of 1D CNN and LSTM (Conv-LSTM). The investigation focuses on four landslides with different geographic locations, geological settings, time step dimensions, and measurement instruments. Two landslides are located in an artificial reservoir context, while the displacement of the other two is influenced just by rainfall. The results reveal that the MLP, GRU, and LSTM models can make reliable predictions in all four scenarios, while the Conv-LSTM model outperforms the others in the Baishuihe landslide, where the landslide is highly seasonal. No evident performance differences were found for landslides inside artificial reservoirs rather than outside. Furthermore, the research shows that MLP is better adapted to forecast the highest displacement peaks, while LSTM and GRU are better suited to model lower displacement peaks. We believe the findings of this research will serve as a precious aid when implementing a DL-based landslide early warning system (LEWS). [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]

In this paper, the authors investigated the dynamic process of a high-level paleolandslide blocking river event-Gangda landslide blocking river event in the deep valley area. An integrated fluid–solid coupling method based on the FDEM-SPH was proposed to study it. In the numerical model, the dynamic process of the landslide is described by FDEM, while SPH describes the behavior of the river water. Based on the back-analysis of the three-dimension modeling, the landslide blocking river simulation results of the FDEM-SPH agree well with the field investigation and PFC3D simulation results. Simulation results of the FDEM-SPH show that the primary process of the Gangda paleolandslide blocking river lasted for 60 s. The maximum overall feature speed of the sliding mass was 33 m/s, while the speed of the sliding mass tip reached a maximum value of 40 m/s. Dynamic fragmentation of the landslide was violent, and both the open mode and sliding mode damage play essential roles in dynamic fragmentation. The variation of kinetic energy, accumulated friction dissipation, and fracture energy of the sliding mass are also shown. The landslide induced an impulse wave after merging into the river, and the wave behavior was further affected by the sliding mass movement. The maximum overall feature speed of the wave was 17 m/s, and the speed of the wave tip reached a maximum value of 33 m/s with a maximum run-up height of 120 m. Finally, a comparison of the FDEM-SPH model and PFC model in the application of landslide blocking river is discussed, and the advantages and limitations of the FDEM-SPH model are examined. [ABSTRACT FROM AUTHOR]

When lakes are dammed by earthquake-induced landslides, they can pose a significant threat to communities and infrastructure both downstream and upstream. To improve means for disaster prevention and mitigation, a field investigation together with the DEM-SPH numerical method was utilized to study a landslide-dammed lake disaster in the Baoxing River after the 2022 Lushan earthquake. The simulation results agreed well with those of the field investigation; the simulated landslide dam and dammed lake were similar to the field observations. The whole process of the formation of the landslide-dammed lake, including the formation of the natural dam, the blocking of the river, and overtopping, was well described. The maximum average velocity and sliding distance of the causative Xinhua landslide were 6.55 m/s and 318 m, respectively. The limit storage capacity of the dammed lake was 513,317 m3; the water level increased under the impoundment, and the maximum river depth was 22.56 m. The fluid pressure increased with river flow direction, and the maximum fluid pressure was 1.81 × 105 Pa. The process of the river blocking disaster was divided into three stages: the impoundment stage, discharge stage, and balanced stage. In the event of a heavy rainfall, the submerged area could increase by 20.8% compared with the current state. The coupled numerical investigation presented in this paper provides new insights into the research and risk assessment of landslide-dammed lakes. [ABSTRACT FROM AUTHOR]

In recent years, landslides induced by underground mining have attracted much attention as they cause great harm and early warning signals are difficult to detect. The key work of the early warning of a mining landslide is to clarify its initiation mechanism and evolution process. Due to the complexity of the deformation and failure of the goaf overburden and the lack of monitoring of the slope evolution process, the deformation and failure law and evolution characteristics of mining landslides have always been difficult to analyze. In this paper, a typical mining landslide, the Madaling landslide, was selected as the study object. The soft-hard interlayer structure of the slope was generalized and explored by centrifuge model tests and a 3D discrete element model. The results showed that the evolution of the Madaling landslide are divided into four stages: (I) the bending and collapse of the goaf overburden, the slope settlement and the formation of tensile cracks at the trailing edge; (II) the upwards extension of the subsidence cracks of the rock mass; (III) the occurrence of shear cracks in the rock mass, with gradual slope deformation as a whole; and (IV) the connection of shear cracks, with the initiation of landslides. The long-term gravity creep of soft rock and the extension of trailing edge tensile cracks cause the internal rock mass of the slope to become the key block controlling slope stability. The slope surface displacement (S)-time (t) curve of mining landslides is divided into the settlement stage, rock mass crack development stage and landslide evolution stage. The formation time of shear cracks in the rock mass crack development stage is a sign of the transformation of subsidence into a landslide. The relationship between the horizontal displacement and the depth of the rock mass effectively reflects the development stage of shear cracks. The horizontal and vertical displacement of the deep rock mass can serve as the early warning criterion for mining landslides. [ABSTRACT FROM AUTHOR]

Worldwide, landslides are the most frequently occurring disaster that is very destructive and unpredictable in nature. A total of 850 landslide events were detected during 2005–2020 in the Tehri region of the Indian Himalayas. Many researchers have conducted landslide susceptibility mapping (LSM) studies for this region using different static landslide-causing factors. However, studies considering dynamic factors in predicting future landslide susceptibility scenarios are inadequate. Hence in this study, both dynamic and static factors were utilized in predicting future landslide susceptibility maps for the year 2050. The paper's main objective is the future prediction of LSM, considering future projections of land use land cover (LULC) and climate variables (precipitation and temperature). To achieve this objective, first, the geospatial database in three temporal categories, 2005–2010, 2010–2015, and 2015–2020, was prepared for the historical landslide events. Second, the landslide-causing factors were optimized and utilized in LSM for 2010, 2015, and 2020. Third, projected LULC map was generated for the year 2050 using the Artificial Neural Network-Cellular Automata (ANN-CA) model. Fourth, CMIP6 climate projection maps were prepared using the Indian Institute of Tropical Meteorology Earth system model (IITM ESM) under four shared socioeconomic pathway (SSP) scenarios. Finally, the projected maps were used as the driving parameter for the future prediction of LSM. The results reveal a high increase in the built-up area (5%) and agriculture land (4%) with a decrease in forest area (10%) in future LULC projections. The results of future LSM prediction under SSP 1–2.6, SSP 2–4.5, SSP 3–7.0, and SSP 5–8.5 climate scenarios show an increase in very high landslide susceptibility class by 2%, 4%, 7%, and 9% respectively. The predicted maps were validated utilizing the Kappa coefficient verifies the reliability of the simulated future results. [ABSTRACT FROM AUTHOR]

An updated and complete landslide inventory is the starting point for an appropriate hazard assessment. This paper presents an improvement for landslide mapping by integrating data from two well-consolidated techniques: Differential Synthetic Aperture Radar (DInSAR) and Landscape Analysis through the normalised channel steepness index (ksn). The southwestern sector of the Sierra Nevada mountain range (Southern Spain) was selected as the case study. We first propose the double normalised steepness (ksnn) index, derived from the ksn index, to remove the active tectonics signal. The obtained ksnn anomalies (or knickzones) along rivers and the unstable ground areas from the DInSAR analysis rapidly highlighted the slopes of interest. Thus, we provided a new inventory of 28 landslides that implies an increase in the area affected by landslides compared with the previous mapping: 33.5% in the present study vs. 14.5% in the Spanish Land Movements Database. The two main typologies of identified landslides are Deep-Seated Gravitational Slope Deformations (DGSDs) and rockslides, with the prevalence of large DGSDs in Sierra Nevada being first revealed in this work. We also demonstrate that the combination of DInSAR and Landscape Analysis could overcome the limitations of each method for landslide detection. They also supported us in dealing with difficulties in recognising this type of landslides due to their poorly defined boundaries, a homogeneous lithology and the imprint of glacial and periglacial processes. Finally, a preliminary hazard perspective of these landslides was outlined. [ABSTRACT FROM AUTHOR]

In this study, a new paradigm compared to traditional numerical approaches to solve the partial differential equation (PDE) that governs the thermo-poro-mechanical behavior of the shear band of deep-seated landslides is presented. In particular, this paper shows projections of the temperature inside the shear band as a proxy to estimate the catastrophic failure of deep-seated landslides. A deep neural network is trained to find the temperature, by using a loss function defined by the underlying PDE and field data of three landslides. To validate the network, we have applied this network to the following cases: Vaiont, Shuping, and Mud Creek landslides. The results show that, by creating and training the network with synthetic data, the behavior of the landslide can be reproduced and allows to forecast the basal temperature of the three case studies. Hence, providing a real-time estimation of the stability of the landslide, compared to other solutions whose stability study has to be calculated individually for each case scenario. Moreover, this study offers a novel procedure to design a neural network architecture, considering stability, accuracy, and over-fitting. This approach could be useful also to other applications beyond landslides. [ABSTRACT FROM AUTHOR]

The reservoir impoundment can lead to accelerated deformation or reactivation of landslides, posing great threats to human lives and dam stability. The primary objective of this paper is to report the movement process of the Tuandigou (TDG) ancient landslide in the Baihetan dam area during reservoir impounding. Field investigation, borehole work, and site monitoring data are used to analyze the deformation behavior and triggering mechanism of the TDG landslide. It is found that the landslide deformation is closely related to the reservoir water level. After the reservoir water level reached the elevation of 810 m on October 12, 2022, the ground cracks continued to expand and gradually linked up, resulting in an accelerated deformation of the TDG landslide. Furthermore, the lithology in the study area also positively contributes to the slide movement. Based on these results, it is anticipated that the present research will provide guidelines for the early warning and control of the TDG landslide during and after the reservoir impounding. [ABSTRACT FROM AUTHOR]

This study explores the whole process of deformation–failure–movement of fault-type slopes under rainfall conditions. Taking the Guanling landslide as an example, this paper simulates the whole process of the Guanling landslide based on FLAC/PFC coupling. The research results show that the simulated Guanling landslide accelerates deformation and failure due to the influence of reverse faults and rainfall. In general, the simulated Guanling landslide first fails in the form of sliding on the left side and then collapsing on the right side. The bottom unit mainly consists of tensile–shear composite failure that is supplemented by shear failure. After the simulated Guanling landslide failure, the average peak velocities basically show a trend of first increasing and then decreasing from top to bottom and from left to right. Overall, the landslide bed is basically in a state of compression, and the impact stress is constantly attenuated with the movement process. The σ1 trace of the landslide bed is basically consistent with the movement direction of the landslide body. As burial depth increases, the trace is obviously deflected downwards. According to the law of conservation of energy, the hydrodynamic work and gravitational potential energy are 2.96 × 1012 J and 4.43 × 1012 J, respectively, and the energy consumption of the peak bonding energy, peak kinetic energy, sliding energy and damping energy are 7.77 × 1011 J, 1.41 × 1012 J, 5.55 × 1012 J and 1.66 × 1012 J, respectively. Finally, the accumulation forms of the landslide bodies in each section after the termination of the simulated Guanling landslide are compared, and the simulation results are consistent with the actual situation, which confirms the reliability of the simulation results. These research results provide a reference for studying the failure mechanisms and movement characteristics of similar geological landslide disasters. [ABSTRACT FROM AUTHOR]

Among the types of landslides that occur in Alpine areas, rockfalls are one of the most frequent and dangerous. Rockfalls affect every type of rock mass with greater or lesser incidence based on the intrinsic characteristics of rocks (structural setting, lithotype, geotechnical quality, weathering conditions, etc.) and climate features of geographic areas considered. In order to assess the hazard and subsequently the risk associated with these phenomena, it is first necessary to identify areas most likely to be affected. This paper presents a methodology applicable on a regional scale to identify the most susceptible Alpine areas to rockfalls. The methodology is based on statistical cross-analyses of environmental factors to identify the Alpine areas most susceptible to rockfalls. The predisposing factors considered to generate a rockfall susceptibility map are both qualitative (geological variables) and quantitative (morphometric, altimetric and climatic variables) and have been compared with a selection of historical data on rockfalls occurrence and distribution (consisting in 2800 events). As result, a classification of the Piemonte's Alpine territory identifying 5 classes characterized by a greater or lesser propensity to generate rockfalls has been obtained. The regional susceptibility map has been subsequently used as basis to identify road networks more exposed to rockfall hazard to obtain a preliminary risk analysis at regional scale. [ABSTRACT FROM AUTHOR]

Back analysis is the most common method to study landslide movements after the event, and it allows us to understand how a landslide evolved along the slope. This paper presents the back-analysis of the Pomarico landslide (Basilicata, Italy) that occurred on January 25th, 2019, on the southwestern slope of the Pomarico hill. The landslide, of rotational clayey retrogressive type—planar sliding, evolved in different phases until it caused a paroxysmal movement in the early afternoon on January 29th, 2019. The landslide caused the collapse of a bulkhead (built at the end of the twentieth century) and of some buildings along the village's main road. In this paper, a multi-layer back-analysis study is presented, based on the limit equilibrium model (LEM), applying the solution proposed by Morgenstern and Price in Geotechnique 15(1):79–93zh, (1965) and implemented in the freeware software SSAP 2010. The analysis allowed the reconstruction of the entire landslide evolution, using geotechnical parameters obtained from both laboratory and in situ tests, and data from the literature. The application of multilayer back-analysis made it possible to avoid the homogenisation of the layers, modelling the event according to the real conditions present on the slope. The use of the SSAP software made it possible to curb the problem related to the theoretical limitation of the shape of the rupture surfaces, by evaluating independently the friction angle locally and by discarding all those surfaces, which, due to this problem, presented a non-reliable factor of safety (FS) value. The modelling revealed a slope that is highly unstable as the height of the water table changes. The FS calculated under water table conditions close to ground level was less than 1 (FS = 0.98), simulating the first landslide movement (November 2018). The subsequent model reconstructed the critical surface responsible for the January 2019 movement and calculated the FS present on the slope (FS = 1.01). Eventually, the paroxysmal event on January 29th, 2019, was modelled, returning an FS of 0.83, and a sliding surface that sets below the bulkhead, causing its failure. Furthermore, the modelling of the slope in the presence of adequate retaining structures demonstrated the (non-) effectiveness of the retaining wall system represented by the bulkhead. The proposed method of analysis suggests further applications in similar complex multi-layer soil-structure interaction scenarios. [ABSTRACT FROM AUTHOR]

This paper proposes a new, physically based, and mathematically consistent method for predicting the evolution of existing landslides and first-failure phenomena based on slope displacement measurements. The method is the latest step in a long-term research program and, as such, uses the preliminary framework introduced in two previous papers. The first characterizes slope movements through a limited number of displacement trends, and the second analyzes their dynamic characteristics. The approach is here extended to the prediction of landslide evolution and its validity and effectiveness are tested on landslides well known in the scientific literature for the accuracy of the studies carried out and, in some cases, for the consequences they have caused. Although the results obtained so far are very encouraging, in full awareness of the relevance and complexity of the subject matter, the authors emphasize that the method should be used, in the current state of knowledge, only by experienced professionals and especially for research purposes. [ABSTRACT FROM AUTHOR]

Length (L), width (W), and aspect ratio (ε, L/W) are elementary parameters widely adopted to characterize landslide geometry. Aspect ratio (ε) had been associated with the propagation of landslide along its movement path. However, current automatic measurement algorithms have not effectively considered the path of landslide movement, therefore only measure "path-independent" instead of "path-dependent" geometric parameters. This paper proposes a new approach for measuring path-dependent landslide L, W, and ε, in which path-dependent landslide L (Lpdep) is measured as the path distance from the landslide crown to its tip along the central line of landslide movement, and path-dependent landslide W (Wpdep) is measured as the weighted average lateral extent, across the central line of landslide movement. Specifically, Wpdep is calculated indirectly through dividing the landslide area by its length (A/Lpdep), and path-dependent landslide ε (εpdep) can be calculated by Lpdep2/A. The central line of landslide movement, i.e., landslide profile, is a three-dimensional (3D) polyline generated by the freely available software ALPA (Automatic Landslide Profile Analysis), and all these path-dependent geometric parameters can be measured either in 3D space or in two-dimensional (2D) space (in the horizontal plane). A case study is presented showing that path-dependent parameters can be effectively measured by the proposed profile-based approach, and could be significantly different from path-independent parameters especially for long-narrow and tortuous landslides. This paper also practically introduces new path-dependent definitions of landslide L, W, and ε, which inspires a worthwhile revisit of nomenclature. An overview shows that significant divergencies exist between current definitions of landslide L, W, and ε. In order to moderate the raised ambiguities and confusions, this paper accordingly suggests a "physically indicative" inclusive nomenclature somehow embracing all current representative definitions, and then appeals for constituting "type-specific" nomenclatures for each landslide type. However, some open questions still exist. We therefore suggest relevant academic organizations to work further on type-specific nomenclatures and effective measurement approaches for landslide L, W, and ε. [ABSTRACT FROM AUTHOR]

From June 10 to 13, 2019, continuous heavy rainfall occurred in Longchuan County, Guangdong Province, yielding a cumulative rainfall of nearly 270 mm. The heavy rainfall triggered a large number of landslide disasters and formed three hardest-hit areas. In this paper, Mibei village, Beiling town, Longchuan County, is chosen as the research object; detailed field investigation data, satellite remote sensing images, rainfall monitoring data, and artificial rainfall physical model test results are integrated; the temporal and spatial distribution characteristics of rainfall-induced group-occurring landslides in the study area are obtained; and the rainfall instability mechanism of granite residual soil slopes is explained. Under the influence of continuous heavy rainfall from June 10 to 13, 2019, 327 landslides developed in Mibei village, Beiling town, and these landslides were mainly distributed in low mountainous areas, of which the sections at elevations from 300 ~ 400 m and slopes ranging from 35 ~ 45° were the most susceptible to landslide disasters. Continuous rainfall on June 10 and 11 was the controlling factor leading to these large number of landslides, with numerous landslides occurring from 20:00 on June 11 to 04:00 on June 13. These group-occurring landslides exhibited the characteristics of a considerable rainfall lag. The deformation and failure characteristics of the numerous observed landslides within the study area were highly similar, mainly involving traction sliding failure, and the sliding mass thickness ranged mostly from 1.5 ~ 3 m. The flow pattern characteristics of unconsolidated deposits after landslide instability were significant. According to the deformation and failure characteristics of landslides and the rainfall infiltration pattern, the development of landslides was divided into stages in this paper. Due to the difference between the rainfall intensity and permeability of granite residual soil, the main influence depth of heavy rainfall was limited to the superficial zone of slopes, which is the main reason why the shallow surface zone was damaged by landslides. Under the action of continuous heavy rainfall, a saturated seepage field was established in the shallow surface zone of slopes. Driven by gravitational potential energy, this led to an uneven distribution of the slope saturation zone. Attenuation of the mechanical strength of saturated soil reduced the slope stability, and sliding failure consequently occurred in the shallow surface saturation zone. In regard to excavated slopes, anti-sliding force reduction and free face formation enhanced the slope's susceptibility to sliding failure under the influence of heavy rainfall, which is also the reason for the large-scale distribution of landslides along the X158 county road. [ABSTRACT FROM AUTHOR]

Landslides frequently disrupt roadway networks in mountainous regions worldwide. Because of the relatively long roadway extension and low roadway density in mountainous regions, the occurrence of a landslide hazard along a local road segment will cause traffic paralysis on the individual roadway and will further impact regional roadway network accessibility. This paper establishes an integrative risk assessment framework based on risk theory and complex network theory to combine the results of landslide susceptibility mapping along roadways and impact assessment on the roadway network. Through an analysis of the relationship between various geo-environmental conditioning factors and historical landslides along roadways, the support vector machine (SVM) model is used to assess landslide susceptibility across the regional roadway network. Both topological connectivity of the entire roadway network and transport accessibility between local residents are considered in the impact assessment on roadway networks. To illustrate the effectiveness of the proposed risk assessment framework, a case study of the roadway network in Fengjie County, China, which is prone to landslide occurrence, is conducted. The resulting landslide risk heatmap of Fengjie County's roadway network is generated using ArcGIS software. The most critical road segments are identified as being highly susceptible to landslides, and if they are disrupted, the entire roadway network will suffer significant performance loss. The results can support adaptive strategies for landslide mitigation, preparedness, and emergency response services, as well as improve roadway plans to reduce exposure and associated consequences by adding new links to the existing roadway network. [ABSTRACT FROM AUTHOR]

In the present paper, an active landslide located to the southeast of the urban area of Alcoy (Alicante, Spain) is analysed. This landslide affects the N-340 road, as well as an area in which some industrial buildings dated from the fifteenth century and catalogued as Places of Cultural Interest are placed. This region is characterised by the existence of a steep relief composed of materials from the Miocene to the present, on which many gravitational movements are developed. The displacements have been analysed using high-resolution spotlight mode SAR images captured by the Spanish SAR satellite PAZ (which operates at X-band), which were processed by advanced differential SAR Interferometry (A-DInSAR). The behaviour of the instability has been determined using InSAR-derived time series displacement maps. Additionally, the study has been completed with a detailed geomorphological mapping at 1:2000 scale and an analysis of structural damage carried out in situ. The landslide was modelled in 3D, from a deterministic and probabilistic point of view, confirming the global instability of the area and the relationship of the landslide with rainfall and the construction of a new road. The integration of all this information obtained and its subsequent analysis has made possible to precisely define the extension of the instability, its kinematics, and to identify the main triggering factors. This information will be crucial for the proper management of this landslide by the local authorities. [ABSTRACT FROM AUTHOR]

Landslide monitoring is a mandatory step in landslide risk assessment. It requires collecting data on landslide conditions (e.g., areal extent, landslide kinematics, surface topography, hydrogeometeorological parameters, and failure surfaces) from different time periods and at different scales, from site-specific to local, regional, and national, to assess landslide activity. In this analysis, we collected information on landslide monitoring techniques from 17 members of the Earth Observation and Geohazards Expert Group (from EuroGeoSurveys) deployed between 2005 and 2021. We examined the types of the 75 recorded landslides, the landslide techniques, spatial resolution, temporal resolution, status of the technique (operational, non-operational), time of using (before the event, during the event, after the event), and the applicability of the technique in early warning systems. The research does not indicate the accuracy of each technique but, rather, the extent to which Geological Surveys conduct landslide monitoring and the predominant techniques used. Among the types of landslides, earth slides predominate and are mostly monitored by geological and engineering geological mapping. The results showed that Geological Surveys mostly utilized more traditional monitoring techniques since they have a broad mandate to collect geological data. In addition, this paper provides new insights into the role of the Geological Surveys on landslide monitoring in Europe and contributes to landslide risk reduction initiatives and commitments (e.g., the Kyoto Landslide Commitment 2020). [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Fragmentation is a common feature of rockfall that exerts a strong control on the trajectories of the generated blocks, the impact energies, and the runout. In this paper, we present a set of four real-scale rockfall tests aimed at studying the fragmentation of the rocky blocks, from the global design of the field procedure to the data analysis and the main results. A total of 124 limestone, dacite, or granite blocks ranging between 0.2 and 5 m3 were dropped from different heights (8.5 to 23.6 m) onto four slopes with different shapes (single or double bench) and slope angles (42º to 71º). The characteristics of the blocks, in particular the size, surface texture and joint condition, were measured before the drops. The trajectories of the blocks and both the initial and the impact velocities were tracked and recorded by means of three high-speed video cameras. A total of 200 block-to-ground impacts have been studied. On average, 40% of the blocks broke upon impact on the slope or on the ground, making it necessary to measure the fragments. The initial and final sizes of the blocks/fragments were measured by hand with tape, though photogrammetric techniques (UAV and terrestrial) were also used for comparison purposes. The information gathered during the field tests provides a deep insight into the fragmentation processes. On the one hand, the high-resolution slow-motion videos help to describe when and how the block breakage takes place and the spatial distribution of the pieces. On the other hand, it is possible to compute the block trajectories, the velocities, and the energy losses using videogrammetry. The results include, for instance, a block average fragmentation of 54% and 14% for the limestone and granitoids, respectively; the systematic inventory of the size fragments, which may be used for fitting the power law distributions; and after each breakage, the total angle of aperture occupied by the fragments has been measured, with values in the range 25º–145º. To figure out the different behavior of the blocks in terms of breakage/no breakage, each block-to-ground impact has been characterized with a set of parameters describing the energy level, the robustness of the substrate, and the configuration of the block contact at the impact point, among others. All these terms are combined in a function F, which is used to adjust the field data. The adjustment has been carried out, first, for the whole 200 events and later for a subset of them. The procedure and the results are described in the paper. Although the discrimination capability of F is moderately satisfactory, it is very sensitive to the test site and setup. It must be highlighted that these field tests are a unique source of data to adjust the parameters of the numerical simulation models in use for rockfall studies and risk mitigation, especially when fragmentation during the propagation is considered. [ABSTRACT FROM AUTHOR]

Rapid flow-like landslides, particularly debris flows and debris avalanches, cause significant economic damage and many victims worldwide every year. They are usually extremely fast with the capability of travelling long distances in short times, sweeping away everything in their path. The principal objective of this paper is to test the ability of the 'GeoFlow-SPH' two-phase model developed by the authors, to reproduce the complex behaviour of natural debris avalanches where pore-water pressure evolution plays a key role. To reach this goal, the model is applied to reproduce the complex dynamic behaviour observed in Johnsons Landing debris avalanche including the observed bifurcation caused by the flowing out of part of the moving mass from the mid-channel. Initial thickness deposit trim-line, distribution of deposit volume, and the average velocities were provided for this real case, making it an appropriate case to validate the developed model. The paper also contributes to evaluate the SPH-FD model's potentialities to simulate the structural countermeasure, like bottom drainage screens, used to reduce the impact of debris flows. The analysis of the results shows the adequacy of the proposed model to solve this complicated geophysical problem. [ABSTRACT FROM AUTHOR]

Landslides are one of the main geological disasters in the riparian zone of the Three Gorges Reservoir Area (TGRA) in China, causing massive losses in terms of industrial and agricultural production and people's lives and property. Due to the difficulty of data collection and lack of facade information, the remote sensing methods currently used for landslide mapping and detection are insufficient. The current landslide information acquisition method is mainly based on visual interpretation and automatic computer classification from aerial orthophotographs, which is subjective and time-consuming and yields low accuracy. New shipborne photogrammetry has unique advantages in small and steep slope landslide detection. In recent years, the deep learning technology represented by convolutional neural networks (CNNs) exhibits more powerful feature learning ability in image recognition than traditional machine learning technology. Meanwhile, transfer learning can make the network suitable for small datasets through fine-tuning, which could transfer the parameters of the pretrained model to the new model in a certain way, thereby increasing the learning efficiency of the model. In this paper, the training of multiple deep learning models is realized through transfer learning, and automatic identification of landslides based on shipborne images is achieved through decision-level fusion. The experimental results show that through the integration of decision-level fusion and transfer learning, it performs well in the classification of landslides based on shipborne images. [ABSTRACT FROM AUTHOR]

Landslide failure near a reservoir generates impulse waves, greatly increasing casualties and property losses. This paper presents a field investigation and potential impulse wave simulation analysis of the Wulipo (WLP) landslide, which deformed after the initial impoundment of the Baihetan Hydropower Station, China. According to geological surveys, the WLP landslide is a weak base-type, weathered, fragmented rockslide with soft rock in the lower portion and hard rock in the upper portion. The deformation and failure mechanism under the action of reservoir water consist of compression, fracturing, and shearing. The periodic fluctuation of reservoir water may cause overall high-speed slip failure of the WLP landslide. According to the inferred failure mode of the WLP landslide, the impulse waves caused by the overall failure of the landslide at the highest (825 m) and lowest (765 m) water levels are predicted. Due to the significant height difference between the center of gravity of the landslide and the reservoir water level, the wave height and fluctuation degree of the overall failure of the WLP landslide for 765 m are significantly greater than those for 825 m. However, the wave flood area for 825 m is larger than that for 765 m. Although the impulse waves triggered by the overall landslide at either water level will not destroy the nearby Menggu community, at 765 m, impulse waves will overflow the bridge deck of Ganhu Road. Therefore, engineering measures such as reducing the landslide volume can lower the secondary disaster risk posed by WLP landslides. This study provides insights into landslide disaster risk in the Baihetan Reservoir region and similar areas worldwide. [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]

Due to their practicability, simple and data-driven empirical models have been extensively developed and applied in practical engineering to predict the run-out distance of landslides. However, the definition of the most appropriate empirical model for specific landslide data is rarely discussed. Moreover, the empirical model is subjected to the high variability of landslide data, which should be quantified into the model to provide more reliable predictions. As such, we propose in this paper, a simple, practical, and probabilistic run-out distance prediction method based on an empirical model and Bayesian method. This method was implemented with a regional landslide inventory compiled from 34 loess landslides in Heifangtai Terrace, Gansu Province, China. In this method, we performed a Bayesian model selection to determine the most appropriate empirical model for the compiled database among the possible candidate models adapted from previous literature. Considering the high variability of data, unknown parameters of the empirical model are regarded as random variables, and their posterior distributions are obtained by Bayesian updating with the compiled database. Then, we developed the probabilistic run-out prediction model to evaluate the run-out distance exceedance probability of landslides based on the most appropriate model and its associated posterior random variable information. We utilized data from two recent landslides that occurred in Heifangtai Terrace to validate the performance of the proposed model. In addition, we produced a run-out distance exceedance probability curve using the proposed method for a potential landslide in Heifangtai Terrace, in which the sliding volume interval is estimated using the sloping local base level (SLBL) method. In general, this study presents a practical method for landslide run-out distance analyses within a probabilistic framework, aiming to provide support for risk-based decisions. [ABSTRACT FROM AUTHOR]

The Jinsha River flows through one of the most geologically complex regions in western China with extremely high altitudes and capricious climates. Frequent landslides (e.g., the Baige landslides on 11 October and 3 November 2018) occurred along its stretch which posed severe damage to bridges, dams, and roads, and put the safety of local residents at risk. Systematic and comprehensive landslide detection has been rarely carried out in the Jinsha River corridor because it is a wide and hard-to-reach region. With 30 years of development, InSAR has become an effective way to map ground motion, which in turn makes it feasible to detect active landslides over wide regions. However, several challenges remain in applying InSAR in the Jinsha River corridor, such as low coherence due to dense vegetation and/or large gradient surface movements, and atmospheric effects due to the spatio-temporal variations of the atmosphere (especially the part due to tropospheric water vapor). In this paper, we propose a framework to overcome the limitations of conventional InSAR through the integration of GACOS-assisted InSAR, advanced SBAS InSAR, and SAR pixel offset tracking techniques. This integrated framework enables us to detect active landslides under complex topographic and climate conditions. Our results show that the detected active landslides are largely controlled by active faults, most of them are found at elevations of 2500–4000 m, their slopes fall between 25 and 45 ∘ facing southwest and northwest, and 83.3% of the active landslides have a NDVI value of less than 0.3. Furthermore, precipitation is one critical triggering factor of active landslides, evidenced by the high temporal correlation between average precipitation and surface displacement time series. [ABSTRACT FROM AUTHOR]

This paper presents a new method to estimate a landslide failure surface and its volume based on a digital elevation model (DEM) using spline curves by assuming values of the tangent of the failure surface along with transversal vertical profiles. The model will give the depth of the probable failure surface plotted using the 2D grid function. We can easily visualize the results by running the codes; the display will show the step-by-step processes involved. The model requires fundamental data inputs, which are readily available online throughout the world, such as a DEM of the study area and a KML file of contour limits of the landslide as input. The calculation procedure is simple; it evaluates the depth of the failure surface at each point of the grid. The cross-section for each point is estimated perpendicular to the slope line joining the highest elevation point and the lowest elevation point within the contour limits. A cubic spline curve is calculated using two endpoints and the first derivative at these points. The Z value is calculated using these parameters at each grid point (centre of a grid), and finally, a 3D failure surface is generated, enabling the volume calculation. MATLAB code has been written to execute this process automatically. This model can easily use input data with very little fieldwork, and the processing can produce a result in a few minutes. We can enrich a real scenario by imposing more constraints on the variation in the cross-section angle based on the limit of the landslide using field measurements. This model is also helpful in predicting the area affected by the mass of the failure surface, and we can determine a predefined hazard zone to avoid any casualties in the future. [ABSTRACT FROM AUTHOR]

On October 2017, due to continuous rainfall, there was a massive river blockage induced by a landslide in Guang'an Village, Chongqing, China. Long-term monitoring analysis has shown that there remain four strong deformation areas on the slope, which seriously threaten the life and property of nearby residents. In this paper, a granular flow model and an elasto-visco-plasticity model were applied to reproduce and predict the landslide event that hit Guang'an Village. The results showed that the landslide gradually moved along the sliding surface, pushing loose deposits and blocking the Xixi River. The numerical reproduction results of the 2017 event are consistent with the actual slope deformation and failure process and deposit morphology. The simulated maximum depth-averaged velocity of this landslide was approximately 1.89 m/s, and the height of the landslide dam was approximately 10 m. After the landslide occurred in 2017, several large deformation areas appeared in the vicinity of the sliding area, and the right rear side of the sliding mass in area III has the largest deformation volume, accompanied by the most developed surface crack and the most intense deformation. There is a risk that the Xixi river will be blocked again. Therefore, with the same parameter and numerical model, a sliding–pushing–blocking dynamic prediction analysis of the strong deformation area III was conducted. The pushing motion of the mass in this area will reactivate the landslide mass observed in 2017. The maximum depth-averaged velocity of deformation area III was 0.5 m/s, and the maximum depth-averaged velocity of landslide deposition was 0.45 m/s. The length of the blocking dam formed by the mass of deformation area III along river was approximately 780 m, 30 m longer than that in 2017. The predicted height of the landslide dam was 14.5 m, approximately 4.5 m higher than that in 2017. The length of the landslide dam reservoir was predicted to be 2.55 km along the Xixi River, which may submerge the Waping Village. This study supports the landslide hazard prevention, reveals the whole movement process of sliding-pushing-blocking, and provides a new research idea and method for the landslide movement prediction. Hence, this study can serve as a reference for the hazard prevention and mitigation of such chain disasters. [ABSTRACT FROM AUTHOR]

As the global climate warms, the frequency of soil slope failures induced by snowmelt is gradually increasing, especially loess landslide disasters in seasonally frozen areas. Landslide disasters in seasonally frozen areas pose a serious threat to human lives and engineering constructions and are gradually drawing greater public attention around the world. However, the mechanism that regulates deformation and failure processes, resulting in snowmelt-driven landslides, remains elusive. The Yili River Valley in Xinjiang, China, is an ideal location for exploring the mechanism of snowmelt-driven landslides. Based on detailed field surveys, remote sensing image identification, meteorological data analysis and loess characteristic tests, this paper takes a representative high-level loess landslide in north-west China as a case in order to explore its evolution history, movement process and resurrection mechanism. It was found that this loess slope had suffered two large-scale sliding failures and different degrees of inherited slope deformation had been found between the two slope failures. The 32 surface cracks identified have a total length of 3,505.10 m, and the total area of the landslide was found to be 135,462m2. The average thickness of the sliding body was about 30 m, and the volume was approximately 504,000 m3. The actual movement time lasted for 32 s with an average moving speed of about 15 m/s. Human grazing activities dominate the formation and development of slope surface cracks in the early stage. The strong water sensitivity of distinctive loess controls soil strength deterioration in the slip zone. Rapid snow removal and infiltration, driven by an abnormal temperature rise in Spring, is the most important triggering factor for slope deformation, evolution and failure. As regional grazing activities increase and global warming intensifies, the potential for resurrection landslides will increase. The results provide essential information for a comprehensive understanding of early warning systems and risk assessment for snowmelt-triggered landslides in cold areas. [ABSTRACT FROM AUTHOR]

Limited by the independence and its defects of each general software package, simultaneous analysis of runoff, seepage, and large-deformation analysis is still an inevitable challenge. Generally, one of seepage, landslide-related large-deformation, and runoff is ignored or indirectly assessed during unsaturated soil landslide runout simulation. To provide a brand new solution, this paper declares a local shear strength (LSS) method to evaluate rainfall/runoff-induced reduction of the unsaturated soil shear strength. After that, a hybrid coupled hydro-mechanical framework is proposed to simulate rainfall/runoff-induced landslide runout within an unsaturated soil slope. The decrease in local shear strength corresponding to the decrease in matric suction is defined by shifting the Mohr–Coulomb (M-C) failure envelope towards compressive stress space during rainfall/runoff infiltration. Based on the proposed local shear strength method, the variable matric suction obtained from the bidirectionally coupled runoff and seepage analysis in FEM is unidirectionally transferred to the variable local shear strength for each soil material point in MPM (i.e., this is a FEM-MPM hybrid coupled model). Then, the correctness of the proposed hybrid coupled hydro-mechanical framework is effectively verified by a hypothetical homogeneous slope model. The results show that the slope stable/unstable state simulated by the proposed hybrid coupled hydro-mechanical framework has a good consistency with that simulated by the shear strength reduction technique (SSRT) and limit-equilibrium method (LEM). Afterward, combined with a case study of a natural landslide in Hokkaido, Japan, it is proved to be effective for simulating landslide runout subjected to rainfall/runoff infiltration by using the proposed hybrid coupled hydro-mechanical framework in an unsaturated soil slope. [ABSTRACT FROM AUTHOR]