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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]

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]

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]

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]

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]

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]

The primary aim of this paper is to highlight the importance of scientific collaboration for landslide disaster risk reduction in Mexico. Drawing upon specific undertakings into applied research, this article attempts to outline the emerging role of the ICL World Centre of Excellence (WCoE) based in the Institute of Geography (IGg) of the National Autonomous University of Mexico (UNAM), Mexico City. This paper begins by offering a brief introduction of the significance of WCoEs in the international landslide disaster research sphere. In the second section, general information of the institutional framework and the IGg-UNAM WCoE is provided. The third part contextualises the landslide research engagement with reference to the activities carried out under the umbrella of the International Consortium on Landslides (ICL). Overall insights are offered in the final section. [ABSTRACT FROM AUTHOR]

In the last 10 years, also due to climate change, extreme rain events have affected the Basilicata region (southern Italy) causing landslides and floods that have damaged urban fabric, commercial activities and transport infrastructures. In many of these cases, the civil protection system, involving national (DPC) and regional (DRPC) Civil Protection Departments, was activated to manage the emergency phase in cooperation with local administrations and scientific institutions, which in this context are referred to as competence centres (CdCs). Among the latter, the Institute of Methodologies for Environmental Analysis (IMAA) of the Italian National Research Council (CNR) has been frequently involved in carrying out geophysical investigations in landslide areas, especially during the post-event phase. This paper reports the main results of the in-field geophysical surveys carried out in two areas of the Basilicata region affected by significant landslides in the last 10 years. The aim of the surveys was to provide the DRPC technicians with a useful subsoil geophysical model to improve the knowledge of the geological setting of the slope, to reconstruct the geometry of landslide body and to highlight high water content areas, in order to support the decision-making process. At the end of the paper, a discussion follows with the lessons learned from each case study along with recommendations on how to possibly improve the application of geophysical techniques in landslide investigations in order to further increase their impact. [ABSTRACT FROM AUTHOR]

Large-scale landslides as a common geological hazard can be easily triggered by natural factors, while it is extremely difficult to be activated by anthropolical blasting operations. The paper presents a comprehensive analysis of characteristics of stress wave propagation in a blasting-triggered large-scale rockslide, termed Wangshan–Zhuakousi Landslide, to investigate the effect of blasting wave on multi-layer interface condition. A detailed analysis of the propagation of P wave in the filling joints was conducted to verify the reliability of the analytical model and the deterioration characteristics of the weak interlayer. The results indicate that the reflection tensile wave can be formed when P wave propagates from the basalt across the tuff interlayer. The theoretical equation for damage assessment of soft rock interlayers is proposed, which can be adopted in calculating the additional stress of soft rock mass in blasting operation. It is found that the material density is the most influential factor in attenuation rate. Besides, the longitudinal wave velocity of materials and the period of incident waves perform the identical effect on the attenuation rate. It is demonstrated that the cumulative failure of tuff layer plays a crucial role in triggering successive sliding. According to the modelling, the tuff interlayer appears to be in a compressed state before the complete damage under P-wave propagation. The locally tensile behaviour can be observed after the complete damage of the tuff. The underlying tuff interlayer forms a discontinuous cumulative damage zone, which significantly triggered major ruptures and destabilized the original bedding rock slope. This paper could prove new insight towards the enforcement of risk assessments and mitigation countermeasures for blasting-induced landslide. [ABSTRACT FROM AUTHOR]

Long-runout landslides can be extremely hazardous and unpredictable. The prediction of long-runout landslides is challenging because it is unclear what factors control their mobility, and it is unclear how to best measure their runout given that they traverse changing topography. In this paper, we document the development and statistical evaluation of a Landslide Runout Score (LRS) system to predict short, medium, and long runout using a new mobility measure, the unitless Runout Number L/A1/2 (where L is landslide length and A is landslide area). The Runout Number has previously been correlated to three geomorphological factors (planimetric curvature, sand content, and upslope contributing area normalized to landslide area), so the LRS uses these factors as inputs. These factors are readily calculated using geographic information systems (GIS) and publicly available data sources. The LRS system predicts runout categories (short, medium, and long) with one-vs.-all accuracies (i.e., percentage of correct predictions within a given class) of 75, 58, and 72%, respectively, for a total weighted accuracy of approximately 65%. The results of this work are summarized in a worksheet that can be used by geologists and engineers to develop preliminary predictions of landslide runout behavior, which can be incorporated into or used alongside regional-scale landslide hazard assessments. [ABSTRACT FROM AUTHOR]

This paper submits a report on the effective adoption of machine learning algorithms for enhancing the reliability of rainfall-induced landslides. The challenges involved in the design of reliable landslide early warning systems (LEWS) and the data-driven context for overcoming these challenges have been presented. The operation of LEWS is explained using the chain of five major components (i) Data collection, (ii) Data transmission, (iii) Modelling, analysis and forecasting, (iv) Warning, and (v) Response. Failure of any of these major components of the LEWS will break the chain of operation of LEWS and the ensued consequences of each component failure are reviewed. Inferences drawn from the analysis of the reliability measures incorporated in 12 LEWS deployments across a dozen locations around the world are also presented. Based on the investigations from 12 LEWS and the real-world experience, we identified that an alternate solution is required for ensuring the reliability of LEWS, especially during disaster scenarios when warnings are crucial, but data availability is a constraint. We recognized that machine learning algorithms can provide an alternate solution and in this paper, we have discussed two machine learning approaches nowcasting and forecasting for enhancing the reliability. Both the algorithms employ historic data of the landslide monitoring parameters to learn the changes materializing in slope leading to landslide incidences. The learned knowledge is used to nowcast and forecast the real-time and future conditions of the slope from the real-time landslide monitoring parameters. In terms of ensuring reliability, (i) Nowcasting algorithm provides an alternate solution if either the Data collection component or Data transmission component of a LEWS fails. (ii) Forecasting algorithm provides extra lead-time for early warning and solves the problem of less lead-time during early warning process. The breakthrough is even when the real-time landslide monitoring parameters are not available for various reasons, these algorithms take the minimal input of rainfall forecast information for nowcasting and forecasting thus restoring the broken chain of operation of LEWS. [ABSTRACT FROM AUTHOR]

Geomorphological analysis of landslide processes in mountainous terrains with difficult access has benefited from virtual representation of topography through the use of high-resolution digital elevation models (DEMs) generated by use of light detecting and ranging. Generic models of overlay and interpolation take advantage of the high-resolution DEMs for volume calculation. However, the advantages and limitations of the models at regional scale have received scant attention. These challenges are magnified in landslide hazard zonation mapping projects at state or national level, where the models need to be implemented for large datasets. To address the above deficiency, this paper presents a means to estimate landslide volume production and distribution by taking full advantage of LiDAR and to standardize landslide volume calculation in a geographic information system (GIS). We implemented two generic landslide volume models by using Python scripts; this is a systematic methodology for modeling volume of shallow and deep-seated landslides. The models were tested in real and theoretical conditions to highlight advantages and limitations. At the same time, we explored how the interpolation model is affected by local altimetric variation. The results show that one of the models can be used to make first-order interpretations regarding volume of eroded debris for landslide deposits at a local or national scale, while the other can help to assess the sequence of landslide activity. Theoretical evaluations show that local altimetric variation of < 1 m could lead to errors of almost 17%. The approach is explored with examples from Sumas Mountain in Whatcom County, Washington, USA. [ABSTRACT FROM AUTHOR]

Landslide is a uniquely dynamic large-deformation process that can present serious threat to human lives and infrastructures. The natural soil properties often exhibit inherent spatial variability, which affects the landslide behavior significantly. This paper focuses on combined Monte Carlo simulation and three-dimensional (3D) dynamic large-deformation finite-element (LDFE) analysis using the coupled Eulerian-Lagrangian method to investigate the whole runout process of landslide induced by the earthquake in spatially varying soil. The results from LDFE analysis show that the mean value of runout distance in spatially varying soil is significantly higher than that of the deterministic value obtained from a homogeneous slope due to the slope failure developed along the weakest path in soils. The mean runout distance increases and converges with increasing slope length in 3D-LDFE stochastic analysis. The advantages and necessities of 3D-LDFE analysis were illustrated by comparing it with two-dimensional (2D) LDFE analysis of landslide in spatially varying soil. The results show that the calculated mean runout distance using 3D-LDFE method is at least 16.1% higher than that calculated using 2D-LDFE analysis. Finally, a linear regression formula was established to estimate the mean runout distance of landslide due to horizontal inertia acceleration. Such a formula may facilitate the risk assessment of landslide in practical engineering. [ABSTRACT FROM AUTHOR]

At about 12:00 a.m., on October 13, 2020, a rapid rotational landslide induced by rainfall swept over Ranger Station-7 in Phong Xuan commune, Phong Dien district, Thua Thien Hue province, Vietnam, claiming the lives of 13 rescue team members. This paper presents an overview of the fatal landslide at Ranger Station-7. The analysis indicates that the entire landslide had a volume of approximately 81,550 m3. The observed topographical features indicate that the landslide mass consists of lower and upper blocks. The lower block of the landslide mass started sliding first; the movement was then followed by the slide of the upper block. [ABSTRACT FROM AUTHOR]

Glacial lakes represent a threat for the populations of the Andes and numerous disastrous glacial lake outburst floods (GLOFs) occurred as a result of sudden dam failures or dam overtoppings triggered by landslides such as rock/ice avalanches into the lake. This paper investigates a landslide-triggered GLOF process chain that occurred on February 23, 2020, in the Cordillera Vilcabamba in the Peruvian Andes. An initial slide at the SW slope of Nevado Salkantay evolved into a rock/ice avalanche. The frontal part of this avalanche impacted the moraine-dammed Lake Salkantaycocha, triggering a displacement wave which overtopped and surficially eroded the dam. Dam overtopping resulted in a far-reaching GLOF causing fatalities and people missing in the valley downstream. We analyze the situations before and after the event as well as the dynamics of the upper portion of the GLOF process chain, based on field investigations, remotely sensed data, meteorological data and a computer simulation with a two-phase flow model. Comparison of pre- and post-event field photographs helped us to estimate the initial landslide volume of 1–2 million m3. Meteorological data suggest rainfall and/or melting/thawing processes as possible causes of the landslide. The simulation reveals that the landslide into the lake created a displacement wave of 27 m height. The GLOF peak discharge at the dam reached almost 10,000 m3/s. However, due to the high freeboard, less than 10% of the lake volume drained, and the lake level increased by 10–15 m, since the volume of landslide material deposited in the lake (roughly 1.3 million m3) was much larger than the volume of released water (57,000 m3, according to the simulation). The model results show a good fit with the observations, including the travel time to the uppermost village. The findings of this study serve as a contribution to the understanding of landslide-triggered GLOFs in changing high-mountain regions. [ABSTRACT FROM AUTHOR]

India ranks first in the world in terms of fatal landslides. Large vulnerable area (0.42 million km2), high population density and monsoon rainfall make India's landslide management and mitigation task difficult. Therefore, an understanding of landslide occurrences and exposure of socio-economic parameters on a national scale is essential to prioritise landslide mitigation efforts. In this paper, a database of 45,334 landslides (polygons) in India mapped by the National Remote Sensing Centre (NRSC) during the 1998–2018 period was compiled and catalogued in a WebGIS platform. High-resolution satellite data such as IRS PAN+LISS-III, Resourcesat LISS-IV Mx, Cartosat, WorldView, Pleiades and GeoEye were used to map landslides as small as 12 m2 to as big as 1,390,350 m2. GIS analysis using the landslide inventory revealed interesting results about control, exposure and pattern of landslide occurrences in India. The Northwest Himalayas contribute 66.5% of landslides in India, followed by the Northeast Himalayas (18.8%) and the Western Ghats (14.7%). The Greater Himalayan sequence consisting of high-grade metamorphic rocks has a considerable control (32%), and the Main Central Thrust is the major regional structure controlling (12%) landslides in India. In the Northeast Himalayas, the size of landslides and the slope gradient controlling landslide occurrence are less in comparison to the Northwest Himalayas and the Western Ghats. Landslides in the Western Ghats are triggered with a lesser amount of rainfall than the Himalayan regions. Exposure analysis using four key socio-economic parameters in the 145 hilly districts shows that Rudraprayag district is most affected by landslides in India. The understanding derived using the landslide database on a national scale will help to prioritise and strengthen landslide disaster risk reduction strategies in India. [ABSTRACT FROM AUTHOR]

This paper proposes a mechanism of earthquake-induced landslides with pulse-like ground motion (PLGM) based on the discontinuous deformation analysis (DDA) method. In recent near-fault earthquake-induced landslides, the two phenomena confusing landslide researchers are observed. One is that large-scale landslides occurred in the area with small PGA but no landslide in the area with large PGA, for example, the landslides occurred during the 2016 Kumamoto earthquake. The other is that one side slopes collapsed while the opposite side slopes back to back remain stable. For example, most west-faced slopes collapsed while the east-faced slopes back to back remain stable in the 2018 Hokkaido earthquake. To explain the two phenomena in near-fault earthquake-induced landslides with PLGM, a symmetrical slope model subjected to different ground motions during these two earthquakes are analyzed and discussed by using the DDA method. The analysis results show that PLGM may be a major triggering factor for co-seismic landslides in the near-fault region, while PGA may not be the right parameter for the initiation of the earthquake-induced landslides. Meanwhile, the aspect of collapsed slope is related to both the velocity pulse of PLGM and strength parameters of the slope. The proposed mechanism for the earthquake-induced landslides in the near-fault region with PLGM considers both tensile and shear strengths of the slope, which can well explain the landslide initiation phenomena in the 2016 Kumamoto earthquake and the 2018 Hokkaido earthquake. The relationship between PLGM and forward directivity effect is also studied in combination with two earthquake cases to more accurately clarify the mechanism of the initiation of co-seismic landslides in the near-fault region. [ABSTRACT FROM AUTHOR]

Landslides are regarded as significant geological hazards across the world, causing serious economic losses and casualties. The understanding on deformation characteristics and failure mechanisms of landslides plays the vital roles in slope stability evaluation and reinforcement design. In this study, the deformation characteristics and failure mechanism of the Xiaomiaoling talus slope were analyzed based on field monitoring data. In addition, as it was difficult to measure the shear strength parameters of the rock–soil mixture due to its complex spatial structure and variable material composition, a displacement back analysis based on the back-propagation neural network (DBA-BPNN) was proposed to determine the shear strength parameters of the rock–soil mixture. The analytical results show that deformation of the Xiaomiaoling talus slope was that of a typical traction landslide, which has the characteristics of progressive failure, and major slope deformation was triggered by excavation and rainfall. According to field monitoring data, the shear strength parameters of the rock–soil mixture could be determined. The predicted cohesion and internal friction angle of the rock–soil mixture were 10.84 kPa and 19.51°, respectively, and the predicted and test values were in good agreement. The method proposed in this paper can provide references for the design and construction in geotechnical engineering. [ABSTRACT FROM AUTHOR]

The displacement of a large slow-moving landslide is accompanied by slope damage, such as fractures, tension cracks, and slope bulging. Studies of these features provide insight into the mechanisms responsible for the deformation. In this paper, we investigate slope damage at the Downie Slide, a very large landslide in British Columbia, Canada, that is slowly moving along two shear zones subparallel to the ground surface. Structural geology, and particularly the morphology of the lower shear zone, strongly controls the deformation and, in turn, the observed internal and surficial slope damage features. We use aerial and underground adit laser scanning and photogrammetry to characterize the geometry of the landslide. We subdivide the slide area into slope damage domains based on the distribution, size, and orientation of the slope damage features. We reconstruct the shape of the two failure surfaces by interpolating borehole inclinometer monitoring data and processing the 3D surfaces in a GIS environment to create aspect and slope maps. We observe a strong correlation between surficial slope damage features and changes in the dip and dip direction of the lower sliding surface. We further infer the presence of a multi-planar failure surface geometry and previously unrecognized structurally controlled damage zones. [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]

The main aim of this study is the description and the analysis of the monitoring strategies implemented within local landslide early warning systems (Lo-LEWS) operational all around the world. Relevant information on 29 Lo-LEWS have been retrieved from peer-reviewed articles published in scientific journals and proceedings of technical conferences, books, reports, and institutional web pages. The first part of the paper describes the characteristics of these early warning systems considering their different components. The main characteristics of each system are summarized using tables with the aim of providing easily accessible information for technicians, experts, and stakeholders involved in the design and operation of Lo-LEWSs. The second part of the paper describes the monitoring networks adopted within the considered systems. Monitoring strategies are classified in terms of monitored activities and methods detailing the parameters and instruments adopted. The latter are classified as a function of the type of landslide being monitored. The discussion focuses on issues relevant for early warning, including appropriateness of the measurements, redundancy of monitoring methods, data analysis, and performance. Moreover, a description of the most used monitoring parameters and instruments for issuing warnings is presented. [ABSTRACT FROM AUTHOR]

The topic of rainfall thresholds for landslide occurrence was thoroughly investigated, producing abundance of case studies at different scales of analysis and several technical and scientific advances. We reviewed the most recent papers published in scientific journals, highlighting significant advances and critical issues. We collected and grouped all the information on rainfall thresholds into four categories: publication details, geographical distribution and uses, dataset features, threshold definition. In each category, we selected descriptive information to characterize each one of the 115 rainfall threshold published in the last 9 years. The main improvements that stood out from the review are the definition of standard procedures for the identification of rainfall events and for the objective definition of the thresholds. Numerous advances were achieved in the cataloguing of landslides too, which can be defined as one of the most important variables, together with rainfall data, for drawing reliable thresholds. Another focal point of the reviewed articles was the increased definition of thresholds with different exceedance probabilities to be employed for the definition of warning levels in landslide early warning systems. Nevertheless, drawbacks and criticisms can be identified in most part of the recent literature on rainfall thresholds. The main issues concern the validation process, which is seldom carried out, and the very frequent lack of explanations for the rain gauge selection procedure. The paper may be used as a guide to find adequate literature on the most used or the most advanced approaches followed in every step of the procedure for defining reliable rainfall thresholds. Therefore, it constitutes a guideline for future studies and applications, in particular in early warning systems. The paper also aims at addressing the gaps that need to be filled to further enhance the quality of the research products in this field. The contribution of this manuscript could be seen not only as a review of the state of the art, but also an effective method to disseminate the best practices among scientists and stakeholders involved in landslide hazard management. [ABSTRACT FROM AUTHOR]

Giant landslides can cause significant damage to their dynamic runout processes. Understanding the mechanics of the runout process is essential for landslide risk assessment and mitigation design. This paper investigates the runout process of the 1.16 × 109 m3 giant Daguangbao landslide triggered by the 2008 Ms 8.0 Wenchuan earthquake. The Daguangbao landslide is a typical bedding-plane slide and the properties of basal sliding surface play a key role in the runout behaviors of the landslide. The material point method (MPM) is adopted as a numerical tool of the simulations. A thermal-based velocity-weakening friction law is used to simulate the contact between the sliding body and the basal sliding surface. A strain-softening constitutive model is used to evaluate the damages inside the landslide body and their effects on the runout behaviors of the landslide. Numerical results reveal that landslide mass slides along the bedding-plane as a whole body initially and then disintegrates during the runout process. The calculated duration of sliding is 64 s and the maximum velocity reaches 60 m/s. The friction coefficient of the slip surface decreases sharply as the landslide body starts to move, and a steady-state friction coefficient μ ≈ 0.06 is reached when the velocity exceeds approximately 20 m/s. Friction degradation of the slip surface shows a sensible influence on the final runout distance and the depth of the deposit zone. The dynamic fragmentation of the landslide body, the final runout distance, and deposition topography are also significantly affected by material softening. [ABSTRACT FROM AUTHOR]

This paper reports a feasible alternative to compile a landslide inventory map (LIM) from remote sensing datasets using the application of an artificial intelligence–driven methodology. A deep convolutional neural network model, called LanDCNN, was developed to generate segmentation maps of landslides, and its performance was compared with the benchmark model, named U-Net, and other conventional object-based methods. The landslides that occurred in Lantau Island, Hong Kong, were taken as the case study, in which the pre- and post-landslide aerial images, and a rasterized digital terrain model (DTM) were used. The assessment reveals that LanDCNN trained with bitemporal images and DTM yields the smoothest and most semantically meaningfully LIM, compared to other methods. This LIM is the most balanced segmentation results, represented by the highest F1 measure among all analyzed cases. With the encoding capability of LanDCNN, the application of DTM as the input renders better LIM production, especially when the landslide signatures are relatively subtle. With the computational setup used in this study, LanDCNN requires ~ 3 min to map landslides from the datasets of approximately 25 km2 in area and with a resolution of 0.5 m. In short, the proposed landslide mapping framework, featured LanDCNN, is scalable to handle the vast amount of remote sensing data from different types of measurements within a short processing period. [ABSTRACT FROM AUTHOR]

Landslide disasters are associated with severe losses on the Loess Plateau of China. Although early warning systems and susceptibility mapping have mitigated this issue to some extent, most methods are qualitative or semi-quantitative in the site-specific range. In this paper, a quantitative spatial distribution model is presented for site-specific loess landslide hazard assessment. Coupled with multi-temporal remote sensing images and high-precision UAV cloud point data, a total of 98 loess landslides that have occurred since 2004 on the Heifangtai terrace were collected to establish a landslide volume-date and retreating distance database. Eleven loess landslides are selected to construct a numerical model for parameter back analysis, and the accuracy of the simulation results is quantitatively evaluated by the centroid distance and overlapping area. Different volumes and receding distance rates of landslides are fitted to determine the relationship between cracks and potential volume, and different volumes and parameters are combined to simulate the spatial distribution of potential loess landslides. The results of this study reveal that landslide volumes mainly range between 1 × 103 and 5 × 105 m3, and the historical occurrence probability reaches 0.551. The optimal parameters are estimated by the maximum likelihood method to obtain a uniform distribution parameter value probability model, and the results show that the error of the estimated length within a range of 0.05 from the optimal parameter does not exceed 15%. In the selected slope slide case, farmland near the toe of the slope primarily includes exposed hazards with probabilities greater than 0.7. This work provides a useful reference for local disaster reduction and a theoretical methodology for hazard assessments. [ABSTRACT FROM AUTHOR]

The reconnaissance, mapping and analysis of kinematic features of slow-moving landslides evolving along medium-deep sliding surfaces in urban areas can be a difficult task due to the presence and interactions of/with anthropic structures/infrastructures and human activities that can conceal morphological signs of landslide activity. The paper presents an integrated approach to investigate the boundaries, type of movement, kinematics and interactions (in terms of damage severity distribution) with the built environment of a roto-translational slow-moving landslide affecting the historic centre of Lungro town (Calabria region, southern Italy). For this purpose, ancillary multi-source data (e.g. geological-geomorphological features and geotechnical properties of geomaterials), both conventional inclinometer monitoring and innovative non-invasive remote sensing (i.e. A-DInSAR) displacement data were jointly analyzed and interpreted to derive the A-DInSAR-geotechnical velocity (DGV) map of the landslide. This result was then cross-compared with detailed information available on the visible effects (i.e. crack pattern and width) on the exposed buildings along with possible conditioning factors to displacement evolution (i.e. remedial works, sub-services, etc.). The full integration of multi-source data available at the slope scale, by maximizing each contribution, provided a comprehensive outline of kinematic-geometric landslide features that were used to investigate the damage distribution and to detect, if any, anomalous locations of damage severity and relative possible causes. This knowledge can be used to manage landslide risk in the short term and, in particular, is propaedeutic to set up an advanced coupled geotechnical-structural model to simulate both the landslide displacements and the behavior of interacting buildings and, therefore, to implement appropriate risk mitigation strategies over medium/long period. [ABSTRACT FROM AUTHOR]

The great M ~ 8 1920 Haiyuan earthquake (HYEQ) was one of the largest and most deadly earthquakes in China in the last century, with ~ 234,000 deaths. The earthquake occurred within the Loess Plateau of northern China, where Quaternary loess deposits form a distinctive blanket across the landscape. Large regions of this loess cover experienced co-seismic landslides. Based on an analysis of the original disaster reports, field surveys, and satellite image interpretation, we have compiled the shaking effects of the earthquake, including the distribution of landslides, fatalities, and structural damage. Landslides triggered by the HYEQ (n > 7,000) are concentrated south of the Haiyuan fault, in a region that has both thick loess cover and long-term relief generated by the drainage network. This distribution is spatially separated from landslides triggered by other earthquakes. We find that in contrast to previous studies, the most important factor in the severe death toll of the HYEQ was the collapse of housing by ground shaking, including collapse of loess house-caves. Landslides were a secondary factor; although up to 32,000 deaths occurred in areas with intense landsliding. Based on the revised distribution pattern of landslides and damage (e.g., house collapses), we suggest that the isoseismal intensity IX line extends south of previous locations. We have also identified 126 dammed lakes created by co-seismic landslides, which form major modifications of this semi-arid landscape. The research methods in this paper, combining historical review, satellite image interpretation, and field validation of landslides, can be used as a reference for studies of other areas affected by historical earthquakes and co-seismic landslides, elsewhere in the Loess Plateau and beyond. [ABSTRACT FROM AUTHOR]

The department plays active roles in the international debris flow and steep creek hazards mitigation committees such as; Association of Geohazard Professionals (AGHP), the Technical committee on slope stability (TC208); International Society for Soil Mechanics and Geotechnical Engineering (ISSMGE), and the Joint technical committee No. 1 (JTC1) on natural slopes and landslides of Federation of the International Geoengineering Societies (FedIGS). The titles of these works are as follows: "AHP Method Applied to Landslide Susceptibility Mapping in pilot sites of Tegucigalpa, 2016" by the IHCIT-UNAH and "Coupling Antecedent Rainfall and Intensity-Duration Thresholds for Landslide Occurrence in Tegucigalpa, Honduras, 2010" by the Department of Civil Engineering of the UNAH. In addition, during that period and within the framework of the Project, ICL/IPLHQ contributed to the preparation and publication of a Paper within the ISDR-ICL Landslide Interactive Teaching Tool section of the Landslide Dynamics book (https://www.researchgate.net/publication/322841159 TXT-tool 450411 How to Make a Database of Landslides in Tegucigalpa Honduras). Camilab has a large landslide simulator (http://www.camilab.unical.it/web/camilab/laboratori), equipped with the most sophisticated technology and tools that allow the reproduction of real cases of slopes subject to instability, the study of physical phenomena underlying the triggering of landslides, and the measurement of the effectiveness of devices designed for the mitigation of landslide risk. [Extracted from the article]

In high mountain canyon regions, SAR geometric distortion in imaging side may have an inevitable impact on InSAR deformation information, so the effective deformation information acquisition is critical for landslide identification and deformation mechanisms analysis. The landslide deformation around the reservoir of Gushui Hydropower Station located in upstream of the Lancang River has been focused on in the study. Using SAR satellite parameters and topographic information, the visibility analysis of deformation in radar line-of-sight (LOS) direction has been carried out, and a method to obtain LOS effective deformation information based on the visibility analysis has been proposed. The small baseline subsets (SBAS) technique is used to process the L-band and C-band SAR data, and the area affected by the geometric distortion in the InSAR result is masked to obtain the deformation information of the effective deformation region. The landslide identification analysis in the reservoir area has been carried out based on the effective deformation information in LOS direction. Thirteen landslides have been identified, and ten of them are new ones. A new large unstable area (New Zhenggang landslide) has been found near the Zhenggang landslide. The geological survey and displacement time series of the Zhenggang landslide reveals that it is in pull-type landslide mode, that is, due to the local instability of the leading edge of a landslide, the support of the trailing edge may be weakened, which may result in the landslide gradually developing backwards and upwards, and finally becoming a large landslide. The impact of peak rainfall and cumulative rainfall during the rainy season on landslide deformation has been verified in this paper. It indicates that the cumulative precipitation is the dominant factor causing the deformation of the landslide, and it shows that the landslide begins the deformation acceleration period about 12 days after the peak precipitation. The results have shown that the proposed visibility analysis method for extracting the effective deformation information of InSAR results can significantly improve landslide identification and analysis in complex terrain. [ABSTRACT FROM AUTHOR]

A year after the establishment of the Sendai Framework for Disaster Risk Reduction 2015–2030 (SFDRR), the science and technology community (STC) endorsed in Geneva the UNISDR Science and Technology Roadmap to Support the Implementation of the SFDRR 2015–2030 (STR-SFDRR). Conducted actions by the International Consortium on Landslides (ICL) reflect priorities and challenges at different scales with regard to the progress of multi-sectoral partnerships, recognising the key role of the STC for the implementation of the SFDRR. Central to such endeavour are the Sendai Landslide Partnerships 2015–2025 and the new-fangled Kyoto Landslide Commitment 2020. While the former was conceived as a strategy for global promotion of understanding and reducing landslide disaster risk, the latter is directed to advocate for harmonic cohesiveness between the Sendai Landslide Partnerships 2015–2025, and the SFDRR, the 2030 Agenda Sustainable Development Goals, the New Urban Agenda and the Paris Climate Agreement. By encompassing the linkages of the contributions of the ICL community to the expected outcomes of the STR-SFDRR, this paper provides valuable input to foster the SFDRR, and provides concrete information on the ongoing ICL initiatives, actions and deliverables for strengthening partnerships and science-informed public policies to reduce landslide disaster risk and to advance Integrated Landslide Disaster Risk Management at different scales. [ABSTRACT FROM AUTHOR]

This paper presents details of the recently reactivated landslide in Wushan Town, Chongqing, China. The landslide was reactivated on July 17, 2019, by slope cutting, and thereafter, entered a state of imminent sliding. The landslide involved 4 million m3 of rock and soil masses, thereby threatening National Road G348 and the safety of 588 residents in 136 households in Xiping Village and over 1000 residents in the Jinke Community. Field investigations, drilling, and in situ monitoring were performed to determine the landslide deformation characteristics and reactivation mechanism. The results show that the regional abundant rainfall, formation lithology, and tectonic effects were responsible for the formation of the ancient Baiyangwan landslide. Moreover, the building load on the rear and middle parts increased the sliding force. Open excavation at the toe decreased the anti-sliding force and directly promoted landslide reactivation. In particular, the groundwater table rise caused by gully filling in recent years also played a key role in the reactivation of the ancient landslide. [ABSTRACT FROM AUTHOR]

Multidisciplinary approaches for the characterization of deep-seated landslides (DSLs) are useful in comprehending the internal and external dynamics of the phenomena. This paper examines and characterizes the active Cañón de Yerbabuena DSL (in Ahualulco, San Luis Potosi, Mexico), for which there are over 30 years of damage records to Federal Highway 63. Hence, the integrated study carried out includes lithological and structural characterization, geomorphological mapping, Persistent Scatterer Interferometry (PSI) analysis, and electrical geophysical surveys. The slope deformation extends over the highly weathered and fractured volcanic rocks of the Ahualulco Volcanic Complex in an area comprising 741,000 m2 and mobilizes an estimated rock volume of 6,950,459 m3. Geomorphological features within the DSL include a counter-scarp, minor scarps, tension cracks, small landslides, avalanche breccias, toppling, and colluvial deposits. Furthermore, the velocity rate in the upper section of the slope reaches up to 10 mm/year with a linear behavior that changes to a faster non-linear deformation (up to 30 mm/year) in the middle-lower part of the DSL. This change is the result of a multiple-complex dynamic linked to the presence of two normal faults fragmenting the middle section of the slope and an interbedded volcaniclastic clayey-siltstone layer acting as a possible sliding plane; the latter being defined by a resistivity survey. The acceleration of the velocity rates at the foot of the DSL are linked to torrential rains in the area and cause the most severe damage to the highway. Furthermore, the general slow deformation within the DSL is triggered by severe erosion from the seasonal river at the foot and the run-off on both flanks. [ABSTRACT FROM AUTHOR]

This paper shows the results of a study aimed at quantitatively estimating—in terms of direct (repair) costs, at large scale (1:5000)—the slow-moving landslide risk to a road network assumed as undamaged as well as the consequences to the same network in damaged conditions. The newly conceived methodological approaches address some challenging tasks concerning (i) the hazard analysis, which is expressed in terms of probability of occurrence of slow-moving landslides with a given intensity level that, in turn, is established based on empirical fragility curves, and (ii) the consequence analysis, which brings to the generation of time-dependent vulnerability curves. Their applicability is successfully tested in a case study in the Campania region (southern Italy) for which both very high-resolution DInSAR data and information gathered from in situ surveys on the severity of damage sustained by the selected road sections are available. Benefits associated with the use of the obtained results in informed decision-making processes are finally discussed. [ABSTRACT FROM AUTHOR]

The accuracy of landslide displacement prediction can effectively prevent casualties and economic losses. To achieve accurate prediction of the Majiagou landslide displacement in the Three Gorges Reservoir (TGR), China, a hybrid machine learning prediction model considering the deformation hysteresis effect is proposed. The real-time deep displacement measurements were captured by using in-place inclinometers with Fiber Bragg grating (FBG) sensors. The time series method was adopted to divide the total displacement into a trend term and periodic term. Trend displacement was determined by the geological condition and predicted by the fitting method. Periodic displacement was controlled by external factors such as rainfall and fluctuation of reservoir water level. Before making the prediction, the grey correlation analysis was adopted to confirm that the fluctuation of the reservoir water level was the main influence factor. In view of the deficiency that current prediction methods could not quantitatively determine the lag time of landslide deformation and thus select the influencing factors empirically, the dynamic analysis of the correlation between periodic influence factors and periodic displacement was carried out in this paper, and the deformation lag time was identified to be 18 days by using set pair analysis (SPA) method. Finally, the optimal influence factors were selected and the prediction model of Majiagou landslide based on support vector machine optimized by particle swarm optimization (SPA-PSO-SVM) was established. Results showed that the root mean square error (RMSE) and the mean absolute percentage error (MAPE) of the proposed SPA-PSO-SVM prediction model are 0.28 and 12.8, respectively. Compared with the PSO-SVM model, the prediction accuracy of the proposed model had been improved significantly. The reliability and effectiveness of the SPA-PSO-SVM prediction model is verified and it has apparent advantages while predicting landslide displacement with deformation hysteresis effect involved. [ABSTRACT FROM AUTHOR]

This paper provides newly found and profound insight into the initiation mechanism of the first Baige landslide occurred on the upper reaches of the Jinsha River in October 10, 2018. The detailed geological characteristics are interpreted by comprehensive field surveys in the past year, which indicate that the Baige landslide developed in the Jinsha River tectonic suture zone, and the active tectogenesis significantly contributes to broken stratigraphic structures and complex spatial distribution of lithologies. The initiation is considered to be long-term creep under the exogenic and endogenic integration, while the active tectogenesis and the influence of serpentinite and foliation, respectively, are the primary exogenic and endogenic factors. In addition, this creep process can be analyzed within three stages: evolution and formation of failure-prone geological structures, progressive deformation and fracturing, and shear failure of the locking section. The distribution and easily degraded behavior of the serpentinite are the fundamental, enabling the formation of failure-prone structures and also responsible for the subsequent deformation evolution. The foliation controls the toppling deformation-failure mode and direction. Furthermore, this catastrophic landslide further reminds us to pay attention to the landslides along the upper reaches of the Jinsha River, and our preliminary results indicate that the distribution characteristics of landslides significantly depend on the Jinsha River tectonic suture zone and the serpentinite mélange belt and thus are always associated with tectonically induced damage. [ABSTRACT FROM AUTHOR]

Rainfall infiltration in unsaturated slopes alters the effective stress through pore water pressure changes, thus causing ground deformation. Although important to assess the timescale over which the margin of safety of a slope decreases, such coupled processes are rarely accounted in the context of spatially distributed hazard assessment procedures. In this paper, a physically based, spatially distributed model accounting for full hydro-mechanical coupling is discussed. The model relies on a vectorized finite element (FE) solver to calculate the stability of deformable unsaturated infinite slopes subjected to transient flow. First, the FE solver is used to study the response of individual slopes to a prolonged rainfall for three scenarios (i.e., rigid, swelling, and collapsible soil). Then, the model is used in the context of spatially distributed computations to assess spatiotemporal variations of factor of safety over a large area. For this purpose, a series of shallow landslides occurred in a mountainous landscape covered by collapsible loess deposits in northwestern China was used as test site. The analyses show that hydro-mechanical couplings affect the performance of the model in terms of computed failure time and areal extent of the unstable zones. Specifically, volume collapse due to suction decrease is found to reduce the time of failure compared with uncoupled computations obtained for a rigid soil scenario. The most substantial advantages of using coupled analyses have been reported with reference to gentle slopes, for which the higher rate of suction reduction driven by volume change was crucial to capture landslide source areas that would otherwise be overlooked by uncoupled analyses. The proposed methodology offers a complete tool for landslide hazard assessment, in that it incorporates sources of coupling between hydrology and mechanics that are crucial to replicate the physics of landslide initiation. [ABSTRACT FROM AUTHOR]

This paper focuses on the evaluation of temporal and spatial propagations of impulse waves using 3D numerical simulation-based models for a potential landslide area in the Tersun Dam reservoir, Northeast Turkey. The topographical model was created using Stereo Lithography files, and a drift-flux model was used to simulate the landslides. The wave generation was simulated using the Reynolds-averaged Navier–Stokes equations. The Drift-Flux approach was selected, and a renormalization group-based k-ε turbulence model was used to create a fluid–solid coupled model. The results show that a 16.5-m impulse wave is created as a result of sliding material hitting the water at a velocity of 16.4 m/s. The wave will reach the opposite shore in 27.4 s, and the run-up height will reach up to 48.8 m. As the wave, with an average velocity of 11.6 m/s in the reservoir, propagates toward the dam body, its height will decrease, and a 3.7-m-high wave will hit the dam in 155 s. A validation analysis performed using empirical equations and laboratory model tests indicates small differences in the results. The main reason for these differences is that the evaluation of wave properties is independent of the type of mass movement in empirical methods. A proper stability analysis is required to have a better estimation of the volume and the velocity of the sliding mass. [ABSTRACT FROM AUTHOR]

Several landslides and floods were triggered by unprecedented, incessant heavy monsoon rainfall from 4 August 2019 to 8 August 2019, along the northern slopes, at Kavalappara, in the Malappuram district and Puthumala, in the Wayanad district of Kerala, India. The geological and geomorphological field settings were analyzed within the sub-basin wide catchment areas, for a preliminary evaluation of the causative factors of the landslides. This paper provides insights into the causative factors of landslides, together with suggestions on appropriate land use planning and deployment of real-time flood and landslide monitoring systems, besides building an adaptable community resilience model in high landslide-prone areas. The structurally disturbed weakened bedrocks and generally incompetent residual alteration products, exacerbated by nonchalant human interventions, reduction of cohesive strength, on super-saturation of underlying earth material, were inferred to be the primary causes of major landslides, triggered during 5 days of excessively heavy rainfall (400% over the normal average). Additionally, massive toe erosion was observed by streams gushing with course clastic sediments and debris. The destructive nature of such multi-hazards was evoked by unscientific modifications of slopes, mismanagement of water drainage, unplanned residential developments, across the channel courses and within active floodplain of rivers, and lack of early warning schemes for landslide. [ABSTRACT FROM AUTHOR]

The paper presents the long-term collaboration of the Czech research group organized under the World Centre of Excellence with Peruvian experts from the National Archaeological Park of Machupicchu (Parque Arqueológico Nacional de Machupicchu). The collaboration monitored potentially dangerous slope movements at the archaeological site. This was achieved with the installation of an environmentally friendly network of dilatometric instruments, taking into consideration the strict requirements for site protection and the provision of long-term, reliable results. The 17-year-long monitoring (not continuous) identified no major hazard to the archaeological site which used to support decision of site managers to limit the entrance of tourists to the Intiwatana hill since 2019. Historical photographs of Czech travellers (from 1949, 1950, 1954, and 1961) were shared with Peruvian experts, who compared them with the oldest photos from the explorer Hiram Bingham and the most recent situation. The photographs were used to document the historical development of selected structures inside Machupicchu as well as landslide occurrences on the surrounding slopes. We think this is a good example of a successful collaboration in the adoption of a robust and reliable monitoring approach into the regular practices of the site mangers. [ABSTRACT FROM AUTHOR]

The monitoring of landslides has a practical application for the prevention of hazards, especially in the case of large deep-seated landslides. Monitoring data are necessary to understand the relationships between movement and triggers, to predict movement, and to establish an early warning system. This paper compares two phenomenological models for the prediction of the movement of the Kostanjek landslide, the largest landslide in the Republic of Croatia. The prediction models are based on a 4-year monitoring data series of landslide movement, groundwater level, and precipitation. The presented models for landslide movement prediction are divided into the model for the prediction of groundwater level from precipitation data and the model for the prediction of landslide velocity from groundwater level data. The statistical techniques used for prediction are multiple linear regression and random forests. For the prediction of groundwater level, 75 variables calculated from precipitation and evapotranspiration data were used, while for the prediction of landslide movement, 10 variables calculated from groundwater level data were used. The prediction results were mutually compared by k-fold cross-validation. The root mean square error analyses of k-fold cross-validation showed that the results obtained from random forests are just slightly better than those from multiple linear regression, in both, the groundwater level and the landslide velocity models, proofing that multiple linear regression has a potential for prediction of landslide movement. [ABSTRACT FROM AUTHOR]

Landslide early warning systems (LEWS) can be categorized into two groups: territorial and local systems. Territorial landslide early warning systems (Te-LEWS) deal with the occurrence of several landslides in wide areas: at municipal/regional/national scale. The aim of such systems is to forecast the increased probability of landslide occurrence in a given warning zone. The performance evaluation of such systems is often overlooked, and a standardized procedure is still missing. This paper describes a new Excel user-friendly tool for the application of the EDuMaP method, originally proposed by (Calvello and Piciullo 2016). A description of indicators used for the performance evaluation of different Te-LEWS is provided, and the most useful ones have been selected and implemented into the tool. The EDuMaP tool has been used for the performance evaluation of the "SMART" warning model operating in Piemonte region, Italy. The analysis highlights the warning zones with the highest performance and the ones that need threshold refinement. A comparison of the performance of the SMART model with other models operating in different Te-LEWS has also been carried out, highlighting critical issues and positive aspects. Lastly, the SMART performance has been evaluated with both the EDuMaP and a standard 2 × 2 contingency table for comparison purposes. The result highlights that the latter approach can lead to an imprecise and not detailed assessment of the warning model, because it cannot differentiate among the levels of warning and the variable number of landslides that may occur in a time interval. [ABSTRACT FROM AUTHOR]

Scientific literature is becoming daily more and more abundant. Scientific and professional journals as primary information sources are competing to each other to attract readership. Their position (ranking, visibility, attractiveness, prestige) in scientific community can be measured by using different journal bibliometric and scientometric parameters, journal impact factor being only one of them. Springer Nature publishes the journal Landslides: Journal of the International Consortium on Landslides since 2004. Being examined in the past by different authors from bibliometric and editorial point of view, this review on the journal's achievements confirmed the high ranking of this journal in the fields of geological and geotechnical engineering and engineering geology. Strong and weak points are discussed from the bibliometric point of view, stressing the need for higher internationality of co-authorship of published articles in order to be true international journal. Continuous publishing and the move to a monthly journal in 2018 will eventually increase journal's h-index and cited half-life of citations, but further editorial efforts should be directed to attract excellent review papers and focused technical notes to increase cites per paper. Until now, the journal Landslides is the foremost journal in the field of landslide disaster risk reduction, and the top young international journal in the field of geological engineering and engineering geology. [ABSTRACT FROM AUTHOR]

Landslide-induced waves are one of the most disastrous hazards that can post a great threat to human lives and properties. At about 4:00 pm, 5 November 2017, a landslide-induced tsunami-like wave suddenly occurred across the Truong river in Bac Tra My District, Quang Nam province, Vietnam. The water wave destroyed six houses at the opposite bank and caused one person dead and three others injured. This study seeks to investigate the initiation mechanism and process of the landslide and its impulse wave. First, we examined landslide characteristics through site investigations, unmanned aerial vehicle (UAV) surveys, and laboratory testing with a series of standard geotechnical tests on collected soil samples. Then, the initiation and motion of the rainfall-induced landslides were reproduced by the integrated landslide simulation model (LS-RAPID). Finally, a combined computer simulation of the landslide motion and its impulse wave was performed by using a landslide-induced tsunami simulation model (LS-Tsunami). In which, output data from the LS-RAPID was used as input parameters for LS-Tsunami. The analysis shows that the rainfall with very high intensity in a short-time period was the triggering factor of the landslide, which is common factor in the study area. The 12-, 24-, and 48-h accumulative precipitation prior to the landslide recorded to 530, 760, and 950 mm, respectively. In addition, the rainfall trigger presented a typical pattern of rainstorm events in a long duration. Simulation results show that the impulse wave was generated by the landslide mass rapidly entering the river, crossing the river, and directly causing the disastrous damage to the resident area opposite site of the fail slope. The landslide moved down at a maximum speed of 16.4 m/s when its body approached the water surface and generated a maximum wave height of 5 m. There is good agreement between the observed geomorphic evidences and water traces on the site and simulation results of the landslide and its impulse wave. The paper provides a good case study on the understanding of the mechanism and dynamic process of the whole event that significantly contribute to potential landslide hazard assessment and future disaster mitigation in the area. [ABSTRACT FROM AUTHOR]

Preliminary knowledge of the surface movement and internal structure of a landslide is essential to improve the understanding of landslide behavior as an initial step to develop suitable mitigation measures. This paper characterizes displacement and internal structure of landslides using multitemporal unmanned aerial vehicles (UAV) and electrical resistivity tomography (ERT) imaging. Multitemporal orthophotos, digital elevation model (DEM) profile lines, DEMs of difference (DoD), and ERT images allowed us to measure the landslide morphometry and areal changes, detect the trend of surface displacement, calculate the surface movement rate and direction, characterize the spatial variability in surface movement rates, and relate spatial variation in displacement to the internal structure of the landslide. The Kalisari Landslide experienced retrogressive movement where the scarp of landslide moved backward 38 m from 2015 to 2019. The surface movement ranged from 0.7 to 8.1 m with the direction mostly to the northeast. The depletion and accumulation ranged from 0.1 to 5 m, with the most active change located northwest of the landslide. The variability in movement was influenced by the spatial variability in the subsurface material inferred from the ERT images, i.e., surface material, clay, weathered breccias (possibly altered), and breccias. The interpretation from multitemporal UAV and ERT images indicates that the Kalisari Landslide has a nonhomogeneous multiple retrogressive behavior with a curved slip surface located at the clay layer. [ABSTRACT FROM AUTHOR]

In this paper, a systematic procedure to assess the feasibility of Advanced Differential Interferometric SAR (A-DInSAR) technique for landslide monitoring using SAR images acquired by Sentinel-1 sensors is presented. The methodology is named "Assessment of the advanced differentiaL interferometric synthetic aperture radar technique Feasibility for large scale lAndslide monitoring – ALFA" and it is structured in two main phases, which includes pre-processing and post-processing elaborations. The methodology was developed and tested in the Alpine sector of the Piedmont region in Italy, which represents a landslide prone area. In particular, ALFA represents a methodology based on previous algorithms available in the literature to assess the a-prior feasibility assessment and post-processing analysis of A-DInSAR data for landslide, which introduces three novel aspects such as (1) a systematic scheme suitable within regional practices; (2) the use of Sentinel-1 data and the development of (3) an index to take into account of the kind of distribution of the measuring points along the landslide. The approach was applied over an area extended about 5300 km2 affected by 5703 landslides mapped in the database of the Piedmont Region (Landslides information system in Piedmont—SIFRAP). Sentinel-1 images covering the period 2014–2018 were analysed. The results show the potential of the Sentinel-1 data for large-scale landslide monitoring. The developed methodology presents reliable tools that could be useful as feasibility for the use of Sentinel-1 data for landslide monitoring at regional and national scale. [ABSTRACT FROM AUTHOR]

Landslides are the second most frequent tsunami source worldwide. However, their complex and diverse nature of origin combined with their infrequent event records make prognostic modelling challenging. In this paper, we present a probabilistic framework for analysing uncertainties emerging from the landslide source process. This probabilistic framework employs event trees and is used to conduct tsunami uncertainty analysis as well as probabilistic tsunami hazard analysis (PTHA). An example study is presented for the Lyngen fjord in Norway. This application uses a mix of empirical landslide data combined with expert judgement to come up with probability maps for tsunami inundation. Based on this study, it is concluded that the present landslide tsunami hazard analysis is largely driven by epistemic uncertainties. These epistemic uncertainties can be incorporated in the probabilistic framework. Conducting a literature analysis, we further show examples of how landslide and tsunami data can be used to better constrain landslide uncertainties, combined with statistical and numerical analysis methods. We discuss how these methods, combined with the probabilistic framework, can be used to improve landslide tsunami hazard analysis in the future. [ABSTRACT FROM AUTHOR]