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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 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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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-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]

A large catastrophic landslide occurred on July 23, 2019, after a continuous heavy rainfall at Jichang (N 26°15′22.99″, E 104°40′6.33″), Shuicheng county, Guizhou province, China. This landslide had a maximum runout distance of 1295 m, a maximum falling height of 477 m between the highest point of the source area and the leading edge, and a maximum speed of 45.5 m/s. The 1.4 million cubic meters of debris instantly buried twenty-one buildings, destroyed six buildings, and resulted in 42 fatalities and 9 people missing. This paper analyzes the extent and mechanisms of initiation and runout distance of the landslide. The landslide occurred in the eluvial and deluvial accumulation of basalt, and its initiation mechanisms were different from the bedrock landslides that are typical of the interbedded soft and hard rocks in Guizhou and basalt landslides in Southwest China. The prolonged heavy rainfall, lush vegetation, and road construction played the roles in landslide initiation. Subsequently, the landslide can be divided into three sliding regions caused by the valley on both sides and the stepped terrain in the middle, and the valley topography enhanced the disaster intensity and reach. [ABSTRACT FROM AUTHOR]

The analysis of the residual hazard existing after the emergency phases generated by the activation or reactivation of landslides is rarely taken into account in a proper manner. However, the assessment of landslide post-failure evolution should represent a key factor to control potential landslide reactivations and prevent new landslide-induced damages. This paper presents the results of a long-term field monitoring activity performed in the years after the emergency phase of the Montaguto (Italy) earthflow reactivation occurred in 2010 as well as the results of 2-D and 3-D numerical analyses aimed at interpreting the post-emergency landslide behaviour. The results of the numerical simulations, which agree well with the in situ monitoring data, allow to define a conceptual model of the earthflow behaviour that is related to the pore water pressure variations resulting from the drained or undrained processes occurring in the landslide body. The study proposed confirms a general reduction of the landslide activity, as well as allows to detect the factors that control the residual activity existing in a specific area of the landslide and to infer possible critical scenarios for landslide reactivations. [ABSTRACT FROM AUTHOR]

An Ms 6.5 earthquake shocked the Ludian County, Yunnan Province, China, on 3 August 2014 and triggered the Hongshiyan landslide dam. The dam, with a height of 83 m and a lake capacity of 260 × 10 m, threatened more than 10,000 people. A unique feature of this landslide dam was that it formed between a man-made dam and a hydropower plant. An existing drainage tunnel connecting the lake and the hydropower plant became a natural drainage conduit for the landslide dam, which played an important role in the mitigation of the landslide dam risks. This paper reports a quantitative risk assessment for the Hongshiyan landslide dam considering both engineering and non-engineering risk mitigation measures. The risk assessment is divided into three stages according to the implementation of two engineering measures: construction of a diversion channel and excavation of a branch drainage tunnel. The dam breaching hydrographs, flood zones, population at risk, and likely fatalities in each of the three stages are analysed. The optimum evacuation strategy in each stage is also studied based on the principle of minimum total consequence. It is found that the diversion channel decreases the dam breaching peak discharge and the associated risks significantly. The branch drainage tunnel prevent the landslide dam from overtopping failure in non-flooded period; however, the landslide dam may fail by overtopping in a future flood if the inflow rate is larger than the outflow rate through the drainage tunnels, resulting in serious losses of lives and properties. The dam breaching risks in all the three stages could be largely reduced by the optimal evacuation decision, which shows that timely evacuation is vital to save life and properties. The study provides a scientific basis for decision making in landslide dam risk management. [ABSTRACT FROM AUTHOR]

This paper presents a new region-based preparatory factor, total flux ( TF), for landslide susceptibility models (LSMs). TF takes into account the topography and hydrology conditions upstream of each gridded data cell and represents the total flux of water in the stream. The results show that TF is strongly associated with the occurrence of landslides and is a good preparatory factor for LSM. Using TF instead of a drainage distance factor in I-Lan region in Taiwan shows an improvement in the accuracy of the cumulative percentage of landslide occurrence of 44 and 14 % for the top 1 and 10 % susceptible areas, respectively. This significant improvement in accuracy in these high-risk areas is critical for preventing and mitigating the economic and human losses due to landslides. [ABSTRACT FROM AUTHOR]

The published version of this article contains error. The author noticed that there are several missing mathematical symbols on the paper. These includes in the following particular sections: Reliability methods, Implementation, Estimation of exceedance probability and Mechanical model and random variables. [ABSTRACT FROM AUTHOR]

Landslide susceptibility assessment over large areas is considered a preliminary step for the planning or design of the most appropriate risk mitigation measures. The use of physically based models is considered a useful tool for landslide susceptibility assessment. Sometimes, using the available geotechnical input data, physically based models can be used to assess landslide susceptibility to obtain a susceptibility map which allows the expert to identify areas where detailed in situ investigations and laboratory tests should be carried out. In this context, the paper proposes a methodology based on the use of TRIGRS to assess landslide susceptibility in an area of about 1 km2 frequently affected by shallow phenomena in weathered gneiss. Owing to the fact that these materials are extremely complex to characterize from a mechanical and hydraulic point of view, the methodology starts with the collection and analysis of the geotechnical data available for weathered gneiss outcropping in the study area. These data are combined with the data provided by scientific literature on soils similar, for genesis and stress history, to those of the studied area. Through the application of TRIGRS, the data are combined in order to obtain the values of parameters that better analyze shallow landslide source areas. Subsequently, using the abovementioned values, several susceptibility maps are obtained. Finally, the most representative shallow landslide susceptibility map for the area is chosen by means of the error index (EI), the true positive fraction (TPF), and the forecasting index (FI). The success of the best map is confirmed by the high value of the area under the receiver operator characteristic curve (AUC) that demonstrates a good level of forecasting ability. [ABSTRACT FROM AUTHOR]

Early warning systems for slope instability are needed to alert users of accelerating slope deformation behaviour, enable evacuation of vulnerable people, and conduct timely repair and maintenance of critical infrastructure. Communities exposed to landslide risk in low- and middle-income countries seldom currently instrument and monitor slopes to provide a warning of instability because existing techniques are complex and prohibitively expensive. Research and field trials have demonstrated conclusively that acoustic emission (AE) monitoring can be an effective approach to detect accelerating slope movements and to subsequently communicate warnings to users. The objective of this study was to develop and assess a simple, robust, low-cost AE monitoring system to warn of incipient landslides, which can be widely deployed and operated by communities globally to help protect vulnerable people. This paper describes a novel AE measurement sensor that has been designed and developed with the cost constrained to a few hundred dollars (US). Results are presented from physical model experiments that demonstrate performance of the AE system in measuring accelerating deformation behaviour, with quantifiable relationships between AE and displacement rates. Exceedance of a pre-determined trigger level of AE can be used to communicate an alarm to users in order to alert them of a slope failure. Use of this EWS approach by communities worldwide would reduce the number of fatalities caused by landslides. [ABSTRACT FROM AUTHOR]

Early warning systems (EWSs) rely on the capacity to forecast a dangerous event with a certain amount of advance by defining warning criteria on which the safety of the population will depend. Monitoring of landslides is facilitated by new technologies, decreasing prices and easier data processing. At the same time, predicting the onset of a rapid failure or the sudden transition from slow to rapid failure and subsequent collapse, and its consequences is challenging for scientists that must deal with uncertainties and have limited tools to do so. Furthermore, EWS and warning criteria are becoming more and more a subject of concern between technical experts, researchers, stakeholders and decision makers responsible for the activation, enforcement and approval of civil protection actions. EWSs imply also a sharing of responsibilities which is often averted by technical staff, managers of technical offices and governing institutions. We organized the First International Workshop on Warning Criteria for Active Slides (IWWCAS) to promote sharing and networking among members from specialized institutions and relevant experts of EWS. In this paper, we summarize the event to stimulate discussion and collaboration between organizations dealing with the complex task of managing hazard and risk related to active slides. [ABSTRACT FROM AUTHOR]

The definition of landslide hazard is a step-like procedure that encompasses the quantification of its spatial and temporal attributes, i.e., a reliable definition of landslide susceptibility and a detailed analysis of landslide recurrence. However, available information is often incomplete, fragmented and unsuitable for reliable quantitative analysis. Nevertheless, landslide hazard evaluation has a key role in the implementation of risk mitigation policies and an effort should be done to retrieve information and make it useful for this purpose. In this research, we go through this topic of optimising the information available in catalogues, starting from landslide inventory review and constitution of a boosted training dataset, propaedeutic for susceptibility analysis based on machine learning methods. The temporal recurrence of landslide events has been approached here either through the definitions of large-scale quantitative hazard descriptors or by analysis of historical rainfall (i.e., the main triggering factor for the considered shallow earth slope failures) databases through the definition of rainfall probability curves. Spatial and temporal attributes were integrated, selecting potential landslide source areas ranked in terms of hazard. Data integration was also pursued through persistent scatterer interferometry analysis which pointed out areas of interest within potential landslide source areas featured by ongoing ground movement. The consequential approach led to the definition of the first hazard product of the city of Rome at a local scale functional for advisory purposes or the statutory level, representing a thematic layer able to orient the risk managers and infrastructure stakeholders. [ABSTRACT FROM AUTHOR]

Landslide risk assessment is often a difficult task due to the lack of temporal data on landslides and triggering events (frequency), run-out distance, landslide magnitude and vulnerability. The probability of occurrence of landslides is often very difficult to predict, as well as the expected magnitude of events, due to the limited data availability on past landslide activity. In this paper, a qualitative procedure for assessing the exposure of elements at risk is presented for an area of the Apulia region (Italy) where no temporal information on landslide occurrence is available. Given these limitations in data availability, it was not possible to produce a reliable landslide hazard map and, consequently, a risk map. The qualitative analysis was carried out using the spatial multi-criteria evaluation method in a global information system. A landslide susceptibility composite index map and four asset index maps (physical, social, economic and environmental) were generated separately through a hierarchical procedure of standardising and weighting. The four asset index maps were combined in order to obtain a qualitative weighted assets map, which, combined with the landslide susceptibility composite index map, has provided the final qualitative landslide exposure map. The resulting map represents the spatial distribution of the exposure level in the study area; this information could be used in a preliminary stage of regional planning. In order to demonstrate how such an exposure map could be used in a basic risk assessment, a quantification of the economic losses at municipal level was carried out, and the temporal probability of landslides was estimated, on the basis of the expert knowledge. Although the proposed methodology for the exposure assessment did not consider the landslide run-out and vulnerability quantification, the results obtained allow to rank the municipalities in terms of increasing exposure and risk level and, consequently, to identify the priorities for designing appropriate landslide risk mitigation plans. [ABSTRACT FROM AUTHOR]

Probabilistic modelling is gaining increased attention in the field of assessing the landslide hazard due to the ability to account for the spatial and temporal uncertainties related to the variability of geological, hydrological, geotechnical, seismological and geomorphological parameters. In this study, a seismic landslide hazard assessment was carried out for Uttarakhand state, located in the Indian Himalayan region. A methodology was developed to model the parametric uncertainties incorporated in the modified Newmark slope stability analysis model, which considers the rock joint shear strength properties in permanent displacement computation. The uncertainties related to input parameters were taken into account by utilizing statistical distributions to represent these parameters. On a pixel-by-pixel basis, several probability density functions were simulated using the Monte Carlo method, and the simulation results were retained throughout the computation process. As a result, there were no constraints on the mathematical complexity or symmetry of the underlying distributions when casting the derived quantities into probabilistic hazard maps. The hazard map showed the probability of exceedance of seismic slope displacement beyond a threshold value of 5 cm. High probability values were observed in the Middle and Greater Himalayas, emphasizing the likelihood of a large number of earthquake-induced landslides in this region. Finally, the results were validated using the landslide inventory of the 1999 Chamoli earthquake. The prepared seismic landslide hazard map will give infrastructural planners and local authorities a tool for evaluating the risk associated with a seismic landslide for land use planning and taking appropriate mitigation measures to reduce the losses. [ABSTRACT FROM AUTHOR]

Seismic slope stability analysis at regional scale (e.g., landslide hazard mapping, infrastructure slope management) is essential in areas that are susceptible to earthquake. Analytical infinite slope model is well suited and widely used for regional analyses which involve hundreds and thousands of slopes. Use of infinite slope model implicitly assumes shallow translational failure mechanism. However, earthquake can also cause deep rotational failure, which is more destructive and should also be considered. This study aims to develop closed-form solutions that can be efficiently used for seismic slope stability analysis at regional scale considering deep rotational failure mechanism. The existing research is critically reviewed first, and their shortcomings are identified. In contrast to the conventional "two-step" approach, a more efficient and versatile "one-step" approach is proposed in this study. The "one-step" approach can be used to calculate factor of safety for pseudo-static analysis and yield coefficient for displacement-based seismic analysis. To consider the effects of uncertainty from soil properties and seismic loading, the "one-step" approach is further extended for probabilistic analysis and application is demonstrated through a case study. The efficiency and versatility of the proposed "one-step" approach make it well suited for regional seismic slope stability analysis. [ABSTRACT FROM AUTHOR]

Earthquake-induced landslides represent a significant seismic hazard since they can largely increase the damage and losses due to a seismic event, an issue that must be considered in land-use and risk management purposes. However, it can be difficult to consider all the natural variables, such as geotechnical parameters, that predispose the occurrence of landslides under a specific dynamic triggering, especially for wide areas. Among these, the most important and critical ones to quantify at large scale, are represented by the hydraulic conditions in both unsaturated and saturated media. For this reason, in this work we present a newly developed GIS tool that was specifically designed for the automation of a pseudo-dynamic Newmark model to estimate the co-seismic displacements over wide areas. The tool takes into account reactivations of landslides under different rupture mechanisms and parametrically weighs the role of variable initial soil moisture or pressure head conditions, as well as the influence of ground shaking resulting from local amplification effects. The proposed tool was tested in the Molise region (central-southern Italy), where almost 23,000 existing landslides have been selected for evaluating potential reactivations. The obtained results point out the importance of local conditions on the displacement amount, even by considering a unique return period of the seismic action. Strengths and weaknesses of the proposed model have been also highlighted in view of potential future applications in the framework of co-seismic landslide risk assessment and mitigation measures. [ABSTRACT FROM AUTHOR]

Landslide run-out modeling is a powerful model-based decision support tool for landslide hazard assessment and mitigation. Most landslide run-out models contain parameters that cannot be directly measured but rely on back-analysis of past landslide events. As field data on past landslide events come with a certain measurement error, the community developed probabilistic calibration techniques. However, probabilistic parameter calibration of landslide run-out models is often hindered by high computational costs resulting from the long run time of a single simulation and the large number of required model runs. To address this computational challenge, this work proposes an efficient probabilistic parameter calibration method by integrating landslide run-out modeling, Bayesian inference, Gaussian process emulation, and active learning. Here, we present an extensive synthetic case study. The results show that our new method can reduce the number of necessary simulation runs from thousands to a few hundreds owing to Gaussian process emulation and active learning. It is therefore expected to advance the current practice of parameter calibration of landslide run-out models. [ABSTRACT FROM AUTHOR]

Landslides represent a serious worldwide hazard, especially in Italy, where exposure to hydrogeological risk is very high; for this reason, a landslide quantitative risk assessment (QRA) is crucial for risk management and for planning mitigation measures. In this study, we present and describe a novel methodological approach of QRA for slow-moving landslides, aiming at national replicability. This procedure has been applied at the basin scale in the Arno River basin (9100 km2, Central Italy), where most landslides are slow-moving. QRA is based on the application of the equation risk = hazard (H) × vulnerability (V) × exposure (E) and on the use of open data with uniform characteristics at the national scale. The study area was divided into a grid with a 1 km2 cell size, and for each cell, the parameters necessary for the risk assessment were calculated. The obtained results show that the total risk of the study area amounts to approximately 7 billion €. The proposed methodology presents several novelties in the risk assessment for the regional/national scale of the analysis, mainly concerning the identification of the datasets and the development of new methodologies that could be applicable over such large areas. The present work demonstrates the feasibility of the methodology and discusses the obtained results. [ABSTRACT FROM AUTHOR]

Landslide hazard mapping is essential for disaster reduction and mitigation. The hazard map produced by the spatiotemporal probability analysis is usually static with false-negative and false-positive errors due to limited data resolution. Here we propose a new method to obtain dynamic landslide hazard maps over the Wushan section of the Three Gorges Reservoir Area by introducing the ground deformation measured by the spaceborne Copernicus Sentinel-1 synthetic aperture radar (SAR) imagery collected from 9/30/2016 to 9/13/2017. We first determine the spatial probability of landslide occurrence predicted by the support vector machine algorithm. We also conducted the statistical analysis on the temporal probability of landslide occurrence under various rainfall conditions (0, 0–50, 50–100, and > 100 mm for the antecedent 5-day total). We initialize a preliminary landslide hazard map by combining the spatial and temporal landslide probabilities. Meanwhile, the ground deformation velocities during the representative dry and wet seasons can be extracted from multi-temporal interferometric SAR (MT-InSAR). Thereafter, the landslide hazard map can be finalized by an empirical assessment matrix considering both the preliminary landslide hazard map and deformation velocities. Our results demonstrate that false-negative and false-positive errors in the landslide hazard map can be effectively reduced with the assistance of the deformation information. Our proposed method can be used to assess the dynamic landslide hazard at higher accuracy. [ABSTRACT FROM AUTHOR]

The potential for widespread landslides is generally increased when extraordinary wet periods occur during times of elevated subsurface hydrologic conditions. A series of storms in early 2018 in Pittsburgh, Pennsylvania, overlapped with a period of increased shallow soil moisture and rising bedrock groundwater levels resulting from seasonally diminished evapotranspiration and induced widespread landslides in the region. Most of the landslides were shallow slope failures in colluvium, landslide deposits, and/or fill. However, deep-seated landslide activity also occurred and corresponded with record cumulative precipitation from late February to April and bedrock groundwater levels rising to an annual high. Landslides blocked or damaged roads, adversely affected multiple houses, disrupted electrical service, crushed vehicles, and resulted in considerable economic losses. The initial landslides occurred during or immediately after a rare period of three successive days of heavy rain that began on February 14. Subsequent landslides between late February and April were induced by multiday storms with smaller rainfall totals. As shallow soil moisture at a monitoring site rose above a volumetric water content of 32%, the mean rainfall intensities necessary to induce slope failure in colluvium and other surficial deposits decreased. Deep-seated landslide movement occurred in the region mostly when the groundwater level in a bedrock observation well was shallower than 1.7 m. The availability of hydrologic and landslide movement monitoring data during this extraordinary series of storms highlighted the evolution of the landslide hazard with changing moisture conditions and yielded insights into potential hydrologic criteria for anticipating future widespread landslides in the region. [ABSTRACT FROM AUTHOR]

A large number of landslides occur in North-Eastern Italy during every rainy period due to the particular hydrogeological conditions of this area. Even if there are no casualties, the economic losses are often significant, and municipalities frequently do not have sufficient financial resources to repair the damage and stabilize all the unstable slopes. In this regard, the research for more economically sustainable solutions is a crucial challenge. Floating composite anchors are an innovative and low-cost technique set up for slope stabilization: it consists in the use of passive sub-horizontal reinforcements, obtained by coupling a traditional self-drilling bar with some tendons cemented inside it. This work concerns the application of this technique according to the observational method described within the Italian and European technical codes and mainly recommended for the design of geotechnical works, especially when performed in highly uncertain site conditions. The observational method prescribes designing an intervention and, at the same time, using a monitoring system in order to correct and adapt the project during realization of the works on the basis of new data acquired while on site. The case study is the landslide of Cischele, a medium landslide which occurred in 2010 after an exceptional heavy rainy period. In 2015, some floating composite anchors were installed to slow down the movement, even if, due to a limited budget, they were not enough to ensure the complete stabilization of the slope. Thanks to a monitoring system installed in the meantime, it is now possible to have a comparison between the site conditions before and after the intervention. This allows the evaluation of benefits achieved with the reinforcements and, at the same time, the assessment of additional improvements. Two stabilization scenarios are studied through an FE model: the first includes the stabilization system built in 2015, while the second evaluates a new solution proposed to further increase the slope stability. [ABSTRACT FROM AUTHOR]

An accurate and timely inventory for major landslides triggered by seismic events is essential for hazard assessment and risk governance. Sentinel-2 MultiSpectral Instrument (MSI) data have been increasingly used for co-seismic landslide mapping in recent years. However, there have been few studies exploring the full use of Sentinel-2 near infrared (NIR) narrow, red-edge, and short-wave infrared (SWIR) bands for landslide mapping. In this study, Sentinel-2 10-m fused NIR narrow, red-edge-1, SWIR-1, and SWIR-2 bands were incorporated into the change detection-based Markov random field (CDMRF) that considers the spectral, spatial, and contextual information of landslides. The effectiveness of the developed method was proved in mapping co-seismic landslides triggered by two major earthquakes in the Yarlung Tsangpo Grand Canyon, China, in 2017 and Hokkaido, Japan, in 2018. The results demonstrated that the mapping performances with the fused Sentinel-2 bands were significantly improved for both seismic events. Such improvement is mainly attributed to the more abundant spectral information provided by the fused Sentinel-2 red-edge, NIR narrow, and SWIR bands. The novelties of this study are (1) the preliminary attempt to use the Sentinel-2 10-m fused bands for landslide mapping, and (2) the synergy of Sentinel-2 red-edge, NIR narrow, and SWIR bands for more reliable landslide mapping. [ABSTRACT FROM AUTHOR]

Assessment of the spatial probability of future landslide occurrences for disaster risk reduction is done through landslide susceptibility modelling. In this study, we investigated the effect of time and space partitioning strategies of samples on the performance of regional landslide susceptibility models on macro-scale mapping in the state of Mizoram, India, covering 21,087 km2 area. We used landslide inventory data of 2014 and 2017 periods consisting of 1205 and 2265 landslides, respectively, to train and test the models with four sampling strategies such as spatial, temporal, temporal (size constrained) and temporal (geographic constrained). We used five commonly inherited models such as multiclass weighted overlay (MCWO), information value (IV), weights of evidence (WoE), logistic regression (LR) and artificial neural network (ANN) to evaluate the effect of sampling strategies on the model performance for regional landslide susceptibility mapping. Validation of model performance was done using receiver operating characteristic (ROC) curve. Traditional spatial sampling strategy applied to landslides in 2014 with a random split in 70:30 proportion provided a high performance of all the five models but failed to predict landslides in 2017. The landslide incidences in 2017, when used for model validation either entirely or in different split conditions (both size and geographic constrained), provided consistent performance, even though the testing sample size is large or have a different spatial disposition, if the training was carried out with non-linear susceptibility models such as LR and ANN using landslide incidences in 2014. Results show the importance of sample selection during validation of landslide susceptibility models on a regional scale. [ABSTRACT FROM AUTHOR]

The Kyoto Landslide Commitment 2020 (KLC 2020; Sassa [3]) has officially been launched since the online launching session of KLC 2020, 5 November 2020 in the midst of COVID19 pandemic (Sassa [4], [5]). In the field of capacity building, SE RIBC offers courses for postgraduate students in landslide mechanics, dynamics and stabilization, and landslide risk mitigation. Since 2011, UB FMG has been a full member of the International Consortium on Landslides (ICL) and has gradually been intensifying its contributions to the ICL worldwide efforts for landslide risk reduction and international promotion of landslide research. Since 2009, ITGIS has been a full member of the International Consortium on Landslides (ICL) and has been gradually intensifying its contributions to the ICL worldwide efforts for landslide risk reduction and international promotion of landslide research. [Extracted from the article]

Communities living in the Chittagong Hill Districts (CHD) of Bangladesh recurrently observe landslide disasters during the monsoon season (June–September). CHD is primarily dominated by three distinct groups of hill communities, namely, urbanised hill (Bengali), indigenous tribal and stateless Rohingya refugees. Landslide vulnerability amongst them is complex and varies between physical, social, economic, environmental, institutional and cultural dimensions. This study aims to understand driving forces of landslide disasters in the region by emphasising human factors. Data from the three contrasting communities were collected through participatory workshops, in-depth interviews and fieldwork observation. The participants were local people and landslide experts who were purposefully selected from five case study communities in the CHD. They ranked different socio-economic problems, identified causes of landslides and proposed landslide mitigation action plans. Results suggest that the urbanised Bengali and Rohingya refugee communities are highly vulnerable to landslides. The urbanised hill communities largely deal with poverty, social injustice, lack of planning regulations and illegal hill cutting issues, whereas the Rohingya refugees' predominant constraints are linked to the ongoing genocide and state-sponsored violence in Myanmar hindering their sustainable repatriation, and their protracted living conditions in Bangladesh. The indigenous tribal communities are comparatively resilient to landslides due to their unique history, traditional knowledge, cultural heritage and lifestyle. Landslides in the CHD should be characterised as socio-natural hazards since the components of landslide disasters are profoundly intertwined with the culture–conflict–corruption nexus. [ABSTRACT FROM AUTHOR]

Rainfall-induced landslide inventories can be compiled using remote sensing and topographical data, gathered using either traditional or semi-automatic supervised methods. In this study, we used the PlanetScope imagery and deep learning convolution neural networks (CNNs) to map the 2018 rainfall-induced landslides in the Kodagu district of Karnataka state in the Western Ghats of India. We used a fourfold cross-validation (CV) to select the training and testing data to remove any random results of the model. Topographic slope data was used as auxiliary information to increase the performance of the model. The resulting landslide inventory map, created using the slope data with the spectral information, reduces the false positives, which helps to distinguish the landslide areas from other similar features such as barren lands and riverbeds. However, while including the slope data did not increase the true positives, the overall accuracy was higher compared to using only spectral information to train the model. The mean accuracies of correctly classified landslide values were 65.5% when using only optical data, which increased to 78% with the use of slope data. The methodology presented in this research can be applied in other landslide-prone regions, and the results can be used to support hazard mitigation in landslide-prone regions. [ABSTRACT FROM AUTHOR]

In this study, based on the frictional velocity-weakening law used in continuum modeling, an adapted Hertz-Mindlin contact model between particles and the ground surface is established to accurately simulate the landslide dynamics process. Moreover, the long-period seismic waves indicating the dynamic characteristics of large landslides are adopted to determine the computational parameters for the discrete element method (DEM). By combining the force-time functions obtained from the long-period seismic waves and numerical modeling, the dynamic parameters and reasonableness of the simulation results of the first Baige landslide on October 11, 2018, in eastern Tibet, China, have been validated. The friction coefficient obtained in this study is also in agreement with the relationship between the volume and friction coefficients reported in the literature. Taking the same model and parameters, the dynamic process leading to the second Baige landslide on November 3, 2018, is analyzed. The modeled profile and position of the maximum thickness of the deposits arising from the two landslides both match the observations well. It is demonstrated that this combination procedure is feasible for the prediction and identification of areas susceptible to large landslides. [ABSTRACT FROM AUTHOR]

Landslides falling into water can trigger tsunamis, which are particularly destructive in the proximity of the landslide impact and in narrow water bodies. The energy transfer mechanism between landslide and water wave is complex, but its understanding is of fundamental importance for the numerical modeling which aims to predict the induced wave hazard. In order to study the involved physical processes, we set up an experimental facility consisting of a landslide generator releasing gravel at high speed in a wave basin. With the aim of estimating the landslide–wave energy transfer, we implemented a simplified 1D conceptual model of landslide motion, including the 3D landslide deformations. We optimized the model with the experimental results. The model results explain that the deformable landslide has an average drag coefficient of 1.26 and a relatively inefficient energy transfer from landslide to wave. Of the landslide energy at impact, the 52% is dissipated by Coulomb basal friction between the slide and the water basin bottom, 42% is dissipated by other processes, including turbulence, and only the remaining 6% is transferred to the wave thus formed. [ABSTRACT FROM AUTHOR]

This work aims to characterize the earthquake-triggered landslides (EQTLs) during the 2017 Jiuzhaigou earthquake and to describe the geological and geomorphological control of landslide hazards. The Jiuzhaigou-EQTLs were studied via field-based investigation and remote sensing techniques during few weeks immediately after the mainshock occurred. The Jiuzhaigou earthquake produced an 11.4-km2 landslide area with 5633 landslides, dominated by shallow disrupted landslides, rock falls, and rockslides/rock avalanche. Statistical analyses show that the Jiuzhaigou-EQTLs had low mobility with a modal apparent friction angle of 40–44°. The Jiuzhaigou-EQTLs mostly occurred at elevations of 2700–3500 m and below 900 m relative relief to the main riverbed, where small-scale landslides were situated at the lower to middle parts of hillslope and large-scale landslides were situated close to the ridge crests. Jiuzhaigou-EQTLs clustered on river valley-side at the upstream of Baihe River (Jiuzhai Paradise and Ganhaizi village), Rize gully, and Danzhu gully on the tectonically active region at the southwest side of seismogenic fault F2. Landslides are prone to failure on the carbonate rocks with anaclinal hillslopes, following the cataclinal and cata-plagioclinal hillslopes. Most Jiuzhaigou-EQTLs preferentially occur on the below low-level break line (L2) in V-shaped valley-side and between first-level (L2) and high-level break line (L1) in U-shaped valley-side formed from long-term paleo-glacier and river incision. We found that landslide frequency is determined by a combination of the earthquake source with ground deformation, slope distribution, and rock strength due to weathering and dissolution along the fractures. Ground surface deformation, rock type (strength), geological structures, and valley-side slope breaks dictated the Jiuzhaigou-EQTL distribution pattern and hazards. This study elucidates the roles that local conditions play in influencing the occurrence, distribution, and frequency of slope failures and provides a better understanding of EQTL phenomenon during the Jiuzhaigou earthquake. [ABSTRACT FROM AUTHOR]

When a life-threatening landslide is recognized, the questions most relevant to community leaders are as follows: is our community safe enough, and if not, how much should we spend on protection? A risk evaluation tool that helps answer these questions, which compares landslide risk faced by a group of people to perceptions of tolerable risk, was first proposed in Hong Kong and is now being used widely in Western Canada within a quantitative risk management framework. After more than a decade of sporadic application in Western Canada, challenges of applying this tool are becoming apparent. For example, its use has resulted in landslide mitigation designs that are unaffordable, and it has failed to persuade funding authorities that proposed risk management solutions are a justifiable allocation of governments' limited resources. This article suggests that the risk evaluation tool designed in Hong Kong should not be universally applied. We propose modifications believed to be more appropriate for Western Canada, including tolerating landslide risk levels similar to other natural hazard types and emphasizing cost-effectiveness of landslide risk reduction options. Although the proposed tools were developed for the sociopolitical context of Western Canada, we hope the discussions included in this article motivate others to modify these risk evaluation tools for other societies and hazard types and that ultimately this will lead to more rational and consistent decisions that, with time, save lives and resources in landslide-prone regions of the world. [ABSTRACT FROM AUTHOR]

The Loess Plateau is the largest loess accumulation area around the world, onto which loess landslides have been occurring frequently each year, thus bringing significant threats to communities there. To mitigate or manage the risk brought by loess landslides, many methods have been developed to gain insights into mechanisms that trigger loess landslides, or to identify regions that are susceptible to landslides through landslide susceptibility mapping. However, none of these methods can be used to quantitatively evaluate possible impact of potentially unstable slopes, which offers important information for risk management, especially for regions with high susceptibility to landslides. This study aims to fill this gap by constructing a loess landslide database first from field investigation. Then, a multivariate model for loess landslide data, including its height, width, area, and length, is developed considering correlation among these parameters. Subsequently, the multivariate model is used to predict statistically and quantitatively impact of a potentially unstable loess slope, in terms of slide width, length, and area, given height of the potentially unstable loess slope. The proposed method is applied to loess landslides occurred in Baoji City for illustration. Results show that the proposed method works reasonably well. In addition, some key equations are provided using results from the multivariate model. With these equations, geotechnical engineers or decision-makers can evaluate possible impact of a potentially unstable loess slope with minimal effort. [ABSTRACT FROM AUTHOR]

Much of contemporary landslide research is concerned with predicting and mapping susceptibility to slope failure. Many studies rely on generalised linear models with environmental predictors that are trained with data collected from within and outside of the margins of mapped landslides. Whether and how the performance of these models depends on sample size, location, or time remains largely untested. We address this question by exploring the sensitivity of a multivariate logistic regression—one of the most widely used susceptibility models—to data sampled from different portions of landslides in two independent inventories (i.e. a historic and a multi-temporal) covering parts of the eastern rim of the Fergana Basin, Kyrgyzstan. We find that considering only areas on lower parts of landslides, and hence most likely their deposits, can improve the model performance by >10% over the reference case that uses the entire landslide areas, especially for landslides of intermediate size. Hence, using landslide toe areas may suffice for this particular model and come in useful where landslide scars are vague or hidden in this part of Central Asia. The model performance marginally varied after progressively updating and adding more landslides data through time. We conclude that landslide susceptibility estimates for the study area remain largely insensitive to changes in data over about a decade. Spatial or temporal stratified sampling contributes only minor variations to model performance. Our findings call for more extensive testing of the concept of dynamic susceptibility and its interpretation in data-driven models, especially within the broader framework of landslide risk assessment under environmental and land-use change. [ABSTRACT FROM AUTHOR]