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The international journal Landslides: Journal of International Consortium on Landslides was established in April 2004. The aims of Landslides are to promote landslide science, technology, and capacity building, and to strengthen global cooperation for landslide risk reduction within the United Nations International Strategy for Disaster Risk Reduction (ISDR). The achievements of the first 5 years from the beginning of 2004 (Vol. 1, No. 1) to the mid-2009 (Vol. 6, No. 2) were reviewed in 2009 (). This article presents the review for the second 5-year period from mid-2009 (Vol. 6, No. 3) to the end of 2013 (Vol. 10, No. 6), focusing on the journal's significance and its impact. We include an analysis of the classifications of articles in Landslides. [ABSTRACT FROM AUTHOR]

The B Fifth World Landslide Forum b is organized under the subtitle Implementing and Monitoring the Sendai Landslide Partnerships 2015-2025, I Voluntary contribution to the Sendai Framework 2015-2030 i , and the I Agenda 2030-Sustainable Development Goals. i All session proceedings to the WLF5 secretariat will be compiled as WLF5 electronic proceedings with ISBN number. Each poster can be cited by its number within the WLF5 electronic posters published by the WLF5 organizing committee. Potential WLF5 supporting organizations willing to support more than 3000 USD or 300,000 JPY are requested to inform it to the financial support evaluation team (FSET) of WLF5 secretariat;. [Extracted from the article]

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]

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

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

The Canadian Cordillera in the province of British Columbia witnesses numerous rock falls every year. Studies on the recorded rock fall data in this area show that rock fall hazard follows weather conditions, especially precipitation and freeze-thaw cycles. This relationship indicates that a weather-based approach can be implemented to estimate possible changes in the rock fall hazard due to climate change. In this paper, we used a statistical approach to quantify the relationship between monthly weather averages and rock fall frequencies for a section of a transportation corridor along the Fraser River in British Columbia, Canada. In this regard, von Mises distributions are used to find the best-fitted models to the monthly precipitation and freeze-thaw cycles, and proper relative weights are applied to the models in order to calibrate them to the rock fall monthly frequency. The calibrated model is used with input data from climatic predictions for 2041–2070 and 2071–2100 to see how rock fall distribution will be affected due to climate change in the future decades. Results show that between 9 and 19%, more rock fall is anticipated in future winters. Rock falls are expected to decrease in other months, especially in October, November, March, and April. This paper presents a method to predict changes in rock fall hazard seasonality due to climate change and illustrates the method with a case study along a section of the Canadian Cordillera. [ABSTRACT FROM AUTHOR]

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

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

Logistic regression studies which assess landslide susceptibility are widely available in the literature. However, a global review of these studies to synthesise and compare the results does not exist. There are currently no guidelines for the selection of covariates to be used in logistic regression analysis, and as such, the covariates selected vary widely between studies. An inventory of significant covariates associated with landsliding produced from the full set of such studies globally would be a useful aid to the selection of covariates in future logistic regression studies. Thus, studies using logistic regression for landslide susceptibility estimation published in the literature were collated, and a database was created of the significant factors affecting the generation of landslides. The database records the paper the data were taken from, the year of publication, the approximate longitude and latitude of the study area, the trigger method (where appropriate) and the most dominant type of landslides occurring in the study area. The significant and non-significant (at the 95 % confidence level) covariates were recorded, as well as their coefficient, statistical significance and unit of measurement. The most common statistically significant covariate used in landslide logistic regression was slope, followed by aspect. The significant covariates related to landsliding varied for earthquake-induced landslides compared to rainfall-induced landslides, and between landslide type. More importantly, the full range of covariates used was identified along with their frequencies of inclusion. The analysis showed that there needs to be more clarity and consistency in the methodology for selecting covariates for logistic regression analysis and in the metrics included when presenting the results. Several recommendations for future studies were given. [ABSTRACT FROM AUTHOR]

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

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

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

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

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

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

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

This paper presents the implementation of a slope stability method for rainfall-induced shallow landslides in CRITERIA-1D, which is an agro-hydrological model based on Richards' equation for transient infiltration and redistribution processes. CRITERIA-1D can simulate the presence and development of roots and canopies over space and time, the regulation of transpiration activity based on real meteorological data, and the evaporation reduction caused by canopies. The slope can be considered composed of a multi-layered soil, leading to the possibility of simulating the bedrock and of setting an initial water table level. CRITERIA-1D can consider different soil horizons characterized by different hydraulic conductivities and soil water retention curves, thus allowing the simulation of capillarity barriers. The validation of the proposed physically based slope stability model was conducted through the simulation of the collected water content and water potential data of an experimental slope. The monitored slope is located close to Montuè, in the north-eastern sector of Oltrepò Pavese (northern Apennines—Italy). Just close to the monitoring station, a shallow landslide occurred in 2014 at a depth of around 100 cm. The results show the utility of agro-hydrological modeling schemes in modeling the antecedent soil moisture condition and in reducing the overestimation of landslides events detection, which is an issue for early warning systems and slope management related to rainfall-induced shallow landslides. The presented model can be used also to test different bioengineering solutions for slope stabilization, especially when data about rooting systems and plant physiology are known. [ABSTRACT FROM AUTHOR]

Interferometric Synthetic Aperture Radar (InSAR) technology is capable of detecting large areas of potentially unstable slopes. However, traditional time-series InSAR methods yield fewer valid measurement points (MPs) in alpine canyon regions. Distributed Scatterer (DS) Interferometry (DSI) technology serves as a potent tool for monitoring surface deformation in complex land cover areas; nonetheless, it grapples with high computational demands and low efficiency when interpreting deformation across extended time series. This study proposes an image compression–based DSI (ICDSI) method, which, building upon the DSI method, utilizes principal component analysis (PCA) to compress multi-temporal SAR images in the time dimension. It develops a module for compressing long-time sequence SAR images, acquires the compressed image (referred to as a virtual image), and integrates the developed image compression module into the DSI data processing flow to facilitate the inversion of long-time sequence InSAR land surface deformation information. To validate and assess the credibility of the ICDSI method, we processed a total of 78 ascending and 81 descending scenes of Sentinel-1A images spanning the period 2019–2021 using Small Baseline Subset (SBAS), DSI, and the ICDSI method proposed in this paper. Subsequently, these methods were applied to detect landscape displacements on both coasts of the Jinsha River Basin. The investigation reveals that the ICDSI method outperforms SBAS and DSI significantly in monitoring landslide displacements, enabling the detection of more measurement points (MPs) while utilizing less raw data. The accomplishments of this research program carry crucial theoretical implications and practical application value for the detection of surface deformation using long-time series InSAR. [ABSTRACT FROM AUTHOR]

A common and largely unresolved problem of national-scale landslide early warning systems is their independent evaluation. In a recent paper, Mondini et al. (Nat Commun 14:2466, 2023) proposed a deep-learning system for short-term forecasting of rain-induced shallow landslides in Italy. Here, we independently evaluate the performance of this national-scale system by demonstrating its application between 1 January and 31 May 2021. For the purpose, we use hourly rainfall measurements from the same rain gauge network and different and independent information on the timing and location of 163 rain-induced landslides obtained from the FraneItalia catalogue that occurred in Italy in a period non considered in the construction of the system (https://zenodo.org/records/7923683). Independent demonstration confirmed the good predictive performance of the forecasting system and revealed no geographical or temporal bias in the forecasts. The analysis also showed that the system was more effective at predicting multiple landslides in the same general area than single landslides. This was a good result as multiple landslides are inherently more dangerous than single failures. Analysis of the few misclassified landslides showed that approximately one-third of the landslides were rockfalls, and for approximately another third there was uncertainty about when or where the landslides occurred. We conclude that, despite the inevitable misclassifications inherent in any probabilistically based national-scale landslide forecasting system, the deep-learning system analysed is well suited for short-term operational forecasting of rain-induced shallow landslides in Italy. [ABSTRACT FROM AUTHOR]

Hazards created by the landslide damming of rivers have become common in tectonically active mountainous areas. However, it remains unclear how tectonic effects may influence the formation and characteristics of landslide dams. The purpose of this paper is to explore how tectonic effects impact the drivers, geomorphic features, and activity characteristics of landslide dams along a fault zone. We investigated 83 landslide dams clustered along a fault zone in the Bailong River Basin. Most of the dams are located in areas of high tectonic stress, resulting from the rapid river incision and destruction of slope structure caused by intense tectonic activities in these areas. Statistical analysis, InSAR monitoring, and field investigation revealed that different tectonic effects were associated with significant differences in the geomorphic features, activity characteristics, and controlling factors of the landslide dams. Thus, we identified three distinct patterns of landslide dams in tectonically active mountainous areas: (1) Topography-driven landslide dams are caused by rapid rock uplift and river incision. Here, the steep terrain enhances the development of small landslides, the narrowness of the channels favors river damming, and the residual deposits on the hillslope remain active. (2) Tectonic activities promote the development of structural planes in the rock mass and reduce its strength, ultimately forming structural plane-controlled landslide dams. Although their volumes are not very large, the strong erosion resistance of rockslides can cause river damming and maintain the stability of deposits. (3) Fractured rock mass-controlled landslide dams are composed of broken rock and fault gouge. The extremely low strength of these materials allows them to form very large landslides that can easily dam the river, and maintain a slow-moving state. Through a geomorphological and geological model, our study offers new insights and enhances the understanding of the formation and characteristics of landslide dams induced by tectonic activity in mountainous regions. [ABSTRACT FROM AUTHOR]

Frictional heating–induced thermal pressurization is a key mechanism responsible for the exceptional long-runout distances and high-speed movement of some massive landslides. In this paper, a novel framework for modelling landslides with frictional heating–induced thermal pressurization is developed based on the material point method (MPM). In this MPM framework, the basal terrain is idealised as a rigid material, while the sliding mass is treated as a thermo-hydro-mechanical porous mixture. The sliding mass interacts with the rigid terrain via the generalized multi-material contact model accounting for frictional heating and water pressurization effect. Special scaling treatment is applied for the temperature and liquid pressure diffusion calculations within the sliding mass to better approximating the thermal pressurization effect within the thin shear band. The validity of the thermal pressurization model and the sensitivity of its parameters have been demonstrated through two benchmark examples, corresponding to thermo-poro-elastic blocks sliding on a horizontal surface with an imposed constant velocity and along an inclined plane under gravity. Finally, the capability of the proposed framework is verified by satisfyingly reproducing both the dynamic runout and the friction-induced thermal pressurization processes of the giant Daguangbao landslide. [ABSTRACT FROM AUTHOR]

All nominations must be submitted before the deadline shown, must be complete in content and requirements and must be submitted directly to the Chair of the ICL Awards Committees (peter.bobrowsky@canada.ca) and the ICL/IPL Secretariat (secretariat@iclhq.org). Nominations will be accepted by the IPL/ICL Secretariat proposed by any ICL member in good standing up to 3 months prior to the annual ICL BOR; the exact date will be advertised by the IPL/ICL Secretariat; Nominations submitted by any ICL member in good standing up to 6 months prior to the forthcoming WLF will be accepted by the committee; the IPL/ICL Secretariat announces the exact deadline submission date. [Extracted from the article]

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]

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

In many catchments prone to debris flows, prevention structures such as check dams and retention basins have been installed to prevent debris flows from impacting the nearby infrastructures. The SPH model adopting the Herschel–Bulkley–Papanastasiou (HBP) constitutive law has shown good potential in modeling the interaction between debris flow and prevention structures. However, the accuracy of this model is not fully satisfactory when modeling the deposition process of debris flow, because the original HBP law is a viscoplastic model which does not consider frictional dissipation. Therefore, in this paper, we proposed a novel SPH model for analyzing the interaction between debris flow and prevention structures, by incorporating a modified HBP law with frictional dissipation into the original SPH model. The proposed model is validated by column collapse and flume benchmark experiments first and then utilized to analyze a real debris flow and its interaction with the prevention structures in the Cancia catchment in northern Italian Alps. The results of the column collapse experiment show that our model exhibits a better performance in simulating the collapse process compared with the original SPH model, and the simulation results of the sand flume test illustrate that the proposed model can accurately predict the impact force of debris flow on the prevention structure. The simulation results of the Cancia debris flow demonstrate that the check dams can dramatically diminish the discharge and the frontal flow velocity of the debris flow, and the peak impact force of debris flow generally decreases with gentler channel slope. Furthermore, various prevention structures show different interaction mechanisms with debris flows: the flat deposition platform mainly dissipates the kinetic energy of the flow, the check dam mainly reduces the peak discharge of the debris flow and intercepts the debris mass, and the retention basin at the outlet contributes to the deposition of debris flow. The proposed novel SPH model is helpful for guiding the optimization design of multiple prevention structures in debris flow gullies. [ABSTRACT FROM AUTHOR]

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

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

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

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

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

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]

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

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

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

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

As a slope may have numerous potential slip surfaces, its failure probability as a system is usually different from that of a single slip surface. Nevertheless, the system failure probability of a slope is often governed by several representative slip surfaces. How to identify the representative slip surfaces is important for system reliability analysis of soil slopes. Previous studies on representative slip surface identification mainly focus on circular slip surfaces within the framework of limit equilibrium methods; there are also relevant researches, albeit limited, within the framework of finite element/finite difference analyses based on the shear strength reduction method. By viewing the task of representative slip surface identification as a multidesign point identification problem, a barrier-based optimization method is suggested in this paper to identify representative slip surfaces of arbitrary shape based on the shear strength reduction method. A multiple starting point strategy is suggested to enhance its efficiency. For the three slopes examined in this paper, the method suggested is capable of identifying representative slip surfaces efficiently without prior assumption on their shapes. [ABSTRACT FROM AUTHOR]

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

The paper presents a methodology to rapidly assess and map the landslide kinematics in areas with dense vegetation cover. The method uses aerial imagery collected with UAVs (Unmanned Aerial Vehicles) and their derived products obtained from the structure from motion technique. The landslide analysed in the current paper occurred in the spring of 2021 and is located in Livadea village from Curvature Subcarpathians, Romania. This landslide affected the houses in the vicinity, and people were relocated because of the risk of landslide reactivation. To mitigate the landslide consequences, a preliminary investigation based on UAV imagery and geological-geomorphological field surveys was carried out to map the active parts of the landslide and establish evacuation measures. Three UAV flights were performed between 6 May and 10 June using DJI Phantom 4 and Phantom 4 RTK UAVs (Real-Time Kinematic Unmanned Aerial Vehicles). Because it is a densely forested area, semi-automated analyses of the landslide kinematics and change detection analysis were not possible. Instead, the landslide displacement rates and the changes in terrain morphology were assessed by manually interpolating the landmarks, mostly tilted trees, collected from all three UAV flights. The results showed an average displacement of approximately 20 m across the landslides, with maximum values reaching 45 m in the transport area and minimum values below 1 m in the toe area. This approach proved quick and efficient for rapid landslide investigations in a densely forested area when fast response and measures are necessary to reduce the landslide consequences. [ABSTRACT FROM AUTHOR]