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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]

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 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]

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]