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Earthquake triggered landslides often pose a great threat to human life and property. Emerging research has been devoted to documenting coseismic landslides failed during or shortly after earthquakes, however, the long-term seismic effect that causes unstable landslides only to accelerate, moderately or acutely, without immediate failures is largely neglected. Here we show the activation and recovery of these earthquake accelerated landslides (EALs) in Central Italy, based on satellite radar observations. Unlike previous studies based on single or discrete landslides, we established a large inventory of 819 EALs and statistically quantified their spatial clustering features against a set of conditioning factors, thus finding that EALs did not rely on strong seismic shaking or hanging wall effects to occur and larger landslides were more likely to accelerate after earthquakes than smaller ones. We also discovered their accelerating-to-recovering sliding dynamics, and how they differed from the collapsed 759 coseismic landslides. These findings contribute to a more comprehensive understanding of the earthquake-triggering landslide mechanism and are of great significance for long-term landslide risk assessment in seismically active areas. This study uses satellite radar observations to investigate the triggering and recovery mechanisms of landslides that are accelerated by earthquakes without immediate failures but showing a prolonged response.

Landslides can occur in different environments and can interact with or fall into water reservoirs or open sea with different characteristics. The subaerial evolution and the transition from subaerial to subaqueous conditions can strongly control the landslide evolution and the generated impulse waves, and consequently the final hazard zonation. We intend to model the landslide spreading, the impact with the water surface and the generation of the impulse wave under different 2D and 3D conditions and settings. We verify the capabilities of a fully 2D and 3D FEM ALE approach to model and analyse near-field evolution. To this aim we validate the code against 2D laboratory experiments for different Froude number conditions (Fr = 1.4, 3.2). Then the Vajont rockslide (Fr = 0.26–0.75) and the consequent impulse wave are simulated in 2D and 3D. The sliding mass is simulated as an elasto-plastic Mohr–Coulomb material and the lake water as a fully inviscid low compressibility fluid. The rockslide model is validated against field observations, including the total duration, the profile and internal geometry of the final deposit, the maximum water run-up on the opposite valley flank and on the rockslide mass. 2D models are presented for both the case of a dry valley and that of the impounded lake. The set of fully 3D simulations are the first ones available and considering the rockslide evolution, propagation and interaction with the water reservoir. Advantages and disadvantages of the modelling approach are discussed.

Large landslides and deep-seated gravitational slope deformations (DSGSD) represent an important geo-hazard in relation to the deformation of large structures and infrastructures and to the associated secondary landslides. DSGSD movements, although slow (from a few millimetres to several centimetres per year), can continue for very long periods, producing large cumulative displacements and undergoing partial or complete reactivation. Therefore, it is important to map the activity of such phenomena at a regional scale. Ground surface displacements at DSGSD typically range close to the detection limit of monitoring equipment but are suitable for synthetic aperture radar (SAR) interferometry. In this paper, permanent scatterers (PSInSAR™) and SqueeSAR™ techniques are used to analyse the activity of 133 DSGSD, in the Central Italian Alps. Statistical indicators for assigning a degree of activity to slope movements from displacement rates are discussed together with methods for analysing the movement and activity distribution within each landslide. In order to assess if a landslide is active or not, with a certain degree of reliability, three indicators are considered as optimal: the mean displacement rate, the activity index (ratio of active PS, displacement rate larger than standard deviation, overall PS) and the nearest neighbor ratio, which allows to describe the degree of clustering of the PS data. According to these criteria, 66% of the phenomena are classified as active in the monitored period 1992–2009. Finally, a new methodology for the use of SAR interferometry data to attain a classification of landslide kinematic behaviour is presented. This methodology is based on the interpretation of longitudinal ground surface displacement rate profiles in the light of numerical simulations of simplified failure geometries. The most common kinematic behaviour is rotational, amounting to 41 DSGSDs, corresponding to the 62.1% of the active phenomena.

This paper tests a multivariate statistical model to simulate rainfall-dependent susceptibility scenarios of shallow landslides. To this end, extreme rainfall events spanning from 1977 to 2021 in the Orba basin (a study area of 595 km2 located in Piedmont, northern Italy) have been considered. First of all, the role of conditioning and triggering factors on the spatial pattern of shallow landslides in areas with complex geological conditions is analysed by comparing their spatial distribution and their influence within logistic regression models, with results showing that rainfall and specific lithological and geomorphological conditions exert the strongest control on the spatial pattern of landslides. Different rainfall-based scenarios were then modelled using logistic regression models trained on different combinations of past events and evaluated using an ensemble of performance metrics. Models calibrated on multiple events outperform the ones based on a single event, since they are capable of compensating for local misleading effects that can arise from the use of a single rainfall event. The best-performing developed model considers all the landslide-triggering rainfall scenarios and two non-triggering intense rainfall events, with a score of 0.90 out of 1 on the multi-criteria TOPSIS-based1 performance index. Finally, a new approach based on misclassification costs is proposed to account for false negatives and false positives in the predicted susceptibility maps. Overall, this approach based on a multi-event calibration and on a misclassification cost analysis shows promise in producing rainfall-dependent shallow landslide susceptibility scenarios that could be used for hazard analyses and early warning systems and could assist decision-makers in developing risk mitigation strategies.

Urban areas are major contributors to the alteration of the local atmospheric and groundwater environment. The impact of such changes on the groundwater thermal regime is documented worldwide by elevated groundwater temperature in city centers with respect to the surrounding rural areas. This study investigates the subsurface urban heat island (SUHI) in the aquifers beneath the Milan city area in northern Italy, and assesses the natural and anthropogenic controls on groundwater temperatures within the urban area by analyzing groundwater head and temperature records acquired in the 2016–2020 period. This analysis demonstrates the occurrence of a SUHI with up to 3 °C intensity and reveals a correlation between the density of building/subsurface infrastructures and the mean annual groundwater temperature. Vertical heat fluxes to the aquifer are strongly related to the depth of the groundwater and the density of surface structures and infrastructures. The heat accumulation in the subsurface is reflected by a constant groundwater warming trend between +0.1 and + 0.4 °C/year that leads to a gain of 25 MJ/m2 of thermal energy per year in the shallow aquifer inside the SUHI area. Future monitoring of groundwater temperatures, combined with numerical modeling of coupled groundwater flow and heat transport, will be essential to reveal what this trend is controlled by and to make predictions on the lateral and vertical extent of the groundwater SUHI in the study area.

Alpine areas are experiencing substantial changes in both temperature and rainfall intensity, both critical triggers for rockfall events. To better understand these evolving climatic scenarios in the Dolomites from 1970 to 2019 and their implications for historical rockfall occurrences, we developed a novel approach based on the frequency analysis of meteorological variables. Our analysis considered key climate variables including mean air temperature, precipitation, thermal amplitude, freeze-thaw cycles, and icing, examined at various aggregation scales. Results unequivocally show a significant warming trend, with the highest warming rates (up to 0.3 °C per decade) observed during spring. This warming has led to an earlier onset of summer and a delayed end of winter, altering seasonal lengths. We also detected a notable decline in cold-related phenomena, with an estimated decrease of 7.3 freeze-thaw days and 2.2 icing days per decade. Precipitation patterns are changing too, with an increasing frequency of high-intensity rainfall events, particularly in winter, and a reduction in low-intensity events across all seasons. The Rescaled Adjusted Partial Sums (RAPS) method further confirmed long-term precipitation trends, revealing that climatic evolution is driven by shifts in variable frequencies rather than just extreme values. Employing a Bayesian method, we investigated the conditional probability of rockfall occurrences knowing that a meteorological variable is within a given range. Our findings reveal several key correlations: in the last decade high-intensity rainfall correlates with rockfalls in autumn, showing conditional probabilities of 12.4 % below 1000 m and 22.2 % between 1000–2000 m. Mean air temperature correlates with rockfalls in summer, for instance, with a 12.7 % probability for 21–24 °C between 1000–2000 m, and in autumn, such as a 2.2 % probability for 17.6–20.8 °C above 2000 m. Temperature amplitude shows high rockfall probabilities in spring, reaching 28.6 % for 8.8–9.9 °C below 1000 m, and in winter, with a 5.8 % probability for 9–10 °C between 1000–2000 m. Beyond these meteorological links, rockfall frequency exhibits three main peaks: November, February-April, and August. Regarding rockfall source aspect, north component has significant increment from 1970–1999 to 2000–2025 (from 4 % to 12 % +3 %) above 2000 m, a pattern likely linked to permafrost thawing. This study underscores the critical influence of changing climate dynamics on rockfall activity in Alpine environments, providing quantitative links between specific meteorological shifts and rockfall occurrence.

There is an increasing need for quantitative rockfall hazard and risk assessment that requires a precise definition of the terms and concepts used for this particular type of landslide. This paper suggests using terms that appear to be the most logic and explicit as possible and describes methods to derive some of the main hazards and risk descriptors. The terms and concepts presented concern the rockfall process (failure, propagation, fragmentation, modelling) and the hazard and risk descriptors, distinguishing the cases of localized and diffuse hazards. For a localized hazard, the failure probability of the considered rock compartment in a given period of time has to be assessed, and the probability for a given element at risk to be impacted with a given energy must be derived combining the failure probability, the reach probability, and the exposure of the element. For a diffuse hazard that is characterized by a failure frequency, the number of rockfalls reaching the element at risk per unit of time and with a given energy (passage frequency) can be derived. This frequency is relevant for risk assessment when the element at risk can be damaged several times. If it is not replaced, the probability that it is impacted by at least one rockfall is more relevant.

A multi-criteria risk analysis to identify and to rank the most critical UNESCO World Heritage Sites (WHSs) in Europe was implemented in the framework of the JPI-CH PROTHEGO project. The presented approach considers three natural geo-hazards (i.e. landsliding, seismic shaking and volcanic activity) for which homogenous European hazard maps are available. The methodology is based on a quantitative and reproducible heuristic assessment of risk through the development of a new UNESCO Risk Index (URI), which combines the level of hazard with a potential damage vector. The latter expresses the expected level of damage as a function of the type of heritage site (monuments, cultural routes, rock-art sites, cultural landscapes, earthworks/hominid sites, walls and natural sites), the position with respect to the ground (underground or overground) and the hazard type. The methodology was applied both to the entire WHS site and to the different properties that compose the site, with the purpose of identifying areas, inside the same site, with different level of risk. At European scale, the spatial distribution of risk reflects the fact that only three hazards were implemented in the analysis so far, with highest values in the Mediterranean area due to the importance of seismic hazard.

Large slow rock-slope deformations, including deep-seated gravitational slope deformations and large landslides, are widespread in alpine environments. They develop over thousands of years by progressive failure, resulting in slow movements that impact infrastructures and can eventually evolve into catastrophic rockslides. A robust characterization of their style of activity is thus required in a risk management perspective. We combine an original inventory of slow rock-slope deformations with different PS-InSAR and SqueeSAR datasets to develop a novel, semi-automated approach to characterize and classify 208 slow rock-slope deformations in Lombardia (Italian Central Alps) based on their displacement rate, kinematics, heterogeneity and morphometric expression. Through a peak analysis of displacement rate distributions, we characterize the segmentation of mapped landslides and highlight the occurrence of nested sectors with differential activity and displacement rates. Combining 2D decomposition of InSAR velocity vectors and machine learning classification, we develop an automatic approach to characterize the kinematics of each landslide. Then, we sequentially combine principal component and K-medoids cluster analyses to identify groups of slow rock-slope deformations with consistent styles of activity. Our methodology is readily applicable to different landslide datasets and provides an objective and cost-effective support to land planning and the prioritization of local-scale studies aimed at granting safety and infrastructure integrity.

This study is devoted to understanding the impact of irregularly shaped rock blocks against a soil buffering layer above a rock shed via numerical simulations by discrete element method (DEM). In the DEM model, the rock block is represented by an assembly of densely packed and bonded spherical particles with the block shape reconstructed from the laser scanning results of a real rock block. The soil buffering layer is modeled as a loose packing of cohesionless frictional spherical particles, while the rock shed is simplified as a layer of fixed particles. The DEM model is first validated by modeling the impact of a cubic block against a soil buffering layer. Then, it is employed to investigate the dynamic interaction between a realistic-shaped rock block and the soil buffering layer. The numerical results show that the geometry of the contact surface between the rock block and soil layer can play a significant influence on the impact force of the rock block and the force acting on the rock shed. For the tested conditions, the distribution of stress on the rock shed can be well described by the Gaussian function, which seems to be independent on the geometry of the contact surface. In addition, the simplification of realistic-shaped rock blocks as spheres in the traditional DEM modeling approaches can significantly underestimate of the impact force. The established modeling strategy serves as a starting point for investigating the rock block shape. The proposed results can contribute to the choice of buffering layer for designing the rock shed.