Landslide Reactivation Research Articles

Benchmarking Physical Model Experiments with Numerical Simulations for the Wangjiashan Landslide-Induced Surge Waves in the Baihetan Reservoir Area

The Baihetan Hydropower Station reservoir area began impoundment in 2021, triggering the reactivation of ancient landslides and the formation of new ones. This not only caused direct landslide disasters but also significantly increased the likelihood of secondary surge wave disasters. This study takes the Wangjiashan (WJS) landslide in the Baihetan reservoir area as an example and conducts large-scale three-dimensional physical model experiments. Based on the results of the physical model experiments, numerical simulation is used as a comparative verification tool. The results show that the numerical simulation method effectively reproduces the formation and propagation process of the WJS landslide-induced surge waves observed in the physical experiments. At the impoundment water level of 825 m, the surge waves generated by the WJS landslide pose potential threats to the Xiangbiling (XBL) residential area. In this study, the numerical simulation based on computational fluid dynamics confirmed the actual propagation forms of the surge waves, aligning well with the results of the physical experiments at a microscopic scale. However, at a macroscopic scale, there is some discrepancy between the numerical simulation results and the physical experiment outcomes, with a maximum error of 25%, primarily stemming from the three-dimensional numerical source model. This study emphasizes the critical role of physical model experiments in understanding and mitigating surge wave disasters in China. Furthermore, physical experiments remain crucial for accurate disaster prediction and mitigation strategies. The theories and methods used in this study will provide important references for future research related to landslide disasters in reservoir areas.

Investigating the reactivation of historical landslides during the 2022 Luding MS6.8 earthquake

On September 5, 2022, a strong earthquake with a magnitude of MS6.8 struck Luding County in Sichuan Province, China, triggering thousands of landslides along the Dadu River in the northwest-southeast (NW-SE) direction. We investigated the reactivation characteristics of historical landslides within the epicentral area of the Luding earthquake to identify the initiation mechanism of earthquake-induced landslides. Records of the two newly triggered and historical landslides were analyzed using manual and threshold methods; the spatial distribution of landslides was assessed in relation to topographical and geological factors using remote sensing images. This study sheds light on the spatial distribution patterns of landslides, especially those that occur above historical landslide areas. Our results revealed a similarity in the spatial distribution trends between historical landslides and new ones induced by earthquakes. These landslides tend to be concentrated within a range of 0.2 km from the river and 2 km from the fault. Notably, both rivers and faults predominantly influenced the reactivation of historical landslides. Remarkably, the reactivated landslides are characterized by their small to medium size and are predominantly situated in historical landslide zones. The number of reactivated landslides surpassed that of previously documented historical landslides within the study area. We provide insights into the critical factors responsible for historical landslides during the 2022 Luding earthquake, thereby enhancing our understanding of the potential implications for future co-seismic hazard assessments and mitigation strategies.

Post-landslide restoration through multistrata agroforestry-based land management in the West Bogor area of Indonesia

The West Bogor area experienced a landslide disaster, causing extensive damage to secondary forest areas, plantations, and residential homes. Despite evacuations, the community persists in using the affected land for agriculture. This study aims to develop a land management model based on agroforestry for post-landslide restoration, to prevent landslide reactivation, and to provide benefits for the local community. Introducing an agroforestry system that includes deep-rooted trees and perennial crops on landslide-prone slopes can improve slope stability by enhancing soil structure and water retention, minimising erosion and landslides. The study examines unexplored aspects of landslide characteristics and zoning as a novel approach to improve mitigation strategies. We classify the post-landslides area into depletion, transition, and accumulation zones. The lithology comprises Breccia with pumice and Andesite gravels, a sandy tuff matrix, and Claystone underneath, acting as the slip surface. The northern landslide has depletion, transition, and accumulation zones ranging from 743 to 710 m above sea level (masl), 710 to 694 masl, and 694 to 676 masl. In the southern landslide, these zones range from 783 to 720 masl, 720 to 705 masl, and 705 to 676 masl. Based on the characteristics of those zones, we develop an agroforestry model in a vertical pattern with species strata, fast-growing local plants, strong and deep roots, and a relatively high evapotranspiration rate. The depletion zone is managed as a complex agroforestry system (forest type) consisting primarily of forest plants and plantation crops. The transition zone is a complex agroforestry (garden type) with plantation crops and some forest plants. The accumulation zone is a simple agroforestry system with seasonal crops. On almost flat land in an accumulation zone suitable for Oryza sativa cultivation, we apply Cocos nucifera as a protective plant. Soil fertility in all zones is improved with organic and inorganic fertilization, and it also increases the mycorrhizal population through the planting of leguminous plants. The multistrata agroforestry model, created and adapted to the specific characteristics and zoning of landslide-prone areas, is expected to significantly enhance landslide restoration and erosion mitigation and reduce the risk of future landslides. Such approaches can be extended to regions with comparable characteristics.

Integrating Dendrogeomorphology into Stress–Strain Numerical Models: An Opportunity to Monitor Slope Dynamic

Monitoring systems are recognized worldwide as fundamental tools for landslide risk management. However, monitoring can be difficult when dealing with large slopes in forested areas. In these situations, dendrogeomorphology can offer a low-cost and low-impact alternative for providing distributed information with an annual temporal resolution. The present study is a first attempt to integrate dendrometric and dendrogeomorphic data into a numerical finite difference model, in order to simulate the stress–strain behavior of the tree-slope system. By using a parametrical approach, the capability of the numerical model to effectively reproduce the tree stem anomalies (i.e., tilting angle, J-shaped feature, and internal stresses causing tree-ring growth anomalies such as eccentric growth and reaction wood) was verified, and the target parameters for the model calibration were identified based on a sensitivity analysis, which emphasized the relevance of the wood deformability; moreover, the interpretation of results allowed to point out different peculiarities (in terms of type of deformation, falling direction, and distribution of internal stresses) for different slope conditions (kinematics and depth of the failure surface) and different zones of the landslide (head scarp, main body, and toe). Afterwards, the modeling approach was applied to the Val Roncaglia landslide (Northen Italy), which involves a complex roto-translational kinematics, characterized by multiple sliding surfaces. The simulated stem anomalies showed good agreement with the ones arising from onsite dendrometric surveys, and they confirmed the conceptual model of the landslide, enabling the planning of further specific investigations. Moreover, the capability of the model in reproducing the tilting angle of trees, if correlated to their eccentricity, could provide a quite long time series (over more than 50–60 years) of the landslide reactivation and allow the use of dendrochronological data for the model calibration, thereby enhancing slope dynamic monitoring and landslide risk management.

Insights into Deformation and Mechanism of a Reactivated Landslide Occurrence from Multi-Source Data: A Case Study in Li County, China

The investigation of reactivated landslides in the alpine-canyon areas suffers the difficult accessibility of precipitous terrain. In particular, when reactivated landslides occur along the major roads, efforts are focused on measuring ground surface displacements during road construction. Nevertheless, the ancient landslide deposits may reactivate after several years of road operation, while they show a stable state during the road construction. The characterization of this type of reactivated landslides is challenging, due to their complex mechanism and the limited monitoring data. Appropriate multi-source data can provide insights into deformation fields and enhance the understanding of landslide mechanisms, ensuring the outperformance of remedial works. This paper reports a recent Tangjiawan reactivated landslide along the Wenchuan-Maerkang Highway in Li County, China. The outcomes, including satellite InSAR, in situ real-time monitoring, and detailed ground and UAV investigation, conducted at this landslide are presented. Early deformation of the reactivated landslide began from 2019, with an InSAR-derived velocity of −11.7 mm/year, furthermore, a significant subsidence of about 21.2 mm, which occurred within a span of only 12 days from 3 June 2020 to 15 June 2020, was observed. The deformation characteristics derived from in situ monitoring during the remedial works were likely firstly associated with the initial unreinforced slope condition and the heavy rainfall. Subsequently, the displacement evolution transformed into deformation induced by time-dependent reduction in slope strength under rainfall conditions. The existing of unconsolidated deposits derived from ancient landslides, along with a fragile geo-structure consisting of rock blocks and gravels interlayered with breccias, exacerbated by large relief created a predisposition for landslide reactivation. Furthermore, 13 days of antecedent cumulative rainfall totaling 224.5 mm directly triggered the occurrence of a landslide event. The significance and implications of integrating multiple monitoring techniques are emphasized.

Development Characteristics and Reactivation Mechanism of a Large-Scale Ancient Landslide in Reservoir Area

The intricate geological conditions of reservoir banks render them highly susceptible to destabilization and damage from fluctuations in water levels. The study area, the Cheyipin section of the Huangdeng Hydroelectric Station, is characterized by numerous ancient landslides of varying scales and ages. In June 2019, during the reservoir filling process of the Huangdeng Hydroelectric Station, a large-scale reactivation of ancient landslides occurred in this area, posing severe threats to riverside infrastructure and human safety, including ground cracking, house cracking, foundation settlement, and road collapse. The reactivation mechanism of ancient landslides at reservoir banks is highly complex due to fluid dynamics. This study conducted field investigations in the Cheyipin landslide area, monitored surface and subsurface deformations using GNSS and inclinometers, and analyzed the distribution characteristics, destruction features, and reactivation mechanisms of the landslides through correlation analysis and numerical calculations. The results indicate that the instability pattern of the slopes manifests as traction-type sliding failure. The slopes do not slide along the ancient sliding surface but along a newly formed arcuate sliding surface, with the direct impact area mainly concentrated near the waterline. The stability of the slopes in this project is closely related to the reservoir water level. It can be assumed that the lowering of the reservoir water level triggered the reactivation of the ancient landslides in the Cheyipin section, while the influence of rainfall can be ignored. To prevent the reactivation of ancient landslides, attention should be focused on the changes in reservoir water level, avoiding rapid adjustments in water level during the initial lowering and final raising of the water level.

Indicating landslide hazard from tree rings – Ecosystem service provided by an alder forest in the hengduan Mts, Sichuan, China

Landslides are destructive geomorphological processes that cause economic and social losses. This stimulates the development of new tools related to landslide hazard. Recently, trees, their growth rings and dendrochronology have become widely used in landslide studies. Thus, this study aims to explore the potential of trees in providing landslide-monitoring ecosystem services through dendrochronology. In our opinion, establishing such an idea would help promote empirical evidence on the efficiency of tree-ring-based tools to decision-makers. We use the example of a landslide in the Moxi basin, Sichuan, China and present results of dendrochronological analysis of growth eccentricity in 48 Nepalese alder (Alnus nepalensis) trees. This analysis provided data on event timing and magnitudes, average frequency and recurrence interval for reactivation of the study landslide, as well as spatial variability of landslide active. Based on dendrochronological data we were also able to determine the current slope balance and general hazard of landslide reactivation on the study slope. Our study shows that trees and dendrochronology can provide data on the activity of landslides that can complement and improve the results of standard engineering methods. Moreover, dendrochronology itself can provide the full information needed for landslide hazard assessment, monitoring and prediction.

Evolution Process of Ancient Landslide Reactivation under the Action of Rainfall: Insights from Model Tests

Under rapid global climate change, the risk of ancient landslide reactivation induced by rainfall infiltration is increasing significantly. The contribution of cracks to the reactivation of ancient landslides, as an evolutionary product, is a topic that deserves attention; however, current research on this issue remains insufficient. In this study, taking the Woda landslide in the upper Jinsha River as a case study, we investigated the reactivation mechanisms of ancient landslides with and without cracks under rainfall based on model tests. The study showed that cracks influence the reactivation range and depth of ancient landslide. In cases where no cracks develop on ancient landslides, rainfall can only cause shallow sliding with failure concentrated at its front edge. Conversely, when cracks develop on ancient landslides, rainwater can quickly infiltrate into the sliding zone along the cracks and induce overall reactivation of the ancient landslide. Furthermore, the reactivation mechanism of ancient landslides without cracks is that the failure of ancient landslide foot results in progressive failure at the front of the ancient landslide. When cracks have developed at ancient landslides, the reactivation mechanism of which involves mid-rear ancient landslide creeping, tensile cracks develop on the mid-rear ancient landslide, with localized sliding at the front edge, tensile cracks extending, local sliding range extending, accelerated creeping, and progressive failure of the mid-rear ancient landslide. These findings shed light on how cracks influence rainfall-induced mechanisms of ancient landslide reactivation and hold great significance for advancing our understanding regarding these mechanisms.

Landslide hazard assessment and mapping at national scale for Malawi

Malawi, a landlocked country characterized by its mountainous terrain crisscrossed by the Great Rift Valley and Lake Malawi, is facing an increasing threat of landslides primarily triggered by heavy rainfall from tropical cyclones and depressions. While there are general landslide susceptibility maps available on a global and continental scale, Malawi lacks its own specific landslide hazard maps that take into account regional nuances, different types of landslides, and their triggering factors. However, factoring in these parameters is essential for accurately quantifying hazards. This contribution aims to fill this gap by proposing hazard maps that consider both spatial and temporal probabilities of landslide events. The methodology employed here is based on quantifying the probability of failure at both spatial and temporal levels, following the guidelines set forth by the Joint Technical Committee – 1 for slopes and landslides (JTC-1). To enhance the accuracy of the landslide inventory, a combination of literature review, visual remote sensing, and field surveys was used. This comprehensive data collection approach included information on the types of landslides, their activity levels, and the periods during which they are most likely to be triggered. Subsequently, susceptibility analyses were conducted for various types of landslides using a data-driven approach. Temporal analyses were carried out, taking into consideration two key factors: (i) the recurrence time of different phenomena, such as debris-flows, debris-slides, and slides from 1946 to 2019, and (ii) the rainfall patterns induced by various tropical meteorological events, as defined by the World Meteorological Organization and Meteo-France. the computation of exceedance probabilities based on the Poisson distribution, predicting the likelihood of landslide reactivation for six different return periods, ranging from 1 to 100 years, following various typical meteorological events. Subsequently, susceptibility analyses were conducted for various types of landslides using a data-driven approach. Temporal analyses were carried out, taking into consideration two key factors: (i) the recurrence time of different phenomena, such as debris-flows, debris-slides, and slides from 1946 to 2019, and (ii) the rainfall patterns induced by various tropical meteorological events, as defined by the World Meteorological Organization and Meteo-France. the computation of exceedance probabilities based on the Poisson distribution, predicting the likelihood of landslide reactivation for six different return periods, ranging from 1 to 100 years, following various typical meteorological events. Ultimately, this methodology facilitates the development of various spatio-temporal landslide risk scenarios on a national scale.

Old Landslide Detection Using Optical Remote Sensing Images Based on Improved YOLOv8

The reactivation of old landslides can be triggered by heavy destructive earthquakes, heavy rainfall, and ongoing human activities, thereby resulting in the occurrence of secondary landslides. However, most existing models are designed for detecting nascent landslides and there are few algorithms for old landslide detection. In this paper, we introduce a novel landslide detection model known as YOLOv8-CW, built upon the YOLOv8 (You Only Look Once) architecture, to tackle the formidable challenge of identifying old landslides. We replace the Complete-IoU loss function in the original model with the Wise-IoU loss function to mitigate the impact of low-quality samples on model training and improve detection recall rate. We integrate a CBAM (Convolutional Block Attention Module) attention mechanism into our model to enhance detection accuracy. By focusing on the southwest river basin of the Sichuan–Tibet area, we collect 558 optical remote sensing images of old landslides in three channels from Google Earth and establish a dataset specifically for old landslide detection. Compared to the original model, our proposed YOLOv8-CW model achieves an increase in detection accuracy of 10.9%, recall rate of 6%, and F1 score from 0.66 to 0.74, respectively. These results demonstrate that our improved model exhibits excellent performance in detecting old landslides within the Sichuan–Tibet area.

Characteristics and mechanism of large deformation of a reservoir colluvial landslide-a case study of the Yulinerzu landslide in Xiluodu Reservoir, China

The reactivation of colluvial landslides in reservoir banks poses a serious threat to the safety of hydropower projects and nearby towns. This study aims to research the morphological evolution of this type of landslides under the action of reservoir water and the impact of morphological evolution on landslide stability. The study focused on the Yulinerzu landslide, a large reactivated colluvial landslide in the Xiluodu Reservoir, China. Field surveys were conducted to analyze the geological structure of the landslide. In situ monitoring and surveys were used to obtain the deformation characteristics and morphological evolution of the landslide. A combined seepage-slope stability analysis was conducted to reveal the deformation mechanism. The results show that the reactivation of the Yulinerzu landslide is dominated by reservoir water fluctuations rather than rainfall. The underlying geological condition of the colluvial landslide is its hydrogeological structure, which causes the landslide to deform in a step-like manner during reservoir operation. With the accumulation of displacement and morphology evolution, the landslide displayed self-stabilizing characteristics. Therefore, in the stability analysis and risk assessment of large deformation landslides, it is essential to take into account not only the hydraulic effects of reservoir fluctuation but also the evolution of landslide morphology.

Deformation Behavior and Reactivation Mechanism of the Dandu Ancient Landslide Triggered by Seasonal Rainfall: A Case Study from the East Tibetan Plateau, China

In recent years, numerous ancient landslides initially triggered by historic earthquakes on the eastern Tibetan Plateau have been reactivated by fault activity and heavy rainfall, causing severe human and economic losses. Previous studies have indicated that short-term heavy rainfall plays a crucial role in the reactivation of ancient landslides. However, the deformation behavior and reactivation mechanisms of seasonal rainfall-induced ancient landslides remain poorly understood. In this paper, taking the Dandu ancient landslide as an example, field investigations, ring shear experiments, and interferometric synthetic aperture radar (InSAR) deformation monitoring were performed. The cracks in the landslide, formed by fault creeping and seismic activity, provide pathways for rainwater infiltration, ultimately reducing the shear resistance of the slip zone and causing reactivation and deformation of the Dandu landslide. The deformation behavior of landslides is very responsive to seasonal rainfall, with sliding movements beginning to accelerate sharply during the rainy season and decelerating during the dry season. However, this response generally lags by several weeks, indicating that rainfall takes time to infiltrate into the slip zone. These research results could help us better understand the reactivation mechanism of ancient landslides triggered by seasonal rainfall. Furthermore, these findings explain why many slope failures take place in the dry season, which typically occurs approximately a month after the rainy season, rather than in the rainy season itself.

Landslide inventory of Puerto Plata, Santiago de los Caballeros and Jarabacoa (Dominican Republic)

ABSTRACT Landslide inventory maps are the first and main information level in studies of reactivation of previous landslides and susceptibility, hazard and risk maps of future landslides. Due to its geographical location and tectonic setting, the Dominican Republic is exposed to several geological risks, among which landslides stand out. In this study, we present a landslide inventory elaborated in three selected zones with particular geographical, lithological, tectonic and climatic characteristics: Puerto Plata, Santiago de los Caballeros and Jarabacoa. A total of 6085 landslides have been recorded (56% in S, 29% in P and 15% in J), which have been classified according to their geometry, propagation mechanism, affected material, thickness and activity. The detected factors that control the distribution and type of gravitational deposits are lithology and geologic structure, as well as rainfall and active faulting along the Camú, Septentrional and Bonao-La Guácara fault zones.

Reactivation of an old landslide in north–central Iran following reservoir impoundment: Results from multisensor satellite time-series analysis

Water impoundment combined with more frequent precipitation extremes due to climate change increases landslide hazards on the slopes surrounding dam reservoirs. In situ monitoring systems in these potential landslide-prone areas are often unavailable, making landslide failures challenging to forecast. This paper describes a multisensor and multivariate remote sensing approach using data from Envisat, Sentinel-1, Landsat and PlanetScope satellites to reconstruct the spatiotemporal evolution of the mechanism and causes of the March 2019 landslide failure backside of the dam reservoir in Hoseynabad-e Kalpush village, north–central Iran. Statistical analysis and time series clustering are performed to derive the main landslide kinematic features from multitemporal interferometric synthetic aperture radar (MT-InSAR) analysis. We also exploit GIS and wavelet analysis to correlate potential external driving factors with landslide kinematics. Envisat and Sentinel-1 MT-InSAR analyses revealed that a previously stable old landslide was reactivated following reservoir impoundment in early 2013. As the reservoir water level rose during the following years up to 34 m in 2019, the landslide displacement rate gradually increased from 3.5 cm/yr to 8.4 cm/yr, and the destabilization gradually propagated upslope. At this stage, seasonal precipitation effects were detected only in the vertical component, indicating swelling and shrinkage movements of the shallower soil layer. The reactivated landslide accelerated and catastrophically failed following the exceptional precipitation in early 2019, producing a horizontal shift of >40 m, detected with optical image digital correlation. In the aftermath, the landslide continued to move with a decreasing trend until final stabilization in October 2021. Our study demonstrates how combined observations derived from multisensor satellite remote sensing data can be used to assess landslide precursors and kinematics, as well as the influence of climatic and anthropogenic factors on the instability of slopes surrounding water reservoirs. This is especially relevant in data-scarce areas.

Regional-scale InSAR investigation and landslide early warning thresholds in Umbria, Italy

In this work, we use conventional two-pass differential satellite interferometry for the investigation of active landslides at regional scale in the Umbria Region of Italy. First, we use InSAR for the detection of active slope movements during the study period (October 2019 to January 2021). Then, we characterize the displacement rates and their variation through time for selected active slopes, by analysing 6-days interferograms. The evolutionary trends of the phenomena are compared to the pertaining climate and soil parameters that are used by the regional landslide early warning system (LEWS) to predict the possibility of landslide occurrence over the territory. We aim to explore the potential of differential interferometry to detect and monitor active landslides and, through that, to validate a geographical LEWS:Through the analysis of stacked interferograms, we identified 256 InSAR deformation signals (IDS) corresponding to active slope movements. Their distribution is compared to the geological map and the landslide inventory, illustrating how active landslides are favoured by weak lithologies and pre-existing slope instability. Also, the relative orientation of the satellite's line of sight with respect to the slope influences the results indicating a bias in the completeness of remotely sensed data. However, in the context of land management and civil protection, the results constitute a valuable information dataset for plans managed by local authorities.The analysis, at the interferogram scale (6-days), of 13 clearly recognizable IDSs illustrates the dynamics of the slopes through the variation of the displacement rates. Due to decorrelation, only about half of the interferograms can be used for this purpose, and interpretation is required. The comparison to the alert thresholds of the Umbria region shows the remarkable relationship between InSAR-derived landslide activity and the parameters used for landslide prediction (i.e., 48 h rainfall and soil moisture conditions). The maximum 48 h cumulative rainfall and the soil saturation index prove effective to predict landslide reactivations and accelerations. We provide uncertainty measures of the landslide prediction through binary classification metrics. Such results are only preliminary, given the limited number of landslides and timespan of our dataset. However, it represents one of the few attempts to validate the forecast of a regional LEWS quantitatively.

The Kangjiapo landslide in Wanzhou district, Chongqing city: reactivation of a deep-seated colluvial landslide in the Three Gorges Reservoir

The Kangjiapo landslide in Wanzhou district, Chongqing city: reactivation of a deep-seated colluvial landslide in the Three Gorges Reservoir

Assessment of check dams and afforestation in mitigating debris flows based on dendrogeomorphic reconstructions, field surveys and semi-empirical models

Check dams and afforestation are two common measures for debris-flow mitigation; however, there is a lack of methods allowing quantitative evaluation of check dam and afforestation effects on debris flow motion and deposition. Laogan Gully is an active debris-flow system with a large landslide on the right hillslope, currently representing the main source of debris flows in the catchment. Several debris flows have occurred since 1985 which led to the construction of check dams and the plantation of silver wattle (Acacia dealbata) trees between 1991 and 1993. These measures significantly reduced the volume of debris flows reaching downstream portions of the system. At the same time, large amounts of sediment were trapped on the gully bed and thereby increased the base level of the torrent, which again tended to stabilize the large landslide on the right hillslope. This paper analyzes sedimentation of debris-flow material on the gully bed and on the fan using 12 trial pits behind the check dams and 6 incised geological sections on the fan. The timing and extent of debris flow motion and deposition was reconstructed using scars and growth suppression events in the annual growth ring series of 306 Acacia trees trapped in the newly formed deposits. The gravitational potential energy of the sediment of the large landslide is calculated according to the change in the erosion base level behind the check dams. On the basis of these results, we constructed a semi-empirical vegetation coverage–landslide energy–debris flow volume model to quantitatively evaluate the effects of the check dams and afforestation on debris-flow mitigation. The model exhibited predictive capabilities for estimating the volume of debris flow generated by the reactivation of landslides. Check dams are found to play a major role in debris-flow mitigation in the early stages, whereas the Acacia trees play an increasingly important role once the check dams are filled. The partial resurgence of the landslide caused a sudden increase in the gravitational potential energy of the sediment source, which in turn affected the effectiveness of the debris-flow mitigation measures.

Understanding slow-moving landslide triggering processes using low-cost passive seismic and inclinometer monitoring

Landslides are a major natural hazard, threatening communities and infrastructures worldwide. Mitigation of these hazards relies on understanding their causes and triggering processes, which critically depend on subsurface characteristics and their variations over time. In this study, we present a novel approach combining passive seismic and low-cost inclinometer monitoring methods to improve the understanding of landslide activation mechanisms and their controls. We evaluate the efficiency of this approach on a shallow, slow-moving landslide directly endangering a road bridge, a bridge that is part of an important emergency response route. Results show the value of combining the two approaches for observing and monitoring landslide hazards. Passive seismic monitoring captures the variation in soil properties (rigidity and density) over time by sensing the variations of the seismic wave velocity (dV/V and its associated correlation coefficient). At the same time, novel low-cost inclinometers are monitoring subsurface deformation (from millimetric to pluricentimetric scale) and temperature. Seismic precursors detected at the bottom sensor a few hours prior to the reactivation are followed by the reactivation of the landslide toe, releasing stresses in the top part that lead to the reactivation of the whole landslide. This reactivation occurs during an episode of heavy rainfall following a 7-month drought. Meanwhile, temperature monitoring enables us to track water infiltration and to highlight its role in the landslide mechanisms. Overall, the combination of the two monitoring methods shows promise for quantifying the sliding mechanisms of landslide reactivations and for designing landslide early warning systems.

MPM-based mechanism and runout analysis of a compound reactivated landslide

Understanding the entire process of hydraulic-related landslide reactivations is crucial for risk assessment, which includes initiation and runout evolves from a small-deformation in the pre-failure stage to large-deformation after failure, with complex interactions between the materials in solid and liquid phases. This paper reproduces the entire process of a reactivated landslide using Material Point Method (MPM). The accuracy of MPM is validated in comparison to Limit Equilibrium Method (LEM) and Finite Element Method (FEM). The effects of antecedent rainfall and pre-existing groundwater on landslide runout and the deposits morphology are discussed. Results show that the antecedent rainwater rises the groundwater level and saturates the front edge of slope where the initial failure occurred. Three computed spatio-temporal distributions of pore water pressure show good agreement and match well with field evidence. The kinematic characteristics show that the landslide has different moving features with different microtopography, which reveals retrogressive failure in front and middle part of slope initially and compound retro- and pro-gressive failures occur at the rear edge. The results of unsaturated two-phase MPM are in better agreement with the measured morphology than full-saturated MPM. The antecedent rainfall and the pre-existing groundwater are the main contributing factors to the landslide runout.

Remote Sensing Monitoring of the Pietrafitta Earth Flows in Southern Italy: An Integrated Approach Based on Multi-Sensor Data

Earth flows are complex gravitational events characterised by a heterogeneous displacement pattern in terms of scale, style, and orientation. As a result, their monitoring, for both knowledge and emergency purposes, represents a relevant challenge in the field of engineering geology. This paper aims to assess the capabilities, peculiarities, and limitations of different remote sensing monitoring techniques through their application to the Pietrafitta earth flow (Southern Italy). The research compared and combined data collected during the main landslide reactivations by different ground-based remote sensors such as Robotic Total Station (R-TS), Terrestrial Synthetic Aperture Radar Interferometry (T-InSAR), and Terrestrial Laser Scanner (TLS), with data being derived by satellite-based Digital Image Correlation (DIC) analysis. The comparison between R-TS and T-InSAR measurements showed that, despite their different spatial and temporal resolutions, the observed deformation trends remain approximately coherent. On the other hand, DIC analysis was able to detect a kinematic process, such as the expansion of the landslide channel, which was not detected by the other techniques used. The results suggest that, when faced with complex events, the use of a single monitoring technique may not be enough to fully observe and understand the processes taking place. Therefore, the limitations of each different technique alone can be solved by a multi-sensor monitoring approach.