Large Landslides Research Articles

Size scaling of large landslides from incomplete inventories

Abstract. Landslide inventories have become cornerstones for estimating the relationship between the frequency and size of slope failures, thus informing appraisals of hillslope stability, erosion, and commensurate hazard. Numerous studies have reported how larger landslides are systematically rarer than smaller ones, drawing on probability distributions fitted to mapped landslide areas or volumes. In these models, much uncertainty concerns the larger landslides (defined here as affecting areas ≥ 0.1 km2) that are rarely sampled and often projected by extrapolating beyond the observed size range in a given study area. Relying instead on size-scaling estimates from other inventories is problematic because landslide detection and mapping, data quality, resolution, sample size, model choice, and fitting method can vary. To overcome these constraints, we use a Bayesian multi-level model with a generalised Pareto likelihood to provide a single, objective, and consistent comparison grounded in extreme value theory. We explore whether and how scaling parameters vary between 37 inventories that, although incomplete, bring together 8627 large landslides. Despite the broad range of mapping protocols and lengths of record, as well as differing topographic, geological, and climatic settings, the posterior power-law exponents remain indistinguishable between most inventories. Likewise, the size statistics fail to separate known earthquakes from rainfall triggers and event-based triggers from multi-temporal catalogues. Instead, our model identifies several inventories with outlier scaling statistics that reflect intentional censoring during mapping. Our results thus caution against a universal or solely mechanistic interpretation of the scaling parameters, at least in the context of large landslides.

Distribution-agnostic landslide hazard modelling via Graph Transformers

In statistical applications, choosing a suitable data distribution or likelihood that matches the nature of the response variable is required. To spatially predict the planimetric area of a landslide population, the most tested likelihood corresponds to the Log-Gaussian case. This causes a limitation that hinders the ability to accurately model both very small and very large landslides, with the latter potentially leading to a dangerous underestimation of the hazard. Here, we test a distribution-agnostic solution via a Graph Transformer Neural Network (GTNN) and implement a loss function capable of forcing the model to capture both the bulk and the right tail of the landslide area distribution. An additional problem with this type of data-driven hazard assessment is that one often excludes slopes with landslide areas equal to zero from the regression procedure, as this may bias the prediction towards small values. Due to the nature of GTNNs, we present a solution where all the landslide area information is passed to the model, as one would expect for architectures built for image analysis. The results are promising, with the landslide area distribution generated by the Wenchuan earthquake being suitably estimated, including both zeros, the bulk and the extremely large cases. We consider this a step forward in the landslide hazard modelling literature, with implications for what the scientific community could achieve in light of a future space–time and/or risk assessment extension of the current protocol.

Exploring the integration of InSAR data into climate‐driven creep models to assess slow‐moving landslide dynamics

AbstractThe deformation behavior of slow‐moving large landslides is often governed by rainfall characteristics. Based on observational data such as precipitation, deformation measurement, and pore water pressure measurements in the slip zone, in many cases a strong correlation between strong rainfall events, a time‐delayed increase of pore water pressures in the slip zone, and, simultaneously to this, an increase of the deformation rate of the landslide can be found. Based on such detailed data, calculation models, which couples the relation between rainfall characteristics and the development of pore water pressures in the slip zone on one hand and the deformation behavior of the slope on the other, can be developed and be used for a better understanding and a prediction of deformation behavior of such slow‐moving landslides. Climate change issues will lead to a change in rainfall frequency and magnitude and annual temperature distribution characteristics in several regions worldwide, which will also lead to changes in the deformation behavior of such large landslides. In this contribution, satellite‐based interferometric synthetic aperture radar (InSAR) data are discussed to be used as source for deformation measurements as bases for prediction models describing the rainfall‐triggered deformation behavior of slow‐moving landslides.

Can satellite InSAR innovate the way of large landslide early warning?

Predicting landslide failure times is an essential component in landslide risk management. Although in-situ sensor-supported landslide early warning systems are still predominantly used, their high cost makes it impractical to monitor all the landslides, thereby posing a major challenge for the effective landslide risk management. Hence, this study investigated this problem from an earth observation perspective and proposed a probabilistic landslide failure time prediction framework integrating Interferometric Synthetic Aperture Radar (InSAR) monitoring information. Accordingly, 30 historical landslides that occurred between 2016 and 2021 in central and western China were collected to evaluate the feasibility of the aforementioned framework. Based on the landslide dataset, the performance of the satellite InSAR technology for landslide failure time prediction is evaluated systematically from an application perspective. It was evident that eleven landslides (36.67 %) were captured by InSAR with accelerated deformation signals before failure, and monitoring data from eight (26.67 %) of them provided enough information for their failure time prediction. Further, a probabilistic method integrating the conventional inverse velocity model and sequential Bayesian updating was proposed to dynamically predict the most likely failure time and related confidence interval. Case studies showed that the proposed method could successfully predict the failure time of the eight landslides, thus demonstrating the feasibility of the framework. Although the current long revisit period of satellites constrains their performance practically, this problem can be solved by advancements in future satellite missions. Thus, we believe that the InSAR era is imminent and will bring substantial values for large landslide early warning.

A rockslide-generated tsunami in a Greenland fjord rang Earth for 9 days.

Climate change is increasingly predisposing polar regions to large landslides. Tsunamigenic landslides have occurred recently in Greenland (Kalaallit Nunaat), but none have been reported from the eastern fjords. In September 2023, we detected the start of a 9-day-long, global 10.88-millihertz (92-second) monochromatic very-long-period (VLP) seismic signal, originating from East Greenland. In this study, we demonstrate how this event started with a glacial thinning-induced rock-ice avalanche of 25 × 106 cubic meters plunging into Dickson Fjord, triggering a 200-meter-high tsunami. Simulations show that the tsunami stabilized into a 7-meter-high long-duration seiche with a frequency (11.45 millihertz) and slow amplitude decay that were nearly identical to the seismic signal. An oscillating, fjord-transverse single force with a maximum amplitude of 5 × 1011 newtons reproduced the seismic amplitudes and their radiation pattern relative to the fjord, demonstrating how a seiche directly caused the 9-day-long seismic signal. Our findings highlight how climate change is causing cascading, hazardous feedbacks between the cryosphere, hydrosphere, and lithosphere.

Reconstruction and verification of mechanism and triggering conditions of the Hermitage landslide at the Vaches Noires cliffs (Normandy)

The Pays d'Auge region is known for its diversity of instability including large and deep-seated landslides. Among them, the “L'Hermitage landslide” is one of the largest historical phenomena in the Vaches Noires area of Normandy, France. As it was triggered during a period when few observations were performed, there is still considerable uncertainty about the conditions under which it was triggered. While it remains an epiphenomenon in the Vaches Noires area, the fact is that the area could be prone to other large landslides. It therefore seems essential and necessary to study these old landslides in order to better understand the evolution of this type of cliff. This article presents a detailed study of this major landslide. The main objective is to reconstruct the evolution of the landslide and verify the conditions that led to its triggering. To this end, a conceptual model of functioning, including a trigger scenario, was proposed using a multidisciplinary approach integrating historical, geomorphological, geological and hydrological data. This scenario was then evaluated and verified by finite-difference numerical modeling (FDM) with FLAC2D®. This approach enabled to propose probable trigger conditions and to identify more precisely the different contributing factors, causes and mechanisms influencing its development.

Geohazard features of the Southern Sardinia

ABSTRACT The Maps of Geohazard features of Southern Sardinia produced in the framework of the Magic project (MArine Geohazard along Italian Coasts) are here presented. The MaGIC project (Marine Geohazard along the Italian Coasts) had the aim of mapping the geohazard in the Italian seas. The features were derived from the digital elevation model interpretation of the seafloor morphology and shallow sub-surface. From the marine geo-hazards point of view, the main critical elements are represented by gravitational mass processes in the canyon heads, some of which, as in the Toro Canyon, are exposed to seismic triggering. In other cases, we observed that gravitational dynamics connected to fluid leakage processes (pockforms). Large landslides and debris avalanches have been detected in Cagliari Gulf, whereas in eastern upper slope, crescent bedforms, occurring in the eastern sector of the upper slope testify to the upward migration of hyperpycnal erosional structures linked to flows from nearby river inputs.

Lake Wellington and West Walker River in Great Basin of western United States: History and genesis

ABSTRACTClosed basins within the Great Basin of the western United States were home to numerous lakes during the Pleistocene. One of these paleolakes along the western edge of the Great Basin, Lake Wellington, once filled a 10 × 25‐km expanse of Smith Valley to depths approaching 90 m. This and other lakes that existed during the Pleistocene are generally considered to be pluvial, indicating contemporaneity with either or all a period of cooler climate, increased rainfall and snowmelt, and relatively reduced rates of evaporation as compared to today. Here we combine the results of 36Cl terrestrial cosmogenic nuclide surface exposure dating with soils and geomorphic observations to suggest Lake Wellington was not a pluvial lake but instead the result of a large landslide prior to ~43 ± 15 ka along the West Walker River where it exited Smith Valley. The observations collected also reveal an ancestral course of the West Walker River ~85 m above the current river grade. Attributing the elevation difference to incision caused by active 0.05 ± 0.01 mm a−1 uplift of the underlying Singatse and assuming the ancestral course followed the same path as today places the age of the paleoriver course at ~1.7 Ma.

The Geomechanics of the Dangkhar Landslide, Himachal Pradesh, India

The Dangkhar Landslide is an extremely large landslide located in the Spiti Valley of Himachal Pradesh, India. The landslide is situated in a remote high mountain desert within the Tethys Himalaya at elevations between 3400 m and 5600 m. It is amongst the five largest continental landslides on earth, covering an area of approximately 54 km2 and having an estimated volume of 15–20 km3. Geomechanical evaluations based on the block theory indicate that the Dangkhar Landslide formed as a result of unfavorable combinations of structural geological features and complex surface morphology. A massive kinematically removable block is created by a regional synclinal flexure that is crosscut and kinematically liberated by bounding side valleys. Three-dimensional block kinematics are necessary to permit the release of the giant block and its sliding along the synclinal flexure. Pseudostatic slope stability sensitivity analyses incorporating estimates of site seismicity and shear strength parameters suggest that earthquake shaking could have triggered instability if the static factor of safety was less than or in the range of about 1.5–1.9. Considering the glacial history of the region, ice debuttressing represents an additional potential triggering mechanism.

Comprehensive Study on the 143 A.D. West Gangu Earthquake in the West Qinling Area, Northeastern Margin of Tibetan Plateau

The 143 A.D. west Gangu earthquake is documented to have occurred in the West Qinling area, which is located on the northeastern margin of the Tibetan Plateau. Initial limited historical records suggest the earthquake took place along the West Qinling fault (WQLF) in the western region of Gangu County. However, the absence of corresponding geological and geomorphological evidence has posed a considerable challenge in accurately quantifying parameters such as the precise location, magnitude, and seismogenic fault segment in earlier investigations. In this study, a comprehensive examination of multiple residual surface rupture zones within the macroseismic zone of this earthquake enabled the determination of the seismogenic structure, magnitude, and rupture zone scale through diverse methodologies, which include field geological investigations, chronology testing, Unmanned Aerial Vehicle (UAV) aerial surveying, and interpretation of landslides along the fault zone. The results reveal that the seismogenic structure of this seismic event is associated with the Zhangxian fault segment of the WQLF, also marked by a dense distribution of large landslides from Zhangxian to Yuanyangzhen. The epicenter was identified at the eastern end of the Zhangxian fault segment of the WQLF. Furthermore, the magnitude of the 143 A.D. west Gangu earthquake is estimated to be approximately Ms 7–7.3, with the residual surface rupture zone intermittently extending over about 22 km and a maximum horizontal dislocation along the rupture zone of 2.8 ± 0.5 m. This detailed investigation contributes foundational insights for further evaluating the seismic risk across various segments of the WQLF.

High-resolution landslide mapping and susceptibility assessment: Landslide temporal variations and vegetation recovery

In mountainous terrains, the frequent landslides and their associated impacts on human lives and the economy is increasing globally. Development of landslide inventory and afterward landslide susceptibility mapping are the main prerequisites for implementing landslide mitigation measures and protection in mountainous regions. The 2005 Kashmir earthquake induced different small and large landslides and some were active for the long term. So far many studies have used medium and high-resolution data to develop landslide inventory. This study aims to develop a 1st detailed, comprehensive and accurate landslide inventory using a very high-resolution image using a semi-automatic technique. The precise landslide inventory is employed to develop an accurate and comprehensive landslide susceptibility map considering the landslide inventory data using a logistic regression training model. Furthermore, the landslide’s temporal recovery from the earthquake and its reactivation due to rainfall in spare vegetation areas have been evaluated. Fine-resolution satellite images of Worldview-2 are applied to develop a detailed landslide inventory using a Support Vector Machine (SVM) classifier. A total of 63,630 landslides were identified using a semi-automatic technique within a study area of 265 km2. From regression modeling, the results show that geology, topography, and road networks have a significant impact on the spatial distribution of landslides. Model performance was evaluated based on the testing data, the model gives an AUC of 0.93 and the kappa value of 0.9353. The spatiotemporal NDVI has been assessed to identify the landslide recovery and its reactivation due to extreme rainfall. The results show that 72.1 % of the landslides occurred in Muzaffarabad formation in the study area. The developed landslide susceptibility map can be further used for land-use planning and implementing mitigation measures for the safety of roads and other infrastructure in the area.

A Thermo-Poro-Mechanics Model Predicts the Transition from Creep to Rapid Movement of Large Landslides

A Thermo-Poro-Mechanics Model Predicts the Transition from Creep to Rapid Movement of Large Landslides

Assessing landslide damming susceptibility in Central Asia

Abstract. Central Asia regions are characterized by active tectonics, high mountain chains with extreme topography with glaciers, and strong seasonal rainfall events. These key predisposing factors make large landslides a serious natural threat in the area, causing several casualties every year. The mountain crests are divided by wide lenticular or narrow, linear intermountain tectonic depressions, which are incised by many of the most important Central Asia rivers and are also subject to major seasonal river flood hazard. This multi-hazard combination is a source of potential damming scenarios, which can bring cascading effects with devastating consequences for the surrounding settlements and population. Different hazards can only be managed with a multi-hazard approach coherent within the different countries, as suggested by the requirements of the Sendai Framework for Disaster Risk Reduction. This work was carried out within the framework of the Strengthening Financial Resilience and Accelerating Risk Reduction in Central Asia (SFRARR) project as part of a multi-hazard approach with the aim of providing a damming susceptibility analysis at a regional scale for Central Asia. To achieve this, a semi-automated GIS-based mapping method, centered on a bivariate correlation of morphometric parameters defined by a morphological index, originally designed to assess the damming susceptibility at basin/regional scale, was modified to be adopted nationwide and applied to spatially assess the obstruction of the river network in Central Asia for mapped and newly formed landslides. The proposed methodology represents an improvement to the previously designed methodology, requiring a smaller amount of data, bringing new preliminary information on damming hazard management and risk reduction, and identifying the most critical area within the Central Asia regions.

Impacts of High-Concentration Turbid Water on the Groundwater Environment of the Tedori River Alluvial Fan in Japan

The occurrence of high-concentration turbid water due to a large landslide in the upper reaches of the Tedori River Basin in Japan in May 2015 led to a rapid decline in the groundwater levels within the alluvial fan. However, factors other than turbid water, such as changes in precipitation patterns, can have a significant impact on groundwater levels but have not been thoroughly investigated. By analyzing the relationship between river water and groundwater levels, we found that by 2018, conditions had returned to those observed prior to the turbidity events. Regarding seepage, we found that approximately 24% of the Tedori River’s discharge contributed to seepage before the turbidity event. In contrast, during the post-turbidity years, seepage decreased between 2015 and 2017 and returned to the pre-turbidity levels by 2018. Furthermore, by constructing a hydrological model and examining the contributions of turbidity and precipitation, we found that in 2015, turbidity contributed to 76% of the groundwater level changes, whereas precipitation accounted for 24%. In contrast, in 2016, turbidity contributed to 67%, while precipitation contributed to 33%. In essence, the first year was characterized by a significant contribution from turbidity, while precipitation also played a significant role in groundwater level fluctuations in the second year.

The effect of lateral thrust on the progressive slope failure under excavation and rainfall conditions

Large landslides can involve the multiple failures of regional slopes. To understand the effect of lateral thrust caused by the failure of one slope on its surroundings, the failures of two adjacent highway slopes in Guangdong Province, China, were investigated in detail. The interactive failure processes and landslide morphological characteristics of the two slopes were first analyzed based on the on-site investigation. Then, a plane mechanical model of a large-scale slope was established to evaluate the significant influence of the lateral thrust generated by the west slope acting on the east excavated slope. Furthermore, the extrusion effect of the west slope was modelled under the alternate excavation disturbance and rainfall by transferring the thrust forces onto the interface elements, and the induced failure mechanism and instability mode of the east slope under lateral thrust were reproduced numerically. The results show that the compression-shear failure occurred at the middle and rear slope bodies because of the lateral thrust, which led to the formation of a thrust landslide and the final instability of the east slope.

Landslide Deposit Erosion and Reworking Documented by Geomatic Surveys at Mount Meager, BC, Canada

Mount Meager is a deeply eroded quaternary volcanic complex located in southwestern British Columbia (BC) and is known for its frequent large landslides. In 2010, the south face of Mount Meager collapsed, generating a long-runout debris avalanche that was one of the largest landslides (50 × 106 m3) in Canadian history. Over the past 14 years, the landslide deposit has been reworked by stream action, delivering large amounts of sediment to Lillooet River, just downstream. In this study, we investigate 10 years of geomorphic evolution of the landslide deposit using orthophotos and digital elevation models (DEMs) generated using Structure from Motion (SfM) photogrammetry on aerial photographs acquired during unmanned aerial vehicle (UAV) and Global Navigation Satellite System (GNSS) surveys. The SfM products were used to produce a series of precise maps that highlight the geomorphological changes along the lower Meager Creek within the runout area of the landslide. Comparison of DEMs produced from 2010, 2012, 2015, and 2019 imagery allowed us to calculate deposit volume changes related to erosion, transport, and redeposition of landslide material. We estimate that about 1.1 × 106 m3 of sediment was eroded from the landslide deposit over the period 2015–2019. About 5.2 × 105 m3 of that sediment was redeposited inside the study area. About 5.8 × 105 m3 of sediment, mainly sand, silt, and clay, were exported from the study area and are being carried by Lillooet River towards Pemberton, 40 km from Mount Meager, and farther downstream. These remobilized sediments likely reduce the Lillooet River channel capacity and thus increase flood hazards to the communities of Pemberton and Mount Currie. Our study indicates a landslide persistence in the landscape, with an estimated 47-year half-life decay, suggesting that higher flood hazard conditions related to increased sediment supply may last longer than previously estimated. This study shows the value of using SfM in tandem with historic aerial photographs, UAV photos, and high-resolution satellite imagery for determining sediment budgets in fluvial systems.

Effects of hydrothermal activity and weathering in the active fault area: formation of large landslide and landslide dam lake, Lake Nakatsuna, Nagano, Japan

Effects of hydrothermal activity and weathering in the active fault area: formation of large landslide and landslide dam lake, Lake Nakatsuna, Nagano, Japan

Space-time modeling of landslide size by combining static, dynamic, and unobserved spatiotemporal factors

Landslide spatial prediction using data-driven models has predominantly concentrated on predicting where landslides may occur. Nevertheless, few researchers have turned to jointly modeling how large and when landslides will be for a given terrain unit. This study proposes a data-driven model capable of estimating how large landslides may be, for the entire Taiwan main island in a fourteen-year time window. To address this task, we implement a space–time generalized additive model to fit the complex relationships between environmental factors and landslide size. In addition to incorporating static and dynamic covariates into the modeling process, the model takes into account spatial and temporal interactions to elucidate the spatiotemporal variations affecting landslide size. To test the effectiveness of the model, we employ a comprehensive set of cross-validation (CV) procedures, includes a randomized 10fold-CV, a spatially constrained CV, a temporal leave-one-year-out CV, and a spatio-temporal CV. The experimental results demonstrate that the space–time model delivers acceptable and interpretable prediction outcomes, demonstrating the ability to predict landslide area for a given slope unit within a specified time period. We believe that the space–time landslide modeling will lay the foundation for landslide community to analyze landslide characteristic within a dynamic context. Furthermore, given its inherent spatio-temporal nature, we anticipate that this approach may pave the way for simulation studies exploring diverse climate scenarios.

Spatial and Temporal Distribution Characteristics of Landslide Surge Based on Large-Scale Physical Modeling Experiment

Surge is a common secondary disaster caused by reservoir landslides. The study of its spatial and temporal distribution characteristics is important since it affects not only the normal operation of reservoirs but also the safety of people residing along the river. This paper presents a large-scale three-dimensional physical modeling experiment using a near-dam high-position landslide project as a prototype. It investigated the relationships between the river course characteristics, the landslide volume, the head wave velocity of the landslide surge, the waveform of surges, and the head wave crest of the wave along the course in depth. The results indicate that the head wave velocity of the landslide surge is basically unchanged during the propagation process, and it is minimally affected by the landslide volume. The waveform distribution characteristics and head wave crests change considerably in the diversion area and the curved areas but remain mostly unchanged in the topographic similarity area. In addition, there is a negative correlation between the head wave crest and the cross-sectional area of the river course. Furthermore, under conditions of a large landslide volume, the influence of the cross-sectional area of the river channel on the wave height of landslide surges becomes more significant. Finally, the maximum wave height along the course may not necessarily occur in the head wave crest; it could occur in the second wave or even the subsequent ones.

Landslide susceptibility evaluation based on landslide classification and ANN-NFR modelling in the Three Gorges Reservoir area, China

The Chongqing section of the Three Gorges Reservoir area (TGRA) is a high-risk geological catastrophe warning zone in the Yangtze River Basin. This study aimed to categorize landslides and improve the accuracy of landslide susceptibility evaluations by combining the artificial neural networks (ANN) and the normalized frequency ratio (NFR) models. Considering that the indicator factors have different effects on various landslides, the landslides were separated into four types, that is, giant, large, medium, and small. Thirteen indicator factors were selected through correlation analysis, including elevation, lithology, precipitation, land use, population density, and so on. Combined with 7777 historical landslide events, an ANN-NFR coupling model was proposed. The susceptibility grade prediction of landslides for the entire region was conducted using a GIS platform. Compared to the NFR model, the ANN-NFR model could improve the accuracy of landslide susceptibility evaluation by approximately 4%. This indicates that the ANN-NFR model is an effective method to calculate the weights of indicator factors. The FR value of large landslides is 9.201 in the very susceptibility region, and the model evaluation effect is superior to that of the other three types of landslides. The success rate of the ANN-NFR model after landslide classification was 78.9%, and the prediction rate was 78.6%, both of which were greater than the unclassified ANN-NFR model. Therefore, the ANN-NFR (landslide classified) model could improve the prediction accuracy and can provide a scientific basis for disaster prevention, mitigation, and management in the TGRA.