Landslide Initiation Research Articles

Evaluating the relation between land use changes and the 2018 landslide disaster in Kerala, India

Land use and land cover changes (LULCCs) in mountainous areas may increase the susceptibility to landslides due to modifications of topography, vegetation, and material characteristics. Understanding the relation between LULCCs and landslide occurrences is important for landslide prevention and land resources management. In this study, these changes were analyzed for the landslides that were triggered during the 2018 monsoon event in Kerala, India. The changes in land use and land cover (LULC) that took place in a period of eight years prior to the 2018 event were analyzed for 4,728 landslide initiation points in the entire state, and for a subset of 2,223 landslides in the most affected district of Idukki. Apart from this, the initiation points were compared to those in steep areas and landslide susceptible zones. For these comparisons, we used LULC datasets for the period between 2000 and 2018 that were obtained from national organizations, derived from satellite image classification and visual interpretation. The LULC datasets lacked coherent classification schemes, so a standardization of LULC types was made. The results of these comparisons reveal that more than half of the landslides (58%) occurred in densely vegetated areas, and that 50% of the landslides that caused damage to buildings and roads originated in forest plantations, followed by built-up areas (25%). For most of the landslide locations (90% in Kerala and 83% in Idukki) there was no noticeable change in major LULC in the period prior to the event. Results of this study indicate that LULCCs for the the period 2010-2018 had less influence on the landslides occurred in 2018.

Field-scale assessment of the unsaturated hydraulic properties of residual soils in southeastern Brazil

Abstract Field tests were carried out to estimate effective unsaturated soil hydraulic properties of layered residual soils in Rio de Janeiro, southeastern Brazil. Data of this type are important for understanding the initiation of rainstorm-induced soil landslides, which often occur in the state of Rio de Janeiro as well as other areas having similar geologic settings and climate conditions. Tests were carried out using a simplified field approach, referred to as the Monitored Infiltration Test, which requires only a tensiometer to measure pressure heads below the wetting front, triggered by flow from a Mariotte bottle which maintains a constant pressure at the top edge of the soil profile. The data can then be analyzed by numerical inversion using the HYDRUS-2D software package. The test is relatively fast since no steady-state flow conditions are needed, and versatile since the test can be carried out quickly on steep slopes with the help of a manual auger. Soil water retention and the unsaturated hydraulic conductivity functions were obtained for a range of young, mature and saprolitic residual soils. The effective hydraulic properties of the distinct residual soil layers can be quite large, reflecting a need to provide a careful analysis of field-scale hydraulic heterogeneity in geotechnical analyses.

Development of a morphometric connectivity model to mitigate sediment derived from storm-driven shallow landslides

In silvopastoral environments, landslide erosion results in loss of productive soils and pasture. Sediment delivered to streams from landslides contributes to the degradation of freshwater and marine receiving environments by smothering benthic habitats and increasing turbidity, light attenuation, and sediment-bound contaminants. Biological mitigation is an important strategy in pastoral environments to combat landslide erosion and improve the health of downstream aquatic ecosystems. Using lasso logistic regression, we investigate determinants of sediment connectivity for a landslide-triggering storm event in 1977 in the Wairarapa, New Zealand. Furthermore, we develop the first morphometric connectivity model to predict the likelihood of sediment delivery to streams following landslide initiation. We explore a range of connectivity scenarios by defining a set of sinks and simulating varying rates of sediment generation during flood events of increasing magnitude. The likelihood of sediment delivery is greatly enhanced where landslide deposits coalesce. Besides scar size variables, overland flow distance and vertical distance to sink were the most important morphometric predictors of connectivity. When scar size variables were removed from the connectivity model, median AUROC was reduced from 0.88 to 0.75.By coupling landslide susceptibility and connectivity predictions in a modular form, we quantify the cost effectiveness of targeted versus non-targeted approaches to shallow landslide mitigation. Sediment delivery ratios range from 0.21 to 0.29, equating to an event sediment yield of 3548 t km−2 to 9033 t km−2. Targeted mitigation of landslide-derived sediment is approximately an order of magnitude more cost-effective than a non-targeted approach. Compared with a pasture-only baseline, a 34% reduction in sediment delivery can be achieved by increasing slope stability through spaced tree planting on 6.5% of the pastoral land. The maximum reduction achievable through comprehensive coverage of widely spaced planting is 56%. The landslide connectivity model provides an objective method to support management decisions relating to mitigation of landslide erosion and sediment delivery to streams.

Inventory of large landslides along the Central Western Andes (ca. 15°–20° S): Landslide distribution patterns and insights on controlling factors

The western flank of the Central Andes hosts some of the largest terrestrial landslides (v > km3), which morphologies are particularly well-preserved due to low erosion rates related to the hyper-arid climate prevailing in this region since the Miocene. First-order questions are pending about the factors controlling the development and the triggering of those large-scale slope failures. Previous studies provided some geomorphological analysis and dating on individual study cases, but a regional-scale vision of landslide processes long the Central Western Andes is missing.Here we report an original inventory of large landslides (areas from 0.1 to 180 km2) established along the western flank of the Central Andes between latitudes ca. 15 and 20° S, and from the Pacific coast to the Altiplano. Based on manual mapping (using satellite images analysis, Google Earth and DEMs analysis) and a compilation of previous works, we inventoried more than a thousand large landslides in this region. We then statistically explored the database according to the landslides typology, size, abundance and relation to geologic, tectonic and climatic settings of the Central Western Andes in order to provide a first insight on their controlling factors. Landslide size-frequency distribution follows a power-law with an exponent of 2.31 ± 0.16 and a cut-off of 4.0 ± 1.9 km2 showing a strong contribution of the largest landslides to the cumulated landslide area. We revealed a dominance of rockslide typology (86%) characterized by in-mass slides, the rest being rock-avalanche type (14%) marked by typical granular-flow morphologies. Combination of specific lithology and great local relief emerge as favorable conditioning factor for large landslide initiation, in particular in the case of river incisions though ignimbrites of the Paleogene-Neogene (Huaylillas Formation), concentrating >30% of the landslides. Moreover, landslide clusters tend to follow crustal faults networks suggesting a long-term control of tectonic activity. Most of the identified landslides are paleo events. We tentatively argue that their triggering could not have been possible in the current hyper-arid conditions of the Atacama Desert and its periphery. Future research providing dating on some of the landslide clusters identified in this study is needed to explore possible temporal correlations between periods of landslide activity and external seismic and/or climatic cycles.

Hybrid stochastic-mechanical modeling of precipitation thresholds of shallow landslide initiation

Numerous early warning systems based on rainfall measurements have been designed to forecast the onset of rainfall-induced shallow landslides. However, their use over large areas poses challenges due to uncertainties related to the interaction among various controlling factors. We propose a hybrid stochastic-mechanical approach to quantify the role of the hydro-mechanical factors influencing slope stability and rank their importance. The proposed methodology relies on a physically based model of landslide triggering and a stochastic approach treating selected model parameters as correlated aleatory variables. The features of the methodology are illustrated by referencing data for Campania, an Italian region characterized by landslide-prone volcanic deposits. Synthetic regional intensity-duration (ID) thresholds are computed through Monte Carlo simulations. Several key variables are treated as aleatoric, constraining their statistical properties through available measurements. The variabilities of topographic features (e.g., slope angle), physical and hydrological properties (e.g., porosity, dry unit weight γd, and saturated hydraulic conductivity, Ks), and pre-rainstorm suction are evaluated to inspect its role on the resulting scatter of ID thresholds. We find that: (1) Ks is most significant for high-intensity storms; (2) in steep slopes, changes in pressure head greatly reduce the timescale of landslide triggering, making the system heavily reliant on initial conditions; (3) for events occurring at long failure times (gentle slopes and/or low-intensity storms), the significance of the evolving stress level (through γd) is highest. The proposed approach can be translated to other regions, expanded to encompass new aleatory variables, and combined with other hydro-mechanical triggering models.

Mass Movement and Initiation of Landslide Dam Burst in the Eastern Ghats, India during the Titli Cyclone

Abstract The article chronicles the landslide dam burst event from the Eastern Ghats region of India during the Titli cyclone on 12th October 2018. The event has been marked as one of the most destructive event in the region due to the abrupt increase in rainfall intensity and duration. The highly weathered profile of the Eastern Ghats metamorphosed terrain favours such fatal incidents, mostly during the monsoon and cyclone season of the Indian subcontinent. Geological and geotechnical investigation of the region followed by numerical modeling has been done to study the cause and mechanism of the incident. From the initiation of landslide to the dam formation and afterwards the bursting event, the whole event has been simulated in the Optum G2-based finite element model. The steep laterite cliff of Eastern Ghats mountains is identified as the primary concern for the stability balance in the region. Real-time monitoring of natural dams is essential to minimize the amount of risk in the region from further occurrence of such landslide-related terminal hazards.

Debris flow initiation in postglacial terrain: Insights from shallow landslide initiation models and geomorphic mapping in Southeast Alaska

AbstractDebris flows pose persistent hazards and shape high‐relief landscapes in diverse physiographic settings, but predicting the spatiotemporal occurrence of debris flows in postglacial topography remains challenging. To evaluate the debris flow process in high‐relief postglacial terrain, we conducted a geomorphic investigation to characterize geologic, glacial, volcanic, and land use contributions to landslide initiation across Southeast Alaska. To evaluate controls on landslide (esp. debris flow) occurrence in Sitka, we used field observation, geomorphic mapping, landslide characteristics as documented in the Tongass National Forest inventory, and a novel application of the shallow landslide model SHALSTAB to postglacial terrain. A complex geomorphic history of glaciation and volcanic activity provides a template for spatially heterogeneous landslide occurrence. Landslide density across the region is highly variable, but debris flow density is high on south‐ or southeast‐facing hillslopes where volcanic tephra soils are present and/or where timber harvest has occurred since 1900. High landslide density along the western coast of Baranof and Kruzof islands coincides with deposition of glacial sediment and thick tephra and exposure to extreme rainfall from atmospheric rivers on south‐facing aspects but the relative contributions of these controls are unclear. Timber harvest has also been identified as an important control on landslide occurrence in the region. Focusing on a subset of geo‐referenced landslides near Sitka, we used the SHALSTAB shallow landslide initiation model, which has been frequently applied in non‐glacial terrain, to identify areas of high landslide potential in steep, convergent terrain. In a validation against mapped landslide polygons, the model significantly outperformed random guessing, with area under the curve (AUC) = 0.709 on a performance classification curve of true positives vs. false positives. This successful application of SHALSTAB demonstrates practical utility for hazards analysis in postglacial landscapes to mitigate risk to people and infrastructure.

Mechanism of rainfall induced landslides in Yunnan Province using multi-scale spatiotemporal analysis and remote sensing interpretation

In order to explore the disaster mechanism of rainfall induced landslides in Yunnan Province, a new method is developed in the field of landslide research, which provides a strong basis for the study of rainfall induced landslide disaster mechanism in different regions. First, the mechanism of rainfall induced landslide initiation and the mechanism of rainfall impacting slope are introduced, and the temporal and spatial characteristics of landslide disasters in Yunnan Province are analyzed comprehensively. The influence of rainfall on landslide density is analyzed in 8 regions randomly, and a semi supervised feature selection algorithm based on manifold is proposed to optimize the remote sensing image of Zhaojiapo rainfall type landslide area in Yunnan Province. The results show that in the province the monthly rainfall days account for a large proportion from June to September, and the months with the largest number of landslides in the whole year are from June to August. The seasonal change of rainfall belt and the frequent occurrence time of landslide disasters and the spatial characteristics of landslide distribution and rainfall distribution basically belong to corresponding relationship; the greater the rainfall of 8 regions, the greater the probability of landslide occurrence, and the greater the landslide density, which belongs to the proportional relationship; the landform characteristics in the optimized remote sensing image can be clearly displayed, and the classification accuracy of the data is enhanced. The results show that based on multi-scale spatiotemporal analysis combined with optimized remote sensing technology, the cause of rainfall induced landslides in Yunnan Province is the change of landslide density caused by heavy rainfall, which eventually leads to disasters. It is conducive to scientifically understanding the inducement of rainfall induced landslides in Yunnan Province, provides a scientific basis for further disaster control and prevention, and provides a new possible method for landslide real-time prediction and alarm system.

Environmental Geological Problems in Southwest China: A Case Study from the Researches of Regional Landslide Hazards

Humans have paid a lot of attention to environmental geological challenges in recent years. Landslides, being one of the most prevalent geological disasters, are characterized by their suddenness and destructiveness. Southwest China is prone to landslides and debris flows due to its unique geological structure. This paper uses landslides in southwest China as an example, focusing on research on landslide initiation mechanisms and outlining modern landslide monitoring devices and prediction models. Landslides are caused by external variables such as persistent precipitation, groundwater movement, and significant seismic activity, as well as interior reasons such as fine particle rearrangement and the action of positive pore water. The reduction of the friction coefficient of the shear surface, which is induced by the increase of the shear rate, the supercritical carbon dioxide and superheated steam of the shear zone, and the mineral recrystallization process on the shear surface, all have an important impact on reducing the friction coefficient of the shear surface, is a key factor in the occurrence of high-speed remote landslides. Real-time landslide monitoring using space-air-ground and acoustic emission technology, as well as the creation of machine learning-based forecast models, have aided in the research of landslide development and early warning.

Characterization and quantitative evaluation of preferential infiltration in loess, based on a soil column field test

Preferential infiltration occurs widely in loess deposits and has a major impact on water movement and redistribution. However, preferential infiltration is a complex process, and previous research has focused principally on its qualitative description. Few studies have carried out infiltration tests to assess preferential infiltration in loess. In this study we conducted five soil column infiltration field tests in the Heifangtai area of Gansu Province, China, to explore preferential infiltration processes and the characteristics of loess. Based on the test data, the preferential infiltration is qualitatively assessed, and a quantitative calculation method for the preferential infiltration of loess with vertical cracks is proposed. The following conclusions were drawn: 1) The relationship between the infiltration rate of loess and the reciprocal of wetting front depth is a positive linear relationship under homogeneous infiltration, but non-linear under preferential infiltration. 2) The degree of preferential infiltration can be determined based on the distance of data points to the infiltration rate-wetting front propagation velocity gradient under homogeneous infiltration. 3) Based on the principle of similarity of infiltration of adjacent soil, we calculate that the preferential infiltration rate of closed cracks is two to three orders of magnitude greater that the homogeneous infiltration rate, and that the contribution of stable preferential infiltration to the total infiltration is about 20%. Quantitative evaluations of preferential infiltration are helpful in calculating slope seepage, evaluating slope stability, and researching landslide initiation mechanisms.

Use of a LiDAR-derived landslide inventory map in assessing Influencing factors for landslide susceptibility of geological units in the Petrinja area (Croatia)

A landslide inventory was created for an area of 22.6 km2 near Petrinja city in northern Croatia, based on the high-resolution LiDAR data complemented by orthophoto maps. A total of 216 landslides were identified, covering 2.91 % of that area. Landslide polygons were overlain on geological units based on the Basic map of SFRY at a scale of 1:100,000 that is the largest scale geological map available for the whole of Croatia. The relationship between landslides and geological units was expressed as a landslide index. Three geological units displayed increased landslide susceptibility. A Pliocene unit clearly had the largest susceptibility, followed by a Palaeocene-Eocene unit, and finally a Badenian unit. Landslide density was analyzed within these geological units to identify influencing factors for landslide initiation. Each geological unit revealed different influencing factors. The Pliocene unit is mostly influenced by bedding plane orientation and local relief. Heterogeneousness lithology is the dominant factor in the Paleocene-Eocene unit, while the Badenian unit demonstrated the least certain interpretation as there are multiple factors involved. The forest road is presumed to be crucial, followed by spring occurrences and proximity to the tectonic boundary. The basic geological map of SFRY proved to be a viable source of geological information for the creation of landslide susceptibility maps at a scale of up to 1:100,000, but with limitations in the case of lithologically heterogeneous geological units. Larger scale maps require more detailed research as landslide susceptibility factors vary in each geological unit.

Warning Models for Landslide and Channelized Debris Flow under Climate Change Conditions in Taiwan

Climate change has caused numerous disasters around the world. It has also influenced the climate of Taiwan, with urban areas exhibiting a temperature increase by 1 °C between 1998 and 2020. In this study, climate change and landslides in Taiwan were statistically analyzed. Cumulative annual precipitation in mountain watersheds in central Taiwan exhibit a declining trend and is lower than that in urban areas. The relatively few typhoons reduced the distribution of rainfall in mountain watersheds and fewer landslides. From 2017 to 2020, typhoon-induced rains caused fewer landslides than did other climate events such as the meiyu front, tropical low pressure, and southwesterly flow events. Three rainfall characteristics of landslide initiation were identified: high rainfall intensity over a short duration (<12 h), high-intensity and prolonged rainfall, and high cumulative rainfall over a long duration (>36 h). Combinations of warning models for landslides in cumulative rainfall–duration plots with rainfall intensity classification and mean rainfall intensity–duration plots with cumulative rainfall classification were presented. In recent (2018–2020) years, climate change has resulted in higher temperatures, less rainfall in mountain watersheds, and a lower rainfall threshold at which landslides are initiated by non-typhoon climate events.

Exploring a landslide inventory created by automated web data mining: the case of Italy

Nowadays, several systems to set up landslide inventories exist although they rarely rely on automated or real-time updates. Mass media can provide reliable info about natural hazard events with a relatively high temporal and spatial resolution. The news publication about a natural disaster inside newspaper or crowdsourcing platforms allows a faster observation, survey, and classification of these phenomena. Several techniques have been developed for data mining inside social media for many natural events, but they have been rarely applied to the automatic extraction of “landslide events”. This source of information allows continuous feedback from real world, and news concerning landslide events can be rapidly collected. In this work, the newspaper articles about landslides in Italy are automatically collected by an existing data mining algorithm, based on a semantic engine. The news has been analysed to assess their distribution over the territory and to verify the possibility of using them for hazard mapping purpose. In 10 years, from 2010 to 2019, the algorithm identified and geolocated 184322 articles referring to 32525 generical events (“news”). At first, the collected data underwent to a manual verification, followed by a classification based on news relevance, localization accuracy and time of publication. Then, these data have been used to identify the areas and the periods most affected by landslide phenomena. The analyses show that almost 42% of Italian municipalities have been affected by landslide. According to the results, the use of data mining is helpful for the creation of landslide databases where the day and the approximative location (municipality) of the possible landslide triggers are known. This database, in turn, can be used for scientific purposes, as the definition of the meteorological condition associated with landslide initiation, the validation of risk maps. It can also be used for a proper land use or risk mitigation planning, since the most landslide-prone municipalities can be defined.

A thermo-hydro-mechanical approach to soil slope stability under climate change

Landslide initiation and dynamics are approached with different methods according to the scale of investigation. Individual landslides are typically described mechanistically, relying on the known coupling between the hydrological input – via atmosphere-soil interaction – and the mechanical response. As the scale increases, fully-coupled hydro-mechanical approaches gradually give way to simpler sequential couplings of hydrological and infinite slope or rigid block models, and finally to geostatistical models. At any scale, while the role of temperature in controlling evapotranspiration and thus the hydrological balance is well recognised, direct thermo-mechanical couplings are systematically neglected unless changes in water phase are involved. This contrasts with abundant experimental evidence of a fully-coupled thermo-hydro-mechanical behaviour of most geomaterials, even in ranges of temperature naturally experienced at the ground surface or in the near subsurface. Here, we review temperature-dependent processes that are potentially relevant to slope stability, focusing in particular on clayey slopes in temperate and warm regions. Our thought-provoking hypothesis is that temperature fluctuations and trends induced by climate change may exert, in short to long terms, a hydro-mechanical forcing on slopes (by altering permeability, water retention capacity, compressibility, shear strength). Together with other known effects (such as altered precipitation patterns and changes in land use), they could affect landslide activity and the distribution and frequency of slope failures. To verify this hypothesis across the scales, systematic field monitoring of temperature-related variables is necessary, together with geostatistical analyses entailing thermal remote sensing products. At the same time, fully-coupled approaches need to be upscaled to permit physically-based catchment- or regional-scale studies accounting for appropriate temperature-related variables and the inherent heterogeneity in materials and boundary conditions.

Comparison of landslide susceptibility models and their robustness analysis: a case study from the NW Himalayas, Pakistan

Machine learning methods are considered as most effective approaches to accomplish landslide susceptibility analysis around the globe. Landslide susceptibility maps (LSMs) have been frequently executed by statistical models in NW Himalaya. However, the comparison and applications of the statistical models with modern machine learning techniques has not been fully explored in this region. Hence, this study aims to compare the predicted performance of statistical and popular machine learning models to explore robust landslide prediction model in the landslide-prone area of NW Himalaya and investigate the compensations and limitations of these models to grasp a more precise and consistent result. This study presented machine learning approaches based on the artificial neural network (ANN), support vector machine (SVM) and logistic regression (LR) and the statistical methods based on the frequency ratio (FR), information value (InfoV) and weight of evidence (WoE). For this purpose, first an inventory map of 1507 landslides was prepared and randomly divided into training (70%) and testing (30%) dataset. Furthermore, 12 landslide conditioning factors (LCFs) were extracted from geospatial dataset to prepare thematic layers in ArcGIS. Thereafter, factor analysis was performed to eliminate colinear and least important variables which can mislead the results. The results showed that all selected LCFs are noncolinear and have significant contribution on landslides initiation, however, lithology, slope angle, annual rainfall and landuse were most influential factors. For modeling purpose, landslide inventory was correlated against all LCFs and trained into six models to produce respective LSMs. Finally, the performance of produced LSM models was validated and compared through area under receiver operating characteristic curve (AUROC), Accuracy, Recall, F 1-score and Cohen’s Kappa coefficients to assess the robustness of employed models. The results exhibit that the performance scores of machine learning models were considerably superior than statistical models. While, the AUROC values based on validation dataset indicate that LR (0.89) has better prediction ability followed by SVM (0.86), ANN (0.84), FR (0.83), InfoV (0.82) and WoE (0.81) in this study. Therefore, it is reasoned out that the machine learning methods are more reliable in generating adequate LSMs. However, the LR is recommended as most efficient model for predicting landslide susceptible zones in study region and thus can be considered as robust model for landslide susceptibility assessment in similar geo-environmental regimes.

Occurrences of Deep-Seated Creeping Landslides in Accordance with Hydrological Water Storage in Catchments

Rainfall is one of the most important triggers of both shallow debris flows and deep-seated landslides. The triggering mechanism involves the process of water infiltration into the failure zone. For deep-seated landslides, the deeper and more extensive failure surfaces delay the effect of the process and thus delay landslide initiations. The delay is difficult to assess, especially if the sites only have scarce or insufficient monitoring data. Under these circumstances, we illustrate that the occurrences of landslides can be estimated by their correlations with the phenomenological water storage index (WSI) of a given catchment. In the present study, a total of five deep-seated landslides in TienChih (4) and SiangYang (1) are investigated. The displacements of the landslides were recorded by global positioning system (GPS) and the WSI was modelled using the tank model. The result demonstrates that the WSI correlates closer in time to the landslide motion than the rainfall, and the WSI thresholds for the landslides are inferred. Thus, this technique can be applied as an associated method to evaluate landslide initiation.

Liquefaction within a bedding fault: Understanding the initiation and movement of the Daguangbao landslide triggered by the 2008 Wenchuan Earthquake (Ms = 8.0)

The Daguangbao landslide was the most catastrophic mass movement triggered by the 2008 Wenchuan earthquake with a magnitude scale of Ms. 8.0. The landslide, which was 4.6 km long and 3.7 km wide, had a volume of approximately 1.2 × 109 m3. Since its occurrence, many assumptions regarding its initiation and movement mechanisms have been made; however, the mechanisms remain unclear. Our recent field evidence suggested that the Daguangbao landslide occurred along a saturated fault parallel to the bedding in the Paleozoic carbonate strata. We therefore examined whether the shear behavior of the fault material could have favored the initiation and movement of the Daguangbao landslide. First, we performed monotonic and cyclic loading tests on samples taken from the bedding fault breccia using ring-shear apparatus. We then conducted Newmark displacement analysis to examine the initiation and motion of the landslide. The laboratory results showed that the carbonate fault breccia on the sliding layer of the landslide has a high liquefaction potential and the friction coefficient at its steady-state under undrained condition could be as small as 0.04. We also found that with increase of shear displacement, the friction coefficients can at first exponentially increase to the peak-failure value and then exponentially decrease to the steady-state value. These relationships between the friction and shear displacements were incorporated in the Newmark analysis of landslide initiation and motion. The numerical calculation results showed that the landslide occurred 36 s after the 2008 Wenchuan earthquake origin time (at its hypocenter), and the landslide mass, with a speed of 94 m/s, collided with a riverbank in the 76th second. We infer that, in addition to the strong seismic force, pore-water pressure built up within the bedding fault during the seismic shaking, enhancing the instability of the Daguangbao slope, and a further increase of pore-water pressure with progress of sliding elevated the mobility of the landslide.

Probabilistic model calibration of spatial variability for a physically-based landslide susceptibility model

ABSTRACT Physically-based landslide susceptibility models have been widely used to predict rainfall-triggered landslides. Calibration of these models is mainly conducted by tuning various inputs, but spatially varying soil properties are not considered. In this study, an efficient probabilistic model calibration method is proposed, which integrates discrete cosine transform (DCT) representation of spatial variability with Bayesian parameter estimation to characterise the spatial variation of soil properties in a physically-based landslide susceptibility model. The efficacy of DCT inversion in representing fields with binary information about landslide occurrence and non-occurrence is investigated. To illustrate the capability and feasibility of the proposed method, a hypothetical example and a case study of a 2014 regional rainfall-triggered landslide event in Qinglian Town, Chongqing, China are presented. The results demonstrate that the location, size and shape of landslide have little influence on the DCT inversion. Considering spatial variation in model calibration can significantly improve model prediction performance for regional rainfall-induced landslides. The topography of the landslide initiation area may affect the influence of the model calibration if the field observations do not agree with the fundamental physics underlying the stability equation used in the model.

Promoting effect of vegetation onthe initiation of landslides induced by typhoon rainstorms

Vegetation is widely used to prevent landslides worldwide. However, rainstorm-induced landslides frequently occur on vegetation-covered slopes during the typhoon season in the southeastern coast of China. The results of a wind tunnel test reveal the effect of typhoon-induced loads on a slope through vegetation. Under a wind speed of 17m/s, the wind load can increase the sliding force by over 10%. Strong typhoon-induced loads cause the vegetation to exert a strong torque on the soil via roots, resulting in crack development. These cracks provide paths for rainwater infiltration and increase the soil permeability coefficient by over 10-fold when the typhoon passes. Therefore, special attention should be paid to the potential impacts of vegetation, especially large trees, on slope stability.

Excess Pore Water Pressure within a Deep-seated Bedding Fault: Understanding of Earthquake Induced Large Landslide Initiation

The 2008 Wenchuan Ms8.0 earthquake triggered a landslide (i.e., Daguangbao landslide) with a volume of near 1.2 billion m3. This landslide involved the Paleozoic carbonate strata and the geological background of the sliding zone is a pre-existing bedding fault with a maximum thickness of 5 m and depth of 400 m in the slope. The bedding fault was the groundwater channel in the slope with saturated rock mass, according to the latest survey. To reveal the initiation mechanism of Daguangbao landslide (hereinafter short for DGB landslide) related to groundwater, a kind of simplified model of hard carbonate rock-cut slope with weak layer has been proposed to generalize bedding fault to weak layer in hard carbonate rock layer. And response characteristics of the model have been simulated by fluid-solid coupling algorithm in the FLAC3D program. The results showed that the hard carbonate rocks above and below the weak layer were different in deformation during strong earthquake, which led to the dynamic behavior of vibratory stamping-tension to the weak layer. And then, the discordant deformation of the two layers resulted in the excess pore water pressure featured with instant amplification and cumulative growth in the saturated water weak layer. So it was inferred that the rapid drop of inside effective stress caused by excess pore water pressure in the weak layer during strong earthquake would give rise to the sudden shearing of stress in the front locked segment of the slope after rapid stress concentration, and consequently trigger the DGB landslide.