Infinite Slope Stability Research Articles

Shallow Landslides Physically Based Susceptibility Assessment Improvement Using InSAR. Case Study: Carpathian and Subcarpathian Prahova Valley, Romania

A multi-temporal satellite radar interferometry technique is used for deriving the actual surface displacement patterns in a slope environment in Romania, in order to validate and improve a landslide susceptibility map. The probability the occurrence of future events is established using a deterministic approach based on a classical one-dimension infinite slope stability model. The most important geotechnical parameters for slope failure in the proposed study area are cohesion, unit weight and friction angle, and the triggering factor is a rapid rise in groundwater table under wetting conditions. Employing a susceptibility analysis using the physically based model under completely saturated conditions proved to be the most suitable scenario for identifying unstable areas. The kinematic characteristics are assessed by the Small BAseline Subsets (SBAS) interferometry technique applied to C-band synthetic aperture radar (SAR) Sentinel-1 imagery. The analysis was carried out mainly for inhabited areas which present a better backscatter return. The validation revealed that more than 22% of the active landslides identified by InSAR were predicted as unstable areas by the infinite slope model. We propose a refinement of the susceptibility map using the InSAR results for unravelling the danger of the worst-case scenario.

Hydrological control of soil thickness spatial variability on the initiation of rainfall-induced shallow landslides using a three-dimensional model

Thickness and stratigraphic settings of soils covering slopes potentially control susceptibility to initiation of rainfall-induced shallow landslides due to their local effect on slope hydrological response. Notwithstanding the relevance of the assessment of hazard to shallow landsliding at a distributed scale by approaches based on a coupled modelling of slope hydrological response and slope stability, the spatial variability of soil thickness and stratigraphic settings are factors poorly considered in the literature. Under these premises, this paper advances the well-known case study of rainfall-induced shallow landslides involving ash-fall pyroclastic soils covering the peri-Vesuvian mountains (Campania, southern Italy). In such a unique geomorphological setting, the soil covering is formed by alternating loose ash-fall pyroclastic deposits and paleosols, with high contrasts in hydraulic conductivity and total thickness decreasing as the slope angle increases, thus leading to the establishment of lateral flow and an increase of pore water pressure in localised sectors of the slope where soil horizon thickness is less. In particular, we investigate the effects, on hillslope hydrological regime and slope stability, of irregular bedrock topography, spatial variability of soil thickness and vertical hydraulic heterogeneity of soil horizons, by using a coupled three-dimensional hydrological and a probabilistic infinite slope stability model. The modelling is applied on a sample mountain catchment, located on Sarno Mountains (Campania, southern Italy), and calibrated using physics-based rainfall thresholds derived from the literature. The results obtained under five simulated constant rainfall intensities (2.5, 5, 10, 20 and 40 mm h−1) show an increase of soil pressure head and major failure probability corresponding to stratigraphic and morphological discontinuities, where a soil thickness reduction occurs. The outcomes obtained from modelling match the hypothesis of the formation of lateral throughflow due to the effect of intense rainfall, which leads to the increase of soil water pressure head and water content, up to values of near-saturation, in narrow zones of the slope, such as those of downslope reduction of total soil thickness and pinching out of soil horizons. The approach proposed can be conceived as a further advance in the comprehension of slope hydrological processes at a detailed scale and their effects on slope stability under given rainfall and antecedent soil hydrological conditions, therefore in predicting the most susceptible areas to initiation of rainfall-induced shallow landslides and the related I-D rainfall thresholds. Results obtained demonstrate the occurrence of a slope hydrological response depending on the spatial variability of soil thickness and leading to focus slope instability in specific slope sectors. The approach proposed is conceived to be potentially exportable to other slope environments for which a spatial modelling of soil thickness would be possible.

Landslide Susceptibility Assessment Using the Scoops3D Model in a Tropical Mountainous Terrain

Many physically-based distributed models study the landslide occurrence using an infinite slope stability analysis, simulating a planar failure, which is not usually applicable to rotational failures and deep landslides. Recently, some three-dimensional distributed physically-based models have been developed that have been applied in different parts of the world. In this research, the Scoops3D model is implemented for a landslide susceptibility analysis in a tropical mountainous terrain of the Colombian Andes (Medellín, Colombia). In addition to identifying the areas susceptible to the occurrence of rotational landslides, the results of the safety factor are analyzed with the areas of associated critical failure surfaces to provide an interpretation and explanation of the simulation results. This is to have a better understanding of how the model works and to facilitate its implementation in landslide hazard assessment. The Scoops3D physicallybased model can be a very useful tool for mass movement risk management projects.

A Landslide Monitoring System for Natural Terrain in Korea: Development and Application in Hazard Evaluations.

This study describes the development of a landslide monitoring system for the purpose of reducing damages caused by landslides in natural terrain. The system was developed to analyze the effects of landslide-inducing rainfall and the behavior of slopes through 12 monitoring stations that are distributed across eight national parks in Korea. Several sensors and a data acquisition equipment to monitor landslide were installed in each station. The composition of the system and its operating program were designed to efficiently manage the sizeable amounts of real-time monitoring data that are collected from the various stations. To test the potential of the developed system for reliable landslide hazard evaluations, data measured over a five-year period by the two monitoring stations in Jirisan National Park were analyzed. Subsequently, the suction stress of the soil over the monitoring period was calculated by applying laboratory test result of the geotechnical and unsaturated soil properties in the analysis domain area. The infinite slope stability analysis combined with an effective stress concept based on the suction stress was applied to calculate the factor of safety. This method also enabled the temporal and quantitative evaluation of slope stability in natural terrain. In addition, based on the monitoring and slope stability analysis results, an analysis for the spatial classification of landslide hazards was conducted. The analysis results quantitatively and statistically demonstrated that 98% of historical landslide initiation areas were classified as high hazard levels.

Improving Spatial Landslide Prediction with 3D Slope Stability Analysis and Genetic Algorithm Optimization: Application to the Oltrepò Pavese

In this study, we compare infinite slope and the three-dimensional stability analysis performed by SCOOPS 3D (software to analyze three-dimensional slope stability throughout a digital landscape). SCOOPS 3D is a model proposed by the U. S. Geological Survey (USGS), the potentialities of which have still not been investigated sufficiently. The comparison between infinite slope and 3D slope stability analysis is carried out using the same hydrological analysis, which is performed with TRIGRS (transient rainfall infiltration and grid-based regional slope-stability model)—another model proposed by USGS. The SCOOPS 3D model requires definition of a series of numerical parameters that can have a significant impact on its own performance, for a given set of physical properties. In the study, we calibrate these numerical parameters through a multi-objective optimization based on genetic algorithms to maximize the model predictability performance in terms of statistics of the receiver operating characteristics (ROC) confusion matrix. This comparison is carried out through an application on a real case study, a catchment in the Oltrepò Pavese (Italy), in which the areas of triggered landslides were accurately monitored during an extreme rainfall on 27–28 April 2009. Results show that the SCOOPS 3D model performs better than the 1D infinite slope stability analysis, as the ROC True Skill Statistic increases from 0.09 to 0.37. In comparison to other studies, we find the 1D model performs worse, likely for the availability of less detailed geological data. On the other side, for the 3D model we find even better results than the two other studies present to date in the scientific literature. This is to be attributed to the optimization process we proposed, which allows to have a greater gain of performance passing from the 1D to the 3D simulation, in comparison to the above-mentioned studies, where no optimization has been applied. Thus, our study contributes to improving the performances of landslide models, which still remain subject to many uncertainty factors.

Slow recovery from soil disturbance increases susceptibility of high elevation forests to landslides

Natural hazards such as shallow landslides are common phenomena that disturb soil and damage forests. Quantifying the recovery of forest vegetation after a hazard is important for determining the window of susceptibility to new disturbance events, especially at high elevations, where extreme weather events are frequent and the growing season is short. Plant roots can reduce the size of this window on unstable hillslopes, by adding mechanical reinforcement (cr) to soil and increasing its matric suction, termed here as hydrological reinforcement (ch). These data are used in landslide models to calculate the Factor of Safety (FoS) of a hillslope. We calculated temporal variations in cr and ch in naturally regenerated mixed, montane forests in the French Alps. In these closed-canopy forests, open-canopy gaps were present, with understory vegetation comprising herbs, forbs and shrubs. At three altitudes (1400, 1700 and 2000 m), we dug small trenches as proxies for shallow landslide events and calculated cr before soil disturbance in both open gaps and closed forests. Then, using monthly tree root initiation and mortality data measured in rhizotrons, we calculated monthly cr for four years after the disturbance. To compare results with cr, ch was estimated using matric suction data that were measured in trenches at 1400 m for >1 year. Temporal FoS was then calculated using an infinite slope stability model.Results showed that finer, short-lived roots contributed little to soil reinforcement compared to thicker, long-lived roots. After disturbance, mean cr (over the entire soil profile) never fully recovered to the initial value at any site, although >90% recovery was observed in open gaps at 1400 m. Mean cr was slow to recover in closed forests, especially at 2000 m, where only 19% recovery occurred after 41 months. The ch in closed forests was considerable during the summer months, but marked increases in soil water moisture resulted in lower FoS, especially during December to April, when soil was near saturation. As cr changed little throughout the year, it was a more reliable contributor to slope stability. Our results show therefore, that particular attention should be paid to high elevation forests after a disturbance. Also, during the process of recovery, the highly variable soil water dynamics in closed forest can result in seasonal hotspots of vulnerability. Therefore, when tree transpiration is low, our results highlight a need for careful monitoring on steep or unstable slopes, especially in disturbed closed-canopy forests.

Mapping method of rainfall-induced landslide hazards by infiltration and slope stability analysis

By utilizing the Green-Ampt infiltration equation and the infinite slope stability model, a method for analyzing shallow slope failures caused by rainfall is developed. With rainfall intensity, soil characteristics, and topography, the modified Green-Ampt infiltration equation is used to estimate the rainfall infiltration capacity and depth of infiltration in a given slope. Assigning the calculated depth of infiltration as the depth of slip surface, the factor of safety of the slope is obtained through the infinite slope stability model. A time-series visualization map of the space-time varying factor of safety is generated when the method is implemented with the aid of Geographic Information System (GIS) software. The model is applied and validated with the landslides that occurred during the October 2019 Typhoon Hagibis in Marumori, Japan. The model results show good agreement with the reported time and depths of failure, and the analysis of the spatial distribution of predicted failures yielded receiver operating characteristic-area under the curve (ROC-AUC) value of 0.90. The applicability of the model can be extended for post-analysis, real-time, or projected assessment of slope stability, depending on the nature of input rainfall data (e.g., historical, real-time, forecast, hypothetical).

Infinite slope stability model and steady-state hydrology-based shallow landslide susceptibility evaluations: The Guneysu catchment area (Rize, Turkey)

The main purpose of this study is to investigate shallow landslide susceptibility by considering the infinite slope stability model and steady-state hydrological conditions. The prediction performance of the Stability Index Mapping (SINMAP) carried out by considering the mechanical and hydrological parameters and soil thickness values for different residual soils derived from different geological formations was evaluated. For this purpose, comprehensive geotechnical site investigations were conducted in the Guneysu catchment area located at the east of Rize in the Eastern Black Sea region of Turkey, where shallow landslides are frequently observed within the residual levels developed as a result of decomposition of magmatic rocks. The research was performed in four stages: (i) The general characteristics of the region were examined; (ii) detailed investigations were carried out for the preparation of shallow landslide inventory of the Guneysu catchment area; (iii) to reveal the mechanical and hydrological characteristics of the residual soils, disturb and undisturbed samplings were carried out, and geophysical investigations were performed; (iv) as the last stage, shallow landslide susceptibility was assessed by implementing the SINMAP mathematical model. As a result, the average accuracy value of the models produced to predict shallow landslide initiation in the region was obtained to be 96.7%. Considering the statistics achieved in this research, it is realized that the differentiation of soil material increases the model prediction capacity. Additionally, pre-evaluations of the mechanical and hydrological characteristics of the residual soils also increase the estimation performance.

Probabilistic evaluation of the seismic stability of infinite submarine slopes integrating the enhanced Newmark method and random field

For evaluation of the seismic stability of submarine slopes, traditional deterministic methods may not reflect the actual situation due to the uncertainties in input values such as seismic acceleration, soil properties, and hydraulic conditions. In this study, probabilistic analyses were conducted based on the use of an enhanced Newmark method to evaluate the seismic stability of submarine slopes. In the probabilistic workflow, the infinite slope model was used, whereby the spatially varied strengths of marine sediments were simulated by non-stationary random fields discretized by the Karhunen-Loeve expansion. The positions of the potential slip surfaces were searched automatically, rather than being predefined as in the traditional limit equilibrium method. Artificial earthquake accelerations, exhibiting the same spectral characteristics, were simulated as the input ground motions in the probabilistic analysis. The pore water pressure generation and dissipation models were incorporated into the enhanced Newmark method when calculating the permanent displacements of the submarine slopes. Monte Carlo simulations were conducted for statistical characterization of the slope displacements and their failure probabilities. The results of the analysis indicate the superiority of the probabilistic framework and demonstrate the significant effects of pore water pressure and the spatial variability of the soil strength on the displacements and failure probabilities of submarine slopes.

Modeling hydrologic processes associated with soil saturation and debris flow initiation during the September 2013 storm, Colorado Front Range

Seven days of extreme rainfall during September 2013 produced more than 1100 debris flows in the Colorado Front Range, about 78% of which occurred on south-facing slopes (SFS). Previously published soil moisture (volumetric water content) observations suggest that SFS were wetter than north-facing slopes (NFS) during the event, which contrasts with soil moisture patterns observed during normal conditions. Various causes have been hypothesized for the preferential saturation of SFS, but those hypotheses remain largely untested. Here, we analyze the soil moisture patterns using additional soil moisture observations, determine the hydrologic processes controlling the preferential saturation of SFS, and evaluate the importance of soil moisture in predicting the debris flow initiation sites. Soil moisture patterns are simulated using the Equilibrium Moisture from Topography, Vegetation, and Soil (EMT + VS) model. Five hypotheses are tested that may have influenced the soil moisture reversal including higher rainfall rates, lower interception rates, lower saturated water content, thinner soils, and reduced deep drainage on SFS. The EMT + VS model is coupled with an infinite slope stability model to produce factor of safety maps. The hypotheses are tested by comparing the modeled soil moisture to soil moisture observations and the debris flow initiation sites. The results suggest that differences in interception and deep drainage between SFS and NFS were primarily responsible for producing wetter SFS, but the soil moisture pattern likely played a smaller role than vegetation and slope in determining where debris flows initiated. The final model predicts instability at approximately 72% of the observed debris flow initiation sites.

Multiseasonal probabilistic slope stability analysis of a large area of unsaturated pyroclastic soils

The analysis of slope stability over large areas is a demanding task for several reasons, such as the need for extensive datasets, the uncertainty of collected data, the difficulty of accounting for site-specific factors, and the considerable computation time required due to the size of investigated areas, which can pose major barriers, particularly in civil protection contexts where rapid analysis and forecasts are essential. However, as the identification of zones of higher failure probability is very useful for stakeholders and decision-makers, the scientific community has attempted to improve capabilities to provide physically based assessments. This study combined a transient seepage analysis of an unsaturated-saturated condition with an infinite slope stability model and probabilistic analysis through the use of a high-computing capacity parallelized platform. Both short- and long-term analyses were performed for a study area, and roles of evapotranspiration, vegetation interception, and the root increment of soil strength were considered. A model was first calibrated based on hourly rainfall data recorded over a 4-day event (December 14–17, 1999) causing destructive landslides to compare the results of model simulations to actual landslide events. Then, the calibrated model was applied for a long-term simulation where daily rainfall data recorded over a 4-year period (January 1, 2005–December 31, 2008) were considered to study the behavior of the area in response to a long period of rainfall. The calibration shows that the model can correctly identify higher failure probability within the time range of the observed landslides as well as the extents and locations of zones computed as the most prone ones. The long-term analysis allowed for the identification of a number of days (9) when the slope factor of safety was lower than 1.2 over a significant number of cells. In all of these cases, zones approaching slope instability were concentrated in specific sectors and catchments of the study area. In addition, some subbasins were found to be the most recurrently prone to possible slope instability. Interestingly, the application of the adopted methodology provided clear indications of both weekly and seasonal fluctuations of overall slope stability conditions. Limitations of the present study and future developments are finally discussed.

A GIS tool for infinite slope stability analysis (GIS-TISSA)

Landslides are one of the most common and a destructive natural hazard in mountainous terrain and thus evaluating their potential locations and the conditions under which they may occur is crucial for their hazard assessment. Shallow landslide occurrence in soil and regolith covered slopes are often modeled using the infinite slope model, which characterizes the slope stability in terms of a factor of safety (FS) value. Different approaches have been followed to also assess and propagate uncertainty through such models. Haneberg (2004) introduced the use of the First Order Second Moment (FOSM) method to propagate input uncertainty through the infinite slope model, further developing the model and implementing it in the PISA-m software package (Haneberg, 2007). Here we present an ArcPy implementation of PISA-m algorithms, which can be run from ESRI ArcMap in an entirely consistent georeferenced framework, and which we call “GIS Tool for Infinite Slope Stability Analysis” (GIS-TISSA). Users can select between different input options, e.g., following a similar input style as for PISA-m, i.e., using an ASCII.csv parameters input file, or providing each input parameter as a raster or constant value, through the program graphic user interface. Analysis outputs can include FS mean and standard deviation estimates, the probability of failure (FS < 1), and a reliability index (RI) calculation for FS. Following the same seismic analysis approach as in PISA-m, the Newmark acceleration can also be done, for which raster files of the mean, standard deviation, probability of exceedance, and RI are also generated. Verification of the code is done by replicating the results obtained with the PISA-m code for different input options, within a 10−5 relative error. Monte Carlo modeling is also applied to validate GIS-TISSA outputs, showing a good overall correspondence. A case study was performed for Kannur district, Kerala, India, where an extensive landslide database for the year 2018 was available. 81.19% of the actual landslides fell in zones identified by the model as unstable. GIS-TISSA provides a user-friendly interface, particularly for those users familiar with ESRI ArcMap, that is fully embedded in a GIS framework and which can then be used for further analysis without having to change software platforms and do data conversions. The ArcPy toolbox is provided as a .pyt file as an appendix as well as hosted at the weblink: https://pages.mtu.edu/~toommen/GeoHazard.html.

Critical Continuous Rainfall Map for Forecasting Shallow Landslide Initiations in Busan, Korea

In recent years, precipitation patterns in Korea have shifted to be characterized as short and intense rainfalls. In consideration of shallow landslide initiations primarily governed by heavy rainfalls at short-time scales that diminish drainage effects, the concept of critical continuous rainfall is proposed as a single-rainfall-variable threshold for shallow landslide forecasting. To generate a critical continuous rainfall map for hillslope areas in a city of Korea (Busan), this study designed and applied a systematic modeling process. As a preparatory stage, input datasets of geo-hydraulic properties and geotechnical properties were assembled using estimation techniques based on experiment data of field samples. The inherent and fixed critical continuous rainfall values for hillslope areas in Busan were derived through one-dimensional infiltration analysis coupled with infinite slope stability calculations. As a result of a detailed analysis of historical rainfall records in a case study area over a period of 11 years, three false forecasting cases were recorded, whereas all landslide-triggering rainfall events were correctly captured with no missed forecasting cases. The results of the case study indicate that the proposed critical continuous rainfall may be useful as an effective and straightforward indicator for forecasting the initiation of shallow landslides.

Probabilistic mapping of earthquake-induced submarine landslide susceptibility in the South-West Iberian margin

The SW Iberian continental margin is well recognized as a tectonically active area, where major canyons and mass wasting events are both present. Earthquake triggered submarine landslides may cause tsunami and result in catastrophic damage to bordering coastal areas. In this setting, submarine landslide susceptibility mapping represents a major step towards a regional risk mitigation strategy. Landslide susceptibility mapping in large offshore areas presents significant challenges as a result of the limited information on controlling variables, large uncertainties in triggering mechanisms and limited geotechnical data. In this study, a geotechnical model-based approach has been followed that narrows the range of controlling factors and, within a probabilistic framework, allows a systematic treatment of parameter uncertainties. This model-based analysis covers the whole SW Iberian margin increasing by three orders of magnitude the areal extent of precedent offshore slope stability susceptibility studies. This jump in spatial scale is facilitated by application of a systematic Bayesian updating procedure, to combine geotechnical information from global databases and that available from regional sites. Seismic shaking is estimated using an available regional database of seismogenic faults. These tools are implemented within a GIS to generate, via Montecarlo simulations, probabilistic landslide susceptibility maps based on two different analytical seismic infinite slope stability models. These models differ mainly in the form of their final results, either as distributions of slope stability safety factors or as distributions of seismic-triggered slope displacements. Receiving Operator Curves are used to assess the different landslide susceptibility predictions obtained against a comprehensive regional database of submarine landslides. It turns out that the models analyzed correctly predict 92% and 82% of the mapped landslide subset chosen for validation for pseudo-static and displacement-based method respectively. This suggests that, within the limits of the currently available databases, seismic events are the dominant factor at the origin of the submarine landslides mapped in the study area. An advantage of the framework presented is that it can quickly incorporate new regional geotechnical information or better regional landslide databases, as they become available.

Modeling the landslide-generated debris flow from formation to propagation and run-out by considering the effect of vegetation

This study aimed to investigate the formation and propagation processes of a landslide-generated debris flow within a small catchment while considering the effects of vegetation. This process is divided into three stages: rainfall infiltration, slope failure, and debris flow routing, according to their different mechanisms. Existing models that involve the effect of vegetation for each stage, including Richards’s model, infinite slope stability model, and the enhanced two-phase debris flow model (Pudasaini 2012), were coupled. The tridiagonal matrix algorithm and finite volume method were applied to solve these equations, respectively. Finally, the approach was tested by application to the 2018 debris flow event in the Yindongzi catchment, China. The results showed that the proposed comprehensive model could effectively describe the behaviors of each stage during the formation and propagation processes of landslide-generated debris flows in vegetated area. The roles of vegetation on each stage, such as root water uptake and root soil reinforcement, were also analyzed by performing several scenarios.

Study on the role of lateral unsaturated flow in triggering slope failure under varying boundary water level conditions

Lateral unsaturated flow was found to contribute little to rainfall-induced shallow landslides, but it can be important when the landslides are triggered under conditions of varying boundary water level. By considering a hillslope with an impermeable bedrock and rising water level at the uphill/downhill boundary, this study explored how lateral unsaturated flow would affect slope stability. Three different Hillslope-Storage Boussinesq models that differ in their treatment of the unsaturated flow were coupled with the same infinite slope stability model to predict and compare the safety factor and instability time. Specific to the situations considered, this study discovered that, despite the inclusion of lateral unsaturated flow effect, the hillslope remains stable when water level at the downhill boundary rises. Under the condition of increasing uphill boundary water level, the consideration of lateral unsaturated flow leads to earlier slope failure for loamy hillslopes with a moderate slope. Also, the lateral unsaturated flow may not induce slope failure for mildly sloping loamy hillslopes and plays an ignorable role when landslides occur fast in very steep hillslopes. By contrast, the lateral unsaturated flow does not affect much the failure of sandy hillslopes. Moreover, when water level at the uphill boundary increases faster, the lateral unsaturated flow exerts a greater influence on initiating slope failure. Further examination shows that intensive rainfall may weaken the effect of lateral unsaturated flow on shallow landslides triggering under the condition of rising uphill boundary water level.

Spatially distributed landslide triggering analyses accounting for coupled infiltration and volume change

Rainfall infiltration in unsaturated slopes alters the effective stress through pore water pressure changes, thus causing ground deformation. Although important to assess the timescale over which the margin of safety of a slope decreases, such coupled processes are rarely accounted in the context of spatially distributed hazard assessment procedures. In this paper, a physically based, spatially distributed model accounting for full hydro-mechanical coupling is discussed. The model relies on a vectorized finite element (FE) solver to calculate the stability of deformable unsaturated infinite slopes subjected to transient flow. First, the FE solver is used to study the response of individual slopes to a prolonged rainfall for three scenarios (i.e., rigid, swelling, and collapsible soil). Then, the model is used in the context of spatially distributed computations to assess spatiotemporal variations of factor of safety over a large area. For this purpose, a series of shallow landslides occurred in a mountainous landscape covered by collapsible loess deposits in northwestern China was used as test site. The analyses show that hydro-mechanical couplings affect the performance of the model in terms of computed failure time and areal extent of the unstable zones. Specifically, volume collapse due to suction decrease is found to reduce the time of failure compared with uncoupled computations obtained for a rigid soil scenario. The most substantial advantages of using coupled analyses have been reported with reference to gentle slopes, for which the higher rate of suction reduction driven by volume change was crucial to capture landslide source areas that would otherwise be overlooked by uncoupled analyses. The proposed methodology offers a complete tool for landslide hazard assessment, in that it incorporates sources of coupling between hydrology and mechanics that are crucial to replicate the physics of landslide initiation.

A two-stage probabilistic approach for the risk assessment of submarine landslides induced by gas hydrate exploitation

In this paper, a two-stage risk assessment approach for gas hydrate exploitation is presented. In the first stage, a probabilistic analysis method is used to evaluate the failure probability of submarine landslide due to hydrate exploitation. The excess pore pressure due to hydrate exploitation is evaluated using a proposed maximum excess pore pressure model, which can consider the equilibrium of pressure and temperature for hydrates during decomposition. An infinite slope stability analysis and Monte Carlo simulations are then used to evaluate the failure probability of the slope with consideration of the generated excess pore pressure. In the second stage, a Bayesian network model to estimate the mortality rate of platform workers is proposed based on the logical sequence of events. The proposed approach is easy to implement and provides an efficient approach for quantitative assessment of human risk. Application of the proposed approach to a potential hydrate exploitation area, the Shenhu area in the South China Sea, is presented. The probabilistic analyses reveal that the production temperature T1 and the slope angle β have significant influences on the failure probability. Analysis of the human risk suggests that the key factors affecting the mortality rate are the distance of the platform to landslide and the equipment time for the workers to equip with lifejackets.

Probabilistic landslide susceptibility analysis in tropical mountainous terrain using the physically based r.slope.stability model

Abstract. Landslides triggered by rainfall are very common phenomena in complex tropical environments such as the Colombian Andes, one of the regions of South America most affected by landslides every year. Currently in Colombia, physically based methods for landslide hazard mapping are mandatory for land use planning in urban areas. In this work, we perform probabilistic analyses with r.slope.stability, a spatially distributed, physically based model for landslide susceptibility analysis, available as an open-source tool coupled to GRASS GIS. This model considers alternatively the infinite slope stability model or the 2.5-D geometry of shallow planar and deep-seated landslides with ellipsoidal or truncated failure surfaces. We test the model in the La Arenosa catchment, northern Colombian Andes. The results are compared to those yielded with the corresponding deterministic analyses and with other physically based models applied in the same catchment. Finally, the model results are evaluated against a landslide inventory using a confusion matrix and receiver operating characteristic (ROC) analysis. The model performs reasonably well, the infinite slope stability model showing a better performance. The outcomes are, however, rather conservative, pointing to possible challenges with regard to the geotechnical and geo-hydraulic parameterization. The results also highlight the importance to perform probabilistic instead of – or in addition to – deterministic slope stability analyses.

A new approach to mapping landslide hazards: a probabilistic integration of empirical and physically based models in the North Cascades of Washington, USA

Abstract. We developed a new approach for mapping landslide hazards by combining probabilities of landslide impacts derived from a data-driven statistical approach and a physically based model of shallow landsliding. Our statistical approach integrates the influence of seven site attributes (SAs) on observed landslides using a frequency ratio (FR) method. Influential attributes and resulting susceptibility maps depend on the observations of landslides considered: all types of landslides, debris avalanches only, or source areas of debris avalanches. These observational datasets reflect the detection of different landslide processes or components, which relate to different landslide-inducing factors. For each landslide dataset, a stability index (SI) is calculated as a multiplicative result of the frequency ratios for all attributes and is mapped across our study domain in the North Cascades National Park Complex (NOCA), Washington, USA. A continuous function is developed to relate local SI values to landslide probability based on a ratio of landslide and non-landslide grid cells. The empirical model probability derived from the debris avalanche source area dataset is combined probabilistically with a previously developed physically based probabilistic model. A two-dimensional binning method employs empirical and physically based probabilities as indices and calculates a joint probability of landsliding at the intersections of probability bins. A ratio of the joint probability and the physically based model bin probability is used as a weight to adjust the original physically based probability at each grid cell given empirical evidence. The resulting integrated probability of landslide initiation hazard includes mechanisms not captured by the infinite-slope stability model alone. Improvements in distinguishing potentially unstable areas with the proposed integrated model are statistically quantified. We provide multiple landslide hazard maps that land managers can use for planning and decision-making, as well as for educating the public about hazards from landslides in this remote high-relief terrain.