Hydrologic conditions responsible for triggering the Sto e landslide, Slovenia

In two events, on November 15 and 17, 2000, near the Mangart Mountain (2679 m a.s.l.), NW Slovenia, two translational landslides (debris flow slides) with a total volume of more than 1.5 million m³ occurred on the Sto e slope composed of morainic material filled with silt fraction. The first landslide was associated with a dry and the second landslide with a wet debris-flow, respectively. The rain gauging station in the village of Log pod Mangartom recorded 1638.4 mm of rainfall (more than 60% of the average annual precipitation) in the 48 days before the events (rainfall intensity of 1.42 mm/h in 1152 h). The recorded rainfall depth has a recurrence interval of more than 100 years. Other recorded rainfall depths of shorter duration (481.6 mm in 7 days, 174.0 mm in 24 h, 70 mm in 1 h) have recurrence intervals of much less than 100 years. A hydrological analysis of the event showed that the increase in runoff coefficients during the wet period in autumn 2000 before the landslide was as high as two- to threefold. An analysis using natural isotopes of δ¹⁸O and tritium of water samples from the Sto e landslide area has shown permanent but slow exfiltration of underground waters from a reservoir in the slope. In the case of low-intensity and long-duration rainfall in autumn 2000, relatively low permeable (10⁻⁷ m/s) morainic material was nearly saturated but remained stable (average porosity 21%, water content 20%, liquid limit 25%) until high artesian pressures up to 100 m developed in the slope by slow exfiltration from the relatively high permeable (10⁻⁵ m/s) massive dolomite. The Sto e landslide (two debris flow slides) was triggered by high artesian pressures built in the slope after long-duration rainfall. The devastating debris-flows formed from the landslide masses by infiltration of rainfall and surface runoff into the landslide masses and by their liquefaction.     

Simulation of a slope failure induced by rainfall infiltration

On 1 November 2000, Typhoon Xangsane brought heavy rainfall that caused serious disasters in many areas of northern Taiwan. A slope located at the upstream of the Shanher Stream in Taipei County failed and induced a debris flow. To investigate the environmental influencing factors that caused the slope failure and the debris flow disaster, laboratory tests and slope stability analysis were performed. A series of tests were conducted to obtain the mechanical and hydraulic properties of the soil under unsaturated and saturated conditions. Then, limit equilibrium method and numerical analysis simulating the process of infiltration were utilized to explore the slope stability, the stress variation, and the pore-water pressure in the soil during rainwater infiltration. The results of the stability analysis show that the rainfall intensity–time history is the most significant influence factor, and the analyzed failure zone and the predicted time when the slope failed are comparable to the field observation.     

Failure processes in a full-scale landslide experiment using a rainfall simulator

A full-scale landslide experiment was conducted to clarify the failure process of a landslide triggered by rainfall, using a loose sandy soil. The experiment used a 23-m long and about 8-m high flume, consisting of three parts: an upper 30° slope section, a lower 10° slope section, and a horizontal section at the foot of the slope. The flume was sprinkled at a constant intensity of 100 mm/h. The landslide occurred first in the upper slope about 154 min after the sprinkling started, following a creep movement within 41 min. The sliding mass slid to a stop in about 5 s, compressing soils in the lower gentle slope and horizontal sections. The dynamic process related to slide movement and the fluctuation of subsurface water pressures during failure were measured and analyzed. Sequential visual observations provided a clear record of the slip surface during failure. The rapid increase of subsurface water pressure in the slope and horizontal soil layers was also recorded during failure. It was inferred that the increased water pressures in the upper slope resulted from collapse of loose soil structure during shearing in the translational slide, whereas those in the lower portion of the slope and horizontal sections resulted from a mix of soil compression and shearing by the sliding mass.     

暴雨型滑坡降水入渗机理分析

暴雨能诱发大量的滑坡发生,在不同地区存在不同的临界降雨值,每当雨强大于该值时,滑坡就会大面积发生,这已经被多起滑坡事件证实。一般地,入渗理论认为,当雨强大于一定值时,入渗率是常量,过强的降水会转化为地表径流,这与雨强越大滑坡越易发生、群发性越强的观察结果不一致。在斜坡体上存在大量的裂隙,它们对降水入渗有着很大的贡献,应用有限单元法分别模拟了有裂隙和无裂隙时斜坡体内瞬态渗流场的变化,并用Bishop法对不同埋深滑动面的稳定性做了计算分析。模拟结果表明：考虑裂隙时的计算结果与宏观观察结果更一致,在评价降雨入渗对斜坡稳定性的影响时,一定要充分考虑裂隙的存在,只有雨强大于一个临界值时才会有充足的降水通过裂隙渗入到斜坡深部,裂隙的影响才显著地表现出来。     

Modeling of rainfall-triggered shallow landslide

By integrating hydrological modeling with the infinite slope stability analysis, a rainfall-triggered shallow landslide model was developed by Iverson (Water Resour Res 36:1897-1910, 2000). In Iverson’s model, the infiltration capacity is assumed to be equivalent to the saturated hydraulic conductivity for finding pressure heads analytically. However, for general infiltration process, the infiltration capacity should vary with time during the period of rain, and the infiltration rate is significantly related to the variable infiltration capacity. To avoid the unrealistically high pressure heads, Iverson employed the beta-line correction by specifying that the simulated pressure heads cannot exceed those given by the beta line. In this study, the suitability of constant infiltration capacity together with the beta-line correction for hydrological modeling and landslide modeling of hillslope subjected to a rainfall is examined. By amending the boundary condition at ground surface of hillslope in Iverson’s model, the modified Iverson’s model with considering general infiltration process is developed to conduct this examination. The results show that the unrealistically high pressure heads from Iverson’s model occur due to the overestimation of infiltration rate induced from the assumption that the infiltration capacity is identical to the saturated hydraulic conductivity. Considering with the general infiltration process, the modified Iverson’s model gives acceptable results. In addition, even though the beta-line correction is applied, the Iverson’s model still produces greater simulated pressure heads and overestimates soil failure potential as compared with the modified Iverson’s model. Therefore, for assessing rainfall-triggered shallow landslide, the use of constant infiltration capacity together with the beta-line correction needs to be replaced by the consideration of general infiltration process.     

National-scale data-driven rainfall induced landslide susceptibility mapping for China by accounting for incomplete landslide data

China is one of the countries where landslides caused the most fatalities in the last decades.The threat that landslide disasters pose to people might even be greater in the future,due to climate change and the increasing urbanization of mountainous areas.A reliable national-scale rainfall induced landslide suscep-tibility model is therefore of great relevance in order to identify regions more and less prone to landslid-ing as well as to develop suitable risk mitigating strategies.However,relying on imperfect landslide data is inevitable when modelling landslide susceptibility for such a large research area.The purpose of this study is to investigate the influence of incomplete landslide data on national scale statistical landslide susceptibility modeling for China.In this context,it is aimed to explore the benefit of mixed effects mod-elling to counterbalance associated bias propagations.Six influencing factors including lithology,slope,soil moisture index,mean annual precipitation,land use and geological environment regions were selected based on an initial exploratory data analysis.Three sets of influencing variables were designed to represent different solutions to deal with spatially incomplete landslide information:Set 1(disregards the presence of incomplete landslide information),Set 2(excludes factors related to the incompleteness of landslide data),Set 3(accounts for factors related to the incompleteness via random effects).The vari-able sets were then introduced in a generalized additive model(GAM:Set 1 and Set 2)and a generalized additive mixed effect model(GAMM:Set 3)to establish three national-scale statistical landslide suscep-tibility models:models 1,2 and 3.The models were evaluated using the area under the receiver operating characteristics curve(AUROC)given by spatially explicit and non-spatial cross-validation.The spatial pre-diction pattern produced by the models were also investigated.The results show that the landslide inven-tory incompleteness had a substantial impact on the outcomes of the statistical landslide susceptibility models.The cross-validation results provided evidence that the three established models performed well to predict model-independent landslide information with median AUROCs ranging from 0.8 to 0.9.However,although Model 1 reached the highest AUROCs within non-spatial cross-validation(median of 0.9),it was not associated with the most plausible representation of landslide susceptibility.The Model 1 modelling results were inconsistent with geomorphological process knowledge and reflected a large extent the underlying data bias.The Model 2 susceptibility maps provided a less biased picture of landslide susceptibility.However,a lower predicted likelihood of landslide occurrence still existed in areas known to be underrepresented in terms of landslide data(e.g.,the Kuenlun Mountains in the northern Tibetan Plateau).The non-linear mixed-effects model(Model 3)reduced the impact of these biases best by introducing bias-describing variables as random effects.Among the three models,Model 3 was selected as the best national-scale susceptibility model for China as it produced the most plausible portray of rainfall induced landslide susceptibility and the highest spatially explicit predictive perfor-mance(median AUROC of spatial cross validation 0.84)compared to the other two models(median AUROCs of 0.81 and 0.79,respectively).We conclude that ignoring landslide inventory-based incomplete-ness can entail misleading modelling results and that the application of non-linear mixed-effect models can reduce the propagation of such biases into the final results for very large areas.     

The influence of rainstorm pattern on shallow landslide

In this study, the influence of the rainstorm pattern on shallow landslide is examined. The physically-based shallow landslide model is used to conduct this examination with considering four representative rainstorm patterns including uniform, advanced, central, and delayed rainstorms. The results show that in spite of the rainfall duration and the rainfall pattern, the rainstorm with less than the minimum landslide-triggering rainfall amount will not trigger landslide. However, for the rainstorm with greater than the minimum landslide-triggering rainfall amount, the occurrence of landslide significantly depends not only on the rainfall duration but also on the rainfall pattern. Among the four representative rainstorm patterns, the delayed rainstorm has the greatest rainfall duration threshold for landslide occurrence, followed by the central rainstorm, and then the uniform rainstorm. In addition, for each rainstorm pattern, the corresponding rainfall duration threshold for landslide occurrence decreases with the increase of rainfall amount, and seems to be constant for large rainfall amount.     

Flood and landslide applications of near real-time satellite rainfall products

Floods and associated landslides account for the largest number of natural disasters and affect more people than any other type of natural disaster. With the availability of satellite rainfall analyses at fine time and space resolution, it has also become possible to mitigate such hazards on a near-global basis. In this article, a framework to detect floods and landslides related to heavy rain events in near-real-time is proposed. Key components of the framework are: a fine resolution precipitation acquisition system; a comprehensive land surface database; a hydrological modeling component; and landslide and debris flow model components. A key precipitation input dataset for the integrated applications is the NASA TRMM-based multi-satellite precipitation estimates. This dataset provides near real-time precipitation at a spatial-temporal resolution of 3 h and 0.25° × 0.25°. In combination with global land surface datasets it is now possible to expand regional hazard modeling components into a global identification/monitoring system for flood/landslide disaster preparedness and mitigation.     

Use of satellite remote sensing data in the mapping of global landslide susceptibility

Satellite remote sensing data has significant potential use in analysis of natural hazards such as landslides. Relying on the recent advances in satellite remote sensing and geographic information system (GIS) techniques, this paper aims to map landslide susceptibility over most of the globe using a GIS-based weighted linear combination method. First, six relevant landslide-controlling factors are derived from geospatial remote sensing data and coded into a GIS system. Next, continuous susceptibility values from low to high are assigned to each of the six factors. Second, a continuous scale of a global landslide susceptibility index is derived using GIS weighted linear combination based on each factor’s relative significance to the process of landslide occurrence (e.g., slope is the most important factor, soil types and soil texture are also primary-level parameters, while elevation, land cover types, and drainage density are secondary in importance). Finally, the continuous index map is further classified into six susceptibility categories. Results show the hot spots of landslide-prone regions include the Pacific Rim, the Himalayas and South Asia, Rocky Mountains, Appalachian Mountains, Alps, and parts of the Middle East and Africa. India, China, Nepal, Japan, the USA, and Peru are shown to have landslide-prone areas. This first-cut global landslide susceptibility map forms a starting point to provide a global view of landslide risks and may be used in conjunction with satellite-based precipitation information to potentially detect areas with significant landslide potential due to heavy rainfall.     

降雨诱发型堆积层滑坡的位移动力学特征分析

根据降雨诱发型堆积层边坡物质组成特点与位移构成性质,建立了边坡粘弹性位移动力学模型与位移动力学方程,并以此为基础系统分析了其位移动力学行为和特征以及位移与地下水位或降雨量定量关系,发现了边坡的位移动力学特征主要取决于边坡的地下水位及其变化规律,且其位移和位移速度在整体失稳前具有强烈的波动性。上述位移动力学规律表明,仅运用位移或位移速度作为单一动力学响应参数来评价与预测该类边坡稳定性存在一定局限性。因此,选择和建立堆积层滑坡的位移多源信息预测理论与模型将具有重要的理论意义与实用价值。     

A loess landslide induced by excavation and rainfall

The initiation of loess landslides is a common engineering problem during the construction of the expressway or other engineering works in the Loess Plateau of China. The engineers and researchers should devote themselves to the prevention of the initiation of loess landslides. This study focused on a loess landslide which was induced during the construction of an expressway in Shanxi Province of China. The stabilities of the loess hillside slope before and after excavation were analyzed using limit equilibrium analysis method and the strength reduction finite element method, respectively. The analysis results indicated that the loess hillside slope, before excavation, was stable under both natural state and rainfall conditions. The collapse of the loess hillside slope, or the initiation of loess landslides, after having been excavated, was induced by excavation and rainfall. The integrated stabilization method including four parts was used to stabilize the loess landslide. The four parts were a reinforced concrete piles row, a rubble concrete retaining wall, alteration of slope geometry, and interception and drainage of water. The initiation of the loess landslide might be evitable if it was given enough attention before excavation; thus, the problem of loess landslides should be paid big attention during the survey and design of the engineering works, not only during the construction.     

Probabilistic landslide susceptibility and factor effect analysis

The susceptibility of landslides and the effect of landslide-related factors at Penang in Malaysia using the geographic information system (GIS) and remote sensing data have been evaluated. Landslide locations were identified in the study area from interpretation of aerial photographs and from field surveys. Topographical and geological data and satellite images were collected, processed, and constructed into a spatial database using GIS and image processing. The factors chosen that influence landslide occurrence were: topographic slope, topographic aspect, topographic curvature and distance from drainage, all from the topographic database; lithology and distance from lineament, taken from the geologic database; land use from Landsat Thermatic Mapper (TM) satellite images; and the vegetation index value from SPOT HRV (High-Resolution Visible) satellite images. Landslide hazardous areas were analyzed and mapped using the landslide-occurrence factors employing the probability–frequency ratio method using the all factors. To assess the effect of these factors, each factor was excluded from the analysis, and its effect verified using the landslide location data. As a result, all factors had relatively positive effects, except lithology, on the landslide susceptibility maps in the study area.     

Systematic GIS-based landslide inventory as the first step for effective landslide-hazard management

The purpose of the so-called IFFI project (Inventario dei Fenomeni Franosi in Italia—Inventory of Landslides in Italy) and of many other related activities carried out by the Centro Regionale per le Ricerche Territoriali e Geologiche of ARPA Piemonte (Agenzia Regionale per la Protezione Ambientale—Regional Agency for Environmental Protection), is to map all the existing landslides in Piemonte (including both results of monitoring data and available historical data). ARPA carried out new systematic surveys using airphoto interpretation and created a specific alphanumeric geological information systems (GIS)-based database to store and process all the collected data. In order to obtain proper landslide-hazard zoning, it is necessary to provide a landslide inventory and to define the relationship between landslides and geological setting. A landslide inventory represents a fundamental base of knowledge, is a very basic tool for land planning, and strongly helps the local authorities in their decision making.     

Spatio-temporal estimation of shallow landslide hazard triggered by rainfall using a three-dimensional model

A shallow landslide triggered by rainfall can be forecast in real-time by modeling the relationship between rainfall infiltration and decrease of slope stability. This paper describes a promising approach that combines an improved three-dimensional slope stability model with an approximate method based on the Green and Ampt model, to estimate the time–space distribution of shallow landslide hazards. Once a forecast of rainfall intensity and slope stability-related data, e.g., terrain and geology data, are acquired, this approach is shown to have the ability to estimate the variation of slope stability of a wide natural area during rainfall and to identify the location of potential failure surfaces. The effectiveness of the estimation procedures described has been tested by comparison with a one-dimensional method and by application to a landslide-prone area in Japan.     

GIS-based weights-of-evidence modelling of rainfall-induced landslides in small catchments for landslide susceptibility mapping

Landslide susceptibility mapping is a vital tool for disaster management and planning development activities in mountainous terrains of tropical and subtropical environments. In this paper, the weights-of-evidence modelling was applied, within a geographical information system (GIS), to derive landslide susceptibility map of two small catchments of Shikoku, Japan. The objective of this paper is to evaluate the importance of weights-of-evidence modelling in the generation of landslide susceptibility maps in relatively small catchments having an area less than 4 sq km. For the study area in Moriyuki and Monnyu catchments, northeast Shikoku Island in west Japan, a data set was generated at scale 1:5,000. Relevant thematic maps representing various factors (e.g. slope, aspect, relief, flow accumulation, soil depth, soil type, land use and distance to road) that are related to landslide activity were generated using field data and GIS techniques. Both catchments have homogeneous geology and only consist of Cretaceous granitic rock. Thus, bedrock geology was not considered in data layering during GIS analysis. Success rates were also estimated to evaluate the accuracy of landslide susceptibility maps and the weights-of-evidence modelling was found useful in landslide susceptibility mapping of small catchments.     

Determination and application of the weights for landslide susceptibility mapping using an artificial neural network

The purpose of this study is the development, application, and assessment of probability and artificial neural network methods for assessing landslide susceptibility in a chosen study area. As the basic analysis tool, a Geographic Information System (GIS) was used for spatial data management and manipulation. Landslide locations and landslide-related factors such as slope, curvature, soil texture, soil drainage, effective thickness, wood type, and wood diameter were used for analyzing landslide susceptibility. A probability method was used for calculating the rating of the relative importance of each factor class to landslide occurrence. For calculating the weight of the relative importance of each factor to landslide occurrence, an artificial neural network method was developed. Using these methods, the landslide susceptibility index (LSI) was calculated using the rating and weight, and a landslide susceptibility map was produced using the index. The results of the landslide susceptibility analysis, with and without weights, were confirmed from comparison with the landslide location data. The comparison result with weighting was better than the results without weighting. The calculated weight and rating can be used to landslide susceptibility mapping.     

Seismically induced landslide displacements: a predictive model

Newmark's model for predicting earthquake-induced landslide displacements provides a simple way to predict the coseismic displacements affecting a sliding mass subject to earthquake loading. In this model, seismic slope stability is measured in terms of critical acceleration, which depends on the mechanical soil properties, pore-pressure distribution, and slope geometry. The triggering seismic forces are investigated in terms of energy radiation from the source, propagation, and site effects, based on 190 accelerometric recordings from 17 Italian earthquakes with magnitudes between 4.5 and 6.8. The method is based on the calibration of relations having the general form of an attenuation law that relates the energy of the seismic forces to the dynamic shear resistances of the sliding mass to propagate the expected landslide displacements as an inverse function of the distance from the fault rupture; the amount of displacement computed through these relations provides a criterion to predict the occurrence of slope failures. Finally, maps showing, in a deterministic and a probabilistic way, the potential of seismically induced landslide displacements are displayed as a tool to provide seismic landslide scenarios and earthquake-induced landslide hazard maps, respectively.