Modeling landslide recurrence in Seattle, Washington, USA

To manage the hazard associated with shallow landslides, decision makers need an understanding of where and when landslides may occur. A variety of approaches have been used to estimate the hazard from shallow, rainfall-triggered landslides, such as empirical rainfall threshold methods or probabilistic methods based on historical records. The wide availability of Geographic Information Systems (GIS) and digital topographic data has led to the development of analytic methods for landslide hazard estimation that couple steady-state hydrological models with slope stability calculations. Because these methods typically neglect the transient effects of infiltration on slope stability, results cannot be linked with historical or forecasted rainfall sequences. Estimates of the frequency of conditions likely to cause landslides are critical for quantitative risk and hazard assessments. We present results to demonstrate how a transient infiltration model coupled with an infinite slope stability calculation may be used to assess shallow landslide frequency in the City of Seattle, Washington, USA. A module called CRF (Critical RainFall) for estimating deterministic rainfall thresholds has been integrated in the TRIGRS (Transient Rainfall Infiltration and Grid-based Slope-Stability) model that combines a transient, one-dimensional analytic solution for pore-pressure response to rainfall infiltration with an infinite slope stability calculation. Input data for the extended model include topographic slope, colluvial thickness, initial water-table depth, material properties, and rainfall durations. This approach is combined with a statistical treatment of rainfall using a GEV (General Extreme Value) probabilistic distribution to produce maps showing the shallow landslide recurrence induced, on a spatially distributed basis, as a function of rainfall duration and hillslope characteristics.     

Mapping susceptibility of rainfall-triggered shallow landslides using a probabilistic approach

To prepare a landslide susceptibility map is essential to identify hazardous regions, construct appropriate mitigation facilities, and plan emergency measures for a region prone to landslides triggered by rainfall. The conventional mapping methods require much information about past landslides records and contributing terrace and rainfall. They also rely heavily on the quantity and quality of accessible information and subjectively of the map builder. This paper contributes to a systematic and quantitative assessment of mapping landslide hazards over a region. Geographical Information System is implemented to retrieve relevant parameters from data layers, including the spatial distribution of transient fluid pressures, which is estimated using the TRIGRS program. The factor of safety of each pixel in the study region is calculated analytically. Monte Carlo simulation of random variables is conducted to process the estimation of fluid pressure and factor of safety for multiple times. The failure probability of each pixel is thus estimated. These procedures of mapping landslide potential are demonstrated in a case history. The analysis results reveal a positive correlation between landslide probability and accumulated rainfall. This approach gives simulation results compared to field records. The location and size of actual landslide are well predicted. An explanation for some of the inconsistencies is also provided to emphasize the importance of site information on the accuracy of mapping results.     

An analysis of storm-induced landslides in colluvial soils overlying mudrock sequences, southeastern Ohio, USA

In late-June, 1998, a series of thunderstorms dropped 16.5 cm (6.5 in.) of rain in a 72-h period over southeastern Ohio, causing extensive flooding, six deaths, and 179 million dollars in property damage. The storms also triggered more than 60 shallow landslides along a 64 km (40-mi) stretch of Interstate 77 between Buffalo and Marietta, Ohio. Almost all of the landslides were translational, occurring along the contact between colluvial soils and the underlying bedrock (shales, claystones, mudstones). Six sites were selected in the affected area for a detailed study of the shallow landslides. At each site, a record was made of the stratigraphy, slope geometry, number of landslides, landslide dimensions, hydrologic conditions, and influence of vegetation on landslide distribution. Both colluvial soils and underlying bedrock were sampled for laboratory investigations, which included determination of natural water content, grain size distribution, Atterberg limits, permeability, slake durability, and shear-strength parameters. Data from laboratory tests were used to perform sensitivity and stability analyses with respect to varying slope angles, strength parameters, and thicknesses of saturated colluvial soil. The results of the study indicate that the shallow landslides along Interstate 77 occurred when the colluvial soils reached 90% to 100% saturation, depending upon the slope angles.     

降雨滑坡预警的概率分析方法

滑坡启动的降雨临界值是滑坡预警的关键,由于研究区资料数据有限,关键值难以确定,降雨滑坡预警效果受影响。所以应该对预警结果进行概率分析。本文利用作者在已开发的降雨滑坡预警系统,提出采用降雨滑坡预警概率分析方法进行预警。通过对滑坡与雨量相关性、降雨滑坡启动值等对滑坡预警的时间概率、空间概率及预警概率进行分析,计算危险区内已发生滑坡频率和降雨滑坡发生频率,得到降雨滑坡预警概率使得预警系统更加可靠。并以沐川县为例对模型进行了算例检验,为更科学的对降雨滑坡进行预警提供方法借鉴。     

A review of landslide hazards in Japan and assessment of their susceptibility using an analytical hierarchic process (AHP) method

In spite of its small size, Japan suffers many landslide disasters due to intense rainfall and earthquakes. This article describes the distribution and topography of these landslides, and a new method of evaluating the susceptibility, the analytical hierarchic process (AHP). The method assigns scores to each factor of micro-topography of landslide-prone areas identified in aerial photographs, and assesses the susceptibility of landslide from the total score. In addition, a method of simulating sliding mass runout is briefly presented for the designating sediment-related disaster warning areas.     

Accuracy assessment of InSAR derived input maps for landslide susceptibility analysis: a case study from the Swiss Alps

In recent years SAR interferometry has become a widely used technique for measuring altitude and displacement of the surface of the earth. Both these capabilities are highly relevant for landslide susceptibility studies. Although there are many problems that make the use of SAR interferometry less suitable for landslide inventory mapping, it’s use in landslide monitoring and in the generation of input maps for landslide susceptibility assessment looks very promising. The present work attempts to evaluate the usefulness and limitations of this technique based on a case study in the Swiss Alps. Input maps were generated from ERS repeat pass data using SAR interferometry. A land cover map has been generated by image classification of multi-temporal SAR intensity images. An InSAR DEM was generated and a number of maps were derived from it, such as slope-, aspect, altitude- and slope form classes. These maps were used to generate landslide and rockfall susceptibility maps, which give fairly well acceptable results. However, a comparison of the InSAR DEM with the conventional Swisstopo DEM, indicated significant errors in the absolute height and slope angles derived from InSAR, especially along the ridges and in the valleys. These errors are caused by low coherence mostly due to layover and shadow effects. Visual comparison of stereo images created from hillshading maps and corresponding DEMs demonstrate that a considerable amount of topographic details have been lost in the InSAR-derived DEM. It is concluded that InSAR derived input maps are not ideal for landslide susceptibility assessment, but could be used if more accurate data is lacking.     

Application of logistic regression for landslide susceptibility zoning of Cekmece Area, Istanbul, Turkey

As a result of industrialization, throughout the world, cities have been growing rapidly for the last century. One typical example of these growing cities is Istanbul, the population of which is over 10 million. Due to rapid urbanization, new areas suitable for settlement and engineering structures are necessary. The Cekmece area located west of the Istanbul metropolitan area is studied, because the landslide activity is extensive in this area. The purpose of this study is to develop a model that can be used to characterize landslide susceptibility in map form using logistic regression analysis of an extensive landslide database. A database of landslide activity was constructed using both aerial-photography and field studies. About 19.2% of the selected study area is covered by deep-seated landslides. The landslides that occur in the area are primarily located in sandstones with interbedded permeable and impermeable layers such as claystone, siltstone and mudstone. About 31.95% of the total landslide area is located at this unit. To apply logistic regression analyses, a data matrix including 37 variables was constructed. The variables used in the forwards stepwise analyses are different measures of slope, aspect, elevation, stream power index (SPI), plan curvature, profile curvature, geology, geomorphology and relative permeability of lithological units. A total of 25 variables were identified as exerting strong influence on landslide occurrence, and included by the logistic regression equation. Wald statistics values indicate that lithology, SPI and slope are more important than the other parameters in the equation. Beta coefficients of the 25 variables included the logistic regression equation provide a model for landslide susceptibility in the Cekmece area. This model is used to generate a landslide susceptibility map that correctly classified 83.8% of the landslide-prone areas.     

Data driven bivariate landslide susceptibility assessment using geographical information systems: a method and application to Asarsuyu catchment, Turkey

In the last decades, landslide hazard assessment has attracted many researchers' attention. A number of parameters are suggested to be responsible to quantitatively explain the mechanism of landslides; many of these parameters are very important and factual. However, some data types and models are site-specific and could not be applied to different locations. Furthermore, the data stored in continuous parameter maps are divided into a number of classes arbitrarily, depending on the vision of the expert. Basically, this division controls the result of bivariate analysis. Besides, the responsible portion of the parameter map controlling the mechanism is also weighted arbitrarily. Based on these two facts, the class boundaries put a prejudice on the produced susceptibility/hazard maps, which result in dependence on the knowledge of the user rather than being dependent on the data and the fact itself. The aim of this study is to refine the previously defined methods in a more data-dependent trend. To achieve this goal, two new concepts: seed cells and percentile maps are introduced. Seed cells are the zones that are considered to represent the best undisturbed morphological decision rules (conditions before landslide occurs) and would be achieved by adding a buffer zone to the crown and flank areas of the landslide. To quantitatively classify the input parameter maps, the data distributions of seed cells in the parameter maps are divided into a number of classes on the basis of their distribution's percentile break-points upon which the parameter maps are directly dependent on the seed cell distributions, hence to the data itself.     

The role of rainfall in the landslide hazard: the case of the Avigliano urban area (Southern Apennines, Italy)

Mass movements varying in type and size, some of which are periodically reactivated, affect the urban area of Avigliano. The disturbed and remoulded masses consist of sandy–silty or silty–clayey plastic material interbedded with stone fragments and conglomerate blocks. Five landslides that were markedly liable to rainfall-associated instability phenomena were selected.

The relationships between landslides and rainfall were investigated using a hydrological and statistical model based on long-term series of daily rainfall data. The model was used to determine the return period of cumulative daily rainfall over 1–180 days. The resulting hydrological and statistical findings are discussed with the aim of identifying the rainfall duration most critical to landslides.

The concept of a precipitation threshold was generalized by defining some probability classes of cumulative rainfall. These classes indicate the thresholds beyond which reactivation is likely to occur. The probability classes are defined according to the return period of the cumulative rainfall concomitant with landslide reactivation.     

A comparison of the GIS based landslide susceptibility assessment methods: multivariate versus bivariate

The purpose of this study is to evaluate and to compare the results of multivariate (logical regression) and bivariate (landslide susceptibility) methods in Geographical Information System (GIS) based landslide susceptibility assessment procedures. In order to achieve this goal the Asarsuyu catchment in NW Turkey was selected as a test zone because of its well-known landslide occurrences interfering with the E-5 highway mountain pass.Two methods were applied to the test zone and two separate susceptibility maps were produced. Following this a two-fold comparison scheme was implemented. Both methods were compared by the Seed Cell Area Indexes (SCAI) and by the spatial locations of the resultant susceptibility pixels.It was found that both of the methods converge in 80% of the area; however, the weighting algorithm in the bivariate technique (landslide susceptibility method) had some severe deficiencies, as the resultant hazard classes in overweighed areas did not converge with the factual landslide inventory map. The result of the multivariate technique (logical regression) was more sensitive to the different local features of the test zone and it resulted in more accurate and homogeneous susceptibility maps.     

浙西梅雨滑坡易发性评价模型对比

新疆巩留县广泛发育冻融降雨型滑坡地质灾害,对其现有的研究多考虑降水,而缺乏温度影响的研究,为此,本文特增加了温度因子来进行巩留县滑坡灾害危险性评价。基于巩留县已发生的682个滑坡灾害点,选取坡度、起伏度、坡向、曲率、温度、距断层距离、距河流距离、距道路距离、工程地质岩组等9个评价因子。采用信息量模型(I)、确定性系数模型(CF)、信息量模型+逻辑回归模型(I+LR)以及确定性系数模型+逻辑回归模型(CF+LR)等4种模型对巩留县滑坡危险性进行了评价,划分为极高、高、中和低4个危险等级分区并进行了精度检验与现场实际验证。结果表明：(1)温度对滑坡有较大的触发作用;(2)耦合模型极高、高危险性分区面积明显低于单一模型极高、高危险性分区面积,其中CF+LR模型的极高、高危险性分区面积最小,低危险性分区面积最大;(3)4种模型ROC精度检验AUC值分别为0.889、0.893、0.895和0.900,均能较为客观地评价巩留县滑坡危险性。CF+LR模型精度最高,且经局部地区现场检验,CF+LR模型评价结果与实际情况也最为相符,研究成果对新疆地区巩留县滑坡地质灾害的预防和治理具有一定的借鉴意义。     

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.     

Movement of the Shuping landslide in the first four years after the initial impoundment of the Three Gorges Dam Reservoir, China

The Shuping landslide was reactivated by the initial impoundment of the Three Gorges Dam Reservoir, China in June 2003. For purposes of landslide disaster mitigation in the reservoir area and identification of landslide movement and deformation caused by reservoir level changes, a monitoring system mainly consisting of drum-style extensometers was installed in the eastern part of the Shuping landslide. Systematic monitoring was started in August 2004 with installation of 13 extensometers above the waterline after the initial impoundment. In August 2006, 11 more drum-style extensometers were installed above the high waterline (175 m) and five flexible extensometers were installed along a longitudinal section in the elongation to low waterline. In this paper, the monitoring results from August 2004 to July 2007 are presented and the deforming of the Shuping landslide caused by both reservoir level changes and rainfall is examined.     

A feasible methodology for landslide susceptibility assessment in developing countries: A case-study of NW Nicaragua after Hurricane Mitch

In October 1998, Hurricane Mitch triggered a large number of landslides (mainly debris flows) in Honduras and Nicaragua, resulting in a high death toll and in considerable damage to property. In recent years, a number of risk assessment methodologies have been devised to mitigate natural disasters. However, due to scarcity of funds and lack of specialised personnel few of these methodologies are accessible to developing countries. To explore the potential application of relatively simple and affordable landslide susceptibility methodologies in such countries, we focused on a region in NW Nicaragua which was among the most severely hit during the Mitch event. Our study included (1) detailed field work to produce a high-resolution inventory landslide map at 1 : 10,000 scale, and (2) a selection of the relevant instability factors from a Terrain Units Map which had previously been generated in a project for rural development. Based on the combination of these two datasets and using GIS tools we developed a comparative analysis of failure-zones and terrain factors in an attempt to classify the land into zones according to the propensity to landslides triggered by heavy rainfalls. The resulting susceptibility map was validated by using a training and a test zone, providing results comparable to those reached in studies based in more sophisticated methodologies. Thus, we provide an example of a methodology which is simple enough to be fully comprehended by non-specialised technicians and which could be of help in landslide risk mitigation through implementation of non-structural measures, such as land planning or emergency measures.     

Understanding rainfall-landslide relationships in man-modified environments: a case-history from Caramanico Terme, Italy

 The expansion of Caramanico Terme in this century has led to the urbanization of marginally stable valley slopes, and this has coincided with the apparent acceleration of landslide processes. Recent landslides on man-modified slopes were caused, but not necessarily triggered, by heavy precipitation (antecedent moisture was a more critical factor than the amount of storm rainfall). Because no important landslides on natural slopes in the same period were reported in the Caramanico area, a clear distinction must be made between natural settings and those modified by man when determining rainfall thresholds for predictive purposes. In recently urbanized mountainous environments, the thresholds used to assess landslide hazards should not be weighted too heavily on old historical records of precipitation and associated mass movements. Instead, more weight ought to be given to the period following the occurrence of any major anthropogenic and natural (e.g. high-magnitude earthquake) modification of slope setting. Received: 19 October 1996 · Accepted: 25 June 1997     

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.     

基于二元统计的区域地质灾害敏感性评价--以四川雅安雨城区为例

借鉴区域地质灾害敏感性评价的各种方法,给出了基于二元统计的地质灾害敏感性评价方法,并在雅安市雨城区地质灾害敏感性评价中进行了试验.本方法主要有以下优点:操作简单、数据容易准备、效率高、评价合理准确、适用于各种精度(比例尺),有着较好的推广应用价值.