A statistical approach to landslide risk modelling at basin scale: from landslide susceptibility to quantitative risk assessment

A procedure for landslide risk assessment is presented. The underlying hypothesis is that statistical relationships between past landslide occurrences and conditioning variables can be used to develop landslide susceptibility, hazard and risk models. The latter require also data on past damages. Landslides occurred during the last 50 years and subsequent damages were analysed. Landslide susceptibility models were obtained by means of Spatial Data Analysis techniques and independently validated. Scenarios defined on the basis of past landslide frequency and magnitude were used to transform susceptibility into quantitative hazard models. To assess vulnerability, a detailed inventory of exposed elements (infrastructures, buildings, land resources) was carried out. Vulnerability values (0–1) were obtained by comparing damages experienced in the past by each type of element with its actual value. Quantitative risk models, with a monetary meaning, were obtained for each element by integrating landslide hazard and vulnerability models. Landslide risk models showing the expected losses for the next 50 years were thus obtained for the different scenarios. Risk values obtained are not precise predictions of future losses but rather a means to identify areas where damages are likely to be greater and require priority for mitigation actions.     

Rainfall patterns and related landslide incidence in the Porretta-Vergato region,Italy

An analysis of landslide occurrence in the low permeability terrain of Porretta-Vergato, Italy, related to prolonged rainfall patterns is presented. Data sets collected over nearly a century are statistically analysed. The pattern of the landslide hazard is considered and related to precipitation at the basin scale in order to enhance the understanding between the two parameters and assess their temporal changes, as well as interrelationships. Landslide incidence generally follows the periodic pattern of precipitation with a lag of approximately six months, which is believed to relate to the time necessary for the ground water to reach a critical level to initiate slope failure. There also appears to be a two-stage pattern of precipitation which induces most landslides: a preparatory period, where the landslide is destabilized and conditioned for slope failure, followed by a more intense period of rainfall that triggers or provokes the event. These initial findings point to the need for further studies to verify such unstable situations.     

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.     

Generation of a landslide risk index map for Cuba using spatial multi-criteria evaluation

This paper explains the procedure for the generation of a landslide risk index map at national level in Cuba, using a semi-quantitative model with ten indicator maps and a cell size of 90 × 90 m. The model was designed and implemented using spatial multi-criteria evaluation techniques in a GIS system. Each indicator was processed, analysed and standardised according to its contribution to hazard and vulnerability. The indicators were weighted using direct, pairwise comparison and rank-ordering weighting methods, and weights were combined to obtain the final landslide risk index map. The results were analysed per physiographic region and administrative units at provincial and municipal levels. The Sierra Maestra mountain system was found to have the largest concentration of high landslide risk index values while the Nipe–Cristal–Baracoa system has the highest absolute values, although they are more dispersed. The results obtained allow designing an appropriated landslide risk mitigation plan at national level and to link the information to the national hurricane early warning system, allowing also warning and evacuation for landslide-prone areas.     

A review of assessing landslide frequency for hazard zoning purposes

The probability of occurrence is one of the key components of the risk equation. To assess this probability in landslide risk analysis, two different approaches have been traditionally used. In the first one, the occurrence of landslides is obtained by computing the probability of failure of a slope (or the reactivation of existing landslides). In the second one, which is the objective of this paper, the probability is obtained by means of the statistical analysis of past landslide events, specifically by the assessment of the past landslide frequency. In its turn, the temporal frequency of landslides may be determined based on the occurrence of landslides or from the recurrence of the landslide triggering events over a regional extent. Hazard assessment using frequency of landslides, which may be taken either individually or collectively, requires complete records of landslide events, which is difficult in some areas. Its main advantage is that it may be easily implemented for zoning. Frequency assessed from the recurrence of landslide triggers, does not require landslide series but it is necessary to establish reliable relations between the trigger, its magnitude and the occurrence of the landslides. The frequency of the landslide triggers can be directly used for landslide zoning. However, because it does not provide information on the spatial distribution of the potential landslides, it has to be combined with landslide susceptibility (spatial probability analysis) to perform landslide hazard zoning. Both the scale of work and availability of data affect the results of the landslide frequency and restrict the spatial resolution of frequency zoning as well. Magnitude–frequency relationships are fundamental elements for the quantitative assessment of both hazard and risk.     

Statistical approach to earthquake-induced landslide susceptibility

Susceptibility analysis for predicting earthquake-induced landslides has most frequently been done using deterministic methods; multivariate statistical methods have not previously been applied. In this study, however, we introduce a statistical methodology that uses the intensity of earthquake shaking as a landslide triggering factor. This methodology is applied in a study of shallow earthquake-induced landslides in central western Taiwan. The results show that we can accurately interpret landslide distribution in the study area and predict the occurrence of landslides in neighboring regions. This susceptibility model is capable of predicting shallow landslides induced during an earthquake scenario with similar range of ground shaking, without requiring the use of geotechnical, groundwater or failure depth data.     

Climatic effect of ENSO associated with landslide occurrence in the Central Andes, Mendoza Province, Argentina

Temporal distribution of landslides can be verified by means of climatic anomalies linked to the ENSO phenomenon. An increasing number of landslides triggered by rainfall have been recorded during warm episodes (El Niño) in the Cordillera Frontal, and a decreasing number during cold episodes (La Niña), concluding that this geological province is mainly influenced by the Pacific Anticyclone. However, slope instability in the Precordillera, located east of the Cordillera Frontal, seems to be mainly influenced by the Atlantic Anticyclone. Analysis of variance shows that there is no significant difference between landslide records and cold-warm episodes, and a higher number of landslides were recorded in years linked to wet periods than during dry periods. Furthermore, the precipitation threshold value associated with landslide occurrence and antecedent precipitation are analysed.     

Uncertainty pattern in landslide susceptibility prediction modelling: Effects of different landslide boundaries and spatial shape expressions

In some studies on landslide susceptibility mapping (LSM), landslide boundary and spatial shape characteristics have been expressed in the form of points or circles in the landslide inventory instead of the accurate polygon form. Different expressions of landslide boundaries and spatial shapes may lead to substantial differences in the distribution of predicted landslide susceptibility indexes (LSIs); moreover, the presence of irregular landslide boundaries and spatial shapes introduces uncertainties into the LSM. To address this issue by accurately drawing polygonal boundaries based on LSM, the uncertainty patterns of LSM modelling under two different landslide boundaries and spatial shapes, such as landslide points and circles, are compared. Within the research area of Ruijin City in China, a total of 370 landslides with accurate boundary information are obtained, and 10 environmental factors, such as slope and lithology, are selected. Then, correlation analyses between the landslide boundary shapes and selected environmental factors are performed via the frequency ratio (FR) method. Next, a support vector machine (SVM) and random forest (RF) based on landslide points, circles and accurate landslide polygons are constructed as point-, circle- and polygon-based SVM and RF models, respectively, to address LSM. Finally, the prediction capabilities of the above models are compared by computing their statistical accuracy using receiver operating characteristic analysis, and the uncertainties of the predicted LSIs under the above models are discussed. The results show that using polygonal surfaces with a higher reliability and accuracy to express the landslide boundary and spatial shape can provide a markedly improved LSM accuracy, compared to those based on the points and circles. Moreover, a higher degree of uncertainty of LSM modelling is present in the expression of points because there are too few grid units acting as model input variables. Additionally, the expression of the landslide boundary as circles introduces errors in measurement and is not as accurate as the polygonal boundary in most LSM modelling cases. In addition, the results under different conditions show that the polygon-based models have a higher LSM accuracy, with lower mean values and larger standard deviations compared with the point- and circle-based models. Finally, the overall LSM accuracy of the RF is superior to that of the SVM, and similar patterns of landslide boundary and spatial shape affecting the LSM modelling are reflected in the SVM and RF models.     

Regional-scale landslide activity and landslide susceptibility zonation in the Nepal Himalaya

Landslide susceptibility zonation mapping is a fundamental procedure for geo-disaster management in tropical and sub-tropical regions. Recently, various landslide susceptibility zonation models have been introduced in Nepal with diverse approaches of assessment. However, validation is still a problem. Additionally, the role of various predisposing causative parameters for landslide activity is still not well understood in the Nepal Himalaya. To address these issues of susceptibility zonation and landslide activity, about 4,000 km² area of central Nepal was selected for regional-scale assessment of landslide activity and susceptibility zonation mapping. In total, 655 new landslides and 9,229 old landslides were identified with the study area with the help of satellite images, aerial photographs, field data and available reports. The old landslide inventory was “blind landslide database” and could not explain the particular rainfall event responsible for the particular landslide. But considering size of the landslide, blind landslide inventory was reclassified into two databases: short-duration high-intensity rainfall-induced landslide inventory and long-duration low-intensity rainfall-induced landslide inventory. These landslide inventory maps were considered as proxy maps of multiple rainfall event-based landslide inventories. Similarly, all 9,884 landslides were considered for the activity assessment of predisposing causative parameters. For the Nepal Himalaya, slope, slope aspect, geology and road construction activity (anthropogenic cause) were identified as most affective predisposing causative parameters for landslide activity. For susceptibility zonation, multivariate approach was considered and two proxy rainfall event-based landslide databases were used for the logistic regression modelling, while a relatively recent landslide database was used in validation. Two event-based susceptibility zonation maps were merged and rectified to prepare the final susceptibility zonation map and its prediction rate was found to be more than 82 %. From this work, it is concluded that rectification of susceptibility zonation map is very appropriate and reliable. The results of this research contribute to a significant improvement in landslide inventory preparation procedure, susceptibility zonation mapping approaches as well as role of various predisposing causative parameters for the landslide activity.     

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.     

The chaotic characteristics of landslide evolution: a case study of Xintan landslide

A new method based on the chaos theory is used to assess the evolution process of a slope system. The method is applied to the Xintan landslide and the results show: (1) the slope movement is a complex process of the slope going in and out of the stable and chaotic state; (2) the method reveals the evolution process of the slope pointing to the slope failure while the observed movement shows a simple monotonic increase with time; (3) the method is not sufficiently mature to precisely predict the time of failure but it has potential for improvement with further research and more field data for analysis.     

Slope stabilization and landslide size on Mt. 99 Peaks after Chichi Earthquake in Taiwan

The study focuses on the landslide characteristics of Mt. 99 Peaks in Nantou County, the most serious landslide prone area caused by Chichi Earthquake in Taiwan. Several investigations and field surveys were made on Mt. 99 Peaks for 5 years to research the landslide area and depth, rainfall trend, and slope stabilization. The total landslide volume caused by the earthquake on Mt. 99 Peaks was about 1.47×10⁶ m³ and the mean landslide thickness was about 0.22 m. Gravel layers with a volume of more than 80% of total soil profile dominated Mt. 99 Peaks. The landslide on Mt. 99 Peaks was induced by heavy rainfall from July to September because the rainfall on Mt. 99 Peaks had a nonuniform distribution in time. Although the vegetation recovery on Mt. 99 Peaks was in progress, the soil slope had remained unstable. As a result, Typhoon Mindulle occurred in July 2004 collapsed the hillslope again after 5 years of Chichi Earthquake. This study suggests that vegetation recovery on Mt. 99 Peaks for 5 years was insufficient to stabilize the landslide affected area.     

国际滑坡研究的新进展

本文根据２０００年６月２６～３０日在英国如开的第八届国际滑坡学术讨论会的主题报告,分组报千和会议文集所报导的近几年来国际滑坡的最新研究成果,对滑坡与全球气候变化、滑坡灾害风险评价、滑坡机制、海岸带滑坡和海底滑坡五个方面作了简要介绍与分析,这些方面的成果代表了当前国际滑坡研究的进展和动态,对促进我国今后的滑坡理论和灾害防治研究将会起到一定的帮助作用。     

A conceptual framework for quantitative estimation of physical vulnerability to landslides

The paper illustrates a method for scenario-based, quantitative estimation of physical vulnerability of the built environment to landslides. The rationale and main features of the procedure are presented in the context of quantitative risk estimation. Vulnerability is defined quantitatively as a function of landslide intensity and the susceptibility of vulnerable elements. Reference terminology is presented and discussed. Models for the quantification of intensity and susceptibility for some categories of elements at risk such as structures and persons are proposed. An example application is illustrated.     

Field variability of landslide model parameters

 A data set of parameters (slope, soil depth and soil shear strength) relevant to spatially distributed modelling of shallow landslides triggered by rain and snowmelt events was determined from field measurements in 250 grid elements of dimensions 25 m (downslope)×10 m (across slope) in an area of 250 m×250 m on a hillslope in Scotland. These data provide an unusually detailed basis for the evaluation of spatial variability and uncertainty in model parameterisation. The variations in slope and soil strength are represented adequately by normal distributions; a Weibull distribution is suggested for the soil depth data. The factor of safety calculated at each point in the grid was shown partially to identify observed landslides, with a number of false predictions of occurrence. Trend analysis and semivariogram analysis of the data set suggest that the use of kriging could improve upon this approach to landslide prediction by providing areal estimates of parameters at the grid element scale with associated error bounds. Received: 30 October 1996 · Accepted: 25 June 1997     

滑坡灾害风险评价的系统分析

从系统理论的观点出发,提出了滑坡灾害复杂大系统的概念,并以这一概念为基础,探讨了滑坡灾害风险特征及滑坡灾害风险评价的基本内容,提出并系统地阐述了以滑坡危险性分析、承灾体易损性分析和滑坡灾害破坏损失评估为核心内容的滑坡灾害风险评价的系统理论。     

Regression models for estimating coseismic landslide displacement

Newmark's sliding-block model is widely used to estimate coseismic slope performance. Early efforts to develop simple regression models to estimate Newmark displacement were based on analysis of the small number of strong-motion records then available. The current availability of a much larger set of strong-motion records dictates that these regression equations be updated. Regression equations were generated using data derived from a collection of 2270 strong-motion records from 30 worldwide earthquakes. The regression equations predict Newmark displacement in terms of (1) critical acceleration ratio, (2) critical acceleration ratio and earthquake magnitude, (3) Arias intensity and critical acceleration, and (4) Arias intensity and critical acceleration ratio. These equations are well constrained and fit the data well (71% < R² < 88%), but they have standard deviations of about 0.5 log units, such that the range defined by the mean ± one standard deviation spans about an order of magnitude. These regression models, therefore, are not recommended for use in site-specific design, but rather for regional-scale seismic landslide hazard mapping or for rapid preliminary screening of sites.     

Using GB-SAR technique to monitor slow moving landslide

A Ground-Based SAR (GB-SAR) interferometer was employed to measure the surface displacements of a landslide occurring in the Carnian Alps, north-eastern Italy, which has affected a national road and seriously damaged a road tunnel still under construction. Moreover, since the landslide is located on the left bank of the Tagliamento River Valley, it is feared that this mass movement might dam the river, creating a basin that would increase natural hazard for the valley inhabitants. The data collected from December 2002 to July 2005 by a conventional monitoring system, consisting of a GPS network and boreholes equipped with inclinometric tubes, showed that the landslide was moving at a quasi-constant rate of about 3 cm per year. Due to the slow deformation rate of the landslide, a recently developed GB-SAR technique based on the analysis of a restricted ensemble of coherent points was used. Two surveys, each lasting two days, were planned in December 2004 and July 2005, in order to map and measure the surface displacements that occurred over time. The results from the radar were compared with the ones derived from the GPS monitoring network. An agreement was achieved among the data collected, showing the capability of the GB-SAR technique to measure displacements even within a time span of several months between the surveys.     

贵州兴义滑坡特征及过程预警研究

本文以新源县喀拉海依苏滑坡隐患体为研究案例,采用COMSOL Multiphysics数值模拟有限元软件,建立了基于非饱和渗流理论与Mohr-Coulomb准则的滑坡稳定性计算模型。根据数值模拟结果得到研究区滑坡体原始应力分布与初始孔隙压力的分布情况、在降雨入渗条件下的内部应力、有效塑性应变、塑性区、含水率、潜在滑移面等的分布情况,并计算得到滑坡体在降雨入渗条件下的安全系数。根据模型计算,滑坡体在不同降雨入渗工况下的安全系数随降雨量的增大而减小,滑坡体在连续降雨5天的临界阈值为218.82 mm,根据安全系数的变化可得到滑坡体临界降雨阈值。该论文提供了一种强度折减安全系数计算降雨型黄土滑坡降雨阈值的数值模拟方法,研究结果为西北地区降雨型黄土滑坡失稳破坏的临界降雨阈值研究提供了一种有效的研究手段。