Interpretation of probability of landsliding triggered by rainfall

This paper is a contribution to an important aspect of the systematic and quantitative assessment of landslide hazard and risk. The focus is on site-specific and detailed assessment for rainfall-triggered landslides and, in particular, on the estimation and interpretation of the temporal probability of landsliding. Historical rainfall data over a 109-year period were analysed with particular reference to a site along the Unanderra and Moss Vale Railway Line in the State of New South Wales, Australia. It is shown that the recurrence interval of landsliding and hence annual probability of occurrence is subject to significant uncertainty and that it cannot be regarded as a constant. Accordingly landslide hazard varies spatially as well as being a function of time. For the example case study considered in this paper the annual probability of landslide occurrence was estimated to be in the range 0.026–0.172. However, the mean annual probability of landslide reactivation was estimated to be in the range 0.037–0.078. Utilisation of methods for probability assessment proposed in this paper will contribute to more realistic assessment of hazard and risk and, therefore, to more efficient risk management.     

Assessing landslide exposure in areas with limited landslide information

Landslide risk assessment is often a difficult task due to the lack of temporal data on landslides and triggering events (frequency), run-out distance, landslide magnitude and vulnerability. The probability of occurrence of landslides is often very difficult to predict, as well as the expected magnitude of events, due to the limited data availability on past landslide activity. In this paper, a qualitative procedure for assessing the exposure of elements at risk is presented for an area of the Apulia region (Italy) where no temporal information on landslide occurrence is available. Given these limitations in data availability, it was not possible to produce a reliable landslide hazard map and, consequently, a risk map. The qualitative analysis was carried out using the spatial multi-criteria evaluation method in a global information system. A landslide susceptibility composite index map and four asset index maps (physical, social, economic and environmental) were generated separately through a hierarchical procedure of standardising and weighting. The four asset index maps were combined in order to obtain a qualitative weighted assets map, which, combined with the landslide susceptibility composite index map, has provided the final qualitative landslide exposure map. The resulting map represents the spatial distribution of the exposure level in the study area; this information could be used in a preliminary stage of regional planning. In order to demonstrate how such an exposure map could be used in a basic risk assessment, a quantification of the economic losses at municipal level was carried out, and the temporal probability of landslides was estimated, on the basis of the expert knowledge. Although the proposed methodology for the exposure assessment did not consider the landslide run-out and vulnerability quantification, the results obtained allow to rank the municipalities in terms of increasing exposure and risk level and, consequently, to identify the priorities for designing appropriate landslide risk mitigation plans.     

Area-scale landslide hazard and risk assessment

The paper deals with a methodology for quantitative landslide hazard and risk assessments over wide-scale areas. The approach was designed to fulfil the following requirements: (1) rapid investigation of large study areas; (2) use of elementary information, in order to satisfy the first requirement and to ensure validation, repetition and real time updating of the assessments every time new data are available; (3) computation of the landslide frequency of occurrence, in order to compare objectively different hazard conditions and to minimize references to qualitative hazard attributes such as activity states. The idea of multi-temporal analysis set forth by Cardinali et al. (Nat Hazards Earth Syst Sci 2:57–72, 2002), has been stressed here to compute average recurrence time for individual landslides and to forecast their behaviour within reference time periods. The method is based on the observation of the landslide activity through aerial-photo surveys carried out in several time steps. The output is given by a landslide hazard map showing the mean return period of landslides reactivation. Assessing the hazard in a quantitative way allows for estimating quantitatively the risk as well; thus, the probability of the exposed elements (such as people and real estates) to suffer damages due to the occurrence of landslides can be calculated. The methodology here presented is illustrated with reference to a sample area in Central Italy (Umbria region), for which both the landslide hazard and risk for the human life are analysed and computed. Results show the powerful quantitative approach for assessing the exposure of human activities to the landslide threat for a best choice of the countermeasures needed to mitigate the risk.An erratum to this article can be found at     

降雨型滑坡时间概率的逻辑回归拟合及连续概率滑坡危险性建模

提高降雨型滑坡危险性预警精度和空间辨识度具有重要意义.以江西宁都县1980—2001年156个降雨型滑坡为例,首先基于传统的EE-D(early effective rainfall-rainfall duration)阈值法计算不同降雨诱发滑坡的时间概率级别;然后以各级别临界降雨阈值曲线对应的时间概率为因变量,并以对应的前期有效降雨量(early effective rainfall,EE)和降雨历时(D)为自变量,采用逻辑回归拟合出上述因变量与自变量之间的非线性关系,得到降雨诱发滑坡的连续概率值;之后对比C5.0决策树和多层感知器的滑坡易发性预测性能;最后利用降雨诱发滑坡的连续概率值与易发性图相耦合以实现连续概率滑坡危险性预警.结果显示：(1)宁都降雨型滑坡连续概率值的逻辑回归方程为1/P=1+e^{4.062+0.747 4×D-0.079 44×EE},其拟合优度为0.983;(2)2002—2003年的20处用于连续概率阈值测试的降雨型滑坡大都落在连续概率值大于0.7的区域,只有4处落在小于0.7的区域;(3)C5.0决策树预测滑坡易发性的精度显著高于多层感知...     

Integration of statistical and heuristic approaches for landslide risk analysis: a case of volcanic mountains in West Java Province,Indonesia

Owing to fragile geo-morphology, extreme climatic conditions, and densely populated settlements and rapid development activities, West Java Province is the most landslide hazardous area in Indonesia. So, a landslide risk map for this province bears a great importance such as for land-use planning. It is however widely accepted that landslide risk analysis is often difficult because of the difficulties involved in landslide hazard assessment and estimation of consequences of future landslide events. For instance, lack of multi-temporal inventory map or records of triggering events is often a major problem in landslide hazard mapping. In this study, we propose a simple technique for converting a landslide susceptibility map into a landslide hazard map, which we have employed for landslide risk analysis in one ideally hazardous part of volcanic mountains in West Java Province. The susceptibility analysis was carried out through correlation between past landslides and eight spatial parameters related to instability, i.e. slope, aspect, relative relief, distance to river, geological units, soil type, land use and distance to road. The obtained susceptibility map was validated using cross-time technique, and was collaborated with the frequency-area statistics to respond to ‘when landslide will occur’ and ‘how large it will be’. As for the judgment of the consequences of future landslides, expert opinion was used considering available literature and characteristic of the study area. We have only considered economic loss in terms of physical damage of buildings, roads and agricultural lands for the landslide risk analysis. From this study, we understand the following: (1) the hazard map obtained from conversion of the susceptibility map gives spatial probability and the area of an expected landslide will be greater than 500m² in the next 2 years, (2) the landslide risk map shows that 24% of the total area is in high risk; 30% in moderate risk; 45% in low risk and no risk covers only 1% of the total area, and (3) the loss will be high in agricultural lands, while it will be low in the road structures and buildings.     

A comparison of the predicted vulnerability zones with the data based on hazard zones of landslide in the Kurseong hill subdivision,Darjeeling district,West Bengal,India

Kurseong hill subdivision, being one of the three (Kurseong, Sadar and Kalingpong) subdivisions of the hilly portions of the Darjeeling district, West Bengal, India, is affected by severe landslide incidence in the rainy season every year. These landslides and related phenomena frequently create social and economic instability disrupting communication system, claiming property and even sometimes life. Curbing landslide threat, therefore, becomes very much essential over this area. Individual landslide treatments are seen to be taken up by the construction engineers and geo-technical experts almost every year from government level. But reoccurrence of landslides on the same spots or surrounding places clearly reveals that construction works and filling procedures (usually taken up) are not the adequate measures to heal up the problem unless the area is treated as zones of landslides than individual spots of landslide occurrences. Therefore, the assessment of spatial probability of landslide occurrence in various magnitudes in the form of landslide vulnerability zones becomes essential. This spatial probability should also be compared with temporal probability based on the data of landslide incidence of the area for justification of match or mismatch between the inference drawn from the diagnostic criteria based assessment of the possibility level of landslide occurrence and the reality of the landslide scenario in the light of historical perspective of the area. This comparison will finally help to achieve the predicted vulnerability zones of landslide with desirable accuracy to put forward for planning decision. Moreover, such predicted vulnerability zonation can be taken as a standard estimate to use in planning purpose for the areas where historical data of landslide incidences are inadequate or unavailable. With this view in mind, the present paper takes an attempt to verify and compare landslide vulnerability zones derived from Spatial Terrain Parameter Evaluation (STPE) and Anthropogenic Criteria Identification (ACI) methods with the landslide hazard zones prepared from historical data, i.e. landslide inventory of certain length of time. Careful observation reveals that different degrees of landslide vulnerability zones significantly correspond with the similar magnitudes of the landslide hazard zones determined by past occurrence data of landslides over this hill subdivision and therefore validate the predictability procedure of landslide vulnerability zonation. The accuracy performance of the landslide vulnerability zonation model has further been verified by the occurrence dataset of landslide events through receiver operating characteristic curve analysis where area under curve evaluation showed 81.77 % correctness.     

滑坡灾害风险评价的关键理论与技术方法

滑坡灾害风险评估主要包括滑坡敏感性分析、危险性评价和风险评估3个不同层次的内容。但是,滑坡地质灾害本身的复杂性和滑坡强度的确定、滑坡发生的时空概率估算、承灾体的易损性时空概率分析等难点问题的存在,无疑阻碍了滑坡风险定量评估的推广和应用。在系统分析国内外滑坡灾害风险评估研究成果的基础上,对滑坡灾害风险评价的技术体系进行了总结,提出了不同层次滑坡灾害的研究内容和相应的评价方法;分析了实现滑坡风险有效评价涉及到的难点问题,并结合降雨和地震诱发的滑坡灾害危险性评价国内外的实践,提出了中国未来滑坡灾害风险评价研究的主要内容和技术方法。     

Landslide hazard and risk mapping at catchment scale in the Arno River basin

We present the methodologies adopted and the outcomes obtained in the analysis of landslide risk in the basin of the Arno River (Central Italy) in the framework of a project sponsored by the Basin Authority of the Arno River, started in the year 2002 and completed at the beginning of 2005. In particular, a complete set of methods and applications for the assessment of landslide susceptibility and risk are described and discussed. A new landslide inventory of the whole area was realized, using conventional (aerial-photo interpretation and field surveys) and non-conventional methods (e.g. remote sensing techniques such as DInSAR and PS-InSAR). The great majority of the mapped mass movements are rotational slides (75%), solifluctions and other shallow slow movements (17%) and flows (5%), while soil slips, and other rapid landslides, seem less frequent everywhere within the basin. The relationships between landslide characteristics and environmental factors have been assessed through statistical analysis. As expected, the results show a strong control of land cover, lithology and morphology on landslide occurrence. The landslide frequency-size distribution shows a typical scaling behaviour already underlined in other landslide inventories worldwide. The assessment of landslide hazard in terms of probability of occurrence in a given time, based for mapped landslides on direct and indirect observations of the state of activity and recurrence time, has been extended to landslide-free areas through the application of statistical methods implemented in an artificial neural network (ANN). Unique conditions units (UCU) were defined by the map overlay of landslide preparatory factors (lithology, land cover, slope gradient, slope curvature and upslope contributing area) and afterwards used to construct a series of model vectors for the training and test of the ANN. Various different ANNs were selected throughout the basin, until each UCU was assigned a degree of membership to a susceptibility and a hazard class. Model validation confirms that prediction results are very good, with an average percentage of correctly recognized mass movements of about 85%. The analysis also revealed the existence of a large number of unmapped mass movements, thus contributing to the completeness of the final inventory. Temporal hazard was estimated via the translation of state of activity in recurrence time and hence probability of occurrence. The following intersection of hazard values with vulnerability and exposure figures, obtained by reclassification of digital vector mapping at 1:10,000 scale, lead to the definition of risk values for each terrain unit for different periods of time into the future. The final results of the research are now undergoing a process of integration and implementation within land planning and risk prevention policies and practices at local and national level.     

Landslide hazard and risk assessment: a?case study from the Hlohovec–Sered’ landslide area in south-west Slovakia

Landslide hazard or susceptibility assessment is based on the selection of relevant factors which play a role on the slope instability, and it is assumed that landslides will occur at similar conditions to those in the past. The selected statistical method compares parametric maps with the landslide inventory map, and results are then extrapolated to the entire evaluated territory with a final product of landslide hazard or susceptibility map. Elements at risk are defined and analyzed in relation with landslide hazard, and their vulnerability is thus established. The landslide risk map presents risk scenarios and expected financial losses caused by landslides, and it utilizes prognoses and analyses arising from the landslide hazard map. However, especially the risk scenarios for future in a selected area have a significant importance, the literature generally consists of the landslide susceptibility assessment and papers which attempt to assess and construct the map of the landslide risk are not prevail. In the paper presented herein, landslide hazard and risk assessment using bivariate statistical analysis was applied in the landslide area between Hlohovec and Sered?? cities in the south-western Slovakia, and methodology for the risk assessment was explained in detail.     

Frequency–volume relation and prediction of rainfall-induced landslides

Rainfall-induced landslides constitute a major public concern in Hong Kong. This paper investigates two aspects of critical importance to landslide hazard and risk assessment and management: magnitude–cumulative frequency relationship for landslides, and relationship between rainfall and the occurrence of landslides. The results indicated that landslides with a failure volume of not less than 4 m³ have a cumulative frequency–size distribution with a power-law dependence on volume of failure. Analysis of rainfall/landslides showed that the 12-hour rolling rainfall is most important in predicting the number of landslides. Failure volume dependency in the relationships between rainfall and the number of landslides is also presented. However, with an increase in failure volume of landslides, the most important rainfall variable may vary from rainfall of short duration (12-h rolling rainfall) to that of relatively long duration (24-h rolling rainfall).     

基于GIS的重庆万州区滑坡灾害危险性评价

在充分调查万州区地质环境及滑坡灾害基本特征的基础上,根据资料的有效性和可获得性,选取地表高程、坡度、地层岩性、地质构造、土地利用类型、区域交通建设及河流侵蚀冲刷7个影响滑坡发生的因素作为评价指标,采用AHP法确定各个指标权重并建立滑坡灾害危险性指数模型,通过GIS系统的空间分析功能进行栅格运算,得出研究区滑坡灾害危险性分区。采用上述指标和方法将重庆市万州区的滑坡灾害划分为极高危险区、高危险区、中危险区、低危险区和极低危险区,划分结果符合该区滑坡灾害的实际情况。     

Exploiting historical rainfall and landslide data in a spatial database for the derivation of critical rainfall thresholds

Critical rainfall thresholds for landslides are powerful tools for preventing landslide hazard. The thresholds are commonly estimated empirically starting from rainfall events that triggered landslides in the past. The creation of the appropriate rainfall–landslide database is one of the main efforts in this approach. In fact, an accurate agreement between the landslide and rainfall information, in terms of location and timing, is essential in order to correctly estimate the rainfall–landslide relationships. A further issue is taking into account the average moisture conditions prior the triggering event, which reasonably may be crucial in determining the sufficient amount of precipitation. In this context, the aim of this paper is exploiting historical landslide and rainfall data in a spatial database for the derivation of critical rainfall thresholds for landslide occurrence in Sicily, southern Italy. The hourly rainfall events that caused landslides occurred in the twentieth century were specifically identified and reconstructed. A procedure was proposed to automatically convert rain guages charts recorded on paper tape into digital format and then to provide the cumulative rainfall hyetograph in digital format. This procedure is based on a segmentation followed by signal recognition techniques which allow to digitalize and to recognize the hyetograph automatically. The role of rainfall prior to the landslide events was taken into account by including in the analysis the rainfall occurred 5, 15 and 30 days before each landslide. Finally, cumulated rainfall duration thresholds for different exceedance probability levels were determined. The obtained thresholds resulted in agreement with the regional curves proposed by other authors for the same area; antecedent rainfall turned out to be particularly important in triggering landslides.     

基于多期DEM和滑坡强度的滑坡风险评估

滑坡危险性定量评估是滑坡风险评估中的关键和难点,也是当前国际风险管理研究中的热点问题.以滑坡密集分布的黑方台南塬为研究区,以32处典型滑坡为研究对象,依据多期三维数字高程模型(DEM),提出了一种基于强度的滑坡危险性定量评估技术方法.根据多期三维地形信息的解译及野外调查,编制多期滑坡分布图,计算滑坡活动的频率.利用GIS技术,利用滑坡体积与速度的乘积计算滑坡强度.将滑坡危险性定义为滑坡频率和滑坡强度的乘积,同时调查和分析了黑方台地区各类承灾体的类型、价值及其在相应滑坡强度下的易损性,在此基础上开展了单体滑坡风险评估和黑方台南塬滑坡风险区划.     

A landslide database for Nicaragua: a tool for landslide-hazard management

A digital landslide database has been created for Nicaragua to provide the scientific community and national authorities with a tool for landslide-hazard assessment, emergency management, land-use planning, development of early warning systems, and the implementation of public and private policies. The Instituto Nicaragüense de Estudios Territoriales (Nicaraguan Geosciences Institute, INETER) began to compile the database in a digital format in 2003 as part of a comprehensive geographical information system for all types of geohazards. Landslide data have been obtained from a variety of sources including newspapers, technical reports, and landslide inventory maps. Inventory maps are largely based on fieldwork and aerial-photo analyses conducted by foreign development agencies in collaboration with INETER and other Nicaraguan institutions. This paper presents the sources of landslide information, introduces the database, and presents the first analyses of the data at national and regional scales. The database currently contains spatial information for about 17,000 landslides that occurred in mountainous and volcanic terrains. Information is mainly recorded for the period 1826–2003, with a large number of events that occurred during the disastrous Hurricane Mitch in October 1998. The oldest historical event is dated at 1570, some events are recorded as prehistorical, and other events have unknown dates of occurrence. Debris flows have been the most common types of landslides, both in volcanic and nonvolcanic areas, but other types, including rockfalls and slides, have also been identified. Intense and prolonged rainfall, often associated with tropical cyclones, and seismic and volcanic activity represent the most important landslide triggers. At a regional scale, the influence of topographic (elevation, slope angle, slope aspect) and lithologic parameters on the occurrence of landslides was analyzed. The development of the database allowed us to define the state of knowledge on landslide processes in the Nicaragua and to provide a preliminary identification of areas affected by landslides.     

GIS技术在区域地震滑坡危险性预测中的应用以龙陵地震滑坡为例

地震滑坡是一种有着严重危害的次生地震灾害形式,形成机制复杂,涉及因素众多。运用G IS丰富的空间分析功能,对地震滑坡的影响因素进行研究,并进行潜在地震滑坡区的预测,是地震滑坡研究领域的一种新的发展趋势。本文在对1976年龙陵地震引发的地震滑坡分布特征研究的基础上,结合前人有关中国西南地区地震滑坡特征的研究成果,应用G IS对该区潜在地震滑坡危险区进行了预测。     

区域滑坡灾害预测预警与风险评价

区域滑坡灾害预测预警是滑坡灾害研究领域的难点和热点。过去10多年来在这方面的研究主要集中在区域降雨与地质环境的结合方面。文章总结了目前国内外滑坡灾害预测预报、预警和风险管理研究现状,认为把滑坡灾害预警预报与风险管理相结合是减灾防灾的需要,也是今后该领域研究的发展趋势。文章从区域滑坡灾害空间预测、时间预警预报的角度提出了滑坡灾害预测预报的分类和理论基础,并在此基础上,利用MapGIS软件平台进行二次开发,建立了基于WebGIS的滑坡灾害信息管理系统和实时预警发布系统。以2004年"云娜"台风期间浙江省永嘉县滑坡灾害预警预报为例,进行了滑坡危险性预测、人口易损性预测、经济易损性预测到风险预测的实例研究。     

Quantitative assessment of landslide hazard along transportation lines using historical records

In this paper, a quantitative landslide hazard model is presented for transportation lines, with an example for a road and railroad alignment, in parts of Nilgiri hills in southern India. The data required for the hazard assessment were obtained from historical records available for a 21-year period from 1987 to 2007. A total of 901 landslides from cut slopes along the railroad and road alignment were included in the inventory. The landslides were grouped into three magnitude classes based on the landslide type, volume, scar depth, and run-out distance. To calculate landslide hazard, we estimated the total number of individual landslides per kilometer of the (rail) road for different return periods, based on the relationship between past landslides (recorded in our database) and triggering events. These were multiplied by the probability that the landslides belong to a given magnitude class. This gives the hazard for a given return period expressed as the number of landslides of a given magnitude class per kilometer of (rail) road. The relationship between the total number of landslides and the return period was established using a Gumbel distribution model, and the probability of landslide magnitude was obtained from frequency–volume statistics. The results of the analysis indicate that the total number of landslides, from 1- to 50-year return period, varies from 56 to 197 along the railroad and from 14 to 82 along the road. In total, 18 hazard scenarios were generated using the three magnitude classes and six return periods (1, 3, 5, 15, 25, and 50 years). The hazard scenarios derived from the model form the basis for future direct and indirect landslide risk analysis along the transportation lines. The model was validated with landslides that occurred in the year 2009.     

Regional landslide-hazard assessment for Seattle, Washington, USA

Landslides are a widespread, frequent, and costly hazard in Seattle and the Puget Sound area of Washington State, USA. Shallow earth slides triggered by heavy rainfall are the most common type of landslide in the area; many transform into debris flows and cause significant property damage or disrupt transportation. Large rotational and translational slides, though less common, also cause serious property damage. The hundreds of landslides that occurred during the winters of 1995–96 and 1996–97 stimulated renewed interest by Puget Sound communities in identifying landslide-prone areas and taking actions to reduce future landslide losses. Informal partnerships between the U.S. Geological Survey (USGS), the City of Seattle, and private consultants are focusing on the problem of identifying and mapping areas of landslide hazard as well as characterizing temporal aspects of the hazard. We have developed GIS-based methods to map the probability of landslide occurrence as well as empirical rainfall thresholds and physically based methods to forecast times of landslide occurrence. Our methods for mapping landslide hazard zones began with field studies and physically based models to assess relative slope stability, including the effects of material properties, seasonal groundwater levels, and rainfall infiltration. We have analyzed the correlation between historic landslide occurrence and relative slope stability to map the degree of landslide hazard. The City of Seattle is using results of the USGS studies in storm preparedness planning for emergency access and response, planning for development or redevelopment of hillsides, and municipal facility planning and prioritization. Methods we have developed could be applied elsewhere to suit local needs and available data.     

Integrating spatial, temporal, and magnitude probabilities for medium-scale landslide risk analysis in Darjeeling Himalayas, India

Landslide risk assessment is based on spatially integrating landslide hazard with exposed elements-at-risk to determine their vulnerability and to express the expected direct and indirect losses. There are three components that are relevant for expressing landslide hazard: spatial, temporal, and magnitude probabilities. At a medium-scale analysis, this is often done by first deriving a landslide susceptibility map, and to determine the three types of probabilities on the basis of landslide inventories linked to particular triggering events. The determination of spatial, temporal, and magnitude probabilities depend mainly on the availability of sufficiently complete historical records of past landslides, which in general are rare in most countries (e.g., India, etc.). In this paper, we presented an approach to use available historical information on landslide inventories for landslide hazard and risk analysis on a medium scale (1:25,000) in a perennially typical data-scarce environment in Darjeeling Himalayas (India). We demonstrate how the incompleteness in the resulting landslide database influences the various components in the calculation of specific risk of elements-at-risk (e.g., buildings, population, roads, etc.). We incorporate the uncertainties involved in the risk estimation and illustrate the range of expected losses in the form of maximum and minimum loss curves. The study demonstrates that even in data-scarce environments, quantitative landslide risk assessment is a viable option, as long as the uncertainties involved are expressed.     

Landslide hazard mapping using logistic regression model in Mackenzie Valley,Canada

A logistic regression model is developed within the framework of a Geographic Information System (GIS) to map landslide hazards in a mountainous environment. A case study is conducted in the mountainous southern Mackenzie Valley, Northwest Territories, Canada. To determine the factors influencing landslides, data layers of geology, surface materials, land cover, and topography were analyzed by logistic regression analysis, and the results are used for landslide hazard mapping. In this study, bedrock, surface materials, slope, and difference between surface aspect and dip direction of the sedimentary rock were found to be the most important factors affecting landslide occurrence. The influence on landslides by interactions among geologic and geomorphic conditions is also analyzed, and used to develop a logistic regression model for landslide hazard mapping. The comparison of the results from the model including the interaction terms and the model not including the interaction terms indicate that interactions among the variables were found to be significant for predicting future landslide probability and locating high hazard areas. The results from this study demonstrate that the use of a logistic regression model within a GIS framework is useful and suitable for landslide hazard mapping in large mountainous geographic areas such as the southern Mackenzie Valley.