Assessment of shallow landslides from Hurricane Mitch in central America using a physically based model

Shallow landslides induced by heavy rainfall events represent one of the most disastrous hazards in mountainous regions because of their high frequency and rapid mobility. Recent advancements in the availability and accessibility of remote sensing data, including topography, land cover and precipitation products, allow landslide hazard assessment to be considered at larger spatial scales. A theoretical framework for a landslide forecasting system was prototyped in this study using several remotely sensed and surface parameters. The applied physical model SLope-Infiltration-Distributed Equilibrium (SLIDE) takes into account some simplified hypotheses on water infiltration and defines a direct relation between factor of safety and the rainfall depth on an infinite slope. This prototype model is applied to a case study in Honduras during Hurricane Mitch in 1998. Two study areas were selected where a high density of shallow landslides occurred, covering approximately 1,200 km². The results were quantitatively evaluated using landslide inventory data compiled by the United States Geological Survey (USGS) following Hurricane Mitch’s landfall. The agreement between the SLIDE modeling results and landslide observations demonstrates good predictive skill and suggests that this framework could serve as a potential tool for the future early landslide warning systems. Results show that within the two study areas, the values of rates of successful estimation of slope failure locations reached as high as 78 and 75%, while the error indices were 35 and 49%. Despite positive model performance, the SLIDE model is limited by several assumptions including using general parameter calibration rather than in situ tests and neglecting geologic information. Advantages and limitations of this physically based model are discussed with respect to future applications of landslide assessment and prediction over large scales.     

基于主控因子分析与GM-IAGA-WNN联合模型的平推式滑坡位移预测研究——以垮梁子滑坡为例

滑坡位移预测模型是滑坡预警系统建立的核心,而模型可靠性与精确性关键在于主控因子的选取与基础理论模型的构建。学者们通过大量滑坡实例研究,已取得了诸多成果,但是由于滑坡位移变化具有强烈的个性特征及趋势发展的不确定性问题,在多因子联合作用下的位移预测模型尚有不足之处。本文以西南地区普遍存在的平推式滑坡——垮梁子滑坡为研究对象,结合前人已有的研究成果,综合考虑坡体内外各项影响因子,利用灰色关联度与相关性分析对坡体变形主控因子进行优化筛选。以此为基础,提出一种基于GM（1,1）灰色模型与改进型自适应遗传算法（IAGA）进行优化的小波神经网络（WNN）联合预测模型构建方案。通过对垮梁子滑坡历时5年的监测数据挖掘分析,得知滑坡变形受累计降雨、渗压、地下水位及土体含水率影响显著,预测结果与实际监测比较吻合。相较于传统BP神经网络模型、小波神经网络模型和未优化遗传算法-小波神经网络联合模型,该联合模型具有更好的稳定性与精度优势,在滑坡预警预报研究中具有良好的应用前景。     

基于H-P滤波法、ARIMA和VAR模型的库区滑坡位移综合预测

受库水位涨落及降雨等影响,库区滑坡位移表现出明显的周期性。基于位移时间序列分析,将滑坡监测位移分解为趋势项与周期项之和。趋势项反映滑坡变形的长期趋势,其主要受滑坡本身地质结构等因素影响。周期项反映滑坡变形的波动性,其主要受外部因素影响。以三峡库区巫山塔坪滑坡为例,考虑长江水位与降雨量影响,采用H-P滤波法从滑坡位移中分解出趋势项及周期项,利用差分自回归滑动平均模型（ARIMA）对趋势项进行平稳处理并计算趋势项预测值,利用向量自回归模型（VAR）计算周期项预测值。趋势项预测值与周期项预测值之和为滑坡位移预测值。与实际监测值及多种方法分析比较,表明综合预测所得结果能较好反映滑坡变形的趋势性和波动性,位移预测效果较好。     

Prototyping an experimental early warning system for rainfall-induced landslides in Indonesia using satellite remote sensing and geospatial datasets

An early warning system has been developed to predict rainfall-induced shallow landslides over Java Island, Indonesia. The prototyped early warning system integrates three major components: (1) a susceptibility mapping and hotspot identification component based on a land surface geospatial database (topographical information, maps of soil properties, and local landslide inventory, etc.); (2) a satellite-based precipitation monitoring system () and a precipitation forecasting model (i.e., Weather Research Forecast); and (3) a physically based, rainfall-induced landslide prediction model SLIDE. The system utilizes the modified physical model to calculate a factor of safety that accounts for the contribution of rainfall infiltration and partial saturation to the shear strength of the soil in topographically complex terrains. In use, the land-surface “where” information will be integrated with the “when” rainfall triggers by the landslide prediction model to predict potential slope failures as a function of time and location. In this system, geomorphologic data are primarily based on 30-m Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data, digital elevation model (DEM), and 1-km soil maps. Precipitation forcing comes from both satellite-based, real-time National Aeronautics and Space Administration (NASA) Tropical Rainfall Measuring Mission (TRMM), and Weather Research Forecasting (WRF) model forecasts. The system’s prediction performance has been evaluated using a local landslide inventory, and results show that the system successfully predicted landslides in correspondence to the time of occurrence of the real landslide events. Integration of spatially distributed remote sensing precipitation products and in-situ datasets in this prototype system enables us to further develop a regional, early warning tool in the future for predicting rainfall-induced landslides in Indonesia.     

A probabilistic model for rainfall—induced shallow landslide prediction at the regional scale

This paper presents a new probabilistic physically-based computational model (called PG_TRIGRS) for the probabilistic analysis of rainfall-induced landslide hazard at a regional scale. The model is based on the deterministic approach implemented in the original TRIGRS code, developed by Baum et al. (USGS Open File Report 02–424, 2002) and Baum et al. (USGS Open File Report 08–1159, 2008). Its key innovative features are: (a) the application of Ordinary Kriging for the estimation of the spatial distributions of the first two statistical moments of the probability density functions of the relevant soil properties over the entire area, based on limited available information gathered from available information from limited site investigation campaigns, and (b) the use of Rosenblueth’s Point Estimate method as a more efficient alternative to the classical Monte Carlo method for the reliability analysis performed at the single-cell level to obtain the probability of failure associated to a given rainfall event. The application of the PG_TRIGRS code to a selected study area located in the Umbria Region for different idealized but realistic rainfall scenarios has demonstrated the computational efficiency and the accuracy of the proposed methodology, assessed by comparing predicted landslide densities with available field observations reported by the IFFI project. In particular, while the model might fail to identify all individual landslide events, its predictions are remarkably good in identifying the areas of higher landslide density.     

Analysis of the relationship of landslide occurrence with rainfall: a case study of Wudu County,China

Landslides are a significant hazard in many parts of the world and represent an important geohazard in China. Rainfall is the primary triggering agent for landslides and often used for prediction slope failures. However, the relationship between rainfall and landslide occurrences is very complex. Great efforts have been made on the study of regional rainfall-induced landslide forecasting models in recent years; still, there is no commonly accepted method for rainfall-induced landslide prediction. In this paper, the quantitative antecedent soil water status (ASWS) model is applied to investigate the influence of daily and antecedent rainfall on the triggering of landslides and debris flows. The study area is Wudu County in Gansu Province, an area which exhibits frequent landslide occurrences. The results demonstrate a significant influence of high intensity rainfall events on landslide triggering. Still, antecedent rainfall conditions are very important and once a threshold of approximately 20 mm is exceeded, landslides and debris flows can occur even without additional rainfall. The study presented could also facilitate the implementation of a regional forecasting scheme once additional validation has been carried out.     

Identification of hazard conditions for mudflow occurrence by hydrological model: Application of FLaIR model to Sarno warning system

Mathematical models for forecasting landslides and mudflow movements triggered by heavy rainfalls are useful tools to develop warning systems and hazard mitigation strategy for loss reduction.

In the present paper, an application of Forecasting of Landslides Induced by Rainfalls (FLaIR) hydrological model, correlating the rainfall amount and landslide or mudflow movement occurrences, will be performed. Model application presented here refers to the mudflows of Sarno, Southern Italy, and is based on hourly precipitation data available from a real-time rain gauge installed immediately after the catastrophic event that occurred on May 1998.

The application is extended from October 1998 to May 2002. The main objective is to perform a backanalysis in order to verify the reliability of the proposed scheme for use in a warning system.

Among the most interesting results of the application, the relatively few false alarms for populations given by the model may be highlighted.

The FLaIR model is more useful when it is integrated with a probabilistic model for forecasting precipitation depths during a storm event at an hourly scale. By stochastic modelling of hourly precipitation, it is possible to estimate the probability of reaching the alarm threshold before allowing civil protection actions.     

基于无限边坡算法的降雨型滑坡预警系统的模型试验研究

滑坡预警系统是减少降雨型滑坡灾害的重要手段,其中针对具体单个滑坡失稳的预警系统,尽管具有较好的物理判断依据,但由于其构造机制较为复杂,目前仍然比较少见。基于无限边坡算法构建了较为简单的实验室降雨型滑坡技术性预警系统,并详细介绍了该系统的预警思路、预警时间计算原理、信息交流及反馈的实现。通过模型试验结果考察该预警系统在估算不同降雨强度及不同初始含水状态下滑坡失稳时间的表现,并对模型试验结果、预警能力进行分析。结果表明,基于无限边坡算法的预警系统的预警能力尽管受到降雨侵蚀、滑动面位置、初始含水状态的影响,但仍然具有较好的准确性。     

洪水预报模型输入与输出的误差分布及相关性分析

降雨预报信息作为洪水预报模型的输入,该信息的准确性直接影响洪水预报模型输出的准确性.为探究模型输入(降雨预报)误差与输出(洪水预报)误差之间的关系,以英那河流域为例,分析了不同雨量等级下,预报模型的输入误差与输出误差的分布规律,并定性分析了两种误差的相关关系.结果表明,降雨量等级若为无雨及小雨时,两种误差不相关;若为中...     

灾难性地震和降雨滑坡的体积与运动距离研究

为预测和评价灾难性滑坡的致灾机制,基于国内近年来地震和降雨诱发的灾难性滑坡资料,对地震和降雨滑坡的等价摩擦系数H/L、最大水平运动距离L、最大垂直运动距离H与滑坡体积V的关系进行分析。研究灾难性地震和降雨滑坡的远程运动特征以及不同规模滑坡最大水平运动和垂直运动的优势距离。研究结果表明,滑坡的H/L、L、H与V具有幂律关系,其中H/L与V具有负幂律关系,L、H与V具有正幂律关系。同一规模等级的地震和降雨诱发灾难性滑坡的水平和垂直运动距离不同。以H/L＝0.42作为滑坡远程运动的标准,地震诱发的灾难性滑坡与滑坡远程运动的关系较小,而降雨诱发灾难性滑坡与滑坡的远程运动的关系较大。根据滑坡运动距离的累积分布表,以80%滑坡的运动距离所分布的范围,建立了滑坡不同规模等级的优势运动距离区间,同规模等级的地震滑坡和降雨滑坡在水平和垂直运动的优势距离区间上存在差异。灾难性滑坡的运程不仅受滑坡体积的控制还与其诱发机制相关,其研究成果可为由地震和降雨诱发的灾难性滑坡的致灾区域和致灾强度的预测和评价提供参考依据。     

Regional prediction of landslide hazard using probability analysis of intense rainfall in the Hoa Binh province, Vietnam

The main objective of this study is to assess regional landslide hazards in the Hoa Binh province of Vietnam. A landslide inventory map was constructed from various sources with data mainly for a period of 21 years from 1990 to 2010. The historic inventory of these failures shows that rainfall is the main triggering factor in this region. The probability of the occurrence of episodes of rainfall and the rainfall threshold were deduced from records of rainfall for the aforementioned period. The rainfall threshold model was generated based on daily and cumulative values of antecedent rainfall of the landslide events. The result shows that 15-day antecedent rainfall gives the best fit for the existing landslides in the inventory. The rainfall threshold model was validated using the rainfall and landslide events that occurred in 2010 that were not considered in building the threshold model. The result was used for estimating temporal probability of a landslide to occur using a Poisson probability model. Prior to this work, five landslide susceptibility maps were constructed for the study area using support vector machines, logistic regression, evidential belief functions, Bayesian-regularized neural networks, and neuro-fuzzy models. These susceptibility maps provide information on the spatial prediction probability of landslide occurrence in the area. Finally, landslide hazard maps were generated by integrating the spatial and the temporal probability of landslide. A total of 15 specific landslide hazard maps were generated considering three time periods of 1, 3, and 5 years.     

诱发池州滑坡的降水特征分析

滑坡是池州最严重的地质灾害.滑坡的形成除与地质条件有关外,降水和人类工程活动是很重要的诱发因素.通过分析大量的滑坡资料和气象(雨量)数据,研究和探讨了滑坡的发生与降水特征之间的关系,发现滑坡的发生与近3天内的降水强度、过程降水总雨量、降水的持续时间等关系十分密切.结合国内邻近省区的分析结论,建立了一个用日综合雨量预测滑坡的数学统计模型,并对池州1995、1998、1999年3次滑坡等重大突发性地质灾害过程进行了检验,效果良好.     

基于GIS和信息量模型的广西花岗岩分布区滑坡易发性评价

广西花岗岩分布区的岩土体结构松散,分布有大量较大厚度的风化坡残积土,受侵蚀、剥蚀、切割作用强烈,局部的水土流失严重,而且该区降雨量丰富,经常会有滑坡地质灾害发生。在统计分析的基础上选取坡度、高程、地质构造、植被、降雨、人类工程活动、滑坡灾害体积密度作为易发性评价指标,基于ArcGIS软件并运用信息量模型对广西花岗岩分布区进行滑坡易发性区划、完成滑坡易发性评价,并与滑坡灾害点和隐患点进行了验证。广西花岗岩区滑坡易发性评价的结果与实际滑坡的分布较为吻合,为广西花岗岩分布区滑坡预警预报及防治工作奠定基础。     

Rainfall-induced landslides in Hulu Kelang area, Malaysia

Hulu Kelang is known as one of the most landslide-prone areas in Malaysia. The area has been constantly hit by landslide hazards since 1990s. This paper provides an insight into the mechanism of rainfall-induced landslide in the Hulu Kelang area. Rainfall patterns prior to the occurrences of five selected case studies were first analyzed. The results showed that daily rainfall information is insufficient for predicting landslides in the area. Rainfalls of longer durations, i.e., 3–30 days prior to the landslides should be incorporated into the prediction model. Numerical simulations on a selected case study demonstrated that both matric suction and factor of safety decreased steadily over time until they reached the lowest values on the day of landslide occurrence. Redistribution of infiltrated rainwater in the soil mass could be a reason for the slow response of failure mechanism to rainfall. Based on 21 rainfall-induced landslides that had occurred in the area, three rainfall thresholds were developed as attempts to predict the occurrence of rainfall-induced landslide. The rainfall intensity–duration threshold developed based on the local rainfall conditions provided a reasonably good prediction to the landslide occurrence. The cumulative 3- versus 30-day antecedent precipitation index threshold chart was capable of giving the most reliable prediction with the limiting threshold line for major landslide yielded a reliability of 97.6 %.     

Evolutionary polynomial regression to alert rainfall-triggered landslide reactivation

The derivation of an alert model for landslide risk management is a paramount problem for those sites which are affected by complex landslides involving strategic infrastructures as well as towns. This is a quite common scenario all over the world and then it is a primary problem for the management of geomorphological risk. Along the Adriatic Coast of south Italy, Petacciato landslide is peculiar, since it showed 11 reactivations between 1924 and 2009. It is a deep-seated landslide, and the history of its reactivations shows that even if generally related to quite abundant rainfall periods, there is no clear correlation between rainfall events and reactivations. For this reason, here, an analysis based on a data-driven evolutionary modeling technique is attempted, in order to identify an alert model based on cumulative rainfall heights. Modeling results are quite interesting and encouraging, since they are able to provide landslide forecasting whereas no false positive are ever returned. This work shows the results of this attempt as well as an analysis of the input to the modeling approach, in order to identify which are those cumulative rainfall heights which are physically sound with respect to the particular landslide.     

Identification of the affected areas by mass movement through a physically based model of landslide hazard combined with an empirical model of debris flow

In tropical areas, mass movements are common phenomena, especially during periods of heavy rainfall, which frequently take place in the summer season. These phenomena have caused loss of life and serious damage to infrastructure and properties. The most prominent of these phenomena are landslides that can produce debris flows. Thus, this article aims at determining affected areas using a model to predict landslide prone areas (SHALSTAB) combined with an empirical model designed to define the debris flow travel distance and area of deposition. The methodology of this work consists of the following steps: (a) elaboration of a digital elevation model (DEM), (b) application of the deterministic SHALSTAB model to locate the landslide prone areas, (c) identification of the debris flow travel distance and area of deposition, and (d) mapping of the affected areas (landslides and debris flows). This work was developed in an area in which many mass movements occurred after intense rainfall during the summer season (February 1996) in the state of Rio de Janeiro, southeast Brazil. All of the scars produced by that event were mapped, allowing for validation of the applied models. The model results show that the mapped landslide locations can adequately be simulated by the model.     

基于位移比模型的三峡库区云阳县域内蠕变型滑坡降雨预警

建立高效合理的区域滑坡灾害降雨预警模型对滑坡防治具有重要意义.然而以往的研究多侧重于临滑预警,对蠕变型滑坡在强降雨工况下的短暂加速变形的预警研究还有待深入.以三峡库区云阳县域内滑坡为例,首先根据滑坡地表位移监测数据的特点对统计样本进行合理筛选.再通过降雨因子与滑坡发生的相关性分析以及对滑坡在降雨条件下位移变化情况的数值模拟,确定了适用于不同时间阶段的降雨统计变量.然后将考虑了滑坡规模特征的滑坡位移比（累计位移与滑坡纵长之比）作为变形指标,分时段统计滑坡地表位移监测数据与历史降雨信息,建立了日降雨数据与月位移数据的对应关系,得到了可用于确定降雨量阈值的位移比模型,并获得了云阳县蠕变型滑坡的五级预警分区.最后分别选用研究区滑坡险情实例、长年位移监测数据及极端降雨事件对模型预警效果进行检验.结果显示基于专业监测数据的位移比模型的滑坡降雨预警结果与实际情况相符,可为蠕变型滑坡的预警预报提供依据.      

Landslides in the North of Lisbon Region (Portugal): Conditioning and triggering factors

Detailed geomorphological mapping carried out in 5 sample areas in the North of Lisbon Region allowed us to collect a set of geological and geomorphological data and to correlate them with the spatial occurrence of landslide. A total of 597 slope movements were identified in a total area of 61.7 km², which represents about 10 landslides per km².The main landslide conditioning factors are: lithology and geological structure, slope angle and slope morphology, land use, presence of old landslides, and human activity.The highest landslide density occurs in Cretaceous marls and marly limestones, but the largest movements are in Jurassic clays, marls and limestones.The landslide density is higher on slopes with gradients above 20 °, but the largest unstable area is found on slopes of 10 ° to 15 °, thus reflecting the presence of the biggest slope movements. There is a correlation between landslides and topographical concavities, a fact that can be interpreted as reflecting the significance of the hydrological regime in slope instability.Concerning land use, the highest density of landslides is found on slopes covered with shrub and undergrowth vegetation.About 26% of the total number of landslides are reactivation events. The presence of old landslides is particularly important in the occurrence of translational slides and complex and composite slope movements.20% of the landslide events were conditioned by anthropomorphic activity. Human's intervention manifests itself in ill-consolidated fills, cuts in potentially unstable slopes and, in a few cases, in the changing of river channels.Most slope movements in the study area exhibit a clear climatic signal. The analysis of rainfall distribution in periods of recognised slope instability allows the distinction of three situations: 1) moderate intensity rainfall episodes, responsible for minor slope movements on the bank of rivers and shallow translational slides, particularly in artificial trenches; 2) high intensity rainfall episodes, originating flash floods and most landslides triggered by bank erosion; 3) long-lasting rainfall periods, responsible for the rise of the groundwater table and triggering of landslides with deeper slip surfaces.     

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.     

降雨引起的边坡位移研究

边坡和滑坡的变形是非常复杂的问题,同时也是滑坡灾害预报与预警的一个重要指标。由降雨引起的边坡位移与边坡体中非饱和土体基质吸力变化产生的应变有关。依据实验数据,笔者对非饱和土中基质吸力变化产生的应变进行了研究。建立了非饱和土应变与含水量的变化关系。在此基础上,对降雨引起的边坡位移机制、规律进行了探讨。并对武都古滑坡蠕滑位移进行了分析。