Approaches for defining thresholds and return periods for rainfall‐triggered shallow landslides

Probabilistic thresholds for triggering shallow landslides by rainfall are developed using two approaches: a logistic regression model and Iverson's physically based model. Both approaches are applied to a 180 km² area in northern Italy. For the physically based model a Monte Carlo approach is used to obtain probabilities of slope failure associated with differing combinations of rainfall intensity and duration as well as differing topographic settings. For the logistic regression model hourly and daily rainfall data and split‐sample testing are used to explore the effect of antecedent rainfall on triggering thresholds. It is demonstrated that both the statistical and physically based models provide stochastic thresholds that express the probability of landslide triggering. The resulting thresholds are comparable, even though the two approaches are conceptually different. The physically based model also provides an estimate of the percentage of potentially unstable areas in which failure can be triggered with a certain probability. The return period of rainfall responsible for landslide triggering is studied by using a Gumbel scaling model of rainfall intensity–duration–frequency curves. It is demonstrated that antecedent rainfall must be taken into account in landslide forecasting, and a method is proposed to correct the rainfall return period by filtering the rainfall maxima with a fixed threshold of antecedent rainfall. This correction produces an increase of the return periods, especially for rainstorms of short duration. Copyright © 2009 John Wiley & Sons, Ltd.     

Testing a model for predicting the timing and location of shallow landslide initiation in soil‐mantled landscapes

The growing availability of digital topographic data and the increased reliability of precipitation forecasts invite modelling efforts to predict the timing and location of shallow landslides in hilly and mountainous areas in order to reduce risk to an ever‐expanding human population. Here, we exploit a rare data set to develop and test such a model. In a 1·7 km² catchment a near‐annual aerial photographic coverage records just three single storm events over a 45 year period that produced multiple landslides. Such data enable us to test model performance by running the entire rainfall time series and determine whether just those three storms are correctly detected. To do this, we link a dynamic and spatially distributed shallow subsurface runoff model (similar to TOPMODEL) to an in?nite slope model to predict the spatial distribution of shallow landsliding. The spatial distribution of soil depth, a strong control on local landsliding, is predicted from a process‐based model. Because of its common availability, daily rainfall data were used to drive the model. Topographic data were derived from digitized 1 : 24 000 US Geological Survey contour maps. Analysis of the landslides shows that 97 occurred in 1955, 37 in 1982 and ?ve in 1998, although the heaviest rainfall was in 1982. Furthermore, intensity–duration analysis of available daily and hourly rainfall from the closest raingauges does not discriminate those three storms from others that did not generate failures. We explore the question of whether a mechanistic modelling approach is better able to identify landslide‐producing storms. Landslide and soil production parameters were ?xed from studies elsewhere. Four hydrologic parameters characterizing the saturated hydraulic conductivity of the soil and underlying bedrock and its decline with depth were ?rst calibrated on the 1955 landslide record. Success was characterized as the most number of actual landslides predicted with the least amount of total area predicted to be unstable. Because landslide area was consistently overpredicted, a threshold catchment area of predicted slope instability was used to de?ne whether a rainstorm was a signi?cant landslide producer. Many combinations of the four hydrological parameters performed equally well for the 1955 event, but only one combination successfully identi?ed the 1982 storm as the only landslide‐producing storm during the period 1980–86. Application of this parameter combination to the entire 45 year record successfully identi?ed the three events, but also predicted that two other landslide‐producing events should have occurred. This performance is signi?cantly better than the empirical intensity–duration threshold approach, but requires considerable calibration effort. Overprediction of instability, both for storms that produced landslides and for non‐producing storms, appears to arise from at least four causes: (1) coarse rainfall data time scale and inability to document short rainfall bursts and predict pressure wave response; (2) absence of local rainfall data; (3) legacy effect of previous landslides; and (4) inaccurate topographic and soil property data. Greater resolution of spatial and rainfall data, as well as topographic data, coupled with systematic documentation of landslides to create time series to test models, should lead to signi?cant improvements in shallow landslides forecasting. Copyright © 2003 John Wiley & Sons, Ltd.     

Modelling catchment‐scale shallow landslide occurrence and sediment yield as a function of rainfall return period

A model‐based method is proposed for improving upon existing threshold relationships which define the rainfall conditions for triggering shallow landslides but do not allow the magnitude of landsliding (i.e. the number of landslides) to be determined. The SHETRAN catchment‐scale shallow landslide model is used to quantify the magnitude of landsliding as a function of rainfall return period, for focus sites of 180 and 45 km² in the Italian Southern Alps and the central Spanish Pyrenees. Rainfall events with intensities of different return period are generated for a range of durations (1‐day to 5‐day) and applied to the model to give the number of landslides triggered and the resulting sediment yield for each event. For a given event duration, simulated numbers of landslides become progressively less sensitive to return period as return period increases. Similarly, for an event of given return period, landslide magnitude becomes less sensitive to event duration as duration increases. The temporal distribution of rainfall within an event is shown to have a significant impact on the number of landslides and the timing of their occurrence. The contribution of shallow landsliding to catchment sediment yield is similarly quantified as a function of the rainfall characteristics. Rainfall intensity–duration curves are presented which define different levels of landsliding magnitude and which advance our predictive capability beyond, but are generally consistent with, published threshold curves. The magnitude curves are relevant to the development of guidelines for landslide hazard assessment and forecasting. Copyright © 2011 John Wiley & Sons, Ltd.     

A new method for spatio-temporal prediction of rainfall-induced landslide

1 Introduction The landslides influences on the human society have become an environment difficult problem not able to be neglected, and according to the priority of harms, harms of landslides are only smaller than those from earthquakes in all sorts of natural hazards[1]. Landslide is part of rock mass, soil mass or their compound mass slides downward along a certain slid- ing surface under the actions of inner and external dy- namics, and it is one severe instability phenomenon of rock and s…     

不同含水率下滑坡滑带土动力特性试验研究

甘肃舟曲泄流坡滑坡地处活跃断层破裂带内,断层活动控制着该滑坡的发育和运动。为了研究该滑坡滑带土的动力特性,采用重塑滑带土样,在固结不排水条件下,利用分级循环加载法开展动三轴试验,重点探讨含水率的变化对滑带土动力特性的影响规律。试验结果表明:含水率一定时,泄流坡滑坡滑带土的动弹性模量随动应变的增大呈指数形式减小;动应变一定时,动弹性模量随含水率的增大而不断减小,且衰减速率随含水率的增大而增大;含水率并不影响动弹性模量-动应变关系曲线的形态,不同含水率下该关系曲线可以进行归一化。滑带土阻尼比随含水率的增大而增大,阻尼比-动应变关系曲线也具有归一化特征。不同含水率下泄流坡滑带土动应力-应变本构关系可以用双曲线模型进行描述。     

A stochastic model describing the impact of daily rainfall depth distribution on the soil water balance

Simplified, vertically-averaged soil moisture models have been widely used to describe and study eco-hydrological processes in water-limited ecosystems. The principal aim of these models is to understand how the main physical and biological processes linking soil, vegetation, and climate impact on the statistical properties of soil moisture. A key component of these models is the stochastic nature of daily rainfall, which is mathematically described as a compound Poisson process with daily rainfall amounts drawn from an exponential distribution. Since measurements show that the exponential distribution is often not the best candidate to fit daily rainfall, we compare the soil moisture probability density functions obtained from a soil water balance model with daily rainfall depths assumed to be distributed as exponential, mixed-exponential, and gamma. This model with different daily rainfall distributions is applied to a catchment in New South Wales, Australia, in order to show that the estimation of the seasonal statistics of soil moisture might be improved when using the distribution that better fits daily rainfall data. This study also shows that the choice of the daily rainfall distributions might considerably affect the estimation of vegetation water-stress, leakage and runoff occurrence, and the whole water balance.     

Investigating the impact of the Chi‐Chi earthquake on the occurrence of debris flows using artificial neural networks

Debris flows have caused enormous losses of property and human life in Taiwan during the last two decades. An efficient and reliable method for predicting the occurrence of debris flows is required. The major goal of this study is to explore the impact of the Chi‐Chi earthquake on the occurrence of debris flows by applying the artificial neural network (ANN) that takes both hydrological and geomorphologic influences into account. The Chen‐Yu‐Lan River watershed, which is located in central Taiwan, is chosen for evaluating the critical rainfall triggering debris flows. A total of 1151 data sets were collected for calibrating model parameters with two training strategies. Significant differences before and after the earthquake have been found: (1) The size of landslide area is proportioned to the occurrence of debris flows; (2) the amount of critical rainfall required for triggering debris flows has reduced significantly, about half of the original critical rainfall in the study case; and (3) the frequency of the occurrence of debris flows is largely increased. The overall accuracy of model prediction in testing phase has reached 96·5%; moreover, the accuracy of occurrence prediction is largely increased from 24 to 80% as the network trained with data from before the Chi‐Chi earthquake sets and with data from the lumped before and after the earthquake sets. The results demonstrated that the ANN is capable of learning the complex mechanism of debris flows and producing satisfactory predictions. Copyright © 2009 John Wiley & Sons, Ltd.     

Prediction of landslide occurrence based on slope-instability analysis and hydrological model simulation

Hillslope failure usually occurs as soil resistance deteriorates in the presence of the acting stress developed by a rising groundwater level during rainstorms. The present study adopted a slope-instability analysis and a hydrological model for landslide prediction during heavy rainstorms. Variation of the groundwater table on hillslope was simulated by using the hydrological model and then the temporal groundwater level at each grid was substituted into the slope-instability analysis to determine the instability of the grids in watersheds for prediction of massive landslides.Hydrological records from two landslide-prone areas in northern Taiwan were collected. Digital elevation model was adopted to obtain the geomorphologic factors required for the slope-instability analysis and the hydrological model. The spatial distribution of soil thickness required for performing the infinite slope model was estimated by using a wetness index. Results showed that the temporal variation of the percentage of unstable grids in the study watersheds basically followed the variation of rainfall hyetographs. The percentage of the unstable grids reached a maximum value when the centroid of the hyetograph passed. A comparison between the landslide records and the model analytical results revealed that a massive landslide might occur if more than 50% of the grids in the subwatershed were classified as unstable in the study areas. The predicted time and location of landslide occurrence were consistent with those obtained from field investigations. It is therefore considered promising to apply the developed analytical method for landslide warning to alleviate the loss of lives and property.     

Point process-based modeling of multiple debris flow landslides using INLA: an application to the 2009 Messina disaster

We develop a stochastic modeling approach based on spatial point processes of log-Gaussian Cox type for a collection of around 5000 landslide events provoked by a precipitation trigger in Sicily, Italy. Through the embedding into a hierarchical Bayesian estimation framework, we can use the integrated nested Laplace approximation methodology to make inference and obtain the posterior estimates of spatially distributed covariate and random effects. Several mapping units are useful to partition a given study area in landslide prediction studies. These units hierarchically subdivide the geographic space from the highest grid-based resolution to the stronger morphodynamic-oriented slope units. Here we integrate both mapping units into a single hierarchical model, by treating the landslide triggering locations as a random point pattern. This approach diverges fundamentally from the unanimously used presence–absence structure for areal units since we focus on modeling the expected landslide count jointly within the two mapping units. Predicting this landslide intensity provides more detailed and complete information as compared to the classically used susceptibility mapping approach based on relative probabilities. To illustrate the model’s versatility, we compute absolute probability maps of landslide occurrences and check their predictive power over space. While the landslide community typically produces spatial predictive models for landslides only in the sense that covariates are spatially distributed, no actual spatial dependence has been explicitly integrated so far. Our novel approach features a spatial latent effect defined at the slope unit level, allowing us to assess the spatial influence that remains unexplained by the covariates in the model. For rainfall-induced landslides in regions where the raingauge network is not sufficient to capture the spatial distribution of the triggering precipitation event, this latent effect provides valuable imaging support on the unobserved rainfall pattern.     

基于降雨入渗的Newmark模型改进及地震滑坡危险性预测研究

滑坡是一种破坏性非常强的地质灾害,其中地震与降雨均为诱导滑坡发生的关键因素。从降雨期间发生地震的角度考虑,基于Green-Ampt降雨入渗模型对Newmark模型进行改进,推导两因素耦合作用下的边坡安全系数F_S。以云南省鲁甸县某一区域为例,分别开展无降雨、降雨无积水与降雨积水三种情况下的地震滑坡危险性预测及坡度与入渗深度因子对位移影响分析。通过比较上述三种情况,得到研究区域内的Newmark累积位移分布及危险性区划。结果表明：与未降雨情况相比,后两种情况下地震滑坡高危险程度区域面积占比计算区域随着降雨时间的增加从1%分别提高至9%、12%,滑坡低危险程度区域面积从51%分别降低至35%、33%;坡度值与入渗深度值越大,滑坡位移越大,危险性越高。Newmark改进模型充分考虑了降雨对地震滑坡产生的促进作用,能更好地反映出研究区每个场点相对的滑坡危险性,对滑坡危险性预测具有一定指导意义。     

Spatio-temporal analysis and simulation on shallow rainfall-induced landslides in China using landslide susceptibility dynamics and rainfall I-D thresholds

An empirical simulation method to simulate the possible position of shallow rainfall-induced landslides in China has been developed. This study shows that such a simulation may be operated in real-time to highlight those areas that are highly prone to rainfall-induced landslides on the basis of the landslide susceptibility index and the rainfall intensity-duration (I-D) thresholds. First, the study on landslide susceptibility in China is introduced. The entire territory has been classified into five categories, among which high-susceptibility regions (Zone 4- ‘High’ and 5-‘Very high’) account for 4.15% of the total extension of China. Second, rainfall is considered as an external triggering factor that may induce landslide initiation. Real-time satellite-based TMPA 3B42 products may provide real rainfall spatial and temporal patterns, which may be used to derive rainfall duration time and intensity. By using a historical record of 60 significant past landslides, the rainfall I-D equation has been calibrated. The rainfall duration time that may trigger a landslide has resulted between 3 hours and 45 hours. The combination of these two aspects can be exploited to simulate the spatiotemporal distribution of rainfall-induced landslide hazards when rainfall events exceed the rainfall I-D thresholds, where the susceptibility category is ‘high’ or ‘very high’. This study shows a useful tool to be part of a systematic landslide simulation methodology, potentially providing useful information for a theoretical basis and practical guide for landslide prediction and mitigation throughout China.     

Rainfall characteristics for shallow landsliding in Seattle,Washington, USA

Shallow landsliding in the Seattle, Washington, area, has caused the occasional loss of human life and millions of dollars in damage to property. The effective management of the hazard requires an understanding of the rainfall conditions that result in landslides. We present an empirical approach to quantify the antecedent moisture conditions and rainstorm intensity and duration that have triggered shallow landsliding using 25 years of hourly rainfall data and a complementary record of landslide occurrence. Our approach combines a simple water balance to estimate the antecedent moisture conditions of hillslope materials and a rainfall intensity–duration threshold to identify periods when shallow landsliding can be expected. The water balance is calibrated with field‐monitoring data and combined with the rainfall intensity–duration threshold using a decision tree. Results are cast in terms of a hypothetical landslide warning system. Two widespread landslide events are correctly identified by the warning scheme; however, it is less accurate for more isolated landsliding. Copyright © 2005 John Wiley & Sons, Ltd.     

Establishing a threshold for rainfall-induced landslides by a kinetic energy–duration relationship

Many investigators have attempted to define the threshold of landslide failure, that is, the level of the selected climatic variable above which a rainfall-induced landslide occurs. Intensity–duration (I–d) relationships are the most common type of empirical thresholds proposed in the literature for predicting landslide occurrence induced by rainfall. Recent studies propose the use of the kinetic power per unit volume of rainfall (J m⁻² mm⁻¹) to quantify the threshold of landslides induced by rainfall. In this paper, the relationship between rainfall duration and kinetic power corresponding to landslides triggered by rain was used to propose a new approach to define the threshold for predicting landslide occurrence. In particular, for the first time, a kinetic power per unit volume of rainfall–duration relationship is proposed for defining the minimum threshold needed for landslide failure. This new method can be applied using commonly used relationship for estimating the kinetic power per unit volume of rainfall and a new equation based on the measured raindrop size distribution. The applicability of this last method was tested using the data of rainfall intensity, duration and median volume diameter for 51 landslides in Taiwan. For the 51 landslides, the comparison between the measured pairs' kinetic power–duration and all selected relationships demonstrated that the equation based on the measured raindrop size distribution is the best method to define the landslide occurrence threshold, as it is both a process-oriented approach and is characterized by the best statistical performance. This last method has also the advantage to allow the forecasting of landslide hazard before the end of the rainfall event, since the rainfall kinetic power threshold value can be exceeded for a time interval less than the event duration.     

Conceptual examination of climate–soil controls upon rainfall partitioning in an open-fractured soil II: Response to a population of storms

A simple two-domain bucket model of fractured soil was coupled with a stochastic model of rainfall variability, in order to investigate the climate and soil controls upon the stochastic properties of the triggering of fracture flow and surface runoff, and the partitioning of rainfall between the matrix and fracture domains and surface runoff. Conventionally, soils are regarded as time domain filters between rainfall and hydrological response. This investigation highlights an additional type of threshold filtering especially important in understanding the infiltration behaviour of fractured soils, for which an event-based characterisation of rainfall in modelling is crucial. A priori-definable indices were derived which are capable of describing elements of this threshold filtering, by allowing the statistical properties of fracture flow- and surface runoff-triggering storms (i.e., mean and variance of storm duration, intensity and effective inter-storm period, as well as cumulative partitioning of rainfall), to be inferred directly from average storm and soil properties. Using these indices, the long-term response of fractured soils, including the long-term hydrological importance of fractures, can be estimated without simulation.     

Analysing the Relationship Between Typhoon‐Triggered Landslides and Critical Rainfall Conditions

Many landslides are triggered by rainfall. Previous studies of the relationship between landslides and rainfall have concentrated on deriving minimum rainfall thresholds that are likely to trigger landslides. Though useful, these minimum thresholds derived from a log–log plot do not offer any measure of confidence in a landslide monitoring or warning system. This study presents a new and innovative method for incorporating rainfall into landslide modelling and prediction. The method involves three steps: compiling radar reflectivity data in a QPESUMS (quantitative precipitation estimation and segregation using multiple sensors) system during a typhoon (tropical hurricane) event, estimating rainfall from radar data and using rainfall intensity and rainfall duration as explanatory variables to develop a landslide logit model. Given the logit model, this paper discusses ways in which the model can be used for computing probabilities of landslide occurrence for a real‐time monitoring system or a warning system, and for delineating and mapping landslides. Copyright © 2007 John Wiley & Sons, Ltd.     

降雨和地震耦合作用对滑坡稳定性的影响——以甘肃西和Ⅲ号滑坡为例

以甘肃省西和县西山Ⅲ号滑坡为例分析了地震与降雨耦合作用对滑坡稳定性的影响。采用GEOSTUDIO软件对其进行了天然及地震降雨耦合作用两种条件下的数值模拟。通过计算结果对比可知,西山Ⅲ号滑坡在天然状态下处于稳定状态;地震降雨耦合作用对西山Ⅲ号滑坡的稳定会起到很强的削弱作用,滑坡将处于失稳状态。在此处采用的计算条件下,相同降雨量下地震与不同降雨强度的耦合作用显示,降雨强度越小雨水入渗相对越多,地震作用下超孔隙水压力影响区域越大,滑坡越不稳定。     

analyses on the triggering facrors of large quantities of landslides in the upper reaches of the minjiang river,sichuan province

Over the past geological and historical period, tens of thousands of landslides occurred in the upper reaches of the Minjiang River, an area which is characterized by alpine valleys and has been densely populated over the past several hundreds of years. Discussing the triggering factor of these landslides is of great significance to geological hazard mitigation and prevention in this region. In this paper, we focus on four aspects of regional rainfall, shape features of landslide slopes, the corresponding relationship between landslide area and earthquake magnitude, and the recurring features of the reconstructed palaeoearthquake record at Diexi. Compared with those in Nepal, both mean seasonal rainfall accumulation and mean daily rainfall for the past 30 years are too low to reach the threshold values triggering landslides in the upper reaches of the Minjiang River. Secondly, landslides in the study area are usually absent of inner gorges(canyon topography)on the hillslope toes, which are confirmed in previous studies as typical features of landslides triggered by storms. Thirdly, wide distribution of the landslides in the study area supports our notion of earthquake-triggering because the landslides triggered by storms commonly distribute locally. Fourthly, periodicity analysis of the reconstructed palaeoearthquake record at Diexi provides a few cycles of twenty to thirty years, possibly corresponding to the earthquakes of magnitudes>5.0 or 5.5 which are believed to have caused soft-sediment deformation in the study area. In contrast, like the 2008 M_S8.0 Wenchuan earthquake, the average recurrence interval of the large earthquakes in the study area is 2.6ka. They caused tens of thousands of landslides and provided more coarse silt particles for the nearby lake sediments at least in 330 years for each time. This is consistent with exponential increase of earthquake magnitude from large to medium and of the landslide area with the increased earthquake magnitude. To sum up, we suggest that tens of thousands of landslides in the upper reaches of the Minjiang River were most likely triggered by earthquakes instead of storms. This preliminary viewpoint needs further examination in the future.     

Extreme learning machine for the displacement prediction of landslide under rainfall and reservoir level

Landslide prediction is always the emphasis of landslide research. Using global positioning system GPS technologies to monitor the superficial displacements of landslide is a very useful and direct method in landslide evolution analysis. In this paper, an EEMD–ELM model [ensemble empirical mode decomposition (EEMD) based extreme learning machine (ELM) ensemble learning paradigm] is proposed to analysis the monitoring data for landslide displacement prediction. The rainfall data and reservoir level fluctuation data are also integrated into the study. The rainfall series, reservoir level fluctuation series and landslide accumulative displacement series are all decomposed into the residual series and a limited number of intrinsic mode functions with different frequencies from high to low using EEMD technique. A novel neural network technique, ELM, is employed to study the interactions of these sub-series at different frequency affecting landslide occurrence. Each sub-series extracted from accumulative displacement of landslide is forecasted respectively by establishing appropriate ELM model. The final prediction result is obtained by summing up the calculated predictive displacement value of each sub. The EEMD–ELM model shows the best accuracy comparing with basic artificial neural network models through forecasting the displacement of Baishuihe landslide in the Three Gorges reservoir area of China.     

Applying Probability Determination to Refine Landslide-triggering Rainfall Thresholds Using an Empirical “Antecedent Daily Rainfall Model”

—Rainfall-triggered landslides constitute a serious hazard and an important geomorphic process in many parts of the world. Attempts have been made at various scales in a number of countries to investigate triggering conditions in order to identify patterns in behaviour and, ultimately, to define or calculate landslide-triggering rainfall thresholds. This study was carried out in three landslide-prone regions in the North Island of New Zealand. Regional landslide-triggering rainfall thresholds were calculated using an empirical “Antecedent Daily Rainfall Model.” In this model, first introduced by, triggering rainfall conditions are represented by a combination of rainfall occurring in a period before the event (antecedent rainfall) and rainfall on the day of the event. A physically-based decay coefficient is derived for each region from the recessional behaviour of storm hydrographs and is used to produce an index for antecedent rainfall. Statistical techniques are employed to obtain the thresholds which best separate the rainfall conditions associated with landslide occurrence from those of non-occurrence or a given probability of occurrence.The resultant regional models are able to represent the probability of occurrence of landsliding events on the basis of rainfall conditions. The calculated thresholds show regional differences in susceptibility of a given landscape to landslide-triggering rainfall. These differences relate to both the landslide database and the difference of existing physical conditions between the regions.     

Controls of preferential orientation of earthquake- and rainfall-triggered landslides in Taiwan's orogenic mountain belt

Landsliding usually occurs on specific hillslope aspect, which may reflect the control of specific geo-environmental factors, triggering factors, or their interaction. To explore this notion, this study used island-wide landslide inventories of the Chi-Chi earthquake in 1999 (M_W = 7.6) and Typhoon Morakot in 2009 in Taiwan to investigate the preferential orientation of landslides and the controls of landslide triggers and geological settings. The results showed two patterns. The orientations of earthquake-triggered landslides were toward the aspect facing away from the epicenter in areas with peak ground acceleration (PGA) ≥ 0.6 g and landslide ratio ≥ 1%, suggesting that the orientations were controlled by seismic wave propagation. Rainfall-triggered landslides tended to occur on dip slopes, instead of the windward slopes, suggesting that geological settings were a more effective control of the mass wasting processes on hillslope scale than the rainfall condition. This study highlights the importance of the endogenic processes, namely seismic wave and geological settings, on the predesigned orientation of landslides triggered by either earthquake or rainfall, which can in turn improve our knowledge of landscape evolution and landslide prediction. © 2019 John Wiley & Sons, Ltd.