四川雅安市雨城区降雨诱发滑坡研究

降雨是滑坡灾害的重要诱发因素之一。通过详细的地质灾害调查及地质灾害与降雨相关关系的研究,得出降雨对雅安雨城区地质灾害诱发作用的规律并进一步定量评价了诱发因素级别。研究发现,降雨量对滑坡的诱发因素可划分为4级,1d降雨量分级临界值分别为20mm、50mm和100mm,3d降雨量分级临界值分别为100mm、150mm和240mm。这一研究成果为雅安雨城区地质灾害预警预报提供了基础。     

滑坡频度-降雨量的分形关系

降雨-滑坡关系是对降雨引发的滑坡进行预报的一个重要基础。本文根据中国浙江省1990～2003年期间有明确日期和坐标记录的1414个滑坡数据和基本覆盖浙江全部陆地区域的1257个雨量站记录的日降雨量数据,研究了滑坡频度-降雨关系。研究结果表明,降雨引发滑坡的频度与降雨量之间遵循分形的幂指数关系,并且在两个尺度的降雨量范围内具有不同的标度指数。按照幂指数关系拟合的两条滑坡累计频度-降雨关系线交点(拐点)处的降雨量(Rin)指出了引发75%左右滑坡的累计降雨阀值的上边界(以THCR表示)。对1d、4d、6d和11d这4个累计降雨时段的研究表明,引发75%左右滑坡的累计降雨阀值分别为205mm(1d)、273mm(4d)、294mm(6d)、315mm(11d)。     

江西省滑坡与降雨的关系研究

对江西全省1973 年~2002 年间1 158个降雨型滑坡,从考虑滑坡所处地层岩性条件和坡向因素的角度,分析了江西省降雨型滑坡发生的概率与降雨的关系。从坡向因素角度分析,江西省有64 %的滑坡发生于阳坡,但降雨对于不同坡向的滑坡影响近乎相同,只是阴坡所需激发雨量和有效临界雨量较阳坡稍大3 %。6 d累计降雨量、14 d有效降雨量(有效降雨系数为0.82)、3 d累计降雨量和9 d累计降雨量分别是变质岩、岩浆岩、碳酸岩和碎屑岩类地层中发育的滑坡所对应的最相关的降雨因子。各岩类地层中发育的滑坡有80 %发生于降雨当日;岩浆岩、变质岩和碎屑岩类滑坡约12 %～14 %滞后于降雨发生,碳酸岩类滑坡有22 %滞后于降雨发生。滞后时间多为1～3 d,其中碳酸岩类滑坡滞后时间相对较短。这与降雨对不同滑体物质成分的渗透性能、岩土体强度弱化程度和速度等有关。     

降雨与滑坡灾害相关性分析及预警预报阀值之探讨

详细研究了深圳市降雨与滑坡的历史资料,对区域性滑坡与降雨量进行偏相关分析,与降雨强度进行相关分析以及与降雨时间进行了系统地统计分析。研究结果表明,（1）滑坡的活动1～4日的降雨量及一次降雨过程的降雨量偏相关系数较大,表明一次性降雨量达到或超过某一数值时区域性滑坡就可以出现;（2）暴雨尤其是大暴雨及特大暴雨与滑坡的关系非常密切,相关系数达0.8以上,大暴雨或特大暴雨具有直接触发滑坡的作用;（3）滑坡活动时间与季节性降雨相对应,季节雨量越多,滑坡亦越多;另外滑坡活动时间与暴雨、大暴雨相吻合或略滞后,滞后时间一般不超过4 d,暴雨的当天及次日发生滑坡的可能性最大。在此基础上,探讨了区域性滑坡发生的临界降雨量和降雨强度阀值。最后将滑坡灾害的地质模型与降雨模型耦合建立了滑坡灾害的空间预警预报区划指标和等级系统,为区域滑坡灾害发生的时间与空间预报预警提供了科学依据。     

降雨型滑坡失稳概率计算方法

为了解决滑坡风险评价中的滑坡失稳概率计算问题,利用前人在降雨阈值的研究成果,结合气象学中降雨概率分布理论,以云南省盐津县庙坝滑坡为例进行计算,建立降雨型滑坡失稳概率计算模型。结果表明,盐津县降雨型滑坡的降雨阈值类型为累积降雨量-历时关系阈值,即为单日降雨阈值,降雨阈值为29.7 mm;盐津县在当日降雨量达到或超过阈值水平时可能诱发滑坡,对滑坡影响的滞后天数最大为5天;庙坝滑坡在8月20—25日6天内单日降雨达到或超过29.7 mm的降雨概率为46.49%;庙坝滑坡在8月25日因前5天或当天单日降雨量超过29.7 mm而失稳的概率为0.2853%。     

福建省台风暴雨条件下滑坡规律性研究

通过对福建省台风暴雨特征的详细分析,结合沿台风“莫拉克”路径对省内新发生滑坡进行的追踪调研,总结出台风暴雨条件下滑坡发生的背景环境,分析了台风暴雨各个特征要素与滑坡之间的关系：滑坡与台风中心登陆区域及登陆后的运行轨迹具有很强的一致性,台风登陆当天降雨对滑坡起决定性作用,当台风登陆当日降雨量达到100 mm或60 mm的3 h极值降雨量时,易触发“即雨即滑”群发式小型浅层土质滑坡。     

降雨引发的兰州黄土滑坡时空规律分析和临界降雨量预测

开展降雨型黄土滑坡预警对于区域性防治滑坡具有重要意义。本研究在收集1985~2015年兰州市降雨型黄土滑坡历史数据的基础上,运用反距离权重插值(IDW)和核密度估算(KDE)方法揭示了降雨引发黄土滑坡的时空分布规律。该文基于统计学的基本原理,运用相关性和偏相关性等方法建立适合兰州市的有效降雨量模型。通过拟合有效降雨量与滑坡因子的线性回归关系,确定引发黄土滑坡的临界降雨量阈值,设定兰州市黄土滑坡的降雨量危险性预警等级。研究表明:(1)兰州市黄土滑坡灾害点沿着黄河及其支流沿岸分布,城关区滑坡点最多且呈环形分布,西固区次之,其他地区分布较少;(2)降雨是兰州市及其周边地区黄土滑坡的关键诱因,10d有效降雨量与滑坡因子均呈现显著正相关特性,其相关系数达到0.698;(3)依据10mm、20mm和40mm临界降雨量阈值将预警等级划分为低、中、高3个危险性等级。     

有限元法分析降雨条件下斜坡稳定性——以邻水县马鞍坪滑坡为例

通过对华蓥市马鞍坪滑坡现有变形监测情况表明,斜坡变形位移与降雨的关系十分密切,马鞍坪滑坡的后部强变形区,斜坡位移与50mm以上持续降雨几乎呈对应关系。研究人员采用工程地质条件分析与有限元分析法相结合,对马鞍坪滑坡进行了滑坡体在降雨条件下饱和一非饱和渗流分析与坡体稳定性分析。分析结果表明,降雨量是华蓥山地区斜坡失稳下滑的主要引发因素,不同雨量决定斜坡演化成滑坡的不同阶段,其影响作用也不近相同。计算结果表明,当日降雨量达到150mm左右且累积降雨量超过300mm的情况下,斜坡土体将出现变形,出现滑坡可能性大。该分析结果为今后华蓥山地区地质灾害监测预警示范中的地质灾害单体预警预报,提供了可靠预警参数。     

浙江省滑坡灾害预警预报的降雨阀值研究

根据浙江省降雨的特点,将降雨分为台风降雨和非台风降雨,采用统计方法研究了区域性滑坡灾害与台风区和非台风区降雨量及降雨强度的相关性,通过相关性分析确定了有效降雨量模型;得到了浙江省区域性滑坡发生的临界降雨量和降雨强度阀值,为实时时间预警提供了定量依据;将滑坡灾害的空间易发性与降雨量和降雨强度相结合确定了滑坡灾害的空间预警区划指标和等级;最后初步研究了滑坡发生的滞后时间。     

四川绵竹清平乡“8.13”文家沟特大泥石流灾害

2010年8月12日15:00时至8月13日2:00时,绵竹清平乡发生强降雨,持续历时10余小时,累计降雨量达227.5mm,22时降雨量加大并持续约2h,降雨强度大于90mm/h。在强降雨的诱发下,暴雨导致文家沟滑     

Rainfall thresholds for prediction of shallow landslides around Chamoli-Joshimath region,Garhwal Himalayas,India

Majority of landslides in the Indian sub-continent are triggered by rainfall. Several attempts in the global scenario have been made to establish rainfall thresholds in terms of intensity-duration and antecedent rainfall models on global, regional and local scales for the occurrence of landslides. However, in the context of the Indian Himalayas, the rainfall thresholds for landslide occurrences are not yet understood fully. Neither on regional scale nor on local scale, establishing such rainfall thresholds for landslide occurrences in Indian Himalayas has yet been attempted. This paper presents an attempt towards deriving local rainfall thresholds for landslides based on daily rainfall data in and around Chamoli-Joshimath region of the Garhwal Himalayas, India. Around 128 landslides taken place in last 4 years from 2009 to 2012 have been studied to derive rainfall thresholds. Out of 128 landslides, however, rainfall events pertaining to 81 landslides were analysed to yield an empirical intensity–duration threshold for landslide occurrences. The rainfall threshold relationship fitted to the lower boundary of the landslide triggering rainfall events is I?=?1.82 D ^?0.23 (I?=?rainfall intensity in millimeters per hour and D?=?duration in hours). It is revealed that for rainfall events of shorter duration (≤24 h) with a rainfall intensity of 0.87 mm/h, the risk of landslide occurrence in this part of the terrain is expected to be high. Also, the role of antecedent rainfall in causing landslides was analysed by considering daily rainfall at failure and different period cumulative rainfall prior to failure considering all 128 landslides. It is observed that a minimum 10-day antecedent rainfall of 55 mm and a 20-day antecedent rainfall of 185 mm are required for the initiation of landslides in this area. These rainfall thresholds presented in this paper may be improved with the hourly rainfall data vis-à-vis landslide occurrences and also data of later years. However, these thresholds may be used in landslide warning systems for this particular region of the Garhwal Himalayas to guide the traffic and provide safety to the tourists travelling along this pilgrim route during monsoon seasons.     

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.     

Early warning system for shallow landslides using rainfall threshold and slope stability analysis

A combined cluster and regression analysis were performed for the first time to identify rainfall threshold that triggers landslide events in Amboori, Kerala, India. Amboori is a tropical area that is highly vulnerable to landslides. The 2, 3, and 5-day antecedent rainfall data versus daily rainfall was clustered to identify a cluster of critical events that could potentially trigger landslides. Further, the cluster of critical events was utilized for regression analysis to develop the threshold equations. The 5-day antecedent (x-variable) vs. daily rainfall (y-variable) provided the best fit to the data with a threshold equation of y = 80.7–0.1981x. The intercept of the equation indicates that if the 5-day antecedent rainfall is zero, the minimum daily rainfall needed to trigger the landslide in the Amboori region would be 80.7 mm. The negative coefficient of the antecedent rainfall indicates that when the cumulative antecedent rainfall increases, the amount of daily rainfall required to trigger monsoon landslide decreases. The coefficient value indicates that the contribution of the 5-day antecedent rainfall is ∼20% to the landslide trigger threshold. The slope stability analysis carried out for the area, using Probabilistic Infinite Slope Analysis Model (PISA-m), was utilized to identify the areas vulnerable to landslide in the region. The locations in the area where past landslides have occurred demonstrate lower Factors of Safety (FS) in the slope stability analysis. Thus, rainfall threshold analysis together with the FS values from slope stability can be suitable for developing a simple, cost-effective, and comprehensive early-warning system for shallow landslides in Amboori and similar regions.     

四川雅安市雨城区地质灾害预警系统研究

区域地质灾害的敏感性评价与诱发因素评价是区域群发性地质灾害预警预报的基础。文中以四川雅安市雨城区为例,系统介绍了区域地质灾害预警预报的方法。在区域地质灾害敏感性评价及区域降雨监测的基础上,研究了降雨诱发区域群发性地质灾害的规律,得出了地质灾害降雨阈值。研究了降雨诱发地质灾害预警预报的方法,建立了大中比例尺的地质灾害预警预报系统,并在四川雅安雨城区开展了系统运行。文中论述的方法可以用于县(市)级的地质灾害预警预报工作。模拟运行及实际运行效果表明,本方法效果较好,能够在类似区域进行推广。     

降雨诱发区域性滑坡预报预警方法研究

论文以四川省雅安市雨城区为研究区，将逻辑回归模型引入区域降雨型滑坡预警预报，建立了同时考虑降雨强度和降雨过程的降雨临界值表达式。在滑坡危险性区划的基础上，研究提出了区域降雨型滑坡预警预报指标，包括临界值降雨指数R和滑坡发生指数L，并利用20台自动遥测雨量计和地质灾害群测群防网络，采用历史记录雨量和预报雨量，建立了区域降雨型滑坡预报预警体系。     

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 %.     

Integrating intensity–duration-based rainfall threshold and antecedent rainfall-based probability estimate towards generating early warning for rainfall-induced landslides in parts of the Garhwal Himalaya,India

In order to generate early warning for landslides, it is necessary to address the spatial and temporal aspects of slope failure. The present study deals with the temporal dimension of slope failures taking into account the most widespread and frequent triggering factor, i.e. rainfall, along the National Highway-58 from Rishikesh to Mana in the Garhwal Himalaya, India. Using the post-processed three-hourly rainfall intensity and duration values from the Tropical Rainfall Measuring Mission-based Multi-satellite Precipitation Analysis and the time-tagged landslide records along this route, an intensity–duration (I–D)-based threshold has been derived as I?=?58.7D ^?1.12 for the rainfall-triggered landslides. The validation of the I–D threshold has shown 81.6 % accuracy for landslides which occurred in 2005 and 2006. From this result, it can be inferred that landslides in the study area can be initiated by continuous rainfall of over 12 h with about 4-mm/h intensity. Using the mean annual precipitation, a normalized intensity–duration relation of NI?=?0.0612D ^?1.17 has also been derived. In order to account for the influence of the antecedent rainfall in slope failure initiation, the daily, 3-day cumulative, and 15- and 30-day antecedent rainfall values associated with landslides had been subjected to binary logistic regression using landslide as the dichotomous dependent variable. The logistic regression retained the daily, 3-day cumulative and 30-day antecedent rainfall values as significant predictors influencing slope failure. This model has been validated through receiver operating characteristic curve analysis using a set of samples which had not been used in the model building; an accuracy of 95.1 % has been obtained. Cross-validation of I–D-based thresholding and antecedent rainfall-based probability estimation with slope failure initiation shows 81.9 % conformity between the two in correctly predicting slope stability. Using the I–D-based threshold and the antecedent rainfall-based regression model, early warning can be generated for moderate to high landslide-susceptible areas (which can be delineated using spatial integration of preconditioning factors). Temporal predictions where both the methods converge indicate higher chances of slope failures for areas predisposed to instability due to unfavourable geo-environmental and topographic parameters and qualify for enhanced slope failure warning. This method can be verified for further rainfall seasons and can also be refined progressively with finer resolutions (spatial and temporal) of rainfall intensity and multiple rain gauge stations covering a larger spatial extent.     

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.     

The determination of statistical and deterministic hydrological landslide-triggering thresholds

 Hydrological landslide-triggering thresholds separate combinations of daily and antecedent rainfall or of rainfall intensity and duration that triggered landslides from those that failed to trigger landslides. They are required for the development of landslide early warning systems. When a large data set on rainfall and landslide occurrence is available, hydrological triggering thresholds are determined in a statistical way. When the data on landslide occurrence is limited, deterministic models have to be used. For shallow landslides directly triggered by percolating rainfall, triggering thresholds can be established by means of one-dimensional hydrological models linked to the infinite slope model. In the case of relatively deep landslides located in topographic hollows and triggered by a slow accumulation of water at the soil-bedrock contact, simple correlations between landslide occurrence and rainfall can no longer be established. Therefore real-time failure probabilities have to be determined using hydrological catchment models in combination with the infinite slope model. Received: 15 October 1997 · Accepted: 25 June 1997