基于有效降雨强度的滑坡灾害危险性预警

选取湖北省恩施地区1 000 km2区域作为典型研究区, 在全面分析该区域历史滑坡资料的基础上, 根据该区滑坡生成与地层岩性之间的关系, 将研究区地层划分为高、中、低3类易发性岩组.分岩组统计降雨监测数据与历史滑坡信息, 得出有效降雨强度与关键降雨持续时间的散点图, 由此确定不同滑坡发生概率的有效降雨强度阈值, 提出该区的滑坡灾害危险性预警判别模型.基于样本区统计数据建立滑坡预测指标体系, 运用GIS得出研究区域的滑坡空间易发性区划结果, 并根据不同易发岩组-有效降雨强度模型, 叠加滑坡灾害易发性分区结果与降雨危险性预警等级分级结果, 对研究区的滑坡灾害危险性进行了预测预警.结果表明: 不同易发岩组-有效降雨强度模型所得预警结果与实际情况吻合, 预警模型具有考虑全面和预警精度高的特点, 在实际预警中切实可用.      

基于I-D-R阈值模型的滑坡气象预警

建立滑坡灾害多维度气象预警判据和划分“网格化”预警单元能够为滑坡灾害气象预警提供科学依据.本文以浙江省金华市磐安县205个降雨型滑坡为研究对象,首先,基于平均有效降雨强度-降雨历时（I-D）阈值模型,采用普通最小二乘回归（OLSQ）和分位数回归（QR）划分临界阈值曲线;其次,引入当日降雨量（R）,进一步优化I-D阈值模型,建立I-D-R阈值模型,采用不同参数估计方法对比不同阈值模型精度,选择最优阈值模型作为磐安县滑坡灾害气象预警判据;最后,基于降雨分布的差异性,在划分地形单元的基础上利用泰森多边形（VD）建立了乡镇级别的“网格化”预警单元.结果显示：（1）I-D-R阈值模型相比于I-D阈值模型具有更好的预警精度,且基于QR的I-D-R阈值模型效果更好,警告及以上阈值等级精度提升到50%,特别注意及以上阈值等级精度提升到88.9%;(2)采用基于QR的I-D-R降雨阈值作为磐安县51个预警单元四级气象预警（红、橙、黄、蓝）的判据,并提出相应的应急响应措施.研究成果提供了一种新的阈值模型,能够为磐安县区域气象预警提供借鉴与参考.     

基于易发性分区的区域滑坡降雨预警阈值确定——以云南龙陵县为例

滑坡所处不同易发等级的区域,降雨预警阈值差别较大。为提高滑坡降雨预警的针对性和准确率,文章以野外地质调查和滑坡易发条件分析为基础,结合信息量模型和层次分析法开展滑坡易发性评价,再通过滑坡发生概率与前期累计降雨量的相关性分析,分区进行滑坡降雨预警阈值模型研究。结果表明：坡度、高程、距断层距离、工程地质岩组、水系是龙陵县滑坡的主要孕灾地质条件;龙陵县滑坡非易发区面积为14.33 km²,低易发区面积为1 053.87 km²,中易发区面积为1 471.65 km²,高易发区面积为254.73 km²;确定单日和前3日为降雨预警时间,分区分时细化了降雨预警阈值模型;对比降雨预警阈值模型应用于龙陵县滑坡监测预警中的前后,预警信息减少了70条,预警准确率提高了14.4%,并实现了镇安镇户帕村施家寨组滑坡的有效预警。文章为区域滑坡降雨预警阈值确定提供了一种较好的参考方法。     

降雨诱发滑坡阶跃型变形的预测分析及应用

滑坡进入蠕动变形阶段之后,往往难以及时开展勘察治理工作,合理的临灾预警是有效减少滑坡灾害损失的重要手段。首先确定降雨诱发“阶跃型”滑坡的预警关键判据为前期降雨、当次降雨、位移速率,并引入“一个降雨过程”定义滑坡监测的降雨区间,将预警过程分为当次降雨和前期降雨+当次降雨两种模式。然后运用最小二乘法确定滑坡“阶跃”变形曲线上的“破坏拐点”和“稳定拐点”用以确定变形加速区间,以此求解前期降雨、当次降雨以及移速率阈值。最后以王家坡滑坡为例,设计了两种模式下的5级递进式分级预警模型。研究表明：（1）前期降雨与当次降雨组成“一个降雨过程”的时间间隔为7 d;（2）王家坡滑坡的位移速率阈值为20 mm/d;（3）前期降雨+当次降雨模式下王家坡滑坡的前期降雨、当次降雨阈值分别为10、15 mm,当次降雨模式下王家坡滑坡的降雨阈值为25 mm。     

基于有效降雨强度和逻辑回归的降雨型 滑坡预测模型

以湖南省张家界市桑植县为研究区,在全面分析近30年降雨及滑坡数据的基础上,对滑坡及滑坡数量与降雨因子的关系开展了统计分析研究。首先确定了区域最佳有效降雨衰减系数,同时分别按滑坡规模、坡度、厚度大小统计了降雨与历史滑坡信息,得出有效降雨强度（I）与持续时间（D）散点图,由此确定各不同概率下诱发滑坡的区域有效降雨强度阈值,并进行了滑坡灾害危险性等级划分。进而,利用部分样本数据进行逻辑回归分析,得到了该研究区的滑坡发生概率预测方程,并给出了降雨强度临界值定量表达式,最后选用实际降雨诱发滑坡事件与未诱发滑坡事件进行对比验证。结果表明,文章所建立的滑坡预测模型准确性较高,预测情况与实际情况比较吻合。     

滑坡降雨阈值模型的应用

在数字高程模型（DEM）的基础上,运用滑坡降雨阈值模型,以楚雄丁家坟一斜坡作为试验研究工点,结合现场勘察、监测数据以及斜坡岩土体主要特性、地形地貌、降雨强度与降雨持续时间、地下水位等因素,模拟斜坡单元产生潜在滑动时的临界降雨量,研究降雨对滑坡发生、分布的影响。研究结果表明：各斜坡单元产生潜在滑动时的临界降雨量各不相同,在不同的降雨量及地下水位条件下滑坡降雨阈值模型模拟的潜在滑坡位置主要位于楚勐公路下边坡处,与实际发生滑坡的位置吻合率达80%以上,滑坡降雨阈值模型可实现对斜坡稳定性进行可视化分析与预测,为降雨型滑坡提供一种有效的预测与分析方法。     

诱发区域性滑坡的降雨阈值

根据降雨预测区域性滑坡是防止滑坡造成人员伤亡的重要途径,其关键是确定诱发滑坡的降雨阈值。按特定地质气象条件分区整理并分析了国内不同地区诱发区域性滑坡的降雨阈值,结果表明降雨阈值与年均降雨量有较大的相关性。除西南部分地区外,我国大部分地区的预报24 h降雨量阈值为年均降雨量(MAP)的0.04~0.08倍,这是因为年均降雨量反映了地区的常规降雨水平,而只有发生非常规的降雨才会诱发滑坡,因而年均降雨量越大对应的降雨阈值越大。分析了目前国内的降雨阈值的确定过程中缺乏详尽数据和论证等问题,并指出了国内的降雨阈值不能反映短时强降雨影响的缺陷。针对国内还没有适用性更广的雨强-持时(I-D)阈值曲线的现状,尝试性地建立了鄂西地区的I-D阈值曲线I=4.0D^-0.51,并在此过程中提出了确定降雨持时D开始时刻的方法。     

基于危险度区划的县级区域降雨引发滑坡的风险预警方法——以四川省米易县降雨滑坡为例

降雨滑坡预警在世界上开展广泛,但对小区域的降雨滑坡风险进行预警尚不多见。以通用的风险定义为基础,首先对目标区域——四川省米易县的地质灾害进行调查,以典型滑坡进行反演,获得灾害的本底因素,按贡献率权重叠加法进行危险度分区;通过对承灾体的调查确定承灾对象,并将各承灾对象按密度由行政界限向1km×1km的网格单元转化后叠加形成易损度分区。二者相乘完成风险分区。对研究区域近5年逐日降雨数据与172条地质灾害记录进行分析,按前期日降雨量模型得到降雨阈值,根据滑坡空间概率和降雨引发滑坡的时间概率叠加的结果,得到研究区域的降雨滑坡概率,对照风险分区,完成风险预警。     

基于“阶跃”变形特征的降雨型滑坡预警判据探讨

由于滑坡灾害的发生具有随机性和复杂性,经常给人们生活造成严重的经济损失,甚至威胁生命安全,故建立滑坡预警判据及模型是减小损失的重要手段。文章首先确定了有效降雨量-变形速率-改进切线角为降雨型阶跃滑坡的预警关键判据。然后运用斜率单变点法确定滑坡“阶跃”变形曲线上的“破坏拐点”和“稳定拐点”并提取变形加速区间的详细信息,以此求解有效降雨量-变形速率-改进切线角综合预警判据阈值。最后,结合麻池村1号滑坡各发展阶段宏观变形特征,将时空演化规律有机结合,建立了4级递进式综合判据分级预警模型。结果表明：麻池村1号滑坡的运移形式为牵引式,在降雨因素的激发下,累计位移曲线呈典型的“阶跃”变形特征;麻池村1号滑坡变形受前期降雨影响,前期降雨与当期降雨组成一个降雨过程的时间间隔阈值为10 d,“前期+当期降雨”模式下对应的降雨阈值分别为24 mm、 32 mm;基于监测数据精细化分析以及改进切线角法获取的麻池村1号滑坡的位移速率阈值为12 mm/d。研究可为此类型滑坡灾害的预警提供理论基础和技术指导。     

重庆地区降雨型滑坡相关性分析及预报模型

整理大量重庆地区降雨诱发滑坡资料,对降雨因子、累积降雨因子、前期降雨衰减系数与滑坡发生与否及滑坡发生数量之间进行系统的相关分析。结果表明:(1)当日降雨量对滑坡的诱发作用最显著,当日最大小时降雨与当日降雨量高度相关;(2)据滑坡发生前日期增加,降雨因子与滑坡相关及偏相关系数随之降低,在据滑坡发生前3d之后已变得很小;(3)大范围滑坡一般在暴雨当日或滞后一天产生,仅有短时强降雨很难诱发大范围滑坡,还需要一定前期降雨量为基础;(4)累积降雨因子与滑坡因子间都显著相关,且随着累积降雨日数的增加,其相关系数先上升后下降;(5)重庆地区适宜的前期降雨衰减系数在0.6左右。在此基础上拟合出了有效累积降雨量-滑坡发生概率预报模型,并使用当日最大小时降雨量-滑坡发生概率模型对其进行修正。最后使用降雨诱发滑坡实例检验该修正模型,结果可为重庆地区降雨型滑坡的预测预报提供科学依据。     

降雨滑坡预警的概率分析方法

滑坡启动的降雨临界值是滑坡预警的关键,由于研究区资料数据有限,关键值难以确定,降雨滑坡预警效果受影响。所以应该对预警结果进行概率分析。本文利用作者在已开发的降雨滑坡预警系统,提出采用降雨滑坡预警概率分析方法进行预警。通过对滑坡与雨量相关性、降雨滑坡启动值等对滑坡预警的时间概率、空间概率及预警概率进行分析,计算危险区内已发生滑坡频率和降雨滑坡发生频率,得到降雨滑坡预警概率使得预警系统更加可靠。并以沐川县为例对模型进行了算例检验,为更科学的对降雨滑坡进行预警提供方法借鉴。     

台风暴雨型滑坡降雨阈值曲线研究以福建地区为例

台风暴雨型滑坡具有群发性、规模小、爆发性强的特点,容易造成严重的人员伤亡和巨大的财产损失。本文应用极值理论分析,以极端降雨重现期的极大值作为标准并计算有效的降雨区间,通过统计分析,确定触发暴雨型滑坡的降雨阈值曲线。应用模型对福建地区台风暴雨型滑坡进行了分析,福建省3个灾害高发区为:南平三明地区、泉州地区和宁德地区。南平三明地区降雨阈值较高,但发生的滑坡数量较多,主要以3d的降雨为主;泉州地区小于3d的连续降雨和大于8d的连续降雨是触发该地滑坡的主要降雨区间;宁德地区对短期降雨较为敏感,滑坡主要由1d的降雨引起。比较分析研究表明,台风地区触发滑坡的降雨阈值要高于非台风地区。降雨型滑坡阈值主要受气候,地质和土壤厚度影响,气候因素为主控因素。     

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.     

Erratum to: Early warning of rainfall-induced shallow landslides and debris flows in the USA

The state of knowledge and resources available to issue alerts of precipitation-induced landslides vary across the USA. Federal and state agencies currently issue warnings of the potential for shallow, rapidly moving landslides and debris flows in a few areas along the Pacific coast and for areas affected by Atlantic hurricanes. However, these agencies generally lack resources needed to provide continuous support or to expand services to other areas. Precipitation thresholds that form the basis of landslide warning systems now exist for a few areas of the USA, but the threshold rainfall amounts and durations vary over three orders of magnitude nationwide and over an order of magnitude across small geographic areas such as a county. Antecedent moisture conditions also have a significant effect, particularly in areas that have distinct wet and dry seasons. Early warnings of shallow landslides that include specific information about affected areas, probability of landslide occurrence, and expected timing are technically feasible as illustrated by a case study from the Seattle, WA area. The four-level warning scheme (Null, Outlook, Watch, Warning) defined for Seattle is based on observed or predicted exceedance of a cumulative precipitation threshold and a rainfall intensity–duration threshold combined with real-time monitoring of soil moisture. Based on analysis of historical data, threshold performance varies according to precipitation characteristics, and threshold exceedance corresponds to a given probability of landslide occurrence. Experience in Seattle during December 2004 and January 2005 illustrates some of the challenges of providing landslide early warning on the USA West Coast.     

Early warning of rainfall-induced shallow landslides and debris flows in the USA

The state of knowledge and resources available to issue alerts of precipitation-induced landslides vary across the USA. Federal and state agencies currently issue warnings of the potential for shallow, rapidly moving landslides and debris flows in a few areas along the Pacific coast and for areas affected by Atlantic hurricanes. However, these agencies generally lack resources needed to provide continuous support or to expand services to other areas. Precipitation thresholds that form the basis of landslide warning systems now exist for a few areas of the USA, but the threshold rainfall amounts and durations vary over three orders of magnitude nationwide and over an order of magnitude across small geographic areas such as a county. Antecedent moisture conditions also have a significant effect, particularly in areas that have distinct wet and dry seasons. Early warnings of shallow landslides that include specific information about affected areas, probability of landslide occurrence, and expected timing are technically feasible as illustrated by a case study from the Seattle, WA area. The four-level warning scheme (Null, Outlook, Watch, Warning) defined for Seattle is based on observed or predicted exceedance of a cumulative precipitation threshold and a rainfall intensity–duration threshold combined with real-time monitoring of soil moisture. Based on analysis of historical data, threshold performance varies according to precipitation characteristics, and threshold exceedance corresponds to a given probability of landslide occurrence. Experience in Seattle during December 2004 and January 2005 illustrates some of the challenges of providing landslide early warning on the USA West Coast.     

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 %.     

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.     

Landslide hazard and cascading effects following the extreme rainfall event on Madeira Island (February 2010)

Heavy rainfall on February 20, 2010, triggered numerous shallow rapid landslides across Madeira Island, a Portuguese archipelago in the North Atlantic. Two days after the extreme rainfall event, a field campaign was started which involved describing and mapping a variety of landslide types and the related losses at 120 different locations throughout the Island. Most of the failures started as debris slides or avalanches at high elevations and transformed into debris flows which rushed downslope into populated coastal areas. Over half of the mapped landslides were located in the central and southern area of the island. A further 1,257 landslide locations were revealed in these areas using remote sensing data which were then assembled in a spatial database. Due to anthropogenic influences caused by urban development and population expansion, the event demonstrated the increased vulnerability of the island??s infrastructure. In order to mitigate future losses, it is important to quantify the typical preparatory factors which contribute to rainfall-induced landslides. This increases our understanding of the hazards and associated risks. The analysis shows that based on their spatial frequency, distribution and in the context of the drainage system, three main factors contribute to the triggering of the landslides due to the heavy rainfall event in February 2010: the characteristic soil type, the land cover and the slope gradient. It can now be recognized that the distribution of landslides is highly dependent on the temporal and spatial distribution of these factors. Furthermore, the anthropogenic impact on the extent of the hazard becomes obvious due to poor settlement planning and drainage system modification.     

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。