地质灾害气象预报预警方法研究

地质灾害的诱发因素包括自然因素和人为因素.人为因素主要与人类工程活动有关.在一定的地质地形条件下,自然诱发因素则主要是降雨.因此在确定的地质环境背景下,研究气象和地质灾害发生的耦合关系极其重要.本文通过总结前人关于地质灾害预报预警的研究,提出了一套完整的应用降雨对地质灾害进行预报预警的方法体系.     

基于Logistic回归及前期有效雨量的降雨诱发型滑坡预测方法

滑坡预测预报一直是国内外滑坡灾害研究的重点。且多数滑坡均属降雨诱发型。为此将降雨诱发型滑坡的研究列为重中之重。但是,降雨与滑坡发生的关系不仅密切,而且非常复杂。除降雨外,还涉及到地质、气象、水文、土壤等多个学科。因此,至今尚未总结出一种对降雨诱发型滑坡进行定量预测预报的成熟方法。文章将Logistic回归模型与前期有效降雨量结合,形成一套对降雨诱发型滑坡进行定量预测预报的方法。并以长江三峡地区为例进行了检验,效果较好。     

湖南省茶陵县地质灾害气象预警区划研究

地质灾害的诱发因素包括自然因素和人为因素,自然因素主要为降雨,在确定的地质背景条件下,研究气象和地质灾害的关系,对今后地质灾害预警预报与地质灾害防灾减灾工作有着重要意义。在总结过去十年地质灾害预警预报工作经验的基础上,提出了地质灾害气象预警区划方法。     

黄土滑坡诱发因素及其形成机理研究

近年来,黄土滑坡灾害发生频率呈现增加的趋势,研究黄土滑坡诱发因素及其形成机理是有效减缓黄土滑坡灾害风险的基础.据西北黄土高原地质灾害详细调查,黄土滑坡的诱发因素有自然和人为2类.自然因素包括地震、河流及沟谷侵蚀、降雨及冻融等;人为因素有堆载与开挖、农业灌溉、修建水库等.研究表明,降水和人类工程活动是最积极的诱发因素.地...     

利用BP神经网络进行水库滑坡变形预测

滑坡变形监测与预测是滑坡预警预报中一种非常重要的途径。文章首先简单介绍了神经网络的基本原理和学习算法,然后利用某水库滑坡24期的GPS地表位移监测数据及其诱发因素即水库水位、降雨等资料,采用BP神经网络模型对该水库滑坡变形进行建模,最后将6期水库水位、降雨等资料输入模型进行滑坡变形预测,结果表明预测结果与实测数据符合性好,总体上能较好反映变形趋势。     

降雨入渗堆积层滑坡的稳定性分析与评价

采用SEEP/W和SLOPE/W程序对一堆积层滑坡进行了渗流分析和稳定性评价,揭示了持续性降雨或大暴雨是诱发堆积层滑坡的主要因素,并可通过稳定性系数与降雨历时的关系曲线知在特定降雨强度下开始滑动的大致时间,可为滑坡灾害的预警预报工作提供参考。     

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。     

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。     

河南灵宝大湖金矿滑坡监测与预警初探

针对大湖金矿滑坡的灾害发育、活动与危害现状,建立了滑坡监测网,进行了滑坡变形及降雨监测;在复杂地质地形条件下,结合降雨情况寻求滑坡与环境（降雨）之间的临界状态值,对大湖滑坡诱发因素进行比较深入的分析和探索,达到滑坡预报预警的目的。     

汶川地震强震区地震诱发滑坡与后期降雨诱发滑坡控制因子耦合分析

本文以汶川地震强震区北川县典型研究区为例,利用高分辨率航片、SPOT5卫星图像对北川县典型研究区进行了512地震之后和924降雨之后诱发的滑坡解译,解译结果显示:512地震诱发滑坡1999个,924强降雨诱发滑坡828个,924强降雨导致原有地震滑坡面积扩大的滑坡150个。研究表明:地震和强降雨都是诱发滑坡的动力成因,924强降雨诱发的滑坡面积是512地震诱发滑坡面积的1/4倍,强降雨诱发滑坡的数量增加了41.4%; 强降雨不仅诱发新的滑坡,而且促使原来地震滑坡复活,并扩大其面积,强降雨导致地震诱发的滑坡面积扩大了原面积的68.7%。同时,在遥感解译数据基础之上,开展地震诱发滑坡与降雨诱发滑坡规模对比和控制因子耦合分析及地震与降雨耦合灾害链模式研究,为进一步分析研究地震灾区滑坡的产生、发展趋势、危险性和风险评价等预测预报提供科学依据,也为汶川震区恢复重建中的减灾防灾提供决策参考。     

Loading/Unloading response ratio theory applied in predicting deep-seated landslides triggering

Rainfall is the most relevant factor for the triggering of landslides and it is characterized by an extreme variability. Rainfall analysis is the most frequently adopted approach for predicting landslides. But predicting deep-seated landslides by means of rainfall thresholds is sometimes inadequate due to the complexity of such slopes. In this paper, further study of the loading / unloading response ratio theory applied in landslides prediction was carried out. As the aforementioned effect and character of rainfall was concerned, a prediction for the known Xintan landslide was carried out by means of the loading / unloading response ratio method, in which the action of water was regarded as loading/unloading measures. The results show that it is an effective method for landslides prediction, and it has an advantage over conventional rainfall thresholds or time sequences analysis method.     

Unload–load displacement response ratio parameter and its application in prediction of debris landslide induced by rainfall

As the traditional displacement value of a landslide is very easy to be affected by rainfall, it is very difficult to establish a stable and uniform destabilized criterion of landslide in terms of the displacement value. So, it determines that establishment of an effective and stable dynamic displacement prediction parameter is very important in forecast of the debris landslides caused by rainfall. In order to determine this kind of prediction parameter, this paper first completes analysis on the relationship between the destabilized mechanism of the debris landslide and the rainfall dynamic rules. The relationship above shows that the periodical change value of rainfall can be taken as the dynamic unload–load parameter (ULP) of landslide, and the homologous change value of mensal landslide displacement can be taken as the stability displacement response parameter (DRP). Then, on the basis of the relationship between the ULP and the stability DRP of this kind of landslide, the unload–load displacement response ratio (ULDRR) appraisal parameter of this kind of landslides is established in this paper. Finally, the stability of typical debris landslides in China has been systematically studied by means of ULDRR appraisal parameter and model. We find that the ULDRR values conform to the evolutional rule of slope deformation and instability. So, it has been proved that the ULDRR parameter and the appraisal model are suitable and effective for prediction and evaluation of stability and evolution rule of rainfall-induced landslides.     

加卸载响应比理论在水库型滑坡时间预测预报中的应用研究

加卸载响应比理论在滑坡时间预测预报中的应用越来越广泛,但基本都以降雨型堆积层滑坡为研究对象,加卸载参数以降雨量、地下水水位为主,而对于水库型滑坡研究较少。为此,提出了一种以库水位为加卸载参数,以滑坡加速度为加卸载响应参数的预测模型,并以典型水库型滑坡——八字门滑坡为例对其准确性进行验证。研究结果表明,以库水位为加卸载参数的加卸载响应比理论预测模型能较准确地对水库型滑坡进行预测预报。     

不同降雨条件下黄土高原浅层滑坡危险性预测评价

黄土地区浅层滑坡发育非常广泛,由于其具有分布规律性差、前期变形迹象小、分布范围大、面小点多等特征,目前还无法进行有效预测,因此给黄土地区工程安全带来严重威胁。根据无限边坡模型,结合降雨入渗-土体强度衰减规律和GIS（地理信息系统）技术,构建了不同降雨条件下黄土地区浅层滑坡发育危险性评价模型,并将该评价模型应用到延河一级支流幸福川流域,预测在有效降雨量30、50、100、200 mm条件下,该流域浅层滑坡发育程度,并与当前较为流行的SINMAP模型(地形稳定性模型)进行对比。结果表明：①不稳定和潜在不稳定浅层滑坡主要分布在末级河流的两侧和源头,稳定和较稳定区域主要分布在一级河流河道两侧和塬面上;通过对比分析,SINMAP模型计算的结果与本文建立的模型在降雨强度30 mm时的计算结果较为一致。②在本文建立的模型评价结果中,随着有效降雨量的增加,F_s（稳定性系数）<1.00的不稳定区域所占比例逐渐增加,从30 mm的1.12%到200 mm的4.79%;相反,稳定区域则出现逐渐减少的趋势。③根据已发生灾害点的分布,随着有效降雨量的增加,研究区域已发生的灾害点分布在F_s<1.25的比例明显增加,从30 mm的62%到200 mm的88%,在SINMAP评价模型中,研究区域已发生的灾害点的64%分布在不稳定和潜在不稳定区域内,说明本文所建立的评价模型具有一定的精度。通过与SINMAP评价模型对比,本文建立的模型主要采用基于降雨入渗规律,而SINMAP评价模型主要基于降雨汇流过程,因此在利用过程中应根据区域特征选择利用。     

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.     

Estimation of rainfall thresholds triggering shallow landslides for an operational warning system implementation

The Piemonte regional warning system service, managed by the Environmental Protection Agency of Piemonte (“ARPA Piemonte” as official Italian acronym), is based on an advanced meteo-hydrological automatic monitoring system, and it is integrated with forecasting activities of severe weather-related natural hazards. At present, a meteo-hydrological chain is operated to provide flood forecasting on the main river pattern. The development of a forecasting tool for shallow landslides triggered by heavy rainfall is presented. Due to the difficulties in modelling shallow landslides triggering in a large and complex area like the Piemonte region, an empirical model is developed on the basis of the correlation between rainfall and previous landslides in historical documents. The research focuses on establishing rainfall thresholds for landslides triggering, differentiating the critical rainfall values through a geological characterisation of the different territories. The period from 1990 to 2002 is considered. A total number of 160 landslides with hourly information and time of triggering are used to calibrate the system. As a first outcome, two different zones have been identified: (1) zones in alpine environments, principally characterised by a bedrock composed of metamorphic rocks, igneous rocks, dolostones or limestones that require high values of critical rainfall and (2) zones in hilly environments, principally characterised by sedimentary bedrock that require low values of critical rainfall. Verification has been performed on a total number of 429 landslides with known date of occurrence. The results show a good agreement with the model with no missed alarms and a very low number of false alarms, thus suggesting an effective operational implementation.     

Relationships between natural terrain landslide magnitudes and triggering rainfall based on a large landslide inventory in Hong Kong

Rain-induced landslides are recognized as one of the most catastrophic hazards on hilly terrains. To develop strategies for landslide risk assessment and management, it is necessary to estimate not only the rainfall threshold for the initiation of landslides, but also the likely magnitudes of landslides triggered by a storm of a given intensity. In this study, the frequency distributions of both open hillside landslides and channelized debris flows in Hong Kong are established on the basis of the Enhanced Natural Terrain Landslide Inventory (ENTLI) with 19,763 records in Hong Kong up to 2013. The landslide magnitudes are measured in terms of the number, scar area, volume, or density of landslides. The mean values of the scar areas and volumes are 55.2 m² and 102.0 m³, respectively, for the open hillside landslides and 91.3 m² and 166.5 m³, respectively, for the channelized debris flows. Empirical correlations between the numbers, scar areas, and volumes of hillside landslides or channelized debris flows and the maximum rolling rainfall intensities of different periods have been derived. The maximum rolling 4- to 24-h rainfall amounts provide better predictions compared with those with the maximum rolling 1-h rainfall. Maximum rolling rainfall intensity-duration thresholds identifying the likely rainfall conditions that yield natural terrain landslides or debris flows of different magnitudes are also proposed. The initiation rainfall thresholds are identified as 75, 90, 100, 120, 150, 180, and 200 mm for the maximum rolling 1-, 2-, 4-, 6-, 8-, 12-, and 24-h rainfall, respectively.     

西安市白鹿塬滑坡发生时间预测研究

笔者通过对白鹿塬区降水与滑坡发生时间之间的规律进行总结，认为本区滑坡发生与降水的丰年有着密切的关系，并应用卡尔曼滤波分析法对白鹿塬区滑坡发生时间进行了全区性预测，提出白鹿塬滑坡发生具有10年左右的中周期，20年左右的长周期，应及早作好防治工作。