Effects of strong ground motion on the susceptibility of gully type debris flows

Typhoon Herb in 1996 caused widespread debris flows in central Taiwan. The 7.3 Chi-Chi earthquake on September 21, 1999, which also took place in central Taiwan, induced many landslides in the region. These landslides turned into debris flows when Typhoon Toraji struck Taiwan in 2001. This research selects three regions which suffered a ground motion class of 5, 6, and 7 on the Richter scale during the Chi-Chi earthquake as study areas. Air photos from 1997 and 2001 of these regions are used to map the gully-type debris flows that took place after Typhoons Herb and Toraji, respectively. The gullies adjacent to the debris flow, but without a trace of debris flows, are also mapped as the non-debris flow data. The topography, hydrogeology, and rainfall factors – where debris flow occurred and in which there was no occurrence of debris flows in these gullies were retrieved from DTM, geological maps, and iso-countour maps, and of rainfall through GIS processing. These characteristic are introduced into a probabilistic neural network to build a predicting model for the probability of the occurrence of debris flows. Three series of cross analyses are conducted to compare the probability of the occurrence of debris flows of the same dataset predicted by different prediction models. The results reveal that the susceptibility of debris flows was elevated after the Chi-Chi earthquake struck. The upsurge of susceptibility was more obvious for the regions that received a higher class of ground motion.     

Time relationship between regional deformation and fabric development in the Peralimala pluton,South India — Inferences from magnetic fabric

Anisotropy of magnetic susceptibility (AMS) is investigated in samples of Peralimala (PM) pluton (ca. 550 Ma) and adjacent gneiss, gabbro, mylonite and amphibolite from the Moyar Shear Zone (MSZ), Southern Granulite Terrane (SGT) with an aim to decipher the time-relationship between fabric development in the pluton and regional tectonics. Magnetic foliation recorded in the PM pluton is sub-parallel to the WNW-ESE striking MSZ. Magnetic foliation and lineation trajectories are sigmoidal and curve into the shear zone. A dextral sense of shear is deciphered from the trajectories, which is similar to that reported within the MSZ in some earlier studies. It is inferred that the PM pluton has developed post-emplacement deformation-fabric related to reactivation of the MSZ during Pan-African age. Based on the data and existing information about regional tectonics of the area, the possibility of the (a) PM pluton being a Deformed Alkali Rock and Carbonatite (DARC) and (b) MSZ marking an ancient suture zone, is discussed.     

Regionalization and susceptibility assessment to daily precipitation extremes in mainland Portugal

The present study aims to identify regions of extreme precipitation in mainland Portugal and to create a single index of extreme precipitation susceptibility (EPSI). For this purpose, twelve extreme precipitation indices were selected from the Expert Team on Climate Change Detection and Indices between 1950 and 2003. By considering only six extreme precipitation indices: R×1day, R×5day, SDII, R20, CWD and R95PTOT for the 10-year return period, between 1950 and 2003, the EPSI was developed to both annual data and meteorological season. The regionalization of extreme precipitation in Portugal were determined using a principal component analysis in T-mode. The results, show three spatial regions obtained from PCA. The three regions were analyzed separate. In the annual EPSI, the highest susceptibility areas are the mountainous regions in northern (e.g. Gerês, Peneda, Alvão, Marão and Montesinho) and central Portugal (e.g. Serra da Estrela), as well as in the Algarve (southern Portugal). Conversely, the lower susceptibility classes are in municipalities of the northeast, Alentejo and along the central-western coast. The results of EPSI show similar results in autumn and winter. In spring, however, the high susceptibility class increases in the Lisbon region and in the Sado Basin. In summer, there is an increase in susceptibility in the northeast, while susceptibility is low over much of Alentejo and Algarve, where precipitation is neglectful. This work presents a first attempt to implement this type of index for mainland Portugal. The first results are very promising, showing a consistent representation of the overall spatial distribution of extreme precipitation susceptibility. The combination of this information by municipalities can be of foremost relevance to civil protection and risk management.     

强磁性体ΔT异常计算的误差分析研究

本文主要针对当前磁法勘探中高精度处理解释的需求,对强磁性体ΔT异常计算存在的误差进行分析研究.我们首先通过理论模型计算试验,证明常规计算采用的投影关系的ΔT与实际测量的模量差ΔT之间的误差E在磁异常幅值大时是明显存在的,其影响不容忽视.其次,当磁性强且剩磁存在时,投影ΔT曲线及其误差曲线在磁化方向与地磁场方向改变时具有一定的对称性;地磁场T0、磁性体形态(如二度水平圆柱体模型的半径r、柱体埋深R)和磁性参数(如磁化率κ)等参数确定的情况下,最大误差值出现在磁性体正上方,且其大小与磁性参数(κ)和模型体规模(如r/R)之间皆是指数关系;另外,研究还发现ΔT的计算误差曲线的一些其他规律特点,如在各纬度带上,ΔT计算误差的最大值Emax曲线的极值主要分布在中纬度地区;磁异常矢量Ta与地磁场T0的夹角θ逐渐变化时,随θ变化Emax曲线的极值分布在θ=90°~120°范围内;当磁异常幅值小于10000nT时,最大误差近似为磁异常矢量垂直于地磁场方向的测点附近的误差值;另外,磁性体(圆柱体为例)的半径(即尺度)与埋深的比值r/R超过0.5,且磁化率超过0.1SI时误差已达到3.9nT,磁化率增大与对应的Emax的值呈指数增长特点.因此,我们的研究表明,在强磁性体、磁异常幅值大的数据处理、反演及解释时,现有方法会产生较大的误差,应该基于严格的模量差ΔT,完善相应的处理以及反演方法.     

Landslide susceptibility analysis with a heuristic approach in the Eastern Alps (Vorarlberg, Austria)

The aim of the study is landslide hazard assessment carried out on a working scale of 1:25 000. The study area within the Northern Calcareous Alps was geologically and geotechnically mapped in order to identify causes and mechanisms of active mass movements. The field surveys were digitised by a Geographical Information System and divided into data layers. The geological units were classified according to their geotechnical properties. All layers were converted into grids and spatially analysed together with a Digital Elevation Model. Comparing the layers with the inventory of active landslides, the prevailing factors leading to sliding movements were identified. Because of the complex tectonic setting and the small number of active landslides, a statistical method of hazard assessment was not applicable. Using the heuristic approach of an index method, the data layers of geotechnical class, bedding conditions, tectonic layouts, slope angles, slope orientations, vegetation and erosion were analysed. The susceptibility of each layer has been evaluated with help of bivariate statistics. The layers have been weighted with indices due to their importance iteratively and were combined into a landslide susceptibility map.     

Comparing models of debris-flow susceptibility in the alpine environment

Debris-flows are widespread in Val di Fassa (Trento Province, Eastern Italian Alps) where they constitute one of the most dangerous gravity-induced surface processes. From a large set of environmental characteristics and a detailed inventory of debris flows, we developed five models to predict location of debris-flow source areas. The models differ in approach (statistical vs. physically-based) and type of terrain unit of reference (slope unit vs. grid cell). In the statistical models, a mix of several environmental factors classified areas with different debris-flow susceptibility; however, the factors that exert a strong discriminant power reduce to conditions of high slope-gradient, pasture or no vegetation cover, availability of detrital material, and active erosional processes. Since slope and land use are also used in the physically-based approach, all model results are largely controlled by the same leading variables.Overlaying susceptibility maps produced by the different methods (statistical vs. physically-based) for the same terrain unit of reference (grid cell) reveals a large difference, nearly 25% spatial mismatch. The spatial discrepancy exceeds 30% for susceptibility maps generated by the same method (discriminant analysis) but different terrain units (slope unit vs. grid cell). The size of the terrain unit also led to different susceptibility maps (almost 20% spatial mismatch). Maps based on different statistical tools (discriminant analysis vs. logistic regression) differed least (less than 10%). Hence, method and terrain unit proved to be equally important in mapping susceptibility.Model performance was evaluated from the percentages of terrain units that each model correctly classifies, the number of debris-flow falling within the area classified as unstable by each model, and through the metric of ROC curves. Although all techniques implemented yielded results essentially comparable; the discriminant model based on the partition of the study area into small slope units may constitute the most suitable approach to regional debris-flow assessment in the Alpine environment.     

Managing expert-information uncertainties for assessing collapse susceptibility of abandoned underground structures

Assessing the collapse susceptibility of abandoned cavities at a regional scale is associated with large uncertainties that are mainly related to the very nature of the phenomena, but also to the difficulty in collecting exhaustive information at such a scale on often “forgotten” structures. In this context, the expert's role is essential, because he is able to synthesize the information resulting from the inventory and from the commonly imprecise, if not vague, criteria on the basis of his experience and his knowledge of the geological, historical, economic regional context.In this article, we propose mathematical tools for representing and processing this information in order to give flexibility to this step and manage the uncertainty inherent in the expert's information. The first tool, based on the weight of evidence theory, is for managing the uncertainty due to the heterogeneous spatial distribution of the data, whereas the second tool, based on the fuzzy set theory, is for managing the imprecision and incompleteness of available data, which hinder the definition of the class boundaries of the quantitative decision criteria. Based on an appropriate representation of the uncertainty sources (related to the input data and to the expert diagnostic), we then propose a methodology that integrates the uncertainty in the final output of the collapse susceptibility assessment and provides a confidence indicator useful within the decision-making process. The proposed methodology is applied to the Arras territory in the North of France, where abandoned chalk pits (dating back to the Roman ages) and war saps located in the vicinity of the First World War front lines (i.e. covered trenches), raise both difficulties for urban planning.     

GIS based landslide susceptibility mapping of Tevankarai Ar sub-watershed, Kodaikkanal, India using binary logistic regression analysis

Landslide susceptibility mapping is the first step in regional hazard management as it helps to understand the spatial distribution of the probability of slope failure in an area.An attempt is made to map the landslide susceptibility in Tevankarai Ar subwatershed,Kodaikkanal,India using binary logistic regression analysis.Geographic Information System is used to prepare the database of the predictor variables and landslide inventory map,which is used to build the spatial model of landslide susceptibility.The model describes the relationship between the dependent variable(presence and absence of landslide) and the independent variables selected for study(predictor variables) by the best fitting function.A forward stepwise logistic regression model using maximum likelihood estimation is used in the regression analysis.An inventory of 84 landslides and cells within a buffer distance of 10m around the landslide is used as the dependent variable.Relief,slope,aspect,plan curvature,profile curvature,land use,soil,topographic wetness index,proximity to roads and proximity to lineaments are taken as independent variables.The constant and the coefficient of the predictor variable retained by the regression model are used to calculate the probability of slope failure and analyze the effect of each predictor variable on landslide occurrence in thestudy area.The model shows that the most significant parameter contributing to landslides is slope.The other significant parameters are profile curvature,soil,road,wetness index and relief.The predictive logistic regression model is validated using temporal validation data-set of known landslide locations and shows an accuracy of 85.29 %.     

曹岘黄土和小良山红土剖面磁化率解释机制的差异

磁化率因其磁测方法快捷、简便和没有破坏性的优点,作为一种研究手段以利用环境物质的磁性来恢复古环境,分析磁性矿物在环境系统中的迁移、转化和组合规律等方面,得到了快速而广泛的应用,取得了一系列研究成果。事实上影响沉积物磁化率的因素是多种多样的,因此在具体应用磁化率分析古环境上要持慎重态度,并结合其他实验结果进行解释,尤其要注意沉积类型的差别对解释磁化率结果的影响。为进一步分析磁化率对古环境的指示意义,本文选取黄土和红土两种沉积类型进行磁化率测试,了解不同类型沉积物的磁化率指示意义,为正确解析磁化率提供依据。