洪水概率预报评价指标研究

在分析现有洪水概率预报评价指标的基础上,建立了洪水概率预报的"精度-可靠度"联合评价指标体系"精度"指标用于评价倾向值预报的准确性,包括确定性系数、相对误差等指标;"可靠度"指标用于评价区间预报的合理性,包括覆盖率、区间离散度等指标其中,在"可靠度"评价指标中,提出了一个新的评价指标,即覆盖率判定系数,用以评价多个区间预报结果的整体合理性以淮河王家坝区间流域为例进行应用研究,结果表明:"精度-可靠度"联合评价指标体系可以对洪水概率预报结果的合理性做出更全面的评价.     

Incorporating inflow uncertainty into risk assessment for reservoir operation

An attempt of using stochastic hydrologic technique to assess the intrinsic risk of reservoir operation is made in this study. A stochastic simulation model for reservoir operation is developed. The model consists of three components: synthetic generation model for streamflow and sediment sequences, one-dimensional delta deposit model for sediment transport processes in reservoirs, and simulation model for reservoir operation. This kind of integrated simulation model can be used to simulate not only the inflow uncertainty of streamflow and sedimentation, but also the variation in operation rules of reservoirs. It is herein used for the risk assessment of a reservoir, and the simulation is performed for different operation scenarios. Simulation for the 100-year period of sediment transport and deposition in the river-reservoir system indicates that the navigation risk is much higher than that of hydropower generation or sediment deposition in the reservoir. The risk of sediment deposition at the river-section near the backwater profile is also high thereby the navigation at the river-segment near this profile takes high risk because of inadequate navigation depth.     

Marginal cost based seismic decision: A probabilistic approach

Among various methodologies for seismic risk mitigation, the use of the marginal cost of saving a life as decision variable, proposed in the literature, does not require to assign a cost to human life. Also, optimization of resources over a territory can be profitably performed in terms of such a variable. Parameters of different type (seismic, structural, financial) enter into the analytical definition of the marginal cost of saving a life. These parameters are subject to intrinsic variability and uncertainty that are bound to affect the decision variable. This aspect was not originally taken into account systematically. The influence of the uncertainty in the various basic parameters on the decision variable is studied here. A procedure is suggested to develop constant probability of exceedance contours of the decision variable versus the design coefficient to be selected. The suggested procedure is exemplified for two regions of different seismicity.     

A probabilistic approach to exposure risk assessment

The introduction of hazardous substances into the environment has long been recognized as being a cause of several diseases in humans, wildlife, and plants. The damaging character of suspected contaminants is usually assessed via a “reject/retain” design with no explicit link between levels of exposure and intensities of the potential adverse health effects even though this connection may be important for the development of public health regulations that limit exposure to hazardous substances. Here, we propose a probabilistic approach to exposure risk assessment as a way around this typical flaw. We develop a Bayesian model using proximity to the source of an alleged contaminant as a surrogate for exposure. Subsequently, we carry out an experimental study based on simulated data to illustrate the model implementation with real world data. We also discuss a possible way of extending the model to accommodate potential heterogeneity in the spatial distribution of the focal disease.     

Improving at-site flood frequency analysis with additional spatial information: a probabilistic regional envelope curve approach

Extreme flood events have detrimental effects on society, the economy and the environment. Widespread flooding across South East Queensland in 2011 and 2013 resulted in the loss of lives and significant cost to the economy. In this region, flood risk planning and the use of traditional flood frequency analysis (FFA) to estimate both the magnitude and frequency of the 1-in-100 year flood is severely limited by short gauging station records. On average, these records are 42 years in Eastern Australia and many have a poor representation of extreme flood events. The major aim of this study is to test the application of an alternative method to estimate flood frequency in the form of the Probabilistic Regional Envelope Curve (PREC) approach which integrates additional spatial information of extreme flood events. In order to better define and constrain a working definition of an extreme flood, an Australian Envelope Curve is also produced from available gauging station data. Results indicate that the PREC method shows significant changes to the larger recurrence intervals (≥100 years) in gauges with either too few, or too many, extreme flood events. A decision making process is provided to ascertain when this method is preferable for FFA.     

Incorporating the LCIA concept into fuzzy risk assessment as a tool for environmental impact assessment

Environmental impact assessment (EIA) is a procedural tool for environmental management that identifies, predicts, evaluates and mitigates the environmental impact of development proposals. In the process of EIA, EIA reports, prepared by developers, are expected to delineate the environmental impact, but in practice they usually determine whether the amounts or concentrations of pollutants comply with the relevant standards. Actually, many analytical tools can improve the analysis of environmental impact in EIA reports, such as life cycle assessment (LCA) and environmental risk assessment (ERA). Life cycle impact assessment (LCIA) is one of steps in LCA that takes account of the causal relationships between environmental hazards and damage. Incorporating the concept of LCIA into an ERA as an integrated tool for the preparation of EIA reports extends the focus of the reports from the regulatory compliance of the environmental impact, to determine the significance of the environmental impact. Sometimes, when using integrated tools, it is necessary to consider fuzzy situations, because of a lack of sufficient information; therefore, so ERA should be generalized to a fuzzy risk assessment (FRA). Therefore, this paper proposes the integration of a LCIA and a FRA as an assessment tool for the preparation of EIA reports, whereby the LCIA clearly identifies the causal linkage for hazard–pathway–receptor–damage and then better explain the significance of the impact; furthermore, a FRA copes with fuzzy and probabilistic situations in the assessment of pollution severity and the estimation of exposure probability. Finally, the use of the proposed methodology is demonstrated in a case study of the expansion plan for the world’s largest plastics processing factory.     

On seasonal and semi-annual approach for flood frequency analysis

As a supplementary method to the conventional flood frequency analysis based on annual peak flows, we propose an approach in this paper to infer the flood frequency distribution on quarterly and semi-annual time scale, which are then converted to annual time scale to obtain the floods corresponding to return periods in unit of year. Two criteria for test of data independence, namely, minimum 7 and 15-day interval between two consecutive peak flows, are tested. The proposed approach was applied to Des Moines River at Fort Dodge, Iowa, USA using its 62 years of observation daily flows. The results show that the estimated floods for given return periods from quarter-annual data series are in general higher than the corresponding estimated floods from semi-annual data series, which is further larger than estimated floods from annual peak flows. The floods estimated from semi-annual data series agree well with the results of previous US Geological Survey study.     

Incorporating uncertainty analysis into a regional IDF formula

A rainfall intensity–duration–frequency (IDF) relationship was generated by pooling annual maximum rainfall series from 14 recording rain gauges in southern Taiwan. Dimensionless frequency curves, plotted by the growth curve method, can be well fitted by regression equations for a duration ranging from 10 mins to 24 hours. As the parameters in regression equations have a good statistical relationship with average annual rainfall, a generalized regional IDF formula was then formulated. The formula, based on average annual rainfall as an index, can be easily applied to non-recording rain gauges. This paper further applies the mean value first-order second moment (MFOSM) method to estimate the uncertainty of the proposed regional IDF formula. From a stochastic viewpoint, the generalized regional IDF formula can accurately simulate the IDF relationship developed using frequency analysis (EV1) at individual stations. The method can provide both rainfall intensity and variance isohyetal maps for various rainfall durations and return periods over the study area. © 1998 John Wiley & Sons, Ltd.     

A non-traditional approach to the analysis of flood hazard for dams

The traditional and still prevailing approach to characterization of flood hazards to dams is the inflow design flood (IDF). The IDF, defined either deterministically or probabilistically, is necessary for sizing a dam, its discharge facilities and reservoir storage. However, within the dam safety risk informed decision framework, the IDF does not carry much relevance, no matter how accurately it is characterized. In many cases, the probability of the reservoir inflow tells us little about the probability of dam overtopping. Typically, the reservoir inflow and its associated probability of occurrence is modified by the interplay of a number of factors (reservoir storage, reservoir operating rules and various operational faults and natural disturbances) on its way to becoming the reservoir outflow and corresponding peak level—the two parameters that represent hydrologic hazard acting upon the dam. To properly manage flood risk, it is essential to change approach to flood hazard analysis for dam safety from the currently prevailing focus on reservoir inflows and instead focus on reservoir outflows and corresponding reservoir levels. To demonstrate these points, this paper presents stochastic simulation of floods on a cascade system of three dams and shows progression from exceedance probabilities of reservoir inflow to exceedance probabilities of peak reservoir level depending on initial reservoir level, storage availability, reservoir operating rules and availability of discharge facilities on demand. The results show that the dam overtopping is more likely to be caused by a combination of a smaller flood and a system component failure than by an extreme flood on its own.     

Seismic response analysis of pipes by a probabilistic approach

Seismic response of buried pipes in longitudinal direction is studied. The effect of the variation of geotechnical properties of the surrounding soil on the stiffness, mass and damping of the soil is considered. The soil–structure interaction depends on pipe stiffness, joint stiffness, the variation of the soil stiffness and the soil mass and damping. Variations of the properties of the surrounding soil along the pipe are described by the random field theory. A numerical model is developed in order to simulate the effects of the variation of the soil on displacements, bending moments in the pipe and also to carry out a statistical analysis. The influence of different parameters regarding design and safety level of the pipe is conducted.     

Multi-criteria decision making under uncertainty for flood mitigation

Designs of flood mitigation infrastructural systems are decision-making which are often made under various uncertainties involving multiple criteria. Under the condition of uncertainties, any chosen design alternative has the likelihood to perform inferior to other unselected designs in terms of the adopted performance indicators. This paper introduces a quantitative risk measure based on the concept of expected opportunity loss (EOL) for evaluating the consequence of making the wrong decision. The EOL can be used to assess the relative performance of multiple decision alternatives and is extended to deal with decision problems involving multiple criteria. In particular, the probabilistic features of the consequences associated with a design alternative is considered and used in the Preference Ranking Organization Method of Enrichment Evaluation (PROMETHEE) MCDM technique. The integration of PROMETHEE and decision making under uncertainty is demonstrated through an example of flood damage mitigation planning.     

Multiple criteria decision making and decision support systems for flood risk management

Multiple criteria decision making (MCDM) is a collection of methodologies to compare, select, or rank multiple alternatives that typically involve incommensurate attributes. MCDM is well-suited for eliciting and modeling the flood preferences of stakeholders and for improving the coordination among flood agencies, organizations and affected citizens. A flood decision support system (DSS) architecture is put forth that integrates the latest advances in MCDM, remote sensing, GIS, hydrologic models, and real-time flood information systems. The analytic network process (ANP) is discussed with application to short-term flood management options for the middle reaches of the Yangtze River. It is shown that DSS and MCDM can improve flood risk planning and management under uncertainty by providing data displays, analytical results, and model output to summarize critical flood information.     

A probabilistic model for real‐time flood warning based on deterministic flood inundation mapping

Deterministic flood inundation mapping is valuable for the investigation of detailed flood depth and extent. However, when these data are used for real‐time flood warning, uncertainty arises while encountering the difficulties of timely response, message interpretation and performance evaluation that makes statistical analysis necessary. By incorporating deterministic flood inundation map outputs statistically by means of logistic regression, this paper presents a probabilistic real‐time flood warning model determining region‐based flood probability directly from rainfall, being efficient in computation, clear in message, and valid in physical meaning. The calibration and validation of the probabilistic model show a satisfactory overall correctness rate, with the hit rate far surpassing the false alarm rate in issuing flood warning for historical events. Further analyses show that the probabilistic model is effective in evaluating the level of uncertainty lying within flood warning which can be reduced by several techniques proposed in order to improve warning performance. Copyright © 2011 John Wiley & Sons, Ltd.     

A new approach for computing a flood vulnerability index using cluster analysis

A Flood Vulnerability Index (FloodVI) was developed using Principal Component Analysis (PCA) and a new aggregation method based on Cluster Analysis (CA). PCA simplifies a large number of variables into a few uncorrelated factors representing the social, economic, physical and environmental dimensions of vulnerability. CA groups areas that have the same characteristics in terms of vulnerability into vulnerability classes. The grouping of the areas determines their classification contrary to other aggregation methods in which the areas' classification determines their grouping. While other aggregation methods distribute the areas into classes, in an artificial manner, by imposing a certain probability for an area to belong to a certain class, as determined by the assumption that the aggregation measure used is normally distributed, CA does not constrain the distribution of the areas by the classes.FloodVI was designed at the neighbourhood level and was applied to the Portuguese municipality of Vila Nova de Gaia where several flood events have taken place in the recent past. The FloodVI sensitivity was assessed using three different aggregation methods: the sum of component scores, the first component score and the weighted sum of component scores.The results highlight the sensitivity of the FloodVI to different aggregation methods. Both sum of component scores and weighted sum of component scores have shown similar results. The first component score aggregation method classifies almost all areas as having medium vulnerability and finally the results obtained using the CA show a distinct differentiation of the vulnerability where hot spots can be clearly identified.The information provided by records of previous flood events corroborate the results obtained with CA, because the inundated areas with greater damages are those that are identified as high and very high vulnerability areas by CA. This supports the fact that CA provides a reliable FloodVI.     

地震岩相识别概率表征方法

储层岩相分布信息是油藏表征的重要参数,基于地震资料开展储层岩相识别通常具有较强的不确定性.传统方法仅获取唯一确定的岩相分布信息,无法解析反演结果的不确定性,增加了油藏评价的风险.本文引入基于概率统计的多步骤反演方法开展地震岩相识别,通过在其各个环节建立输入与输出参量的统计关系,然后融合各环节概率统计信息构建地震数据与储层岩相的条件概率关系以反演岩相分布概率信息.与传统方法相比,文中方法通过概率统计关系表征了地震岩相识别各个环节中地球物理响应关系的不确定性,并通过融合各环节概率信息实现了不确定性传递的数值模拟,最终反演的岩相概率信息能够客观准确地反映地震岩相识别结果的不确定性,为油藏评价及储层建模提供了重要参考信息.模型数据和实际资料应用验证了方法的有效性.     

Sensitivity analysis on the input parameters in probabilistic seismic hazard assessment

A project has been implemented in recent years for assessing seismic hazard in the Italian territory on probabilistic bases, to be used as scientific background for the revision of the current seismic zonation. A consolidated approach was considered for the purpose; seismic hazard was estimated in terms of peak ground acceleration and macroseismic intensity. As the computer code employed allows the user to make specific choices on some input data, some rather unorthodox decisions were taken regarding earthquake catalogue completeness, seismicity rates, boundaries of the seismogenic zones, definition of the maximum magnitude, attenuation relation, etc. The overwhelming amount of geological and seismological data for Italy (just consider, for example, that the earthquake catalogue collects events which occurred over the last ten centuries) permits the operator to make different choices, more or less cautiously. It is quite interesting, then, to evaluate the influence of the specific choices on the final hazard results as a comparison to traditional possibilities. The tests performed clearly indicate the critical choices and quantify their contribution. In particular, we consider thorough comprehension of the space geometry of the earthquake source boundaries and the adequacy of the attenuation relation in modelling the radiation pattern very important.     

A Bayesian probabilistic approach for structure damage detection

A Bayesian probabilistic approach is presented for the damage detection of multistorey frame structures. In this paper, a Bayesian probabilistic approach is applied to identify multiple damage locations using estimated modal parameters when (1) the measurement data are potentially corrupted with noise, (2) only a small number of degrees of freedom are measured, and (3) a few fundamental modes are estimated. To reduce the potentially intensive computational cost of the proposed method, a branch-and-bound search scheme is proposed and a simplified approach for the modelling of multistorey frame structures is employed. A six-storey shear frame example and two multistorey frame examples, with multiple damage locations, are presented to illustrate the applicability of the proposed approach. © 1997 John Wiley & Sons, Ltd.     

Hydrological and flood hazard assessment using a coupled modelling approach for a mountainous catchment in Portugal

Floods may lead to destruction of property, to damage to the environment and ultimately to loss of lives. Although it is not possible to avoid them, they are enhanced by human activities that increase the probability of occurrence of these natural events. Preliminary flood risk assessment and determination of areas of potential significant flood risk is mandatory according to the European Floods Directive (2007). In order to meet the established legislation, a methodology was developed that couples two modelling approaches: the Hydrological Simulation Program—FORTRAN (HSPF) and IBER. A target watershed, with complex orography and known to be vulnerable to flood hazards, is selected: the Vez River (northern Portugal). The performance of the HSPF model, driven by a climate gridded dataset, was assessed, followed by the reconstruction of the flow rate in the catchment for the period from 1950 to 2015. The results hint at an agreement between simulated and observed daily flow rates, with high coefficient of determination value and of the Nash–Sutcliffe coefficient of efficiency (> 0.7 daily timescale). A satisfactory performance was also found in reproducing flood peak events. An average deviation of 10% was found between observed and simulated flood peaks. The output of HSPF was subsequently used to drive IBER, thus determining flood hazard areas for a 10, 50 and 100-year return periods. The methodology presented here provides basic tools for decision-makers to evaluate hydrologic responses to climate data, namely the determination of flood hazard maps, but also risk assessment, water management, environmental protection and sustainability.     

A methodology for the probabilistic assessment of behaviour factors

Given the importance that traditional force-based seismic design still currently exhibits, studies addressing issues related to the definition of the behaviour factor values are considered to be of most interest. A probabilistic methodology is proposed for the calibration of the q-factor relating its value with two fundamental parameters, the displacement ductility capacity measured at a relevant location of the structure and the failure probability P _f . The general foundation of this procedure is based on the probabilistic quantification of the seismic action and, by applying a transformation procedure, of the structural seismic demand in terms of displacement ductility. By recalling well established structural reliability procedures and by making use of nonlinear analysis methods, both static and dynamic, a general probabilistic framework, which is able to relate the ductility capacity, the failure probability P _f and the behaviour factor, is defined. In order to illustrate some of the potentialities of the methodology, an application example is presented, addressing the q-factor assessment for a set of regular and irregular reinforced concrete frame structures, enforcing a given P _f and two different ductility levels.     

Application of a Monte‐Carlo simulation approach for the probabilistic assessment of seismic hazard for geographically distributed portfolio

The conventional integral approach is very well established in probabilistic seismic hazard assessment (PSHA). However, Monte‐Carlo (MC) simulations can become an efficient and flexible alternative against conventional PSHA when more complicated factors (e.g. spatial correlation of ground shaking) are involved. This study aims at showing the implementation of MC simulation techniques for computing the annual exceedance rates of dynamic ground‐motion intensity measures (GMIMs) (e.g. peak ground acceleration and spectral acceleration). We use multi‐scale random field technique to incorporate spatial correlation and near‐fault directivity while generating MC simulations to assess the probabilistic seismic hazard of dynamic GMIMs. Our approach is capable of producing conditional hazard curves as well. We show various examples to illustrate the potential use of the proposed procedures in the hazard and risk assessment of geographically distributed structural systems. Copyright © 2015 John Wiley & Sons, Ltd.