对两种国外地震风险性评估方法的探讨

国外水电工程地震风险性评估方法与国内有很大不同,一般采用确定性方法和概率性方法,本文对这两种方法进行了探讨。确定性方法比较简单,对资料的要求相对较低一些,而概率性方法比较繁琐,对资料的要求也较高,需要专门的计算软件。同时,确定性方法只能提供MCE、MDE和OBE值,而概率性方法不仅能提供MCE、MDE和OBE值,还能提供不同超越概率水平的地震动峰值加速度及其反应谱。     

Uncertainty and precaution in European environmental risk assessment of chemicals.

It is recognised that there is a need for a proper treatment and transparency of uncertainty in risk assessment and management, especially in view of the upcoming proposed new chemical policy REACH, which delegates the responsibility for conducting risk assessments to industry. The current EU risk assessment for new and existing substances is largely deterministic and prudential measures are implicitly embedded in calculation schemes and rules. In this paper, a more probabilistic approach to risk assessment is advocated. The advantage is twofold: 1) inherent variability and other uncertainty pertaining to exposure and effects are transparently taken into account, while at the same time 2) issues of caution are explicitly transferred to the risk management phase. The result of a probabilistic risk assessment as suggested is improved transparency with quantitative and qualitative uncertainty estimates. Such uncertainty information can be used to discuss precautionary measures in the context of risk management.     

确定概率临界滑面的简化方法

在对边坡进行可靠度分析时,通常选择强度参数均值对应的确定性临界滑面或最小可靠度指标对应的概率临界滑面作为计算滑面,前者为确定性滑面上的可靠度指标,并非最小可靠度指标,后者作为边坡体系可靠度的近似,更接近于边坡的实际破坏概率。Hassan和Wolff提出的概率临界滑面计算方法,基于强度参数的特定组合,进行几次稳定性计算确定最小可靠度指标对应滑面,简便易行,无需专用的可靠度分析软件。为证明该方法的有效性,将其运用到简单均质土坡与非均质土坡算例中,并将工程中常用的正交设计运用到可靠度计算中,进行对比分析。结果显示:2个算例均表明最小可靠度指标对应的滑面与参数组合中最小安全系数对应的滑面相一致。因此,在工程应用中,可以采用该方法确定最小可靠度指标对应滑面。     

The Role of Aquifer Media in Improving the Quality of Seawater Feed to Desalination Plants

This study focuses on the role of the aquifer media as a tool to improve the characteristics of seawater used to supply desalination plants, quantifying this improvement from a considerable number of physicochemical parameters. The evolution of both physicochemical and microbiological characteristics through the aquifer media has been determined by comparing the composition of seawater samples taken via direct intake with those taken over the aquifer. The principal improvements are due to the process of filtration. It includes up to a 95 % reduction in turbidity and up to a 50 % reduction in SDI. Other parameters are also notably reduced (TOC is cut on average by 60 %, and DO by 80 %), due to bacterial activity in the aquifer. It is also important to highlight that only a short distance of water flow through the aquifer is needed to achieve this improvement. If it used the seabed as the filter material, it would be necessary a minimum thickness of a few meters due to biological activity involves a significant increase in the TOC. An understanding of the filtration processes occurring within the aquifer provides a natural analogue on which future improvements in artificial pre-treatment can be based.     

Stochastic and Robust Multi-Objective Optimal Management of Pumping from Coastal Aquifers Under Parameter Uncertainty

Combined simulation-optimization approaches have been used as tools to derive optimal groundwater management strategies to maintain or improve water quality in contaminated or other aquifers. Surrogate models based on neural networks, regression models, support vector machies etc., are used as substitutes for the numerical simulation model in order to reduce the computational burden on the simulation-optimization approach. However, the groundwater flow and transport system itself being characterized by uncertain parameters, using a deterministic surrogate model to substitute it is a gross and unrealistic approximation of the system. Till date, few studies have considered stochastic surrogate modeling to develop groundwater management methodologies. In this study, we utilize genetic programming (GP) based ensemble surrogate models to characterize coastal aquifer water quality responses to pumping, under parameter uncertainty. These surrogates are then coupled with multiple realization optimization for the stochastic and robust optimization of groundwater management in coastal aquifers. The key novelty in the proposed approach is the capability to capture the uncertainty in the physical system, to a certain extent, in the ensemble of surrogate models and using it to constrain the optimization search to derive robust optimal solutions. Uncertainties in hydraulic conductivity and the annual aquifer recharge are incorporated in this study. The results obtained indicate that the methodology is capable of developing reliable and robust strategies for groundwater management.     

Fuzzy stochastic damage mechanics (FSDM) based on fuzzy auto-adaptive control theory

In order to fully interpret and describe damage mechanics,the origin and development of fuzzy stochastic damage mechanics were introduced based on the analysis of the harmony of damage,probability,and ...     

Flood Forecasting and Decision Making in the new Millennium. Where are We?

This paper reviews the development of real time flood forecasting systems from the early 1970 approaches to the recent probabilistic ones. A preliminary discussion on the motivations for developing real time flood forecasting systems is introduced to explain their evolution in the last four to five decades. It will be shown how recent probabilistic flood forecasts are more robust and effective than the traditional deterministic ones. In particular, when combined with Bayesian decision approaches, probabilistic forecasts are the most appropriate tools for rational decision making in flood warning and flood management.Moreover, they allow taking into account the information from several models to be taken into account by combining into a unique predictive density the deterministic predictions of several hydrological or hydraulic models of a different nature, while in the multi-temporal forecasting extensions, they provide to answers questions such as: Which is the probability of overtopping a dyke in the next 24 h? When will this event be more likely to occur during the next 24 h?The work concludes with a discussion on the still unresolved problems, namely how decisions makers can fully take advantage of the probabilistic forecasts and how these forecasts must be communicated to them in order to meet this objective.     

Probabilistic Flow Duration Curves for Small Hydro Plant Design and Performance Evaluation

This work presents a probabilistic flow duration methodology that can be used as an alternative approach to the design and performance evaluation of small hydro plants. The probabilistic approach aims to quantify the uncertainty influencing the design and the economic outlook of small hydropower projects thus overcoming the limitations of the standard design methodology, which is based on a deterministic approach through the historical flow duration curve at the intake site. A simple probabilistic flow duration curve model is presented and an applied case study of the probabilistic approach to plant design and evaluation is provided based on a typical small hydro plant in a mountainous region of Greece. The results of this study however are not restricted to any particular geographical region and the methodology can be applied in a variety of hydrologic and economic environments.     

A Novel Approach Using Hybrid Fuzzy Vertex Method-MATLAB Framework Based on GMS Model for Quantifying Predictive Uncertainty Associated with Groundwater Flow and Transport Models

Identification of the uncertain parameters, which affecting on the qualitative behavior of the aquifer, and determining their effect on the uncertainty of the simulated nitrate concentration (NC) is one of the major challenges in the qualitative monitoring of aquifers. In this study, in order to determine the quantitative amount of uncertainty related to the simulated nitrate, an approach based on a hybrid of Groundwater Modeling System (GMS) model and Fuzzy Vertex Method (FVM) method was developed using the developed code for the relationship between aquifer simulation model and MATLAB environment. In this model, hydraulic conductivity, NC in aquifer recharge sources, longitudinal dispersivity coefficient, and specific storage parameters were considered as uncertain parameters in the distributed simulation model of the Ardabil aquifer. In the proposed approach, first the quantitative and qualitative (QQ) model of the aquifer was prepared using the GMS model and calibrated. Then, using the FVM method and developed MATALB code, the uncertain values appropriate for each of the aquifer active cells were determined. The results obtained from the monthly NC uncertainty show that with increasing the level of uncertainty, the uncertainty of the simulated NC increases significantly. For example, can be mentioned a 14-fold increase in the number of cells with variation of NC less than 10% in the September month. Also, the lowest and highest variation in the deterministic amount of NC is related to the months of Nov. and Sep. with concentration variations equal to [??8.5, 8.35] and [??23.43, 19.8] mg/L, respectively. The findings of this study show that the application of at least 10% uncertainty in the deterministic values of the simulated NC is necessary to provide a suitable view for quality monitoring of aquifer. A quantitative amount of monthly uncertainty in areas with nitrate concentrations greater than 50 mg/L indicates that the amount of uncertainty in these areas is higher than areas with nitrate concentrations less than 50 mg/L. This leads to errors in the monitoring of contaminated areas to eliminate contamination and quality restoration. Also, centralization of uncertainty is mainly concentrated in the northeastern, western and southwestern parts of Ardabil plain and the severity of uncertainty in the mentioned areas increases with the intensification of uncertainty and continues to the central areas. Finally, it must be said that hydraulic conductivity and NC in aquifer recharge sources, respectively, play the most important role in creating uncertainty and is necessary to be considered in the NC simulation models.

     

高拱坝-地基体系整体稳定概率地震风险分析

为了进行高拱坝的地震风险分析,引入幂指数函数的形式来描述地震危险性曲线,结合概率地震需求模型和地震易损性曲线函数,推求了概率地震风险分析解析函数。基于此,以实际工程为例,开展了高拱坝-地基体系整体稳定概率地震风险分析。在概率统计框架下,以高拱坝-地基系统整体抗震稳定安全评价为研究目标,在考虑坝基岩体内控制性滑动块体滑裂面力学参数不确定性基础上,构建了概率地震风险分析模型,得到了基于残余滑动位移的高拱坝-地基体系年超越概率曲线。从而给出了设计基准期限内,高拱坝-地基体系整体地震稳定达到不同性能水平的概率,为其在极限地震下的抗震安全评价提供依据,同时为现有基于准则的混凝土坝抗震安全决策转向基于风险概率的安全决策提供科学依据。     

贝叶斯概率水文预报研究进展与展望

水文预报不可避免地存在着输入、水文模型参数和结构等不确定性,导致预报结果也具有不确定性。因此,定量估计水文预报的不确定性,实现概率水文预报,不仅可得到比确定性预报更高的精度,而且还能为决策者提供更丰富的预报信息。本文根据不确定性来源的不同,从输入资料、模型结构、模型参数和综合不确定性等方面,详细综述了贝叶斯水文概率预报的研究进展,归纳了精度评定指标和效果检验方法,并展望了贝叶斯概率水文预报未来的研究重点和方向:(1)科学有效地解释、沟通和传播水文预报不确定性信息和概率水文预报产品;(2)建立水文集合概率预报框架,估计并降低水文预报的总不确定性;(3)开展考虑预报变量时空相关性的贝叶斯概率水文预报研究;(4)深入推动概率水文预报信息在风险决策中的应用。     

A Fuzzy-Stochastic Modeling Approach for Multiple Criteria Decision Analysis of Coupled Groundwater-Agricultural Systems

The complexity of water resources management increases when decisions about environmental and social issues are considered in addition to economic efficiency. Such complexities are further compounded by multiple uncertainties about the consequences of potential management decisions. In this paper, a new fuzzy-stochastic multiple criteria decision-making approach is proposed for water resources management in which a variety of criteria in terms of economic, environmental and social dimensions are identified and taken into account. The goal is to evaluate multiple conflicting criteria under uncertainties and to rank a set of management alternatives. The methodology uses a simulation-optimization water management model of a strongly interacting groundwater-agriculture system to enumerate criteria based on these bio-physical process interactions. Fuzzy and/or qualitative information regarding the decision-making process for which quantitative data is not available are evaluated in linguistic terms. Afterwards, Monte Carlo simulation is applied to combine these information and to generate a probabilistic decision matrix of management alternatives versus criteria in an uncertain environment. Based on this outcome, total performance values of the management alternatives are calculated using ordered weighted averaging. The proposed approach is applied to a real world example, where excessive groundwater withdrawal from the coastal aquifer for irrigated agriculture has resulted in saltwater intrusion, threatening the economical basis of farmers and associated societal impacts. The analysis has provided potential decision alternatives which can serve as a platform for negotiation and further exploration. Furthermore, sensitivity of different inputs to resulting rankings is investigated. It is found that decision makers’ risk aversion and risk taking attitude may yield different rankings. The presented approach suits to systematically quantify both probabilistic and fuzzy uncertainties associated with complex hydrosystems management.     

Particle Filters to Estimate Properties of Confined Aquifers

Involving a limited resource, the assessment of groundwater aquifers is of utmost importance. A key component of any such assessment is the determination of key properties that permit water resource managers to estimate aquifer drawdown and safe yield. This paper presents a particle filtering approach to estimate aquifer properties from transient data sets, leveraging recently published analytically-derived models for confined aquifers and using sample-based approximations of underlying probability distributions. The approach is examined experimentally through validation against three common aquifer testing problems: determination of (i) transmissivity and storage coefficient from non-leaky confined aquifer performance tests, (ii) transmissivity, storage coefficient, and vertical hydraulic conductivity from leaky confined aquifer performance tests, and (iii) transmissivity and storage coefficient from non-leaky confined aquifer performance tests with noisy data and boundary effects. On the first two well-addressed problems, the results using the particle filter approach compare favorably to those obtained by other published methods. The results to the third problem, which the particle filter approach can tackle more naturally than the previously-published methods, underscore the flexibility of particle filtering and, in turn, the promise such methods offer for a myriad of other geoscience problems.     

Mitigating Socio-Economic-Environmental Impacts During Drought Periods by Optimizing the Conjunctive Management of Water Resources

Multi-period optimization of conjunctive water management can utilize reservoirs and aquifer carry-over to alleviate drought impacts. Stakeholders’ socio-economic and environmental indices can be used to minimize the socio-economic and environmental costs associated with water shortages in drought periods. The knowledge gap here is the evaluation and inclusion of the socio-economic and environmental value of conjunctive water management in terms of its drought mitigation capability. In this paper, an integrated water quantity-quality optimization model that considers socio-economic and environmental indices is developed. The model considers and integrates reservoir and aquifer carry-over, river-aquifer interaction and water quality with stakeholders’ socio-economic indices of production, net income and labor force employment to evaluate the socio-economic and environmental value of conjunctive water management. Total dissolved solid (TDS) is used as the water quality index for environmental assessments. The model is formulated as a multi-period nonlinear optimization model, with analysis determining the optimal decisions for reservoir release and withdrawal from the river and aquifer in different months to maximize the socio-economic indices of stakeholders within the environmental constraints. The proposed model is used in Zayandehrood water resource system in Iran, which suffers from water supply and pollution problems. Model analysis results show that conjunctive water use in the Zayandehrood water basin reduces salinity by 50 % in the wetland and keeps water supply reduction during a drought under 10 % of irrigation demand.     

基于多步预报模型的径流中长期预测研究

针对径流中长期预报模型中广泛存在的不确定性问题,在以往研究的基础上,建立了基于最近邻回归预测的径流中长期多步概率预报模型。介绍了该模型的计算方法和重要参数的选取方法,并应用于实例预报工作中。结果表明,该模型在4个月的预报期内,具有较高的概率预报精度。同时,该模型概念清晰,避免了参数不确定性,不仅能够提供常规的确定性预报结果,而且能够给出在不同置信水平下的预报置信区间,便于量化预报风险,为最优决策的制定提供依据。     

改进的模糊综合评价法在渡槽风险评价中的应用

在传统的模糊综合评价方法中常利用主因素决定型算子、主因素突出型算子以及加权平均型算子通过模糊关系矩阵R将因素模糊向量A变换为等级模糊向量B,而传统的主因素型算子容易忽视各因素的综合影响以及重要性程度低但风险高的因素的影响,加权平均型算子不利于凸显不利因素对风险评价结果的决定性作用。所以本文在主因素突出型算子以及加权平均型算子的基础上加以改进得到两种新的Fuzzy合成算子:λ型Fuzzy合成算子和全优型Fuzzy合成算子。通过典型算例分析表明:λ型Fuzzy合成算子可以通过调整λ值改变对有利因素或不利因素的重视程度,而全优型Fuzzy合成算子可以避免有利因素对整体评价结果起到决定性影响,同时考虑到不利因素的决定性作用。两种算子在实际渡槽工程风险评估中的应用表明:在采用同一类模糊单值化的情况下,采用λ型Fuzzy合成算子和全优型Fuzzy合成算子得到的渡槽风险评价结果基本一致,两种新算子能够反映实际工程的安全特点,全面考虑实际工程的不利因子和有利因子对风险评价结果的影响和作用。     

基于k-最近邻筛选的BMA集合预报模型研究

针对冗余训练样本会降低BMA参数求解效率与精度问题,本文提出在BMA运算之前采用k-最近邻(k-nearest neighbor)算法筛选有价值训练样本,并用于BMA参数求解的改进模型。模拟试验在淮河王家坝站进行,分别以k-最近邻筛选、不筛选两种方案为BMA提供训练样本,统计分析两种方案中王家坝站流量模拟结果,评价BMA改进法的性能。模拟结果显示,采用k-最近邻样本筛选方法后,BMA模型对洪水过程以及洪峰的预报精度提升明显;概率预报结果的离散程度降低的同时,可靠性程度获得提升。k-最近邻样本筛选方法的引入,能够有效去除BMA模型训练样本中的冗余数据,以少量的样本获得更可靠的模型参数,改善集合预报性能。     

2D Finite Element Inundation Modelling in Anabranching Channels with Sparse Data: Examination of Uncertainties

Flood inundation modelling in developing countries is severely limited by the lack of high resolution terrain data and suitable imagery to map flood extents. This study assessed the predictive uncertainty of modelled flood extents generated from TELEMAC2D model using low-cost, sparse input data commonly available in developing countries. We studied a river reach characterised by anabranching channels and river islands in eastern India. In this complex fluvial setting, we analysed computational uncertainty as a function of error in both satellite-derived flood-extent maps using a Generalised Likelihood Uncertainty Estimation (GLUE)-based approach. The model performance was quite sensitive to the uncertainty in the inflow hydrograph, particularly close to the flood peak. Evaluation of the flood inundation probability map, conditioned upon deterministic and probabilistic observed flood extents, reveals that the effect of using probabilistic observed data is only evident for portions of the model domain where the model output is free from consistent bias (over or under prediction) likely created by the imperfect terrain data.     

Groundwater Overexploitation Causing Land Subsidence: Hazard Risk Assessment Using Field Observation and Spatial Modelling

Hazard risk assessment of land subsidence is a complicated issue aiming at identifying areas with potentially high environmental hazard due to land subsidence. The methods of hazard risk assessment of land subsidence were reviewed and a new systematic approach was proposed in this study. Quantitative identification of land subsidence is important to the hazard risk assessment. Field observations using extensometers were used to determine assessment indexes and estimate weights of each index. Spatial modelling was also established in ArcGIS to better visualize the assessment data. These approaches then were applied to the Chengnan region, China as a case study. Three factors, thickness of the second confined aquifer, thickness of the soft clay and the annual recovery rate of groundwater level were incorporated into the hazard risk assessment index system. The weights of each index are 0.33, 0.17 and 0.5 respectively. The zonation map shows that the high, medium and low risk ranked areas for land subsidence account for 9.5?%, 44.7?% and 45.8?% of the total area respectively. The annual recovery rate of groundwater level is the major factor raising land subsidence hazard risk in approximately half of the study area.     

The Value of Snow Depletion Forecasting Methods Towards Operational Snowmelt Runoff Estimation Using MODIS and Numerical Weather Prediction Data

Forecasting streamflow mainly due to snowmelt in the mountainous eastern part of Turkey is important in terms of effective management of water resources at the headwaters of Euphrates River, where large dam reservoirs are located. Monitoring Snow Covered Area (SCA) and modeling snowmelt forms the backbone of the forecasting studies as the snowmelt dominating runoff constitutes approximately 2/3 of total annual volume of runoff during spring and early summer. Two main motivations of the study are; firstly, to assess the methodologies to forecast SCA using Moderate Resolution Imaging Spectroradiometer (MODIS) data and derive Snow Depletion Curve (SDC) for each elevation zone. Secondly, to forecast 1?day ahead daily discharges using the derived SDCs and Numerical Weather Prediction (NWP) data corrected specifically for the area. The Upper Euphrates Basin (10,275?km²) is selected as the pilot basin and MODIS daily snow cover products are analyzed for the snowmelt season. Four different methodologies are proposed and assessed to forecast SDCs; simple averaging, temperature based, stochastic modeling and probabilistic approach. SDCs are derived for the water years 2006?C2010, 4?years data are used to derive the equations of the methodologies and 1?year is used to verify their skills. Forecasting discharges 1?day ahead with Snowmelt Runoff Model using NWP data is the second part of the study. Impact of forecasted SDCs with different methodologies is examined with the model. Model applications provide promising results both for the forecasting of SCA and runoff with an overall Model Efficiency higher than 0.60 and 0.85, respectively.