水资源(环境)承载力的研究进展

概述了水资源(环境)承载力的概念、特征、量化评价方法及其总体研究的进展。强调了系统研究水资源(环境)承载力的重要现实意义。指出了当前研究中的不足,以及有待进一步研究的问题。     

水资源系统中的不确定性及风险分析方法

水资源系统中广泛存在着不确定性，其对系统的影响很大，甚至带来灾害性风险(如洪水、干旱)，是风险产生的根本原因，也是水资源系统研究遇到的难点问题之一。从分析总结水资源系统中存在的不确定性因素的类型(包括随机性、模糊性、灰色性及未确知性)及研究方法，提出水资源不确定性系统的概念。从不确定性因素的数学处理方法入手，介绍基于概率统计学(处理随机性)的风险计算模型，提出基于模糊隶属度(处理模糊性)、基于灰数(处理灰性)和基于未确知数(处理未确知性)的风险计算模型。为水资源系统风险规划与管理奠定基础。     

Stochastic multimedia risk assessment for a site with contaminated groundwater

 There exist many sites with contaminated groundwater because of inappropriate handling or disposal of hazardous materials or wastes. Health risk assessment is an important tool to evaluate the potential environmental and health impacts of these contaminated sites. It is also becoming an important basis for determining whether risk reduction is needed and what actions should be initiated. However, in research related to groundwater risk assessment and management, consideration of multimedia risk assessment and the separation of the uncertainty due to lack of knowledge and the variability due to natural heterogeneity are rare. This study presents a multimedia risk assessment framework with the integration of multimedia transfer and multi-pathway exposure of groundwater contaminants, and investigates whether multimedia risk assessment and the separation of uncertainty and variability can provide a better basis for risk management decisions. The results of the case study show that a decision based on multimedia risk assessment may differ from one based on risk resulting from groundwater only. In particular, the transfer from groundwater to air imposes a health threat to some degree. By using a methodology that combines Monte Carlo simulation, a rank correlation coefficient, and an explicit decision criterion to identify information important to the decision, the results obtained when uncertainty and variability are separate differ from the ones without such separation. In particular, when higher percentiles of uncertainty and variability distributions are considered, the method separating uncertainty and variability identifies TCE concentration as the single most important input parameter, while the method that does not distinguish the two identifies four input parameters as the important information that would influence a decision on risk reduction.     

Problems in the application of the SSHAC probability method for assessing earthquake hazards at Swiss nuclear power plants

From 2000 to 2004 a large scale probabilistic seismic hazard analysis (PEGASOS) was created and performed as a research project, sponsored by the Swiss NPP utilities to improve the assessment methodology for seismic risk in support of the plant-specific seismic PRAs. The project followed the methodology of the SSHAC [Senior Seismic Hazard Analysis Committee (SSHAC), 1997. Recommendations for Probabilistic Seismic Hazard Analysis: Guidance on Uncertainty and Use of Experts. NU-REG/CR-6372] procedures at its most elaborate way—level 4. Before practical implementation was to occur, a detailed review was performed including validation tests and analysis of uncertainty propagation. This paper presents the main results of the review. The review revealed that current PSHA (Probabilistic Seismic Hazard Analysis) methodology as based on logic trees, in conjunction with the SSHAC procedures, potentially leads to a significant overestimation of the seismic hazard in areas with low seismic activity due to the inherent possibilities of unconstrained accumulation of uncertainties. The preliminary results of the project did not pass any of our logical geological–scientific benchmark tests used in our attempts to perform a validation process of the PEGASOS analysis method. Some of the problems encountered are of generic nature and shall be studied carefully before making the decision of whether or not the Swiss nuclear power industry will adopt the recommended use of SSHAC-procedures as a basis for the evaluation of the seismic hazard for individual nuclear power plant seismic PRA without correction.     

基于涡度相关技术估算植被/大气间净CO2交换量中的不确定性

近年来，涡度相关技术的进步使陆地生态系统CO2通量的长期和连续观测成为可能。目前，涡度相关技术是全球通量观测网络（FLUXNET）测定植被/大气间CO2通量的主要技术手段，但绝大部分CO2通量观测站点都处于非典型的理想条件下，不能完全满足涡度相关技术的基本假设条件，从而导致基于涡度相关技术估算植被/大气间净生态系统CO2交换量的不确定性。系统介绍了涡度相关技术的基本假设，基本理论公式和误差的类型与特征等理论问题，重点阐述了通量测定中仪器本身的物理限制、二维和三维的气流运动、数据处理的方法和夜间通量的低估等不确定性的主要来源，并据此对通量观测研究中需要优先考虑的问题提出一些建议。研究认为数据质量控制与分析以及误差评价是不同通量站点间的结果比较和全球尺度综合分析的过程中需重点考虑的问题。     

Modeling uncertainty in stability analysis for design of embankment dams on difficult foundations

A probabilistic 3-D slope stability analysis model (PTDSSAM) is developed to evaluate the stability of embankment dams and their foundations under conditions of staged construction taking into consideration uncertainty, spatial variabilities and correlations of shear strength parameters, as well as the uncertainties in pore water pressure. The model has the following capabilities: (1) conducting undrained shear strength analysis (USA) and effective stress analysis (ESA) slope stability analysis of staged construction, (2) incorporation of field monitored data of pore water pressure, and (3) incorporation of increase of undrained shear strength with depth, effective stress, and pore water pressure dissipation. The PTDSSAM model is incorporated in a computer program that can analyze slopes located in multilayered deposits, considering the total slope width.

The main outputs of the program are the geometric parameters of the most critical sliding surface (i.e., center of rotation/radius of rotation and critical width of failure), mean 2-D safety factor, mean 3-D safety factor, squared coefficient of variation of resisting moment, and the probability of slope failure. The program is applied to a case study, Karameh dam in Jordan. Monitored data of induced pore water pressure in the dam embankment and soft foundation were gathered during dam construction.

The stability of Karameh dam embankment and foundation was evaluated during staged construction using deterministic and probabilistic analysis. Foundation stability was evaluated based on the monitored data of pore water pressure.

The study showed that the mean values of the corrective factors which account for the discrepancies between the in situ and laboratory-measured values of soil properties and for the modeling errors have significant influence on the 2-D safety factor, 3-D safety factor, slope probability of failure, and on the expected failure width.

The degree of spatial correlation associated with shear strength parameters within a soil deposit also influences the probability of slope failure and the expected failure width. This correlation is quantified by scale of fluctuation. It is found that a larger scale of fluctuation gives an increase in the probability of slope failure and a reduction in the critical failure width.     

ITOM: an interval-parameter two-stage optimization model for stochastic planning of water resources systems

Planning of water resources systems is often associated with many uncertain parameters and their interrelationships are complicated. Stochastic planning of water resources systems is vital under changing climate and increasing water scarcity. This study proposes an interval-parameter two-stage optimization model (ITOM) for water resources planning in an agricultural system under uncertainty. Compared with other optimization techniques, the proposed modeling approach offers two advantages: first, it provides a linkage to pre-defined water policies, and; second, it reflects uncertainties expressed as probability distributions and discrete intervals. The ITOM is applied to a case study of irrigation planning. Reasonable solutions are obtained, and a variety of decision alternatives are generated under different combinations of water shortages. It provides desired water-allocation patterns with respect to maximum system benefits and highest feasibility. Moreover, the modeling results indicate that an optimistic water policy corresponding to higher agricultural income may be subject to a higher risk of system-failure penalties; while, a too conservative policy may lead to wastage of irrigation supplies.     

Numerical investigation of the impact of uncertainties in satellite rainfall estimation and land surface model parameters on simulation of soil moisture

This study aims at evaluating the uncertainty in the prediction of soil moisture (1D, vertical column) from an offline land surface model (LSM) forced by hydro-meteorological and radiation data. We focus on two types of uncertainty: an input error due to satellite rainfall retrieval uncertainty, and, LSM soil-parametric error. The study is facilitated by in situ and remotely sensed data-driven (precipitation, radiation, soil moisture) simulation experiments comprising a LSM and stochastic models for error characterization. The parametric uncertainty is represented by the generalized likelihood uncertainty estimation (GLUE) technique, which models the parameter non-uniqueness against direct observations. Half-hourly infra-red (IR) sensor retrievals were used as satellite rainfall estimates. The IR rain retrieval uncertainty is characterized on the basis of a satellite rainfall error model (SREM). The combined uncertainty (i.e., SREM + GLUE) is compared with the partial assessment of uncertainty. It is found that precipitation (IR) error alone may explain moderate to low proportion of the soil moisture simulation uncertainty, depending on the level of model accuracy—50–60% for high model accuracy, and 20–30% for low model accuracy. Comparisons on the basis of two different sites also yielded an increase (50–100%) in soil moisture prediction uncertainty for the more vegetated site. This study exemplified the need for detailed investigations of the rainfall retrieval-modeling parameter error interaction within a comprehensive space-time stochastic framework for achieving optimal integration of satellite rain retrievals in land data assimilation systems.     

Application of uncertainty analysis to groundwater pollution modeling

Prediction and evaluation of pollution of the subsurface environment and planning remedial actions at existing sites may be useful for siting and designing new land-based waste treatment or disposal facilities. Most models used to make such predictions assume that the system behaves deterministically. A variety of factors, however, introduce uncertainty into the model predictions. The factors include model and pollution transport parameters and geometric uncertainty. The Monte Carlo technique is applied to evaluate the uncertainty, as illustrated by applying three analytical groundwater pollution transport models. The uncertainty analysis provides estimates of statistical reliability in model outputs of pollution concentration and arrival time. Examples are provided that demonstrate: (a) confidence limits around predicted values of concentration and arrival time can be obtained, (b) the selection of probability distributions for input parameters affects the output variables, and (c) the probability distribution of the output variables can be different from that of the input variables, even when all input parameters have the same probability distribution     

初论工程地质量化预测结论的可靠性

本文首次提出一个新的研究领域-工程地质量化预测(评价)结论的可靠性分析。文中论述了不确定性的来源、种类和可发现性,初步建立了单种方法预测结论的显著性,多种方法预测的综合结论及其可靠性的一套判别方法。在膨胀土边坡坍塌规模预测中的应用表明使用这套方法能明显地提高预测的准确性。