水资源规划优化技术研究进展及展望

针对水资源管理与规划的发展过程,详细论述了水资源系统管理与规划中常用优化技术的国内外研究成果及应用.根据是否考虑了模型参数或者模型结构的不确定性问题,将优化模型分为确定性优化模型和不确定性优化模型,通过对不确定性优化模型的介绍指出了不确定性优化是当前水资源系统优化的热点,同时指出未来不确定性优化技术发展方向等.     

电位滴定法测定水中氯化物的不确定度评定研究

根据《测量不确定度评定与表示》（JJF 1059.1-2012）,建立了实验室电位滴定仪测定水中氯化物不确定度数学模型,分析了整个过程各种不确定度的影响因素,量化各不确定度分量,计算合成不确定度和扩展不确定度.本次测量结果为（110±6.18） mg/L,合成相对不确定度值为0.028 1,扩展不确定度为6.18 mg/L.电位滴定仪测定氯化物的不确定度主要来源是样品重复测定和滴定终点体积读数.     

重量法测定空气中PM10的不确定度评定研究

根据重量法测定空气中PM10的测量方法原理,建立数学模型,从样品现场采样与实验室测试相结合引入各不确定度分量,分析并评定用重量法测定空气中PM10的不确定度,确定合成不确定和扩展不确定度.从各不确定度分量大小,最终确定测量不确定度影响因素并加以有效控制及改进,改善测量方法和手段,从而为空气中PM10浓度测定提高检测数据准确性,提供科学检测依据.     

决策偏好对水环境污染物总量分配的影响

总量分配是制订污染物总量控制方案的基础,在总量分配方法中合理性指数法是由各因素组成的多目标分配方法,是一种新方法、新思路.由于各因素之间对应的权重系数通常采用专家打分方法确定,因此,分析主观性对总量分配结果的影响具有十分重要的意义.论文共选取30名不同领域的人员,并进行分组,分别对权重因子进行打分,分析不同类型的人员权重分布的特点及其对污染物总量分配结果的影响.研究结果表明:不同人员的权重因子对污染物总量分配具有一定的影响,对最后的决策结果产生一定的不确定性.从分配总量结果来看,TN、TP平均值分别为1.64万t·a-1、0.12万t·a-1,标准差分别为0.16万t·a-1、0.014万t·a-1,变差系数分别为0.09、0.12;从分组结果来看,管理人员一致性较好,研究人员一致性较差.总体而言,综合考虑不同领域专家的意见是保持总量分配方案的合理性和一致性的有效途径.     

水中氯化物测定的不确定度评定

根据硝酸银滴定法测定水中氯化物的含量，分析了该方法测量不确定度的来源，评定了水中氯化物的测量不确定度，在各不确定度中，以标准溶液配制与样品分析时滴定消耗的硝酸银体积引入的不确定度较大。     

THE INFLUENCE OF (x,y) UNCERTAINTY ON PREDICTION ERROR AND CONTOUR LINES FROM A THREE-DIMENSIONAL SURFACE1

ABSTRACT: In geohydrology, three-dimensional surfaces are typically represented as a series of contours. Water levels, saturated thickness, precipitation, and geological formation boundaries are a few examples of this practice. These surfaces start as point measurements that are then analyzed to interpolate between the known point measurements. This first step typically creates a raster or a set of grid points. In modeling, subsequent processing uses these to represent the shape of a surface. For display, they are usually converted to contour lines. Unfortunately, in many field applications, the (x, y) location on the earth's surface is much less confidently known than the data in the z dimension. To test the influence of (x, y) locational accuracy on z dimension point predictions and their resulting contours, a Monte Carlo study was performed on water level data from northwestern Kansas. Four levels of (x, y) uncertainty were tested ranging in accuracy from one arc degree-minute (± 2384 feet in the x dimension and ± 3036 feet in the y dimension) to Global Positioning Systems (GPS) accuracy (± 20 feet for relatively low cost systems). These span the range of common levels of locational uncertainty in data available to hydrologists in the United States. This work examines the influence that locational uncertainty can have on both point predictions and contour lines. Results indicate that overall mean error exhibits a small sensitivity to locational uncertainty. However, measures of spread and maximum errors in the z domain are greatly affected. In practical application, this implies that estimates over large regions should be asymptotically consistent. However, local errors in z can be quite large and increase with (x, y) uncertainty.     

MASS BALANCE MODEL ESTIMATION OF PHOSPHORUS CONCENTRATIONS IN RESERVOIRS1

ABSTRACT: Mass balance models have been common tools in lake quality management for some years. However, verification for use on reservoirs, especially in the Western United States, has been seriously lacking, In this study, such a verification is attempted using data from the U.S EPA National Eutrophication Survey. Several models from the literature are compared for accuracy in application to the western reservoir data. Model standard error and correlation between estimated and observed reservoir phosphorus concentrations are the Criteria used for comparison. Standard errors am further used to calculate uncertainty of trophic state classification based on estimated phosphorus concentration. The model proposed by Dillon and Rigler (1974) proved most accurate, with a correlation coefficient of 0.86 and standard error of 0.2, based on logarithmic transformed values. Deficiencies in the other models appear to & from coefficients fit to lake data and from inappropriate model formulation.     

SMOOTHED DATA AND GRADIENTS USING SLIDING POLYNOMIALS WITH OPTIONAL CONTROLS1

ABSTRACT: Sliding polynomials differ from other piecewise interpolation and smoothing methods in their functional continuity at the nodes. This functional continuity was used to establish optional spacing of nodes and optional boundary controls in data smoothing while still maintaining mathematically continuous rates or gradients. Cyclic as well as noncyclic data can be smoothed. Variance of the individual nodal values. derived through least-squares optimization, can be calculated using the rigorously determined weighting coefficients between data points and nodes. Such nodal variances are estimates of localized uncertainty in the data which complement the localization of smoothing through use of piecewise functions. Choice of controls in smoothing and calculation of variance have been incorporated in a computer program for user convenience.     

PROBABILISTIC METHODS IN STREAM QUALITY MANAGEMENT1

ABSTRACT. Recent advances in water quality modelling have pointed out the need for stochastic models to simulate the probabilistic nature of water quality. However, often all that is needed is an estimate of the uncertainty in predicting water quality variables. First order analysis is a simple method of providing an estimate in the uncertainty in a deterministic model due to uncertain parameters. The method is applied to the simplified Streeter-Phelps equations for DO and BOD; a more complete Monte Carlo simulation is used to check the accuracy of the results. The first order analysis is found to give accurate estimates of means and variances of DO and BOD up to travel times exceeding the critical time. Uncertainty in travel time and the BOD decay constant are found to be most important for small travel times; uncertainty in the reaeration coefficient dominates near the critical time. Uncertainty in temperature was found to be a negligible source of uncertainty in DO for all travel times.     

COMPLEMENTING OBJECTIVE PROCEDURES WITH SUBJECTIVE PRIORS IN WATER RESOURCE PLANNING1

A procedure is outlined which allows consideration of both objective and subjective indicators to establish priorities in plan implementation of water resource development. The objective procedure utilizes stepwise multiple discriminant analysis to predict community performance regarding planned project implementation, based on previous project implementation in the Northeast. The subjective procedure incorporates prior probabilities developed by the planner, based on observation and experience gained through the planning process. The proposed analysis could eliminate waste through better allocation of planning funds to implementation studies exhibiting higher probability of early implementation.