AN APPROACH TO MARGINAL ECONOMIC ANALYSIS OF HYDROMETRIC DATA COLLECTION1

ABSTRACT: A procedure for computing the benefit/cost ratio of a hydrometric network is outlined. It consists of two steps: firstly, establishing a relationship between hydrometric station density and error in hydrologic parameters and, secondly, relating hydrologic eror to changes in project cost. The procedure was applied to both the whole Canadian hydrometric network and the provincial networks.     

A PROCEDURE FOR ASSESSING WATER RESOURCES FOR URBAN PLANNING1

A matrix has been developed to guide the assessment of urban water resources. The matrix provides a means for determining the relative importance of water-related problems, and for identifying the data needed to evaluate these problems for the purpose of urban planning. The matrix columns list nine categories of potential water-related urban problems. The rows list 51 categories of data inputs which may be needed to evaluate the potential problems. The inputs include standard types of basic hydrologic data, information based on analysis and interpretation of these data, and information on the interfacing factors of climate, land, and culture. A system is described for ranking the relative importance of the problem categories and data inputs on a numerical scale of 0 to 3. From this, an index is derived that evaluates the relative importance of each input item to an overall program for water resource assessment. From the completed matrix the hydrologist can determine the availability of data to meet the identified requirements. Judgement can then be made as to priorities on work elements to provide the planner with maximum information in minimum time. The matrix also provides a basis for the development of programs and their funding in order to overcome critical data deficiencies.     

ON THE MISMATCH BETWEEN DATA AND MODELS OF HYDROLOGIC AND WATER RESOURCE SYSTEMS1

ABSTRACT: Many difficulties exist in the matching of models with data. This paper identifies elements of this problem and discusses considerations involved in model evaluation. The well known multivariate linear regression model is used to illustrate the distinctions between accuracy and precision and between estimation and prediction (because the model is commonly misused.) No amount of additional data will improve the accuracy of a poor model. A high R², while indicative of a good matching between the observed data and model estimates, is a poor criterion for judging adequacy of the model to make good predictions of future events. Model evaluation also includes the problem of introducing secondary data and proxy variables into a model. Secondary data frequently enter, for example, the mass, energy and water budget equations because of difficulties in measuring the primary variables. Proxy variables arise because of a desire to collapse a vector of incomparable values, say, of water quality into a single number. Review of the above issues indicates that model evaluation is a multi-criterion problem, often imbedded in a larger framework where models are intended to meet multiple objectives. The mismatch of models and data has increasing legal and social consequences.     

DATA REQUIREMENTS OF A WATER QUALITY MANAGEMENT PROGRAM1

ABSTRACT: Routine data collection currently consumes a large amount of the total resources devoted to water quality management. All too often data collection becomes an end in itself, with little thought given to the purpose of the data collection. The problem generally stems from a lack of proper routine surveillance system design and a failure on the part of the designers to initially identify the data needs of the management program. This study attempts, in a general way, to delineate the data needs of a water quality management program. This first required an identification of the activities involved in water quality management. The activities were then discussed in terms of the types of information needed to successfully complete their assigned tasks. Several detailed examples are given. The results of the discussion are summarized and several strategies are proposed to relate the results to surveillance system design.     

卫星云图资料同化中的质量控制及其在暴雨预报中的应用

以1996年7月3—4日发生在江淮流域的暴雨过程为例，依据统计特征分别以常规分析场、正涡度高值区、高低空急流为标准，设计了三套方案对卫星反演湿度场进行质量控制。利用中尺度MM5模式进行的数值试验表明：经质量控制后预报场得到较大改善；质量控制方案的优劣对预报效果有直接影响，选择合理的控制标准是重要的。     

灾害等级的综合评价

灾害等级被用来表示灾害所造成的社会损失大小。主成分分析法能够在保证原始数据信息损失最小的情况下，以少数的综合变量取代原有的多维变量，使数据结构大为简化，并且客观地确定权数，避免了主观随意性，因而是灾害等级综合评价的一种简单易行的有效方法。通过主成分分析，可以为减灾和分区和分级管理提供重要的理论依据。     

TILE RATIONALE FOR OHIO'S DETERGENT PHOSPHORUS BAN1

ABSTRACT: Ohio signed into law a detergent phosphorus ban on March 26, 1988. This law limits the elemental phosphorus content of household laundry detergents to 0.5 percent in all 35 Lake Erie counties in Ohio by 1990. Ohio's detergent phosphorus ban will help non-compliant municipal wastewater treatment plants achieve compliance with the 1 mg/L effluent phosphorus standard. By limiting the phosphorus content of household laundry detergents, Ohio will also benefit from less phosphorus entering surface waters from combined sewer overflows, communities with treatment plant bypasses, and riparian homes with septic tanks. This is important because most of the phosphorus in laundry detergents is in the bioavailable form and Ohio's Lake Erie shoreline is particularly sensitive to Cladophora problems. When viewed in conjunction with reduced chemical costs for phosphorus removal and savings in sludge disposal costs, Ohio's detergent phosphorus ban is a pragmatic component of an international phosphorus management strategy to protect the Great Lakes.     

TREND ASSESSMENT OF WATER QUALITY TIME SERIES1

ABSTRACT A general methodology is described for identifying and statistically modeling trends which may be contained in a water quality time series. A range of useful exploratory data analysis tools are suggested for discovering important patterns and statistical characteristics of the data such as trends caused by external interventions. To estimate the entries in an evenly spaced time series when data are available at irregular time intervals, a new procedure based upon seasonal adjustment is described. Intervention analysis is employed at the confirmatory data analysis stage to rigorously model changes in the mean levels of a series which are identified using exploratory data analysis techniques. Furthermore, intervention analysis can be utilized for estimating missing observations when they are not too numerous. The effects of cutting down a forest upon various water quality variables and also the consequences of acid rain upon the alkalinity in a stream provide illustrative applications which demonstrate the effectiveness of the methodology.     

基于DEA技术的污水处理定价模型

应用DEA方法评价污水处理厂相对效率，并将评价结果用于污水处理定价，得到了基于DEA方法的污水处理定价模型。该模型克服了现有定价模型的不足，体现了政府对污水处理厂生产效率的要求，能够促使企业提高效率并降低成本。     

A GENETIC ALGORITHM TO REDUCE STREAM CHANNEL CROSS SECTION DATA1

A genetic algorithm (GA) was used to reduce cross section data for a hypothetical example consisting of 41 data points and for 10 cross sections on the Kootenai River. The number of data points for the Kootenai River cross sections ranged from about 500 to more than 2,500. The GA was applied to reduce the number of data points to a manageable dataset because most models and other software require fewer than 100 data points for management, manipulation, and analysis. Results indicated that the program successfully reduced the data. Fitness values from the genetic algorithm were lower (better) than those in a previous study that used standard procedures of reducing the cross section data. On average, fitnesses were 29 percent lower, and several were about 50 percent lower. Results also showed that cross sections produced by the genetic algorithm were representative of the original section and that near‐optimal results could be obtained in a single run, even for large problems. Other data also can be reduced in a method similar to that for cross section data.