QUANTITATIVE METHODS FOR PRELIMINARY DESIGN OF WATER QUALITY SURVEILLANCE SYSTEMS1

ABSTRACT: To assure attainment and maintenance of desired water quality levels in our rivers and streams, systematic monitoring must be performed. A preliminary phase of the design of water quality surveillance systems is the specification of sampling frequencies and station locations throughout the basin; that is, the development of an adequate space/time sampling plan. The purpose of this paper is to present some quantitative methods which have been developed to identify candidate sets of sampling frequencies and station locations, and to establish priorities for implementing the different frequencies and locations. These methods are useful in the cost/effectiveness trade-off analyses in surveillance system design, and are based on the surveillance system objective of pollution abatement in which it is desired to detect violations in state-federal water quality standards. A spatial priority measure is developed which is dependent both on the water quality profile in the stream and on the information obtained from monitoring stations located in other reaches. Also, a temporal sampling priority rating is presented which is a measure of the effectiveness of the surveillance system with respect to its ability to detect the violations in the standards. To illustrate the quantitative methods, the procedures are applied to the Wabash River Basin.     

A BASKET SAMPLING TECHNIQUE FOR QUANTIFYING RIVERINE MACROBENTHOS1

ABSTRACT: A colonization basket sampling technique for quantifying macroinvertebrates in rubble bottom rivers is described. Basket samplers were compared to collections made using the Surber square foot sampler. Procedure for processing samples is described. Macrobenthos were more accurately quantified by basket samplers because both surface and subsurface habitats were sampled, no organisms were lost when samplers were removed from the river, substrata and associated fauna were retained for analysis, and the samplers were used efficiently in both pool and riffle areas. At the end of the colonization period the basket samplers contained one-half cubic foot of river bottom and macroinvertebrates spatially arranged within.     

NONPARAMETRIC STATISTICAL METHODS IN URBAN HYDROLOGIC RESEARCH1

ABSTRACT. In urban hydrologic studies, it is often necessary to determine the effect of changes in urban land use patterns on such runoff characteristics as flood peaks and flow volumes. Nonparametric statistical methods have certain properties that make them a valuable tool for detecting hydrologic change caused by a treatment, such as urbanization, that changes watershed over a period of time. As many hydrologists do not have a working familiarity with nonparametric methods, a number of them are used for illustrative purposes to analyze the effect of urbanization on 24 years of annual flood peaks for a Louisville, Kentucky, watershed. In the example, urbanization was found to increase the central tendency, but not the dispersion of the peaks. Peak flows modeled by holding watershed parameters constant were also found to be increasing because of an upward trend in precipitation. By following the numerical examples in the paper and looking up test statistics in referenced sources, the reader can easily apply these methods to other situations.     

图书防灾与保护措施

图书的防灾与保护是图书馆工作的重要内容.重点阐述了纸型图书防灾和保护工作的重要意义,分析了损坏纸型图书载体的因素,提出了图书防灾与保护的措施以及图书馆所担负的责任和义务.     

WATER QUALITY SAMPLING SCHEMES FOR VARIABLE FLOW CANALS AT REMOTE SITES1

ABSTRACT: Growing interest in water quality has resulted in the development of monitoring networks and intensive sampling for various constituents. Common purposes are regulatory, source and sink understanding, and trend observations. Water quality monitoring involves monitoring system design; sampling site instrumentation; and sampling, analysis, quality control, and assurance. Sampling is a process to gather information with the least cost and least error. Various water quality sampling schemes have been applied for different sampling objectives and time frames. In this study, a flow proportional composite sampling scheme is applied to variable flow remote canals where the flow rate is not known a priori. In this scheme, historical weekly flow data are analyzed to develop high flow and low flow sampling trigger volumes for auto‐samplers. The median flow is used to estimate low flow sampling trigger volume and the five percent exceedence probability flow is used for high flow sampling trigger volume. A computer simulation of high resolution sampling is used to demonstrate the comparative bias in load estimation and operational cost among four sampling schemes. Weekly flow proportional composite auto‐sampling resulted in the least bias in load estimation with competitive operational cost compared to daily grab, weekly grab sampling and time proportional auto‐sampling.     

The role of analytical science in natural resource decision making

There is a continuing debate about the proper role of analytical (positivist) science in natural resource decision making. Two diametrically opposed views are evident, arguing for and against a more extended role for scientific information. The debate takes on a different complexion if one recognizes that certain kinds of problem, referred to here as “wicked” or “trans-science” problems, may not be amenable to the analytical process. Indeed, the mistaken application of analytical methods to trans-science problems may not only be a waste of time and money but also serve to hinder policy development. Since many environmental issues are trans-science in nature, then it follows that alternatives to analytical science need to be developed. In this article, the issues involved in the debate are clarified by examining the impact of the use of analytical methods in a particular case, the spruce budworm controversy in New Brunswick. The article ends with some suggestions about a “holistic” approach to the problem.     

Resampling methods for evaluating classification accuracy of wildlife habitat models

Predictive models of wildlife-habitat relationships often have been developed without being tested The apparent classification accuracy of such models can be optimistically biased and misleading. Data resampling methods exist that yield a more realistic estimate of model classification accuracy These methods are simple and require no new sample data. We illustrate these methods (cross-validation, jackknife resampling, and bootstrap resampling) with computer simulation to demonstrate the increase in precision of the estimate. The bootstrap method is then applied to field data as a technique for model comparison We recommend that biologists use some resampling procedure to evaluate wildlife habitat models prior to field evaluation.     

WATER SUPPLY FORECASTING IN REAL-TIME1

ABSTRACT: A strategy is developed for making seasonal water supply forecasts in real time. It links the traditional regression based forecasting techniques to real-time data acquired by systems such as the Soil Conservation Service SNOTEL and the U.S. Bureau of Reclamation Hydromet networks. The concept is based on interpolating between the forecast values obtained by using real-time data in the forecast equations that bracket the real time. Different interpolation procedures were examined and the procedure for calculating confidence intervals about the forecast estimates is developed. The entire conceptual procedure is demonstrated using data from the Reynolds Creek, Idaho, experimental watershed maintained by the USDA-ARS Northwest Watershed Research Center in Boise, Idaho.     

Soil Moisture Data Assimilation Using Support Vector Machines and Ensemble Kalman Filter1

Abstract: A hybrid data assimilation (DA) methodology that combines two state‐of‐the‐art techniques, support vector machines (SVMs) and ensemble Kalman filter (EnKF), is applied for soil moisture DA in this work. The SVM methodology provides a statistically sound and robust approach to solving the inverse problem, and thus to building statistical models. EnKF is an extension of the Kalman Filter (KF), a well‐known tool in prediction updating. In the present research, ground measurements were used to build a SVM‐type soil moisture predictor. Subsequent observations and their statistics were assimilated to update predictions from the SVM model by coupling it with EnKF. In this way, both model predictions and ground data, as well as their statistics, are fused thus minimizing the prediction error and making the predictions and observations statistically consistent. The results are shown for two approaches; one in which update is done at every time step and the other which assumes that data is only available at alternate time steps (in window of 10 time steps) and hence update is performed at those occasions. The SVM‐EnKF coupling is shown to improve soil moisture forecasts in an example using data from the Soil Climate Analysis Network site at Ames, Iowa.     

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.