No abstract

The statistical characteristics of' the accuracy of' regression analyses as used in surface water regionalization are investigated by simulating logarithmic regressions of the streamflow parameters, mean and standard deviation, derived from synthetic streamflow sequences. Accuracy is measured in terms of equivalent years of at-site record. A procedure for the design of'surface water data networks that accounts for the statistical nature of the estimates of' parameter accuracy is presented.In the planning process for the development of the water resources of a particular region, certain inputs of' hydrologic information are desirable. At the present time, however, the optimum level of hydrologic information demanded at any specific step of the planning process has not been defined. In

Two network-design technologies are compared by random sub-sampling of actual streamflow data. The technologies, Network Analysis for Regional Information (NARI) and Network Analysis Using Generalized Least Squares (NAUGLS), have a common objective, viz. to maximize regional information within a limited budget and time horizon. The data used for intercomparison are from a network of 146 streamgauges in the central part of the United States. In general, the results for the illustrative example indicate that the NAUGLS method conveys more information than the NARI method to the network designer interested in maximizing regional information about mean annual flows with a limited budget.Comparaison des systèmes pour la planification des réseaux hydrologiques Résumé Deux systèmes pour la planification des réseaux sont comparés par un sous-échantillonage au hasard des données d'écoulement réelles. Les systèmes, Analyse des Réseaux pour Information Régionale (NARI) et Analyse des Réseaux par une Méthode Généralisée de Régression Linéaire (NAUGLS) ont un objectif commun, tirer les meilleurs connaissances d'une région, étant donné que les dépenses et le temps sont limités. Les données employées pour la comparaison viennent d'un réseau de stations de jaugeage du centre des États Unis. Les résultats du cas pris pour exemple indiquent que le contrôleur d'un réseau qui dispose de fonds limitées préférerait en général le système NAUGLS au système NARI pour une meilleur évaluation des debits moyens annuels d'une région.

The autocorrelation structure of monthly streamflows, a nonstationary process, is developed from a mathematical model that assumes that monthly precipitation is an independent series and that the base flow of the stream is derived from a linear aquifer. Under these assumptions the first-order autocorrelation coefficients of streamflow are found to vary seasonally, as do other statistics such as monthly means and standard deviations. Comparison of the autocorrelation coefficients predicted by the model with those computed from an actual streamflow record of 58 years indicates that the seasonality of streamflow is well represented by the model. The effects of autocorrelation of streamflows onhydrologic analysis have been demonstrated in many investigations. Among these are the studies of LeopoM [1959], Matalas and Langbein [1962], and Lloyd [1963], which have shown that autocorrelation reduces the reliability of the estimates of other statistical parameters of streamflow and increases the storage requirements for regulation of a stream. These two facets working in unison demonstrate the important role that information concerning the magnitude of autocorrelation plays in the development of the water resource. In spite of its importance, very little has been done to increase understanding of the controlling mechanisms of autocorrelation. I'gemelsfelder [1960] hypothesized that carry-over storage in the groundwater phase of the hydrologic cycle is one of the prime sources of persistence, a positive form of autocorrelation, in streamflows. He developed a descriptive model [Wemelsfelder, 1964] of streamflow that accounts for this component of autocorrelation, but no attempt was made to derive predictive relations. However, Fiering [1967], using a similar model, developed an estimator of the autocorrelation function of annual streamflow. One of the assumptions imbedded in Fiering's analysis is that the time series of streamflows is a stationary process. Because of the seasonality exhibited by monthly series of streamflows the requirement of stationarity negates the use of Fiering's function in the estimation of the autocorrelation coefficients of monthly series.This paper presents a model of streamflow that accounts for the nonstationarity of the monthly series. The model parameters yield estimates of the autocorrelation coefficients of the streamflows of contiguous months. MODEL OF DiSCRETIZED STREAMFLOWThe usual taxonomy of stochastic processes has an initial division of all processes into two classes: discrete parameter processes, in which a process is described or observed only at discrete time intervals, and continuous parameter processes, in which a process is defined at any time during the interval in which the process is functioning. Streamflow is a phenomenon that is a member of the latter class. In dealing with streamflow, however, hydrologists invariably use discrete series of observations, usually either the average rates or the volumes of discharge during specified intervals of time, Copyright (b !974 by the...

No abstract