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• W626494033 abstract "Log -transmissivity, specific capacity, and water -level data from the Tajo River basin surrounding Madrid, Spain, are analyzed by geostatistical methods. The existing logtransmissivity data base is augmented with the aid of regression on logspecific capacities. The augmented set of data is used to obtain estimates of mean logtransmissivities over finite sub -regions of the aquifer by kriging. Kriging of water -level data at selected points in the aquifer is accomplished by removing the drift through an iterative generalized least squares procedure. The covariance matrices of the logtransmissivity and water -level estimation errors are used to investigate the structure of these errors. Introduction Ground -water models are becoming increasingly more sophisticated. However, development of a systematic interpretation of the input data has been largely ignored. Kriging may be one way to fill this gap. In the past, transmissivity and head data were estimated subjectively by hand -contouring, thereby including the modeler's biases. The degree of interpretation varied widely. To minimize this subjectivity, automatic contouring and interpolation techniques have been developed. However, most of these methods disregard knowledge of the physical variability of the data (Delhomme, 1978). A measure of the reliability of such contours is lacking. Kriging provides such a measure. Kriging is an interpolation technique based on the geostatistical theory of Matheron (1963). This technique has the ability to yield unbiased estimates with minimum estimation error variance as well as a total covariance matrix of the estimation errors. The theory assumes that spatial fluctuation of the data (i.e., logtransmissivity or hydraulic head) about their mean values can be considered a realization of an intrinsic stochastic process. The process can be characterized by an ensemble mean and a semi -variogram function which are considered uniform over large segments of the aquifer. While log transmissivity can be assumed to fluctuate about a constant mean value, the fluctuations of head occur about a regional trend, or drift, in the general direction of the gradient. This drift must be removed before the head fluctuations can be treated as an intrinsic process. Prior to kriging, the semi -variogram is determined based on field observations of data. Using the semi -variogram, kriging computes estimates at selected points, or average values over discrete subregions of the aquifer. The method also computes the variance of the estimation error (kriging variance) at each point, or subregion, and the covariance matrix associated with these errors. The kriging error is dependent on the natural fluctuation of the observed data as described by the variogram function, on measurement errors, and on the relative locations of the measurement points. One may therefore expect the kriging errors to be smallest in the vicinity of observation points, and largest far from these points. This paper describes the application of kriging to logtransmissivity and water -level data from the Madrid basin, Spain. Similar applications to ground -water basins in Arizona were reported earlier by Binsariti (1980) and Clifton and Neuman (1982). Setting of Study Area The study area is an elongate northeast -trending alluvial basin surrounding Madrid, Spain (Figure 1). The basin is approximately 160 kilometers long and between 30 and 60 kilometers wide. The study area is bounded to the north and northwest by the Central Range and to the south and east by the Tajo, Jarana, and Henares Rivers." @default.
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• W626494033 date "1982-04-24" @default.
• W626494033 modified "2023-09-26" @default.
• W626494033 title "Geostatistical Analysis of Aquifer Test and Water Level Data from the Madrid Basin, Spain" @default.
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