Ameliorate network monitoring of water table by geostatistical methods

Abstract

Amjad HJ, Fereydoon F, Bozorgmehr A

Data collection of alluvium aquifers is done by using quality-quantity networks because of their spread and continuity.In designing such a network, it is necessary to pay attention on distribution of variables all over the aquifer so that variables represent whole of aquifer correctly. This happens when the network is able to predict the affects of climatical, natural etc parameters on the aquifers. Besides it should provide the maximum amount of information of the studied aquifers. At present, these networks are designed based on some empirical parameters. As a result, these cannot correctly represent the aquifer and some scientific methods needs to be benefited from to modify the empirical methods. Therefore using geostatistical methods seems to be necessary. The major difference is that a relation can be detected between quantities of a variation in different samples and distance and direction of the samples, while in the statistical methods, the quantites are haphazardly. It means that in the statistical methods, quantity of the variation in one sample doesn’t provide any information about its quantity in other samles. In this case study, using geostatistical methods, water table network is optimized for the aquifer of Ardestan Plain. Gaussian model - as the best fitted model on Kriging geostatistical method - is used in this case study. Optimization has two main steps: in the first step, using cross validation method, possibility of deleting some wells is studied based on the differences between predicted and measured values of them during a suitable statistical period; if the difference is negligible, these wells can be omitted, based on the iso-standard deviation map, and in the next step based on some suitable points, the best combination of points, by try % error method and a computerized model is determined to be added to the network in such a way that estimations cause minimum standard deviation. At the end of this study, the network was optimized by deleting a piezometer and adding two other ones.

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