Two-dimensional characterization of hydraulic heterogeneity by multiple pumping tests

Li, W.; Englert, A.; Cirpka, O.A.; Vanderborght, J.; Vereecken, H.

doi:10.1029/2006WR005333

Journal Article

PreJuSER-57153

Two-dimensional characterization of hydraulic heterogeneity by multiple pumping tests

Li, W. ; Englert, A.FZJ* ; Cirpka, O. A. ; Vanderborght, J.FZJ* ; Vereecken, H.FZJ*

2007
AGU Washington, DC

Water resources research 43, WO04433 (2007) [10.1029/2006WR005333]

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Please use a persistent id in citations: http://hdl.handle.net/2128/19879 doi:10.1029/2006WR005333

Abstract: [1] The conventional analysis of pumping tests by type-curve methods is based on the assumption of a homogeneous aquifer. Applying these techniques to pumping test data from real heterogeneous aquifers leads to estimates of the hydraulic parameters that depend on the choice of the pumping and observation well positions. In this paper, we test whether these values may be viewed as pseudo-local values of transmissivity and storativity, which can be interpolated by kriging. We compare such estimates to those obtained by geostatistical inverse modeling, where heterogeneity is assumed in all stages of estimation. We use drawdown data from multiple pumping tests conducted at the test site in Krauthausen, Germany. The geometric mean values of transmissivity and storativity determined by type-curve analysis are very close to those obtained by geostatistical inversion, but the conventional approach failed to resolve the spatial variability of transmissivity. In contrast, the estimate from geostatistical inversion reveals more structure. This indicates that the estimates of the type-curve approaches can not be treated as pseudo-local values. Concerning storativity, both analysis methods show strong fluctuations. Because the variability of all terms making up the storativity is small, we believe that the estimated variability of storativity is biased. We examine the influence of measurement error on estimating structural parameters of covariance functions in the inversion. We obtain larger correlation lengths and smaller prior variances if we trust the measured data less.

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