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[1] The relevance of aquifer heterogeneity for flow and transport is recognized broadly; however, its characterization is hampered by the inaccessibility of the subsurface. Time-lapse electrical resistivity tomography (ERT) offers the possibility of imaging noninvasively subsurface transport. We present results of two tracer tests that were carried out successively in a shallow aquifer at the Krauthausen test site (Germany). The breakthroughs of an electrically conductive and a resistive tracer were monitored with ERT and local multilevel groundwater samplers (MLS) along two cross sections perpendicular to the mean flow direction. Sinking of the conductive salt tracer due to density effects was observed with ERT. We applied a stream tube model to characterize the spatially variable transport. ERT-derived stream tube parameters showed similar patterns for the two tracer experiments, reflecting the effect of aquifer heterogeneity on transport. MLS data did not show similar spatial patterns, which indicates that these measurements may be prone to subtle changes of the flow field in the small sampling volume and mixing within screened wells. Between 50% and 10% of the tracer was recovered in the ERT-derived breakthrough curves. Compared with transport simulations in a homogeneous aquifer, ERT-derived time-integrated changes in electrical conductivity were locally larger but focused in a smaller area. MLS data indicated that in this area, ERT did not underestimate the tracer recovery. The relatively low tracer recovery was attributed to undetected tracer breakthrough in regions with low ERT sensitivity and in regions where the length of the tracer plume and the electrical conductivity contrast were small.

[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.

We jointly invert field data of flowmeter and multiple pumping tests in fully screened wells to estimate hydraulic conductivity using a geostatistical method. We use the steady-state drawdowns of pumping tests and the discharge profiles of flowmeter tests as our data in the inference. The discharge profiles need not be converted to absolute hydraulic conductivities. Consequently, we do not need measurements of depth-averaged hydraulic conductivity at well locations. The flowmeter profiles contain information about relative vertical distributions of hydraulic conductivity, while drawdown measurements of pumping tests provide information about horizontal fluctuation of the depth-averaged hydraulic conductivity. We apply the method to data obtained at the Krauthausen test site of the Forschungszentrum Jülich, Germany. The resulting estimate of our joint three-dimensional (3D) geostatistical inversion shows an improved 3D structure in comparison to the inversion of pumping test data only.