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This paper demonstrates quantitative reasoning to separate the dataset of spatially distributed variables into different entities and subsequently characterize their geostatistical properties, properly. The main contribution of the paper is a statistical based algorithm that matches the manual distinction results. This algorithm is based on measured data and is generally applicable. In this paper, it is successfully applied at two datasets of saturated hydraulic conductivity (K) measured at the Borden (Canada) and the Lauswiesen (Germany) aquifers. The boundary layer was successfully delineated at Borden despite its only mild heterogeneity and only small statistical differences between the divided units. The methods are verified with the more heterogeneous Lauswiesen aquifer K data-set, where a boundary layer has previously been delineated. The effects of the macro- and the microstructure on solute transport behaviour are evaluated using numerical solute tracer experiments. Within the microscale structure, both Gaussian and non-Gaussian models of spatial dependence of K are evaluated. The effects of heterogeneity both on the macro- and the microscale are analysed using numerical tracer experiments based on four scenarios: including or not including the macroscale structures and optimally fitting a Gaussian or a non-Gaussian model for the spatial dependence in the micro-structure. The paper shows that both micro- and macro-scale structures are important, as in each of the four possible geostatistical scenarios solute transport behaviour differs meaningfully. [ABSTRACT FROM AUTHOR]

Summary Two dominant processes determine solute concentration in groundwater: vertical infiltration and horizontal advection. The goal of this paper is to incorporate both processes into a geostatistical model for spatial estimation of solute concentrations in groundwater. A multivariate copula-based methodology is demonstrated that considers infiltration via the marginal distribution and solute transport via the multivariate spatial dependence structure. The novel approach is compared to traditional methods as Ordinary- and External Drift Kriging. Leave-one-out cross-validation demonstrates that the novel approach estimates better both in concentration and in probability space, and improves the quantification and quality of uncertainty. The gain in uncertainty reduction is equivalent to at least a few hundred additional observations when Ordinary Kriging was used. Both censored and not-censored measurements are included. An ideal neighborhood size is estimated via cross-validation. The methodology is general and can incorporate other kinds of secondary information. It can be used to evaluate effects of land use changes. [ABSTRACT FROM AUTHOR]