Significant progress has been made with regard to the quantitative
integration of geophysical and hydrological data at the local scale.
However, extending the corresponding approaches to the regional scale
represents a major, and as-of-yet largely unresolved, challenge.
To address this problem, we have developed a downscaling procedure
based on a non-linear Bayesian sequential simulation approach. The
basic objective of this algorithm is to estimate the value of the
sparsely sampled hydraulic conductivity at non-sampled locations
based on its relation to the electrical conductivity, which is available
throughout the model space. The in situ relationship between the
hydraulic and electrical conductivities is described through a non-parametric
multivariate kernel density function. This method is then applied
to the stochastic integration of low-resolution, re-
gional-scale electrical resistivity tomography (ERT) data in combination
with high-resolution, local-scale downhole measurements of the hydraulic
and electrical conductivities. Finally, the overall viability of
this downscaling approach is tested and verified by performing and
comparing flow and transport simulation through the original and
the downscaled hydraulic conductivity fields. Our results indicate
that the proposed procedure does indeed allow for obtaining remarkably
faithful estimates of the regional-scale hydraulic conductivity structure
and correspondingly reliable predictions of the transport characteristics
over relatively long distances.