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 scale of a
field site represents a major, and as-of-yet largely unresolved,
challenge. To address this problem, we have developed downscaling
procedure based on a non-linear Bayesian sequential simulation approach.
The main 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 logged at collocated
wells and surface resistivity measurements, which are available throughout
the studied site. The in situ relationship between the hydraulic
and electrical conductivities is described through a non-parametric
multivariatekernel density function. Then a stochastic integration
of low-resolution, large-scale electrical resistivity tomography
(ERT) data in combination with high-resolution, local-scale downhole
measurements of the hydraulic and electrical conductivities is applied.
The overall viability of this downscaling approach is tested and
validated by comparing flow and transport simulation through the
original and the upscaled hydraulic conductivity fields. Our results
indicate that the proposed procedure allows obtaining remarkably
faithful estimates of the regional-scale hydraulic conductivity structure
and correspondingly reliable predictions of the transport characteristics
over relatively long distances.