Quantifying the spatial configuration of hydraulic conductivity (K)
in heterogeneous geological environments is essential for accurate
predictions of contaminant transport, but is difficult because of
the inherent limitations in resolution and coverage associated with
traditional hydrological measurements. To address this issue, we
consider crosshole and surface-based electrical resistivity geophysical
measurements, collected in time during a saline tracer experiment.
We use a Bayesian Markov-chain-Monte-Carlo (McMC) methodology to
jointly invert the dynamic resistivity data, together with borehole
tracer concentration data, to generate multiple posterior realizations
of K that are consistent with all available information. We do this
within a coupled inversion framework, whereby the geophysical and
hydrological forward models are linked through an uncertain relationship
between electrical resistivity and concentration. To minimize computational
expense, a facies-based subsurface parameterization is developed.
The Bayesian-McMC methodology allows us to explore the potential
benefits of including the geophysical data into the inverse problem
by examining their effect on our ability to identify fast flowpaths
in the subsurface, and their impact on hydrological prediction uncertainty.
Using a complex, geostatistically generated, two-dimensional numerical
example representative of a fluvial environment, we demonstrate that
flow model calibration is improved and prediction error is decreased
when the electrical resistivity data are included. The worth of the
geophysical data is found to be greatest for long spatial correlation
lengths of subsurface heterogeneity with respect to wellbore separation,
where flow and transport are largely controlled by highly connected
flowpaths.