A number of studies have shown that time-lapse crosshole geophysical
data can provide valuable information regarding the hydraulic properties
of the unsaturated zone. The stochastic inversion of such data can
yield estimates of uncertainties in such properties, which are valuable
for hydrological characterization. Here, we investigate the effect
on output parameter uncertainties of accounting for realistic correlation
between the hydraulic model parameters in the inversion procedure.
We do this within a Bayesian framework using a Markov-chain-Monte-Carlo
(McMC) strategy, and we investigate the particular problem of estimating
vadose zone hydraulic properties from ground-penetrating radar (GPR)
data collected during a 1-D infiltration experiment. Our results
clearly indicate that prior information on the correlation between
model parameters has the effect of noticeably reducing posterior
parameter uncertainties and hence, if available, should be included
in such inversions.