The ground-penetrating radar (GPR) geophysical method has the potential
to provide valuable information on the hydraulic properties of the
vadose zone because of its strong sensitivity to soil water content.
In particular, recent evidence has suggested that the stochastic
inversion of crosshole GPR traveltime data can allow for a significant
reduction in uncertainty regarding subsurface van Genuchten-Mualem
(VGM) parameters. Much of the previous work on the stochastic estimation
of VGM parameters from crosshole GPR data has considered the case
of steady-state infiltration conditions, which represent only a small
fraction of practically relevant scenarios. We explored in detail
the dynamic infiltration case, specifically examining to what extent
time-lapse crosshole GPR traveltimes, measured during a forced infiltration
experiment at the Arreneas field site in Denmark, could help to quantify
VGM parameters and their uncertainties in a layered medium, as well
as the corresponding soil hydraulic properties. We used a Bayesian
Markov-chain-Monte-Carlo inversion approach. We first explored the
advantages and limitations of this approach with regard to a realistic
synthetic example before applying it to field measurements. In our
analysis, we also considered different degrees of prior information.
Our findings indicate that the stochastic inversion of the time-lapse
GPR data does indeed allow for a substantial refinement in the inferred
posterior VGM parameter distributions compared with the corresponding
priors, which in turn significantly improves knowledge of soil hydraulic
properties. Overall, the results obtained clearly demonstrate the
value of the information contained in time-lapse GPR data for characterizing
vadose zone dynamics.