Time-lapse geophysical data acquired during transient hydrological
experiments are being increasingly employed to estimate subsurface
hydraulic properties at the field scale. In particular, crosshole
ground-penetrating radar (GPR) data, collected while water infiltrates
into the subsurface either by natural or artificial means, have been
demonstrated in a number of studies to contain valuable information
concerning the hydraulic properties of the unsaturated zone. Previous
work in this domain has considered a variety of infiltration conditions
and different amounts of time-lapse GPR data in the estimation procedure.
However, the particular benefits and drawbacks of these different
strategies as well as the impact of a variety of key and common assumptions
remain unclear. Using a Bayesian Markov-chain-Monte-Carlo stochastic
inversion methodology, we examine in this paper the information content
of time-lapse zero-offset-profile (ZOP) GPR traveltime data, collected
under three different infiltration conditions, for the estimation
of van Genuchten-Mualem (VGM) parameters in a layered subsurface
medium. Specifically, we systematically analyze synthetic and field
GPR data acquired under natural loading and two rates of forced infiltration,
and we consider the value of incorporating different amounts of time-lapse
measurements into the estimation procedure. Our results confirm that,
for all infiltration scenarios considered, the ZOP GPR traveltime
data contain important information about subsurface hydraulic properties
as a function of depth, with forced infiltration offering the greatest
potential for VGM parameter refinement because of the higher stressing
of the hydrological system. Considering greater amounts of time-lapse
data in the inversion procedure is also found to help refine VGM
parameter estimates. Quite importantly, however, inconsistencies
observed in the field results point to the strong possibility that
posterior uncertainties are being influenced by model structural
errors, which in turn underlines the fundamental importance of a
systematic analysis of such errors in future related studies.