Geophysical methods have the potential to provide valuable information
on hydrological properties in the unsaturated zone. In particular,
time-lapse geophysical data, when coupled with a hydrological model
and inverted stochastically, may allow for the effective estimation
of subsurface hydraulic parameters and their corresponding uncertainties.
In this study, we use a Bayesian Markov-chain-Monte-Carlo (MCMC)
inversion approach to investigate how much information regarding
vadose zone hydraulic properties can be retrieved from time-lapse
crosshole GPR data collected at the Arrenaes field site in Denmark
during a forced infiltration experiment.