Time-lapse crosshole ground-penetrating radar (GPR) data, collected
while infiltration occurs, can provide valuable information regarding
the hydraulic properties of the unsaturated zone. In particular,
the stochastic inversion of such data provides estimates of parameter
uncertainties, which are necessary for hydrological prediction and
decision making. Here, we investigate the effect of different infiltration
conditions on the stochastic inversion of time-lapse, zero-offset-profile,
GPR data. Inversions are performed using a Bayesian Markov-chain-Monte-Carlo
methodology. Our results clearly indicate that considering data collected
during a forced infiltration test helps to better refine soil hydraulic
properties compared to data collected under natural infiltration
conditions