Summary We investigated the effect of initial moisture contents and mode of application on the displacement of multiple conservative tracers through undisturbed columns of a Humic Gleysol. Bromide was applied at the soil surface and chloride was injected at 5 cm depth. The columns were irrigated with deuterium‐enriched water. A dual‐porosity model and two single‐porosity models were calibrated separately to Br– and Cl– elution curves in the two columns. Elution curves were almost identical for Br– and Cl– under initially wet conditions, whereas the displacement of Br– was faster than that of Cl– in the initially dry column, indicating rapid transport with preferential flow. Only the dual‐porosity model described the long‐tailing breakthrough of Cl– in the initially dry column adequately. The parameter values giving acceptable fits for ‘Br dry’ were not compatible with the description of the three other elution curves, which could be adequately modelled with a single set of parameter values. The estimated set of common parameters was validated by comparing with the elution curves of deuterium water, nitrate and sulphate, as well as with resident tracer concentrations at four depths. The results showed that solutes can be displaced much faster when applied at the surface of initially dry soil than when applied to wet soil or when resident in the soil matrix. The simulation results suggest that solute transport under initially dry conditions was governed by preferential flow of infiltration water through macropores by‐passing the matrix due to shrinkage cracks and water repellence of matrix surfaces.

An important step in numerical modeling is the determination of model parameters. Because of practical limitations, as well as time and financial constraints, inverse algorithms have in recent years presented an attractive alternative to direct methods of parameter estimation. In this study we linked the inverse algorithm of SUFI with the simulation program LEACHM to study N turnover of an agricultural field. Addressing the inherent modeling uncertainties, we introduce the concept of conditioned parameter distributions as being a more appropriate alternative to best‐fit parameters Conditioned parameter distributions are quantified within uncertainty domains, and the task of an inverse model then is to reduce or condition this domain through minimization of an appropriate objective function. Propagating the uncertainty in the conditioned parameter distributions will result in simulations where most of the measurements are respected or fall within the 95% confidence interval of the Bayesian distribution (95PCIBD). In this study we used measured pressure heads and NO3 concentrations to estimate 12 hydraulic parameters and up to 14 N turnover–related parameters. Most of the measurements in three soil layers fell within the 95PCIBD. Exceptions were some observed pressure heads corresponding to intense rainfall events and periods of soil freezing, as well as some high NO3 concentrations in the subsoil between 40‐ and 70‐cm depth. We attributed the discrepancies to processes that were not addressed by the simulation model such as freezing and short‐circuiting due to macropore flow.

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