In terrestrial ecosystem studies, water drainage and nutrient leaching in the soil profile are estimated with hydrological models. Comparing modeled results to empirical data or comparing data from different models is, however, difficult because the uncertainty of input–output budget predictions is often unknown. In this study, we developed a procedure combining a Generalized Likelihood Uncertainty Estimation and a Monte-Carlo modeling approach to estimate uncertainty in model parameter estimates and model outputs water drainage and nutrient leaching fluxes for the WatFor water balance model. This procedure was then applied to compare different model optimization strategies (daily soil moisture measurements, monthly measurements of chloride concentrations in soil solution, and the elution of a concentrated chloride) at the same experimental site in a 90-year-old European beech (Fagus sylvatica L.) forest in Brittany (France). We show that the monitoring data of natural variations of chloride concentrations in soil solution were the most efficient dataset to calibrate the WatFor model compared to the soil moisture and chloride tracing experimental data. We also show that water tracing experimental data are the most efficient data to estimate the preferential flow generation model parameters. The optimization strategy had little influence on the predicted water drainage flux and nutrient leaching flux at the root zone boundary on a yearly time scale but influenced water and nutrient fluxes in the topsoil layers.