In terrestrial ecosystem studies, nutrient input-output (mass balance) budgets are valuable tools to assess trends and nutrient dynamics. The change in the plant-available store of a given nutrient in the soil is estimated from the difference between the input (atmospheric deposition, mineral weathering) and output (net immobilization in aboveground biomass and nutrient leaching below the rooting zone) fluxes of the nutrient to the ecosystem. The water drainage and nutrient leaching fluxes 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 model fitted parameters and model output data is often unknown. Quantifying uncertainties in models is essential to i) critically evaluate models and ii) supply reliable data to support ecosystem management decisions and policies. The first aim of this study was to develop 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. By applying this procedure, the second aim was 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) and critically evaluate the use of chloride concentration datasets to calibrate forest hydrological models. At this site, we show that the monitoring data of natural variations of chloride concentrations in soil solution was 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.