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Over the past years, Bayesian calibration methods have been successfully applied to calibrate ecosystem models. Bayesian methods combine prior probability distributions of model parameters, based on assumptions about their magnitude and uncertainty, with estimates of the likelihood of the simulation results by comparison with observed values. Bayesian methods also quantify the uncertainty in the updated posterior parameters, which can be used to perform an analysis of model output uncertainty. In this paper, we apply Bayesian techniques to calibrate the VSD soil acidification model for 182 intensively monitored forest sites in Europe. Prior distributions for the model parameters were based on available literature. Since the available literature shows a strong dependence of some VSD parameters on, for example, soil texture, prior distributions were allowed to depend on soil group (i.e. group of soils with similar texture or C/N ratio). The likelihood was computed by comparing modelled soil solution concentrations with observed concentrations for the period 1996-2001. Markov Chain Monte Carlo (MCMC) was used to sample the posterior parameter space. Two calibration approaches were applied. In the singe-site calibration, the plots are calibrated separately to obtain plot-specific posterior distributions. In the multi-site approach priors are assumed constant in space for each soil group, and all plots are calibrated simultaneously yielding one posterior probability distribution for each soil group.Results from the single-site calibrations show that the model performs much better after calibration compared to a run with standard input parameters. Posterior distributions for HAl equilibrium constants narrow down, thus decreasing parameter uncertainty. For base cation weathering of coarse texture soils the posterior distribution is shifted to larger values, indicating an initial underestimation of the weathering rate for these soils. Results for the 2 parameters related to nitrogen modelling show that the nitrogen processes model formulations in VSD may have to be reconsidered as no evidence is found for a relationship between nitrogen immobilization and the C/N ratio of the soil, as assumed in VSD. The multi-site calibration also strongly decreases model error for most model output parameters, but model error is larger than the median model error from the singe-site calibration.Because the large number of plots calibrated at the same time provides very many observations, the Markov chain converges to a very narrow parameter space, leaving little room for posterior parameter uncertainty. For an uncertainty analysis with VSD on the European scale, this study provides promising results, but more work is needed to investigate how the results can be used on a European scale by looking at regional patterns in calibrated parameters from the site calibration or by calibrating for regions instead of all of Europe.

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The Netherlands is "well known" for its nitrogen problems; it has one of the highest reactive nitrogen (Nr) emission densities in the world. It is a small country at the delta of several large European rivers. Ever since the industrial revolution, there has been a growing excess of nutrients and related emissions into the atmosphere (ammonia, nitrogen oxides and nitrous oxide) and into groundwater and surface water (nitrate), leading to a large range of cascading environmental impacts. Vehicular traffic, sewage and animal husbandry are the main sources of oxidized and reduced forms of Nr. This paper provides an overview of the origin and fate of nitrogen in the Netherlands, the various reported impacts of nitrogen, the Dutch and European policies to reduce nitrogen emissions and related impacts. In addition, ways are presented to go forward to potentially solve the problems in a European perspective. Solutions include the improvement of nitrogen efficiencies in different systems, technological options and education.