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Abstract. This study provides results for the optimization strategy of highly parameterized models, especially with a high number of unknown input parameters and joint problems in terms of sufficient parameter space. Consequently, the uncertainty in model parameterization and measurements must be considered when highly variable nitrogen losses, e.g. N leaching, are to be predicted. The Bayesian calibration methodology was used to investigate the parameter uncertainty of the process-based CoupModel. Bayesian methods link prior probability distributions of input parameters to likelihood estimates of the simulation results by comparison with measured values. The uncertainty in the updated posterior parameters can be used to conduct an uncertainty analysis of the model output. A number of 24 model variables were optimized during 20 000 simulations to find the "optimum" value for each parameter. The likelihood was computed by comparing simulation results with observed values of 23 output variables including soil water contents, soil temperatures, groundwater level, soil mineral nitrogen, nitrate concentrations below the root zone, denitrification and harvested carbon from grassland plots in Northern Germany for the period 1997-2002. The posterior parameter space was sampled with the Markov Chain Monte Carlo approach to obtain plot-specific posterior parameter distributions for each system. Posterior distributions of the parameters narrowed down in the accepted runs, thus uncertainty decreased. Results from the single-plot optimization showed a plausible reproduction of soil temperatures, soil water contents and water tensions in different soil depths for both systems. The model performed better for these abiotic system properties compared to the results for harvested carbon and soil mineral nitrogen dynamics. The high variability in modeled nitrogen leaching showed that the soil nitrogen conditions are highly Correspondence to: Y. Conrad (yconrad@hydrology-uni-kiel.de) uncertain associated with low modeling efficiencies. Simulated nitrate leaching was compared to more general, sitespecific estimations, indicating a higher leaching during the seepage periods for both simulated grassland systems.

Land-use and crop-biomass development influence water and nutrient dynamics in soils. Nitrate-N leaching rises with increasing mineral-N input during seepage periods in sandy soils. Leaching of N was simulated for two grassland treatments (N0: unfertilized; N300: highly fertilized: 300 kg mineral N ha -1 y -1 ) using CoupModel. Parameter uncertainty was considered by an automated calibration based on the General Likelihood Uncertainty Estimation (GLUE) approach. The results of this optimization approach showed a realistic reproduction of abiotic and biotic patterns of the system. Modeled abiotic parameters, i.e., soil temperatures and water contents in different soil depths, led to plausible results with minor differences between fertilization levels and some respect for discrepancies due to heterogeneous soil conditions. Groundwater levels were slightly underestimated by CoupModel with a smoother dynamic than measured in both treatments. CoupModel provided plausible results for nitrate-N concentrations below the rooting zone at 60 cm depth with higher uncertainty ranges than standard deviations of the measurements. Simulated nitrate-N concentrations of the unfertilized grassland confirmed the measurements, and no potential environmental risk for water bodies existed according to the standards of the European Drinking Water Directive. The model reproduced satisfactorily the observations for the N300 system with a slightly overestimation in 40% of the simulated seepage periods. Higher uncertainties were found for the simulated N300 than for the N0 plot. Modeled N flows below the rooting zone of 11 kg nitrate-N ha -1 per seepage period were comparable to calculations of the model "Büchter" with a mean of 10 kg nitrate-N ha -1 . For the N300 plot, a more than twice as high N-leaching amount of 74 kg nitrate-N ha -1 was modeled compared to a calculated average of 30 kg nitrate-N ha -1 . Finally, small-scale modeling can provide plausible results for nitrate-N leaching on plot-scale when uncertainties in soil water, N flows, and biomass growth are considered.

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