Uncertainties in model structures have been recognised often to be the main source of uncertainty in predictive model simulations. Despite this knowledge, uncertainty studies are traditionally limited to a single deterministic model and the uncertainty addressed by a parameter uncertainty study. The extent to which a parameter uncertainty study may encompass model structure errors in a groundwater model is studied in a case study. Three groundwater models were constructed on the basis of three different hydrogeological interpretations. Each of the models was calibrated inversely against groundwater heads and streamflows. A parameter uncertainty analysis was carried out for each of the three conceptual models by Monte Carlo simulations. A comparison of the predictive uncertainties for the three conceptual models showed large differences between the uncertainty intervals. Most discrepancies were observed for data types not used in the model calibration. Thus uncertainties in the conceptual models become of increasing importance when predictive simulations consider data types that are extrapolates from the data types used for calibration.

A physically based, coupled and distributed hydrologic model has been set up for the Ringkøbing Fjord catchment, Denmark. This transient model, built with the MIKE SHE/MIKE 11 code, comprises all major components of the terrestrial water cycle, including a three‐dimensional finite‐difference groundwater model. The dynamic coupling of the hydrologic processes secures physically sound feedback and makes the model an ideal tool for evaluating the overall water balance and quantifying potential water balance issues. Historically, failure to obtain water balance closure has been a persistent and much debated problem in Denmark, presumably arising mainly from uncertainties in precipitation, actual evapotranspiration, and groundwater flow to the sea. In this study, the water balance issues were addressed within the modeling framework through analysis of different rain gauge catch corrections and potential evapotranspiration input. The analysis focused on the effect of different rain gauge catch corrections on the model performance, the optimized parameter sets, and state variables not included in the model calibration. The results suggest that water balance problems can be reduced by using a dynamic rain gauge catch correction based on daily wind speed and temperature fields. The model optimization and performance evaluation revealed, however, that several parameter sets gave similar performance compared with the observed groundwater head and river discharge data but still resulted in significant differences with respect to internal water fluxes such as evapotranspiration, groundwater recharge, and stream flow components. New observational data are needed to constrain the model further and thus reduce the water balance uncertainties convincingly.