Flooding is a serious threat in many regions in the world and is a problem of international interest. Hydrodynamic models are used for the prediction of flood water levels to support flood safety and are often applied in a deterministic way. However, the modelling of river processes involves numerous uncertainties. Previous research has shown that the hydraulic roughness is one of the main sources of in hydrodynamic computations. Knowledge of the type and magnitude of uncertainties is crucial for a meaningful interpretation of the model outcomes and the usefulness of model outcomes in decision making. The objective of this thesis is to quantify the uncertainties in the hydraulic roughness that contribute most to the uncertainty in the water levels and quantify their contribution to the uncertainty for the 2D hydrodynamic WAQUA model for the river Waal under design conditions. This research showed that the uncertainty of a complex model factor, such as the hydraulic roughness, can be quantified explicitly. The hydraulic roughness has been unravelled in separate components, which have been quantified separately and then combined and propagated through the model. In chapter 2, a method is presented to identify the sources of uncertainty in an environmental model. In chapter 3, expert opinion is used to determine the sources of uncertainty that contributed most to the uncertainty in the design water levels. Chapter 4 describes the quantification of the uncertainty in the bedform roughness and in chapter 5 the uncertainty in bedform roughness is combined with the uncertainty in the vegetation roughness. The results show a best estimate of the uncertainty range under design conditions, due to roughness, given that we did not account for the effect of calibration. The final uncertainty range is significant in view of Dutch river management practise. The research demonstrates that the uncertainties in a modelling study can be made explicit. The process of uncertainty analysis helps in raising the awareness of the uncertainties and enhances communication about the uncertainties among both scientists and decision makers.