In the contemporary world, many environmental and water resources related decisions rely upon wide range of modelling results. However, models are simplified representations of reality and their predictions are generally imperfect due to the inherent uncertainties emanating from various sources. Therefore, there is an increasing demand to incorporate uncertainty analysis as part of the modelling process. This PhD thesis was, thus, focused on uncertainty quantification and reduction in hydrological modelling through use of both existing and newly introduced methodologies. The viability of these methodologies was evaluated using the Statkraft’s hydrological model and relevant climatic and physiographic datasets from the Nea-catchment, Norway. One of the major outcomes of the PhD work was the adoption of an existing rejectionist uncertainty analysis framework as a ‘tool’ to find the useful out of the imperfect models. Further, machine learning models were coupled with the adopted uncertainty analysis framework in order to minimize the computational time when using this approach. Another major achievement was the improvement in the efficiency of two data assimilation schemes in their capability to extract the available information from the assimilated dataset and thus reducing the prediction uncertainty. The research outputs from this work are expected to contribute to the society at various levels. For example, they will aid water managers in quantifying and reducing modelling uncertainty and thereby in making rational decisions with regards to use of the valuable, but often scarce, water resources.