In this study, we develop a novel sensitivity analysis approach based on Sobol’ SA, utilizing multiple hydrological signatures that are useful to constrain model outcomes. Our hypothesis is that the extra information provided by signatures would facilitate a more comprehensive evaluation of the model parameterization compared to the traditional sensitivity analysis using time series-focused aggregate summary statistics. In addition, the systematic application of the sensitivity analysis to groups of parameters which are linked to certain model structures could assist in the identification of the most suitable and consistent structure. To test these hypotheses, the proposed signature-based sensitivity analysis, as well as the traditional approach are applied to a range of model structures developed within a computationally efficient and flexible hydrological modelling framework called RAVEN. The main objective of the numerical experiments in this study is to demonstrate the applicability of the proposed sensitivity analysis approach in view of the model structure selection and, thus, the model structure uncertainty reduction.