The consistency of hydrological models, i.e. their ability to reproduce observed system dynamics, needs to be improved to increase their predictive power. As using streamflow data alone to calibrate models is not sufficient to constrain them and render them consistent, other strategies must be considered, in particular using additional types of data. The aim of this study was to test whether simultaneous calibration of dissolved organic carbon (DOC) and nitrate (NO3-) concentrations along with streamflow improved the hydrological consistency of a parsimonious solute-transport model. A multi-objective approach with four calibration scenarios was used to evaluate the model's predictions for an intensive agricultural headwater catchment. After calibration, the model reasonably simultaneously reproduced the dynamics of discharge and DOC and NO3- concentrations in the stream of the headwater catchment from 2008-2016. Evaluation using independent datasets indicated that the model usually reproduced dynamics of groundwater level and soil moisture in upslope and riparian zones correctly for all calibration scenarios. Using daily stream concentrations of DOC and NO3- along with streamflow to calibrate the model did not improve its ability to predict streamflow for calibration or evaluation periods. The approach significantly improved the representation of groundwater storage and to a lesser extent soil moisture in the upslope zone but not in the riparian zone. Parameter uncertainty decreased when the model was calibrated using solute concentrations, except for parameters related to fast and slow reservoir flow. This study shows the added value of using multiple types of data along with streamflow, in particular DOC and NO3- concentrations, to constrain hydrological models to improve representation of internal hydrological states and flows. With the increasing availability of solute data from catchment monitoring, this approach provides an objective way to improve the consistency of hydrological models that can be used with confidence to evaluate scenarios.