Using expert knowledge to increase realism in environmental system models can dramatically reduce the need for calibration

Abstract

Conceptual environmental systems models, such as rainfall runoff models, generally rely on calibration for parameter identification. Increasing complexity of this type of model for better representation of hydrological process heterogeneity typically makes parameter identification more difficult. Although various, potentially valuable, strategies for better parameter identification were developed in the past, strategies to impose general conceptual understanding regarding how a catchment works into the process of parameterizing a conceptual model has still not been fully explored. In this study we assess the effect of imposing semi-quantitative, relational expert knowledge into the model development and parameter selection, efficiently exploiting the complexity of a semi-distributed model formulation. Making use of a topography driven rainfall-runoff modeling (FLEX-TOPO) approach, a catchment was delineated into three functional units, i.e. wetland, hillslope and plateau. Ranging from simplicity to complexity, three model set-ups, FLEXA, FLEXB and FLEXC have been developed based on these functional units. While FLEXA is a lumped representation of the study catchment, the semidistributed formulations FLEXB and FLEXC introduce increasingly more complexity by distinguishing 2 and 3 functional units, respectively. In spite of increased complexity, FLEXB and FLEXC allow modelers to compare parameters as well as states and fluxes of their different functional units to each other. Based on these comparisons, expert knowledge based, semi-quantitative relational constraints have been imposed on three models structures. More complexity of models allows more imposed constraints. It was shown that a constrained but uncalibrated semi-distributed model, FLEXC, can predict runoff with similar performance than a calibrated lumped model, FLEXA. In addition, when constrained and calibrated, the semi-distributed model FLEXC exhibits not only higher performance but also reduced uncertainty for prediction, compared to the calibrated, lumped FLEXA model.