Using expert knowledge to increase realism in environmental system models can dramatically reduce the need for calibration
- 1Water Resources Section, Faculty of Civil Engineering and Geosciences, Delft University of Technology, Delft, the Netherlands
- 2Public Research Center – Gabriel Lippmann, Belvaux, Luxembourg
- 3Eawag, Swiss Federal Institute of Aquatic Science and Technology, Dübendorf, Switzerland
Abstract. Conceptual environmental system models, such as rainfall runoff models, generally rely on calibration for parameter identification. Increasing complexity of this type of models for better representation of hydrological process heterogeneity, typically makes parameter identification more difficult. Although various, potentially valuable, approaches for better parameter estimation have been developed, strategies to impose general conceptual understanding of how a catchment works into the process of parameter estimation has not been fully explored. In this study we assess the effects of imposing semi-quantitative, relational inequality constraints, based on expert-knowledge, for model development and parameter specification, 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 simple to complex, three model setups, FLEXA, FLEXB and FLEXC were developed based on these functional units, where FLEXA is a lumped representation of the study catchment, and the semi-distributed formulations FLEXB and FLEXC progressively introduce more complexity. 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, allowing the formulation of constraints that limit the feasible parameter space. We show that by allowing for more landscape-related process heterogeneity in a model, e.g., FLEXC, the performance increases even without traditional calibration. The additional introduction of relational constraints further improved the performance of these models.