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Conceptual environmental system models, such asrainfall runoff models, generally rely on calibration for parameteridentification. Increasing complexity of this type of models for betterrepresentation of hydrological process heterogeneity, typically makesparameter identification more difficult. Although various, potentiallyvaluable, approaches for better parameter estimation have been developed,strategies to impose general conceptual understanding of how a catchmentworks into the process of parameter estimation has not been fully explored.In this study we assess the effects of imposing semi-quantitative, relationalinequality constraints, based on expert-knowledge, for model development andparameter specification, efficiently exploiting the complexity of asemi-distributed model formulation. Making use of a topography drivenrainfall-runoff modeling (FLEX-TOPO) approach, a catchment was delineatedinto three functional units, i.e., wetland, hillslope and plateau. Rangingfrom simple to complex, three model setups, FLEXA,FLEXB and FLEXC were developed based on thesefunctional units, where FLEXA is a lumped representation of thestudy catchment, and the semi-distributed formulations FLEXB andFLEXC progressively introduce more complexity. In spite ofincreased complexity, FLEXB and FLEXC allow modelersto compare parameters, as well as states and fluxes, of their differentfunctional units to each other, allowing the formulation of constraints thatlimit the feasible parameter space. We show that by allowing for morelandscape-related process heterogeneity in a model, e.g., FLEXC,the performance increases even without traditional calibration. Theadditional introduction of relational constraints further improved theperformance of these models

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