Abstract
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.
Highlights
Lumped conceptual and distributed physically based models are the two endpoints of the modeling spectrum, ranging from simplicity to complexity, which here is defined as the number of model parameters
FLEXB, run with the set of constrained but uncalibrated parameters shows a substantial improvement in overall performance (ENS,median = 0.56, ENS,log,median = 0.33, ENS,FDC,median = 0.87) compared to FLEXA, as FLEXB allows for more process heterogeneity but, more importantly, it is conditioned with an increased number of constraints
This study has tested whether a topography-driven semidistributed formulation of a catchment-scale conceptual model, conditioned by expert knowledge based relational parameter and process constraints, can increase the level of process realism and predictive power while reducing the need for calibration
Summary
Lumped conceptual and distributed physically based models are the two endpoints of the modeling spectrum, ranging from simplicity to complexity, which here is defined as the number of model parameters. Physically based models are typically applied under the assumptions that (a) the spatial resolution and the complexity of the model are warranted by the available data, and (b) the catchment response is a mere aggregation of small scale processes. These two fundamental assumptions are commonly violated. Lumped conceptual models require less data for model parameters estimation This advantage comes at the expense of considerable limitations. Representing system integrated processes, model structures and parameters are not directly linked to observable quantities
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