Many environmental systems models, such as conceptual rainfall-runoff models,rely on model calibration for parameter identification. For this, an observedoutput time series (such as runoff) is needed, but frequently not available(e.g., when making predictions in ungauged basins). In this study, we providean alternative approach for parameter identification using constraints basedon two types of restrictions derived from prior (or expert) knowledge. Thefirst, called parameter constraints, restricts the solution spacebased on realistic relationships that must hold between the different modelparameters while the second, called process constraints requiresthat additional realism relationships between the fluxes and state variablesmust be satisfied. Specifically, we propose a search algorithm for findingparameter sets that simultaneously satisfy such constraints, based onstepwise sampling of the parameter space. Such parameter sets have thedesirable property of being consistent with the modeler's intuition of howthe catchment functions, and can (if necessary) serve as prior informationfor further investigations by reducing the prior uncertainties associatedwith both calibration and prediction.
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