Many environmental systems models, such as conceptual rainfall-runoff models, rely on model calibration for parameter identification. For this, an observed output time series (such as runoff) is needed, but frequently not available. Here, we explore another way to constrain the parameter values of semi-distributed conceptual models, based on two types of restrictions derived from prior (or expert) knowledge. The first, called “parameter constraints”, restrict the solution space based on realistic relationships that must hold between the different parameters of the model while the second, called “process constraints” require that additional realism relationships between the fluxes and state variables must be satisfied. Specifically, we propose a strategy for finding parameter sets that simultaneously satisfy all such constraints, based on stepwise sampling of the parameter space. Such parameter sets have the desirable property of being consistent with the modeler’s intuition of how the catchment functions, and can (if necessary) serve as prior information for further investigations by reducing the prior uncertainties associated with both calibration and prediction.