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The dialog between experimentalist and modeler in catchment hydrology has been minimal to date. The experimentalist often has a highly detailed yet highly qualitative understanding of dominant runoff processes; thus there is often much more information content on the catchment than we use for calibration of a model. While modelers often appreciate the need for “hard data” for the model calibration process, there has been little thought given to how modelers might access this “soft” or process knowledge. We present a new method where soft data (i.e., qualitative knowledge from the experimentalist that cannot be used directly as exact numbers) are made useful through fuzzy measures of model simulation and parameter value acceptability. We developed a three‐box lumped conceptual model for the Maimai catchment in New Zealand, a particularly well‐studied process‐hydrological research catchment. The …