This article describes an alternative to the optimization strategies classically adopted to calibrate the parameters of rainfall-runoff models. This new method, called discrete parameterization, relies on the sole use of the prior information on parameters gained on other catchments. The optimum parameter set is simply searched within a collection (a library) of predefined optima. This library is composed of parameter sets representing a large number of actual catchments. The method was tested on a set of 900 catchments (from Australia, France, and the United States) using two daily lumped rainfall-runoff models and was compared to more classical calibration approaches. Results are very similar for both models. Although the discrete parameterization method is not as efficient as a classical global search calibration approach when long time series are available for calibration, it provides more robust parameter sets when flow time series available for calibration becomes shorter than 2 years. This makes the method particularly interesting in the cases of poorly gauged catchments where available flow records are short. In case of limited data, the advantage of the proposed approach over the classical calibration approaches was more significant for the more complex model. On our set of 900 catchments, the optimum parameter set is generally selected among parameter sets from the library corresponding to catchments spatially close to the studied catchment. However other criteria of physical similarity may be relevant to select the donor catchment in the library.