Predictions in ungauged basins (PUB) are one of the major challenges in hydrology. Physically-based distributed models are ideal for PUB because they can account most of the heterogeneity in the system. Cibin et al.1 proposed a method to derive the probability distribution function (PDF) of the sensitive parameters using a single likelihood (a global measure over the entire ranges of flow) and use them for PUB. Instead of considering a single criterion for deriving the PDF, a multi-criteria approach that can account variation in sensitivity of parameters and model performance in different flow ranges may be a better approach for identifying the parameter characteristics in terms of their PDF. The study proposes a method to minimize the predictive uncertainty of distributed models by deriving the PDF of sensitive parameters based on the Bayesian approach. The method employs Monte Carlo simulations of parameter sets generated by ‘Latin Hypercube Sampling.’ Within the Monte Carlo simulations, those parameter sets that produced reasonably good performance in all ranges of flow are used for estimating multi-criteria index and updating will continue till both (prior and posterior) PDFs converge in successive cycles. These PDFs, which are derived using gauged basin data, are then transferred to hydrologically similar ungauged basins for generating ensembles of simulations. The proposed methodology is illustrated through a case study of a watershed in USA. The Soil and Water Assessment Tool model was considered for the application. The study also discusses a comparison of PUB using a single criterion approach and a multi-criteria approach. It is observed that confidence band for predictions by proposed approach is narrow and the number of cycles required for deriving the PDF is less as compared with the former. © 2011 by World Scientific Publishing Co. Pte. Ltd. All rights reserved.