Doubt regarding the applicability of laboratory results to alluvial streams has led some to develop sediment transport predictors based solely on field data, and most current sediment transport formulae have typically been calibrated at least partially on field data. This paper examines the transferability of flume results to the field by exploring the extent to which a unified approach to the prediction of (1) flow regime, (2) depth, and (3) total sediment transport can be developed entirely with laboratory data. Relevance vector machine (RVM)-based probabilistic models were constructed with only laboratory data, and their performances were tested against field data and found to be comparable with or better than currently available methods. Comparison of a laboratory-trained RVM with a field-trained RVM suggests that the prediction performances of the two models for unseen field data are not statistically different given the prediction uncertainty. For transferability, the choice of predictor variables is important with successful predictors being characterized by similar probability distribution in the laboratory and field data, e.g., as quantified by the Kullback-Leibler divergence.