Bayesian sediment transport model for unisize bed load

Fluvial sediment transport studies have long underscored the difficulty in reliably estimating transport model parameters, collecting accurate observations, and making predictions because of measurement error, natural variability, and conceptual model uncertainty. Thus, there is a need to identify modeling frameworks that accommodate these realities while incorporating functional relationships, providing probability‐based predictions, and accommodating for conceptual model discrimination. Bayesian statistical approaches have been widely used in a number of disciplines to accomplish just this, yet applications in sediment transport are few. In this paper we propose and demonstrate a Bayesian statistical approach to a simple sediment transport problem as a means to overcome some of these challenges. This approach provides a means to rigorously estimate model parameter distributions, such as critical shear, given observations of sediment transport; provides probabilistically based predictions that are robust and easily interpretable; facilitates conceptual model discrimination; and incorporates expert judgment into model inference and predictions. We demonstrate a simple unisize sediment transport model and test it against simulated observations for which the “true” model parameters are known. Experimental flume observations were also used to assess the proposed model's robustness. Results indicate that such a modeling approach is valid and presents an opportunity for more complex models to be built in the Bayesian framework.