Performance of bedload transport equations in a mixed bedrock-alluvial channel environment

Mixed bedrock-alluvial channel beds are common in mountain rivers. It is well-known that in such system the balance between sediment supply and transport capacity defines either the level of the exposure of bedrock (in the riverbed) or its coverage by alluvial material. We investigate whether applying the representation of the energy losses (caused by macro-roughness) developed for alluvial channels improve the capacity of bedload transport equations to predict the bedload transport in a mixed bedrock-alluvial stream. Therefore, the purpose of this study was to evaluate the performances of five bedload equations against field measurements, while considering the effects of macro-roughness on the equations and the limited availability of mobile sediment. We analyzed the data collected in a stream located in southern Brazil, which has a 2.3% gradient and mixed bed conditions. Optimal results in estimating the bedload transport rate were obtained when using the reduced shear stress (tau') and reduction factor of the available bed material (Frm) together in the five tested equations. Analyzing the two approaches separately, the implementation of tau' proved to be more critical for improving the performance of the equations than using Frm alone. Among all the equations, Reeking (2013) presented the best result for the case in which 85.2% of the estimates fell within one order of magnitude of the measured transport rates (0.1 < r > 10.0); this was followed by the Reeking (2010) (70.4% of estimates within one order of magnitude) and Meyer-Peter and Willer (1948) (37% of estimates within one order of magnitude) equations. In conclusion, we observed that even by implementing flow resistance and with the limited availability of mobile sediment, the equations overestimated the bedload transport rate in the studied river reach, indicating an underestimation of energy loss in the flow resistance equations of mixed bedrock-alluvial channels.