Observations show that instream vegetation has a strong impact on bedload transport. However, there is a scarcity of sediment transport predictors that directly account for the effects of plants, and existing methods, based on re-calculation of roughness coefficients, may present some inconsistencies. The approach herein proposed extends Einstein's parameters to include the effects of vegetation on sediment transport for implementation in existing bedload predictors of the form Φ = f(Ψ). The new formulations are applicable in presence of submerged and emergent vegetation and reduce to the original Einstein's parameters if vegetation is absent. Calibration is based on laboratory data. For this purpose, an extensive experimental program was carried out on a tilting glass-walled flume with medium-sand bed and plants represented by aluminum cylinders. Validation is based on published bedload measurements from the literature. The results show a much better agreement between measurements and predictions when applying the extended parameters compared to using Einstein's original ones. Predicted bedload rates have, on average, the same order of magnitude of the measured ones, and quantitative agreement is substantially increased. Clear improvements were also observed when comparing the results with the bedload predictions of Baptist's (2005) method, which is based on the re-calculation of bed roughness in the presence of vegetation.

An advection-diffusion model is proposed to simulate large wood transport during high flows. The mathematical model is derived from the wood mass balance, taking into consideration both the wood mass concentration and the log orientation, which affects log transport and, most importantly, wood accumulation. Focusing on wood mass transport, the advection-diffusion equation is implemented in a hydrodynamic model to provide a one-way coupled solution of the flow and of the floating wood mass. The model is tested on a large series of flume experiments, involving at least 30 logs and different control parameters (flow Froude number, log length, diameter, release point). The validation through the experimental data shows that the proposed model can predict the correct displacement of the most probable position of the logs and to simulate with a sufficient accuracy the planar diffusion of the wooden mass. Transversal wood distribution is more accurate than the streamwise one, indicating that a higher control on the longitudinal diffusion needs to be implemented.