An analysis of nonlinearity effects on bedload transport prediction
Résumé
Because bedload equations are nonlinear and because parameters describing the flow and the bed can have large variance, different results are expected when integrating bedload over a cross section with respect to spatially variable local data (2D), or when computing bedload from cross-section averaged data, which reduces the problem to uniform conditions (1D). I have shown evidence of these effects by comparing 1D (flume-derived) equations to 2D field measurements, and by comparing a 2D (field-derived) equation to 1D flume measurements, concluding that different equations should be used depending on whether local or averaged data are used. However, whereas nonlinearity effects are considerable for low-transport stages, they tend to disappear for higher flow conditions. I propose probability distribution functions describing the variance in flow and bed grain size distribution (GSD) and I used the width-integrated bedload data (implicitly containing the natural variance in bed and flow parameters) to calibrate these functions. The method consisted of using a Monte Carlo approach to match the measured 2D bedload transport rates with 1D computations artificially reproducing the natural variance associated with the mean input parameters. The Wilcock and Crowe equation was used for the 1D computations because it was considered representative of 1D transport. The results suggest that nonlinearity effects are mostly sensitive to the variance in shear stress, modelled here with a gamma function, whose shape coefficient alpha was shown to increase linearly with the transport stage. This variance in shear stress suggests that even for very low flow conditions, shear stress can locally exceed the critical shear stress for the bed armour, generating local armour break-up. This could explain why the bedload GSD is usually very similar to subsurface GSD, even in the presence of complete armour.
Origine : Fichiers produits par l'(les) auteur(s)
Loading...