Calcul de coefficient de piégeage pour l'infiltration de sédiments fins | Computations of trapping coefficient for fine sediment infiltration

River habitats are among the most important environmental issues at stake in recent years. Indeed, they have a strong impact on biodiversity. The numerous structures that are built in rivers such as dams, hydroelectric plants, dikes, etc. have dramatically changed the sediment dynamics in some rivers. This fact sometimes causes the clogging of the river bed, i.e. infiltration of very fine sediments within the coarser matrix forming the bed. It is essential to understand the dynamic of fine sediments within the bed to quantify how clogging develops. A trapping coefficient is usually used to describe clogging. It quantifies the amount (or the proportion) of fine sediment that is blocked by a layer of gravels when travelling down into the bed. Most previous models are based on fitting steady-state profiles of fine sediment contents using results of laboratory experiments to compute empirical trapping coefficient. In this article, we explore the Lauk stochastic model (Lauck 1991) that is based on a geometrical analysis. The cumulative pore size distribution of the bed is computed and compared to the grain size distribution of the fine sediment. The convolution between these two distributions gives the percentage of fine sediment that is trapped by the bed. To compute the pore size distribution, a biased coarse sediment frequency distribution is used to take into account the different probabilities for coarse grains to contribute to a pore depending on their size. This computation can be updated as fine sediment infiltrates and modifies the grain size distribution of a certain layer. In this article, we present the computation of the trapping coefficient and analyze how the variation of the trapping coefficient with respect to fine content is affected by the bias applied in coarse sediment frequency distribution. It appears that whereas the bias has little effect on the trapping coefficient for a matrix of gravel void of fine sediments, it has a strong impact on how this coefficient varies with a small amount of sediment trapped within the bed.