Suspended vegetation's ability to handle solute accumulation in natural waters through their roots has sparked significant interest and research in using them to improve water quality. This research semi-analytically investigates the unsteady solute cloud evolution in channels with suspended vegetation after the point source release on the water surface, by solving the solute transport model with spatially variable flow field coefficients. Results show that regardless of gap ratio (the ratio of the height of the gap region to the water depth) and vegetation density, the solute cloud evolution in the preasymptotic stage (the dimensionless time ⟨Dz*⟩t*/h*2<1, where ⟨Dz*⟩ is the depth-averaged vertical turbulent diffusion coefficient. t* is the time, and h* is water depth.) can be featured by: the cloud centroid shifts upstream; the longitudinal dispersion coefficient increases; the solute cloud is positively skewed; and the solute accumulates in the vegetation layer on the upstream side and the bottom boundary layer on the downstream side. At around ⟨Dz*⟩t*/h*2∼1, the cloud centroid position and the longitudinal dispersion coefficient approach their asymptotic values, and the solute cloud is basically considered to follow the normal distribution longitudinally. In terms of the influences of configurations on the solute cloud during the whole dispersion process, the cloud centroid moves faster and reaches a farther asymptotic location due to a more serious blockage effect for a smaller gap ratio or larger vegetation density. In addition, a small gap ratio enhances the skewness and kurtosis of the cloud, while the vegetation density has little effect on them. Benefiting from the combined action of the violent velocity shear and the strong shear-scale diffusion which dominates the vertical mixing, the smallest gap ratio or highest vegetation density promotes the vertical mixing and yields the largest asymptotic longitudinal dispersion coefficient. Consequently, the vertical concentration distribution is the most uniform and the longitudinal concentration distribution is the flattest in these two cases.
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