RIVERBED MORPHOLOGY, HYDRODYNAMICS AND HYPORHEIC EXCHANGE PROCESSES
Hyporheic exchange is key to buffer water quality and temperatures in streams and rivers, while also providing localized downwelling and upwelling microhabitats. In this research, the effect of geomorphological parameters on hyporheic exchange has been assessed from a physical standpoint: surface and subsurface flow fields, pressure distribution across the sediment/water interface and the residence time in the bed.
First, we conduct a series of numerical simulations to systematically explore how the fractal properties of bedforms are related to hyporheic exchange.We compared the average interfacial flux and residence time distribution in the hyporheic zone with respect to the magnitude of the power spectrum and the fractal dimension of riverbeds. The results show that the average interfacial flux increases logarithmically with respect to the maximum spectral density whereas it increases exponentially with respect to fractal dimension.
Second, we demonstrate how the Froude number affects the free-surface profile, total head over sediment bed and hyporheic flux. When the water surface is fixed,the vertical velocity profile from the bottom to the air-water interface follows the law of the wall so that the velocity at the air-water interface has the maximum value. On the contrary, in the free-surface case, the velocity at the interface no longer has the maximum value: the location having the maximum velocity moves closer to the sediment bed. This results in increasing velocity near the bed and larger head gradients, accordingly.
Third,we investigate how boulder spacing and embeddedness affect the near-bed hydrodynamics and the surface-subsurface water exchange.When the embeddedness is small, the recirculation vortex is observed in both closely-packed and loosely-packed cases, but the size of vortex was smaller and less coherent in the closely-packed case. For these dense clusters, the inverse relationship between embeddedness and flux no longer holds. As embeddedness increases, the subsurface flowpaths move in the lateral direction, as the streamwise route is hindered by the submerged boulder. The average residence time therefore decreases as the embeddedness increases.
Lastly, we propose a general artificial neural network for predicting the pressure field at the channel bottom using point velocities at different level. We constructed three different data-driven models with multivariate linear regression, local linear regression and artificial neural network. The input variable is velocity in x, y, and z directions and the target variable is pressure at the sediment bed. Our artificial neural network model produces consistent and accurate prediction performance under various conditions whereas other linear surrogate models such as linear multivariate regression and local linear multivariate regression significantly depend on input variable.
As restoring streams and rivers has moved from aesthetics and form to a more holistic approach that includes processes, we hope our study can inform designs that benefit both structural and functional outcomes. Our results could inform a number of critical processes, such as biological filtering for example. It is possible to use our approach to predict hyporheic exchange and thus constrain the associated biogeochemical processing under different topographies. As river restoration projects become more holistic, geomorphological, biogeochemical and hydro-ecological aspects should also be considered.
- Doctor of Philosophy
- Civil Engineering
- West Lafayette