| Abstract/Description or Keywords |
This thesis investigates the potential application of a two-dimensional depth-averaged sediment transport and morphological model on a large braided river system and examines its capability to build a computational gravel budget and predict the morphological changes. The Lower Fraser River gravel reach is characterized by an irregularly sinuous single-thread channel split around large gravel bars and vegetated islands, and riverbed aggradation because of gradual gravel deposition over the years, bank hardening and channel confinement. Gravel removal from selected locations is considered as one of the viable management options to maintain the safety and integrity of the existing flood protection system along the reach. Therefore, any gravel removal plan in this reach requires a reliable sediment budget estimation and identification of deposition zones. It is also required to examine the possible future morphological changes with and without gravel removal and to assess its impact on design flood level. The main objective of this study is to build a computational sediment (gravel) budget for the 33 km long gravel reach that extends from Agassiz-Rosedale Bridge to Sumas Mountain near Chilliwack. In this study, a two-dimensional depth-averaged curvilinear mathematical model MIKE 21C was modified and applied to predict the gravel bedload transport and detect the change of morphology for the next 10 years period. A gravel transport formula was coded and added into the MIKE 21C model. Sediment transport code modification and application has been done side by side in a trial and error fashion. This is the first use of a conventional two-dimensional depth-averaged model for the entire gravel reach of the Lower Fraser River within affordable computational effort. The model application was successful in term of gravel budgeting, aggradation and degradation zones identification and long-term morphological change prediction, with some limitations and drawbacks. Further modification and model testing with recent bedload data is recommended. |
