Estuaries are highly dynamic systems, subject to continuous morphological change, which results from complex interactions and feedbacks between the hydrodynamic processes, sediment transport processes and the ecology. The prediction of morphological change in estuaries is therefore difficult but is necessary to help protect a range of human interests and estuarine ecosystems. Existing methods use detailed process modelling (Bottom-Up methods) or rely on data analysis and the development simple equilibrium relationships (Top-Down methods). Bottom-Up methods are able to make accurate predictions of change over short timescales but suffer from long simulation times and an accumulation of errors when applied over medium and long timescales, while Top-Down methods are better suited for predicting long term trends in morphological behaviour. A need currently exists for new, improved methods to predict changes occurring over medium timescales (one year to several decades). This thesis presents a new, Cellular Automata based, estuarine morphodynamic model, which divides the estuary into an array of cells and uses simplified representations of the hydrodynamic and sediment transport processes together with empirical rules to represent salt marsh ecology. The model has been developed to focus on high level interaction and feedback effects between these processes in order to identify potential medium term morphological changes that may occur in response to environmental change or engineering works. The model has been tested using a series of sensitivity tests and idealised test scenarios for a simple generic estuary and was found to have successfully generated qualitatively realistic results. The model is robust and computationally very efficient. Further work is now needed to calibrate and verify the model using datasets from real estuaries. Future improvements may also include the addition of ocean waves, littoral wave driven sand transport and improvements to the methodology in order to further enhance the computational efficiency.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:669459 |
| Date | January 2016 |
| Creators | Bentley, Ian |
| Publisher | University of Glasgow |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://theses.gla.ac.uk/6821/ |
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