Coastal areas are heavily populated in countries around the world and are a source of economic activity, both recreational and industrial. Waves and tides interact with sediments in a dynamic equilibrium which leads to coastal morphological changes at different temporal and spatial scales. Natural or human-induced changes in this equilibrium may lead to an alteration of the coastline causing environmental or economic impacts. Coastal defences are often needed in order to protect specific areas and reduce such impacts. Therefore, understanding the impact that coastal defence structures have on coastal morphological changes is important for coastal managers. There are different methods to study morphological changes in coastal areas. Process-based numerical models are powerful and precise tools but they are more effective for small to medium spatial scales (km) and short to medium-term temporal scales. Data-driven methods have been proven useful to study morphological changes in the long-term. However, data is not always available in the quantity or quality needed for such methods to provide meaningful results. This study uses jointly process-based numerical models, COAST2D, and data-driven methods, Empirical Orthogonal Functions method (EOF), taking advantage of the strengths of both methods to overcome their own weaknesses. A novel methodology for EOF components extrapolation, named Dynamic EOF method, is developed. Results show that, COAST2D is an efficient tool to simulate morphological changes in the scale of months and kilometres. These scales exceed the ones reached previously by the model, increasing the confidence on its capabilities. The Dynamic EOF method, which extrapolates both temporal and spatial EOF components, was found to yield better results than previous attempts using the EOF method to extrapolate results beyond the training period based on EOF temporal component extrapolation only. Keywords: EOF method, Dynamic EOF method, forecasting, shore-parallel breakwaters, morphological changes, COAST2D.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:709576 |
| Date | January 2016 |
| Creators | Alvarez Martinez, Fernando Manuel |
| Publisher | Cardiff University |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://orca.cf.ac.uk/99018/ |
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