MODELLING LONG-TERM COASTAL MORPHOLOGY USING EOF METHOD

Proceedings of the International Conference on Coastal Engineering

Fernando Alvarez¹  Shunqi Pan¹ 

[1] Cardiff University

关键词: EOF method;    parameterisation;    shore-parallel breakwaters;    morphological modelling;   

DOI  :  10.9753/icce.v34.sediment.65

学科分类：建筑学

来源: Coastal Engineering Research Council

【 摘 要 】

Coastline is constantly changing due to the action of wind, waves, tides and sea level variations. Coastal erosion and coastal flooding become increasingly concerned for coastal engineers and coastal zone managers. With global warming due to the climate change which leads to the sea level rise, the frequency and severity of storms are increasing, so that coastal defence becomes even more challenging. In the past decades, various coastal defence structures have been built worldwide to protect our coasts and coastal environment. These structures include sea wall, breakwaters, groynes, and other forms, which often are the combinations of those mentioned, in addition to the soft engineering approaches, such as beach nourishment. To ensure the coastal defence structures to be effectively functional for their design life, it requires the designers to fully understand the effects of the structures on the hydrodynamics and morphodynamics in the surrounding areas, and the response of the coastline in long term. In recent years, the data-driven models have been widely used for long-term modelling of shoreline changes, as such Empirical Orthogonal Functions (EOF) method or one-line model, but require extensively the field data. This paper is to present the details of the EOF analysis using the results obtained from a process-based model COAST2D and the parameterisation procedure for predicting longer term morphological changes from the extrapolated spatial and temporal EOF components. The results presented in this paper illustrate the novelty and effectiveness of the approach as a practical tool for long-term morphological predictions.

【 授权许可】

Unknown