Ensemble Hindcasting of Coastal Wave Heights

• Published in: Journal of marine science and engineering Vol. 11; no. 6; p. 1110
• Main Authors: Pais, Namitha Viona, Ravishanker, Nalini, O’Donnell, James, Shaffer, Ellis
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.06.2023
• Subjects: Airports; Analysis; Autoregressive models; Beach erosion; Coastal engineering; Coastal erosion; Coastal structures; Coastal zone management; Empirical analysis; Environmental protection; Environmental risk; extreme value analysis; Extreme values; GARCH; Hindcasting; Mathematical models; Missing data; Parameters; Prediction models; Regression models; return values; Seasons; Sensors; Shore protection; Statistical analysis; Statistical models; threshold regression; Time series; Value analysis; Waterfront development; Wave height; wave heights; Wave parameters; Wind; Wind data; Wind speed; Wind waves; United States > US
• ISSN: 2077-1312; 2077-1312
• DOI: 10.3390/jmse11061110

Long records of wave parameters are central to the estimation of coastal flooding risk and the causes of coastal erosion. This paper leverages the predictive power of wave height history and correlations with wind speed and direction to build statistical models for time series of wave heights to develop a method to fill data-gaps and extend the record length of coastal wave observations. A threshold regression model is built where the threshold parameter, based on lagged wind speed, explains the nonlinear associations, and the lagged predictors in the model are based on a well-established empirical wind-wave relationship. The predictive model is completed by addressing the residual conditional heteroscedasticity using a GARCH model. This comprehensive model is trained on time series data from 2005 to 2013, using wave height and wind data both observed from a buoy in Long Island Sound. Subsequently, replacing wind data with observations from a nearby coastal station provides a similar level of predictive accuracy. This approach can be used to hindcast wave heights for past decades given only wind information at a coastal station. These hindcasts are used as a representative of the unobserved past to carry out extreme value analysis by fitting Generalized Pareto (GP) distribution in a peaks over threshold (POT) framework. By analyzing longer periods of data, we can obtain reliable return value estimates to help design better coastal protection structures.