Understanding the variability of an extreme storm tide along a coastline
Lewis, M.; Schumann, G.; Bates, P.; Horsburgh, K. ORCID: https://orcid.org/0000-0003-4803-9919. 2013 Understanding the variability of an extreme storm tide along a coastline. Estuarine, Coastal and Shelf Science, 123. 19-25. 10.1016/j.ecss.2013.02.009
Full text not available from this repository.Abstract/Summary
Correctly determining the peak storm tide height along the coastline, and expressing the associated natural variability, is essential for a robust prediction of coastal flood risk. A new approach is proposed that calculates a storm tide relationship (relative to a tide gauge) by using a storm surge model to describe the natural spatial variability based on the features of a large number of very high storm tides. Two historic flood events (1953 and 2007) were used to validate this characteristics approach along the East Anglia coastline (U.K.), and predicted water-levels were found to be in good agreement with tide gauge observations (Root Mean Squared Error of 36 cm), especially when compared to the method of assuming a storm tide of constant return period (Root Mean Squared Error of 59 cm). Detailed observations of storm tide height between tide gauge locations are rare; therefore, Synthetic Aperture Radar (SAR) was employed to calculate the LiDAR geo-referenced storm tide height along the North Somerset coastline of the Bristol Channel (U.K.). Two SAR observed “extreme” storm tide events were used to validate the characteristics approach between tide gauges (Root Mean Squared Error of 1.2 m and 0.7 m), and indicated the presence of localised wave effects to the observed storm tide height that could have a significant effect to flood risk estimates.
Item Type: | Publication - Article |
---|---|
Digital Object Identifier (DOI): | 10.1016/j.ecss.2013.02.009 |
ISSN: | 02727714 |
Date made live: | 14 May 2013 09:33 +0 (UTC) |
URI: | https://nora.nerc.ac.uk/id/eprint/501897 |
Actions (login required)
View Item |
Document Downloads
Downloads for past 30 days
Downloads per month over past year