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Storm surges are the most important driver of flooding in many coastal areas. Understanding the spatial extent of storm surge events has important financial and practical implications for flood risk management, reinsurance, infrastructure reliability and emergency response. In this paper, we apply a new tracking algorithm to a high-resolution surge hindcast (CODEC, 1980–2017) to characterize the spatial dependence and temporal evolution of extreme surge events along the coastline of the UK and Ireland. We quantify the severity of each spatial event based on its footprint extremity to select and rank the collection of events. Several surge footprint types are obtained based on the most impacted coastal stretch from each particular event, and these are linked to the driving storm tracks. Using the collection of the extreme surge events, we assess the spatial distribution and interannual variability of the duration, size, severity, and type. We find that the northeast coastline is most impacted by the longest and largest storm surge events, while the English Channel experiences the shortest and smallest storm surge events. The interannual variability indicates that the winter seasons of 1989-90 and 2013–14 were the most serious in terms of the number of events and their severity, based on the return period along the affected coastlines. The most extreme surge event and the highest number of events occurred in the winter season 1989–90, while the proportion of events with larger severities was higher during the winter season 2013–14. This new spatial analysis approach of surge extremes allows us to distinguish several categories of spatial footprints of events around the UK/Ireland coast and link these to distinct storm tracks. The spatial dependence structures detected can improve multivariate statistical methods which are crucial inputs to coastal flooding assessments.
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A metastatistical frequency analysis of extreme storm surge hazard along the US coastline
Frequency analysis of extreme storm surge is crucial for coastal flood risk assessments. To date, such analyses are based on traditional extreme value theory (EVT) and its associated generalized extreme value (GEV) distribution. The metastatistical extreme value distribution (MEVD) provides a new approach that can alleviate limitations of EVT. This paper provides a comparison between the GEV distribution and the MEVD on their ability to predict “unseen” upper-tail quantiles of storm surge along the US coastline. We analyze the error structure of these distributions by performing a cross-validation experiment where we repeatedly divide the data record into a calibration and validation set, respectively, and then compute the predictive non-dimensional error. We find that the MEVD provides comparable estimates of extreme storm surge to those of the GEV distribution, with discrepancies being subtle and dependent on tide gauge location and calibration set length. Additionally, we show that predictions from the MEVD are more robust with less variability in error. Finally, we illustrate that the employment of the MEVD, as opposed to classical EVT, can lead to remarkable differences in design storm surge height; this has serious implications for engineering applications at sites where the novel MEVD is found more appropriate.
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- PAR ID:
- 10523836
- Publisher / Repository:
- Taylor & Francis
- Date Published:
- Journal Name:
- Coastal Engineering Journal
- Volume:
- 66
- Issue:
- 2
- ISSN:
- 2166-4250
- Page Range / eLocation ID:
- 380 to 394
- Subject(s) / Keyword(s):
- Extreme Sea Level Generalized extreme value Distribution Metastatistical extreme value distribution
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1080/21664250.2024.2338323
