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Abstract Storm surge events are a key driver of widespread flooding, particularly when combined with astronomical tides superimposed on mean sea level (MSL). Coastal storms exhibit seasonal variability which translates into a seasonal cycle in storm surge activity. Understanding changes in the seasonal storm surge cycle is critical as both changes in the amplitude and the phase may alter the flood potential, especially when compounded with changes in the MSL cycle. Here, a comprehensive analysis of the storm surge seasonal cycle and its links to the MSL seasonal cycle is performed using tide gauge observations from a quasi‐global data set. Harmonic analysis is used to assess the mean and changing storm surge seasonal cycles over time. Extreme value analysis is applied to explore the effect of seasonal changes on storm surge return levels. We also quantify the influence of large‐scale climate modes, and we compare how the seasonality of storm surge and MSL have changed relative to each other. The peak of the storm surge cycle typically occurs during winter for tide gauges outside of tropical cyclone regions, where there is also greater variability in the phase of the storm surge cycle. The timing of the peak varied by more than a month at 21% of the tide gauges analyzed. The MSL and storm surge cycles peaked at least once within 30 days over the historic records at 74% of tide gauges.
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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
