A metastatistical frequency analysis of extreme storm surge hazard along the US coastline

ABSTRACT

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.

KEYWORDS:

• Coastal hazards
• extreme storm surge
• extreme value theory
• metastatistics

Acknowledgments

Data sources and R packages used for this analysis have been recognized with in-text references. All authors would like to thank the three anonymous reviewers whose comments helped significantly improve the quality of this work. The first author would also like to acknowledge Ph.D. researcher Da-young Kang for aiding with esthetics of the figures.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Supplementary material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/21664250.2024.2338323

Additional information

Funding

This study is funded by the National Science Foundation awards [#2223893 and #2238000].