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….Read more

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