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Analyzing Monthly Extreme Sea Levels with a Time-Dependent GEV Model

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  • 1 Ocean and Coastal Research Group, Departamento Ciencias y Técnicas del Agua y del Medio Ambiente, Universidad de Cantabria, Santander, Spain
  • | 2 Departamento Matemática Aplicada y Ciencias de la Computación, Universidad de Cantabria, Santander, Spain
  • | 3 Ocean and Coastal Research Group, Departamento Ciencias y Técnicas del Agua y del Medio Ambiente, Universidad de Cantabria, Santander, Spain
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Abstract

A statistical model to analyze different time scales of the variability of extreme high sea levels is presented. This model uses a time-dependent generalized extreme value (GEV) distribution to fit monthly maxima series and is applied to a large historical tidal gauge record (San Francisco, California). The model allows the identification and estimation of the effects of several time scales—such as seasonality, interdecadal variability, and secular trends—in the location, scale, and shape parameters of the probability distribution of extreme sea levels. The inclusion of seasonal effects explains a large amount of data variability, thereby allowing a more efficient estimation of the processes involved. Significant correlation with the Southern Oscillation index and the nodal cycle, as well as an increase of about 20% for the secular variability of the scale parameter have been detected for the particular dataset analyzed. Results show that the model is adequate for a complete analysis of seasonal-to-interannual sea level extremes providing time-dependent quantiles and confidence intervals.

Corresponding author address: Fernando J. Méndez, Ocean and Coastal Research Group, Dpto. Ciencias y Técnicas del Agua y del Medio Ambiente, Universidad de Cantabria, Avda. de los Castros s/n, 39005 Santander, Spain. Email: mendezf@unican.es

Abstract

A statistical model to analyze different time scales of the variability of extreme high sea levels is presented. This model uses a time-dependent generalized extreme value (GEV) distribution to fit monthly maxima series and is applied to a large historical tidal gauge record (San Francisco, California). The model allows the identification and estimation of the effects of several time scales—such as seasonality, interdecadal variability, and secular trends—in the location, scale, and shape parameters of the probability distribution of extreme sea levels. The inclusion of seasonal effects explains a large amount of data variability, thereby allowing a more efficient estimation of the processes involved. Significant correlation with the Southern Oscillation index and the nodal cycle, as well as an increase of about 20% for the secular variability of the scale parameter have been detected for the particular dataset analyzed. Results show that the model is adequate for a complete analysis of seasonal-to-interannual sea level extremes providing time-dependent quantiles and confidence intervals.

Corresponding author address: Fernando J. Méndez, Ocean and Coastal Research Group, Dpto. Ciencias y Técnicas del Agua y del Medio Ambiente, Universidad de Cantabria, Avda. de los Castros s/n, 39005 Santander, Spain. Email: mendezf@unican.es

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