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\begin{document}

\title{Trivariate probabilistic assessments of the compound flooding events
using Semiparametric Fully Nested Archimedean (FNA) copula approach}



\author[1]{Shahid Latif}%
\author[1]{Slobodan  Simonovic}%
\affil[1]{Western University Department of Civil and Environmental Engineering}%


\vspace{-1em}



  
  \date{May 25, 2024}


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\begin{abstract}
Flooding in coastal areas can result from the joint occurrence of
multiple individual flood variables, resulting in compound flooding (CF)
events. Individual variables may not be extreme but can result in a
severe coastal impact if they occur in close succession or simultaneous.
Bivariate joint distribution analysis is ineffective in assessing the
likelihood of joint occurrence, thus demanding a more advanced higher
dimensional probability framework. Incorporating higher dimensional
joint simulation via traditional symmetric 3-D Archimedean or Elliptical
copulas has statistical limits and would be incapable of preserving all
lower-level dependencies. The heterogeneous dependency in hydrologic
consequences can be modelled effectively via the fully nested
Archimedean (FNA) copulas. Incorporating FNA under parametric
distribution settings is not flexible enough since it is restricted by
the prior distributional assumption of the function type for both its
marginal density functions and copulas in parametric fittings. This
study introduces a multivariate FNA copula under semiparametric
distribution settings. The presented approach is based on relaxing the
modelling of univariate marginal behaviour without any distributional
assumption via the nonparametric kernel density estimation (KDE). The
univariate marginal distribution of all the flood characteristics is
constructed via normal KDE. The performance of FNA with nonparametric
marginals outperforms the FNA copula built under parametric settings.
The derived semiparametric FNA is applied to a case study in compounding
the joint impact of rainfall, storm surge and river discharge
observations on the west coast of Canada. The presented copula-based
joint modelling is employed in multivariate analysis of flood risks in
trivariate primary joint and conditional joint return periods. The
trivariate hydrologic risk associated with compound events is analyzed
using the failure probability (FP) statistics. Investigation reveals
that trivariate hydrologic events produce a higher failure probability
than bivariate (or univariate) events; neglecting trivariate joint
analysis would underestimate FP. Also, it indicates that trivariate
hydrologic risk values would increase with an increase in service time
of the hydraulic facilities.%
\end{abstract}%



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\textbf{Hosted file}

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\textbf{Hosted file}

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\end{document}

