Proceedings of the Thirty-second (2022) International Ocean and Polar Engineering Conference
Shanghai, China, June 5–10, 2022
Copyright © 2022 by the International Society of Offshore and Polar Engineers (ISOPE)
ISBN 978-1-880653-81-4; ISSN 1098-6189

www.isope.org

Characterization of Storm Events for Coastal Flood Hazard Assessment:
Sensitivity of the Spatial Structure Variable to the number of simulations
Luca Martinelli, Piero Ruol, Chiara Favaretto
ICEA Department, University of Padova
Padova, Italy

reliable joint statistics (Coles & Tawn, 1994) in the framework of coastal
flooding hazard assessment.
The assessment of the coastal flooding return period is closely related tothe method to identify independent events, i.e. sea storms. A detailed
review of sea storm analysis is presented in Martzikos et al. (2021).
During a single sea storm, different combinations of simultaneous ζ andHs can cause flooding. In fact, the typical approaches would be to selectan “a priori” combination such as: (i) maximum Hs and simultaneousvalue of ζ, (ii) maximum ζ and simultaneous value of Hs or (iii) maximaof Hs and ζ during the storm regardless of their concomitance. However,these simple approaches overlook the correlation between the variables
or consider one to be of primary importance.
The authors (Favaretto et al, 2021) proposed a methodology based on thestructure variable method (so-called by Coles & Tawn, 1994), that
accounts for correlation within each extreme event (Zachary et al., 1998).
Through a structure variable approach, Mazas & Hamm (2017) analyzed
the sampling of the time series based on single events. They proposed aclassification in order to help the choice of the sampling method.
Similarly, Arnoux et al. (2021) present a method to include damage forthe coastal flooding return periods computation. In particular, they tested
several rules, that combine wave parameters and water level, to explain
damages reported in a historical dataset. These authors found that thebest rule also gives the most consistent result in terms of occurrenceprobability.
The method, described in this paper, named Spatial Structure Variable,
evaluates the joint statistics of significant wave heights Hs and waterlevels ζ focusing on the selection of the sample pair that characterizes
each extreme sea storm event. The pair is selected by maximizing aspatial structure variable, that is a linear combination of ζ and Hs, withcoefficients specific to each point of the area at risk.
This paper aims at addressing the untackled problem of the balance
between the accuracy of the results and the computational demand of theproposed method. In fact, the Spatial Structure Variable approachrequires the analysis of a large number of flooding maps establishedthrough numerical modeling. Therefore, guidelines on the number ofsimulations are hereafter investigated for an example case application.
In the following paragraph, the spatial structure variable method is firstdescribed. Then the effect of the number of modeled maps is analyzed
for a case study in the Venetian littoral. Finally, conclusions are drawn.

ABSTRACTThe authors recently proposed a method to define reliable joint statistics
of significant wave heights Hs and water levels  in order to assess thecoastal flood hazard. The method is termed Spatial Structure Variableapproach and focuses on the selection of the sample pair thatcharacterizes each sea storm, to evaluate the occurrence probability ofextreme events.
The pair is selected by maximizing a spatial structure variable, that is afunction (a linear combination) of Hs and , defined for each point of thearea at risk. The structure variable represents the sensitivity of theflooding process to Hs and , as found by analyzing a large set ofinundation maps (90) produced through a Simplified Shallow-Water
numerical model (SSW).
The aim of this paper is to provide guidelines on the number ofsimulations required and show an application of the method for a large
stretch of the coast of the Venetian region (Italy).
KEYWORDS: coastal flooding; numerical model; bivariate statisticalanalysis; sample pairs; Venetian littoral
INTRODUCTION
Coastal flooding is expected to be one of the primary impacts of climatechange, requiring significant economic and social effort to preserve
coastal areas (Diaz, 2016, Tomoil et al. 2020). The evaluation of hazards
associated with coastal flooding is therefore one of the most important
aspects of coastal planning. Several approaches for hazard and risk
assessment were developed (e.g. Di Risio et al, 2016). A recent review
is proposed by Foti et al. (2020).
The maps can, in some cases, directly express the hazard in terms offailure probabilities. Failure in this study is defined as a marine
inundation that exceeds a predefined limit. This quantitative approachrequires the evaluation of the effects due to the combined occurrence ofsea levels and waves. Obviously, the correct selection of sample pairs(wave heights and sea levels) during a sea storm is essential to define2622