A stochastic storm surge generator
as a tool for integrated risk
analyses
Siegen
area at risk flood defence structures
storm surge
Source Pathway Receptor
SP1: Extreme storm surges SP2: Failure probability SP3: Damage assessment
SP4: Integration (Risk analysis, risk assessment and risk management)
area at risk flood defence structures
storm surge
Source Pathway Receptor
Source Pathway Receptor
SP1: Extreme storm surges SP2: Failure probability SP3: Damage assessment
SP4: Integration (Risk analysis, risk assessment and risk management)
Introduction
The joint research project XtremRisK (www.xtremrisk.de) brings together scientists from different universities and agencies to perform integrated risk analyses for Sylt and Hamburg based on the source-pathway-receptor concept.
Partners:
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Introduction
(1) A stochastic storm surge generator is developed, allowing the simulation of a large number of synthetic, physically consistent and high resolution storm surge events as a basis for the statistical
assessment.
(2) The complete storm surge curve is considered to model the failure mechanisms of flood defense structures and to
deter-mine the potential losses. A multivariate statistical approach
implicitly considering the complete storm surge curve when estimating occurrence probabilities is presented.
Two improvements in comparison to former studies (risk analyses): Main objective:
Estimating the occurrence probabilities of extreme storm surge events, which are the outcome of empirical analyses (LSBG Hamburg).
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Data basis
 Input for the statistical model are observed high
resolution storm surge curves identified by applying a POT-Method
 Available data: 1936 to 2008 for Hörnum;
1900 to 2008 for Cuxhaven
The problem of tide-surge separation:
 But, separation supposes a certain degree of knowledge of the site specific tide-surge interaction
 As joint probability methods are not implemented in German coastal design strategies (up to now), information about the non-linear
relationships between the storm surge components are rather limited  Extensive empirical (and numerical) studies are conducted within the
project to obtain further insights into the underlying processes  Separating stochastic components before
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Data basis
 Input for the statistical model are observed high
resolution storm surge curves identified by applying a POT-Method
 Available data: 1936 to 2008 for Hörnum;
1900 to 2008 for Cuxhaven
The problem of tide-surge separation:
 But, separation supposes a certain degree of knowledge of the site specific tide-surge interaction
 As joint probability methods are not implemented in German coastal design strategies (up to now), information about the non-linear
relationships between the storm surge components are rather limited  Extensive empirical (and numerical) studies are conducted within the
project to obtain further insights into the underlying processes  Separating stochastic components before
conducting statistical analyses is recommended
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Parameterization
 A typical storm surge event in the German Bight consists of a
maximum of 3 high tides (> 99% of the observed extreme events) Â An extreme event consisting of 3 tides is parameterized using 19 sea
level and 6 time parameters
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Monte-Carlo-Simulations
 Common parametric distribution functions are fitted to the time series of the 25 parameters and Monte-Carlo-Simulations are
conducted
 Observed dependencies between the sea level parameters are modeled and assigned to the simulation results
 Results from numerical model studies (MUSE-project) are considered as
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Reconstruction and simulation results
 For the following statistical assessment, the parameters Highest turning point and
Intensity | Fullness are considered
 1,000,000 synthetic storm surge events are simulated and can potentially serve as input for a large number of investigations
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Multivariate statistical assessment
 Here, the simulation results serve as a basis to estimate the
occurrence probabilities of extreme storm surge curves
 Joint probabilities of the parameters S and F are calculated based on
2-dimensional Archimedean-Copula-Functions
 Copulas are very flexible joint distributions, accounting for the
structure of dependence overlooking the margins
 As the underlying data set consists of 1,000,000 pairs of S and F, non-parametric Kernel-Density-Functions (KDF) are used as marginal distributions
 Based on GoF-tests, the Gumbel Copula is identified to be able to model the structure of dependence between S and F and is used for the multivariate statistical assessment of the simulation results
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Multivariate statistical assessment
 Joint occurrence probabilities of the parameters S and F for the tide gauge of Hörnum
Min. height of flood protection
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Multivariate statistical assessment
 Combined interim results of the empirical and statistical analyses for the tide gauge of Cuxhaven
Recurrence interval ≈
2.500 years
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Discussion and summary
 The stochastic storm surge generator provides an innovative tool to improve the accuracy of the results from integrated risk analyses
 The presented multivariate statistical model contributes to overcome an existing deficiency when estimating occurrence probabilities of extreme storm surges to be considered for risk analyses
 The feature of Copulas provides the possibility to extend the statistical model to allow the consideration of additional parameters (such as significant wave heights or wave periods)
 The combined results from empirical studies, numerical simulations and statistical analyses serve as reliable hydrodynamic boundary
Thomas Wahl
University of Siegen
Research Institute for Water and Environment (fwu) Fon +49 271 740 3462
E-Mail: [email protected]