Assessing the hydrodynamic boundary conditions for risk analyses in coastal areas: a stochastic storm surge model

Abstract. This paper describes a methodology to stochastically simulate a large number of storm surge scenarios (here: 10 million). The applied model is very cheap in computation time and will contribute to improve the overall results from integrated risk analyses in coastal areas. Initially, the observed storm surge events from the tide gauges of Cuxhaven (located in the Elbe estuary) and Hornum (located in the southeast of Sylt Island) are parameterised by taking into account 25 parameters (19 sea level parameters and 6 time parameters). Throughout the paper, the total water levels are considered. The astronomical tides are semidiurnal in the investigation area with a tidal range >2 m. The second step of the stochastic simulation consists in fitting parametric distribution functions to the data sets resulting from the parameterisation. The distribution functions are then used to run Monte-Carlo-Simulations. Based on the simulation results, a large number of storm surge scenarios are reconstructed. Parameter interdependencies are considered and different filter functions are applied to avoid inconsistencies. Storm surge scenarios, which are of interest for risk analyses, can easily be extracted from the results.

[1]  T. Wahl,et al.  Improved estimates of mean sea level changes in the German Bight over the last 166 years , 2011 .

[2]  J. Jensen,et al.  Modellgestützte Untersuchungen zu Sturmfluten mit sehr geringen Eintrittswahrscheinlichkeiten an der deutschen Nordseeküste , 2006 .

[3]  Extremwertstatistische Analyse von historischen, beobachteten und modellierten Wasserständen an der deutschen Ostseeküste , 2009 .

[4]  Khaled H. Hamed,et al.  Flood Frequency Analysis , 1999 .

[5]  B. Gouldby,et al.  Statistical simulation of flood variables: incorporating short‐term sequencing , 2008 .

[6]  M. Kendall A NEW MEASURE OF RANK CORRELATION , 1938 .

[7]  Eric P. Smith,et al.  An Introduction to Statistical Modeling of Extreme Values , 2002, Technometrics.

[8]  Jürgen Jensen,et al.  Assessing the hydrodynamic boundary conditions for risk analyses in coastal areas: a multivariate statistical approach based on Copula functions , 2012 .

[9]  S. P. Simonovic,et al.  Bivariate flood frequency analysis: Part 1. Determination of marginals by parametric and nonparametric techniques , 2008 .

[10]  T. Wahl,et al.  On analysing sea level rise in the German Bight since 1844 , 2010 .

[11]  Dominic E. Reeve Risk and Reliability: Coastal and Hydraulic Engineering , 2009 .

[12]  S. Coles,et al.  An Introduction to Statistical Modeling of Extreme Values , 2001 .

[13]  S. Nash,et al.  Numerical methods and software , 1990 .

[14]  Hocine Oumeraci,et al.  INTEGRATED FLOOD RISK ANALYSIS FOR EXTREME STORM SURGES (XTREMRISK) , 2011 .

[15]  B. Anderson,et al.  The rising tide: assessing the risks of climate change and human settlements in low elevation coastal zones , 2007 .

[16]  Wilson H. Tang,et al.  Probability Concepts in Engineering: Emphasis on Applications to Civil and Environmental Engineering , 2006 .

[17]  Bernard Bobée,et al.  Towards operational guidelines for over-threshold modeling , 1999 .

[18]  Irving I. Gringorten,et al.  A plotting rule for extreme probability paper , 1963 .

[19]  R. Nicholls,et al.  Sea-level rise and its possible impacts given a ‘beyond 4°C world’ in the twenty-first century , 2011, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[20]  Bastian Klein Ermittlung von Ganglinien für die risikoorientierte Hochwasserbemessung von Talsperren , 2009 .

[21]  S. Simonovic,et al.  Bivariate flood frequency analysis. Part 2: a copula‐based approach with mixed marginal distributions , 2009 .

[22]  Jürgen Jensen,et al.  A MULTIVARIATE STATISTICAL MODEL FOR ADVANCED STORM SURGE ANALYSES IN THE NORTH SEA , 2011 .