Assessing the hydrodynamic boundary conditions for risk analyses in coastal areas: a multivariate statistical approach based on Copula functions

Self Cite

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 Hörnum (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.

Section: Monte-carlo-simulationsmentioning

confidence: 99%

Section: Introduction and Objectivesmentioning

confidence: 99%

Section: Introduction and Objectivesmentioning

confidence: 99%

See 1 more Smart Citation

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

Wahl

Mudersbach

Jensen

2011

Self Cite

“…For example, after the closure of the Maeslant barrier and the Haringvliet dam, the water level within the delta depends on the fluvial flow, the storage capacity and the closure duration (Zhong et al, 2012). Recent research (De Michele et al, 2007;Wahl et al, 2012) has shown how to include more variables in the probabilistic analysis of the hydrodynamic boundary conditions. In this paper, more associated variables will be taken into account in the high water level frequency estimation.…”

Section: Introductionmentioning

confidence: 99%

A joint probability approach using a 1-D hydrodynamic model for estimating high water level frequencies in the Lower Rhine Delta

Zhong

Overloop

Gelder

2013

The Lower Rhine Delta, a transitional area between the River Rhine and Meuse and the North Sea, is at risk of flooding induced by infrequent events of a storm surge or upstream flooding, or by more infrequent events of a combination of both. A joint probability analysis of the astronomical tide, the wind induced storm surge, the Rhine flow and the Meuse flow at the boundaries is established in order to produce the joint probability distribution of potential flood events. Three individual joint probability distributions are established corresponding to three potential flooding causes: storm surges and normal Rhine discharges, normal sea levels and high Rhine discharges, and storm surges and high Rhine discharges. For each category, its corresponding joint probability distribution is applied, in order to stochastically simulate a large number of scenarios. These scenarios can be used as inputs to a deterministic 1-D hydrodynamic model in order to estimate the high water level frequency curves at the transitional locations. The results present the exceedance probability of the present design water level for the economically important cities of Rotterdam and Dordrecht. The calculated exceedance probability is evaluated and compared to the governmental norm. Moreover, the impact of climate change on the high water level frequency curves is quantified for the year 2050 in order to assist in decisions regarding the adaptation of the operational water management system and the flood defense system

“…5, sequences and duration of storms are also important. Therefore future work to statistically analyse storm events could benefit from methods which can incorporate more parameters (including also those previously mentioned such as river levels) (Wahl et al, 2012).…”

Section: P Wadey Et Al: Assessment and Comparison Of Extreme Seamentioning

confidence: 99%

Assessment and comparison of extreme sea levels and waves during the 2013/14 storm season in two UK coastal regions

Wadey¹,

Brown²,

Haigh³

et al. 2015

Abstract. The extreme sea levels and waves experienced around the UK's coast during the 2013/14 winter caused extensive coastal flooding and damage. Coastal managers seek to place such extremes in relation to the anticipated standards of flood protection, and the long-term recovery of the natural system. In this context, return periods are often used as a form of guidance. This paper provides these levels for the winter storms, and discusses their application to the given data sets for two UK case study sites: Sefton, northwest England, and Suffolk, east England. Tide gauge records and wave buoy data were used to compare the 2013/14 storms with return periods from a national data set, and also joint probabilities of sea level and wave heights were generated, incorporating the recent events. The 2013/14 high waters and waves were extreme due to the number of events, as well as the extremity of the 5 December 2013 "Xaver" storm, which had a high return period at both case study sites. The nationalscale impact of this event was due to its coincidence with spring high tide at multiple locations. Given that this event is such an outlier in the joint probability analyses of these observed data sets, and that the season saw several events in close succession, coastal defences appear to have provided a good level of protection. This type of assessment could in the future be recorded alongside defence performance and upgrade. Ideally other variables (e.g. river levels at estuarine locations) would also be included, and with appropriate offsetting for local trends (e.g. mean sea-level rise) so that the storm-driven component of coastal flood events can be determined. This could allow long-term comparison of storm severity, and an assessment of how sea-level rise influences return levels over time, which is important for consideration of coastal resilience in strategic management plans.