Suchergebnisse

Abstract. This paper presents an advanced approach to statistically analyse storm surge events. In former studies the highest water level during a storm surge event usually was the only parameter that was used for the statistical assessment. This is not always sufficient, especially when statistically analysing storm surge scenarios for event-based risk analyses. Here, Archimedean Copula functions are applied and allow for the consideration of further important parameters in addition to the highest storm surge water levels. First, a bivariate model is presented and used to estimate exceedance probabilities of storm surges (for two tide gauges in the German Bight) by jointly analysing the important storm surge parameters "highest turning point" and "intensity". Second, another dimension is added and a trivariate fully nested Archimedean Copula model is applied to additionally incorporate the significant wave height as an important wave parameter. With the presented methodology, reliable and realistic exceedance probabilities are derived and can be considered (among others) for integrated flood risk analyses contributing to improve the overall results. It is highlighted that the concept of Copulas represents a promising alternative for facing multivariate problems in coastal engineering.

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.

Abstract. In this paper, a methodology to analyse observed sea level rise (SLR) in the German Bight, the shallow south-eastern part of the North Sea, is presented. The paper focuses on the description of the methods used to generate and analyse mean sea level (MSL) time series. Parametric fitting approaches as well as non-parametric data adaptive filters, such as Singular System Analysis (SSA) are applied. For padding non-stationary sea level time series, an advanced approach named Monte-Carlo autoregressive padding (MCAP) is introduced. This approach allows the specification of uncertainties of the behaviour of smoothed time series near the boundaries. As an example, the paper includes the results from analysing the sea level records of the Cuxhaven tide gauge and the Heligoland tide gauge, both located in the south-eastern North Sea. For comparison, the results from analysing a worldwide sea level reconstruction are also presented. The results for the North Sea point to a weak negative acceleration of SLR since 1844 with a strong positive acceleration at the end of the 19th century, to a period of almost no SLR around the 1970s with subsequent positive acceleration and to high recent rates.

Worldwide consumer response toward food products made from genetically modified (GM) ingredients has been largely negative. However, the majority of the previous studies on consumer attitudes towards food products were conducted in developed countries in Europe as well as Japan. The small number of studies conducted in developing nations obtain different results from those of the developed world. This paper considers the motivations for consumer attitudes towards genetically modified foods in developing nations. We conclude that the generally positive perception towards GM foods in developing nations stems from more urgent needs in terms of food availability and nutritional content. Additionally, perceived levels of risk may be smaller due to trust in government, positive perceptions of science, and positive media influences. This is contrary to the smaller benefits and higher perceived risks found in many developed countries, and hence, the rational for low or non-acceptance of GM foods in those countries.