Compound flood events: analysing the joint occurrence of extreme river discharge events and storm surges in northern and central Europe

The simultaneous occurrence of extreme events gained more and more attention from scientific research in the last couple of years. Compared to the occurrence of single extreme events, co-occurring or compound extremes may substantially increase risks. To adequately address such risks, improving our understanding of compound flood events in Europe is necessary and requires reliable estimates of their probability of occurrence together with potential future changes. In this study compound flood events in northern and central Europe were studied using a Monte Carlo-based approach that avoids the use of copulas. Second, we investigate if the number of observed compound extreme events is within the expected range of 2 standard deviations of randomly occurring compound events. This includes variations of several parameters to test the stability of the identified patterns. Finally, we analyse if the observed compound extreme events had a common large-scale meteorological driver. The results of our investigation show that rivers along the west-facing coasts of Europe experienced a higher amount of compound flood events than expected by pure chance. In these regions, the vast majority of the observed compound flood events seem to be related to the cyclonic westerly general weather pattern (Großwetterlage).</p

Estimating near-shore wave statistics from regional hindcasts using downscaling techniques

Several downscaling techniques, comprising fully dynamical and statistical--dynamical methods applied to near-shore local wave climate, are tested and assessed in terms of wave statistics with respect to the added value that can be achieved compared to larger scale data. The techniques are applied for the example of Helgoland, a small island in the German Bight. It was found that an improved representation could generally be obtained from all downscaling techniques by comparing the near-shore wave climate. Based on a balance between the required computer resources and the improvements achieved, it is suggested, to this end, that a dynamical--statistical approach based on high-resolution coastal wave modeling and linear regression provides the optimal choice

Future storm surge impacts on insurable losses for the North Sea region

The influence of climate change on storm surges including increased mean sea level change and the associated insurable losses are assessed for the North Sea basin. In doing so, the newly developed approach couples a dynamical storm surge model with a loss model. The key element of the approach is the generation of a probabilistic storm surge event set. Together with parametrizations of the inland propagation and the coastal protection failure probability this enables the estimation of annual expected losses. The sensitivity to the parametrizations is rather weak except when the assumption of high level of increased mean sea level change is made. Applying this approach to future scenarios shows a substantial increase of insurable losses with respect to the present day. Superimposing different mean sea level changes shows a nonlinear behavior at the country level, as the future storm surge changes are higher for Germany and Denmark. Thus, the study exhibits the necessity to assess the socio-economic impacts of coastal floods by combining the expected sea level rise with storm surge projections

German Bight residual current variability on a daily basis: principal components of multi-decadal barotropic simulations

Time variability of Eulerian residual currents in the German Bight (North Sea) is studied drawing on existing multi-decadal 2D barotropic simulations (1.6 km resolution) for the period Jan. 1958–Aug. 2015. Residual currents are calculated as 25 h means of velocity fields stored every hour. Principal component analysis (PCA) reveals that daily variations of these residual currents can be reasonably well represented in terms of only 2–3 degrees of freedom, partly linked to wind directions. The daily data refine monthly data already used in the past. Unlike existing classifications based on subjective assessment, numerical principal components (PCs) provide measures of strength and can directly be incorporated into more comprehensive statistical data analyses. Daily resolution in particular fits the time schedule of data sampled at the German Bight long-term monitoring station at Helgoland Roads. An example demonstrates the use of PCs and corresponding empirical orthogonal functions (EOFs) for the interpretation of short-term variations of these local observations. On the other hand, monthly averaging of the daily PCs enables to link up with previous studies on longer timescales