Determining return water levels at ungauged coastal sites: a case study for northern Germany

Abstract

We estimate return periods and levels of extreme still water levels for the highly vulnerable and historically and culturally important small marsh islands known as the Halligen, located in the Wadden Sea offshore of the coast of northern Germany. This is a challenging task as only few water level records are available for this region, and they are currently too short to apply traditional extreme value analysis methods. Therefore, we use the Regional Frequency Analysis (RFA) approach. This originates from hydrology but has been used before in several coastal studies and is also currently applied by the local federal administration responsible for coastal protection in the study area. The RFA enables us to indirectly estimate return levels by transferring hydrological information from gauged to related ungauged sites. Our analyses highlight that this methodology has some drawbacks and may over- or underestimate return levels compared to direct analyses using station data. To overcome these issues, we present an alternative approach, combining numerical and statistical models. First, we produced a numerical multidecadal model hindcast of water levels for the entire North Sea. Predicted water levels from the hindcast are bias corrected using the information from the available tide gauge records. Hence, the simulated water levels agree well with the measured water levels at gauged sites. The bias correction is then interpolated spatially to obtain correction functions for the simulated water levels at each coastal and island model grid point in the study area. Using a recommended procedure to conduct extreme value analyses from a companion study, return water levels suitable for coastal infrastructure design are estimated continuously along the entire coastline of the study area, including the offshore islands. A similar methodology can be applied in other regions of the world where tide gauge observations are sparse.