A new modelling framework for regional assessment of extreme sea levels and associated coastal flooding along the German Baltic Sea coast

Abstract

Abstract. Hydrodynamic models are increasingly being used in recent years to map coastal floodplains on local to continental scales. On regional scales, however, high computational costs and the need for high-resolution data limit their application. Additionally, model validation constitutes a major concern, as in-situ data are hardly available or limited in spatial coverage to small parts of the study region. Here we address these challenges by developing a modelling framework, which couples a hydrodynamic coastal inundation model covering the German Baltic Sea coast with a hydrodynamic coastal ocean model of the western Baltic Sea, to produce high resolution (50 m) regional scale flood maps for the entire German Baltic Sea coast. Using a LiDAR derived digital elevation model with 1 m horizontal resolution, we derive and validate the elevation of dikes and natural flood barriers such as dunes. Using this model setup, we simulate a storm surge event from January 2019, a surge with a return period of 200 years and two sea-level rise scenarios for the year 2100 (200-year event plus 1 m and 1.5 m). We validate the simulated flood extents by comparing them to inundation maps derived from Sentinel-1 SAR satellite imagery, acquired between 1.5 and 3.5 hours after the peak of the 2019 surge, covering a large part of the study region. Our results confirm that the German Baltic Sea coast is exposed to coastal flooding, with flood extent varying between 118 km2 and 1016 km2 for the 2019 storm surge and a 200-year return water level plus 1.5 m of sea-level rise, respectively. Hotspots of coastal flooding are mostly located in the federal state of Mecklenburg Western Pomerania. Our results emphasise the importance of current plans to update coastal protection schemes along the German Baltic Sea coast over the course of the 21st century in order to prevent large-scale damage in the future.

A Comprehensive Probabilistic Flood Assessment Accounting for Hydrograph Variability of ESL Events

“…Therefore, specifically for localities with gently sloping and large low‐lying areas, we recommend taking a large number of hydrographs into consideration, when modeling floods. Last, as a result of progress in high‐performance computing, hydrodynamic models of lower complexity can be successfully employed to dynamically simulate flooding at larger scale (Kiesel et al., 2023; Vousdoukas et al., 2016). Nevertheless, at this scale, flood plains are often obtained from a single event with a specific return‐period.…”

How Sea Level Rise May Hit You Through the Backdoor: Changing Extreme Water Levels in Shallow Coastal Lagoons

“…The tidal period is set to 24 hr since tides in the Gulf of Mexico are diurnal. The surge amplitude is chosen to be twice the tidal amplitude a : a surge = 2 a since the tidal amplitudes in the Gulf of Mexico are small $$\mathcal{O}(a)=1$$ m. The timescale for the storm surge is chosen with αT = 6 hr which corresponds approximately to a 24‐hr event which is realistic for both the Gulf of Mexico (Familkhalili et al., 2020) and the Baltic Sea (Kiesel et al., 2023). The freshwater pulse amplitude Q amp is chosen with 1,000 m 3 s −1 , which is a value in the range of the 90th percentile discharge, see Table 1.…”

“…For the Baltic Sea lagoons, a corresponds to the 99.75th percentile (see Table S2 in Supporting Information ) of the water level distribution and T = 24 hr (a typical length of an EWL event Kiesel et al. (2023)). The bias and RMSE in the last column refer to the 3‐week time series comparison.…”

Regional assessment of extreme sea levels and associated coastal flooding along the German Baltic Sea coast

“…Data availability. The flood boundary stations, associated waterlevel time series representing the four storm surge scenarios (the 2019 surge, 200-year event, 200-year event plus 1 m SLR, and 200-year event plus 1.5 m SLR), simulated flood characteristics (flood extent and depth), spatially explicit results of the extreme value analysis for every grid cell of the coastal ocean model, modelled monthly peak water levels between 1961 and 2018 for every grid cell of the coastal ocean model, and modelled time series of water levels during the 2019 storm surge and for the entire hindcast period are freely available from Kiesel et al (2023) (https://doi.org/10.5281/zenodo.7886455).…”