Quantification of morphodynamic variability and sea state damping of plates at the nearshore area in the East Frisian North Sea
Introduction
Coastal areas are of great interest as present and future climate change impacts, such as sea level rise, will put increasing pressure on humans living in those regions. Also the possibility of an increase in extreme storm events, which may damage coastal areas, is conceivable (Weisse et al., 2012; Bengtsson et al., 2009). For the North Atlantic and the North Sea, various studies that have analyzed the wave climate identified an increasing trend in ocean wave heights (Bacon and Carter, 1991; Weisse and Gunther, 2007; Grabemann and Weisse, 2008; Mori et al., 2013; Vanem and Walker, 2013). To sufficiently assess coastal management risks and evaluate human interferences, detailed knowledge about the wave climate is of particular interest, not only for coastal protection, but also for offshore structures and operations (Wang et al., 2012; Kaiser and Niemeyer, 2007; Teich et al., 2018).
For such purposes, the state of the art third generation wave model SWAN (Simulating WAves Nearshore) is often applied for modeling design waves, wave climate, and special events in shelf seas, coastal, and nearshore areas (Booij et al., 1999; Hoque et al., 2019; Vieira et al., 2020; Sebastian et al., 2014). In many cases, static bathymetries are used for those applications (Hoque et al., 2019; Akpinar et al., 2016; Kutupoğlu et al., 2018; Sierra et al., 2017). But SWAN modeling with dynamical bathymetries is also an important and promising approach in coastal risk assessment. Thus, some studies estimated morphological impacts on the wave climate in coastal and nearshore areas (Dallas and Barnard, 2011; Eshleman et al., 2007). Due to local changes in the morphodynamic conditions and the interaction of numerous different influences, global wave climate issues are very heterogeneous on a local scale (Wang et al., 2012; Kaiser and Niemeyer, 2007). Various studies analyzed those morphological conditions and sediment dynamics of ebb-tidal deltas (ETD) in coastal areas (Hansen et al., 2013; Anthony, 2013; Bergillos et al., 2016). ETDs consist of sediments from ebb-tidal currents, deposited seawards of the tidal inlet (Dallas and Barnard, 2011). They are subject to rapid changes by migration, erosion, and accretion (Castelle et al., 2007).
The ETD sandbanks off the Norderney coastline have a major impact on the local wave climate, since they are a natural barrier against incoming waves, especially during extreme events for which only few in-situ buoy data are available (Niemeyer, 1979; Niemeyer and Kaiser, 1999). Wave breaking causes the sea state energy to be dissipated on the sandbanks before reaching the islands beaches, while many influences affect the damping, whose complexity is not yet entirely known even due to missing measurements (Niemeyer and Kaiser, 1999). In order to consider local morphodynamic influences on the sea state, small-scale and high-resolution analyses are necessary. Until now, no quantification of this damping effect, especially for the specific impact on swell waves, has yet been done. In particular, the influence of specific small-scale morphological changes or rather gaps in the ETD sandbanks on the sea state in this area is of great interest for coastal protection, as the ETD sandbanks are constantly exposed to morphological changes.
Thus, the main objective of this study was to quantify the influence of the ETD sandbank dynamics on the sea state and to analyze their damping effect by validated SWAN simulations for representative storm events at Tidal High Water (THW) and for swell in specific.
In chapter 2 the research area and dataset are presented. In chapter 3 the ETD sandbank dynamics (migration velocities and changes in size and shape) are analyzed with GIS based on five different bathymetries from 1995, 2005, 2006, 2008, and 2015. Chapter 4 presents the methods for data analysis and numerical modeling with SWAN. Based on the previous analyzes, chapter 5 focuses on the modeling results of the nearshore sea state of Norderney with dynamic bathymetries in SWAN to isolate and quantify the ETD influences on the sea state damping. Therefore, five representative storm cases are simulated and the respective damping effects are discussed. The Norderney nearshore wave climate is also characterized in this chapter to distinguish natural variability from morphological influences. Further evaluations transfer some general findings of the modeled sea state damping effect to the buoy measurements, unaffected and affected by the morphodynamic of the ETD sandbanks off the Norderney coast. Chapter 6 then provides appropriate conclusions.
Section snippets
Research area
The research area covers the nearshore zone of the coastal area of the East Frisian island of Norderney in the northern German North Sea. It extends from about 7.05° to 7.3° E and 53.85°–53.65° N. Norderney is separated from the island of Juist in the west by the Norderneyer Seegat and from Baltrum in the east by the Wichter Ee (Fig. 1). The main wind direction of the research area is southwest. The long-term Mean High Water (MHW) is 1.24 m above the German reference height (NHN), which is
ETD dynamics analysis methods
In a first step an analysis of the ETD sandbank movements and dynamics was performed with QGIS using the DTMs and aerial photos (QGIS Development Team, 2019). A detailed description of the meta data of the DTMs and the aerial photos is given in Table 1. The five different bathymetries from the DTMs were selected for the highest degree of variability in their characteristics of the sandbanks size and shape. Differences between the aerial photos and the DTM analysis can be explained by the
Wave parameter postprocessing
The buoys postprocessing software W@ves21 provided wave parameters from a spectral analysis at 30-min time periods (history data files) (Datawell, 2014). Since all data should be processed consistently and the buoys software had changed over the years, the raw (directional) surface elevation data of the 30-min periods were used in addition to the history data files. Based on the buoys raw data, a data preprocessing was performed with the MATLAB toolbox WAFO to calculate the continuous variance
Natural wave climate variability
For a first overview, detailed descriptive statistics of the different buoy positions are given in Table 5. With increasing coastal proximity, the mean significant wave height decreased from 1.20 m to 0.63 m, mainly caused by depth-induced wave breaking. Concurrently, the mean wave period and the quotient of its standard deviation and mean value increased slightly with increasing coastal proximity. This could indicate a variability of different sea states and wave transformation
Conclusion
This study quantitatively determined a significant influence of the ETD sandbank dynamics on the sea state damping at the nearshore area of Norderney. Non-linear influences, mainly caused by the ETD sandbanks, clearly characterized the wave climate in the nearshore area. Sizes and shapes of single ETD plates showed high dynamics, with mean movement velocities of to , depending on the position of the plates.
By SWAN modeling, significant differences of the relative damping effects
CRediT authorship contribution statement
Christoph Jörges: Conceptualization, Methodology, Software, Validation, Formal analysis, Data curation, Writing - original draft, Writing - review & editing, Visualization. Cordula Berkenbrink: Conceptualization, Methodology, Resources, Data curation, Writing - review & editing. Jannis Heil: Writing - review & editing. Britta Stumpe: Conceptualization, Writing - review & editing, Supervision.
Declaration of competing interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgements
The authors thank the Coastal Research Station of the Lower Saxony Water Management, Coastal Defence and Nature Conservation Agency (NLWKN) for providing the buoy measurement data used in this study.
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
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Cited by (2)
Prediction and reconstruction of ocean wave heights based on bathymetric data using LSTM neural networks
2021, Ocean EngineeringCitation Excerpt :The normalization only refers to the min and max of the training data, to ensure the models did not see any information (e.g., the range) of the test data during the training process. Since the ETD sandbanks exert a huge and dynamical influence on the sea state damping in the nearshore area of Norderney – depending on their shape and size the damping effect varies on average by 14% – better reconstruction and prediction performances were expected by including the bathymetric data (Jörges et al., 2021). In order to avoid overfitting and to achieve a better generalization of the models, a dimension reduction of the bathymetries was carried out.