EGU2020-19940
https://doi.org/10.5194/egusphere-egu2020-19940
EGU General Assembly 2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Application of an artificial neural network to generate wave projections at southern African coasts

Felix Soltau1, Matthias Hirt1, Jessica Kelln1, Sara Santamaria-Aguilar2, Sönke Dangendorf3, and Jürgen Jensen1
Felix Soltau et al.
  • 1University of Siegen, Research Institute for Water and Environment, Civil Engineering, Siegen, Germany (felix.soltau@uni-siegen.de)
  • 2Christian-Albrechts-University, Coastal Risks and Sea-Level Rise | Research Group, Institute of Geography, Kiel, Germany
  • 3Old Dominion University, Norfolk, VA, USA

In the past decades, severe so called ‚compound events‘ led to critical high water levels at the coasts of southern Africa and as a consequence to property damage and loss of human life. The co-occurrence of storm surges, wind waves, heavy precipitation and resulting runoff increases the risk of coastal flooding and exacerbates the impacts along the vulnerable southern African coasts (e.g. Couasnon et al. 2019). To mitigate these high-impacts, it is essential to understand the underlying processes and driving factors (Wahl et al. 2015). As compound flooding events at southern African coasts are dominated by wind waves, it is of great importance to investigate the regional wave climate to understand the wave forcing as well as the origin of the wave energy.

Wind waves around southern African coasts are affected by the complex interactions between the Agulhas current and the atmosphere. In the research project CASISAC* we analyse the present evolution of the Agulhas Current system and quantify its impact on the future regional wave climate. Ocean waves contributing to high sea levels can be generated offshore resulting in swell or closer to the coasts by strong onshore winds. To identify responsible atmospheric pressure fields that force high wind wave events we apply a hybrid approach: (1) linking south hemispheric pressure fields with offshore wave data using an artificial neural network and (2) determine the prevailing nearshore wave conditions by regional numerical wave propagation models (SWAN). By validating the modelled nearshore wave data from hindcast runs with wave buoy records, this approach allows us to predict future extreme wind wave events and thus potential flooding. In a next step, extreme value analysis is used to determine future return periods of extreme wave events. These results can contribute to the development of more reliable adaptive protection strategies for southern African coast.

*CASISAC (Changes in the Agulhas System and its Impact on Southern African Coasts: Sea level and coastal extremes) is funded by the German Federal Ministry of Education and Research (BMBF) through the project management of Projektträger Jülich PTJ under the grant number 03F0796C

 

Couasnon, Eilander, Muis, Veldkamp, Haigh, Wahl, Winsemius, Ward (2019): Measuring compound flood potential from river discharge and storm surge extremes at the global scale and its implications for flood hazard. In: Natural Hazards and Earth System Sciences, Discussion Paper, S. 1–24. DOI: 10.5194/nhess-2019-205, in review.
Wahl, Jain, Bender, Meyers, Luther (2015): Increasing risk of compound flooding from storm surge and rainfall for major US cities. In: Nature Climate Change 5 (12), S. 1093–1097. DOI: 10.1038/nclimate2736.

How to cite: Soltau, F., Hirt, M., Kelln, J., Santamaria-Aguilar, S., Dangendorf, S., and Jensen, J.: Application of an artificial neural network to generate wave projections at southern African coasts, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19940, https://doi.org/10.5194/egusphere-egu2020-19940, 2020

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