Mathematical Modelling of Catastrophic Surge and Seiche Events in the Azov Sea Using Remote Sensing Data

Introduction. This work is devoted to the mathematical modelling of extreme sea level fluctuations in the Azov Sea using remote sensing data. The aim of the study is to develop and apply a mathematical model that allows more accurate prediction of surge and seiche events caused by extreme wind conditions. The relevance of the work is due to the need to improve the forecasts of hydrodynamic processes in shallow water bodies (such as the Azov Sea), where such phenomena can have significant economic and ecological consequences. The goal of this work is to develop and apply a mathematical model for predicting extreme sea level fluctuations in the Azov Sea caused by wind conditions.
Materials and Methods. The study is based on the analysis of remote sensing data and observations of wind speed and direction over the Azov Sea. The primary method used is mathematical modelling, which includes solving the system of shallow water hydrodynamics equations. Wind condition data were collected from November 20 to 25, 2019, during which catastrophic sea level fluctuations were observed. The model considers the components of water flow velocity, water density, hydrodynamic pressure, gravitational acceleration, and turbulence exchange coefficients.
Results. The modelling showed that prolonged easterly winds with speeds up to 22 m/s led to significant surge and seiche fluctuations in sea level. The maximum amplitudes of fluctuations were recorded in the central part of the Taganrog Bay, where the wind direction and speed remained almost constant throughout the observation period. Data from various platforms located in different parts of the Azov Sea confirmed a significant decrease in water level in the northeast and an increase in the southwest.
Discussion and Conclusions. The study results confirm that using mathematical models in combination with remote sensing data allows more accurate predictions of extreme sea level fluctuations. This is important for developing measures to prevent and mitigate the consequences of surge and seiche events in coastal areas. In the future, it is necessary to improve models by including additional factors such as climate change and anthropogenic impact on the Azov Sea ecosystem.

1. Alekseenko Е., Roux B., Sukhinov А., Kotarba R., Fougere D. Coastal hydrodynamics in a windy lagoon. Nonlinear Processes in Geophysics. 2013;20(2):189–198. https://doi.org/10.1016/j.compfluid.2013.02.003

2. Holthuijsen L.H. Waves in Oceanic and Coastal Waters. Cambridge University Press. 2007. https://doi.org/10.1017/CBO9780511618536

3. Kirby J.T. (2013). Advanced Numerical Models for Simulating Tsunami Waves and Runup. World Scientific Publishing Company. https://doi.org/10.1142/9101

4. Komen G.J., Cavaleri L., Donelan M., Hasselmann K., Hasselmann S., Janssen P.A.E.M. (1994). Dynamics and Modelling of Ocean Waves. Cambridge University Press. https://doi.org/10.1017/CBO9780511628955

5. Mei C.C., Stiassnie M., Yue D.K.P. Theory and Applications of Ocean Surface Waves: Linear Aspects. World Scientific Publishing Company. 2005. https://doi.org/10.1142/5678

6. The official website of the Unified State Information System on the Situation in the World Ocean. URL: http://esimo.ru/portal/ (accessed: 11.06.2024)

7. Protsenko S., Sukhinova T. Mathematical modeling of wave processes and transport of bottom materials in coastal water areas taking into account coastal structures. MATEC Web of Conferences. 2017;132(2):04002. https://doi.org/10.1051/matecconf/201713204002

8. Sukhinov A.I., Chistyakov A.E., Protsenko E.A. Mathematical Modeling of Sediment Transport in the Coastal Zone of Shallow Reservoirs. Mathematical Models and Computer Simulations. 2014;6(4):351–363. https://doi.org/10.1134/S2070048214040097

9. Panasenko N.D. Forecasting the Coastal Systems State using Mathematical Modelling Based on Satellite Images. Computational Mathematics and Information Technologies. 2023;7(4):54‒65. https://doi.org/10.23947/2587-8999-2023-7-4-54-65

10. Sukhinov A.I., Protsenko S.V., Panasenko N.D. Mathematical modeling and ecological design of the marine systems taking into account multi-scale turbulence using remote sensing data. Computational Mathematics and Information Technologies. 2022;6(3):104‒113. https://doi.org/10.23947/2587-8999-2022-1-3-104-113

11. Sukhinov A., Panasenko N., Simorin A. Algorithms and programs based on neural networks and local binary patterns approaches for monitoring plankton populations in sea systems. E3S Web of Conferences. 2022;363:02027. https://doi.org/10.1051/e3sconf/202236302027

12. Xu W., Yin X., Zhang W. A Review of Applications of Neural Networks in Coastal and Ocean Engineering. Ocean Engineering. 2019;186:106092. https://doi.org/10.1016/j.oceaneng.2019.106092

13. Zhang L., Wang L., Yang Z. Deep Learning for Remote Sensing Image Analysis: A Comprehensive Review. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2018;11(10):3713‒3723. https://doi.org/10.1109/JSTARS.2018.2857630

14. The official website of Earth observing system. URL: https://eos.com/landviewer/account/pricing (accessed: 12.06.2024)

15. The official website of NASA Worldview. URL: https://worldview.earthdata.nasa.gov (accessed: 12.06.2024)