Articles | Volume 16, issue 5
https://doi.org/10.5194/os-16-1047-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/os-16-1047-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
11 Sep 2020
Research article |
| 11 Sep 2020
| 11 Sep 2020
Variability of distributions of wave set-up heights along a shoreline with complicated geometry
Laboratory of Wave Engineering, Department of Cybernetics, School of
Science, Tallinn University of Technology, Akadeemia tee 21, Tallinn, 12618,
Estonia
Estonian Academy of Sciences, Kohtu 6, Tallinn, 10130, Estonia
Katri Pindsoo
Laboratory of Wave Engineering, Department of Cybernetics, School of
Science, Tallinn University of Technology, Akadeemia tee 21, Tallinn, 12618,
Estonia
Nadezhda Kudryavtseva
Laboratory of Wave Engineering, Department of Cybernetics, School of
Science, Tallinn University of Technology, Akadeemia tee 21, Tallinn, 12618,
Estonia
Maris Eelsalu
Laboratory of Wave Engineering, Department of Cybernetics, School of
Science, Tallinn University of Technology, Akadeemia tee 21, Tallinn, 12618,
Estonia
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- Directional variation of return periods of water level extremes in Moonsund and in the Gulf of Riga, Baltic Sea R. Männikus & T. Soomere https://doi.org/10.1016/j.rsma.2022.102741
- Inherent non-stationarity in the GEV distribution for extreme sea levels: Implications for coastal vulnerability in the Baltic Sea K. Viigand et al. https://doi.org/10.1016/j.oceaneng.2025.122681
- Forecasting sea level maxima using Machine learning with explainability and extreme value analysis S. Rajabi-Kiasari et al. https://doi.org/10.1016/j.jag.2025.105064
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- Sea level dynamics and coastal erosion in the Baltic Sea region R. Weisse et al. https://doi.org/10.5194/esd-12-871-2021
- Quantifying exposedness of the eastern Baltic Sea shores with respect to extremely high and low water levels K. Viigand et al. https://doi.org/10.1016/j.ecss.2025.109267
- Spatial and temporal variability of wave energy resource in the eastern Pacific from Panama to the Drake passage M. Eelsalu et al. https://doi.org/10.1016/j.renene.2024.120180
- Event-based wave statistics for the Baltic Sea J. Björkqvist et al. https://doi.org/10.5194/sp-4-osr8-10-2024
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- Numerical simulations of wave climate in the Baltic Sea: a review T. Soomere https://doi.org/10.1016/j.oceano.2022.01.004
- Variability and extremes of the Caspian Sea's modelled wave climate A. Giudici et al. https://doi.org/10.1016/j.oceaneng.2025.123077
17 citations as recorded by crossref.
- Sandy beach evolution in the low-energy microtidal Baltic Sea: Attribution of changes to hydrometeorological forcing M. Eelsalu et al. https://doi.org/10.1016/j.geomorph.2022.108383
- Where can simple wave models replace sophisticated ones in the Gulf of Riga? R. Männikus et al. https://doi.org/10.3176/earth.2026.08
- Directional variation of return periods of water level extremes in Moonsund and in the Gulf of Riga, Baltic Sea R. Männikus & T. Soomere https://doi.org/10.1016/j.rsma.2022.102741
- Inherent non-stationarity in the GEV distribution for extreme sea levels: Implications for coastal vulnerability in the Baltic Sea K. Viigand et al. https://doi.org/10.1016/j.oceaneng.2025.122681
- Forecasting sea level maxima using Machine learning with explainability and extreme value analysis S. Rajabi-Kiasari et al. https://doi.org/10.1016/j.jag.2025.105064
- Morphodynamics of two Mediterranean microtidal beaches presenting permanent megacusps under the influence of waves and strong offshore winds P. Feyssat et al. https://doi.org/10.1016/j.csr.2023.105160
- Reconstruction of Baltic Gridded Sea Levels from Tide Gauge and Altimetry Observations Using Spatiotemporal Statistics from Reanalysis J. Elken et al. https://doi.org/10.3390/rs16152702
- Baltic sea wave climate in 1979–2018: Numerical modelling results A. Sokolov & B. Chubarenko https://doi.org/10.1016/j.oceaneng.2024.117088
- Sea level dynamics and coastal erosion in the Baltic Sea region R. Weisse et al. https://doi.org/10.5194/esd-12-871-2021
- Quantifying exposedness of the eastern Baltic Sea shores with respect to extremely high and low water levels K. Viigand et al. https://doi.org/10.1016/j.ecss.2025.109267
- Spatial and temporal variability of wave energy resource in the eastern Pacific from Panama to the Drake passage M. Eelsalu et al. https://doi.org/10.1016/j.renene.2024.120180
- Event-based wave statistics for the Baltic Sea J. Björkqvist et al. https://doi.org/10.5194/sp-4-osr8-10-2024
- Quantification of longshore sediment transport and compartments in urban areas: A case study of shores of Tallinn, the Baltic Sea M. Eelsalu et al. https://doi.org/10.1016/j.rsma.2023.103199
- Long-Term and Decadal Sea-Level Trends of the Baltic Sea Using Along-Track Satellite Altimetry M. Mostafavi et al. https://doi.org/10.3390/rs16050760
- Obliquely Incident Nonlinear Internal Waves on a Shallow Shelf C. Papoutsellis et al. https://doi.org/10.1029/2025JC022650
- Numerical simulations of wave climate in the Baltic Sea: a review T. Soomere https://doi.org/10.1016/j.oceano.2022.01.004
- Variability and extremes of the Caspian Sea's modelled wave climate A. Giudici et al. https://doi.org/10.1016/j.oceaneng.2025.123077
Saved (final revised paper)
Latest update: 20 Oct 2026
Short summary
Extreme water levels are often created by several drivers with different properties. For example, the contribution from the water volume of the Baltic Sea follows a Gaussian distribution, but storm surges represent a Poisson process. We show that wave set-up heights (the third major component of high water levels) usually follow an exponential distribution and thus also represent a Poisson process. However, at some locations set-up heights better match an inverse Gaussian (Wald) distribution.
Extreme water levels are often created by several drivers with different properties. For...
