Articles | Volume 20, issue 1
https://doi.org/10.5194/os-20-21-2024
https://doi.org/10.5194/os-20-21-2024
Technical note
 | 
12 Jan 2024
Technical note |  | 12 Jan 2024

Technical note: Extending sea level time series for the analysis of extremes with statistical methods and neighbouring station data

Kévin Dubois, Morten Andreas Dahl Larsen, Martin Drews, Erik Nilsson, and Anna Rutgersson

Related authors

Influence of data source and copula statistics on estimates of compound flood extremes in a river mouth environment
Kévin Dubois, Morten Andreas Dahl Larsen, Martin Drews, Erik Nilsson, and Anna Rutgersson
Nat. Hazards Earth Syst. Sci., 24, 3245–3265, https://doi.org/10.5194/nhess-24-3245-2024,https://doi.org/10.5194/nhess-24-3245-2024, 2024
Short summary

Related subject area

Approach: Numerical Models | Properties and processes: Sea level, tides, tsunamis and surges
The characteristics of tides and their effects on the general circulation of the Mediterranean Sea
Bethany McDonagh, Emanuela Clementi, Anna Chiara Goglio, and Nadia Pinardi
Ocean Sci., 20, 1051–1066, https://doi.org/10.5194/os-20-1051-2024,https://doi.org/10.5194/os-20-1051-2024, 2024
Short summary
Effects of sea level rise and tidal flat growth on tidal dynamics and geometry of the Elbe estuary
Tara F. Mahavadi, Rita Seiffert, Jessica Kelln, and Peter Fröhle
Ocean Sci., 20, 369–388, https://doi.org/10.5194/os-20-369-2024,https://doi.org/10.5194/os-20-369-2024, 2024
Short summary
Uncertainties and discrepancies in the representation of recent storm surges in a non-tidal semi-enclosed basin: a hindcast ensemble for the Baltic Sea
Marvin Lorenz and Ulf Gräwe
Ocean Sci., 19, 1753–1771, https://doi.org/10.5194/os-19-1753-2023,https://doi.org/10.5194/os-19-1753-2023, 2023
Short summary
Observations and modeling of tidally generated high-frequency velocity fluctuations downstream of a channel constriction
Håvard Espenes, Pål Erik Isachsen, and Ole Anders Nøst
Ocean Sci., 19, 1633–1648, https://doi.org/10.5194/os-19-1633-2023,https://doi.org/10.5194/os-19-1633-2023, 2023
Short summary

Cited articles

Andersson, M.: Climate Adaptation by Managed Realignment. Future mean and extreme sea levels, SMHI, Report number: 2021/912/9.5, 16–17, 2021. 
Andrée, E., Su, J., Dahl Larsen, M. A., Drews, M., Stendel, M., and Skovgaard Madsen, K.: The role of preconditioning for extreme storm surges in the western Baltic Sea, Nat. Hazards Earth Syst. Sci., 23, 1817–1834, https://doi.org/10.5194/nhess-23-1817-2023, 2023. 
Bellinghausen, K., Hünicke, B., and Zorita, E.: Short-term prediction of extreme sea-level at the Baltic Sea coast by Random Forests, Nat. Hazards Earth Syst. Sci. Discuss. [preprint], https://doi.org/10.5194/nhess-2023-21, 2023. 
Bernier, N. B., Thompson, K. R., Ou, J., and Ritchie, H.: Mapping the return periods of extreme sea levels: Allowing for short sea level records, seasonality, and climate change, Glob. Planet. Change, 57, 139–150, https://doi.org/10.1016/j.gloplacha.2006.11.027, 2007. 
Breiman, L.: Random Forests, Mach. Learn., 45, 5–32, https://doi.org/10.1023/A:1010933404324, 2001. 
Download
Short summary
Coastal floods occur due to extreme sea levels (ESLs) which are difficult to predict because of their rarity. Long records of accurate sea levels at the local scale increase ESL predictability. Here, we apply a machine learning technique to extend sea level observation data in the past based on a neighbouring tide gauge. We compared the results with a linear model. We conclude that both models give reasonable results with a better accuracy towards the extremes for the machine learning model.