Articles | Volume 23, issue 12
https://doi.org/10.5194/nhess-23-3685-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-23-3685-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
30 Nov 2023
Research article |
| 30 Nov 2023
| 30 Nov 2023
Bayesian extreme value analysis of extreme sea levels along the German Baltic coast using historical information
Leigh Richard MacPherson
CORRESPONDING AUTHOR
Faculty of Agricultural and Environmental Sciences, University of Rostock, 18059 Rostock, Germany
Arne Arns
Faculty of Agricultural and Environmental Sciences, University of Rostock, 18059 Rostock, Germany
Svenja Fischer
Institute of Engineering Hydrology and Water Resources Management, Ruhr University Bochum, 44801 Bochum, Germany
Fernando Javier Méndez
Departamento de Ciencias y Técnicas del Agua y del Medio Ambiente, E.T.S.I de Caminos, Canales y Puertos, Universidad de Cantábria, 39005 Santander, Spain
Jürgen Jensen
Research Institute for Water and Environment, University of Siegen, 57076 Siegen, Germany
Related authors
Leigh R. MacPherson, Arne Arns, Svenja Fischer, Fernando J. Méndez, and Jürgen Jensen
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2021-406, https://doi.org/10.5194/nhess-2021-406, 2022
Preprint withdrawn
Short summary
Short summary
Extreme sea levels represent one of the most damaging natural hazards due to their potential to cause flooding. We developed a new method which incorporates historical information with systematically recorded sea levels, leading to improved estimates of extreme sea levels with reduced uncertainties. Such information helps to improve coastal flood risk analyses, which in turn allows for more efficient planning of coastal protection measures.
Sameen Bushra, Jeniya Shakya, Céline Cattoën, Svenja Fischer, and Markus Pahlow
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-244, https://doi.org/10.5194/essd-2025-244, 2025
Revised manuscript accepted for ESSD
Short summary
Short summary
To support comparative hydrology and climate impact research, we present a large-sample dataset with hourly streamflow and hydrometeorological data from 369 catchments across Aotearoa New Zealand. It includes detailed catchment attributes and represents diverse hydrological regimes. This open-access resource enables model evaluation and international comparisons and helps fill a key regional gap in global hydrological data.
Christophe Cudennec, Ernest Amoussou, Yonca Cavus, Pedro L. B. Chaffe, Svenja Fischer, Salvatore Grimaldi, Jean-Marie Kileshye Onema, Mohammad Merheb, Maria-Jose Polo, Eric Servat, and Elena Volpi
Proc. IAHS, 385, 501–511, https://doi.org/10.5194/piahs-385-501-2025, https://doi.org/10.5194/piahs-385-501-2025, 2025
Toshio Koike, Shinji Egashira, Miho Ohara, Abdul Wahid Mohamed Rasmy, Tomoki Ushiyama, Mamoru Miyamoto, Daisuke Harada, Kensuke Naito, Christophe Cudennec, and Svenja Fischer
Proc. IAHS, 386, 353–354, https://doi.org/10.5194/piahs-386-353-2025, https://doi.org/10.5194/piahs-386-353-2025, 2025
Angélique Melet, Roderik van de Wal, Angel Amores, Arne Arns, Alisée A. Chaigneau, Irina Dinu, Ivan D. Haigh, Tim H. J. Hermans, Piero Lionello, Marta Marcos, H. E. Markus Meier, Benoit Meyssignac, Matthew D. Palmer, Ronja Reese, Matthew J. R. Simpson, and Aimée B. A. Slangen
State Planet, 3-slre1, 4, https://doi.org/10.5194/sp-3-slre1-4-2024, https://doi.org/10.5194/sp-3-slre1-4-2024, 2024
Short summary
Short summary
The EU Knowledge Hub on Sea Level Rise’s Assessment Report strives to synthesize the current scientific knowledge on sea level rise and its impacts across local, national, and EU scales to support evidence-based policy and decision-making, primarily targeting coastal areas. This paper complements IPCC reports by documenting the state of knowledge of observed and 21st century projected changes in mean and extreme sea levels with more regional information for EU seas as scoped with stakeholders.
Leigh R. MacPherson, Arne Arns, Svenja Fischer, Fernando J. Méndez, and Jürgen Jensen
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2021-406, https://doi.org/10.5194/nhess-2021-406, 2022
Preprint withdrawn
Short summary
Short summary
Extreme sea levels represent one of the most damaging natural hazards due to their potential to cause flooding. We developed a new method which incorporates historical information with systematically recorded sea levels, leading to improved estimates of extreme sea levels with reduced uncertainties. Such information helps to improve coastal flood risk analyses, which in turn allows for more efficient planning of coastal protection measures.
Cited articles
Arns, A., Wahl, T., Haigh, I. D., Jensen, J., and Pattiaratchi, C.: Estimating Extreme Water Level Probabilities: A Comparison of the Direct Methods and Recommendations for Best Practise, Coast. Eng., 81, 51–66, https://doi.org/10.1016/j.coastaleng.2013.07.003, 2013. a, b, c
Arns, A., Wahl, T., Haigh, I. D., and Jensen, J.: Determining Return Water Levels at Ungauged Coastal Sites: A Case Study for Northern Germany, Ocean Dynam., 65, 539–554, https://doi.org/10.1007/s10236-015-0814-1, 2015. a
Bardet, L., Duluc, C.-M., Rebour, V., and L'Her, J.: Regional Frequency Analysis of Extreme Storm Surges along the French Coast, Nat. Hazards Earth Syst. Sci., 11, 1627–1639, https://doi.org/10.5194/nhess-11-1627-2011, 2011. a
Benito, G., Lang, M., Barriendos, M., Llasat, M. C., Francés, F., Ouarda, T., Thorndycraft, V., Enzel, Y., Bardossy, A., Coeur, D., and Bobée, B.: Use of Systematic, Palaeoflood and Historical Data for the Improvement of Flood Risk Estimation. Review of Scientific Methods, Nat. Hazards, 31, 623–643, https://doi.org/10.1023/B:NHAZ.0000024895.48463.eb, 2004. a, b
Bork, I., Rosenhagen, G., and Müller-Navarra, S.: Modelling the Extreme Storm Surge in the Western Baltic Sea on November 13, 1872, Revisited, Küste, 92, 3, https://doi.org/10.18171/1.092103, 2022. a
Bulteau, T., Idier, D., Lambert, J., and Garcin, M.: How Historical Information Can Improve Estimation and Prediction of Extreme Coastal Water Levels: Application to the Xynthia Event at La Rochelle (France), Nat. Hazards Earth Syst. Sci., 15, 1135–1147, https://doi.org/10.5194/nhess-15-1135-2015, 2015. a, b, c, d, e, f
Coles, S.: An Introduction to Statistical Modeling of Extreme Values, in: Springer Series in Statistics, Springer, London, ISBN 978-1-84996-874-4, ISBN 978-1-4471-3675-0, https://doi.org/10.1007/978-1-4471-3675-0, 2001. a, b
Coles, S. and Tawn, J.: Bayesian Modelling of Extreme Surges on the UK East Coast, Philos. T. Roy. Soc. A, 363, 1387–1406, https://doi.org/10.1098/rsta.2005.1574, 2005. a, b, c
Coles, S., Pericchi, L. R., and Sisson, S.: A Fully Probabilistic Approach to Extreme Rainfall Modeling, J. Hydrology, 273, 35–50, https://doi.org/10.1016/S0022-1694(02)00353-0, 2003. a, b, c
Dangendorf, S., Arns, A., Pinto, J. G., Ludwig, P., and Jensen, J.: The Exceptional Influence of Storm `Xaver' on Design Water Levels in the German Bight, Environ. Res. Lett., 11, 054001, https://doi.org/10.1088/1748-9326/11/5/054001, 2016. a
El Adlouni, S. and Ouarda, T. B. M. J.: Joint Bayesian Model Selection and Parameter Estimation of the Generalized Extreme Value Model with Covariates Using Birth-Death Markov Chain Monte Carlo, Water Resour. Res., 45, W06403, https://doi.org/10.1029/2007WR006427, 2009. a
Frau, R., Andreewsky, M., and Bernardara, P.: The Use of Historical Information for Regional Frequency Analysis of Extreme Skew Surge, Nat. Hazards Earth Syst. Sci., 18, 949–962, https://doi.org/10.5194/nhess-18-949-2018, 2018. a
Gaál, L., Szolgay, J., Kohnová, S., Hlavčová, K., and Viglione, A.: Inclusion of Historical Information in Flood Frequency Analysis Using a Bayesian MCMC Technique: A Case Study for the Power Dam Orlík, Czech Republic, Contrib. Geophys. Geod., 40, 121–147, https://doi.org/10.2478/v10126-010-0005-5, 2010. a
Gaume, E.: Flood Frequency Analysis: The Bayesian Choice, WIREs Water, 5, e1290, https://doi.org/10.1002/wat2.1290, 2018. a
Haigh, I. D., Nicholls, R., and Wells, N.: A Comparison of the Main Methods for Estimating Probabilities of Extreme Still Water Levels, Coast. Eng., 57, 838–849, https://doi.org/10.1016/j.coastaleng.2010.04.002, 2010. a, b
Haigh, I. D., Marcos, M., Talke, S. A., Woodworth, P. L., Hunter, J. R., Hague, B. S., Arns, A., Bradshaw, E., and Thompson, P.: Tide-gauge data, GESLA-3 [data set], https://gesla787883612.wordpress.com/downloads/ (last access: 2 August 2023), 2021. a
Haigh, I. D., Marcos, M., Talke, S. A., Woodworth, P. L., Hunter, J. R., Hague, B. S., Arns, A., Bradshaw, E., and Thompson, P.: GESLA Version 3: A Major Update to the Global Higher-Frequency Sea-Level Dataset, Geosci. Data J., 10, 293–314, https://doi.org/10.1002/gdj3.174, 2022. a
Hamdi, Y., Bardet, L., Duluc, C.-M., and Rebour, V.: Use of Historical Information in Extreme-Surge Frequency Estimation: The Case of Marine Flooding on the La Rochelle Site in France, Nat. Hazards Earth Syst. Sci., 15, 1515–1531, https://doi.org/10.5194/nhess-15-1515-2015, 2015. a, b
Hastings, W. K.: Monte Carlo Sampling Methods Using Markov Chains and Their Applications, Biometrika, 57, 97–109, https://doi.org/10.1093/biomet/57.1.97, 1970. a
Hofstede, J. and Hamann, M.: The 1872 Catastrophic Storm Surge at the Baltic Sea Coast of Schleswig-Holstein; Lessons Learned?, Küste, 92, 1, https://doi.org/10.18171/1.092101, 2022. a, b, c, d
Hurst, H. E.: Long-Term Storage Capacity of Reservoirs, T. Am. Soci. Civ. Eng., 116, 770–799, 1951. a
Isikwue, M. O., Onoja, S. B., and Naakaa, D. S.: Classical and Bayesian Markov Chain Monte Carlo (MCMC) Modeling of Extreme Rainfall (1979–2014) in Makurdi, Nigeria, Int. J. Water Resour. Environ. Eng., 7, 123–131, https://doi.org/10.5897/IJWREE2015.0588, 2015. a
Jensen, J. and Müller-Navarra, S. H.: Storm Surges on the German Coast, Küste, 74, 92–124, 2008. a
Kelln, J., Dangendorf, S., Calafat, F., Patzke, J., Jensen, J., and Fröhle, P.: A novel tide gauge dataset for the Baltic Sea – Part 1: Spatial features and temporal variability of the seasonal sea level cycle, EGU General Assembly, https://www.researchgate.net/publication/316472804_A_novel_tide_gauge_dataset_for_the_Baltic_Sea_-_Part_1_Spatial_features_and_temporal_variability_of_the_seasonal_sea_level_cycle (last acces: 28 November 2023), 2017. a
Kwiatkowski, D., Phillips, P. C., Schmidt, P., and Shin, Y.: Testing the Null Hypothesis of Stationarity against the Alternative of a Unit Root: How Sure Are We That Economic Time Series Have a Unit Root?, J. Economet., 54, 159–178, 1992. a
Ludwig, P., Ehmele, F., Franca, M. J., Mohr, S., Caldas-Alvarez, A., Daniell, J. E., Ehret, U., Feldmann, H., Hundhausen, M., Knippertz, P., Küpfer, K., Kunz, M., Mühr, B., Pinto, J. G., Quinting, J., Schäfer, A. M., Seidel, F., and Wisotzky, C.: A Multi-Disciplinary Analysis of the Exceptional Flood Event of July 2021 in Central Europe – Part 2: Historical Context and Relation to Climate Change, Nat. Hazards Earth Syst. Sci., 23, 1287–1311, https://doi.org/10.5194/nhess-23-1287-2023, 2023. a
MacPherson, L. R., Arns, A., Dangendorf, S., Vafeidis, A. T., and Jensen, J.: A Stochastic Extreme Sea Level Model for the German Baltic Sea Coast, J. Geophys. Res.-Oceans, 124, 2054–2071, https://doi.org/10.1029/2018JC014718, 2019. a, b, c, d
Mazas, F. and Hamm, L.: A Multi-Distribution Approach to POT Methods for Determining Extreme Wave Heights, Coast. Eng., 58, 385–394, https://doi.org/10.1016/j.coastaleng.2010.12.003, 2011. a
MELUND – Ministerium für Energiewende, Landwirtschaft, Umwelt, Natur und Digitalisierung des Landes Schleswig-Holstein: Generalplan Küstenschutz Des Landes Schleswig-Holstein, https://www.schleswig-holstein.de/DE/fachinhalte/K/kuestenschutz/Downloads/Generalplan.pdf?__blob=publicationFile&v=3 (last access: 28 November 2023), 2022. a
Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H., and Teller, E.: Equation of State Calculations by Fast Computing Machines, J. Chem. Phys., 21, 1087–1092, 1953. a
MLUV – Ministerium für Landwirtschaft, Umwelt und Verbraucherschutz Mecklenburg-Vorpommern: Regelwerk Küstenschutz Mecklenburg-Vorpommern – Bemessungswasserstand und Referenzhoch-wasserstand, Nr. 2–5, https://www.stalu-mv.de/serviceassistent/download?id=1639581 (last access: 28 November 2023), 2012. a, b, c, d, e
Mohr, S., Ehret, U., Kunz, M., Ludwig, P., Caldas-Alvarez, A., Daniell, J. E., Ehmele, F., Feldmann, H., Franca, M. J., Gattke, C., Hundhausen, M., Knippertz, P., Küpfer, K., Mühr, B., Pinto, J. G., Quinting, J., Schäfer, A. M., Scheibel, M., Seidel, F., and Wisotzky, C.: A Multi-Disciplinary Analysis of the Exceptional Flood Event of July 2021 in Central Europe – Part 1: Event Description and Analysis, Nat. Hazards Earth Syst. Sci., 23, 525–551, https://doi.org/10.5194/nhess-23-525-2023, 2023. a
Mudersbach, C. H. and Jensen, J.: Extremwertstatistische Analyse von Historischen, Beobachteten Und Modellierten Wasserständen an Der Deutschen Ostseeküste, Küste, 75, 131–161, 2009. a
Prosdocimi, I.: German Tanks and Historical Records: The Estimation of the Time Coverage of Ungauged Extreme Events, Stoch. Environ. Res. Risk A., 32, 607–622, https://doi.org/10.1007/s00477-017-1418-8, 2018. a
PSMSL – Permanent Service for Mean Sea Level: Tide gauge data, PSMSL [data set], http://www.psmsl.org/data/obtaining/ (last access: 2 August 2023), 2023. a
Pugh, D. and Vassie, J.: Applications of the Joint Probability Method for Extreme Sea Level Computations, Proc. Inst. Civ. Eng., 69, 959–975, https://doi.org/10.1680/iicep.1980.2179, 1980. a
Reis, D. S. and Stedinger, J. R.: Bayesian MCMC Flood Frequency Analysis with Historical Information, J. Hydrol., 313, 97–116, https://doi.org/10.1016/j.jhydrol.2005.02.028, 2005. a, b, c, d
Rohmer, J., Lincke, D., Hinkel, J., Le Cozannet, G., Lambert, E., and Vafeidis, A. T.: Unravelling the Importance of Uncertainties in Global-Scale Coastal Flood Risk Assessments under Sea Level Rise, Water, 13, 774, https://doi.org/10.3390/w13060774, 2021. a
Rosenhagen, G. and Bork, I.: Rekonstruktion Der Sturmwetterlage Vom 13. November 1872, Küste, 75, 51–70, 2009. a
Tawn, J., Vassie, J., and Gumbel, E.: Extreme Sea Levels; the Joint Probabilities Method Revisited and Revised., Proc. Inst. Civ. Eng., 87, 429–442, https://doi.org/10.1680/iicep.1989.2975, 1989. a
Tawn, J. A.: Estimating Probabilities of Extreme Sea-Levels, J. Roy. Stat. Soc. Ser. C, 41, 77–93, https://doi.org/10.2307/2347619, 1992. a
Wahl, T., Haigh, I. D., Nicholls, R. J., Arns, A., Dangendorf, S., Hinkel, J., and Slangen, A. B. A.: Understanding Extreme Sea Levels for Broad-Scale Coastal Impact and Adaptation Analysis, Nat. Commun., 8, 16075, https://doi.org/10.1038/ncomms16075, 2017. a, b, c
Weiss, J., Bernardara, P., and Benoit, M.: Formation of Homogeneous Regions for Regional Frequency Analysis of Extreme Significant Wave Heights, J. Geophys. Res.-Oceans, 119, 2906–2922, https://doi.org/10.1002/2013JC009668, 2014. a
Woodworth, P. L., Hunter, J. R., Marcos, M., Caldwell, P., Menéndez, M., and Haigh, I.: Towards a global higher-frequency sea level dataset, Geosci. Data J., 3, 50–59, https://doi.org/10.1002/gdj3.42, 2017. a
Short summary
Efficient adaptation planning for coastal flooding caused by extreme sea levels requires accurate assessments of the underlying hazard. Tide-gauge data alone are often insufficient for providing the desired accuracy but may be supplemented with historical information. We estimate extreme sea levels along the German Baltic coast and show that relying solely on tide-gauge data leads to underestimations. Incorporating historical information leads to improved estimates with reduced uncertainties.
Efficient adaptation planning for coastal flooding caused by extreme sea levels requires...
Altmetrics
Final-revised paper
Preprint