Articles | Volume 22, issue 2
https://doi.org/10.5194/nhess-22-431-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/nhess-22-431-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
14 Feb 2022
Research article |
| 14 Feb 2022
| 14 Feb 2022
Statistical estimation of spatial wave extremes for tropical cyclones from small data samples: validation of the STM-E approach using long-term synthetic cyclone data for the Caribbean Sea
Ryota Wada
CORRESPONDING AUTHOR
Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan
Jeremy Rohmer
BRGM, Orleans, France
Yann Krien
SHOM, DOPS/HOM/REC, Toulouse, France
Philip Jonathan
Shell Research Limited, London SE1 7NA, United Kingdom
Department of Mathematics and Statistics, Lancaster University LA1 4YF, United Kingdom
Related authors
Ryota Wada, Amir R. Nejad, Kazuhiro Iijima, Junji Shimazaki, Mihaela Ibrion, Shinnosuke Wanaka, Hideo Nomura, Yoshitaka Mizushima, Takuya Nakashima, and Ken Takagi
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-74, https://doi.org/10.5194/wes-2025-74, 2025
Preprint under review for WES
Short summary
Short summary
This paper identifies technology gaps on the path to achieve sustainable development of floating offshore wind in Japan. The key challenges emphasizing on unique conditions such as earthquakes and tropical cyclones are addressed. In Japan the absence of oil & gas has led to social challenges like lack of supply chain, infrastructure, and human resources in offshore wind. Site selection, design & operation, industry development, and contribution to national energy policy "S + 3E" are studied.
Heiko Goelzer, Constantijn J. Berends, Fredrik Boberg, Gael Durand, Tamsin Edwards, Xavier Fettweis, Fabien Gillet-Chaulet, Quentin Glaude, Philippe Huybrechts, Sébastien Le clec'h, Ruth Mottram, Brice Noël, Martin Olesen, Charlotte Rahlves, Jeremy Rohmer, Michiel van den Broeke, and Roderik S. W. van de Wal
EGUsphere, https://doi.org/10.5194/egusphere-2025-3098, https://doi.org/10.5194/egusphere-2025-3098, 2025
This preprint is open for discussion and under review for The Cryosphere (TC).
Short summary
Short summary
We present an ensemble of ice sheet model projections for the Greenland ice sheet. The focus is on providing projections that improve our understanding of the range future sea-level rise and the inherent uncertainties over the next 100 to 300 years. Compared to earlier work we more fully account for some of the uncertainties in sea-level projections. We include a wider range of climate model output, more climate change scenarios and we extend projections schematically up to year 2300.
Sophie Lecacheux, Jeremy Rohmer, Eva Membrado, Rodrigo Pedreros, Andrea Filippini, Déborah Idier, Servane Gueben-Vénière, Denis Paradis, Alice Dalphinet, and David Ayache
EGUsphere, https://doi.org/10.5194/egusphere-2024-3615, https://doi.org/10.5194/egusphere-2024-3615, 2025
This preprint is open for discussion and under review for Natural Hazards and Earth System Sciences (NHESS).
Short summary
Short summary
This study comparer three data-driven methodologies to overcome the computational burden of numerical simulations for early warning purpose. They are all based on the statistical analysis of pre-calculated databases, to downscale total sea levels and predict marine flooding maps from offshore metocean forecasts. Conclusions highlight the relevance of metamodel-based approaches for fast prediction and the added value of precalculated databases during the prepardness phase.
Ryota Wada, Amir R. Nejad, Kazuhiro Iijima, Junji Shimazaki, Mihaela Ibrion, Shinnosuke Wanaka, Hideo Nomura, Yoshitaka Mizushima, Takuya Nakashima, and Ken Takagi
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-74, https://doi.org/10.5194/wes-2025-74, 2025
Preprint under review for WES
Short summary
Short summary
This paper identifies technology gaps on the path to achieve sustainable development of floating offshore wind in Japan. The key challenges emphasizing on unique conditions such as earthquakes and tropical cyclones are addressed. In Japan the absence of oil & gas has led to social challenges like lack of supply chain, infrastructure, and human resources in offshore wind. Site selection, design & operation, industry development, and contribution to national energy policy "S + 3E" are studied.
Mirna Badillo-Interiano, Jérémy Rohmer, Gonéri Le Cozannet, and Virginie Duvat
EGUsphere, https://doi.org/10.5194/egusphere-2024-3884, https://doi.org/10.5194/egusphere-2024-3884, 2025
Short summary
Short summary
Small islands face growing threats from climate change. In this context, we consider it important to explore new modeling approaches to improve climate risk assessments in these islands. Our study propose a probabilistic approach using Bayesian Networks (BNs). We developed a expert-based BN model to assess the risk to habitability on four atoll islands in the Indian and Pacific Oceans. The findings suggests that BNs could be used to assess climate-related risk and other adaptation problems.
Jeremy Rohmer, Heiko Goelzer, Tamsin Edwards, Goneri Le Cozannet, and Gael Durand
EGUsphere, https://doi.org/10.5194/egusphere-2025-52, https://doi.org/10.5194/egusphere-2025-52, 2025
Short summary
Short summary
Developing robust protocols to design multi-model ensembles is of primary importance for the uncertainty quantification of sea level projections. Here, we set up a series of computer experiments to reflect design decisions for the prediction of future sea level contribution of the Greenland ice sheet. We show the importance of including the most extreme climate scenario, and the benefit of having diversity in numerical models for ice sheet modelling and regional climate assessments.
Jeremy Rohmer, Stephane Belbeze, and Dominique Guyonnet
SOIL, 10, 679–697, https://doi.org/10.5194/soil-10-679-2024, https://doi.org/10.5194/soil-10-679-2024, 2024
Short summary
Short summary
Machine learning (ML) models have become key ingredients for digital soil mapping. To explain why the ML model is reliable, we apply a popular method from explainable artificial intelligence to the uncertainty prediction, with an application to the mapping of hydrocarbon pollutants on urban soil. We show the benefit of a joint analysis of the influence on the best estimate and the uncertainty to improve communication with end users and support decisions regarding covariates’ characterisation.
Jeremy Rohmer, Remi Thieblemont, Goneri Le Cozannet, Heiko Goelzer, and Gael Durand
The Cryosphere, 16, 4637–4657, https://doi.org/10.5194/tc-16-4637-2022, https://doi.org/10.5194/tc-16-4637-2022, 2022
Short summary
Short summary
To improve the interpretability of process-based projections of the sea-level contribution from land ice components, we apply the machine-learning-based
SHapley Additive exPlanationsapproach to a subset of a multi-model ensemble study for the Greenland ice sheet. This allows us to quantify the influence of particular modelling decisions (related to numerical implementation, initial conditions, or parametrisation of ice-sheet processes) directly in terms of sea-level change contribution.
Jeremy Rohmer, Deborah Idier, Remi Thieblemont, Goneri Le Cozannet, and François Bachoc
Nat. Hazards Earth Syst. Sci., 22, 3167–3182, https://doi.org/10.5194/nhess-22-3167-2022, https://doi.org/10.5194/nhess-22-3167-2022, 2022
Short summary
Short summary
We quantify the influence of wave–wind characteristics, offshore water level and sea level rise (projected up to 2200) on the occurrence of flooding events at Gâvres, French Atlantic coast. Our results outline the overwhelming influence of sea level rise over time compared to the others. By showing the robustness of our conclusions to the errors in the estimation procedure, our approach proves to be valuable for exploring and characterizing uncertainties in assessments of future flooding.
Rémi Thiéblemont, Gonéri Le Cozannet, Jérémy Rohmer, Alexandra Toimil, Moisés Álvarez-Cuesta, and Iñigo J. Losada
Nat. Hazards Earth Syst. Sci., 21, 2257–2276, https://doi.org/10.5194/nhess-21-2257-2021, https://doi.org/10.5194/nhess-21-2257-2021, 2021
Short summary
Short summary
Sea level rise and its acceleration are projected to aggravate coastal erosion over the 21st century. Resulting shoreline projections are deeply uncertain, however, which constitutes a major challenge for coastal planning and management. Our work presents a new extra-probabilistic framework to develop future shoreline projections and shows that deep uncertainties could be drastically reduced by better constraining sea level projections and improving coastal impact models.
Raphaël Cécé, Didier Bernard, Yann Krien, Frédéric Leone, Thomas Candela, Matthieu Péroche, Emmanuel Biabiany, Gael Arnaud, Ali Belmadani, Philippe Palany, and Narcisse Zahibo
Nat. Hazards Earth Syst. Sci., 21, 129–145, https://doi.org/10.5194/nhess-21-129-2021, https://doi.org/10.5194/nhess-21-129-2021, 2021
Short summary
Short summary
The present innovative modeling aims to combine the most realistic simulated strongest gusts driven by tornado-scale vortices within the eyewall and the most realistic complex terrain effects. The present modeling method could be easily extended to other small mountainous islands to improve the understanding of observed past damage and to develop safer urban management and appropriate building standards.
Stéphane Abadie, Alexandre Paris, Riadh Ata, Sylvestre Le Roy, Gael Arnaud, Adrien Poupardin, Lucie Clous, Philippe Heinrich, Jeffrey Harris, Rodrigo Pedreros, and Yann Krien
Nat. Hazards Earth Syst. Sci., 20, 3019–3038, https://doi.org/10.5194/nhess-20-3019-2020, https://doi.org/10.5194/nhess-20-3019-2020, 2020
Short summary
Short summary
The tsunami which could be generated by a potential flank collapse of the Cumbre Vieja volcano in La Palma, Canary Islands, is evaluated through a numerical simulation based on an advanced and finely calibrated model. Then the consequences of such an event for Europe, France and Guadeloupe island are investigated using different numerical models for propagation. The impacts vary from negligible to very significant depending on the location considered.
Cited articles
Barbier, E. B.: Policy: Hurricane Katrina's lessons for the world, Nat. News, 524, 285, https://doi.org/10.1038/524285a, 2015. a
Bloemendaal, N., Muis, S., Haarsma, R. J., Verlaan, M., Apecechea, M. I.,
de Moel, H., Ward, P. J., and Aerts, J. C.: Global modeling of tropical cyclone storm surges using high-resolution forecasts, Clim. Dynam., 52,
5031–5044, 2019. a
Bloemendaal, N., Haigh, I. D., de Moel, H., Muis, S., Haarsma, R. J., and
Aerts, J. C.: Generation of a global synthetic tropical cyclone hazard dataset using STORM, Scient. Data, 7, 1–12, 2020. a
Dasgupta, R., Basu, M., Kumar, P., Johnson, B. A., Mitra, B. K., Avtar, R., and Shaw, R.: A rapid indicator-based assessment of foreign resident preparedness in Japan during Typhoon Hagibis, Int. J. Disast. Risk Reduct., 51, 101849, https://doi.org/10.1016/j.ijdrr.2020.101849, 2020. a
Davison, A. C.: Statistical models, Cambridge University Press, Cambridge, UK, ISBN 978-0-521-73449-3, 2003. a
de Zea Bermudez, P. and Kotz, S.: Parameter estimation of the generalized
Pareto distribution – Part I, J. Stat. Plan. Inference, 140, 1353–1373,
2010a. a
de Zea Bermudez, P. and Kotz, S.: Parameter estimation of the generalized
Pareto distribution – Part II, J. Stat. Plan. Inference, 140, 1374–1388,
2010b. a
Desarthe, J.: Ouragans et submersions dans les Antilles françaises (XVIIe-XXe siècle) – Hurricanes and Storm-surge in French Antilles (17th–20th century), Études caribéennes, https://doi.org/10.4000/etudescaribeennes.7176, 2015. a
Furrer, R. and Naveau, P.: Probability weighted moments properties for small
samples, Stat. Probabil. Lett., 70, 190–195, 2007. a
Hosking, J. R. M. and Wallis, J. R.: Parameter and Quantile Estimation for the Generalized Pareto Distribution, Technometrics, 29, 339–349, 1987. a
Jevrejeva, S., Bricheno, L., Brown, J., Byrne, D., De Dominicis, M., Matthews, A., Rynders, S., Palanisamy, H., and Wolf, J.: Quantifying processes contributing to marine hazards to inform coastal climate resilience assessments, demonstrated for the Caribbean Sea, Nat. Hazards Earth Syst. Sci., 20, 2609–2626, https://doi.org/10.5194/nhess-20-2609-2020, 2020. a
Jonathan, P., Randell, D., Wadsworth, J., and Tawn, J.: Uncertainties in
return values from extreme value analysis of peaks over threshold using the
generalised Pareto distribution, Ocean Eng., 220, 107725, https://doi.org/10.1016/j.oceaneng.2020.107725, 2021. a
Kennedy, A. B., Westerink, J. J., Smith, J. M., Hope, M. E., Hartman, M.,
Taflanidis, A. A., Tanaka, S., Westerink, H., Cheung, K. F., Smith, T., Hamann, M., Minamide, M., Ota, A., and Dawson, C.: Tropical cyclone inundation potential on the Hawaiian Islands of Oahu and Kauai, Ocean Model., 52, 54–68, 2012. a
Knapp, K. R., Kruk, M. C., Levinson, D. H., Diamond, H. J., and Neumann, C. J.: The international best track archive for climate stewardship (IBTrACS)
unifying tropical cyclone data, B. Am. Meteorol. Soc., 91, 363–376, 2010. a
Koussoula-Bonneton, A.: Le passage dévastateur d'un ouragan:
conséquences socio-économiques. Le cas du cyclone Hugo en Guadeloupe,
La Météorologie, https://doi.org/10.4267/2042/53441, 1994. a
Krien, Y., Dudon, B., Roger, J., and Zahibo, N.: Probabilistic hurricane-induced storm surge hazard assessment in Guadeloupe, Lesser Antilles, Nat. Hazards Earth Syst. Sci., 15, 1711–1720, https://doi.org/10.5194/nhess-15-1711-2015, 2015. a, b, c, d
Krien, Y., Wada, R., Rohmer, J., and Jonathan, P.: Synthetic tropical cyclone
data for the Caribbean Sea, Zenodo [data set], https://doi.org/10.5281/zenodo.4627903, 2021. a, b
Lecacheux, S., Pedreros, R., Le Cozannet, G., Thiébot, J., De La Torre, Y., and Bulteau, T.: A method to characterize the different extreme waves for islands exposed to various wave regimes: a case study devoted to Reunion Island, Nat. Hazards Earth Syst. Sci., 12, 2425–2437, https://doi.org/10.5194/nhess-12-2425-2012, 2012. a, b
Liese, F. and Vajda, I.: On divergences and informations in statistics and
information theory, IEEE T. Inform. Theor., 52, 4394–4412, 2006. a
Lin, N., Emanuel, K., Oppenheimer, M., and Vanmarcke, E.: Physically based
assessment of hurricane surge threat under climate change, Nat. Clim. Change, 2, 462–467, 2012. a
McInnes, K. L., Walsh, K. J., Hoeke, R. K., O'Grady, J. G., Colberg, F., and
Hubbert, G. D.: Quantifying storm tide risk in Fiji due to climate variability and change, Global Planet. Change, 116, 115–129, 2014. a
Merrifield, M., Becker, J., Ford, M., and Yao, Y.: Observations and estimates
of wave-driven water level extremes at the Marshall Islands, Geophys. Res. Lett., 41, 7245–7253, 2014. a
Nadal-Caraballo, N. C., Campbell, M. O., Gonzalez, V. M., Torres, M. J., Melby, J. A., and Taflanidis, A. A.: Coastal Hazards System: A Probabilistic Coastal Hazard Analysis Framework, J. Coast. Res., 95, 1211–1216, 2020. a
Simpson, R. H. and Saffir, H.: The hurricane disaster potential scale,
Weatherwise, 27, 169–186, https://doi.org/10.1080/00431672.1974.9931702, 1974. a
Stephens, S. A. and Ramsay, D.: Extreme cyclone wave climate in the Southwest
Pacific Ocean: Influence of the El Niño Southern Oscillation and projected climate change, Global Planet. Change, 123, 13–26, 2014. a
Vickery, P., Skerlj, P., and Twisdale, L.: Simulation of hurricane risk in the US using empirical track model, J. Struct. Eng., 126, 1222–1237, 2000. a
Wada, R., Jonathan, P., Waseda, T., and Fan, S.: Estimating Extreme Waves in the Gulf of Mexico Using a Simple Spatial Extremes Model, in: Proceedings of the ASME 2019 38th International Conference on Ocean, Offshore and Arctic Engineering, Volume 9: Rodney Eatock Taylor Honoring Symposium on Marine and Offshore Hydrodynamics, Takeshi Kinoshita Honoring Symposium on Offshore Technology, 9–14 June 2019, Glasgow, Scotland, UK, V009T13A007, https://doi.org/10.1115/OMAE2019-95442, 2019. a
Wada, R., Jonathan, P., and Waseda, T.: Spatial Features of Extreme Waves in Gulf of Mexico, in: Proceedings of the ASME 2020 39th International Conference on Ocean, Offshore and Arctic Engineering, Volume 6B: Ocean Engineering, 3–7 August 2020, Virtual, Online, V06BT06A007, https://doi.org/10.1115/OMAE2020-19190, 2020. a, b, c
Wu, J.: Wind-stress coefficients over sea surface from breeze to hurricane,
J. Geophys. Res.-Oceans, 87, 9704–9706, 1982. a
Zahibo, N., Pelinovsky, E., Talipova, T., Rabinovich, A., Kurkin, A., and
Nikolkina, I.: Statistical analysis of cyclone hazard for Guadeloupe, Lesser
Antilles, Atmos. Res., 84, 13–29, 2007. a
Short summary
Characterizing extreme wave environments caused by tropical cyclones in the Caribbean Sea near Guadeloupe is difficult because cyclones rarely pass near the location of interest. STM-E (space-time maxima and exposure) model utilizes wave data during cyclones on a spatial neighbourhood. Long-duration wave data generated from a database of synthetic tropical cyclones are used to evaluate the performance of STM-E. Results indicate STM-E provides estimates with small bias and realistic uncertainty.
Characterizing extreme wave environments caused by tropical cyclones in the Caribbean Sea near...
Special issue
Altmetrics
Final-revised paper
Preprint