Articles | Volume 22, issue 10
https://doi.org/10.5194/nhess-22-3167-2022
© Author(s) 2022. 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-22-3167-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
06 Oct 2022
Research article |
| 06 Oct 2022
| 06 Oct 2022
Partitioning the contributions of dependent offshore forcing conditions in the probabilistic assessment of future coastal flooding
BRGM, 3 av. C. Guillemin, 45060 Orléans CEDEX 2, France
Deborah Idier
BRGM, 3 av. C. Guillemin, 45060 Orléans CEDEX 2, France
Remi Thieblemont
BRGM, 3 av. C. Guillemin, 45060 Orléans CEDEX 2, France
Goneri Le Cozannet
BRGM, 3 av. C. Guillemin, 45060 Orléans CEDEX 2, France
François Bachoc
Institut de Mathématiques de Toulouse, 118 Rte de Narbonne, 31400 Toulouse, France
Related authors
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.
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.
Ryota Wada, Jeremy Rohmer, Yann Krien, and Philip Jonathan
Nat. Hazards Earth Syst. Sci., 22, 431–444, https://doi.org/10.5194/nhess-22-431-2022, https://doi.org/10.5194/nhess-22-431-2022, 2022
Short summary
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.
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.
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.
Susan E. Hanson, Robert J. Nicholls, Floris R. Calkoen, Gonéri Le Cozannet, and Arjen P. Luijendijk
EGUsphere, https://doi.org/10.5194/egusphere-2025-2371, https://doi.org/10.5194/egusphere-2025-2371, 2025
This preprint is open for discussion and under review for Natural Hazards and Earth System Sciences (NHESS).
Short summary
Short summary
The CoasTER geographic database provides erosion relevant characteristics for Europe’s coastal floodplains. It builds on earlier erosion research and includes a coastal geomorphological typology incorporating modification in the form of hard engineering and infrastructure. Analysis shows 27 % of coastal floodplains have shown significant erosion over the last 40 years. Nearly 3,500 km will require additional or new management to protect developed areas and infrastructure if the trend continues.
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.
Alexander Bisaro, Giulia Galluccio, Elisa Fiorini Beckhauser, Fulvio Biddau, Ruben David, Floortje d'Hont, Antonio Góngora Zurro, Gonéri Le Cozannet, Sadie McEvoy, Begoña Pérez Gómez, Claudia Romagnoli, Eugenio Sini, and Jill Slinger
State Planet, 3-slre1, 7, https://doi.org/10.5194/sp-3-slre1-7-2024, https://doi.org/10.5194/sp-3-slre1-7-2024, 2024
Short summary
Short summary
This paper assesses coastal adaptation governance by examining socio-economic and political contexts, reviewing policy frameworks, and identifying challenges. Results show that regional and basin-scale frameworks lack sea level rise provisions, but significant national progress is observed. The main governance challenges are time horizons and uncertainty, coordination, and social vulnerability. These, however, can be addressed if flexible planning and nature-based solutions are implemented.
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.
Ryota Wada, Jeremy Rohmer, Yann Krien, and Philip Jonathan
Nat. Hazards Earth Syst. Sci., 22, 431–444, https://doi.org/10.5194/nhess-22-431-2022, https://doi.org/10.5194/nhess-22-431-2022, 2022
Short summary
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.
Ioannis A. Daglis, Loren C. Chang, Sergio Dasso, Nat Gopalswamy, Olga V. Khabarova, Emilia Kilpua, Ramon Lopez, Daniel Marsh, Katja Matthes, Dibyendu Nandy, Annika Seppälä, Kazuo Shiokawa, Rémi Thiéblemont, and Qiugang Zong
Ann. Geophys., 39, 1013–1035, https://doi.org/10.5194/angeo-39-1013-2021, https://doi.org/10.5194/angeo-39-1013-2021, 2021
Short summary
Short summary
We present a detailed account of the science programme PRESTO (PREdictability of the variable Solar–Terrestrial cOupling), covering the period 2020 to 2024. PRESTO was defined by a dedicated committee established by SCOSTEP (Scientific Committee on Solar-Terrestrial Physics). We review the current state of the art and discuss future studies required for the most effective development of solar–terrestrial physics.
Davide Zanchettin, Sara Bruni, Fabio Raicich, Piero Lionello, Fanny Adloff, Alexey Androsov, Fabrizio Antonioli, Vincenzo Artale, Eugenio Carminati, Christian Ferrarin, Vera Fofonova, Robert J. Nicholls, Sara Rubinetti, Angelo Rubino, Gianmaria Sannino, Giorgio Spada, Rémi Thiéblemont, Michael Tsimplis, Georg Umgiesser, Stefano Vignudelli, Guy Wöppelmann, and Susanna Zerbini
Nat. Hazards Earth Syst. Sci., 21, 2643–2678, https://doi.org/10.5194/nhess-21-2643-2021, https://doi.org/10.5194/nhess-21-2643-2021, 2021
Short summary
Short summary
Relative sea level in Venice rose by about 2.5 mm/year in the past 150 years due to the combined effect of subsidence and mean sea-level rise. We estimate the likely range of mean sea-level rise in Venice by 2100 due to climate changes to be between about 10 and 110 cm, with an improbable yet possible high-end scenario of about 170 cm. Projections of subsidence are not available, but historical evidence demonstrates that they can increase the hazard posed by climatically induced sea-level rise.
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.
Gonéri Le Cozannet, Déborah Idier, Marcello de Michele, Yoann Legendre, Manuel Moisan, Rodrigo Pedreros, Rémi Thiéblemont, Giorgio Spada, Daniel Raucoules, and Ywenn de la Torre
Nat. Hazards Earth Syst. Sci., 21, 703–722, https://doi.org/10.5194/nhess-21-703-2021, https://doi.org/10.5194/nhess-21-703-2021, 2021
Short summary
Short summary
Chronic flooding occurring at high tides under calm weather conditions is an early impact of sea-level rise. This hazard is a reason for concern on tropical islands, where coastal infrastructure is commonly located in low-lying areas. We focus here on the Guadeloupe archipelago, in the French Antilles, where chronic flood events have been reported for about 10 years. We show that the number of such events will increase drastically over the 21st century under continued growth of CO2 emissions.
Cited articles
Abily, M., Bertrand, N., Delestre, O., Gourbesville, P., and Duluc, C. M.:
Spatial Global Sensitivity Analysis of High Resolution classified topographic data use in 2D urban flood modelling, Environ. Modell. Softw., 77, 183–195, 2016.
Anderson, B., Borgonovo, E., Galeotti, M., and Roson, R.:
Uncertainty in climate change modelling: can global sensitivity analysis be of help?, Risk Anal., 34, 271–293, 2014.
Ardhuin, F., Rogers, W. E., Babanin, A. V., Filipot, J., Magne, R., Roland, A., Van der Westhuysen, A., Queffeulou, P., Lefevre, J., Aouf, L., and Collard, F.:
Semiempirical dissipation source functions for ocean waves. Part I: Definition, calibration, and validation, J. Phys. Oceanogr., 40, 917–941, 2010.
Athanasiou, P., van Dongeren, A., Giardino, A., Vousdoukas, M. I., Ranasinghe, R., and Kwadijk, J.:
Uncertainties in projections of sandy beach erosion due to sea level rise: an analysis at the European scale, Sci. Rep., 10, 11895, https://doi.org/10.1038/s41598-020-68576-0, 2020.
Bamber, J. L. and Aspinall, W. P.:
An expert judgement assessment of future sea level rise from the ice sheets, Nat. Clim. Change, 3, 424–427, 2013.
Betancourt, J., Bachoc, F., Klein, T., Idier, D., Pedreros, R., and Rohmer, J.:
Gaussian process metamodeling of functional-input code for coastal flood hazard assessment, Reliab. Eng. Syst. Safe., 198, 106870, https://doi.org/10.1016/j.ress.2020.106870, 2020.
Bevacqua, E., Vousdoukas, M. I., Zappa, G., Hodges, K., Shepherd, T. G., Maraun, D., Mentaschi, L., and Feyen, L.:
More meteorological events that drive compound coastal flooding are projected under climate change, Communications Earth & Environment, 1, 1–11, 2020.
Broto, B., Bachoc, F., and Depecker, M.:
Variance reduction for estimation of Shapley effects and adaptation to unknown input distribution, SIAM/ASA Journal of Uncertainty Quantification, 8, 693–716, 2020.
Cagigal, L., Rueda, A., Anderson, D. L., Ruggiero, P., Merrifield, M. A., Montaño, J., Coco, G., and Méndez, F. J.:
A multivariate, stochastic, climate-based wave emulator for shoreline change modelling, Ocean Model., 154, 151–184, 2020.
Callaghan, D. P., Nielsen, P., Short, A., and Ranasinghe, R. W. M. R. J. B.:
Statistical simulation of wave climate and extreme beach erosion, Coastal Engineering, 55, 375–390, 2008.
Chaumillon, E., Bertin, X., Fortunato, A. B., Bajo, M., Schneider, J.-L., Dezileau, L., Walsh, J. P., Michelot, A., Chauveau, E., Créach, A., Hénaff, A., Sauzeau, T., Waeles, B., Gervais, B., Jan, G., Baumann, J., Breilh, J.-F., and Pedreros, R.: Storm-induced marine flooding: Lessons from a multidisciplinary approach, Earth-Sci. Rev., 165, 151–184, 2017.
Coles, S.:
An Introduction to Statistical Modeling of Extreme Values, Springer Verlag, London, 2001.
Coles, S., Heffernan, J., and Tawn, J.:
Dependence Measures for Extreme Value Analyses, Extremes, 2, 339–365, 1999.
Coles, S. G. and Tawn, J. A.:
Modelling extreme multivariate events, J. Roy. Stat. Soc. Ser. B Met., 53, 377–392, 1991.
De Conto, R. M., Pollard, D., Alley, R. B., Velicogna, I., Gasson, E., Gomez, N., Sadai, S., Condron, A., Gilford, D. M., Ashe, E. L., and Kopp, R. E.:
The Paris Climate Agreement and future sea-level rise from Antarctica, Nature, 593, 83–89, 2021.
Demange-Chryst, J., Bachoc, F., and Morio, J.:
Shapley effect estimation in reliability-oriented sensitivity analysis with correlated inputs by importance sampling, arXiv [preprint], arXiv:2202.12679, 2022.
Do, N. C. and Razavi, S.:
Correlation effects? A major but often neglected component in sensitivity and uncertainty analysis, Water Resour. Res., 56, e2019WR025436, https://doi.org/10.1029/2019WR025436, 2020.
Fox-Kemper, B., Hewitt, H. T., Xiao, C., Aðalgeirsdóttir, G., Drijfhout, S. S., Edwards, T. L., Golledge, N. R., Hemer, M., Kopp, R. E., Krinner, G., Mix, A., Notz, D., Nowicki, S., Nurhati, I. S., Ruiz, L., Sallée, J.-B., Slangen, A. B. A., and Yu, Y.: Ocean, Cryosphere and Sea Level Change, in: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Masson-Delmotte, V., Zhai, P., Pirani, A., Connors, S. L., Péan, C., Berger, S., Caud, N., Chen, Y., Goldfarb, L., Gomis, M. I., Huang, M., Leitzell, K., Lonnoy, E., Matthews, J. B. R., Maycock, T. K., Waterfield, T., Yelekçi, O., Yu, R., and Zhou, B., Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 1211–1362, https://doi.org/10.1017/9781009157896.011, 2021.
Gouldby, B., Méndez, F. J., Guanche, Y., Rueda, A., and Mínguez, R.:
A methodology for deriving extreme nearshore sea conditions for structural design and flood risk analysis, Coastal Engineering, 88, 15–26, 2014.
Hastie, T., Tibshirani, R., and Friedman, J.:
The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer-Verlag, New York, 2009.
Heffernan, J. E. and Tawn, J. A.:
A conditional approach for multivariate extreme values (with discussion), J. Roy. Stat. Soc. Ser. B Met., 66, 497–546, 2004.
Idier, D., Rohmer, J., Pedreros, R., Le Roy, S., Lambert, J., Louisor, J., Le Cozannet, G., and Le Cornec, E.:
Coastal flood: a composite method for past events characterisation providing insights in past, present and future hazards—joining historical, statistical and modelling approaches, Nat. Hazards, 101, 465–501, 2020a.
Idier, D., Pedreros, R., Rohmer, J., and Le Cozannet, G.:
The effect of stochasticity of waves on coastal flood and its variations with sea-level rise, Journal of Marine Science and Engineering, 8, 798, https://doi.org/10.3390/jmse8100798, 2020b.
Idier, D., Aurouet, A., Bachoc, F., Baills, A., Betancourt, J., Gamboa, F., Klein, T., López-Lopera, A. F., Pedreros, R., Rohmer, J., and Thibault, A.: A User-Oriented Local Coastal Flooding Early Warning System Using Metamodelling Techniques, Journal of Marine Science and Engineering, 9, 1191, https://doi.org/10.3390/jmse9111191, 2021.
Idrissi, M. I., Chabridon, V., and Iooss, B.:
Developments and applications of Shapley effects to reliability-oriented sensitivity analysis with correlated inputs, Environ. Modell. Softw., 143, 105115, https://doi.org/10.1016/j.envsoft.2021.105115, 2021.
Iooss, B. and Lemaître, P.:
A review on global sensitivity analysis methods, in: Uncertainty management in simulation-optimization of complex systems, edited by: Dellino, G. and Meloni, C., Springer, Boston, MA, 101–122, 2015.
Iooss, B. and Prieur, C.:
Shapley effects for Sensitivity Analysis with correlated inputs: Comparisons with Sobol' Indices, Numerical Estimation and Applications, Int. J. Uncertain. Quan., 9, 493–514, 2019.
Iooss, B., Da Veiga, S., Janon, A., and Pujol, G.:
sensitivity: Global Sensitivity Analysis of Model Outputs, R package version 1.27.0, https://CRAN.R-project.org/package=sensitivity (last access: 15 September 2022), 2021.
Jane, R., Cadavid, L., Obeysekera, J., and Wahl, T.:
Multivariate statistical modelling of the drivers of compound flood events in south Florida, Nat. Hazards Earth Syst. Sci., 20, 2681–2699, https://doi.org/10.5194/nhess-20-2681-2020, 2020.
Keef, C., Papastathopoulos, I., and Tawn, J. A.:
Estimation of the conditional distribution of a multivariate variable given that one of its components is large: Additional constraints for the Heffernan and Tawn model, J. Multivariate Anal., 115, 396–404, 2013.
Kessy, A., Lewin, A., and Strimmer, K.:
Optimal whitening and decorrelation, Am. Stat., 72, 309–314, 2018.
Kopp, R. E., Horton, R. M., Little, C. M., Mitrovica, J. X., Oppenheimer, M., Rasmussen, D. J., Strauss, B. H., and Tebaldi, C.: Probabilistic 21st and 22nd century sea‐level projections at a global network of tide‐gauge sites, Earth's future, 2, 383–406,
https://doi.org/10.1002/2014EF000239, 2014.
Le Cornec, E., Le Bris, E., and Van Lierde, M.: Atlas des risques littoraux sur le departement du Morbihan. Phase 1: Recensement et Consequences des tempetes et coups de vent majeurs, GEOS-AEL and DHI Technical report, 476 pp., 2012 (in French).
Le Cozannet, G., Rohmer, J., Cazenave, A., Idier, D., van De Wal, R., De Winter, R., Pedreros, R., Balouin, Y., Vinchon, C., and Oliveros, C.:
Evaluating uncertainties of future marine flooding occurrence as sea-level rises, Environ. Modell. Softw., 73, 44–56, 2015.
Le Cozannet, G., Bulteau, T., Castelle, B., Ranasinghe, R., Wöppelmann, G., Rohmer, J., Bernon, N., Idier, D., Louisor, J., and Salas-y-Mélia, D.:
Quantifying uncertainties of sandy shoreline change projections as sea level rises, Sci. Rep., 9, 1–11, 2019a.
Le Cozannet, G., Thiéblemont, R., Rohmer, J., Idier, D., Manceau, J. C. and Quique, R.:
Low-end probabilistic sea-level projections, Water, 11, 1507, https://doi.org/10.3390/w11071507, 2019b.
Marcos, M., Rohmer, J., Vousdoukas, M. I., Mentaschi, L., Le Cozannet, G., and Amores, A.:
Increased extreme coastal water levels due to the combined action of storm surges and wind waves, Geophys. Res. Lett., 46, 4356–4364, 2019.
Minasny, B. and McBratney, A. B.:
A conditioned Latin hypercube method for sampling in the presence of ancillary information, Comput. Geosci., 32, 1378–1388, 2006.
Morim, J., Trenham, C., Hemer, M., Wang, X. L., Mori, N., Casas-Prat, M., Semedo, A., Shimura, T., Timmermans, B., Camus, P., Bricheno, L., Mentaschi, L., Dobrynin, M., Feng, Y., and Erikson, L.: A global ensemble of ocean wave climate projections from CMIP5-driven models, Scientific Data, 7, 1–10, 2020.
Northrop, P. J., Attalides, N., and Jonathan, P.:
Cross-validatory extreme value threshold selection and uncertainty with application to ocean storm severity, J. Roy. Stat. Soc. C-App., 66, 93–120, 2017.
Oppenheimer, M., Glavovic, B. C., Hinkel, J., van de Wal, R., Magnan, A. K., Abd-Elgawad, A., Cai, R., Cifuentes-Jara, M., DeConto, R. M., Ghosh, T., Hay, J., Isla, F., Marzeion, B., Meyssignac, B., and Sebesvari, Z.: Sea Level Rise and Implications for Low-Lying Islands, Coasts and Communities, in: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate, edited by: Pörtner, H.-O., Roberts, D. C., Masson-Delmotte, V., Zhai, P., Tignor, M., Poloczanska, E., Mintenbeck, K., Alegría, A., Nicolai, M., Okem, A., Petzold, J., Rama, B., and Weyer, N. M., Cambridge University Press, Cambridge, UK and New York, NY, USA, 321–445, https://doi.org/10.1017/9781009157964.006, 2019.
Outten, S. and Sobolowski, S: Extreme wind projections over Europe from the Euro-CORDEX regional climate models, Weather and Climate Extremes, 33, 100363, 2021.
Owen, A. B.:
Sobol' indices and Shapley value, SIAM/ASA Journal of Uncertainty Quantification, 2, 245–251, 2014.
Razavi, S., Jakeman, A., Saltelli, A., Prieur, C., Iooss, B., Borgonovo, E., Plischke, E., Lo Piano, S., Iwanaga, T., Becker, W., Tarantola, S., Guillaume, J. H. A., Jakeman, J., Gupta, H., Melillo, N., Rabitti, G., Chabridon, V., Duan, Q., Sun, X., Smith, S., Sheikholeslami, R., Hosseini, N., Asadzadeh, M., Puy, A., Kucherenko, S., and Maier, H. R.:
The Future of Sensitivity Analysis: An essential discipline for systems modeling and policy support, Environ. Modell. Softw., 137, 104954, https://doi.org/10.1016/j.envsoft.2020.104954, 2021.
Rohmer, J., Idier, D., and Pedreros, R.:
A nuanced quantile random forest approach for fast prediction of a stochastic marine flooding simulator applied to a macrotidal coastal site, Stoch. Env. Res. Risk A., 34, 867–890, 2020.
Roustant, O., Ginsbourger, D., and Deville, Y.:
DiceKriging, DiceOptim: Two R packages for the analysis of computer experiments by kriging-based metamodeling and optimization, J. Stat. Softw., 51, 1–55, 2012.
Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., Saisana, M., and Tarantola, S. (Eds.): Global sensitivity analysis: the primer, JohnWiley & Sons, 2008.
Shapley, L. S.:
A value for n-person games, in: Contributions to the Theory of Games, Volume II, Annals of Mathematics Studies, edited by: Kuhn, H. and Tucker, A. W., Princeton University Press, Princeton, NJ, 307–317, 1953.
Song, E., Nelson, B., and Staum, J.:
Shapley effects for global sensitivity analysis: Theory and computation, SIAM/ASA Journal on Uncertainty Quantification, 4, 1060–1083, 2016.
Southworth, H., Heffernan, J. E., and Metcalfe, P.D: texmex: Statistical modelling of extreme values, R package version 2.4.8, https://cran.r-project.org/web/packages/texmex/index.html (last access: 15 September 2022), 2020.
UNFCCC:
Report of the Conference of the Parties on its twenty-first session, held in Paris from 30 November to 13 December 2015, Addendum part two: Action taken by the Conference of the Parties at its twenty-first session, FCCC/CP/2015/10/Add.1, Bonn, Germany, 2016.
Wahl, T., Jain, S., Bender, J., Meyers, S. D., and Luther, M. E.:
Increasing risk of compound flooding from storm surge and rainfall for major US cities, Nat. Clim. Change, 5, 1093–1097, 2015.
Wahl, T., Haigh, I. D., Nicholls, R. J., Arns, A., Dangendorf, S., Hinkel, J., and Slangen, A. B.:
Understanding extreme sea levels for broad-scale coastal impact and adaptation analysis, Nature Commun., 8, 1–12, 2017.
Ward, P. J., Couasnon, A., Eilander, D., Haigh, I. D., Hendry, A., Muis, S., Veldkamp, T. I. E., Winsemius, H. C., and Wahl, T.:
Dependence between high sea-level and high river discharge increases flood hazard in global deltas and estuaries, Environ. Res. Lett., 13, 084012, https://doi.org/10.1088/1748-9326/aad400, 2018.
Williams, C. K. and Rasmussen, C. E.:
Gaussian processes for machine learning, MIT Press, Cambridge, MA, 2006.
Wong, T. E. and Keller, K.:
Deep uncertainty surrounding coastal flood risk projections: A case study for New Orleans, Earths Future, 5, 1015–1026, 2017.
Zhu, X. and Sudret, B.:
Global sensitivity analysis for stochastic simulators based on generalized lambda surrogate models, Reliab. Eng. Syst. Safe., 214, 107815, https://doi.org/10.1016/j.ress.2021.107815, 2021.
Zijlema, M., Stelling, G., and Smit, P.:
SWASH: An operational public domain code for simulating wave fields and rapidly varied flows in coastal waters, Coastal Engineering, 58, 992–1012, 2011.
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.
We quantify the influence of wave–wind characteristics, offshore water level and sea level rise...
Altmetrics
Final-revised paper
Preprint