Articles | Volume 25, issue 11
https://doi.org/10.5194/nhess-25-4527-2025
© Author(s) 2025. 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-25-4527-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Assessing atoll island future habitability in the context of climate change using Bayesian networks
Mirna Badillo-Interiano
CORRESPONDING AUTHOR
BRGM, 45000, Orléans, France
UMR LIENSs, La Rochelle University-CNRS, 7266, La Rochelle, France
Jérémy Rohmer
BRGM, 45000, Orléans, France
Gonéri Le Cozannet
BRGM, 45000, Orléans, France
Virginie Duvat
UMR LIENSs, La Rochelle University-CNRS, 7266, La Rochelle, France
Institut Universitaire de France, Paris, France
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Hideo Aochi, Masumi Yamada, Tung-Cheng Ho, Gonéri Le Cozannet, Arno Christian Hammann, and Ruth Mottram
EGUsphere, https://doi.org/10.5194/egusphere-2025-3803, https://doi.org/10.5194/egusphere-2025-3803, 2025
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The 2017 Landslide-made tsunami in Greenland occurred in a context of global warming and heavily impacted local communities. We analyze this event using seismic data to reconstruct the whole chain of processes from the landslide to the tsunami. Our results validate a new approach to analyze crustal deformations caused by tsunami propagation in fjords, suggesting that alert systems based on seismic data are feasible, potentially allowing to reduce tsunami risks in polar regions.
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
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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).
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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
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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.
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
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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
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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
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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
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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
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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.
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
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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
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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
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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.
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Short summary
Small islands face increasing threats from climate change. In this context, exploring new modeling approaches is needed to improve climate risk assessments. We applied Bayesian Networks to assess the risk to future habitability on four atoll islands. The findings show that Bayesian Networks are powerful tools for efficiently assessing climate-related risks by combining expert judgments and confidence levels, providing a comprehensive framework to assess risks in data-limited island settings.
Small islands face increasing threats from climate change. In this context, exploring new...
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