Articles | Volume 22, issue 1
https://doi.org/10.5194/nhess-22-1-2022
© 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-22-1-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
The influence of infragravity waves on the safety of coastal defences: a case study of the Dutch Wadden Sea
Christopher H. Lashley
Department of Hydraulic Engineering, Delft University of Technology, Stevinweg 1, 2628 CN Delft, the Netherlands
Center for Applied Coastal Research, University of Delaware, Newark, DE 19716, United States
Sebastiaan N. Jonkman
Department of Hydraulic Engineering, Delft University of Technology, Stevinweg 1, 2628 CN Delft, the Netherlands
Jentsje van der Meer
Department of Hydraulic Engineering, Delft University of Technology, Stevinweg 1, 2628 CN Delft, the Netherlands
Van der Meer Consulting, P.O. Box 11, 8490 AA Akkrum, the Netherlands
Coastal & Urban Risk & Resilience Department, IHE Delft, Westvest 7, 2611 AX Delft, the Netherlands
Jeremy D. Bricker
CORRESPONDING AUTHOR
Department of Hydraulic Engineering, Delft University of Technology, Stevinweg 1, 2628 CN Delft, the Netherlands
Civil and Environmental Engineering Department, University of Michigan, 2350 Hayward St., Ann Arbor, MI 48109-2125, USA
Vincent Vuik
Department of Hydraulic Engineering, Delft University of Technology, Stevinweg 1, 2628 CN Delft, the Netherlands
Related authors
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Laurie van Gijzen, Alexander M. Bakker, and Sebastiaan N. Jonkman
EGUsphere, https://doi.org/10.5194/egusphere-2025-5801, https://doi.org/10.5194/egusphere-2025-5801, 2025
This preprint is open for discussion and under review for Natural Hazards and Earth System Sciences (NHESS).
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Future sea level rise will likely challenge many low-lying deltas in the world. A possible mitigation strategy discharging the incoming river discharge with large pump-sluice stations. In this study we determine the required pump and sluice capacity by including the new concept of operational discharge capacity. We found that including operational discharge capacity led to an significant increase in flood frequency, making it crucial to account for in the design of pump-sluice stations.
Roberto Bentivoglio, Sebastiaan Nicolas Jonkman, Elvin Isufi, and Riccardo Taormina
EGUsphere, https://doi.org/10.5194/egusphere-2025-5582, https://doi.org/10.5194/egusphere-2025-5582, 2025
This preprint is open for discussion and under review for Natural Hazards and Earth System Sciences (NHESS).
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Obtaining probabilistic flood maps with numerical models is very time-consuming. Deep learning models can speed this up, but their predictions are hard to verify without reference data, and they ignore structures like dikes or canals. This work introduces a mass-based validation measure to assess prediction plausibility and adapts a graph-based model to include hydraulic structures, enabling realistic, large-scale probabilistic flood mapping in the Netherlands.
Roberto Bentivoglio, Elvin Isufi, Sebastiaan Nicolas Jonkman, and Riccardo Taormina
Nat. Hazards Earth Syst. Sci., 25, 335–351, https://doi.org/10.5194/nhess-25-335-2025, https://doi.org/10.5194/nhess-25-335-2025, 2025
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Deep learning methods are increasingly used as surrogates for spatio-temporal flood models but struggle with generalization and speed. Here, we propose a multi-resolution approach using graph neural networks that predicts dike breach floods across different meshes, topographies, and boundary conditions with high accuracy and up to 1000× speed-ups. The model also generalizes to larger more complex case studies with just one additional simulation for fine-tuning.
Hanqing Xu, Elisa Ragno, Sebastiaan N. Jonkman, Jun Wang, Jeremy D. Bricker, Zhan Tian, and Laixiang Sun
Hydrol. Earth Syst. Sci., 28, 3919–3930, https://doi.org/10.5194/hess-28-3919-2024, https://doi.org/10.5194/hess-28-3919-2024, 2024
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A coupled statistical–hydrodynamic model framework is employed to quantitatively evaluate the sensitivity of compound flood hazards to the relative timing of peak storm surges and rainfall. The findings reveal that the timing difference between these two factors significantly affects flood inundation depth and extent. The most severe inundation occurs when rainfall precedes the storm surge peak by 2 h.
Roberto Bentivoglio, Elvin Isufi, Sebastiaan Nicolas Jonkman, and Riccardo Taormina
Hydrol. Earth Syst. Sci., 27, 4227–4246, https://doi.org/10.5194/hess-27-4227-2023, https://doi.org/10.5194/hess-27-4227-2023, 2023
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To overcome the computational cost of numerical models, we propose a deep-learning approach inspired by hydraulic models that can simulate the spatio-temporal evolution of floods. We show that the model can rapidly predict dike breach floods over different topographies and breach locations, with limited use of ground-truth data.
Roberto Bentivoglio, Elvin Isufi, Sebastian Nicolaas Jonkman, and Riccardo Taormina
Hydrol. Earth Syst. Sci., 26, 4345–4378, https://doi.org/10.5194/hess-26-4345-2022, https://doi.org/10.5194/hess-26-4345-2022, 2022
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Deep learning methods have been increasingly used in flood management to improve traditional techniques. While promising results have been obtained, our review shows significant challenges in building deep learning models that can (i) generalize across multiple scenarios, (ii) account for complex interactions, and (iii) perform probabilistic predictions. We argue that these shortcomings could be addressed by transferring recent fundamental advancements in deep learning to flood mapping.
Julius Schlumberger, Christian Ferrarin, Sebastiaan N. Jonkman, Manuel Andres Diaz Loaiza, Alessandro Antonini, and Sandra Fatorić
Nat. Hazards Earth Syst. Sci., 22, 2381–2400, https://doi.org/10.5194/nhess-22-2381-2022, https://doi.org/10.5194/nhess-22-2381-2022, 2022
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Flooding has serious impacts on the old town of Venice. This paper presents a framework combining a flood model with a flood-impact model to support improving protection against future floods in Venice despite the recently built MOSE barrier. Applying the framework to seven plausible flood scenarios, it was found that individual protection has a significant damage-mediating effect if the MOSE barrier does not operate as anticipated. Contingency planning thus remains important in Venice.
Hanqing Xu, Zhan Tian, Laixiang Sun, Qinghua Ye, Elisa Ragno, Jeremy Bricker, Ganquan Mao, Jinkai Tan, Jun Wang, Qian Ke, Shuai Wang, and Ralf Toumi
Nat. Hazards Earth Syst. Sci., 22, 2347–2358, https://doi.org/10.5194/nhess-22-2347-2022, https://doi.org/10.5194/nhess-22-2347-2022, 2022
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A hydrodynamic model and copula methodology were used to set up a joint distribution of the peak water level and the inland rainfall during tropical cyclone periods, and to calculate the marginal contributions of the individual drivers. The results indicate that the relative sea level rise has significantly amplified the peak water level. The astronomical tide is the leading driver, followed by the contribution from the storm surge.
Manuel Andres Diaz Loaiza, Jeremy D. Bricker, Remi Meynadier, Trang Minh Duong, Rosh Ranasinghe, and Sebastiaan N. Jonkman
Nat. Hazards Earth Syst. Sci., 22, 345–360, https://doi.org/10.5194/nhess-22-345-2022, https://doi.org/10.5194/nhess-22-345-2022, 2022
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Extratropical cyclones are one of the major causes of coastal floods in Europe and the world. Understanding the development process and the flooding of storm Xynthia, together with the damages that occurred during the storm, can help to forecast future losses due to other similar storms. In the present paper, an analysis of shallow water variables (flood depth, velocity, etc.) or coastal variables (significant wave height, energy flux, etc.) is done in order to develop damage curves.
Roberto Bentivoglio, Elvin Isufi, Sebastian Nicolaas Jonkman, and Riccardo Taormina
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2021-614, https://doi.org/10.5194/hess-2021-614, 2021
Manuscript not accepted for further review
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Deep Learning methods have been increasingly used in flood mapping as an alternative to traditional modeling techniques. While promising results have been obtained, our review shows significant challenges in building Deep Learning models that can generalize across multiple scenarios, account for complex interactions, and provide probabilistic predictions. We argue that these shortcomings could be addressed by transferring recent fundamental advancements in Deep Learning.
Zhan Hu, Pim W. J. M. Willemsen, Bas W. Borsje, Chen Wang, Heng Wang, Daphne van der Wal, Zhenchang Zhu, Bas Oteman, Vincent Vuik, Ben Evans, Iris Möller, Jean-Philippe Belliard, Alexander Van Braeckel, Stijn Temmerman, and Tjeerd J. Bouma
Earth Syst. Sci. Data, 13, 405–416, https://doi.org/10.5194/essd-13-405-2021, https://doi.org/10.5194/essd-13-405-2021, 2021
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Erosion and accretion processes govern the ecogeomorphic evolution of intertidal (salt marsh and tidal flat) ecosystems and hence substantially affect their valuable ecosystem services. By applying a novel sensor, we obtained unique high-resolution daily bed-level change datasets from 10 marsh–mudflat sites in northwestern Europe. This dataset has revealed diverse spatial bed-level change patterns over daily to seasonal scales, which are valuable to theoretical and model development.
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Short summary
Many coastlines around the world have shallow foreshores (e.g. salt marshes and mudflats) that reduce storm waves and the risk of coastal flooding. However, most of the studies that tried to quantify this effect have excluded the influence of very long waves, which often dominate in shallow water. Our newly developed framework addresses this oversight and suggests that safety along these coastlines may be overestimated, since these very long waves are largely neglected in flood risk assessments.
Many coastlines around the world have shallow foreshores (e.g. salt marshes and mudflats) that...
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