Articles | Volume 25, issue 4
https://doi.org/10.5194/nhess-25-1353-2025
© 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-25-1353-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
09 Apr 2025
Research article |
| 09 Apr 2025
| 09 Apr 2025
Accelerating compound flood risk assessments through active learning: A case study of Charleston County (USA)
Lucas Terlinden-Ruhl
CORRESPONDING AUTHOR
Department of Hydraulic Engineering, Delft University of Technology, Delft, the Netherlands
Department of Inland and Water Systems, Deltares, Delft, the Netherlands
Anaïs Couasnon
Department of Inland and Water Systems, Deltares, Delft, the Netherlands
Institute for Environmental Studies (IVM), Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
Dirk Eilander
Department of Inland and Water Systems, Deltares, Delft, the Netherlands
Institute for Environmental Studies (IVM), Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
Gijs G. Hendrickx
Department of Hydraulic Engineering, Delft University of Technology, Delft, the Netherlands
Patricia Mares-Nasarre
Department of Hydraulic Engineering, Delft University of Technology, Delft, the Netherlands
José A. Á. Antolínez
Department of Hydraulic Engineering, Delft University of Technology, Delft, the Netherlands
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Doris M. Vertegaal, Bart J. J. M. van den Hurk, Anaïs Couasnon, Natalia Aleksandrova, Tycho Bovenschen, Fernaldi Gradiyanto, Tim W. B. Leijnse, Henrique M. D. Goulart, and Sanne Muis
Nat. Hazards Earth Syst. Sci., 26, 1417–1433, https://doi.org/10.5194/nhess-26-1417-2026, https://doi.org/10.5194/nhess-26-1417-2026, 2026
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This study highlights the need to disentangle climate change effects on flood drivers using storyline attribution. Whether information is presented as change in one or multiple drivers, or as change in hazard or impact, determines the attribution statement. For compound flooding from tropical cyclone Idai, that hit Mozambique in 2019, we attribute 1–19 % of the flood hazard and 8–35 % of the damage to climate change. The attribution framework can be applied to other events worldwide.
Huazhi Li, Robert A. Jane, Dirk Eilander, Alejandra R. Enríquez, Toon Haer, and Philip J. Ward
Nat. Hazards Earth Syst. Sci., 26, 391–409, https://doi.org/10.5194/nhess-26-391-2026, https://doi.org/10.5194/nhess-26-391-2026, 2026
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We assess the likelihood of widespread compound flooding along the U.S. coastline. Using a large set of generated plausible events preserving observed dependence, we find that nearly half of compound floods on the West coast affect multiple sites. Such events are rarer on the East coast while most compound events affect single sites on the Gulf coast. Our results underscore the importance of including spatial dependence in compound flood risk assessment and can help in better risk management.
Tim H. J. Hermans, Chiheb Ben Hammouda, Simon Treu, Timothy Tiggeloven, Anaïs Couasnon, Julius J. M. Busecke, and Roderik S. W. van de Wal
Nat. Hazards Earth Syst. Sci., 25, 4593–4612, https://doi.org/10.5194/nhess-25-4593-2025, https://doi.org/10.5194/nhess-25-4593-2025, 2025
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We studied the performance of different types of neural networks at predicting extreme storm surges. We found that that performance improves when during model training, storm surges that are rarer are given a higher weight than moderate storm surges. Additionally, we found that the performance of some of the neural networks approaches that of a state-of-the-art hydrodynamic model. This is promising for the future application of neural networks to climate model simulations.
Tarun Sadana, Jeroen C. J. H. Aerts, Dirk Eilander, Bruno Merz, Hans de Moel, Tim Busker, Veerle Bril, and Jens de Bruijn
EGUsphere, https://doi.org/10.5194/egusphere-2025-4387, https://doi.org/10.5194/egusphere-2025-4387, 2025
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We evaluated a global flood model using satellite data from 499 historical flood events across 96 countries. Our study shows that larger upstream river basins are modelled more accurately, while using observed river gauges and high-resolution elevation data can improve results. Our findings highlight the importance of large-scale validation and sensitivity analyses to enhance future global flood hazard assessments and prediction accuracy.
Daan Cornelis Hulskemper, José A. Á. Antolínez, Roderik Lindenbergh, and Katharina Anders
EGUsphere, https://doi.org/10.5194/egusphere-2025-4964, https://doi.org/10.5194/egusphere-2025-4964, 2025
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We developed a new method to automatically detect and group short-term topographic changes on sandy beaches using continuous 3D laser scans collected over three years. By distinguishing variations in patterns of sand deposition and erosion, the approach allows scientists to study how beaches change at different moments in time and link these changes to environmental conditions like winds, waves or bulldozers, improving understanding and prediction of dynamics of sandy beaches.
Irene Benito, Jeroen C. J. H. Aerts, Philip J. Ward, Dirk Eilander, and Sanne Muis
Nat. Hazards Earth Syst. Sci., 25, 2287–2315, https://doi.org/10.5194/nhess-25-2287-2025, https://doi.org/10.5194/nhess-25-2287-2025, 2025
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Global flood models are key to the mitigation of coastal flooding impacts, yet they still have limitations when providing actionable insights locally. We present a multiscale framework that couples dynamic water level and flood models and bridges the fully global and local modelling approaches. We apply it to three historical storms. Our findings reveal that the importance of model refinements varies based on the study area characteristics and the storm’s nature.
Joshua Green, Ivan D. Haigh, Niall Quinn, Jeff Neal, Thomas Wahl, Melissa Wood, Dirk Eilander, Marleen de Ruiter, Philip Ward, and Paula Camus
Nat. Hazards Earth Syst. Sci., 25, 747–816, https://doi.org/10.5194/nhess-25-747-2025, https://doi.org/10.5194/nhess-25-747-2025, 2025
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Compound flooding, involving the combination or successive occurrence of two or more flood drivers, can amplify flood impacts in coastal/estuarine regions. This paper reviews the practices, trends, methodologies, applications, and findings of coastal compound flooding literature at regional to global scales. We explore the types of compound flood events, their mechanistic processes, and the range of terminology. Lastly, this review highlights knowledge gaps and implications for future practices.
Robert McCall, Curt Storlazzi, Floortje Roelvink, Stuart G. Pearson, Roel de Goede, and José A. Á. Antolínez
Nat. Hazards Earth Syst. Sci., 24, 3597–3625, https://doi.org/10.5194/nhess-24-3597-2024, https://doi.org/10.5194/nhess-24-3597-2024, 2024
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Accurate predictions of wave-driven flooding are essential to manage risk on low-lying, reef-lined coasts. Models to provide this information are, however, computationally expensive. We present and validate a modeling system that simulates flood drivers on diverse and complex reef-lined coasts as competently as a full-physics model but at a fraction of the computational cost to run. This development paves the way for application in large-scale early-warning systems and flood risk assessments.
Willem J. van Verseveld, Albrecht H. Weerts, Martijn Visser, Joost Buitink, Ruben O. Imhoff, Hélène Boisgontier, Laurène Bouaziz, Dirk Eilander, Mark Hegnauer, Corine ten Velden, and Bobby Russell
Geosci. Model Dev., 17, 3199–3234, https://doi.org/10.5194/gmd-17-3199-2024, https://doi.org/10.5194/gmd-17-3199-2024, 2024
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We present the wflow_sbm distributed hydrological model, recently released by Deltares, as part of the Wflow.jl open-source modelling framework in the programming language Julia. Wflow_sbm has a fast runtime, making it suitable for large-scale modelling. Wflow_sbm models can be set a priori for any catchment with the Python tool HydroMT-Wflow based on globally available datasets, which results in satisfactory to good performance (without much tuning). We show this for a number of specific cases.
Eric Mortensen, Timothy Tiggeloven, Toon Haer, Bas van Bemmel, Dewi Le Bars, Sanne Muis, Dirk Eilander, Frederiek Sperna Weiland, Arno Bouwman, Willem Ligtvoet, and Philip J. Ward
Nat. Hazards Earth Syst. Sci., 24, 1381–1400, https://doi.org/10.5194/nhess-24-1381-2024, https://doi.org/10.5194/nhess-24-1381-2024, 2024
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Current levels of coastal flood risk are projected to increase in coming decades due to various reasons, e.g. sea-level rise, land subsidence, and coastal urbanization: action is needed to minimize this future risk. We evaluate dykes and coastal levees, foreshore vegetation, zoning restrictions, and dry-proofing on a global scale to estimate what levels of risk reductions are possible. We demonstrate that there are several potential adaptation pathways forward for certain areas of the world.
Kees Nederhoff, Maarten van Ormondt, Jay Veeramony, Ap van Dongeren, José Antonio Álvarez Antolínez, Tim Leijnse, and Dano Roelvink
Geosci. Model Dev., 17, 1789–1811, https://doi.org/10.5194/gmd-17-1789-2024, https://doi.org/10.5194/gmd-17-1789-2024, 2024
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Forecasting tropical cyclones and their flooding impact is challenging. Our research introduces the Tropical Cyclone Forecasting Framework (TC-FF), enhancing cyclone predictions despite uncertainties. TC-FF generates global wind and flood scenarios, valuable even in data-limited regions. Applied to cases like Cyclone Idai, it showcases potential in bettering disaster preparation, marking progress in handling cyclone threats.
Dirk Eilander, Anaïs Couasnon, Frederiek C. Sperna Weiland, Willem Ligtvoet, Arno Bouwman, Hessel C. Winsemius, and Philip J. Ward
Nat. Hazards Earth Syst. Sci., 23, 2251–2272, https://doi.org/10.5194/nhess-23-2251-2023, https://doi.org/10.5194/nhess-23-2251-2023, 2023
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This study presents a framework for assessing compound flood risk using hydrodynamic, impact, and statistical modeling. A pilot in Mozambique shows the importance of accounting for compound events in risk assessments. We also show how the framework can be used to assess the effectiveness of different risk reduction measures. As the framework is based on global datasets and is largely automated, it can easily be applied in other areas for first-order assessments of compound flood risk.
Job C. M. Dullaart, Sanne Muis, Hans de Moel, Philip J. Ward, Dirk Eilander, and Jeroen C. J. H. Aerts
Nat. Hazards Earth Syst. Sci., 23, 1847–1862, https://doi.org/10.5194/nhess-23-1847-2023, https://doi.org/10.5194/nhess-23-1847-2023, 2023
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Coastal flooding is driven by storm surges and high tides and can be devastating. To gain an understanding of the threat posed by coastal flooding and to identify areas that are especially at risk, now and in the future, it is crucial to accurately model coastal inundation and assess the coastal flood hazard. Here, we present a global dataset with hydrographs that represent the typical evolution of an extreme sea level. These can be used to model coastal inundation more accurately.
Dirk Eilander, Anaïs Couasnon, Tim Leijnse, Hiroaki Ikeuchi, Dai Yamazaki, Sanne Muis, Job Dullaart, Arjen Haag, Hessel C. Winsemius, and Philip J. Ward
Nat. Hazards Earth Syst. Sci., 23, 823–846, https://doi.org/10.5194/nhess-23-823-2023, https://doi.org/10.5194/nhess-23-823-2023, 2023
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In coastal deltas, flooding can occur from interactions between coastal, riverine, and pluvial drivers, so-called compound flooding. Global models however ignore these interactions. We present a framework for automated and reproducible compound flood modeling anywhere globally and validate it for two historical events in Mozambique with good results. The analysis reveals differences in compound flood dynamics between both events related to the magnitude of and time lag between drivers.
D. Hulskemper, K. Anders, J. A. Á. Antolínez, M. Kuschnerus, B. Höfle, and R. Lindenbergh
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-2-W2-2022, 53–60, https://doi.org/10.5194/isprs-archives-XLVIII-2-W2-2022-53-2022, https://doi.org/10.5194/isprs-archives-XLVIII-2-W2-2022-53-2022, 2022
Panagiotis Athanasiou, Ap van Dongeren, Alessio Giardino, Michalis Vousdoukas, Jose A. A. Antolinez, and Roshanka Ranasinghe
Nat. Hazards Earth Syst. Sci., 22, 3897–3915, https://doi.org/10.5194/nhess-22-3897-2022, https://doi.org/10.5194/nhess-22-3897-2022, 2022
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Sandy dunes protect the hinterland from coastal flooding during storms. Thus, models that can efficiently predict dune erosion are critical for coastal zone management and early warning systems. Here we develop such a model for the Dutch coast based on machine learning techniques, allowing for dune erosion estimations in a matter of seconds relative to available computationally expensive models. Validation of the model against benchmark data and observations shows good agreement.
Dirk Eilander, Willem van Verseveld, Dai Yamazaki, Albrecht Weerts, Hessel C. Winsemius, and Philip J. Ward
Hydrol. Earth Syst. Sci., 25, 5287–5313, https://doi.org/10.5194/hess-25-5287-2021, https://doi.org/10.5194/hess-25-5287-2021, 2021
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Digital elevation models and derived flow directions are crucial to distributed hydrological modeling. As the spatial resolution of models is typically coarser than these data, we need methods to upscale flow direction data while preserving the river structure. We propose the Iterative Hydrography Upscaling (IHU) method and show it outperforms other often-applied methods. We publish the multi-resolution MERIT Hydro IHU hydrography dataset and the algorithm as part of the pyflwdir Python package.
Cited articles
Anderson, D., Rueda, A., Cagigal, L., Antolinez, J. A. A., Mendez, F. J., and Ruggiero, P.: Time‐Varying Emulator for Short and Long‐Term Analysis of Coastal Flood Hazard Potential, J. Geophys. Res.-Oceans, 124, 9209–9234, https://doi.org/10.1029/2019jc015312, 2019. a
Antolínez, J. A. A., Méndez, F. J., Anderson, D., Ruggiero, P., and Kaminsky, G. M.: Predicting Climate‐Driven Coastlines With a Simple and Efficient Multiscale Model, J. Geophys. Res.-Earth Surface, 124, 1596–1624, https://doi.org/10.1029/2018jf004790, 2019. a
Apel, H., Martínez Trepat, O., Hung, N. N., Chinh, D. T., Merz, B., and Dung, N. V.: Combined fluvial and pluvial urban flood hazard analysis: concept development and application to Can Tho city, Mekong Delta, Vietnam, Nat. Hazards Earth Syst. Sci., 16, 941–961, https://doi.org/10.5194/nhess-16-941-2016, 2016. a, b
Arns, A., Wahl, T., Haigh, I., 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
Bakker, T. M., Antolínez, J. A., Leijnse, T. W., Pearson, S. G., and Giardino, A.: Estimating tropical cyclone-induced wind, waves, and surge: A general methodology based on representative tracks, Coast. Eng., 176, 104154, https://doi.org/10.1016/j.coastaleng.2022.104154, 2022. a, b
Barnard, P., Befus, K. M., Danielson, J. J., Engelstad, A. C., Erikson, L., Foxgrover, A., Hardy, M. W., Hoover, D. J., Leijnse, T., Massey, C., McCall, R., Nadal-Caraballo, N., Nederhoff, K., Ohenhen, L., O'Neill, A., Parker, K. A., Shirzaei, M., Su, X., Thomas, J. A., van Ormondt, M., Vitousek, S. F., Vos, K., and Yawn, M. C.: Future coastal hazards along the U.S. North and South Carolina coasts, U.S. Geological Survey [data set], https://doi.org/10.5066/P9W91314, 2023. a
Barnard, P. L., Erikson, L. H., Foxgrover, A. C., Hart, J. A. F., Limber, P., O'Neill, A. C., van Ormondt, M., Vitousek, S., Wood, N., Hayden, M. K., and Jones, J. M.: Dynamic flood modeling essential to assess the coastal impacts of climate change, Sci. Rep., 9, 4309, https://doi.org/10.1038/s41598-019-40742-z, 2019. a, b
Bates, P. D., Horritt, M. S., and Fewtrell, T. J.: A simple inertial formulation of the shallow water equations for efficient two-dimensional flood inundation modelling, J. Hydrol., 387, 33–45, https://doi.org/10.1016/j.jhydrol.2010.03.027, 2010. a, b, c
Bates, P. D., Quinn, N., Sampson, C., Smith, A., Wing, O., Sosa, J., Savage, J., Olcese, G., Neal, J., Schumann, G., Giustarini, L., Coxon, G., Porter, J. R., Amodeo, M. F., Chu, Z., Lewis‐Gruss, S., Freeman, N. B., Houser, T., Delgado, M., Hamidi, A., Bolliger, I., E. McCusker, K., Emanuel, K., Ferreira, C. M., Khalid, A., Haigh, I. D., Couasnon, A., E. Kopp, R., Hsiang, S., and Krajewski, W. F.: Combined Modeling of US Fluvial, Pluvial, and Coastal Flood Hazard Under Current and Future Climates, Water Resour. Res., 57, e2020WR02867, https://doi.org/10.1029/2020wr028673, 2021. a
Bates, P. D., Savage, J., Wing, O., Quinn, N., Sampson, C., Neal, J., and Smith, A.: A climate-conditioned catastrophe risk model for UK flooding, Nat. Hazards Earth Syst. Sci., 23, 891–908, https://doi.org/10.5194/nhess-23-891-2023, 2023. a, b, c
Bedford, T. and Cooke, R. M.: Vines–a new graphical model for dependent random variables, Ann. Stat., 30, 1031–1068, https://doi.org/10.1214/aos/1031689016, 2002. a
Bevacqua, E., Maraun, D., Hobæk Haff, I., Widmann, M., and Vrac, M.: Multivariate statistical modelling of compound events via pair-copula constructions: analysis of floods in Ravenna (Italy), Hydrol. Earth Syst. Sci., 21, 2701–2723, https://doi.org/10.5194/hess-21-2701-2017, 2017. a, b
Blöschl, G.: Three hypotheses on changing river flood hazards, Hydrol. Earth Syst. Sci., 26, 5015–5033, https://doi.org/10.5194/hess-26-5015-2022, 2022. a
Camus, P., Mendez, F. J., Medina, R., and Cofiño, A. S.: Analysis of clustering and selection algorithms for the study of multivariate wave climate, Coast. Eng., 58, 453–462, https://doi.org/10.1016/j.coastaleng.2011.02.003, 2011. a
City of Charleston: Sea Level Rise Strategy, City of Charleston, https://www.charleston-sc.gov/DocumentCenter/View/10089, (last access: 6 August 2024), 2015. a
Cloern, J. E., Knowles, N., Brown, L. R., Cayan, D., Dettinger, M. D., Morgan, T. L., Schoellhamer, D. H., Stacey, M. T., van der Wegen, M., Wagner, R. W., and Jassby, A. D.: Projected Evolution of California's San Francisco Bay-Delta-River System in a Century of Climate Change, PLoS ONE, 6, e24465, https://doi.org/10.1371/journal.pone.0024465, 2011. a
Cokelaer, T., Kravchenko, A., Varma, A., L, B., Eadi Stringari, C., Brueffer, C., Broda, E., Pruesse, E., Singaravelan, K., Russo, S., and Li, Z.: cokelaer/fitter: v1.7.0, Zenodo [code], https://doi.org/10.5281/zenodo.10459943, 2024. a
Couasnon, A., Eilander, D., Muis, S., Veldkamp, T. I. E., Haigh, I. D., Wahl, T., Winsemius, H. C., and Ward, P. J.: Measuring compound flood potential from river discharge and storm surge extremes at the global scale, Nat. Hazards Earth Syst. Sci., 20, 489–504, https://doi.org/10.5194/nhess-20-489-2020, 2020. a
Couasnon, A., Scussolini, P., Tran, T. V. T., Eilander, D., Muis, S., Wang, H., Keesom, J., Dullaart, J., Xuan, Y., Nguyen, H. Q., Winsemius, H. C., and Ward, P. J.: A Flood Risk Framework Capturing the Seasonality of and Dependence Between Rainfall and Sea Levels – An Application to Ho Chi Minh City, Vietnam, Water Resour. Res., 58, e2021WR030002, https://doi.org/10.1029/2021wr030002, 2022. a, b, c, d, e, f
Cushing, W. M., Taylor, D., Danielson, J. J., Poppenga, S., Beverly, S., and Shogib, R.: Topobathymetric Model of the Coastal Carolinas, 1851 to 2020, U.S. Geological Survey [data set], https://doi.org/10.5066/P9MPA8K0, 2022. a
Czado, C.: Analyzing Dependent Data with Vine Copulas, Springer, ISBN 978-3-030-13785-4, https://doi.org/10.1007/978-3-030-13785-4, 2019. a, b
Czado, C. and Nagler, T.: Vine Copula Based Modeling, Annu. Rev. Stat. Appl., 9, 453–477, https://doi.org/10.1146/annurev-statistics-040220-101153, 2022. a
Danielson, J. J., Poppenga, S. K., Brock, J. C., Evans, G. A., Tyler, D. J., Gesch, D. B., Thatcher, C. A., and Barras, J. A.: Topobathymetric Elevation Model Development using a New Methodology: Coastal National Elevation Database, J. Coast. Res., 76, 75–89, https://doi.org/10.2112/si76-008, 2016. a
Deltares: Delft3D-FM User Manual, Deltares, https://content.oss.deltares.nl/delft3d/D-Flow_FM_User_Manual.pdf (last access: 6 April 2025), 2022. a
Deltares: Delft FIAT, Deltares, https://deltares.github.io/Delft-FIAT/stable/ (last access: 29 September 2024), 2024. a
DHI: MIKE 21 Flow Model User Guide, DHI, https://manuals.mikepoweredbydhi.help/2017/Coast_and_Sea/M21HD.pdf (last access: 6 August 2024), 2017. a
Diermanse, F. L. M., De Bruijn, K. M., Beckers, J. V. L., and Kramer, N. L.: Importance sampling for efficient modelling of hydraulic loads in the Rhine–Meuse delta, Stoch. Env. Res. Risk A., 29, 637–652, https://doi.org/10.1007/s00477-014-0921-4, 2014. a
Dißmann, J., Brechmann, E., Czado, C., and Kurowicka, D.: Selecting and estimating regular vine copulae and application to financial returns, Comput. Stat. Data An., 59, 52–69, https://doi.org/10.1016/j.csda.2012.08.010, 2013. a
Efron, B.: Bootstrap Methods: Another Look at the Jackknife, Ann. Stat., 7, 1–26, https://doi.org/10.1214/aos/1176344552, 1979. a
Eilander, D., Boisgontier, H., Bouaziz, L. J. E., Buitink, J., Couasnon, A., Dalmijn, B., Hegnauer, M., de Jong, T., Loos, S., Marth, I., and van Verseveld, W.: HydroMT: Automated and reproducible model building and analysis, Journal of Open Source Software, 8, 4897, https://doi.org/10.21105/joss.04897, 2023a. a
Eilander, D., Couasnon, A., Sperna Weiland, F. C., Ligtvoet, W., Bouwman, A., Winsemius, H. C., and Ward, P. J.: Modeling compound flood risk and risk reduction using a globally applicable framework: a pilot in the Sofala province of Mozambique, Nat. Hazards Earth Syst. Sci., 23, 2251–2272, https://doi.org/10.5194/nhess-23-2251-2023, 2023b. a, b, c, d, e, f, g, h, i, j, k, l
Fraehr, N., Wang, Q. J., Wu, W., and Nathan, R.: Assessment of surrogate models for flood inundation: The physics-guided LSG model vs. state-of-the-art machine learning models, Water Res., 252, 121202, https://doi.org/10.1016/j.watres.2024.121202, 2024. a, b
Gori, A., Lin, N., and Xi, D.: Tropical Cyclone Compound Flood Hazard Assessment: From Investigating Drivers to Quantifying Extreme Water Levels, Earths Future, 8, e2020EF001660, https://doi.org/10.1029/2020ef001660, 2020. a, b
Gouldby, B., Wyncoll, D., Panzeri, M., Franklin, M., Hunt, T., Hames, D., Tozer, N., Hawkes, P., Dornbusch, U., and Pullen, T.: Multivariate extreme value modelling of sea conditions around the coast of England, P. I. Civil Eng. Mar. En., 170, 3–20, https://doi.org/10.1680/jmaen.2016.16, 2017. a, b, c
Gumbel, E. J.: The Return Period of Flood Flows, Ann. Math. Stat., 12, 163–190, https://doi.org/10.1214/aoms/1177731747, 1941. a
Haer, T., Botzen, W. W., Zavala-Hidalgo, J., Cusell, C., and Ward, P. J.: Economic evaluation of climate risk adaptation strategies: Cost-benefit analysis of flood protection in Tabasco, Mexico, Atmósfera, 30, 101–120, https://doi.org/10.20937/atm.2017.30.02.03, 2017. a
Hendry, A., Haigh, I. D., Nicholls, R. J., Winter, H., Neal, R., Wahl, T., Joly-Laugel, A., and Darby, S. E.: Assessing the characteristics and drivers of compound flooding events around the UK coast, Hydrol. Earth Syst. Sci., 23, 3117–3139, https://doi.org/10.5194/hess-23-3117-2019, 2019. a
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz‐Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.: The ERA5 global reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020. a
Hirabayashi, Y., Mahendran, R., Koirala, S., Konoshima, L., Yamazaki, D., Watanabe, S., Kim, H., and Kanae, S.: Global flood risk under climate change, Nat. Clim. Change, 3, 816–821, https://doi.org/10.1038/nclimate1911, 2013. a
Hodges, J. L.: The significance probability of the smirnov two-sample test, Ark. Mat., 3, 469–486, https://doi.org/10.1007/bf02589501, 1958. a
Homer, C., Dewitz, J., Jin, S., Xian, G., Costello, C., Danielson, P., Gass, L., Funk, M., Wickham, J., Stehman, S., Auch, R., and Riitters, K.: Conterminous United States land cover change patterns 2001–2016 from the 2016 National Land Cover Database, ISPRS J. Photogramm., 162, 184–199, https://doi.org/10.1016/j.isprsjprs.2020.02.019, 2020. a
Ishibashi, H. and Hino, H.: Stopping Criterion for Active Learning Based on Error Stability, arXiv [preprint], https://doi.org/10.48550/arXiv.2104.01836, 9 April 2021. a
Jane, R. A., Malagón‐Santos, V., Rashid, M. M., Doebele, L., Wahl, T., Timmers, S. R., Serafin, K. A., Schmied, L., and Lindemer, C.: A Hybrid Framework for Rapidly Locating Transition Zones: A Comparison of Event‐ and Response‐Based Return Water Levels in the Suwannee River FL, Water Resour. Res., 58, e2022WR032481, https://doi.org/10.1029/2022wr032481, 2022. a, b, c, d, e, f
Jongman, B.: Effective adaptation to rising flood risk, Nat. Commun., 9, 1986, https://doi.org/10.1038/s41467-018-04396-1, 2018. a
Kennard, R. W. and Stone, L. A.: Computer Aided Design of Experiments, Technometrics, 11, 137–148, https://doi.org/10.1080/00401706.1969.10490666, 1969. a
Klijn, F., Kreibich, H., de Moel, H., and Penning-Rowsell, E.: Adaptive flood risk management planning based on a comprehensive flood risk conceptualisation, Mitig. Adapt. Strat. Gl., 20, 845–864, https://doi.org/10.1007/s11027-015-9638-z, 2015. a, b
Koks, E., Jongman, B., Husby, T., and Botzen, W.: Combining hazard, exposure and social vulnerability to provide lessons for flood risk management, Environ. Sci. Policy, 47, 42–52, https://doi.org/10.1016/j.envsci.2014.10.013, 2015. a, b
Leijnse, T., van Ormondt, M., Nederhoff, K., and van Dongeren, A.: Modeling compound flooding in coastal systems using a computationally efficient reduced-physics solver: Including fluvial, pluvial, tidal, wind- and wave-driven processes, Coast. Eng., 163, 103796, https://doi.org/10.1016/j.coastaleng.2020.103796, 2021. a, b, c, d, e, f
MacKay, D. J. C.: Information-Based Objective Functions for Active Data Selection, Neural Comput., 4, 590–604, https://doi.org/10.1162/neco.1992.4.4.590, 1992. a, b
Mann, H. B. and Whitney, D. R.: On a Test of Whether one of Two Random Variables is Stochastically Larger than the Other, Ann. Math. Stat., 18, 50–60, https://doi.org/10.1214/aoms/1177730491, 1947. a
Marra, J., Sweet, W., Leuliette, E., Kruk, M., Genz, A., Storlazzi, C., Ruggiero, P., Leung, M., Anderson, D. L., Merrifield, M., Becker, J., Robertson, I., Widlansky, M. J., Thompson, P. R., Mendez, F., Rueda, A., Antolinez, J. A. A., Cagigal, L., Menendez, M., Lobeto, H., Obeysekera, J., and Chiesa, C.: Advancing best practices for the analysis of the vulnerability of military installations in the Pacific Basin to coastal flooding under a changing climate – RC-2644, U.S. Department of Defense Strategic Environmental Research and Development Program, https://pubs.usgs.gov/publication/70244064 (last access: 19 January 2025), 2023. a
Moftakhari, H., Schubert, J. E., AghaKouchak, A., Matthew, R. A., and Sanders, B. F.: Linking statistical and hydrodynamic modeling for compound flood hazard assessment in tidal channels and estuaries, Adv. Water Resour., 128, 28–38, https://doi.org/10.1016/j.advwatres.2019.04.009, 2019. a
Moradian, S., AghaKouchak, A., Gharbia, S., Broderick, C., and Olbert, A. I.: Forecasting of compound ocean-fluvial floods using machine learning, J. Environ. Manage., 364, 121295, https://doi.org/10.1016/j.jenvman.2024.121295, 2024. a, b
Morales-Nápoles, O.: Counting Vines, World Scientific, ISBN 9789814299886, 189–218, https://doi.org/10.1142/9789814299886_0009, 2010. a
Morales-Nápoles, O., Rajabi-Bahaabadi, M., Torres-Alves, G. A., and 't Hart, C. M. P.: Chimera: An atlas of regular vines on up to 8 nodes, Scientific Data, 10, 337, https://doi.org/10.1038/s41597-023-02252-6, 2023. a, b
Morris, J. T. and Renken, K. A.: Past, present, and future nuisance flooding on the Charleston peninsula, PLOS ONE, 15, e0238770, https://doi.org/10.1371/journal.pone.0238770, 2020. a
Muis, S., Güneralp, B., Jongman, B., Aerts, J. C., and Ward, P. J.: Flood risk and adaptation strategies under climate change and urban expansion: A probabilistic analysis using global data, Sci. Total Environ., 538, 445–457, https://doi.org/10.1016/j.scitotenv.2015.08.068, 2015. a
Nagler, T. and Vatter, T.: pyvinecopulib, Zenodo [code], https://doi.org/10.5281/zenodo.10435751, 2023. a, b
Nederhoff, K., Leijnse, T. W. B., Parker, K., Thomas, J., O'Neill, A., van Ormondt, M., McCall, R., Erikson, L., Barnard, P. L., Foxgrover, A., Klessens, W., Nadal-Caraballo, N. C., and Massey, T. C.: Tropical or extratropical cyclones: what drives the compound flood hazard, impact, and risk for the United States Southeast Atlantic coast?, Nat. Hazards, 120, 8779–8825, https://doi.org/10.1007/s11069-024-06552-x, 2024. a, b, c, d
Neumann, B., Vafeidis, A. T., Zimmermann, J., and Nicholls, R. J.: Future Coastal Population Growth and Exposure to Sea-Level Rise and Coastal Flooding – A Global Assessment, PLOS ONE, 10, e0118571, https://doi.org/10.1371/journal.pone.0118571, 2015. a
Olsen, A., Zhou, Q., Linde, J., and Arnbjerg-Nielsen, K.: Comparing Methods of Calculating Expected Annual Damage in Urban Pluvial Flood Risk Assessments, Water, 7, 255–270, https://doi.org/10.3390/w7010255, 2015. a
Parker, K., Erikson, L., Thomas, J., Nederhoff, K., Barnard, P., and Muis, S.: Relative contributions of water-level components to extreme water levels along the US Southeast Atlantic Coast from a regional-scale water-level hindcast, Nat. Hazards, 117, 2219–2248, https://doi.org/10.1007/s11069-023-05939-6, 2023. a, b, c
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E.: Scikit-learn: Machine Learning in Python, J. Mach. Learn. Res., 12, 2825–2830, 2011. a
Rueda, A., Gouldby, B., Méndez, F., Tomás, A., Losada, I., Lara, J., and Díaz‐Simal, P.: The use of wave propagation and reduced complexity inundation models and metamodels for coastal flood risk assessment, J. Flood Risk Manag., 9, 390–401, https://doi.org/10.1111/jfr3.12204, 2015. a, b, c, d, e, f
Samadi, V. and Lunt, S.: Historical Floods of South Carolina, Clemson University, https://lgpress.clemson.edu/publication/historical-floods-of-south-carolina/ (last access: 15 July 2024), 2023. a
Schwarz, G.: Estimating the Dimension of a Model, Ann. Stat., 6, 461–464, https://doi.org/10.1214/aos/1176344136, 1978. a
Swain, D. L., Wing, O. E. J., Bates, P. D., Done, J. M., Johnson, K. A., and Cameron, D. R.: Increased Flood Exposure Due to Climate Change and Population Growth in the United States, Earths Future, 8, e2020EF001778, https://doi.org/10.1029/2020ef001778, 2020. a
Terlinden-Ruhl, L.: Compound_TGP, Zenodo [code], https://doi.org/10.5281/zenodo.13910108, 2024. a
Tomar, A. and Burton, H. V.: Active learning method for risk assessment of distributed infrastructure systems, Comput.-Aided Civ. Inf., 36, 438–452, https://doi.org/10.1111/mice.12665, 2021. a, b, c
UNDRR: The human cost of disasters: an overview of the last 20 years (2000–2019), UNDRR, https://www.undrr.org/publication/human-cost-disasters-overview-last-20-years-2000-2019 (last access: 12 July 2024), 2020. a
United States Census Bureau: https://www2.census.gov/geo/maps/DC2020/DC20BLK/st45_sc/cousub/, last access: 6 May 2024. a
USACE Hydrologic Engineering Center: HEC-RAS Documentation, USACE Hydrologic Engineering Center, https://www.hec.usace.army.mil/confluence/rasdocs (last access: 8 April 2025), 2025. a
U.S. Department of Agriculture: U.S. General Soil Map (STATSGO2) for Florida, Georgia, South Carolina, North Carolina and Virginia, U.S. Department of Agriculture, https://gdg.sc.egov.usda.gov/ (last access: 8 January 2021), 2020. a
van Ormondt, M., Leijnse, T., de Goede, R., Nederhoff, K., and van Dongeren, A.: Subgrid corrections for the linear inertial equations of a compound flood model – a case study using SFINCS 2.1.1 Dollerup release, Geosci. Model Dev., 18, 843–861, https://doi.org/10.5194/gmd-18-843-2025, 2025. a, b
Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T., Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J., van der Walt, S. J., Brett, M., Wilson, J., Millman, K. J., Mayorov, N., Nelson, A. R. J., Jones, E., Kern, R., Larson, E., Carey, C. J., Polat, İ., Feng, Y., Moore, E. W., VanderPlas, J., Laxalde, D., Perktold, J., Cimrman, R., Henriksen, I., Quintero, E. A., Harris, C. R., Archibald, A. M., Ribeiro, A. H., Pedregosa, F., van Mulbregt, P., and SciPy 1.0 Contributors: SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python, Nat. Methods, 17, 261–272, https://doi.org/10.1038/s41592-019-0686-2, 2020. a, b, c, d
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, https://doi.org/10.1038/nclimate2736, 2015. a
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. a
Williams, J., Horsburgh, K. J., Williams, J. A., and Proctor, R. N. F.: Tide and skew surge independence: New insights for flood risk, Geophys. Res. Lett., 43, 6410–6417, https://doi.org/10.1002/2016gl069522, 2016. a, b, c
Winter, B., Schneeberger, K., Förster, K., and Vorogushyn, S.: Event generation for probabilistic flood risk modelling: multi-site peak flow dependence model vs. weather-generator-based approach, Nat. Hazards Earth Syst. Sci., 20, 1689–1703, https://doi.org/10.5194/nhess-20-1689-2020, 2020. a
Woodward, M., Kapelan, Z., and Gouldby, B.: Adaptive Flood Risk Management Under Climate Change Uncertainty Using Real Options and Optimization, Risk Anal., 34, 75–92, https://doi.org/10.1111/risa.12088, 2013. a
Woodward, M., Gouldby, B., Kapelan, Z., and Hames, D.: Multiobjective Optimization for Improved Management of Flood Risk, J. Water Res. Pl, 140, 201–215, https://doi.org/10.1061/(asce)wr.1943-5452.0000295, 2014. a
Wu, W., Westra, S., and Leonard, M.: Estimating the probability of compound floods in estuarine regions, Hydrol. Earth Syst. Sci., 25, 2821–2841, https://doi.org/10.5194/hess-25-2821-2021, 2021. a
Wyncoll, D. and Gouldby, B.: Integrating a multivariate extreme value method within a system flood risk analysis model, J. Flood Risk Manag., 8, 145–160, https://doi.org/10.1111/jfr3.12069, 2013. a
Short summary
This study develops a conceptual framework that uses active learning to accelerate compound flood risk assessments. A case study of Charleston County shows that the framework achieves faster and more accurate risk quantification compared to the state-of-the-art. This win–win allows for an increase in the number of flooding parameters, which results in an 11.6 % difference in the expected annual damages. Therefore, this framework allows for more comprehensive compound flood risk assessments.
This study develops a conceptual framework that uses active learning to accelerate compound...
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