Articles | Volume 26, issue 3
https://doi.org/10.5194/nhess-26-1417-2026
© Author(s) 2026. 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-26-1417-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
19 Mar 2026
Research article |
| 19 Mar 2026
| 19 Mar 2026
Climate and impact attribution of compound flooding induced by tropical cyclone Idai in Mozambique
Doris M. Vertegaal
CORRESPONDING AUTHOR
Deltares, Delft, the Netherlands
Institute for Environmental Studies (IVM), Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
Bart J. J. M. van den Hurk
Deltares, Delft, the Netherlands
Institute for Environmental Studies (IVM), Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
Anaïs Couasnon
Deltares, Delft, the Netherlands
Institute for Environmental Studies (IVM), Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
Natalia Aleksandrova
Deltares, Delft, the Netherlands
Tycho Bovenschen
Deltares, Delft, the Netherlands
Fernaldi Gradiyanto
Deltares, Delft, the Netherlands
Tim W. B. Leijnse
Deltares, Delft, the Netherlands
Henrique M. D. Goulart
Deltares, Delft, the Netherlands
Institute for Environmental Studies (IVM), Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
Sanne Muis
Deltares, Delft, the Netherlands
Institute for Environmental Studies (IVM), Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
Related authors
No articles found.
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
Short summary
Short summary
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.
Kai Kornuber, Emanuele Bevacqua, Mariana Madruga de Brito, Wiebke S. Jäger, Pauline Rivoire, Cassandra D. W. Rogers, Fabiola Banfi, Fulden Batibeniz, James Carruthers, Carlo de Michele, Silvia de Angeli, Cristina Deidda, Marleen C. de Ruiter, Andreas H. Fink, Henrique M. D. Goulart, Katharina Küpfer, Patrick Ludwig, Douglas Maraun, Gabriele Messori, Shruti Nath, Fiachra O’Loughlin, Joaquim G. Pinto, Benjamin Poschlod, Alexandre M. Ramos, Colin Raymond, Andreia F. S. Ribeiro, Deepti Singh, Laura Suarez Gutierrez, Philip J. Ward, and Christopher J. White
EGUsphere, https://doi.org/10.5194/egusphere-2025-4683, https://doi.org/10.5194/egusphere-2025-4683, 2025
Short summary
Short summary
Impacts from extreme weather events are becoming increasingly severe under global warming, in particular when events occur simultaneously or successively. While these complex event combinations are often difficult to analyse as impact data, early warning schemes or modelling frameworks might not be fit for purpose. In this perspective we reflect on the usability of compound event research to bridge the gap between academic research and real-world applications, by formulating a set of guidelines.
Natalia Aleksandrova, Jelmer Veenstra, and Sanne Muis
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-471, https://doi.org/10.5194/essd-2025-471, 2025
Preprint under review for ESSD
Short summary
Short summary
Extreme sea levels during storms can lead to coastal flooding and sediment displacement. In the paper, we present an expanded global dataset of sea levels, produced using a global ocean model driven by ERA5 reanalysis – a state-of-the-art dataset of historical weather and climate. The strengths and limitations of the dataset are discussed by comparing it to observed sea levels. The dataset covers a period of 1950–2024, improving our understanding of the global frequency of extreme water levels.
Lou Brett, Christopher J. White, Daniela I. V. Domeisen, Bart van den Hurk, Philip Ward, and Jakob Zscheischler
Nat. Hazards Earth Syst. Sci., 25, 2591–2611, https://doi.org/10.5194/nhess-25-2591-2025, https://doi.org/10.5194/nhess-25-2591-2025, 2025
Short summary
Short summary
Compound events, where multiple weather or climate hazards occur together, pose significant risks to both society and the environment. These events, like simultaneous wind and rain, can have more severe impacts than single hazards. Our review of compound event research from 2012–2022 reveals a rise in studies, especially on events that occur concurrently, hot and dry events, and compounding flooding. The review also highlights opportunities for research in the coming years.
Rita Lecci, Robyn Gwee, Kun Yan, Sanne Muis, Nadia Pinardi, Jun She, Martin Verlaan, Simona Masina, Wenshan Li, Hui Wang, Salvatore Causio, Antonio Novellino, Marco Alba, Etiënne Kras, Sandra Gaytan Aguilar, and Jan-Bart Calewaert
EGUsphere, https://doi.org/10.5194/egusphere-2025-1763, https://doi.org/10.5194/egusphere-2025-1763, 2025
Short summary
Short summary
This study explored how sea level is changing along the China-Europe Sea Route. By combining satellite and in-situ observations with advanced modeling, the research identified ongoing sea level rise and an increasing frequency of extreme water level events in some regions. These findings underscore the importance of continued monitoring and provide useful knowledge to support long-term planning, coastal resilience, and informed decision-making.
Lucas Terlinden-Ruhl, Anaïs Couasnon, Dirk Eilander, Gijs G. Hendrickx, Patricia Mares-Nasarre, and José A. Á. Antolínez
Nat. Hazards Earth Syst. Sci., 25, 1353–1375, https://doi.org/10.5194/nhess-25-1353-2025, https://doi.org/10.5194/nhess-25-1353-2025, 2025
Short summary
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.
Maarten van Ormondt, Tim Leijnse, Roel de Goede, Kees Nederhoff, and Ap van Dongeren
Geosci. Model Dev., 18, 843–861, https://doi.org/10.5194/gmd-18-843-2025, https://doi.org/10.5194/gmd-18-843-2025, 2025
Short summary
Short summary
Accurate flood risk assessments are crucial for storm protection. To achieve efficiency, computational costs must be minimized. This paper introduces a novel subgrid approach for linear inertial equations (LIEs) with bed level and friction variations, implemented in the Super-Fast INundation of CoastS (SFINCS) model. Pre-processed lookup tables enhance simulation precision with lower costs. Validations show significant accuracy improvement even at coarser resolutions.
José A. Jiménez, Gundula Winter, Antonio Bonaduce, Michael Depuydt, Giulia Galluccio, Bart van den Hurk, H. E. Markus Meier, Nadia Pinardi, Lavinia G. Pomarico, and Natalia Vazquez Riveiros
State Planet, 3-slre1, 3, https://doi.org/10.5194/sp-3-slre1-3-2024, https://doi.org/10.5194/sp-3-slre1-3-2024, 2024
Short summary
Short summary
The Knowledge Hub on Sea Level Rise (SLR) has done a scoping study involving stakeholders from government and academia to identify gaps and needs in SLR information, impacts, and policies across Europe. Gaps in regional SLR projections and uncertainties were found, while concerns were raised about shoreline erosion and emerging problems like saltwater intrusion and ineffective adaptation plans. The need for improved communication to make better decisions on SLR adaptation was highlighted.
Nadia Pinardi, Bart van den Hurk, Michael Depuydt, Thorsten Kiefer, Petra Manderscheid, Lavinia Giulia Pomarico, and Kanika Singh
State Planet, 3-slre1, 2, https://doi.org/10.5194/sp-3-slre1-2-2024, https://doi.org/10.5194/sp-3-slre1-2-2024, 2024
Short summary
Short summary
The Knowledge Hub on Sea Level Rise (KH-SLR), a joint effort between JPI Climate and JPI Oceans, addresses the critical need for science-based information on sea level changes in Europe. The KH-SLR actively involves stakeholders through a co-design process discussing the impacts, adaptation planning, and policy requirements related to SLR in Europe. Its primary output is the KH Assessment Report (KH-AR), which is described in this volume.
Bart van den Hurk, Nadia Pinardi, Alexander Bisaro, Giulia Galluccio, José A. Jiménez, Kate Larkin, Angélique Melet, Lavinia Giulia Pomarico, Kristin Richter, Kanika Singh, Roderik van de Wal, and Gundula Winter
State Planet, 3-slre1, 1, https://doi.org/10.5194/sp-3-slre1-1-2024, https://doi.org/10.5194/sp-3-slre1-1-2024, 2024
Short summary
Short summary
The Summary for Policymakers compiles findings from “Sea Level Rise in Europe: 1st Assessment Report of the Knowledge Hub on Sea Level Rise”. It covers knowledge gaps, observations, projections, impacts, adaptation measures, decision-making principles, and governance challenges. It provides information for each European basin (Mediterranean, Black Sea, North Sea, Baltic Sea, Atlantic, and Arctic) and aims to assist policymakers in enhancing the preparedness of European coasts for sea level rise.
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
Short summary
Short summary
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
Short summary
Short summary
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.
Henrique M. D. Goulart, Irene Benito Lazaro, Linda van Garderen, Karin van der Wiel, Dewi Le Bars, Elco Koks, and Bart van den Hurk
Nat. Hazards Earth Syst. Sci., 24, 29–45, https://doi.org/10.5194/nhess-24-29-2024, https://doi.org/10.5194/nhess-24-29-2024, 2024
Short summary
Short summary
We explore how Hurricane Sandy (2012) could flood New York City under different scenarios, including climate change and internal variability. We find that sea level rise can quadruple coastal flood volumes, while changes in Sandy's landfall location can double flood volumes. Our results show the need for diverse scenarios that include climate change and internal variability and for integrating climate information into a modelling framework, offering insights for high-impact event assessments.
Chiem van Straaten, Dim Coumou, Kirien Whan, Bart van den Hurk, and Maurice Schmeits
Weather Clim. Dynam., 4, 887–903, https://doi.org/10.5194/wcd-4-887-2023, https://doi.org/10.5194/wcd-4-887-2023, 2023
Short summary
Short summary
Variability in the tropics can influence weather over Europe. This study evaluates a summertime connection between the two. It shows that strongly opposing west Pacific sea surface temperature anomalies have occurred more frequently since 1980, likely due to a combination of long-term warming in the west Pacific and the El Niño Southern Oscillation. Three to six weeks later, the distribution of hot and cold airmasses over Europe is affected.
Giorgia Di Capua, Dim Coumou, Bart van den Hurk, Antje Weisheimer, Andrew G. Turner, and Reik V. Donner
Weather Clim. Dynam., 4, 701–723, https://doi.org/10.5194/wcd-4-701-2023, https://doi.org/10.5194/wcd-4-701-2023, 2023
Short summary
Short summary
Heavy rainfall in tropical regions interacts with mid-latitude circulation patterns, and this interaction can explain weather patterns in the Northern Hemisphere during summer. In this analysis we detect these tropical–extratropical interaction pattern both in observational datasets and data obtained by atmospheric models and assess how well atmospheric models can reproduce the observed patterns. We find a good agreement although these relationships are weaker in model data.
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
Short summary
Short summary
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.
Emma E. Aalbers, Erik van Meijgaard, Geert Lenderink, Hylke de Vries, and Bart J. J. M. van den Hurk
Nat. Hazards Earth Syst. Sci., 23, 1921–1946, https://doi.org/10.5194/nhess-23-1921-2023, https://doi.org/10.5194/nhess-23-1921-2023, 2023
Short summary
Short summary
To examine the impact of global warming on west-central European droughts, we have constructed future analogues of recent summers. Extreme droughts like 2018 further intensify, and the local temperature rise is much larger than in most summers. Years that went hardly noticed in the present-day climate may emerge as very dry and hot in a warmer world. The changes can be directly linked to real-world events, which makes the results very tangible and hence useful for climate change communication.
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
Short summary
Short summary
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.
Mariana C. A. Clare, Tim W. B. Leijnse, Robert T. McCall, Ferdinand L. M. Diermanse, Colin J. Cotter, and Matthew D. Piggott
Nat. Hazards Earth Syst. Sci., 22, 2491–2515, https://doi.org/10.5194/nhess-22-2491-2022, https://doi.org/10.5194/nhess-22-2491-2022, 2022
Short summary
Short summary
Assessing uncertainty is computationally expensive because it requires multiple runs of expensive models. We take the novel approach of assessing uncertainty from coastal flooding using a multilevel multifidelity (MLMF) method which combines the efficiency of less accurate models with the accuracy of more expensive models at different resolutions. This significantly reduces the computational cost but maintains accuracy, making previously unfeasible real-world studies possible.
Tim Willem Bart Leijnse, Alessio Giardino, Kees Nederhoff, and Sofia Caires
Nat. Hazards Earth Syst. Sci., 22, 1863–1891, https://doi.org/10.5194/nhess-22-1863-2022, https://doi.org/10.5194/nhess-22-1863-2022, 2022
Short summary
Short summary
Deriving reliable estimates of design conditions resulting from tropical cyclones is a challenge of high relevance to coastal engineering. Here, having few historical observations is overcome by using the Tropical Cyclone Wind Statistical Estimation Tool (TCWiSE) to create thousands of synthetic realizations, representative of 1000 years of tropical cyclone activity for the Bay of Bengal. The use of synthetic tracks is shown to provide more reliable wind speed, storm surge and wave estimates.
Ruud T. W. L. Hurkmans, Bart van den Hurk, Maurice J. Schmeits, Fredrik Wetterhall, and Ilias G. Pechlivanidis
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2021-604, https://doi.org/10.5194/hess-2021-604, 2022
Manuscript not accepted for further review
Short summary
Short summary
Seasonal forecasts can help in safely and efficiently managing a fresh water reservoir in the Netherlands. We compare hydrological forecast systems of the river Rhine, the lakes most important source and analyze forecast skill for over 1993–2016 and for specific extreme years. On average, forecast skill is high in spring due to Alpine snow and smaller in summer. Dry summers appear to be more predictable, skill increases with event extremity. In those cases, seasonal forecasts are valuable tools.
Martin Wegmann, Yvan Orsolini, Antje Weisheimer, Bart van den Hurk, and Gerrit Lohmann
Weather Clim. Dynam., 2, 1245–1261, https://doi.org/10.5194/wcd-2-1245-2021, https://doi.org/10.5194/wcd-2-1245-2021, 2021
Short summary
Short summary
Northern Hemisphere winter weather is influenced by the strength of westerly winds 30 km above the surface, the so-called polar vortex. Eurasian autumn snow cover is thought to modulate the polar vortex. So far, however, the modeled influence of snow on the polar vortex did not fit the observed influence. By analyzing a model experiment for the time span of 110 years, we could show that the causality of this impact is indeed sound and snow cover can weaken the polar vortex.
Henrique M. D. Goulart, Karin van der Wiel, Christian Folberth, Juraj Balkovic, and Bart van den Hurk
Earth Syst. Dynam., 12, 1503–1527, https://doi.org/10.5194/esd-12-1503-2021, https://doi.org/10.5194/esd-12-1503-2021, 2021
Short summary
Short summary
Agriculture is sensitive to weather conditions and to climate change. We identify the weather conditions linked to soybean failures and explore changes related to climate change. Additionally, we build future versions of a historical extreme season under future climate scenarios. Results show that soybean failures are likely to increase with climate change. Future events with similar physical conditions to the extreme season are not expected to increase, but events with similar impacts are.
Víctor M. Santos, Mercè Casas-Prat, Benjamin Poschlod, Elisa Ragno, Bart van den Hurk, Zengchao Hao, Tímea Kalmár, Lianhua Zhu, and Husain Najafi
Hydrol. Earth Syst. Sci., 25, 3595–3615, https://doi.org/10.5194/hess-25-3595-2021, https://doi.org/10.5194/hess-25-3595-2021, 2021
Short summary
Short summary
We present an application of multivariate statistical models to assess compound flooding events in a managed reservoir. Data (from a previous study) were obtained from a physical-based hydrological model driven by a regional climate model large ensemble, providing a time series expanding up to 800 years in length that ensures stable statistics. The length of the data set allows for a sensitivity assessment of the proposed statistical framework to natural climate variability.
Cited articles
Adeke, D. P. and Mugume, S. N.: A methodology for development of flood-depth-velocity damage functions for improved estimation of pluvial flood risk in cities, J. Hydrol., 653, 132736, https://doi.org/10.1016/J.JHYDROL.2025.132736, 2025.
Athanase, M., Sánchez-Benítez, A., Monfort, E., Jung, T., and Goessling, H. F.: How climate change intensified storm Boris' extreme rainfall, revealed by near-real-time storylines, Commun. Earth Environ., 5, 676, https://doi.org/10.1038/s43247-024-01847-0, 2024a.
Athanase, M., Sánchez-Benítez, A., Goessling, H. F., Pithan, F., and Jung, T.: Projected amplification of summer marine heatwaves in a warming Northeast Pacific Ocean, Commun. Earth Environ., 5, 53, https://doi.org/10.1038/s43247-024-01212-1, 2024b.
Barriopedro, D., Jiménez-Esteve, B., Collazo, S., Garrido-Perez, J. M., Johnson, J. E., and García-Herrera, R.: A Multimethod Attribution Analysis of Spain's 2024 Extreme Precipitation Event, B. Am. Meteor. Soc., 106, E2440–E2460, https://doi.org/10.1175/BAMS-D-25-0049.1, 2025.
Benito, I., Aerts, J. C. J. H., Eilander, D., Ward, P. J., and Muis, S.: Stochastic coastal flood risk modelling for the east coast of Africa, npj Natural Hazards, 1, 10, https://doi.org/10.1038/s44304-024-00010-1, 2024.
Benito, I., Aerts, J. C. J. H., Ward, P. J., Eilander, D., and Muis, S.: A multiscale modelling framework of coastal flooding events for global to local flood hazard assessments, Nat. Hazards Earth Syst. Sci., 25, 2287–2315, https://doi.org/10.5194/nhess-25-2287-2025, 2025.
Bourdin, S., Camargo, S. J., Lee, C. Y., Lin, J., Vrac, M., Vaittinada Ayar, P., and Faranda, D.: Improving analogues-based detection & attribution approaches for hurricanes, Environ. Res. Lett., 20, 024042, https://doi.org/10.1088/1748-9326/ADAA8D, 2025.
Buchhorn, M., Smets, B., Bertels, L., De Roo, B., Lesiv, M., Tsendbazar, N. E., Linlin, L., and Tarko, A.: Copernicus Global Land Service: Land Cover 100m: Version 3 Globe 2015-2019: Product User Manual (Dataset v3.0, doc issue 3.3, Zenodo, https://doi.org/10.5281/zenodo.3938963, 2020.
Callaghan, M., Schleussner, C. F., Nath, S., Lejeune, Q., Knutson, T. R., Reichstein, M., Hansen, G., Theokritoff, E., Andrijevic, M., Brecha, R. J., Hegarty, M., Jones, C., Lee, K., Lucas, A., van Maanen, N., Menke, I., Pfleiderer, P., Yesil, B., and Minx, J. C.: Machine-learning-based evidence and attribution mapping of 100,000 climate impact studies, Nat. Clim. Change, 11, 966–972, https://doi.org/10.1038/s41558-021-01168-6, 2021.
Camargo, S. J., Murakami, H., Bloemendaal, N., Chand, S. S., Deshpande, M. S., Dominguez-Sarmiento, C., González-Alemán, J. J., Knutson, T. R., Lin, I. I., Moon, I. J., Patricola, C. M., Reed, K. A., Roberts, M. J., Scoccimarro, E., Tam, C. Y., Wallace, E. J., Wu, L., Yamada, Y., Zhang, W., and Zhao, H.: An update on the influence of natural climate variability and anthropogenic climate change on tropical cyclones, Tropical Cyclone Research and Review, 12, 216–239, https://doi.org/10.1016/J.TCRR.2023.10.001, 2023.
Carlson, C. J., Mitchell, D., Carleton, T., Chersich, M., Gibb, R., Lavelle, T., Lukas-Sithole, M., North, M., Lippi, C., New, M., Ryan, S. J., Shumba, S., and Trisos, C.: Designing and describing climate change impact attribution studies: a guide to common approaches, EarthArXiv, https://doi.org/10.31223/X5CD7M, 2024.
Clarke, B., Otto, F., and Jones, R.: When don't we need a new extreme event attribution study?, Clim. Change, 176, 60, https://doi.org/10.1007/S10584-023-03521-4, 2023.
Coumou, D., Arias, P. A., Bastos, A., Gonzales, C. K. G., Hegerl, G. C., Hope, P., Jack, C., Otto, F., Saeed, F., Serdeczny, O., Shepherd, T. G., and Vautard, R.: How can event attribution science underpin financial decisions on Loss and Damage?, PNAS Nexus, 3, 277, https://doi.org/10.1093/PNASNEXUS/PGAE277, 2024.
Davenport, F. V, Burke, M., and Diffenbaugh, N. S.: Contribution of historical precipitation change to US flood damages, P. Natl. Acad. Sci. USA, 118, e2017524118, https://doi.org/10.1073/pnas.2017524118, 2021.
Delgado, J. M. P. Q., Guimarães, A. S., De Freitas, V. P., Antepara, I., Kočí, V., and Černý, R.: Salt Damage and Rising Damp Treatment in Building Structures, Adv. Mater. Sci. Eng., 2016, 1280894, https://doi.org/10.1155/2016/1280894, 2016.
Deltares: HydroMT-FIAT: Automated and reproducible Delft-FIAT model building, https://github.com/Deltares/hydromt_fiat/releases/tag/v0.5.7 (last access: 11 September 2025), 3 September 2025.
Deng, E., Xiang, Q., Chan, J. C. L., Dong, Y., Tu, S., Chan, P. W., and Ni, Y. Q.: Increasing temporal stability of global tropical cyclone precipitation, NPJ Clim. Atmos. Sci., 8, 11, https://doi.org/10.1038/s41612-025-00896-2, 2025.
Dullaart, J. C. M., Muis, S., Bloemendaal, N., and Aerts, J. C. J. H.: Advancing global storm surge modelling using the new ERA5 climate reanalysis, Clim. Dynam., 54, 1007–1021, https://doi.org/10.1007/S00382-019-05044-0, 2020.
Dullaart, J. C. M., de Vries, H., Bloemendaal, N., Aerts, J. C. J. H., and Muis, S.: Improving our understanding of future tropical cyclone intensities in the Caribbean using a high-resolution regional climate model, Sci. Rep., 14, 6108, https://doi.org/10.1038/s41598-023-49685-y, 2024.
Eilander, D., Couasnon, A., Leijnse, T., Ikeuchi, H., Yamazaki, D., Muis, S., Dullaart, J., Haag, A., Winsemius, H. C., and Ward, P. J.: A globally applicable framework for compound flood hazard modeling, Nat. Hazards Earth Syst. Sci., 23, 823–846, https://doi.org/10.5194/nhess-23-823-2023, 2023a.
Eilander, D., Boisgontier, H., Bouaziz, L. J. e., Buitink, J., Couasnon, A., Dalmijn, B., Hegnauer, M., de Jong, T., Loos, S., Marth, I., and Verseveld, W. van: HydroMT: Automated and reproducible model building and analysis, J. Open Source Softw., 8, 4897, https://doi.org/10.21105/JOSS.04897, 2023b.
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, 2023c.
Eilander, D., de Goede, R., Leijnse, T., van Ormondt, M., Nederhoff, K., and Winsemius, H. C.: HydroMT-SFINCS, Zenodo, https://doi.org/10.5281/zenodo.13693006, 2024.
Eilander, D., Boisgontier, H., van Verseveld, W., Bouaziz, L., Hegnauer, M., Buitink, J., and Dalmijn, B.: hydroMT-wflow, Zenodo, https://doi.org/10.5281/zenodo.15182718, 2025.
Faranda, D., Bourdin, S., Ginesta, M., Krouma, M., Noyelle, R., Pons, F., Yiou, P., and Messori, G.: A climate-change attribution retrospective of some impactful weather extremes of 2021, Weather Clim. Dynam., 3, 1311–1340, https://doi.org/10.5194/wcd-3-1311-2022, 2022.
Feser, F. and Shepherd, T. G.: The concept of spectrally nudged storylines for extreme event attribution, Commun. Earth Environ., 6, 677, https://doi.org/10.1038/s43247-025-02659-6, 2025.
Frame, D. J., Wehner, M. F., Noy, I., and Rosier, S. M.: The economic costs of Hurricane Harvey attributable to climate change, Clim. Change, 160, 271–281, https://doi.org/10.1007/S10584-020-02692-8, 2020.
Gahtan, J. , Knapp, K. R., Schreck, C. J., Diamond, H. J., Kossin, J. P., and Kruk, M. C.: International Best Track Archive for Climate Stewardship (IBTrACS) Project, Version 4r01, ALL, NOAA National Centers for Environmental Information, https://doi.org/10.25921/82ty-9e16, 2024.
GEBCO Bathymetric Compilation Group 2024: GEBCO 2024 Grid – a continuous terrain model of the global oceans and land, NERC EDS British Oceanographic Data Centre, NOC, https://doi.org/10.5285/1c44ce99-0a0d-5f4f-e063-7086abc0ea0f, 2024.
Goulart, H. M. D., Benito Lazaro, I., van Garderen, L., van der Wiel, K., Le Bars, D., Koks, E., and van den Hurk, B.: Compound flood impacts from Hurricane Sandy on New York City in climate-driven storylines, Nat. Hazards Earth Syst. Sci., 24, 29–45, https://doi.org/10.5194/nhess-24-29-2024, 2024.
Goulart, H. M. D., Athanasiou, P., van Ginkel, K., van der Wiel, K., Winter, G., Pinto, I., and van den Hurk, B.: Exploring coastal climate adaptation through storylines: Insights from cyclone Idai in Beira, Mozambique, Cell Reports Sustainability, 2, 100270, https://doi.org/10.1016/J.CRSUS.2024.100270, 2025.
Government of Mozambique: Mozambique Cyclone Idai Post Disaster Needs Assessment Conference Version, https://www.undp.org/publications/mozambique-cyclone-idai-post-disaster-needs-assessment-pdnadna (last access: 21 May 2025), 2019.
Green, J., Haigh, I. D., Quinn, N., Neal, J., Wahl, T., Wood, M., Eilander, D., de Ruiter, M., Ward, P., and Camus, P.: Review article: A comprehensive review of compound flooding literature with a focus on coastal and estuarine regions, Nat. Hazards Earth Syst. Sci., 25, 747–816, https://doi.org/10.5194/nhess-25-747-2025, 2025.
Grimaldi, S., Salamon, P., Disperati, J., Zsoter, E., Russo, C., Ramos, A., Carton De Wiart, C., Barnard, C., Hansford, E., Gomes, G., and Prudhomme, C.: River discharge and related forecasted data from the Global Flood Awareness System, European Commission, Joint Research Centre (JRC), https://doi.org/10.24381/cds.a4fdd6b9, 2023.
Grimley, L. E., Hollinger Beatty, K. E., Sebastian, A., Bunya, S., and Lackmann, G. M.: Climate change exacerbates compound flooding from recent tropical cyclones, npj Natural Hazards, 1, 45, https://doi.org/10.1038/s44304-024-00046-3, 2024.
Guzman, O. and Jiang, H.: Global increase in tropical cyclone rain rate, Nat. Commun., 12, 5344, https://doi.org/10.1038/s41467-021-25685-2, 2021.
Hawker, L., Uhe, P., Paulo, L., Sosa, J., Savage, J., Sampson, C., and Neal, J.: A 30 m global map of elevation with forests and buildings removed, Environ. Res. Lett., 17, 024016, https://doi.org/10.1088/1748-9326/AC4D4F, 2022.
Hegerl, G. C., Hoegh-Guldberg, O., Casassa, G., Hoerling, M., Kovats, S., Parmesan, C., Pierce, D., and Stott, P.: Good Practice Guidance Paper on Detection and Attribution Related to Anthropogenic Climate Change, in: Meeting Report of the Intergovernmental Panel on Climate Change Expert Meeting on Detection and Attribution of Anthropogenic Climate Change, edited by: Stocker, T., Field, C., Dahe, Q., Plattner, G.-K., Tignor, M., Midgley, P., and Ebi, K., IPCC Working Group I Technical Support Unit, University of Bern, Bern, Switzerland, https://www.wcrp-climate.org/images/summer_school/ICTP_2014/documents/IPCC_DA_GoodPracticeGuidancePaper.pdf (last access: 1 May 2024), 2010.
Herfort, B., Lautenbach, S., Porto De Albuquerque, J., Anderson, J., and Zipf, A.: A spatio-temporal analysis investigating completeness and inequalities of global urban building data in OpenStreetMap, Nat. Commun., 14, 3985, https://doi.org/10.1038/s41467-023-39698-6, 2023.
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. N.: The ERA5 global reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/QJ.3803, 2020.
Holland, G.: A Revised Hurricane Pressure–Wind Model, Mon. Weather Rev., 136, 3432–3445, https://doi.org/10.1175/2008MWR2395.1, 2008.
Holland, G. J., Belanger, J. I., and Fritz, A.: A revised model for radial profiles of hurricane winds, Mon. Weather Rev., 138, 4393–4401, https://doi.org/10.1175/2010MWR3317.1, 2010.
Hope, P., Cramer, W., van Aalst, M., Flato, G., Frieler, K., Gillett, N., Huggel, C., Minx, J., Otto, F., Parmesan, C., Rogelj, J., Rojas, M., Seneviratne, S. I., Slangen, A., Stone, D., Terray, L., Vautard, R., and Zhang, X.: Cross-Working Group Box ATTRIBUTION | Attribution in the IPCC Sixth Assessment Report, in: Climate Change 2022: Impacts, Adaptation and Vulnerability, Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Pörtner, H.-O., Roberts, D. C., Tignor, M., Poloczanska, E. S., Mintenbeck, K., Alegría, A., Craig, M., Langsdorf, S., Löschke, S., Möller, V., Okem, A., and Rama, B., Cambridge University Press, Cambridge, UK and New York, NY, USA, 149–152, https://www.ipcc.ch/report/ar6/wg2/downloads/report/IPCC_AR6_WGII_CCB-CWGB_Compilation.pdf (last access: 2 June 2025), 2022.
Huizinga, J., Moel, H. D., and Szewczyk, W.: Global flood depth-damage functions: Methodology and the database with guidelines, Publications Office of the European Union, Luxembourg (Luxembourg), https://doi.org/10.2760/16510, 2017.
Intergovernmental Panel on Climate Change (IPCC): Climate Change 2014: Synthesis Report, Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Core Writing Team, Pachauri, R. K., and Meyer L. A., IPCC, Geneva, Switserland, 151, https://www.ipcc.ch/report/ar5/syr/ (last access: 29 June 2024), 2014.
Intergovernmental Panel on Climate Change (IPCC): Key Risks across Sectors and Regions, Climate Change 2022 – Impacts, Adaptation and Vulnerability, 2411–2538, https://doi.org/10.1017/9781009325844.025, 2023a.
Intergovernmental Panel on Climate Change (IPCC): Ocean, Cryosphere and Sea Level Change, in: Climate Change 2021 – The Physical Science Basis: Working Group I Contribution to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge, 1211–1362, https://doi.org/10.1017/9781009157896.011, 2023b.
Jaafar, H. and Ahmad, F.: GCN250, global curve number datasets for hydrologic modeling and design, figshare, https://doi.org/10.6084/m9.figshare.7756202.v1, 2019.
Jahanshahi, A. and Booij, M. J.: Exploring controls on rainfall-runoff events: spatial dynamics of event runoff coefficients in Iran, Hydrolog. Sci. J., 68, 954–966, https://doi.org/10.1080/02626667.2023.2193297, 2023.
Joint Research Center and Copernicus Emergency Management Service: River discharge and related forecasted data from the Global Flood Awareness System, Early Warning Data Store (EWDS), https://doi.org/10.24381/cds.a4fdd6b9, 2020.
Kernkamp, H. W. J., Van Dam, A., Stelling, G. S., and De Goede, E. D.: Efficient scheme for the shallow water equations on unstructured grids with application to the Continental Shelf, Ocean Dynam., 61, 1175–1188, https://doi.org/10.1007/S10236-011-0423-6, 2011.
Kim, D., Park, D. S. R., Nam, C. C., and Bell, M. M.: The parametric hurricane rainfall model with moisture and its application to climate change projections, NPJ Clim. Atmos. Sci., 5, 86, https://doi.org/10.1038/S41612-022-00308-9, 2022.
Kind, J., Wouter Botzen, W. J., and Aerts, J. C. J. H.: Accounting for risk aversion, income distribution and social welfare in cost-benefit analysis for flood risk management, Wiley Interdiscip. Rev. Clim. Change, 8, e446, https://doi.org/10.1002/WCC.446, 2017.
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. Meteor. Soc., 91, 363–376, https://doi.org/10.1175/2009BAMS2755.1, 2010.
Knutson, T., Camargo, S. J., Chan, J. C. L., Emanuel, K., Ho, C. H., Kossin, J., Mohapatra, M., Satoh, M., Sugi, M., Walsh, K., and Wu, L.: Tropical Cyclones and Climate Change Assessment: Part I: Detection and Attribution, B. Am. Meteor. Soc., 100, 1987–2007, https://doi.org/10.1175/BAMS-D-18-0189.1, 2019.
Knutson, T., Camargo, S. J., Chan, J. C. L., Emanuel, K., Ho, C. H., Kossin, J., Mohapatra, M., Satoh, M., Sugi, M., Walsh, K., and Wu, L.: Tropical Cyclones and Climate Change Assessment: Part II: Projected Response to Anthropogenic Warming, B. Am. Meteor. Soc., 101, E303–E322, https://doi.org/10.1175/BAMS-D-18-0194.1, 2020.
Lehner, B., Messager, M. L., Korver, M. C., and Linke, S.: Global hydro-environmental lake characteristics at high spatial resolution, Sci. Data, 9, 351, https://doi.org/10.1038/s41597-022-01425-z, 2022.
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.
Leijnse, T. W. B., van Dongeren, A., van Ormondt, M., de Goede, R., and Aerts, J. C. J. H.: The importance of waves in large-scale coastal compound flooding: A case study of Hurricane Florence (2018), Coast. Eng., 199, 104726, https://doi.org/10.1016/j.coastaleng.2025.104726, 2025.
Lin, P., Pan, M., Allen, G., Frasson, R., Zeng, Z., Yamazaki, D., and Wood, E.: Global estimates of reach-level bankfull river width leveraging big-data geospatial analysis, Zenodo, https://doi.org/10.5281/zenodo.3552776, 2019.
Linke, S., Lehner, B., Ouellet Dallaire, C., Ariwi, J., Grill, G., Anand, M., Beames, P., Burchard-Levine, V., Maxwell, S., Moidu, H., Tan, F., and Thieme, M.: Global hydro-environmental sub-basin and river reach characteristics at high spatial resolution, Sci. Data, 6, 283, https://doi.org/10.1038/s41597-019-0300-6, 2019.
Liu, M., Vecchi, G. A., Smith, J. A., and Knutson, T. R.: Causes of large projected increases in hurricane precipitation rates with global warming, npj Climate and Atmospheric Science, 2, 1–5, https://doi.org/10.1038/s41612-019-0095-3, 2019a.
Liu, M., Vecchi, G. A., Smith, J. A., and Knutson, T. R.: Causes of large projected increases in hurricane precipitation rates with global warming, NPJ Clim. Atmos. Sci., 2, 38, https://doi.org/10.1038/s41612-019-0095-3, 2019b.
Lloyd, E. A. and Shepherd, T. G.: Climate change attribution and legal contexts: evidence and the role of storylines, Clim. Change, 167, 28, https://doi.org/10.1007/S10584-021-03177-Y, 2021.
Massari, C., Pellet, V., Tramblay, Y., Crow, W. T., Gründemann, G. J., Hascoetf, T., Penna, D., Modanesi, S., and Brocca, L.: On the relation between antecedent basin conditions and runoff coefficient for European floods, J. Hydrol., 625, 130012, https://doi.org/10.1016/j.jhydrol.2023.130012, 2023.
Mekonnen, K., Velpuri, N. M., Leh, M., Akpoti, K., Owusu, A., Tinonetsana, P., Hamouda, T., Ghansah, B., Paranamana, T. P., and Munzimi, Y.: Accuracy of satellite and reanalysis rainfall estimates over Africa: A multi-scale assessment of eight products for continental applications, J. Hydrol. Reg. Stud., 49, 101514, https://doi.org/10.1016/J.EJRH.2023.101514, 2023.
Mengel, M., Treu, S., Lange, S., and Frieler, K.: ATTRICI v1.1 – counterfactual climate for impact attribution, Geosci. Model Dev., 14, 5269–5284, https://doi.org/10.5194/gmd-14-5269-2021, 2021.
Mester, B., Vogt, T., Bryant, S., Otto, C., Frieler, K., and Schewe, J.: Human displacements from Tropical Cyclone Idai attributable to climate change, Nat. Hazards Earth Syst. Sci., 23, 3467–3485, https://doi.org/10.5194/nhess-23-3467-2023, 2023.
Nederhoff, K., van Ormondt, M., Veeramony, J., van Dongeren, A., Antolínez, J. A. Á., Leijnse, T., and Roelvink, D.: Accounting for uncertainties in forecasting tropical-cyclone-induced compound flooding, Geosci. Model Dev., 17, 1789–1811, https://doi.org/10.5194/gmd-17-1789-2024, 2024.
Nhundu, K., Sibanda, M., and Chaminuka, P.: Economic Losses from Cyclones Idai and Kenneth and Floods in Southern Africa: Implications on Sustainable Development Goals, in: Cyclones in Southern Africa: Volume 3: Implications for the Sustainable Development Goals, Springer International Publishing, Cham, 289–303, https://doi.org/10.1007/978-3-030-74303-1_19, 2021.
Noy, I., Stone, D., and Uher, T.: Extreme events impact attribution: A state of the art, Cell Reports Sustainability, 1, 100101, https://doi.org/10.1016/J.CRSUS.2024.100101, 2024.
OpenStreetMap contributors: OpenStreetMap, https://www.openstreetmap.org (last access: 3 September 2025), 2025.
U.S. Bureau of Labor Statistics: Archived Consumer Price Index Supplemental Files, https://www.bls.gov/cpi/tables/supplemental-files/, last access: 27 August 2025.
van Dongeren, A., Battjes, J., Janssen, T., van Noorloos, J., Steenhauer, K., Steenbergen, G., and Reniers, A.: Shoaling and shoreline dissipation of low-frequency waves, J. Geophys. Res.-Oceans, 112, 2011, https://doi.org/10.1029/2006JC003701, 2007.
van Garderen, L., Feser, F., and Shepherd, T. G.: A methodology for attributing the role of climate change in extreme events: a global spectrally nudged storyline, Nat. Hazards Earth Syst. Sci., 21, 171–186, https://doi.org/10.5194/nhess-21-171-2021, 2021.
van Oldenborgh, G. J., van der Wiel, K., Kew, S., Philip, S., Otto, F., Vautard, R., King, A., Lott, F., Arrighi, J., Singh, R., and van Aalst, M.: Pathways and pitfalls in extreme event attribution, Clim. Change, 166, 13, https://doi.org/10.1007/s10584-021-03071-7, 2021.
van Ormondt, M., Leijnse, T., Nederhoff, K., de Goede, R., van Dongeren, A., Bovenschen, T., and van Asselt, K.: SFINCS: Super-Fast INundation of CoastS model Version 2.2.0 col d'Eze Release 2025.01, Zenodo, https://doi.org/10.5281/zenodo.13691724, 2025a.
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, 2025b.
Otto, F. E. L., Zachariah, M., Wolski, P., Pinto, I., Barimalala, R., Nhamtumbo, B., Bonnet, R., Vautard, R., Philip, S., Kew, S., Luu, L. N., Heinrich, D., Vahlberg, M., Singh, R., Arrighi, J., Thalheimer, L., Van Aalst, M., Li, S., Sun, J., Vecchi, G., and Harrington, L. J.: https://www.worldweatherattribution.org/climate-change-increased-rainfall-associated-with-tropical-cyclones-hitting-highly-vulnerable-communities-in-madagascar-mozambique-malawi/ (last access: 15 April 2025), 2022.
Paprotny, D., 't Hart, C. M. P., and Morales-Nápoles, O.: Evolution of flood protection levels and flood vulnerability in Europe since 1950 estimated with vine-copula models, Nat. Hazards, 121, 6155–6184, https://doi.org/10.1007/S11069-024-07039-5, 2025.
Patricola, C. M. and Wehner, M. F.: Anthropogenic influences on major tropical cyclone events, Nature, 563, 339–346, https://doi.org/10.1038/s41586-018-0673-2, 2018.
Pekel, J. F., Cottam, A., Gorelick, N., and Belward, A. S.: High-resolution mapping of global surface water and its long-term changes, Nature, 540, 418–422, https://doi.org/10.1038/nature20584, 2016.
Perkins-Kirkpatrick, S. E., Alexander, L. V., King, A. D., Kew, S. F., Philip, S. Y., Barnes, C., Maraun, D., Stuart-Smith, R. F., Jézéquel, A., Bevacqua, E., Burgess, S., Fischer, E., Hegerl, G. C., Kimutai, J., Koren, G., Lawal, K. A., Min, S.-K., New, M., Odoulami, R. C., Patricola, C. M., Pinto, I., Ribes, A., Shaw, T. A., Thiery, W., Trewin, B., Vautard, R., Wehner, M., and Zscheischler, J.: Frontiers in attributing climate extremes and associated impacts, Frontiers in Climate, 6, https://doi.org/10.3389/fclim.2024.1455023, 2024.
Philip, S., Kew, S., van Oldenborgh, G. J., Otto, F., Vautard, R., van der Wiel, K., King, A., Lott, F., Arrighi, J., Singh, R., and van Aalst, M.: A protocol for probabilistic extreme event attribution analyses, Adv. Stat. Climatol. Meteorol. Oceanogr., 6, 177–203, https://doi.org/10.5194/ASCMO-6-177-2020, 2020.
Philp, T. J., Champion, A. J., Hodges, K. I., Pigott, C., MacFarlane, A., Wragg, G., and Zhao, S.: Identifying Limitations when Deriving Probabilistic Views of North Atlantic Hurricane Hazard from Counterfactual Ensemble NWP Re-forecasts, in: Hurricane Risk in a Changing Climate, edited by: Collins, J. M. and Done, J. M., Springer International Publishing, Cham, 233–254, https://doi.org/10.1007/978-3-031-08568-0_10, 2022.
Pistrika, A., Tsakiris, G., and Nalbantis, I.: Flood Depth-Damage Functions for Built Environment, Environmental Processes, 1, 553–572, https://doi.org/10.1007/S40710-014-0038-2, 2014.
Probst, P. and Annunziato, A.: Tropical Cyclone Idai: analysis of the wind, rainfall and storm surge impact, European Commission – Joint Research Centre, https://reliefweb.int/report/mozambique/tropical-cyclone-idai-analysis-wind-rainfall-and-storm-surge-impact-9-april-2019 (last access: 4 June 2025), 2019.
Puig, D., Adger, N. W., Barnett, J., Vanhala, L., and Boyd, E.: Improving the effectiveness of climate change adaptation measures, Clim. Change, 178, 7, https://doi.org/10.1007/S10584-024-03838-8, 2025.
Rentschler, J., Avner, P., Marconcini, M., Su, R., Strano, E., Vousdoukas, M., and Hallegatte, S.: Global evidence of rapid urban growth in flood zones since 1985, Nature, 622, 87–92, https://doi.org/10.1038/S41586-023-06468-9, 2023.
Roelvink, D., van Ormondt, M., Reyns, J., and van der Lugt, M.: SnapWave: fast, implicit wave transformation from offshore to nearshore, Geosci. Model Dev., 18, 9469–9495, https://doi.org/10.5194/gmd-18-9469-2025, 2025.
Rogers, J. S., Maneta, M. M., Sain, S. R., Madaus, L. E., and Hacker, J. P.: The role of climate and population change in global flood exposure and vulnerability, Nat. Commun., 16, 1287, https://doi.org/10.1038/S41467-025-56654-8, 2025.
Rogger, M., Agnoletti, M., Alaoui, A., Bathurst, J. C., Bodner, G., Borga, M., Chaplot, V., Gallart, F., Glatzel, G., Hall, J., Holden, J., Holko, L., Horn, R., Kiss, A., Kohnová, S., Leitinger, G., Lennartz, B., Parajka, J., Perdigão, R., Peth, S., Plavcová, L., Quinton, J. N., Robinson, M., Salinas, J. L., Santoro, A., Szolgay, J., Tron, S., van den Akker, J. J. H., Viglione, A., and Blöschl, G.: Land use change impacts on floods at the catchment scale: Challenges and opportunities for future research, Water Resour. Res., 53, 5209–5219, https://doi.org/10.1002/2017WR020723, 2017.
Samadi, V., Fowler, H. J., Lamond, J., Wagener, T., Brunner, M., Gourley, J., Moradkhani, H., Popescu, I., Wasko, C., Wright, D., Wu, H., Zhang, K., Arias, P. A., Duan, Q., Nazemi, A., van Oevelen, P. J., Prein, A. F., Roundy, J. K., Saberian, M., and Umutoni, L.: The Needs, Challenges, and Priorities for Advancing Global Flood Research, WIREs Water, 12, e70026, https://doi.org/10.1002/wat2.70026, 2025.
Sánchez-Benítez, A., Goessling, H., Pithan, F., Semmler, T., and Jung, T.: The July 2019 European Heat Wave in a Warmer Climate: Storyline Scenarios with a Coupled Model Using Spectral Nudging, J. Climate, 35, 2373–2390, https://doi.org/10.1175/JCLI-D-21-0573.1, 2022.
Sauer, I. J., Reese, R., Otto, C., Geiger, T., Willner, S. N., Guillod, B. P., Bresch, D. N., and Frieler, K.: Climate signals in river flood damages emerge under sound regional disaggregation, Nat. Commun., 12, 2128, https://doi.org/10.1038/s41467-021-22153-9, 2021.
Schubert-Frisius, M., Feser, F., von Storch, H., and Rast, S.: Optimal Spectral Nudging for Global Dynamic Downscaling, Mon. Weather Rev., 145, 909–927, https://doi.org/10.1175/MWR-D-16-0036.1, 2017.
Scussolini, P., Luu, L. N., Philip, S., Berghuijs, W. R., Eilander, D., Aerts, J. C. J. H., Kew, S. F., van Oldenborgh, G. J., Toonen, W. H. J., Volkholz, J., and Coumou, D.: Challenges in the attribution of river flood events, WIREs Climate Change, 15, e874, https://doi.org/10.1002/WCC.874, 2024.
Seneviratne, S. I., Zhang, X., Adnan, M., Badi, W., Dereczynski, C., Di Luca, A., Ghosh, S., Iskander, I., Kossin, J., and Lewis, S.: Weather and climate extreme events in a changing climate, 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, 1513–1766, https://doi.org/10.1017/9781009157896.013, 2021.
Shepherd, T. G.: A Common Framework for Approaches to Extreme Event Attribution, Curr. Clim. Change Rep., 2, 28–38, https://doi.org/10.1007/S40641-016-0033-Y, 2016.
Sillmann, J., Shepherd, T. G., van den Hurk, B., Hazeleger, W., Martius, O., Slingo, J., and Zscheischler, J.: Event-Based Storylines to Address Climate Risk, Earths Future, 9, e2020EF001783, https://doi.org/10.1029/2020EF001783, 2021.
Smiley, K. T., Noy, I., Wehner, M. F., Frame, D., Sampson, C. C., and Wing, O. E. J.: Social inequalities in climate change-attributed impacts of Hurricane Harvey, Nat. Commun., 13, 3418, https://doi.org/10.1038/s41467-022-31056-2, 2022.
Smith, A. B. and Katz, R. W.: US billion-dollar weather and climate disasters: Data sources, trends, accuracy and biases, Nat. Hazards, 67, 387–410, https://doi.org/10.1007/S11069-013-0566-5, 2013.
Stott, P. A., Stone, D. A., and Allen, M. R.: Human contribution to the European heatwave of 2003, Nature, 432, 610–614, https://doi.org/10.1038/NATURE03089, 2004.
Strauss, B. H., Orton, P. M., Bittermann, K., Buchanan, M. K., Gilford, D. M., Kopp, R. E., Kulp, S., Massey, C., de Moel, H., and Vinogradov, S.: Economic damages from Hurricane Sandy attributable to sea level rise caused by anthropogenic climate change, Nat. Commun., 12, 2720, https://doi.org/10.1038/s41467-021-22838-1, 2021.
Thieken, A. H., Müller, M., Kreibich, H., and Merz, B.: Flood damage and influencing factors: New insights from the August 2002 flood in Germany, Water Resour. Res., 41, W12430, https://doi.org/10.1029/2005WR004177, 2005.
Thompson, C., Barthe, C., Bielli, S., Tulet, P., and Pianezze, J.: Projected Characteristic Changes of a Typical Tropical Cyclone under Climate Change in the South West Indian Ocean, Atmosphere, 12, 232, https://doi.org/10.3390/atmos12020232, 2021.
Thompson, V., Ermis, S., and Athanase, M.: The need for multi-method extreme event attribution, Weather, https://doi.org/10.1002/wea.7779, 2025.
Treu, S., Muis, S., Dangendorf, S., Wahl, T., Oelsmann, J., Heinicke, S., Frieler, K., and Mengel, M.: Reconstruction of hourly coastal water levels and counterfactuals without sea level rise for impact attribution, Earth Syst. Sci. Data, 16, 1121–1136, https://doi.org/10.5194/essd-16-1121-2024, 2024.
Tu, S., Chan, J. C. L., Xu, J., Zhong, Q., Zhou, W., and Zhang, Y.: Increase in tropical cyclone rain rate with translation speed, Nat. Commun., 13, 7325, https://doi.org/10.1038/s41467-022-35113-8, 2022.
UN Office for the Coordination of Humanitarian Affairs (OCHA): Mozambique: Cyclone Idai & Floods Situation Report No. 2, ReliefWeb, https://reliefweb.int/report/mozambique/mozambique-cyclone-idai-floods-situation-report-no-2-3-april-2019 (last access: 4 June 2025), 2019.
Veenstra, J.: dfm_tools: A Python package for pre- and postprocessing D-FlowFM model input and output files (v0.35.0), Zenodo, https://doi.org/10.5281/zenodo.14901200, 2025.
van Verseveld, W. J., Weerts, A. H., Visser, M., Buitink, J., Imhoff, R. O., Boisgontier, H., Bouaziz, L., Eilander, D., Hegnauer, M., ten Velden, C., and Russell, B.: Wflow_sbm v0.7.3, a spatially distributed hydrological model: from global data to local applications, Geosci. Model Dev., 17, 3199–3234, https://doi.org/10.5194/gmd-17-3199-2024, 2024.
van Verseveld, W., Visser, M., Buitink, J., Bouaziz, L., Boisgontier, H., Bootsma, H., Weerts, A., Baptista, C. F., Pronk, M., Eilander, D., Hartgring, S., Dalmijn, B., Hofer, J., Hegnauer, M., and Mendoza, R.: Deltares/Wflow.jl, GitHub, https://github.com/Deltares/Wflow.jl/releases/tag/v0.8.1 (last access: 21 July 2025), 2025.
Vertegaal, D. M., Aleksandrova, N., Bovenschen, T., Couasnon, A., and Goulart, H. M. D.: Climate and impact attribution of TC Idai, Zenodo [data set], https://doi.org/10.5281/zenodo.17107289, 12 September 2025.
Wagenaar, B. H., Augusto, O., Ásbjörnsdóttir, K., Akullian, A., Manaca, N., Chale, F., Muanido, A., Covele, A., Michel, C., Gimbel, S., Radford, T., Girardot, B., and Sherr, K.: Developing a representative community health survey sampling frame using open-source remote satellite imagery in Mozambique, Int. J. Health Geogr., 17, 37, https://doi.org/10.1186/s12942-018-0158-4, 2018a.
Wagenaar, D., Slager, K., and Calero, J. S.: Delft-FIAT: An open-source flood impact analysis tool, Zenodo, https://doi.org/10.5281/zenodo.1400183, 2017.
Wagenaar, D., Lüdtke, S., Schröter, K., Bouwer, L. M., and Kreibich, H.: Regional and Temporal Transferability of Multivariable Flood Damage Models, Water Resour. Res., 54, 3688–3703, https://doi.org/10.1029/2017WR022233, 2018b.
Wang, J., Chen, Y., Tett, S. F. B., Stone, D., Nie, J., Feng, J., Yan, Z., Zhai, P., and Ge, Q.: Storyline attribution of human influence on a record-breaking spatially compounding flood-heat event, Sci. Adv., 9, eadi2714, https://doi.org/10.1126/sciadv.adi2714, 2023.
Wang, X., Verlaan, M., Veenstra, J., and Lin, H. X.: Data-assimilation-based parameter estimation of bathymetry and bottom friction coefficient to improve coastal accuracy in a global tide model, Ocean Sci., 18, 881–904, https://doi.org/10.5194/os-18-881-2022, 2022.
Ward, P. J., Jongman, B., Salamon, P., Simpson, A., Bates, P., De Groeve, T., Muis, S., De Perez, E. C., Rudari, R., Trigg, M. A., and Winsemius, H. C.: Usefulness and limitations of global flood risk models, Nat. Clim. Change, 5, 712–715, https://doi.org/10.1038/nclimate2742, 2015.
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.
Warren, M.: Why Cyclone Idai is one of the Southern Hemisphere's most devastating storms, Nature, https://doi.org/10.1038/D41586-019-00981-6, 2019.
Wilkerson, G. V.: Improved bankfull discharge prediction using 2-year recurrence-period discharge, J. Am. Water Resour. Assoc., 44, 243–257, https://doi.org/10.1111/J.1752-1688.2007.00151.X, 2008.
Williamson, C., McCordic, C., and Doberstein, B.: The compounding impacts of Cyclone Idai and their implications for urban inequality, Int. J. Disast. Risk Re., 86, 103526, https://doi.org/10.1016/J.IJDRR.2023.103526, 2023.
Wing, O. E. J., Bates, P. D., Quinn, N. D., Savage, J. T. S., Uhe, P. F., Cooper, A., Collings, T. P., Addor, N., Lord, N. S., Hatchard, S., Hoch, J. M., Bates, J., Probyn, I., Himsworth, S., Rodríguez González, J., Brine, M. P., Wilkinson, H., Sampson, C. C., Smith, A. M., Neal, J. C., and Haigh, I. D.: A 30 m Global Flood Inundation Model for Any Climate Scenario, Water Resour. Res., 60, e2023WR036460, https://doi.org/10.1029/2023WR036460, 2024.
World Meteorological Organisation (WMO): Tropical Cyclone Idai hits Mozambique, https://wmo.int/media/news/tropical-cyclone-idai-hits-mozambique (last access: 4 June 025), 2019.
Yamada, Y., Satoh, M., Sugi, M., Kodama, C., Noda, A. T., Nakano, M., and Nasuno, T.: Response of Tropical Cyclone Activity and Structure to Global Warming in a High-Resolution Global Nonhydrostatic Model, J. Climate, 30, 9703–9724, https://doi.org/10.1175/JCLI-D-17-0068.1, 2017.
Yamazaki, D., Ikeshima, D., Sosa, J., Bates, P. D., Allen, G. H., and Pavelsky, T. M.: MERIT Hydro: A High-Resolution Global Hydrography Map Based on Latest Topography Dataset, Water Resour. Res., 55, 5053–5073, https://doi.org/10.1029/2019WR024873, 2019.
Zhou, Q., Zhang, Y., Chang, K., and Brovelli, M. A.: Assessing OSM building completeness for almost 13,000 cities globally, Int. J. Digit. Earth, 15, 2400–2421, https://doi.org/10.1080/17538947.2022.2159550, 2022.
Zscheischler, J., Westra, S., Van Den Hurk, B. J. J. M., Seneviratne, S. I., Ward, P. J., Pitman, A., Aghakouchak, A., Bresch, D. N., Leonard, M., Wahl, T., and Zhang, X.: Future climate risk from compound events, Nat. Clim. Change, 8, 469–477, https://doi.org/10.1038/s41558-018-0156-3, 2018.
Zsoter, E., Harrigan, S., Barnard, C., Wetterhall, F., Ferrario, I., Mazzetti, C., Alfieri, L., Salamon, P., and Prudhomme, C.: River discharge and related forecasted data from the Global Flood Awareness System, European Commission, Joint Research Centre (JRC), https://doi.org/10.24381/cds.a4fdd6b9, 2021.
Short summary
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.
This study highlights the need to disentangle climate change effects on flood drivers using...
Altmetrics
Final-revised paper
Preprint