Articles | Volume 24, issue 1
https://doi.org/10.5194/nhess-24-29-2024
© Author(s) 2024. 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-24-29-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
10 Jan 2024
Research article |
| 10 Jan 2024
| 10 Jan 2024
Compound flood impacts from Hurricane Sandy on New York City in climate-driven storylines
Henrique M. D. Goulart
CORRESPONDING AUTHOR
Climate Adaptation and Disaster Risk Management, Deltares, Delft, the Netherlands
Institute for Environmental Studies, VU University Amsterdam, Amsterdam, the Netherlands
Irene Benito Lazaro
Institute for Environmental Studies, VU University Amsterdam, Amsterdam, the Netherlands
Linda van Garderen
Institute of Coastal Research – Analysis and Modelling, Helmholtz-Zentrum Hereon, Geesthacht, Germany
Karin van der Wiel
Royal Netherlands Meteorological Institute (KNMI), De Bilt, the Netherlands
Dewi Le Bars
Royal Netherlands Meteorological Institute (KNMI), De Bilt, the Netherlands
Elco Koks
Institute for Environmental Studies, VU University Amsterdam, Amsterdam, the Netherlands
Bart van den Hurk
Climate Adaptation and Disaster Risk Management, Deltares, Delft, the Netherlands
Institute for Environmental Studies, VU University Amsterdam, Amsterdam, the Netherlands
Related authors
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
Short summary
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.
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.
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.
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
Short summary
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.
Daniëlle Bintanja, Hylke de Vries, and Karin van der Wiel
EGUsphere, https://doi.org/10.5194/egusphere-2026-517, https://doi.org/10.5194/egusphere-2026-517, 2026
This preprint is open for discussion and under review for Earth System Dynamics (ESD).
Short summary
Short summary
We present a method to separate uncertainties from global and regional climate processes within the Hawkins & Sutton (2009) framework, partitioning sources of uncertainty in future climate projections. This helps interpret sources of uncertainty in future climate projections and informs adaptation planning. Focusing on regional projections reduces total uncertainty, highlighting the value of isolating regional uncertainties when developing regional climate information.
René M. van Westen, Karin van der Wiel, Swinda K. J. Falkena, and Frank Selten
Hydrol. Earth Syst. Sci., 29, 6607–6630, https://doi.org/10.5194/hess-29-6607-2025, https://doi.org/10.5194/hess-29-6607-2025, 2025
Short summary
Short summary
The Atlantic Meridional Overturning Circulation (AMOC) moderates the European climate. The AMOC is a tipping element and may collapse to a substantially weaker state under climate change. Such an event induces global and regional climate shifts. The European hydroclimate becomes drier under an AMOC collapse, this response is not considered in the
standardhydroclimate projections. Our results indicate a considerable influence of the AMOC on the European hydroclimate.
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.
René M. van Westen, Caroline A. Katsman, and Dewi Le Bars
EGUsphere, https://doi.org/10.5194/egusphere-2025-5102, https://doi.org/10.5194/egusphere-2025-5102, 2025
Short summary
Short summary
The Atlantic Meridional Overturning Circulation (AMOC) modulates the global climate and dynamic sea level. A transition of the AMOC to a much weaker state would cause a redistribution of dynamic sea level across the global ocean surface. Here, we analyse climate model simulations to investigate dynamic sea-level changes associated with a collapsing AMOC. Under an AMOC collapse, the dynamic sea level rises substantially in the North Atlantic Ocean.
Erwin Lambert, Dewi Le Bars, Eveline van der Linden, André Jüling, and Sybren Drijfhout
Earth Syst. Dynam., 16, 1303–1323, https://doi.org/10.5194/esd-16-1303-2025, https://doi.org/10.5194/esd-16-1303-2025, 2025
Short summary
Short summary
Ocean warming around Antarctica leads to ice melting and sea-level rise. The meltwater that flows into the surrounding ocean can lead to enhanced warming of the seawater, thereby again increasing melting and sea-level rise. This process, however, is not currently included in climate models. Through a simple mathematical approach, we find that this process can lead to more melting and greater sea-level rise, possibly increasing the Antarctic contribution to 21st century sea-level rise by 80 %.
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.
Timothy Tiggeloven, Colin Raymond, Marleen C. de Ruiter, Jana Sillmann, Annegret H. Thieken, Sophie L. Buijs, Roxana Ciurean, Emma Cordier, Julia M. Crummy, Lydia Cumiskey, Kelley De Polt, Melanie Duncan, Davide M. Ferrario, Wiebke S. Jäger, Elco E. Koks, Nicole van Maanen, Heather J. Murdock, Jaroslav Mysiak, Sadhana Nirandjan, Benjamin Poschlod, Peter Priesmeier, Nivedita Sairam, Pia-Johanna Schweizer, Tristian R. Stolte, Marie-Luise Zenker, James E. Daniell, Alexander Fekete, Christian M. Geiß, Marc J. C. van den Homberg, Sirkku K. Juhola, Christian Kuhlicke, Karen Lebek, Robert Šakić Trogrlić, Stefan Schneiderbauer, Silvia Torresan, Cees J. van Westen, Judith N. Claassen, Bijan Khazai, Virginia Murray, Julius Schlumberger, and Philip J. Ward
EGUsphere, https://doi.org/10.5194/egusphere-2025-2771, https://doi.org/10.5194/egusphere-2025-2771, 2025
Short summary
Short summary
Natural hazards like floods, earthquakes, and landslides are often interconnected which may create bigger problems than when they occur alone. We studied expert discussions from an international conference to understand how scientists and policymakers can better prepare for these multi-hazards and use new technologies to protect its communities while contributing to dialogues about future international agreements beyond the Sendai Framework and supporting global sustainability goals.
Dewi Le Bars, Iris Keizer, and Sybren Drijfhout
Ocean Sci., 21, 1303–1314, https://doi.org/10.5194/os-21-1303-2025, https://doi.org/10.5194/os-21-1303-2025, 2025
Short summary
Short summary
While preparing a new set of sea level scenarios for the Netherlands, we found out that many climate models overestimate the changes in ocean circulation for the last 30 years. To quantify this effect, we defined three methods that rely on diverse and independent observations: tide gauges, satellite altimetry, temperature and salinity in the ocean, land ice melt, etc. Based on these observations, we define a few methods to select models and discuss their advantages and disadvantages.
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
Short summary
Short summary
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.
Nicole van Maanen, Joël J.-F. G. De Plaen, Timothy Tiggeloven, Maria Luisa Colmenares, Philip J. Ward, Paolo Scussolini, and Elco Koks
Nat. Hazards Earth Syst. Sci., 25, 2075–2080, https://doi.org/10.5194/nhess-25-2075-2025, https://doi.org/10.5194/nhess-25-2075-2025, 2025
Short summary
Short summary
Understanding coastal flood protection is vital for assessing risks from natural disasters and climate change. However, current global data on coastal flood protection are limited and based on simplified assumptions, leading to potential uncertainties in risk estimates. As a step in this direction, we propose a comprehensive dataset, COASTtal flood PROtection Standards within EUrope (COASTPROS-EU), which compiles coastal flood protection standards in Europe.
Sadhana Nirandjan, Elco E. Koks, Mengqi Ye, Raghav Pant, Kees C. H. Van Ginkel, Jeroen C. J. H. Aerts, and Philip J. Ward
Nat. Hazards Earth Syst. Sci., 24, 4341–4368, https://doi.org/10.5194/nhess-24-4341-2024, https://doi.org/10.5194/nhess-24-4341-2024, 2024
Short summary
Short summary
Critical infrastructures (CIs) are exposed to natural hazards, which may result in significant damage and burden society. Vulnerability is a key determinant for reducing these risks, yet crucial information is scattered in the literature. Our study reviews over 1510 fragility and vulnerability curves for CI assets, creating a unique publicly available physical vulnerability database that can be directly used for hazard risk assessments, including floods, earthquakes, windstorms, and landslides.
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.
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.
Laura Muntjewerf, Richard Bintanja, Thomas Reerink, and Karin van der Wiel
Geosci. Model Dev., 16, 4581–4597, https://doi.org/10.5194/gmd-16-4581-2023, https://doi.org/10.5194/gmd-16-4581-2023, 2023
Short summary
Short summary
The KNMI Large Ensemble Time Slice (KNMI–LENTIS) is a large ensemble of global climate model simulations with EC-Earth3. It covers two climate scenarios by focusing on two time slices: the present day (2000–2009) and a future +2 K climate (2075–2084 in the SSP2-4.5 scenario). We have 1600 simulated years for the two climates with (sub-)daily output frequency. The sampled climate variability allows for robust and in-depth research into (compound) extreme events such as heat waves and droughts.
Iris Keizer, Dewi Le Bars, Cees de Valk, André Jüling, Roderik van de Wal, and Sybren Drijfhout
Ocean Sci., 19, 991–1007, https://doi.org/10.5194/os-19-991-2023, https://doi.org/10.5194/os-19-991-2023, 2023
Short summary
Short summary
Using tide gauge observations, we show that the acceleration of sea-level rise (SLR) along the coast of the Netherlands started in the 1960s but was masked by wind field and nodal-tide variations. This finding aligns with global SLR observations and expectations based on a physical understanding of SLR related to global warming.
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.
Sigrid Jørgensen Bakke, Niko Wanders, Karin van der Wiel, and Lena Merete Tallaksen
Nat. Hazards Earth Syst. Sci., 23, 65–89, https://doi.org/10.5194/nhess-23-65-2023, https://doi.org/10.5194/nhess-23-65-2023, 2023
Short summary
Short summary
In this study, we developed a machine learning model to identify dominant controls of wildfire in Fennoscandia and produce monthly fire danger probability maps. The dominant control was shallow-soil water anomaly, followed by air temperature and deep soil water. The model proved skilful with a similar performance as the existing Canadian Forest Fire Weather Index (FWI). We highlight the benefit of using data-driven models jointly with other fire models to improve fire monitoring and prediction.
Eveline C. van der Linden, Dewi Le Bars, Erwin Lambert, and Sybren Drijfhout
The Cryosphere, 17, 79–103, https://doi.org/10.5194/tc-17-79-2023, https://doi.org/10.5194/tc-17-79-2023, 2023
Short summary
Short summary
The Antarctic ice sheet (AIS) is the largest uncertainty in future sea level estimates. The AIS mainly loses mass through ice discharge, the transfer of land ice into the ocean. Ice discharge is triggered by warming ocean water (basal melt). New future estimates of AIS sea level contributions are presented in which basal melt is constrained with ice discharge observations. Despite the different methodology, the resulting projections are in line with previous multimodel assessments.
Elco E. Koks, Kees C. H. van Ginkel, Margreet J. E. van Marle, and Anne Lemnitzer
Nat. Hazards Earth Syst. Sci., 22, 3831–3838, https://doi.org/10.5194/nhess-22-3831-2022, https://doi.org/10.5194/nhess-22-3831-2022, 2022
Short summary
Short summary
This study provides an overview of the impacts to critical infrastructure and how recovery has progressed after the July 2021 flood event in Germany, Belgium and the Netherlands. The results show that Germany and Belgium were particularly affected, with many infrastructure assets severely damaged or completely destroyed. This study helps to better understand how infrastructure can be affected by flooding and can be used for validation purposes for future studies.
Weihua Zhu, Kai Liu, Ming Wang, Philip J. Ward, and Elco E. Koks
Nat. Hazards Earth Syst. Sci., 22, 1519–1540, https://doi.org/10.5194/nhess-22-1519-2022, https://doi.org/10.5194/nhess-22-1519-2022, 2022
Short summary
Short summary
We present a simulation framework to analyse the system vulnerability and risk of the Chinese railway system to floods. To do so, we develop a method for generating flood events at both the national and river basin scale. Results show flood system vulnerability and risk of the railway system are spatially heterogeneous. The event-based approach shows how we can identify critical hotspots, taking the first steps in developing climate-resilient infrastructure.
Philip J. Ward, James Daniell, Melanie Duncan, Anna Dunne, Cédric Hananel, Stefan Hochrainer-Stigler, Annegien Tijssen, Silvia Torresan, Roxana Ciurean, Joel C. Gill, Jana Sillmann, Anaïs Couasnon, Elco Koks, Noemi Padrón-Fumero, Sharon Tatman, Marianne Tronstad Lund, Adewole Adesiyun, Jeroen C. J. H. Aerts, Alexander Alabaster, Bernard Bulder, Carlos Campillo Torres, Andrea Critto, Raúl Hernández-Martín, Marta Machado, Jaroslav Mysiak, Rene Orth, Irene Palomino Antolín, Eva-Cristina Petrescu, Markus Reichstein, Timothy Tiggeloven, Anne F. Van Loon, Hung Vuong Pham, and Marleen C. de Ruiter
Nat. Hazards Earth Syst. Sci., 22, 1487–1497, https://doi.org/10.5194/nhess-22-1487-2022, https://doi.org/10.5194/nhess-22-1487-2022, 2022
Short summary
Short summary
The majority of natural-hazard risk research focuses on single hazards (a flood, a drought, a volcanic eruption, an earthquake, etc.). In the international research and policy community it is recognised that risk management could benefit from a more systemic approach. In this perspective paper, we argue for an approach that addresses multi-hazard, multi-risk management through the lens of sustainability challenges that cut across sectors, regions, and hazards.
Elisabeth Tschumi, Sebastian Lienert, Karin van der Wiel, Fortunat Joos, and Jakob Zscheischler
Biogeosciences, 19, 1979–1993, https://doi.org/10.5194/bg-19-1979-2022, https://doi.org/10.5194/bg-19-1979-2022, 2022
Short summary
Short summary
Droughts and heatwaves are expected to occur more often in the future, but their effects on land vegetation and the carbon cycle are poorly understood. We use six climate scenarios with differing extreme occurrences and a vegetation model to analyse these effects. Tree coverage and associated plant productivity increase under a climate with no extremes. Frequent co-occurring droughts and heatwaves decrease plant productivity more than the combined effects of single droughts or heatwaves.
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.
Weihua Zhu, Kai Liu, Ming Wang, Sadhana Nirandjan, and Elco Koks
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2021-277, https://doi.org/10.5194/nhess-2021-277, 2021
Manuscript not accepted for further review
Short summary
Short summary
We use multi-source empirical damage data to generate vulnerability curves and assess the risk of transportation infrastructure to rainfall-induced hazards. The results show large variations in the shape of the vulnerability curves and risk of railway infrastructure in China across the different regions. The usage of multi-source empirical data offer opportunities to perform risk assessments that include spatial detail among regions.
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
Aerts, J. C., Botzen, W. J., de Moel, H., and Bowman, M.: Cost estimates for flood resilience and protection strategies in New York City, Ann. NY Acad. Sci., 1294, 1–104, https://doi.org/10.1111/nyas.12200, 2013. a
Aerts, J. C. J. H., Botzen, W. J. W., Emanuel, K., Lin, N., de Moel, H., and Michel-Kerjan, E. O.: Evaluating Flood Resilience Strategies for Coastal Megacities, Science, 344, 473–475, https://doi.org/10.1126/science.1248222, 2014. a
Amante, C. J., Love, M., Carignan, K., Sutherland, M. G., MacFerrin, M., and Lim, E.: Continuously Updated Digital Elevation Models (CUDEMs) to Support Coastal Inundation Modeling, Remote Sensing, 15, 1702, https://doi.org/10.3390/rs15061702, 2023. a
Baatsen, M., Haarsma, R. J., Delden, A. J. V., and de Vries, H.: Severe Autumn storms in future Western Europe with a warmer Atlantic Ocean, Clim. Dynam., 45, 949–964, https://doi.org/10.1007/s00382-014-2329-8, 2015. a
Bartholomeus, R. P., van der Wiel, K., van Loon, A. F., van Huijgevoort, M. H., van Vliet, M. T., Mens, M., Muurling-van Geffen, S., Wanders, N., and Pot, W.: Managing water across the flood–drought spectrum: Experiences from and challenges for the Netherlands, Cambridge Prisms: Water, 1, e2, https://doi.org/10.1017/wat.2023.4, 2023. a, b
Bevacqua, E., Vousdoukas, M. I., Zappa, G., Hodges, K., Shepherd, T. G., Maraun, D., Mentaschi, L., and Feyen, L.: More meteorological events that drive compound coastal flooding are projected under climate change, Communications Earth & Environment, 1, 47, https://doi.org/10.1038/s43247-020-00044-z, 2020. a
Bloemendaal, N., Muis, S., Haarsma, R. J., Verlaan, M., Apecechea, M. I., de Moel, H., Ward, P. J., and Aerts, J. C.: Global modeling of tropical cyclone storm surges using high-resolution forecasts, Clim. Dynam., 52, 5031–5044, https://doi.org/10.1007/s00382-018-4430-x, 2019. a, b
Bony, S., Stevens, B., Frierson, D. M., Jakob, C., Kageyama, M., Pincus, R., Shepherd, T. G., Sherwood, S. C., Siebesma, A. P., Sobel, A. H., Watanabe, M., Webb, and Clouds, M. J.: Clouds, circulation and climate sensitivity, Nat. Geosci., 8, 261–268, 2015. a
Buchhorn, M., Smets, B., Bertels, L., Roo, B. D., Lesiv, M., Tsendbazar, N.-E., Herold, M., and Fritz, S.: Copernicus Global Land Service: Land Cover 100 m: collection 3: epoch 2015: Globe, Zenodo [data set], https://doi.org/10.5281/zenodo.3939038, 2020. a
Chang, S. E.: Socioeconomic impacts of infrastructure disruptions, in: Oxford research encyclopedia of natural hazard science, https://doi.org/10.1093/acrefore/9780199389407.013.66, 2016. a
Cooperative Institute for Research in Environmental Sciences (CIRES) at the University of Colorado: Continuously Updated Digital Elevation Model (CUDEM) – 1/9 Arc-Second Resolution Bathymetric-Topographic Tiles, NOAA National Centers for Environmental Information [data set], https://doi.org/10.25921/ds9v-ky35, 2014. a
Deser, C., Knutti, R., Solomon, S., and Phillips, A. S.: Communication of the role of natural variability in future North American climate, Nat. Clim. Change, 2, 775–779, https://doi.org/10.1038/nclimate1562, 2012. a
Done, J. M., Bruyère, C. L., Ge, M., and Jaye, A.: Internal variability of North Atlantic tropical cyclones, J. Geophys. Res.-Atmos., 119, 6506–6519, https://doi.org/10.1002/2014JD021542, 2014. a, b
Done, J. M., PaiMazumder, D., Towler, E., and Kishtawal, C. M.: Estimating impacts of North Atlantic tropical cyclones using an index of damage potential, Climatic Change, 146, 561–573, 2018. a
Dullaart, J. C., Muis, S., Bloemendaal, N., Chertova, M. V., Couasnon, A., and Aerts, J. C.: Accounting for tropical cyclones more than doubles the global population exposed to low-probability coastal flooding, Communications Earth & Environment, 2, 135, https://doi.org/10.1038/s43247-021-00204-9, 2021. 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
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 (v0.9.1), Zenodo [code], https://doi.org/10.5281/zenodo.10143631, 2023c. a
Feser, F. and Barcikowska, M.: The influence of spectral nudging on typhoon formation in regional climate models, Environ. Res. Lett., 7, 014024, https://doi.org/10.1088/1748-9326/7/1/014024, 2012. a
Gelaro, R., McCarty, W., Suárez, M. J., Todling, R., Molod, A., Takacs, L., Randles, C. A., Darmenov, A., Bosilovich, M. G., Reichle, R., Wargan, K., Coy, L., Cullather, R., Draper, C., Akella, S., Buchard, V., Conaty, A., da Silva, A. M., Gu, W., Kim, G.-K., Koster, R., Lucchesi, R., Merkova, D., Nielsen, J. E., Partyka, G., Pawson, S., Putman, W., Rienecker, M., Schubert, S. D., Sienkiewicz, M., and Zhao, B.: The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2), J. Climate, 30, 5419–5454, https://doi.org/10.1175/JCLI-D-16-0758.1, 2017. a
Gerritsen, H.: What happened in 1953? The Big Flood in the Netherlands in retrospect, Philos. T. R. Soc. A, 363, 1271–1291, https://doi.org/10.1098/rsta.2005.1568, 2005. a
Goulart, H. M. D.: dumontgoulart/sandy_impacts_storylines: v1.0.1 – publication initial version (v1.0.1), Zenodo [code], https://doi.org/10.5281/zenodo.10209795, 2023. a
Goulart, H. M. D., van der Wiel, K., Folberth, C., Balkovic, J., and van den Hurk, B.: Storylines of weather-induced crop failure events under climate change, Earth Syst. Dynam., 12, 1503–1527, https://doi.org/10.5194/esd-12-1503-2021, 2021. a
Goulart, H. M. D., van der Wiel, K., Folberth, C., Boere, E., and van den Hurk, B.: Increase of Simultaneous Soybean Failures Due To Climate Change, Earth's Future, 11, e2022EF003106, https://doi.org/10.1029/2022EF003106, 2023. a, b
Gutmann, E. D., Rasmussen, R. M., Liu, C., Ikeda, K., Bruyere, C. L., Done, J. M., Garrè, L., Friis-Hansen, P., and Veldore, V.: Changes in Hurricanes from a 13-Yr Convection-Permitting Pseudo–Global Warming Simulation, J. Climate, 31, 3643–3657, https://doi.org/10.1175/JCLI-D-17-0391.1, 2018. a, b, c, d, e
Haasnoot, M., Kwadijk, J., van Alphen, J., Bars, D. L., van den Hurk, B., Diermanse, F., van der Spek, A., Essink, G. O., Delsman, J., and Mens, M.: Adaptation to uncertain sea-level rise; how uncertainty in Antarctic mass-loss impacts the coastal adaptation strategy of the Netherlands, Environ. Res. Lett., 15, 034007, https://doi.org/10.1088/1748-9326/ab666c, 2020. a
Haklay, M. and Weber, P.: OpenStreetMap: User-Generated Street Maps, IEEE Pervasive Computing, 7, 12–18, https://doi.org/10.1109/MPRV.2008.80, 2008. a
Hall, J. W., Aerts, J. C. J. H., Ayyub, B. M., Hallegatte, S., Harvey, M., Hu, X., Koks, E. E., Lee, C., Liao, X., Mullan, M., Pant, R., Paszkowski, A., Rozenberg, J., Sheng, F., Stenek, V., Thacker, S., Väänänen, E., Vallejo, L., Veldkamp, T. I. E., van Vliet, M., Wada, Y., Ward, P., Watkins, G., and Zorn, C.: Adaptation of Infrastructure Systems: Background Paper for the Global Commission on Adaptation, Oxford: Environmental Change Institute, University of Oxford, http://www.gca.org/ (last access: 6 January 2024), 2019. a
Hall, T. M. and Sobel, A. H.: On the impact angle of Hurricane Sandy's New Jersey landfall, Geophys. Res. Lett., 40, 2312–2315, https://doi.org/10.1002/grl.50395, 2013. a
Hamed, R., Vijverberg, S., Van Loon, A. F., Aerts, J., and Coumou, D.: Persistent La Niñas drive joint soybean harvest failures in North and South America, Earth Syst. Dynam., 14, 255–272, https://doi.org/10.5194/esd-14-255-2023, 2023. a
Hawker, L. and Neal, J.: FABDEM V1-0, University of Bristol [data set], https://doi.org/10.5523/bris.25wfy0f9ukoge2gs7a5mqpq2j7, 2021. a
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. a
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. a, b
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., Chiara, G. D., 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. a
Hill, K. A. and Lackmann, G. M.: The Impact of Future Climate Change on TC Intensity and Structure: A Downscaling Approach, J. Climate, 24, 4644–4661, https://doi.org/10.1175/2011JCLI3761.1, 2011. a
Hinkel, J., Lincke, D., Vafeidis, A. T., Perrette, M., Nicholls, R. J., Tol, R. S. J., Marzeion, B., Fettweis, X., Ionescu, C., and Levermann, A.: Coastal flood damage and adaptation costs under 21st century sea-level rise, P. Natl. Acad. Sci. USA, 111, 3292–3297, https://doi.org/10.1073/pnas.1222469111, 2014. a
Hodges, K., Cobb, A., and Vidale, P. L.: How well are tropical cyclones represented in reanalysis datasets?, J. Climate, 30, 5243–5264, 2017. a
IPCC, 2021: 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, 2391 pp., https://doi.org/10.1017/9781009157896, 2021. a, b, c, d, e
IPCC: Climate Change 2022: Impacts, Adaptation and Vulnerability, Summary for Policymakers, Cambridge University Press, Cambridge, UK and New York, USA, ISBN 9781009325844, 2022. a
Jaafar, H. H., Ahmad, F. A., and El Beyrouthy, N.: GCN250, new global gridded curve numbers for hydrologic modeling and design, Sci. Data, 6, 145, https://doi.org/10.1038/s41597-019-0155-x, 2019. a
Jongman, B.: Effective adaptation to rising flood risk, Nat. Commun., 9, 1986, https://doi.org/10.1038/s41467-018-04396-1, 2018. a
Kalnay, E., Kanamitsu, M., Kistler, R., Collins, W., Deaven, D., Gandin, L., Iredell, M., Saha, S., White, G., Woollen, J., Zhu, Y., Chelliah, M., Ebisuzaki, W., Higgins, W., Janowiak, J., Mo, K. C., Ropelewski, C., Wang, J., Leetmaa, A., Reynolds, R., Jenne, R., and Joseph, D.: The NCEP/NCAR 40-year reanalysis project, B. Am. Meteorol. Soc., 77, 437–472, https://doi.org/10.1175/1520-0477(1996)077<0437:TNYRP>2.0.CO;2, 1996. a
Kazakov, E., Sekste, A., and Malikin, M.: OSM Road Completeness, https://www.kontur.io/blog/osm-road-completeness/ (last access: 28 August 2023), 2023. a
Kernkamp, H. W., 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, 2011. a
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. Meteorol. Soc., 91, 363–376, 2010. a
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. Meteorol. Soc., 101, E303–E322, https://doi.org/10.1175/BAMS-D-18-0194.1, 2020. a, b, c, d
Koks, E. E., Rozenberg, J., Zorn, C., Tariverdi, M., Vousdoukas, M., Fraser, S. A., Hall, J., and Hallegatte, S.: A global multi-hazard risk analysis of road and railway infrastructure assets, Nat. Commun., 10, 2677, https://doi.org/10.1038/s41467-019-10442-3, 2019. a, b
Koks, E. E., Bars, D. L., Essenfelder, A., Nirandjan, S., and Sayers, P.: The impacts of coastal flooding and sea level rise on critical infrastructure: a novel storyline approach, Sustainable and Resilient Infrastructure, 8, 237–261, https://doi.org/10.1080/23789689.2022.2142741, 2023. a, b, c
Kunz, M., Mühr, B., Kunz-Plapp, T., Daniell, J. E., Khazai, B., Wenzel, F., Vannieuwenhuyse, M., Comes, T., Elmer, F., Schröter, K., Fohringer, J., Münzberg, T., Lucas, C., and Zschau, J.: Investigation of superstorm Sandy 2012 in a multi-disciplinary approach, Nat. Hazards Earth Syst. Sci., 13, 2579–2598, https://doi.org/10.5194/nhess-13-2579-2013, 2013. a, b, c, d, e
Lackmann, G. M.: Hurricane Sandy before 1900 and after 2100, B. Am. Meteorol. Soc., 96, 547–560, https://doi.org/10.1175/BAMS-D-14-00123.1, 2015. a
Le Bars, D.: Uncertainty in Sea Level Rise Projections Due to the Dependence Between Contributors, Earth's Future, 6, 1275–1291, https://doi.org/10.1029/2018EF000849, 2018. a, b
Lehner, F. and Deser, C.: Origin, importance, and predictive limits of internal climate variability, Environ. Res., 2, 023001, https://doi.org/10.1088/2752-5295/accf30, 2023. 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
Lin, N., Kopp, R. E., Horton, B. P., and Donnelly, J. P.: Hurricane Sandy's flood frequency increasing from year 1800 to 2100, P. Natl. Acad. Sci. USA, 113, 12071–12075, https://doi.org/10.1073/pnas.1604386113, 2016. a
Liu, K., Wang, Q., Wang, M., and Koks, E. E.: Global transportation infrastructure exposure to the change of precipitation in a warmer world, Nat. Commun., 14, 2541, https://doi.org/10.1038/s41467-023-38203-3, 2023. a
Liu, M., Vecchi, G. A., Smith, J. A., and Murakami, H.: Projection of Landfalling–Tropical Cyclone Rainfall in the Eastern United States under Anthropogenic Warming, J. Climate, 31, 7269–7286, https://doi.org/10.1175/JCLI-D-17-0747.1, 2018. a
Lockwood, J. W., Oppenheimer, M., Lin, N., Kopp, R. E., Vecchi, G. A., and Gori, A.: Correlation Between Sea-Level Rise and Aspects of Future Tropical Cyclone Activity in CMIP6 Models, Earth's Future, 10, e2021EF002462, https://doi.org/10.1029/2021EF002462, 2022. a
Martinez, G. S., Linares, C., Ayuso, A., Kendrovski, V., Boeckmann, M., and Diaz, J.: Heat-health action plans in Europe: Challenges ahead and how to tackle them, Environ. Res., 176, 108548, https://doi.org/10.1016/j.envres.2019.108548, 2019. a
Mechler, R., Hochrainer, S., Aaheim, A., Salen, H., and Wreford, A.: Modelling economic impacts and adaptation to extreme events: Insights from European case studies, Mitig. Adapt. Strat. Gl., 15, 737–762, 2010. a
Mei, W., Xie, S.-P., Zhao, M., and Wang, Y.: Forced and Internal Variability of Tropical Cyclone Track Density in the Western North Pacific, J. Climate, 28, 143–167, https://doi.org/10.1175/JCLI-D-14-00164.1, 2015. a, b
Meinshausen, M., Smith, S. J., Calvin, K., Daniel, J. S., Kainuma, M. L., Lamarque, J.-F., Matsumoto, K., Montzka, S. A., Raper, S. C., Riahi, K., Thomson, A., Velders, G. J. M., and van Vuuren, D. P. P.: The RCP greenhouse gas concentrations and their extensions from 1765 to 2300, Climatic Change, 109, 213–241, 2011. a
Muis, S., Apecechea, M. I., Dullaart, J., de Lima Rego, J., Madsen, K. S., Su, J., Yan, K., and Verlaan, M.: A High-Resolution Global Dataset of Extreme Sea Levels, Tides, and Storm Surges, Including Future Projections, Front. Mar. Sci., 7, 263, https://doi.org/10.3389/fmars.2020.00263, 2020. a
Nicholls, R. J. and Cazenave, A.: Sea-level rise and its impact on coastal zones, Science, 328, 1517–1520, 2010. a
Nirandjan, S., Koks, E. E., Ward, P. J., and Aerts, J. C.: A spatially-explicit harmonized global dataset of critical infrastructure, Sci. Data, 9, 150, https://doi.org/10.1038/s41597-022-01218-4, 2022. a, b
OCM Partners: 2017 NYC Topobathy Lidar DEM: New York City, NOAA National Centers for Environmental Information, https://www.fisheries.noaa.gov/inport/item/64732 (last access: 6 January 2024), 2017. a
O'Neill, B. C., Tebaldi, C., van Vuuren, D. P., Eyring, V., Friedlingstein, P., Hurtt, G., Knutti, R., Kriegler, E., Lamarque, J.-F., Lowe, J., Meehl, G. A., Moss, R., Riahi, K., and Sanderson, B. M.: The Scenario Model Intercomparison Project (ScenarioMIP) for CMIP6, Geosci. Model Dev., 9, 3461–3482, https://doi.org/10.5194/gmd-9-3461-2016, 2016. a
Oppenheimer, M., Glavovic, B. C., Hinkel, J., van de Wal, R., Magnan, A. K., Abd-Elgawad, A., Cai, R., Cifuentes-Jara, M., DeConto, R. M., Ghosh, T., Hay, J., Isla, F., Marzeion, B., Meyssignac, B. , and Sebesvari, Z.: Sea Level Rise and Implications for Low-Lying Islands, Coasts and Communities, in: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate, edited by: Pörtner, H.-O., Roberts, D. C., Masson-Delmotte, V., Zhai, P., Tignor, M., Poloczanska, E., Mintenbeck, K., Alegría, A., Nicolai, M., Okem, A., Petzold, J., Rama, B., and Weyer, N. M., Cambridge University Press, Cambridge, UK and New York, NY, USA, 321–445, https://doi.org/10.1017/9781009157964.006, 2019. a
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. a
Prein, A., Gobiet, A., Truhetz, H., Keuler, K., Goergen, K., Teichmann, C., Fox Maule, C., Van Meijgaard, E., Déqué, M., Nikulin, G., Vautard, R., Colette, A., Kjellström, E., and Jacob, D.: Precipitation in the EURO-CORDEX 0.11∘ and 0.44∘ simulations: high resolution, high benefits?, Clim. Dynam., 46, 383–412, https://doi.org/10.1007/s00382-015-2589-y, 2016. a
Qiu, J., Liu, B., Yang, F., Wang, X., and He, X.: Quantitative Stress Test of Compound Coastal-Fluvial Floods in China's Pearl River Delta, Earth's Future, 10, e2021EF002638, https://doi.org/10.1029/2021EF002638, 2022. a, b
Rosenzweig, C. and Solecki, W.: Hurricane Sandy and adaptation pathways in New York: Lessons from a first-responder city, Global Environ. Chang., 28, 395–408, https://doi.org/10.1016/j.gloenvcha.2014.05.003, 2014. a, b
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. a, b, c, d
Schwarzwald, K. and Lenssen, N.: The importance of internal climate variability in climate impact projections, P. Natl Acad. Sci. USA, 119, e2208095119, https://doi.org/10.1073/pnas.2208095119, 2022. a, b, c
Sebastian, A., Bader, D., Nederhoff, C., Leijnse, T., Bricker, J., and Aarninkhof, S.: Hindcast of pluvial, fluvial, and coastal flood damage in Houston, Texas during Hurricane Harvey (2017) using SFINCS, Nat. Hazards, 109, 2343–2362, 2021. a
Shepherd, T. G.: Storyline approach to the construction of regional climate change information, P. Roy. Soc. A-Math. Phy., 475, 20190013, https://doi.org/10.1098/rspa.2019.0013, 2019. a
Shepherd, T. G., Boyd, E., Calel, R. A., Chapman, S. C., Dessai, S., Dima-West, I. M., Fowler, H. J., James, R., Maraun, D., Martius, O., Senior, C. A., Sobel, A. H., Stainforth, D. A., Tett, S. F. B., Trenberth, K. E., van den Hurk, B. J. J. M., Watkins, N. W., Wilby, R. L., and Zenghelis, D. A.: Storylines: an alternative approach to representing uncertainty in physical aspects of climate change, Climatic Change, 151, 555–571, https://doi.org/10.1007/s10584-018-2317-9, 2018. a, b
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, Earth's Future, 9, e2020EF001783, https://doi.org/10.1029/2020EF001783, 2020. a
Stevens, B., Giorgetta, M., Esch, M., Mauritsen, T., Crueger, T., Rast, S., Salzmann, M., Schmidt, H., Bader, J., Block, K., Brokopf, R., Fast, I., Kinne, S., Kornblueh, L., Lohmann, U., Pincus, R., Reichler, T., and Roeckner, E.: Atmospheric component of the MPI-M Earth System Model: ECHAM6, J. Adv. Model. Earth Sy., 5, 146–172, https://doi.org/10.1002/jame.20015, 2013. a
Stott, P. A., Christidis, N., Otto, F. E., Sun, Y., Vanderlinden, J. P., van Oldenborgh, G. J., Vautard, R., von Storch, H., Walton, P., Yiou, P., and Zwiers, F. W.: Attribution of extreme weather and climate-related events, Wires Clim. Change, 7, 23–41, https://doi.org/10.1002/wcc.380, 2016. a
Tebaldi, C., Debeire, K., Eyring, V., Fischer, E., Fyfe, J., Friedlingstein, P., Knutti, R., Lowe, J., O'Neill, B., Sanderson, B., van Vuuren, D., Riahi, K., Meinshausen, M., Nicholls, Z., Tokarska, K. B., Hurtt, G., Kriegler, E., Lamarque, J.-F., Meehl, G., Moss, R., Bauer, S. E., Boucher, O., Brovkin, V., Byun, Y.-H., Dix, M., Gualdi, S., Guo, H., John, J. G., Kharin, S., Kim, Y., Koshiro, T., Ma, L., Olivié, D., Panickal, S., Qiao, F., Rong, X., Rosenbloom, N., Schupfner, M., Séférian, R., Sellar, A., Semmler, T., Shi, X., Song, Z., Steger, C., Stouffer, R., Swart, N., Tachiiri, K., Tang, Q., Tatebe, H., Voldoire, A., Volodin, E., Wyser, K., Xin, X., Yang, S., Yu, Y., and Ziehn, T.: Climate model projections from the Scenario Model Intercomparison Project (ScenarioMIP) of CMIP6, Earth Syst. Dynam., 12, 253–293, https://doi.org/10.5194/esd-12-253-2021, 2021. a
Trenberth, K. E., Cheng, L., Jacobs, P., Zhang, Y., and Fasullo, J.: Hurricane Harvey Links to Ocean Heat Content and Climate Change Adaptation, Earth's Future, 6, 730–744, https://doi.org/10.1029/2018EF000825, 2018. a
van den Hurk, B. J., Pacchetti, M. B., Boere, E., Ciullo, A., Coulter, L., Dessai, S., Ercin, E., Goulart, H. M., Hamed, R., Hochrainer-Stigler, S., Koks, E., Kubiczek, P., Levermann, A., Mechler, R., van Meersbergen, M., Mester, B., Middelanis, R., Minderhoud, K., Mysiak, J., Nirandjan, S., van den Oord, G., Otto, C., Sayers, P., Schewe, J., Shepherd, T. G., Sillmann, J., Stuparu, D., Vogt, T., and Witpas, K.: Climate impact storylines for assessing socio-economic responses to remote events, Climate Risk Management, 40, 100500, https://doi.org/10.1016/j.crm.2023.100500, 2023a. a
van den Hurk, B. J., White, C. J., Ramos, A. M., Ward, P. J., Martius, O., Olbert, I., Roscoe, K., Goulart, H. M., and Zscheischler, J.: Consideration of compound drivers and impacts in the disaster risk reduction cycle, Iscience, 26, 106030, https://doi.org/10.1016/j.isci.2023.106030, 2023b. a, b
van Garderen, L. and Mindlin, J.: A storyline attribution of the 2011/2012 drought in Southeastern South America, Weather, 77, 212–218, https://doi.org/10.1002/wea.4185, 2022. a, b, c
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. a, b
van Ormondt, M., Leijnse, T., Nederhoff, K., de Goede, R., van Dongeren, A., and Bovenschen, T.: SFINCS: Super-Fast INundation of CoastS model (2.0.3 Cauberg Release Q4 2023), Zenodo [code], https://doi.org/10.5281/zenodo.10118583, 2023. a
von Storch, H., Langenberg, H., and Feser, F.: A Spectral Nudging Technique for Dynamical Downscaling Purposes, Mon. Weather Rev., 128, 3664–3673, https://doi.org/10.1175/1520-0493(2000)128<3664:ASNTFD>2.0.CO;2, 2000. a, b, c
Weisse, R. and Feser, F.: Evaluation of a method to reduce uncertainty in wind hindcasts performed with regional atmosphere models, Coast. Eng., 48, 211–225, https://doi.org/10.1016/S0378-3839(03)00027-9, 2003. a
Woodruff, J. D., Irish, J. L., and Camargo, S. J.: Coastal flooding by tropical cyclones and sea-level rise, Nature, 504, 44–52, 2013. a
Yamada, Y., Kodama, C., Satoh, M., Nakano, M., Nasuno, T., and Sugi, M.: High-Resolution Ensemble Simulations of Intense Tropical Cyclones and Their Internal Variability During the El Niños of 1997 and 2015, Geophys. Res. Lett., 46, 7592–7601, https://doi.org/10.1029/2019GL082086, 2019. a
Yates, D., Luna, B. Q., Rasmussen, R., Bratcher, D., Garre, L., Chen, F., Tewari, M., and Friis-Hansen, P.: Stormy Weather: Assessing Climate Change Hazards to Electric Power Infrastructure: A Sandy Case Study, IEEE Power and Energy Magazine, 12, 66–75, https://doi.org/10.1109/MPE.2014.2331901, 2014. a, b, c, d, e, f
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. a
Zio, E.: Challenges in the vulnerability and risk analysis of critical infrastructures, Reliab. Eng. Syst. Safe., 152, 137–150, https://doi.org/10.1016/j.ress.2016.02.009, 2016. a
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.
We explore how Hurricane Sandy (2012) could flood New York City under different scenarios,...
Altmetrics
Final-revised paper
Preprint