Mapping coastal storm surges fills a gap in flood risk modelling
Coastal floods are among the most damaging natural hazards, and they worsen as sea levels rise. However, traditional flood risk models tend to assess one location at a time, ignoring how extreme sea levels in different areas may be connected. That is a dangerous oversight. The EU-funded SpaDeRisks project, coordinated by Philip Ward at Vrije Universiteit Amsterdam, set out to fill that gap by mapping the spatial dependencies of storm surges on a global scale. By identifying how and when different coastlines are likely to be hit at the same time, the project opens new doors for smarter disaster response, urban planning and insurance design.
New insights into global flood risk patterns
Until now, spatial flood footprints had mostly been studied in rivers, not oceans. However, as SpaDeRisks researcher Alejandra Enriquez(opens in new window), explains: “Studying the spatial dependencies of extreme sea levels will help determine what locations are commonly impacted simultaneously, and that hopefully can help emergency managers.” By analysing global numerical models and tide gauge data, the project identified patterns in extreme sea levels. Different coastal areas impacted simultaneously could stress national response systems and lead to underestimation of economic damage. The research also highlighted seasonal and decadal changes in storm surge behaviour. Spatial footprints shift over time, influenced by seasonal weather patterns. Climate patterns might also play a role in some areas, like El Niño(opens in new window) and the North Atlantic Oscillation(opens in new window), although further research is needed. “We found evidence that some of these spatial patterns may be influenced by larger climate systems,” reveals Enriquez. These insights are critical for flood risk models. “Ignoring spatial dependencies can affect flood response and insurance planning, as the economic damage may be underestimated if these dependencies are not taken into account,” adds Enriquez.
Predicting future flood hotspots due to tides
Undertaken with the support of the Marie Skłodowska-Curie Actions(opens in new window) programme, SpaDeRisks also found that long-term tidal cycles can significantly alter flood probabilities over time. These predictable cycles mean that certain years or regions may face elevated flood risk, even without rising sea levels or major storms. “Our research has identified periods of elevated coastal flood risk due to the combination of extreme storm surges and long-term tidal cycles,” notes Enriquez. “Since astronomical tides are fully predictable, we can identify in advance the years and locations that will face heightened flood risk and prepare accordingly.” This non-stationary view of flood risk challenges traditional models and suggests that flood planning must consider timing as much as location.
Tools for communities and climate planning
During the course of the project, Enriquez contributed to the launch of HazardAware.org an open-access tool that helps residents across the Gulf of Mexico assess their homes’ exposure to coastal and weather-related hazards. The tool shows annual losses, risk profiles, socio-vulnerability indices and even suggests protective measures. It merges the project’s modelling advances with real-world accessibility, bringing science directly into the hands of those most at risk.
From research to smarter planning
With sea levels rising and coastlines under pressure, SpaDeRisks offers a roadmap for future risk assessments. Its global synthetic dataset of storm surges provides new modelling possibilities for scientists, governments and insurers alike. “By identifying when and where flood risk is highest, we can help communities prepare more effectively and allocate resources more efficiently,” says Enriquez. In doing so, SpaDeRisks sets the stage for more accurate insurance pricing, better urban design, and stronger cross-border climate adaptation, turning science into resilience.
Keywords
SpaDeRisks, storm surges, flood risk model, coastal flood, tidal cycles, sea levels rise, climate adaptation, synthetic dataset