Improved flood predictions through satellite data and modelling
Climate change poses an increasing threat to millions of people living in coastal cities worldwide. By 2100, more than 97 million people could face an expanded risk of flooding. Traditional models rely heavily on local measurements, which are often lacking in vulnerable areas, especially in the Global South.
The ESA-funded FRACCEO project, led by Deltares with partners TU Munich, TU Delft, and the Nansen Environmental and Remote Sensing Center (NERSC), addresses this gap by using satellite data of sea levels and wave heights. The aim is to use this data to improve the accuracy of coastal water level and flood models even in regions without local measurements. In this way, FRACCEO helps governments and policymakers better understand how extreme water levels are changing and how they can prepare for them.
A globally applicable method
FRACCEO combines satellite data from the ESA Climate Change Initiative with Delft3D Flexible Mesh and SFINCS, Deltares’ models for tides, storm surges, and coastal flooding. Another goal is to make coastal water level and flood modelling more consistent worldwide through a common vertical reference framework.
The method will be tested in Hamburg (Germany) and Beira (Mozambique) and applied in Durban, Cochin, Manila, and Naga City to demonstrate its global applicability.
Scientific innovation
FRACCEO uses satellite altimetry to detect extreme sea levels and waves, even in locations without ground-based measurements. By comparing these data with historical events such as Storm Pia (North Sea) and Cyclone Idai (Mozambique), the models can be calibrated and validated, resulting in more reliable and reproducible outcomes.
“Improved flood predictions through satellite data and modelling will help vulnerable regions better anticipate and prepare for the impacts of climate change,” explains Dr. Björn Backeberg, Project Lead at Deltares
Deltares’ role
As the lead partner, Deltares provides the core hydrodynamic modelling through Delft3D-FM and SFINCS. Drawing on experience from projects such as Destination Earth, COMPASS and EOatSee, Deltares ensures that satellite data are directly linked to decision-support tools for climate adaptation and flood management.
By combining satellite data with hydrodynamic models, we are creating a science-based foundation for cities to prepare for future extreme water levels and floods, even where local data are scarce.
Dr. Björn Backeberg, Project Lead, Deltares
Collaboration
Together with our partners from TU Munich, NERSC, and TU Delft, we are developing an open-access methodology that cities worldwide can use to strengthen their flood preparedness and climate resilience.
“For me personally, the collaboration has been very rewarding,” says Björn. “Each partner brings their own strengths, and I’ve learned a great deal from working so closely together across disciplines.”
“One of the best things about working on this project is the open and positive spirit of the team. Everyone is genuinely motivated to push the state of the art and tackle challenges as they come. I appreciate how we support each other in finding solutions, whether it’s aligning modelling approaches or integrating satellite data more effectively. It’s inspiring to be part of a group so committed to making flood risk information more accessible and usable worldwide.”
Future
The FRACCEO project will run for 18 months, until February 2027. After that, Deltares will translate the improved coastal water level time series into practical indicators that support adaptation planning in coastal communities.
These indicators will provide local authorities with clear, science-based signals showing when conditions are changing and when adaptation actions should be considered. Developing such indicators on a global scale requires large satellite datasets, hydrodynamic models, and diverse local elevation and exposure data to be carefully harmonised. This process ensures that results are consistent, reliable, and applicable across different coastal regions worldwide.
