Summary
Coastal cities are increasingly exposed to climate-related flood risks due to sea-level rise and extreme weather events. This project aims to develop and test a globally applicable methodology that combines Earth Observation (EO) data — particularly Essential Climate Variables addressed by ESA's Climate Change Initiative (CCI) —with hydrodynamic modelling to enhance coastal water level forecasts and urban flood risk assessments. Focusing on Hamburg and Beira, the project aims to advance the scientific baseline by improving uncertainty quantification, validating combined EO-hydrodynamic approaches, and exploring their applicability across diverse urban contexts, including cities in the Global South. The methodology is co-developed with stakeholders to ensure relevance, usability, and uptake of project outcomes. The work aims to support ESA’s Climate Change Initiative and aims to contribute to the IPCC Special Report on Climate Change and Cities by addressing priority knowledge gaps and demonstrating the added value of EO.

Project background
Coastal cities are increasingly vulnerable to climate-related hazards such as sea-level rise, storm surges, and extreme precipitation events. These risks are amplified by urbanisation and socio-economic inequalities, particularly in the Global South. Yet, current flood risk assessments often lack the spatial resolution, data integration, and uncertainty quantification needed to support effective climate adaptation planning.
This project responds to a key challenge identified by ESA’s Climate Change Initiative: the need for robust, transferable methodologies to assess and monitor climate impacts and responses at the city scale. It aims to provide input into the upcoming IPCC Special Report on Climate Change and Cities (due 2027), which calls for improved urban-scale climate science, including the use of Earth Observation (EO) data to understand hazards, risks, and adaptation options (IPCC outline).
Scientifically, the project addresses gaps in combining EO-derived climate data —especially Essential Climate Variables generated via the ESA Climate Change Initiative (CCI) — with hydrodynamic models to forecast coastal water levels and assess urban flood risks. It seeks to advance the state-of-the-art by:
- Quantifying uncertainties in EO-hydrodynamic modelling,
- Validating methods across diverse urban contexts (e.g., Hamburg and Beira),
- Exploring transferability to cities in the Global South.
Policy-wise, the project aligns with the Paris Agreement and the European Green Deal, supporting climate resilience through improved data-driven decision-making. It also contributes to the Global Climate Observing System (GCOS) goals for urban climate monitoring (GCOS Implementation Plan).
The open-source methods will be co-developed and validated with stakeholders, ensuring that outputs are relevant, usable, and adopted by local authorities, planners, and climate service providers. This open approach aims to enhance the societal impact and operational potential of the research.
By demonstrating the added value of EO data and open science workflows, the project aims to lay the groundwork for future operational climate services tailored to urban needs.

Aims and Objectives
The project aims to develop and demonstrate a globally transferable methodology that combines Earth Observation (EO) data with hydrodynamic modelling to improve flood risk assessments in coastal urban areas. By leveraging data records generated via ESA’s Climate Change Initiative (CCI) for GCOS-defined Essential Climate Variables (ECVs), the project seeks to enhance the accuracy and usability of coastal water level forecasts and flood impact assessments under current and future climate conditions.
The overarching goal is to support climate adaptation and resilience planning in cities, particularly in the context of increasing risks from sea-level rise, storm surges, and extreme weather events. The project contributes to ESA’s Climate Change Initiative and aims to inform the scientific priorities of the upcoming IPCC Special Report on Climate Change and Cities.
Specific objectives include:
1. Developing and validating a methodology to combine EO data and hydrodynamic modelling
- Combine satellite altimetry data with high-resolution hydrodynamic models
- Improve uncertainty quantification and validation techniques.
2. Apply and test the methodology in diverse case study cities
- Detailed validation of the approach will be performed for Hamburg (Germany) and Beira (Mozambique) representing different geographic, socio-economic, and climatic contexts.
- The global transferability of the approach to other urban areas, including cities in the Global South, will be tested.
3. Identify and address key scientific and policy knowledge gaps
- Advance the state-of-the-art and use of EO data in urban flood risk modelling.
- Support the Global Climate Observing System (GCOS) urban monitoring goals and contribute to the IPCC Special Report on Cities.
4. Co-develop the methodology with stakeholders
- Engage local stakeholders during the project.
- Ensure that outputs are relevant, usable, and aligned with real-world decision-making needs.
5. Promote open science and future operational uptake
- Publish algorithms, workflows, and data products in open-access platforms.
- Provide a scientific roadmap for scaling the methodology into operational climate services.
Through these objectives, the project aims to demonstrate the added value of EO in urban climate risk assessment and contribute timely, policy-relevant research to the international climate science and adaptation community.
The project aims to leverage Earth Observation data to enhance simulations of urban climate conditions — particularly extreme heat and flooding — in Africa’s rapidly expanding cities. This will address an information gap and generate valuable knowledge and datasets, supporting efforts to better understand and manage urban climate risks in African cities and provide essential scientific evidence to inform global assessments, including the forthcoming IPCC Special Report on Climate Change and Cities.
Project plan
The project is structured around five interlinked work packages (WPs), each addressing a specific phase of the project lifecycle: from scientific scoping to stakeholder engagement, technical development, validation, and dissemination.
WP1, led by Deltares, focuses on reviewing the state-of-the-art, identifying knowledge gaps, and gathering user needs through literature analysis and stakeholder workshops with stakeholders in Hamburg and Beira. This ensures that the methodology is grounded in both scientific evidence and real-world requirements.
WP2, led by TUM, develops the core scientific methodology. This includes detecting sea level and sea state extremes from Earth Observation (EO) data, using these with hydrodynamic models, and referencing outputs to a common vertical datum. The work is supported by TU Delft (vertical referencing) and NERSC (validation and clustering of EO data).
WP3, led by Deltares, implements and validates the methodology through simulations in case study cities. It focuses on leveraging EO-derived sea level data, validating model outputs against in situ observations, and quantifying uncertainties using statistical techniques.
WP4, led by NERSC, syntheses the scientific findings into a roadmap for future research and operationalisation. It also ensures that all code and data products are archived in open-access repositories to support transparency and reuse.
WP5, also led by Deltares, manages the project, coordinates communication, and oversees outreach. This includes maintaining the project website, producing promotional materials, and engaging with the broader scientific and policy communities.
The combines satellite Earth Observation (EO) data, hydrodynamic model outputs, and ancillary datasets to improve flood risk assessments in coastal cities under climate change.
Altimetry Data
A core component of the project is the use of satellite altimetry data to detect and analyse sea level and sea state extremes. These data are primarily sourced from ESA’s Climate Change Initiative (CCI) Sea Level and Sea State products, which provide long-term, consistent Essential Climate Variable (ECV) datasets. The altimetry data are processed to:
- Detect extreme sea level events using statistical filtering and smoothing techniques (e.g., 1-Hz smoothing),
- Validate hydrodynamic model outputs,
- Improve open boundary conditions for coastal models.
Altimetry-derived sea level and wave height data are validated against in situ observations (e.g., tide gauges) and used to compute wave setup contributions to total water levels.
Hydrodynamic Models
The project employs two hydrodynamic modelling systems:
- SFINCS (Super-Fast INundation of CoastS), a rapid flood model for high-resolution urban-scale simulations,
- Delft3D-FM, a flexible mesh model for simulating coastal hydrodynamics and storm surge.
These models are used to simulate coastal water levels under current and future climate scenarios. They are calibrated and validated using EO data and in situ observations, and their outputs are referenced to a common vertical datum using workflows developed by TU Delft.
Destination Earth (DestinE) Data
FRACCEO aligns with the objectives of the European Commission’s Destination Earth (DestinE) initiative. Where available, DestinE datasets, such as high-resolution digital twins and climate projections, will be used to provide boundary conditions and forcing data for hydrodynamic models.
Ancillary Data
Additional datasets include:
- Digital Elevation Models (DEMs) for urban topography,
- Land use and infrastructure maps,
- In situ observations (e.g., tide gauges, meteorological data).
The consortium for the FRACCEO project comprises four organisations:
Project Prime
Deltares
- Björn Backeberg – Scientific Lead and Project Manager
- Sanne Muis – WP1.1, WP1.2, WP2.2, WP3 Lead, WP4
- Natalia Aleksandrova – WP1.1, WP1.2, WP2.2, WP3.2, WP4
- Gundula Winter – WP1.1, WP1.2, WP2.2, WP3.2, WP4
Partners
NERSC
- Antonio Bonaduce – WP1.1, WP2.4, WP3.4, WP4
- Fabio Mangini – WP1.1, WP2.4, WP3.4, WP4
- Roshin P. Raj – WP1.1, WP2.4, WP3.4, WP4
TUDelft
- Cornelis Slobbe – WP1.1, WP2.3, WP3.3, WP4
- Pavel Ditmar – WP1.1, WP2.3, WP3.3, WP4
DGFI-TUM
- Marcello Passaro – WP1.1, WP2.1, WP3.1, WP4
- Michael Hart-Davis – WP1.1, WP2.1, WP3.1, WP4
- Jemma Johnson – WP1.1, WP2.1, WP3.1, WP4