Earth Observation data for Advancing Flood Forecasting (EO4FLOOD)

Floods are among the most destructive natural hazards worldwide, causing severe impacts on human health, the environment, cultural heritage, and economies. In Europe alone, floods have resulted in approximately 4,000 fatalities and €274 billion in economic losses over the past 50 years, with even more dramatic consequences in developing countries. As climate change increases the frequency and intensity of extreme events, there is an urgent need for innovative and reliable flood forecasting systems capable of reducing societal and environmental impacts.

The EO4FLOOD project (Earth Observation Data for Advancing Flood Forecasting), aims to demonstrate the maturity and effectiveness of cutting-edge satellite data in enhancing flood forecasting systems.

The project leverages advanced Earth Observation (EO) products and state-of-the-art algorithms to improve the accuracy, reliability, and timeliness of hydrological and hydraulic predictions. By integrating satellite observations into modeling frameworks, EO4FLOOD contributes to the development of more robust and operational flood forecasting systems.

Project Structure

EO4FLOOD is built around three key pillars:

1. Development of an Advanced Open EO Dataset

The project develops the EO4FLOOD dataset, an advanced and open Earth Observation dataset that integrates the latest products from ESA and non-ESA satellite missions.

The dataset ensures global coverage with high spatial and temporal resolution and provides critical variables for flood forecasting, including: precipitation, soil moisture, snow variables, flood extent, river discharge.

This dataset is designed to support the global scientific community by providing consistent, ready-to-use EO information for hydrological applications.

2. Integration into Flood Forecasting Models

The EO4FLOOD dataset is integrated into flood forecasting systems through: hydrological models, hydraulic models, flood inundation models, machine learning techniques.

The integration supports flood prediction up to 7 days in advance, improving forecast accuracy and timeliness while also quantifying predictive uncertainty.

EO data are tested in different components of the modeling chain, including model calibration, forcing data, initial conditions and data assimilation.

Three rainfall–runoff models (HYPE, GHM, and MGB) and one Artificial Intelligence (AI) model are used within the project framework.

3. Demonstration for Science and Society

EO4FLOOD demonstrates how the combined use of EO data and advanced models can significantly improve flood forecasting and risk management.

The project also investigates the influence of human activities, such as land-use changes and dam construction, on flood dynamics, contributing to improved disaster preparedness, water resource management, and evidence-based policymaking.

 

Testing Basins and Modeling Framework

The modeling framework is implemented across selected areas within five major river basins representing diverse hydro-climatic conditions: Torne, Negro, Congo, Niger and Brahmaputra

In addition, the EO4FLOOD dataset is provided for major European river basins, including: Po, Danube, Rhine and Ebro

To optimize the use of available forecasting tools, EO4FLOOD develops a hybrid modeling approach that combines the strengths of physics-based hydrological and hydraulic models with advanced Artificial Intelligence techniques.

Expected Impact

The insights derived from EO4FLOOD will:

• refine current flood forecasting methodologies
• provide a benchmark framework for future hydrological research
• enhance the operational use of satellite data
• support global strategies for flood risk reduction and water resource management

By integrating next-generation satellite products with advanced modeling techniques, EO4FLOOD delivers a robust and scalable framework for predicting flood events and mitigating their impacts on society and the environment.