Advancing Flood Forecasting in Large River Basins Using Multi-Mission Satellite Data: the EO4FLOOD project

Floods are among the most destructive natural hazards worldwide, causing severe impacts on human health, ecosystems, cultural heritage and economies. Over the past decades, both developed and developing regions have experienced increasing flood-related losses, a trend that is expected to intensify under climate change due to shifts in precipitation patterns and the frequency of extreme events. In many large river basins, particularly in data-scarce regions, flood forecasting remains highly uncertain because of limited in situ observations and complex hydrological and hydraulic dynamics.

EO4FLOOD is an ESA-funded project aimed at demonstrating the added value of advanced Earth Observation (EO) data for improving flood forecasting at regional to continental scales. The project focuses on the integration of multi-mission satellite observations with hydrological and hydrodynamic modelling frameworks to support flood prediction up to seven days in advance, with an explicit treatment of uncertainty.

A key outcome of EO4FLOOD is the development of a comprehensive and openly available EO-based dataset designed to support flood modelling and forecasting studies. The dataset covers nine large and hydrologically complex river basins worldwide, selected to represent a wide range of climatic, physiographic and anthropogenic conditions, and characterized by limited or heterogeneous availability of ground-based observations. It integrates high-resolution satellite products from ESA and non-ESA missions, including precipitation, soil moisture, snow variables, flood extent, water levels and satellite-derived river discharge.

Within EO4FLOOD, these EO datasets are combined with hydrological and hydraulic models, enhanced by machine learning techniques, to improve flood prediction skill and to better quantify predictive uncertainty in data-scarce environments. The project also investigates the role of human interventions, such as reservoirs and land-use changes, in modulating flood dynamics across the selected basins.By making this multi-variable EO dataset publicly available, EO4FLOOD aims to support the broader hydrological community in testing, benchmarking and developing flood modelling and forecasting approaches in challenging large-basin settings. The project provides a unique opportunity to explore the potential and limitations of EO-driven flood forecasting and contributes to advancing the use of satellite observations for global flood risk assessment and management.