EO4FLOOD
Earth observation data for
Advancing Flood Forecasting

PROJECT DESCRIPTION

Floods rank among the most destructive natural disasters, causing significant harm to human health, the environment, cultural heritage, and economies. In Europe alone, floods have led to approximately 4,000 fatalities and $274 billion in economic losses over the past 50 years, with even more severe impacts in developing countries. As climate change accelerates the frequency and intensity of floods, there is an urgent need for innovative flood forecasting systems that can effectively reduce societal impacts.

The EO4FLOOD Project (Earth Observation data for Advancing Flood Forecasting) funded by ESA aims at demonstrating the maturity and effectiveness of cutting-edge satellite data in enhancing flood forecasting systems. The project focuses on leveraging advanced satellite technologies and algorithms to improve the accuracy and timeliness of existing hydrological and hydraulic models, resulting in more reliable and precise flood predictions.

EO4FLOOD is structured around three key pillars:

Development of an Advanced Open Earth Observation Dataset

(EO4FLOOD dataset) that leverages the latest products from both ESA and non-ESA satellite missions, ensuring global coverage with high spatial and temporal resolutions. This dataset provides viable information to the global scientific community for enhanced flood forecasting by offering critical information on key variables such as precipitation, soil moisture, snow, flood extent and river discharge.

Integration of the EO4FLOOD Dataset

into Flood Forecasting Models through the combination of hydrological, hydraulic, and flood models with machine learning techniques to predict floods up to 7 days in advance. This integration enables more accurate and timely predictions that can be crucial for effective disaster preparedness and response, also assessing predictive uncertainty.

Demonstration of EO Data and Models

for Science and Society to show how the integration of EO data and models can improve flood forecasting and risk management. The initiative is addressed to explore the impact of human activities, such as land use changes or dam construction on flood dynamics, contributing to better disaster preparedness and policy-making.

The EO4FLOOD project is based on the use of the last frontiers in terms of advanced algorithms and satellite products to feed hydrological and hydraulic modelling to enhance flood forecasting systems and deliver a robust framework for predicting flood events and managing their impacts on society and the environment.

EO4FLOOD approach and testing river basins

EO4FLOOD will test the impact of EO through calibration, forcing data, initial condition and data assimilation in three rainfall-runoff models (Hype, GHM, MGB) and one AI model. The testing modelling framework will be implemented over selected areas within five specific basins (Torne, Negro, Congo, Niger and Brahmaputra) . The dataset of EO will be provided also in the bigger European basins like Po, Danube, Rhine and Ebro. To optimize the use of the available tools for flood forecasting, we will develop a hybrid approach that integrates the strengths of a physics-based approach and advanced AI techniques.

The insights derived from this research will not only refine current methodologies but also serve as a benchmark for future hydrological studies and applications aimed at mitigating flood impacts and enhancing water resource management strategies globally.