Advancing the prediction of extreme hydrological events
About the project
While CEMS (Copernicus Emergency Management Service) currently provides an operational global Early Warning System (EWS) for floods, there’s room for improvement in the accuracy of its hydrological simulations, particularly in regions of the global south. Furthermore, despite the significant benefits they offer in addressing challenges related to the food-energy-water nexus, operational drought forecasts are not yet issued.
SEED-FD aims to harness the wealth of environmental information provided by Copernicus to enhance the quality of CEMS flood and drought forecasts and bolster its hydrological monitoring capabilities.
Project aims
Enhance the CEMS hydrological model
for better representing the range of hydroclimatic processes worldwide.
Enhancing CEMS
hydrological simulations and forecasts globally through:
More realistic hydrological initial conditions by forcing CEMS HFMC with blended EO-based observations instead of (modelled) reanalysis meteorological data.
Data Assimilation of river discharge and water level to correct model hydrological states at the beginning of the forecast.
Near real-time post-processing of hydrological forecasts using Artificial Intelligence/Machine Learning techniques to integrate observational data to reduce modelling and forecasting errors.
Expanding the CEMS EWS forecast product portfolio for floods and droughts
by developing new extreme hydrometeorological event detection algorithms applicable worldwide: Flash Drought forecast product, Seasonal Drought forecast product, Drought tracking product, Flash Flood forecast product.
Enhance the CEMS hydrological model
for better representing the range of hydroclimatic processes worldwide. This means integrating new technologies and improved hydrological processes into the CEMS Hydrological Model Chain framework.
Enhancing CEMS
hydrological simulations and forecasts globally through:
More realistic hydrological initial conditions by forcing CEMS HFMC with blended EO-based observations instead of (modelled) reanalysis meteorological data.
Data Assimilation of river discharge and water level to correct model hydrological states at the beginning of the forecast.
Near real-time post-processing of hydrological forecasts using Artificial Intelligence/Machine Learning techniques to integrate observational data to reduce modelling and forecasting errors.
Expanding the CEMS EWS forecast product portfolio for floods and droughts
by developing new extreme hydrometeorological event detection algorithms applicable worldwide: Flash Drought forecast product, Seasonal Drought forecast product, Drought tracking product, Flash Flood forecast product.
Use cases
The project is composed of two distinct phases – a scientific development phase and a scale-up validation phase – linked together by a prototyping stage. Use cases are essential in SEED-FD, providing real-world scenarios to test and refine our hydrological models and forecasting tools. By focusing on specific regions, the project ensures its solutions are effective, adaptable, and reliable, ultimately enhancing disaster preparedness and resilience across diverse environments.
Development Phase: Danube and Bhima
In the development phase, SEED-FD focuses on the Danube and Bhima River Basins. These data-rich regions serve as testbeds to implement and evaluate evolutions before they reach maturity, benchmarking against the existing CEMS EWS capability for forecasting floods and droughts. The Danube flows through diverse climates in Central and Eastern Europe, while the Bhima in India experiences monsoon-driven weather, enhancing model accuracy and adaptability.
Validation Phase: Juba-Shebelle, Niger and Paraná
During the validation phase, SEED-FD expands efforts to the Juba-Shebelle, Niger and Paraná River Basins. The Juba-Shebelle Basin in the Horn of Africa faces semi-arid conditions and frequent droughts. The Niger River in West Africa experiences seasonal floods, while the Paraná River in South America has significant hydrological variability. These areas represent diverse hydrological challenges, allowing to fine-tune the forecasting models and enhance prediction accuracy.
Validation Phase: World
In the final step of the validation phase, SEED-FD demonstrates the global applicability of new hydrometeorological extreme event detection and prediction products. By applying advanced algorithms to existing CEMS datasets, SEED-FD compares results with documented real-life events. This aims to enhance global disaster preparedness, providing precise flood and drought predictions around the world.
Partners
The SEED-FD project is a collaborative initiative carried out by a diverse and competent consortium of organisations, each contributing their unique expertise:
Magellium (France, prime), ECMWF (European Centre for Medium-Range Weather Forecasts, science leader), CNR-IRPI (Italy, Research Institute for Geo-Hydrological Protection of the Italian National Research Council), ICPAC (Kenya, Intergovernmental Authority on Development (IGAD) Climate Prediction and Application Center), IIASA (Austria, International Institute for Applied Systems Analysis), vorteX-io (France), POLIMI (Italy, Politecnico di Milano), Design & Data (Germany), JRC (European Union, Joint Research Centre)
To read more about each consortium member and their specific roles in the project, please click here.
ContaCT
The SEED-FD project consortium is coordinated by:
Magellium SAS
1 Rue Ariane
31520 Ramonville-Saint-Agne
France
If you have any questions, you can contact the SEED-FD team via email.
