Hydroterra+ (H+) is a project funded by ESA (European Space Agency) dedicated to Earth observation, focusing specifically on the rapid processes of the water cycle (from a few hours to a few days) through the study of a new SAR satellite. More precisely, it represents the scientific feasibility and applicability study for the eponymous mission under ESA’s Earth Explorers 12 program.
The project targets Europe, the Mediterranean basin, and parts of Africa, regions particularly vulnerable to the impacts of climate change. The H+ mission proposes the use of a synthetic aperture radar (SAR) in a quasi-geostationary orbit, enabling continuous observation with high temporal resolution. This approach aims to address a critical observational gap, as current satellite missions cannot provide adequate data for regional-scale processes with high temporal frequency.
The Hydroterra+ project focuses on four main areas of study and monitoring:
- Monitoring mesoscale convective systems (MCS): These systems are responsible for extreme weather events and a significant portion of tropical rainfall.
- Quantifying the diurnal water cycle balance: This includes a particular focus on soil moisture, irrigation, and vegetation phenomena.
- Studying the cryospheric water balance: The focus here is on snow accumulation and melting, poorly observed yet crucial processes for water availability in mountainous regions.
- Monitoring ground movement: This involves detecting phenomena linked to earthquakes, volcanoes, and landslides.
The contribution of CIMA Research Foundation
CIMA Research Foundation serves as the scientific coordinator and represents the Mission Advisory Group for the project. Our work involves developing high-resolution models to improve the prediction of extreme weather events, particularly in areas with complex topography. By testing hypothetical satellite data from the project, we integrate soil moisture and water vapor observations into numerical forecasting models to enhance prediction accuracy. Additionally, our activities include improving satellite data assimilation techniques for forecasting models, with the aim of enhancing Early Warning Systems (EWS).
