Summary
Climate change and anthropogenic modifications are bringing multiple changes in different regions of the world, affecting the patterns of rainfall, evapotranspiration and stored terrestrial water, which will increase the probability of climate disasters, such as agricultural losses, water scarcity, and famine. Drought monitoring and hydrological early warning system are important tools for water resources management, and they must be further complemented by forecasting facilities that are well integrated with the EU’s Earth Observation data. In this project, based on Forootan and Mehrnegar's expertise, an accurate and efficient, as well as physically and mathematically consistence Bayesian-based Data Assimilation (DA) framework(s) will be developed to integrate the benefits of synergistically available satellite geodetic and Earth Observation (EO) data and the state-of-the-art of hydrological models to better understand and forecast the recent and future spatial-temporal changes in continental water storage and water fluxes. The proposed Multi-Sensor Bayesian Data Assimilation (MuSe-BDA) are unique in terms of flexibility to assimilate various satellite data, and they are computationally efficient.
Building on the effort in MuSe-BDA, this is the first attempt to simultaneously merge multi-land surface models with satellite-derived Surface Soil Moisture (SSM), Surface Water Level (SWL) anomaly from satellite altimetry, Land Surface Temperature (LST) from remote sensing data, and gravity field estimates from GRACE and GRACE-FO missions. The application will be demonstrated in simulating and forecasting episodic large-scale droughts within Europe (north and south) and USA (e.g., California and Texas) covering 2003-onward with an unprecedented spatial resolution of 0.05° (~5 km) at daily temporal rate, which is essential for practical applications such as agricultural early warning and the assimilation of satellite data ensures the compatibility with the real world.
Building on the effort in MuSe-BDA, this is the first attempt to simultaneously merge multi-land surface models with satellite-derived Surface Soil Moisture (SSM), Surface Water Level (SWL) anomaly from satellite altimetry, Land Surface Temperature (LST) from remote sensing data, and gravity field estimates from GRACE and GRACE-FO missions. The application will be demonstrated in simulating and forecasting episodic large-scale droughts within Europe (north and south) and USA (e.g., California and Texas) covering 2003-onward with an unprecedented spatial resolution of 0.05° (~5 km) at daily temporal rate, which is essential for practical applications such as agricultural early warning and the assimilation of satellite data ensures the compatibility with the real world.
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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/101068561 |
| Start date: | 08-09-2022 |
| End date: | 07-09-2024 |
| Total budget - Public funding: | - 230 774,00 Euro |
Cordis data
Original description
Climate change and anthropogenic modifications are bringing multiple changes in different regions of the world, affecting the patterns of rainfall, evapotranspiration and stored terrestrial water, which will increase the probability of climate disasters, such as agricultural losses, water scarcity, and famine. Drought monitoring and hydrological early warning system are important tools for water resources management, and they must be further complemented by forecasting facilities that are well integrated with the EU’s Earth Observation data. In this project, based on Forootan and Mehrnegar's expertise, an accurate and efficient, as well as physically and mathematically consistence Bayesian-based Data Assimilation (DA) framework(s) will be developed to integrate the benefits of synergistically available satellite geodetic and Earth Observation (EO) data and the state-of-the-art of hydrological models to better understand and forecast the recent and future spatial-temporal changes in continental water storage and water fluxes. The proposed Multi-Sensor Bayesian Data Assimilation (MuSe-BDA) are unique in terms of flexibility to assimilate various satellite data, and they are computationally efficient.Building on the effort in MuSe-BDA, this is the first attempt to simultaneously merge multi-land surface models with satellite-derived Surface Soil Moisture (SSM), Surface Water Level (SWL) anomaly from satellite altimetry, Land Surface Temperature (LST) from remote sensing data, and gravity field estimates from GRACE and GRACE-FO missions. The application will be demonstrated in simulating and forecasting episodic large-scale droughts within Europe (north and south) and USA (e.g., California and Texas) covering 2003-onward with an unprecedented spatial resolution of 0.05° (~5 km) at daily temporal rate, which is essential for practical applications such as agricultural early warning and the assimilation of satellite data ensures the compatibility with the real world.
Status
SIGNEDCall topic
HORIZON-MSCA-2021-PF-01-01Update Date
09-02-2023
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