Projects per year
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, Bayesian-based Data Assimilation (DA) framework(s) will be developed to merge available satellite data with hydrological models to better understand and forecast the recent and future spatial-temporal changes in continental water storage and water fluxes.
|Effective start/end date||08/09/2022 → 07/08/2024|
UN Sustainable Development Goals
In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This project contributes towards the following SDG(s):
Bayesian Frameworks for Separating Global Ocean Mass, Hydrology, and Surface Deformation Signals from GRACE Data
01/10/2017 → 31/12/2020
- 1 Journal article
Making the Best Use of GRACE, GRACE-FO and SMAP Data Through a Constrained Bayesian Data-Model IntegrationMehrnegar, N., Schumacher, M., Jagdhuber, T. & Forootan, E., Sept 2023, In: Water Resources Research. 59, 9, e2023WR034544.
Research output: Contribution to journal › Journal article › Research › peer-reviewOpen AccessFile