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Abstract: Machine Learning Based Dynamic Integration of Grace-Based Water Storage into Global Hydrological Models (AGU25)

Machine Learning Based Dynamic Integration of Grace-Based Water Storage into Global Hydrological Models

Fitsume T Wolkeba, University of Alabama, Tuscaloosa, AL, United States, Mesfin Mergia Mekonnen, University of Alabama, Department of Civil, Construction and Environmental Engineering, Tuscaloosa, United States, Hamid Moradkhani, The University of Alabama, Center for Complex Hydrosystems Research, Tuscaloosa, United States and Mukesh Kumar, University of Alabama, Tuscaloosa, United States
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