Improving Local Groundwater Monitoring with High-Resolution Downscaling of GRACE Data in Phoenix, Arizona Using Machine Learning

Rayhan, Md

The deadline to submit a session, town hall, or pre-conference workshop proposal for AGU25 has passed. Please search for and review proposed sessions below. Please note this site is frozen as of 4/29/25 and will not reflect any changes to proposals made after this date.

Conveners will be contacted by the AGU25 Program Committee with any requests for mergers or edits to proposals. Conveners will be notified of the acceptance of their proposal in mid-June.

Abstract submissions will also open in mid-June and the deadline for abstract submissions is 30 July, 2025.

Please visit the AGU25 website for more information or please contact AGU with any questions.

Abstract: Improving Local Groundwater Monitoring with High-Resolution Downscaling of GRACE Data in Phoenix, Arizona Using Machine Learning (AGU25)

Improving Local Groundwater Monitoring with High-Resolution Downscaling of GRACE Data in Phoenix, Arizona Using Machine Learning

Md Rayhan¹, Karem Abdelmohsen², Hrishi Chandanpurkar² and James S Famiglietti², (1)Arizona State University, School of Civil, Environmental, and Sustainable Engineering, Tempe, AZ, United States, (2)Arizona State University, School of Sustainability, Tempe, United States