An Integrated Geodetic Approach To Understand Central Valley Water Resources
Profile
An Integrated Geodetic Approach To Understand Central Valley Water Resources
PIs: Venkat Chandrasekaran and Andrew M. Stuart
Research Team: Mike Turmon, PhD
Division of Engineering and Applied Science, CMS
Water Resources Initiative
This project will create a physically-informed machine learning model for the relationship between land subsidence and groundwater abundance in California's Central Valley aquifer system. Integrating observable deformation and pressure relationships using Interferometric Synthetic Aperture Radar (InSAR) and Global Positioning System (GPS) measurements of surface deformation with groundwater withdrawal measurement patterns, we aim to develop a new statistical framework for future subsidence responses to groundwater use. The resulting model opens up the possibility of machine learning-based decision making for sustainable groundwater management and infrastructure development.