CMX Student/Postdoc Seminar

Overdrafting of groundwater induced by erratic changes in climate has severely stressed California's Central Valley (CV) aquifer system, leading to environmental hazards such as land subsidence. Designing hazard-free groundwater management strategies requires spatio-temporally continuous predictions of how the groundwater table and land surface respond to water recharge and discharge. This generally is challenging in CV due to the sparse and noisy nature of available well head data, missing information on groundwater pumping rates and hydrogeological heterogeneity. To address these challenges, we propose a machine learning (ML) approach to estimating CV groundwater levels and land subsidence continuously across space and time. Our preliminary investigations consist of employing Gaussian process (GP) regression on available well head data and InSAR remote sensing measurements of surface deformation in southern CV. We propose a linear model to capture the seasonal and long-term temporal trends observed in the raw data. Spatial continuity of model parameters is imposed with multi-output GPs. We discuss the linear model of co-regionalization for building permissible covariance kernels in the multivariate setting. We demonstrate the applicability of proposed GP modeling approach to real data in the CV, along with a discussion on advantages and limitations.