Assessing Groundwater Level Change and Aquifer Parameters Across Los Angeles and Orange County Using InSAR Measurements and Machine Learning

Combining observation well data and Interferometric Synthetic Aperture Radar (InSAR) deformation has shown great promise in groundwater levels monitoring and aquifer parameters estimation, which is crucial for groundwater management and related natural hazards anticipating. However, insufficient observation wells bring a drawback of this means in obtaining high spatial resolution results. Here, we employ a machine learning approach, the optimizable Gaussian regression learner, to integrate these two datasets to derive pixel-scale groundwater levels across Los Angeles and Orange County from Jun. 2015 to Nov. 2021. The results are validated with groundwater levels revealed from relative changes in seismic velocity (dv/v) measured at a dense network. With the innovative results, we found an interesting phenomenon that the groundwater levels show an overall rising trend during the study period, which is prominent in the San Gabriel Valley and the Coastal Plain of Orange County (CPOC) basins with mean rate of 0.78 ± 0.04 and 0.31 ± 0.05 m/yr, respectively. Besides, the strongest seasonal groundwater level changes are observed in the CPOC with mean annual amplitude of 0.97 ± 0.16 mm, which results in seasonal ground deformation for 3.58 ± 0.51 mm. Moreover, by analyzing the long-term hydraulic head records in these two basins, we identify that the rising trends should be attributed to ∼ 6.5-year interannual variations. Our study underlines the importance of integrating multitemporal InSAR data with hydraulic head measurements to accurately quantify changes in groundwater levels, determine aquifer parameters, and assess variations in groundwater storage.