Machine learning based downscaling of GRACE-estimated groundwater in Central Valley, California

• Published in: The Science of the total environment 2023-03, Vol.865, p.161138-161138, Article 161138
• Main Authors: Agarwal, Vibhor, Akyilmaz, Orhan, Shum, C.K., Feng, Wei, Yang, Ting-Yi, Forootan, Ehsan, Syed, Tajdarul Hassan, Haritashya, Umesh K., Uz, Metehan
• Format: Article
• Language: English
• Subjects: algorithms; altimetry; aquifers; California; climate; drought; environment; forestry equipment; GRACE; gravimetry; Groundwater; irrigation; Machine learning; radar; Remote sensing; satellites; subsidence; water shortages; water table
• ISSN: 0048-9697; 1879-1026
• DOI: 10.1016/j.scitotenv.2022.161138

California's Central Valley, one of the most agriculturally productive regions, is also one of the most stressed aquifers in the world due to anthropogenic groundwater over-extraction primarily for irrigation. Groundwater depletion is further exacerbated by climate-driven droughts. Gravity Recovery and Climate Experiment (GRACE) satellite gravimetry has demonstrated the feasibility of quantifying global groundwater storage changes at uniform monthly sampling, though at a coarse resolution and is thus impractical for effective water resources management. Here, we employ the Random Forest machine learning algorithm to establish empirical relationships between GRACE-derived groundwater storage and in situ groundwater level variations over the Central Valley during 2002–2016 and achieved spatial downscaling of GRACE-observed groundwater storage changes from a few hundred km to 5 km. Validations of our modeled groundwater level with in situ groundwater level indicate excellent Nash-Sutcliffe Efficiency coefficients ranging from 0.94 to 0.97. In addition, the secular components of modeled groundwater show good agreements with those of vertical displacements observed by GPS, and CryoSat-2 radar altimetry measurements and is perfectly consistent with findings from previous studies. Our estimated groundwater loss is about 30 km3 from 2002 to 2016, which also agrees well with previous studies in Central Valley. We find the maximum groundwater storage loss rates of −5.7 ± 1.2 km3 yr−1 and -9.8 ± 1.7 km3 yr−1 occurred during the extended drought periods of January 2007–December 2009, and October 2011–September 2015, respectively while Central Valley also experienced groundwater recharges during prolonged flood episodes. The 5-km resolution Central Valley-wide groundwater storage trends reveal that groundwater depletion occurs mostly in southern San Joaquin Valley collocated with severe land subsidence due to aquifer compaction from excessive groundwater over withdrawal. [Display omitted] •Machine Learning approach to integrate in-situ groundwater and remote sensing data•Groundwater storage variations for one and a half-decade at 5 km resolution•Model the impact of two droughts on groundwater storage variations•Novel application for broader applicability of GRACE gravimetry data