Agarwal, Vibhor

Akyilmaz, Orhan

Forootan, Ehsan

Haritashya, Umesh K.

Shum, CK

Syed, Tajdarul Hassan

Uz, Metehan

Wei, Feng

Yang, Ting-Yi

English

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 km 3 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 km 3 yr −1 and -9.8 ± 1.7 km 3 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.</p

Feng, Wei

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

https://vbn.aau.dk/files/508873030/Vibhor2023_Accepted.pdf

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

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