Machine Learning application for modeling high-resolution groundwater storage variations in North China Plain

Abstract

Abstract North China Plain (NCP) in China is an important agricultural region increasingly dependent on groundwater to meet the demands of water for irrigation which consequently has resulted in groundwater depletion. Quantifying spatio-temporal variations of groundwater storage (GWS) is important in NCP for monitoring groundwater depletion. Gravity Recovery and Climate Experiment (GRACE) satellite data provide the potential for quantifying regional GWS changes. However, its coarse spatial resolution and errors in disaggregation have limited the application of GRACE for localized groundwater studies, which are essential for effective groundwater management. We, therefore, implement a Random Forest (RF) Machine Learning (ML) model to establish an empirical relationship between GRACE-derived Terrestrial Water Storage variations (TWS), hydro-meteorological variables, and available in situ groundwater level data for shallow and deep aquifers. In-situ and RF modeled groundwater level variations show a high correlation during training and validation. Therefore, the modeled empirical relationship was extended to the whole of NCP to produce monthly GWS variations at 5 km resolution. This resolution is similar to previous downscaling studies. Deep aquifers show rapid GWS losses compared to shallow aquifers suggesting a relatively slow recharge process of the deep layers of the groundwater reservoirs. The methodology presented in this paper shows an effective downscaling of GRACE mass change observations for localized GWS assessment which can also be replicated in other regions.