Abstract

Groundwater is an essential natural resource for human societies and ecosystems. Traditional groundwater monitoring relies on in situ wells, which are susceptible to discontinuity, influencing water resource management. To overcome this deficiency, this study proposes a remote sensing-based groundwater level (GWL) monitoring system that uses machine learning (ML) algorithms and remotely sensed hydrological parameters to reconstruct well-specific GWL time series. Four machine learning algorithms, including K-Nearest Neighbor (KNN), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and a weight-mean Ensemble strategy, were adopted to construct the models at each well individually for monitoring GWL. Specifically, the GWL data for ~770 wells across the conterminous United States (CONUS) were modeled using remotely sensed precipitation (P), evapotranspiration (ET), terrestrial water storage anomaly (TWSA), and soil moisture (SM) datasets during the period from 2004 to 2019. Afterwards, the performances of models were evaluated during an independent period from 2020 to 2023. The results show that the Ensemble model outperforms the individual baseline models evaluated in this study (i.e., KNN, RF, and XGBoost), achieving a mean coefficient of determination (R2) of 0.81, root mean square error (RMSE) of 0.34 m, normalized RMSE (NRMSE) of 11.8%, and Nash–Sutcliffe efficiency (NSE) of 0.78. The results demonstrate that the proposed system can effectively reconstruct GWL dynamics for most wells. This can be a compensation for missing records for hydrologically significant wells, which are those with historical groundwater observations.

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