Downscaling Groundwater Storage Data in China to a 1-km Resolution Using Machine Learning Methods

Abstract

High-resolution and continuous hydrological products have tremendous importance for the prediction of water-related trends and enhancing the capability for sustainable water resources management under climate change and human impacts. In this study, we used the random forest (RF) and extreme gradient boosting (XGBoost) methods to downscale groundwater storage (GWS) from 1° (~110 km) to 1 km by downscaling Gravity Recovery and Climate Experiment (GRACE) and Global Land Data Assimilation System (GLDAS) data from 1° (~110 km) and 0.25° (~25 km) respectively, to 1 km for China. Three evaluation metrics were employed for the testing dataset for 2004−2016: The R2 ranged from 0.77−0.89 for XGBoost (0.74−0.86 for RF), the correlation coefficient (CC) ranged from 0.88−0.94 for XGBoost (0.88−0.93 for RF) and the root-mean-square error (RMSE) ranged from 0.37−2.3 for XGBoost (0.4−2.53 for RF). The R2 of the XGBoost models for GLDAS was 0.64−0.82 (0.63−0.82 for RF), the CC was 0.80−0.91 (0.80−0.90 for RF) and the RMSE was 0.63−1.75 (0.63−1.77 for RF). The downscaled GWS derived from GRACE and GLDAS were validated using in situ measurements by comparing the time series variations and the downscaled products maintained the accuracy of the original data. The interannual changes within 9 river basins between pre- and post-downscaling were consistent, emphasizing the reliability of the downscaled products. Ultimately, annual downscaled TWS, GLDAS and GWS products were provided from 2004 to 2016, providing a solid data foundation for studying local GWS changes, conducting finer-scale hydrological studies and adapting water resources management and policy formulation to local condition.