A Comparative Study of Downscaling Methods for Groundwater Based on GRACE Data Using RFR and GWR Models in Jiangsu Province, China - Rihui Yang; Yuqing Zhong; Xiaoxiang Zhang; Aizemaitijiang Maimaitituersun; Xiaohan Ju (Remote Sensing)
The Gravity Recovery and Climate Experiment (GRACE) introduces a new approach to accurately monitor, in real time, regional groundwater resources, which compensates for the limitations of traditional hydrological observations in terms of spatiotemporal resolution. Currently, observations of groundwater storage changes in Jiangsu Province face issues such as low spatial resolution, limited applicability of the downscaling models, and insufficient water resource observation data. This study based on GRACE employs Random Forest Regression (RFR) and Geographically Weighted Regression (GWR) methods in order to obtain high-resolution information on groundwater storage change. The results indicate that among the established 66 × 158 local GWR models, the coefficient of determination (R2) ranges from 0.39 to 0.88, with a root mean squared error (RMSE) of approximately 2.60 cm. The proportion of downscaling models with an R2 below 0.5 was 18.52%. Similarly, the RFR models trained on the above time series grid data achieved an R2 of 0.50, with the RMSE fluctuating around 1.59 cm. In the results validation, the monthly correlation coefficients between the GWR downscaling results and the data of measured stations ranged from 0.37 to 0.66, with 53.33% of the stations having a coefficient greater than 0.5. The seasonal correlation coefficients ranged from 0.41 to 0.62, with 60% of the stations exceeding 0.5. The correlation coefficients for the RFR downscaling results ranged from 0.44 to 0.88, with seasonal correlation coefficients ranging from 0.49 to 0.84. Only one station had a correlation coefficient below 0.5 for both monthly and seasonal results. In the validation of the correlation accuracy between the downscaling results and the measured groundwater levels, the Random Forest model demonstrated better predictive performance, which offers distinct advantages in improving the spatial resolution of groundwater storage changes in Jiangsu Province.