Learning-based Reconstruction of GRACE Data Based on Changes in Total Water Storage and Its Accuracy Assessment
Su Yong,*, Yang Yi-Fei, Yang Yi-Yu1
1. School of Civil Engineering and Geomatics, Southwest Petroleum University, Chengdu 610500, China
2. School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China
3. Hubei Luojia Laboratory, Wuhan 430079, China
4. Xizang Autonomous Region Key Laboratory of Satellite Remote Sensing and Application, Lhasa 851400, China
Abstract Since April 2002, the Gravity Recovery and Climate Experiment Satellite (GRACE) has provided monthly total water storage anomalies (TWSAs) on a global scale. However, these TWSAs are discontinuous because some GRACE observation data are missing. This study presents a combined machine learningbased modeling algorithm without hydrological model data. The TWSA time-series data for 11 large regions worldwide were divided into training and test sets. Autoregressive integrated moving average (ARIMA), long short-term memory (LSTM), and an ARIMA–LSTM combined model were used. The model predictions were compared with GRACE observations, and the model accuracy was evaluated using fi ve metrics: the Nash–Sutcliff e effi ciency coeffi cient (NSE), Pearson correlation coeffi cient (CC), root mean square error (RMSE), normalized RMSE (NRMSE), and mean absolute percentage error. The results show that at the basin scale, the mean CC, NSE, and NRMSE for the ARIMA–LSTM model were 0.93, 0.83, and 0.12, respectively. At the grid scale, this study compared the spatial distribution and cumulative distribution function curves of the metrics in the Amazon and Volga River basins. The ARIMA–LSTM model had mean CC and NSE values of 0.89 and 0.61 and 0.92 and 0.61 in the Amazon and Volga River basins, respectively, which are superior to those of the ARIMA model (0.86 and 0.48 and 0.88 and 0.46, respectively) and the LSTM model (0.80 and 0.41 and 0.89 and 0.31, respectively). In the ARIMA–LSTM model, the proportions of grid cells with NSE > 0.50 for the two basins were 63.3% and 80.8%, while they were 54.3% and 51.3% in the ARIMA model and 53.7% and 43.2% in the LSTM model. The ARIMA–LSTM model significantly improved the NSE values of the predictions while guaranteeing high CC values in the GRACE data reconstruction at both scales, which can aid in fi lling in discontinuous data in temporal gravity fi eld models.
Fund: This study was financially supported by The National Natural Science Foundation of China (42374004), the Open Fund of Hubei Luojia Laboratory (220100045), and the Natural Science Foundation of Sichuan Province (2022NSFSC1047).
Corresponding Authors: Su Yong (Email: suyongme@foxmail.com).
E-mail: suyongme@foxmail.com
About author: Su Yong graduated from Southwest Jiaotong University with a PhD in Geodesy and Surveying Engineering. He is currently an associate professor at the School of Civil Engineering and Geomatics at Southwest Petroleum University. His main research interests include satellite gravity measurement data processing and related theories and techniques for high-precision gravity field model calculation.
Cite this article:
. Learning-based Reconstruction of GRACE Data Based on Changes in Total Water Storage and Its Accuracy Assessment[J]. APPLIED GEOPHYSICS, 2025, 22(2): 365-382.
2025, Vol. 22 
