Learning-based Reconstruction of GRACE Data Based on Changes in Total Water Storage and Its Accuracy Assessment

IF 0.7 4区 地球科学 Q4 GEOCHEMISTRY & GEOPHYSICS
Yong Su, Yi-Fei Yang, Yi-Yu Yang
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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 learning-based 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 five metrics: the Nash–Sutcliffe efficiency coefficient (NSE), Pearson correlation coefficient (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 filling in discontinuous data in temporal gravity field models..

基于总蓄水量变化的 GRACE 数据学习型重构及其精度评估
自 2002 年 4 月以来,重力恢复和气候实验卫星(GRACE)提供了全球范围内的月度总蓄水量异常值(TWSAs)。然而,由于 GRACE 的一些观测数据缺失,这些总蓄水量异常值是不连续的。本研究在没有水文模型数据的情况下提出了一种基于机器学习的组合建模算法。全球 11 个大区域的 TWSA 时间序列数据被分为训练集和测试集。使用了自回归综合移动平均(ARIMA)、长短期记忆(LSTM)和 ARIMA-LSTM 组合模型。模型预测结果与 GRACE 观测结果进行了比较,并使用五个指标对模型精度进行了评估:纳什-苏特克利夫效率系数(NSE)、皮尔逊相关系数(CC)、均方根误差(RMSE)、归一化均方根误差(NRMSE)和平均绝对百分比误差。结果表明,在流域尺度上,ARIMA-LSTM 模型的平均 CC、NSE 和 NRMSE 分别为 0.93、0.83 和 0.12。在网格尺度上,本研究比较了亚马逊河流域和伏尔加河流域指标的空间分布和累积分布函数曲线。ARIMA-LSTM 模型在亚马逊河流域和伏尔加河流域的平均 CC 值和 NSE 值分别为 0.89 和 0.61 以及 0.92 和 0.61,优于 ARIMA 模型(分别为 0.86 和 0.48 以及 0.88 和 0.46)和 LSTM 模型(分别为 0.80 和 0.41 以及 0.89 和 0.31)。在 ARIMA-LSTM 模型中,两个流域 NSE 为 0.50 的网格单元比例分别为 63.3% 和 80.8%,而在 ARIMA 模型中分别为 54.3% 和 51.3%,在 LSTM 模型中分别为 53.7% 和 43.2%。ARIMA-LSTM模型显著提高了预测的NSE值,同时保证了GRACE数据重建在两个尺度上的高CC值,这有助于填补时间重力场模型中的不连续数据。
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来源期刊
Applied Geophysics
Applied Geophysics 地学-地球化学与地球物理
CiteScore
1.50
自引率
14.30%
发文量
912
审稿时长
2 months
期刊介绍: The journal is designed to provide an academic realm for a broad blend of academic and industry papers to promote rapid communication and exchange of ideas between Chinese and world-wide geophysicists. The publication covers the applications of geoscience, geophysics, and related disciplines in the fields of energy, resources, environment, disaster, engineering, information, military, and surveying.
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