GRACE/GRACE-FO空窗期的陆地水储量变化数据间断补偿:以全球典型流域为例

Data filling of terrestrial water storage anomaly during the gap period of GRACE/GRACE-FO:a case study of global typical basins

陆地水储量异常(TWSA)的长期可持续监测对研究水循环过程、合理配置水资源等具有重要的科学意义,针对GRACE与GRACE-FO数据之间存在空窗期问题,本文引入了奇异谱分析(SSA)与ARMA模型的组合方法对TWSA的间断进行补偿.为比较SSA+ARMA方法在典型流域的适用性,将GRACE球谐系数(SH)反演的2003年1月至2012年12月的TWSA作为训练样本,2013年1月至2016年8月的TWSA作为真值,分别利用ARMA、小波神经网络(WNN)、SSA和SSA+ARMA方法,在亚马逊流域(AZRB)、多瑙河流域(DNRB)、恒河流域(GNRB)、密西西比河流域(MSRB)、尼日尔河流域(NGRB)、伏尔加河流域(VGRB)、叶尼塞河流域(YNSRB)、长江流域(YZRB)八个典型流域进行预测试验,并采用均方根误差(RMSE)、纳什效率系数(NSE)、相关系数(R)技术指标评定不同方法的预测精度.预测试验结果表明,四种方法均在NGRB的预测效果最好,该流域TWSA序列周期特征最为明显;四种方法中,SSA+ARMA方法预测精度较高且相对稳定,多数流域的RMSE在4 cm以内,NSE值在0.75以上,R值在0.85以上.后续以2003年1月至2016年8月TWSA作为样本,利用SSA+ARMA方法补偿2016年9月至2020年2月的TWSA间断结果,并结合GRACE-FO SH反演的TWSA结果、Mascons结果、GLDAS数据评定补偿结果的精度.结果表明,TWSA间断补偿结果有较高的精度,与后续GRACE-FO SH反演TWSA结果有很强的一致性,多数流域的RMSE在4 cm以内,NSE值在0.8以上,R值在0.9以上.且与Mascons、GLDAS结果的符合精度亦能反映补偿结果的可靠性.上述研究表明,利用SSA+ARMA方法可以对GRACE/GRACE-FO空窗期的TWSA间断进行有效补偿.

The long-term monitoring of TWSA(Terrestrial Water Storage Anomaly)is of great scientific significance to the research on water circulation and the allocation of water resources.Therefore,the problem caused by the data window period between GRACE(Gravity Recovery and Climate Experiment)and GRACE-FO(GRACE Follow-On)must be solved.In this paper,SSA(Singular Spectrum Analysis)+ARMA(Autoregressive Moving Average)is used to realize an iterative prediction and gap compensation of TWSA for the data window period.The TWSA of GRACE SH(Spherical Harmonic)inversion between January 2003 and December 2012 are as training samples and the TWSA between January 2013 and August 2016 as true values.Under this premise,through three approaches:ARMA,Wavelet Neural Network(WNN),SSA and SSA+ARMA,forecast tests are respectively carried out in eight typical basins:AZRB(Amazon River Basin),DNRB(Danube River Basin),GNRB(Ganges River Basin),MSRB(Mississippi River Basin),NGRB(Niger River Basin),VGRB(Volga River Basin),YNSRB(Yenisei River Basin)and YZRB(Yangtze River Basin).The accuracy of each approach is measured by RMSE(Root Mean Square Error),NSE(Nash-Sutcliffe Efficiency Coefficient)and R(Correlation Coefficient).The results show that prediction appears best in NGRB because there are strong periodic signals in TWSA time series.Also among the three approaches,SSA is shown to have the highest precision with the RMSE values of most basins less than 4 cm,the relevant NSE over 0.75 and the R above 0.85.So it is demonstrated that SSA+ARMA is excellent in identifying and extracting effective information from complicated signals.Following the first test,the TWSA from January 2003 to August 2016 are taken as training samples,and SSA approach is used to predict the TWSA from September 2016 to February 2020 and fill the data gaps.Through a validation comparing data from the TWSA of GRACE SH inversion,Mascons and GLDAS(Global Land Data Assimilation System),the filling results of TWSA have a high accuracy.Moreover,the results turn out more consistent with the TWSA of GRACE-FO SH inversion,and the RMSE values of most basins are less than 4 cm,the relevant NSE values are above 0.8 and the R values are above 0.9.To some extent,the consistency with Mascons and GLDAS data also proves the validity of the results.In conclusion,the research demonstrates that it is feasible and effective to use SSA+ARMA prediction to realize a data filling of TWSA during the gap period of GRACE/GRACE-FO.