Abstract This paper first discusses the accuracy and feasibility of using different activation functions of BP and RBF neural network methods to fill the gap data of GRACE and GRACE-FO satellites, and fills in the missing data based on the optimal scheme. We use the ITSG-Grace2018 and ITSG-Grace operational time-varying gravity field models to derive the changes of TWS in the Yangtze river basin (YRB) from 2002 to 2020, and finally, combining with the GLDAS model, precipitation, temperature, the Yangtze River Water Resources Bulletin and other data, we comprehensively analyze the changes of TWS. The research results show that: 1) The BP neural network algorithm with the hidden layer activation function as the rectified linear unit (ReLU) is effective in filling the data gap between GRACE and GRACE-FO satellite missions; 2) TWS changes in the YRB have certain regional differences, which are mainly manifested in TWS increase in eastern part of the upstream and most part of the midstream at a rate of about 5 mm/a, and decline in the upstream mid-west, while the downstream is basically unchanged. The GRACE/GRACE-FO long-term series time-varying model can reflect the drought in 2019 and the floods in 2017 and 2019 in the YRB.
XUAN Jianhao,CHEN Zhiwei,ZHANG Xingfu et al. Combining GRACE and GRACE-FO to Derive Terrestrial Water Storage Changes in the Yangtze River Basin from 2002 to 2020[J]. jgg, 2021, 41(9): 961-966.
XUAN Jianhao,CHEN Zhiwei,ZHANG Xingfu et al. Combining GRACE and GRACE-FO to Derive Terrestrial Water Storage Changes in the Yangtze River Basin from 2002 to 2020[J]. jgg, 2021, 41(9): 961-966.