干旱区生态环境知识资源中心(Arid): Bayesian convolutional neural networks for predicting the terrestrial water storage anomalies during GRACE and GRACE-FO gap

Arid

DOI	10.1016/j.jhydrol.2021.127244
	Bayesian convolutional neural networks for predicting the terrestrial water storage anomalies during GRACE and GRACE-FO gap
	Mo, Shaoxing; Zhong, Yulong; Forootan, Ehsan; Mehrnegar, Nooshin; Yin, Xin; Wu, Jichun; Feng, Wei; Shi, Xiaoqing
通讯作者	Wu, JC (corresponding author)，Nanjing Univ, Sch Earth Sci & Engn, Minist Educ, Key Lab Surficial Geochem, Nanjing, Peoples R China. ; Feng, W (corresponding author)，Sun Yat Sen Univ, Sch Geospatial Engn & Sci, Zhuhai, Peoples R China.
来源期刊	JOURNAL OF HYDROLOGY
ISSN	0022-1694
EISSN	1879-2707
出版年	2022
卷号	604
英文摘要	The monthly terrestrial water storage anomaly (TWSA) observations during the gap period between the Gravity Recovery and Climate Experiment (GRACE) satellite and its Follow-On (GRACE-FO) are missing, leading to discontinuity in the time series, and thus, impeding full utilization and analysis of the data. Despite previous efforts undertaken to tackle this issue, a gap-filling TWSA product with desirable accuracy at a global scale is still lacking. In this study, a straightforward and hydroclimatic data-driven Bayesian convolutional neural network (BCNN) is proposed to bridge this gap. Benefiting from the excellent capability of BCNN in handling image data and the integration of recent deep learning advances (including residual-skip connections and spatial-channel attentions), the proposed method can automatically extract informative features for TWSA predictions from multiple predictor data. The BCNN predictions are compared with reanalyzed/simulated TWSA, Swarm solution, and the TWSA prediction products generated by three recent studies, using commonly used accuracy metrics. Results demonstrate BCNN's superior performance to obtain higher-quality TWSA predictions, particularly in relatively arid regions. Additionally, a comparison with two independent datasets at the basin scale further suggests that the BCNN-infilled TWSA is reliable to bridge the gap and enhance data consistency. Our gap-filling product can ultimately contribute to correcting the bias in long-term trend estimates, maintaining the continuity of TWSA time series and thus benefiting subsequent applications desiring continuous data records.
英文关键词	GRACE Bayesian convolutional neural network Gap filling ERA5 Deep learning
类型	Article
语种	英语
开放获取类型	Green Submitted
收录类别	SCI-E
WOS记录号	WOS:000751993100002
WOS关键词	ENCODER-DECODER NETWORKS ; DATA ASSIMILATION ; GROUNDWATER ; DROUGHT ; DEPLETION ; TRENDS
WOS类目	Engineering, Civil ; Geosciences, Multidisciplinary ; Water Resources
WOS研究方向	Engineering ; Geology ; Water Resources
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/376906
作者单位	[Mo, Shaoxing; Wu, Jichun; Shi, Xiaoqing] Nanjing Univ, Sch Earth Sci & Engn, Minist Educ, Key Lab Surficial Geochem, Nanjing, Peoples R China; [Zhong, Yulong] China Univ Geosci Wuhan, Sch Geog & Informat Engn, Wuhan, Peoples R China; [Forootan, Ehsan; Mehrnegar, Nooshin] Aalborg Univ, Dept Planning, Geodesy Grp, Aalborg, Denmark; [Yin, Xin] Nanjing Hydraul Res Inst, State Key Lab Hydrol Water Resources & Hydraul En, Nanjing, Peoples R China; [Feng, Wei] Sun Yat Sen Univ, Sch Geospatial Engn & Sci, Zhuhai, Peoples R China; [Feng, Wei] Chinese Acad Sci, Innovat Acad Precis Measurement Sci & Technol, Inst Geodesy & Geophys, State Key Lab Geodesy & Earths Dynam, Wuhan, Peoples R China
推荐引用方式 GB/T 7714	Mo, Shaoxing,Zhong, Yulong,Forootan, Ehsan,et al. Bayesian convolutional neural networks for predicting the terrestrial water storage anomalies during GRACE and GRACE-FO gap[J],2022,604.
APA	Mo, Shaoxing.,Zhong, Yulong.,Forootan, Ehsan.,Mehrnegar, Nooshin.,Yin, Xin.,...&Shi, Xiaoqing.(2022).Bayesian convolutional neural networks for predicting the terrestrial water storage anomalies during GRACE and GRACE-FO gap.JOURNAL OF HYDROLOGY,604.
MLA	Mo, Shaoxing,et al."Bayesian convolutional neural networks for predicting the terrestrial water storage anomalies during GRACE and GRACE-FO gap".JOURNAL OF HYDROLOGY 604(2022).