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A data-driven approach to generate past GRACE-like terrestrial water storage solution by calibrating the land surface model simulations

文献类型：期刊论文

作者Jing, Wenlong1,2,3; Di, Liping3; Zhao, Xiaodan1,2; Yao, Ling2,4,5; Xia, Xiaolin1,2; Liu, Yangxiaoyue1,2; Yang, Ji1,2; Li, Yong1,2; Zhou, Chenghu1,2,4,5
刊名ADVANCES IN WATER RESOURCES
出版日期2020-09-01
卷号143页码:17
关键词Terrestrial water storage Machine learning GRACE The Nile River basin
ISSN号0309-1708
DOI10.1016/j.advwatres.2020.103683
通讯作者Yao, Ling(yaoling@lreis.ac.cn)
英文摘要The Gravity Recovery and Climate Experiment (GRACE) satellites provide unprecedented perspectives to hydrologists and geoscientists for observing and understanding the variation of terrestrial water storage (TWS) at continental to global scales. However, there are few reliable datasets of past TWS variations before GRACE observations were available (pre-2002). To fill this gap, we attempt to develop an approach to calibrate TWS anomalies (TWSA) data of past decades based on available GRACE solution and land surface model simulations, and a case study was conducted at the Nile River basin. Two ensemble learning algorithms, the Random Forest (RF) and the eXtreme Gradient Boost (XGB), combined with a spatially moving window structure, are used to build the reconstruction model, respectively. Reconstructed TWSA are validated against a precipitation-evapotranspiration index as well as other GRACE-based reconstructed TWSA datasets. Results show that the XGB model performs slightly better than the RF model in reconstructing GRACE TWSA data. The TWSA produced by the two ensemble learning algorithms are comparable and better than other examined reconstructed GRACE-like datasets, and are well correlation with original GRACE solution and past precipitation-evapotranspiration series. The profile soil moisture and groundwater storage show significant contributions to the RF and XGB model, but their variable importance values present different spatial patterns in the RF and XGB model. Further experiments are expected to investigate the contribution of human-induced factors to simulate terrestrial water storage dynamics, especially in intensely managed basins. Rather than modifying the structure and inputs of land surface models, this study provides an alternative way of improving the TWSA estimations of global land surface models and extending time range of GRACE datasets. The experiments are expected to promote and enrich the integration of physical and machine-learning models for optimal simulationsin geoscience research.
WOS关键词SOIL-MOISTURE ; RANDOM FOREST ; DATA ASSIMILATION ; FEATURE-SELECTION ; CLIMATE ; GROUNDWATER ; MACHINE ; DROUGHT ; CHINA ; EVAPOTRANSPIRATION
资助项目National Natural Science Foundation of China, China[41801362] ; National Natural Science Foundation of China, China[41976190] ; National Postdoctoral Program for Innovative Talents, China[BX20200100] ; National Earth System Science Data Sharing Infrastructure, China[2005DKA32300] ; Guangdong Provincial Science and Technology Program, China[2018B030324001] ; GDAS's Project of Science and Technology Development, China[2020GDASYL20200104003] ; GDAS's Project of Science and Technology Development, China[2016GDASRC-0211] ; GDAS's Project of Science and Technology Development, China[2017GDASCX-0601] ; GDAS's Project of Science and Technology Development, China[2018GDASCX-0101] ; GDAS's Project of Science and Technology Development, China[2018GDASCX-0403] ; GDAS's Project of Science and Technology Development, China[2019GDASYL-0502001] ; GDAS's Project of Science and Technology Development, China[2019GDASYL-0301001] ; GDAS's Project of Science and Technology Development, China[2019GDASYL-0302001] ; GDAS's Project of Science and Technology Development, China[2019GDASYL-0501001] ; GDAS's Project of Science and Technology Development, China[2019GDASYL-0401001] ; Key Special Project for Introduced Talents Team of Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), China[GML2019ZD0301] ; Guangdong Innovative and Entrepreneurial Research Team Program, China[2016ZT06D336]
WOS研究方向Water Resources
语种英语
WOS记录号WOS:000564000700004
出版者ELSEVIER SCI LTD
资助机构National Natural Science Foundation of China, China ; National Postdoctoral Program for Innovative Talents, China ; National Earth System Science Data Sharing Infrastructure, China ; Guangdong Provincial Science and Technology Program, China ; GDAS's Project of Science and Technology Development, China ; Key Special Project for Introduced Talents Team of Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), China ; Guangdong Innovative and Entrepreneurial Research Team Program, China
源URL[http://ir.igsnrr.ac.cn/handle/311030/157950]  
专题中国科学院地理科学与资源研究所
通讯作者Yao, Ling
作者单位1.Guangzhou Inst Geog, Key Lab Guangdong Utilizat Remote Sensing & Geog, Guangdong Open Lab Geospatial Informat Technol &, Guangzhou 510070, Peoples R China
2.Southern Marine Sci & Engn Guangdong Lab, Guangzhou 511458, Peoples R China
3.George Mason Univ, Ctr Spatial Informat Sci & Syst, 4087 Univ Dr, Fairfax, VA 22030 USA
4.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China
5.Nanjing Normal Univ, Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Peoples R China
推荐引用方式
GB/T 7714		 Jing, Wenlong,Di, Liping,Zhao, Xiaodan,et al. A data-driven approach to generate past GRACE-like terrestrial water storage solution by calibrating the land surface model simulations[J]. ADVANCES IN WATER RESOURCES,2020,143:17.
APA Jing, Wenlong.,Di, Liping.,Zhao, Xiaodan.,Yao, Ling.,Xia, Xiaolin.,...&Zhou, Chenghu.(2020).A data-driven approach to generate past GRACE-like terrestrial water storage solution by calibrating the land surface model simulations.ADVANCES IN WATER RESOURCES,143,17.
MLA Jing, Wenlong,et al."A data-driven approach to generate past GRACE-like terrestrial water storage solution by calibrating the land surface model simulations".ADVANCES IN WATER RESOURCES 143(2020):17.

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