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Can Terrestrial Water Storage Dynamics be Estimated From Climate Anomalies?

文献类型：期刊论文

作者Jing, Wenlong1,2,3; Zhao, Xiaodan1; Yao, Ling2,4,5; Di, Liping3; Yang, Ji1,2; Li, Yong1,2; Guo, Liying3; Zhou, Chenghu1,2,4,5
刊名EARTH AND SPACE SCIENCE
出版日期2020-03-01
卷号7期号:3页码:19
DOI10.1029/2019EA000959
通讯作者Di, Liping(ldi@gmu.edu)
英文摘要Freshwater stored on land is an extremely vital resource for all the terrestrial life on Earth. But our ability to record the change of land water storage is weak despite its importance. In this study, we attempt to establish a data-driven model for simulating terrestrial water storage dynamics by relating climate forcings with terrestrial water storage anomalies (TWSAs) from the Gravity Recovery and Climate Experiment (GRACE) satellites. In the case study in Pearl River basin, China, the relationships were learned by using two ensemble learning algorithms, the Random Forest (RF) and eXtreme Gradient Boost (XGB), respectively. The TWSA in the basin was reconstructed back to past decades and compared with the TWSA derived from global land surface models. As a result, the RF and XGB algorithms both perform well and could nicely reproduce the spatial pattern and value range of GRACE observations, outperforming the land surface models. Temporal behaviors of the reconstructed TWSA time series well capture those of both GRACE and land surface models time series. A multiscale GRACE-based drought index was proposed, and the index matches the Standardized Precipitation-Evapotranspiration Index time series at different time scales. The case analysis for years of 1963 and 1998 indicates the ability of the reconstructed TWSA for identifying past drought and flood extremes. The importance of different input variables to the TWSA estimation model was quantified, and the precipitation of the prior 2 months is the most important variable for simulating the TWSA of the current month in the model. Results of this study highlight the great potentials for estimating terrestrial water storage dynamics from climate forcing data by using machine learning to achieve comparable results than complex physical models.
WOS关键词PEARL RIVER-BASIN ; GROUNDWATER DEPLETION ; REGIONAL CLIMATE ; GRAVITY RECOVERY ; URBAN-GROWTH ; GRACE ; MODEL ; CHINA ; DROUGHT ; INDEX
资助项目National Natural Science Foundation of China[41801362] ; National Natural Science Foundation of China[41976190] ; Natural Science Foundation of Guangdong Province, China[2018A030310470] ; Guangdong Provincial Science and Technology Program[2018B030324001] ; GDAS's Project of Science and Technology Development[2016GDASRC-0211] ; GDAS's Project of Science and Technology Development[2017GDASCX-0601] ; GDAS's Project of Science and Technology Development[2018GDASCX-0101] ; GDAS's Project of Science and Technology Development[0403] ; GDAS's Project of Science and Technology Development[2019GDASYL-0502001] ; GDAS's Project of Science and Technology Development[0301001] ; GDAS's Project of Science and Technology Development[0302001] ; GDAS's Project of Science and Technology Development[0501001] ; GDAS's Project of Science and Technology Development[0401001] ; Key Special Project for Introduced Talents Team of Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou)[GML2019ZD0301] ; Guangdong Innovative and Entrepreneurial Research Team Program[2016ZT06D336] ; U.S. National Science Foundation[CNS-1739705]
WOS研究方向Astronomy & Astrophysics ; Geology
语种英语
WOS记录号WOS:000529137300013
出版者AMER GEOPHYSICAL UNION
资助机构National Natural Science Foundation of China ; Natural Science Foundation of Guangdong Province, China ; Guangdong Provincial Science and Technology Program ; GDAS's Project of Science and Technology Development ; Key Special Project for Introduced Talents Team of Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou) ; Guangdong Innovative and Entrepreneurial Research Team Program ; U.S. National Science Foundation
源URL[http://ir.igsnrr.ac.cn/handle/311030/159902]  
专题中国科学院地理科学与资源研究所
通讯作者Di, Liping
作者单位1.Guangzhou Inst Geog, Guangdong Open Lab Geospatial Informat Technol &, Key Lab Guangdong Utilizat Remote Sensing & Geog, Guangzhou, Peoples R China
2.Southern Marine Sci & Engn Guangdong Lab, Guangzhou, Peoples R China
3.George Mason Univ, Ctr Spatial Informat Sci & Syst, Fairfax, VA 22030 USA
4.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing, Peoples R China
5.Nanjing Normal Univ, Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing, Peoples R China
推荐引用方式
GB/T 7714		 Jing, Wenlong,Zhao, Xiaodan,Yao, Ling,et al. Can Terrestrial Water Storage Dynamics be Estimated From Climate Anomalies?[J]. EARTH AND SPACE SCIENCE,2020,7(3):19.
APA Jing, Wenlong.,Zhao, Xiaodan.,Yao, Ling.,Di, Liping.,Yang, Ji.,...&Zhou, Chenghu.(2020).Can Terrestrial Water Storage Dynamics be Estimated From Climate Anomalies?.EARTH AND SPACE SCIENCE,7(3),19.
MLA Jing, Wenlong,et al."Can Terrestrial Water Storage Dynamics be Estimated From Climate Anomalies?".EARTH AND SPACE SCIENCE 7.3(2020):19.

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来源：地理科学与资源研究所

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