干旱区生态环境知识资源中心(Arid): Evaluation of global terrestrial evapotranspiration using state-of-the-art approaches in remote sensing, machine learning and land surface modeling

Arid

DOI	10.5194/hess-24-1485-2020
	Evaluation of global terrestrial evapotranspiration using state-of-the-art approaches in remote sensing, machine learning and land surface modeling
	Pan, Shufen 1; Pan, Naiqing 1,2; Tian, Hanqin 1; Friedlingstein, Pierre 3; Sitch, Stephen 4; Shi, Hao 1; Arora, Vivek K.5; Haverd, Vanessa 6; Jain, Atul K.7; Kato, Etsushi 8; Lienert, Sebastian 9; Lombardozzi, Danica 10; Nabel, Julia E. M. S.11; Otte, Catherine 12; Poulter, Benjamin 13; Zaehle, Soenke 14; Running, Steven W.15
通讯作者	Pan, Shufen
来源期刊	HYDROLOGY AND EARTH SYSTEM SCIENCES
ISSN	1027-5606
EISSN	1607-7938
出版年	2020
卷号	24 期号:3 页码:1485-1509
英文摘要	Evapotranspiration (ET) is critical in linking global water, carbon and energy cycles. However, direct measurement of global terrestrial ET is not feasible. Here, we first reviewed the basic theory and state-of-the-art approaches for estimating global terrestrial ET, including remote-sensing-based physical models, machine-learning algorithms and land surface models (LSMs). We then utilized 4 remote-sensing-based physical models, 2 machine-learning algorithms and 14 LSMs to analyze the spatial and temporal variations in global terrestrial ET. The results showed that the ensemble means of annual global terrestrial ET estimated by these three categories of approaches agreed well, with values ranging from 589.6 mm yr(-1) (6.56 x 10(4) km(3) yr(-1)) to 617.1 mm yr(-1) (6.87 x 10(4) km(3) yr(-1)). For the period from 1982 to 2011, both the ensembles of remote-sensing-based physical models and machine-learning algorithms suggested increasing trends in global terrestrial ET (0.62 mm yr(-2) with a significance level of p < 0.05 and 0.38 mm yr(-2) with a significance level of p < 0.05, respectively). In contrast, the ensemble mean of the LSMs showed no statistically significant change (0.23 mm yr(-2), p > 0.05), although many of the individual LSMs reproduced an increasing trend. Nevertheless, all 20 models used in this study showed that anthropogenic Earth greening had a positive role in increasing terrestrial ET. The concurrent small interannual variability, i.e., relative stability, found in all estimates of global terrestrial ET, suggests that a potential planetary boundary exists in regulating global terrestrial ET, with the value of this boundary being around 600mm yr(-1). Uncertainties among approaches were identified in specific regions, particularly in the Amazon Basin and arid/semiarid regions. Improvements in parameterizing water stress and canopy dynamics, the utilization of new available satellite retrievals and deep-learning methods, and model-data fusion will advance our predictive understanding of global terrestrial ET.
类型	Article
语种	英语
国家	USA ; Peoples R China ; England ; Canada ; Australia ; Japan ; Switzerland ; Germany ; France
开放获取类型	Green Submitted, gold, Green Published
收录类别	SCI-E
WOS记录号	WOS:000522832500001
WOS关键词	SUPPORT VECTOR MACHINE ; PLANT FUNCTIONAL TYPES ; ATMOSPHERE WATER FLUX ; LATENT-HEAT FLUX ; LEAF-AREA INDEX ; VEGETATION INDEX ; EDDY COVARIANCE ; NEURAL-NETWORKS ; CLIMATE MODELS ; USE EFFICIENCY
WOS类目	Geosciences, Multidisciplinary ; Water Resources
WOS研究方向	Geology ; Water Resources
来源机构	Commonwealth Scientific and Industrial Research Organisation
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/314699
作者单位	1.Auburn Univ, Int Ctr Climate & Global Change Res, Sch Forestry & Wildlife Sci, Auburn, AL 36832 USA; 2.Chinese Acad Sci, Res Ctr Ecoenvironm Sci, State Key Lab Urban & Reg Ecol, Beijing 100085, Peoples R China; 3.Univ Exeter, Coll Engn Math & Phys Sci, Exeter EX4 4QF, Devon, England; 4.Univ Exeter, Coll Life & Environm Sci, Exeter EX4 4RJ, Devon, England; 5.Univ Victoria, Canadian Ctr Climate Modelling & Anal, Environm Canada, Victoria, BC V8W 2Y2, Canada; 6.CSIRO Oceans & Atmosphere, GPO Box 1700, Canberra, ACT 2601, Australia; 7.Univ Illinois, Dept Atmospher Sci, 105 S Gregory Ave, Urbana, IL 61801 USA; 8.IAE, Minato Ku, Tokyo 1050003, Japan; 9.Univ Bern, Phys Inst, Climate & Environm Phys, Bern, Switzerland; 10.Natl Ctr Atmospher Res, Climate & Global Dynam Lab, Boulder, CO 80305 USA; 11.Max Planck Inst Meteorol, Bundesstr 53, D-20146 Hamburg, Germany; 12.Orme Des Merisiers, LSCE IPSL CNRS, F-91191 Gif Sur Yvette, France; 13.NASA, Goddard Space Flight Ctr, Biospher Sci Lab, Code 916, Greenbelt, MD 20771 USA; 14.Max Planck Inst Biogeochem, POB 600164,Hans Knoll Str 10, D-07745 Jena, Germany; 15.Univ Montana, Coll Forestry & Conservat, Numer Terradynam Simulat Grp, Missoula, MT 59812 USA
推荐引用方式 GB/T 7714	Pan, Shufen,Pan, Naiqing,Tian, Hanqin,et al. Evaluation of global terrestrial evapotranspiration using state-of-the-art approaches in remote sensing, machine learning and land surface modeling[J]. Commonwealth Scientific and Industrial Research Organisation,2020,24(3):1485-1509.
APA	Pan, Shufen.,Pan, Naiqing.,Tian, Hanqin.,Friedlingstein, Pierre.,Sitch, Stephen.,...&Running, Steven W..(2020).Evaluation of global terrestrial evapotranspiration using state-of-the-art approaches in remote sensing, machine learning and land surface modeling.HYDROLOGY AND EARTH SYSTEM SCIENCES,24(3),1485-1509.
MLA	Pan, Shufen,et al."Evaluation of global terrestrial evapotranspiration using state-of-the-art approaches in remote sensing, machine learning and land surface modeling".HYDROLOGY AND EARTH SYSTEM SCIENCES 24.3(2020):1485-1509.