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Predicting Forest Evapotranspiration by Coupling Carbon and Water Cycling Based on a Critical Stomatal Conductance Model
Liu, Zhengjia; Wu, Chaoyang; Wang, Sisi
2017-10-01
Source PublicationIEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
ISSN1939-1404
Volume10Issue:10Pages:4469-4477
Corresponding AuthorWu, Chaoyang(hefery@163.com)
AbstractQuantifying forest evapotranspiration (ET) is essential for understanding of climatic response of forest carbon and water cycling. However, there are still large uncertainties in forest ET predictions, especially in plant transpiration (PT). The poor estimations of forestETand PT are largely attributed to the neglect of wet canopy evaporation and uncertainties in the stomatal conductance. Thus, by coupling a revised Ball-Woodrow-Berry (BWB) model, a precipitation intercepted algorithm and the gross primary production (GPP) model to Shuttleworth-Wallace (SW) model, this study introduced a modified SW (SWm) model. The performances of this model were subsequently tested in three different forest sites with long-term observed records. Compared with previous models, SWm had a canopy stomatal scheme with stronger ecological significance and simpler GPP estimation scheme. Our analyses reveal the following. 1) SWm evidently improves the agreements between estimated and measured ET compared to original SW (R-2 increasing by 0.19-0.68). SWm could more accurately partition PT and evaporation, when compared with an earlier BWB-based SW (R-2 increasing by similar to 0.03). This finding also supports the use of Lohammer function in semiempirical model of stomatal conductance. 2) Accurate predictions of GPP are helpful for improving ET estimations in SWm (r = 0.73, p < 0.01), suggesting that carbon and water fluxes are inherently linked. 3) In addition to GPP, leaf area index evidently affects the performances of estimated ET in SWm. These results suggest that critically coupling carbon and water cycling are very important for improving forest ET prediction.
KeywordCanopy stomatal conductance evapotranspiration (ET) gross primary production (GPP) remote sensing Shuttleworth-Wallace (SW) model
DOI10.1109/JSTARS.2017.2715077
WOS KeywordGROSS PRIMARY PRODUCTION ; SHUTTLEWORTH-WALLACE ; SURFACE-TEMPERATURE ; DATA SET ; MODIS ; TRANSPIRATION ; VEGETATION ; ECOSYSTEM ; PRODUCTIVITY ; HUMIDITY
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[41601582] ; National Natural Science Foundation of China[41371013] ; National Natural Science Foundation of China[41522109] ; China Postdoctoral Science Foundation[2016M590149]
Funding OrganizationNational Natural Science Foundation of China ; China Postdoctoral Science Foundation
WOS Research AreaEngineering ; Physical Geography ; Remote Sensing ; Imaging Science & Photographic Technology
WOS SubjectEngineering, Electrical & Electronic ; Geography, Physical ; Remote Sensing ; Imaging Science & Photographic Technology
WOS IDWOS:000412626500020
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation statistics
Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/62081
Collection中国科学院地理科学与资源研究所
Corresponding AuthorWu, Chaoyang
AffiliationChinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China
Recommended Citation
GB/T 7714
Liu, Zhengjia,Wu, Chaoyang,Wang, Sisi. Predicting Forest Evapotranspiration by Coupling Carbon and Water Cycling Based on a Critical Stomatal Conductance Model[J]. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,2017,10(10):4469-4477.
APA Liu, Zhengjia,Wu, Chaoyang,&Wang, Sisi.(2017).Predicting Forest Evapotranspiration by Coupling Carbon and Water Cycling Based on a Critical Stomatal Conductance Model.IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,10(10),4469-4477.
MLA Liu, Zhengjia,et al."Predicting Forest Evapotranspiration by Coupling Carbon and Water Cycling Based on a Critical Stomatal Conductance Model".IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 10.10(2017):4469-4477.
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