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Premium Determinants of the Asymmetric Parameter in the Generalized Complementary Principle of Evaporation
Author(s)
Wang Liming,
Tian Fuqiang,
Han Songjun,
Wei Zhongwang
Publication year2020
Publication title
water resources research
Resource typeJournals
PublisherWiley-Blackwell
Abstract The complementary principle, which was first proposed by Bouchet (1963), illustrates a complementary relationship among the actual evaporation, the potential evaporation, and the apparent potential evaporation. It has generated increasing attention for estimating evaporation by using only routinely observed meteorological variables (radiation, wind speed, air temperature, and humidity) without complex surface property parameters. However, this principle still poses great challenges because of the underlying uncertainties in estimating its critical parameter, namely, asymmetric parameter b . In this study, we adopted a sigmoid generalized complementary function and utilized the eddy covariance (EC) data from 217 sites around the world to determine b values in different ecosystems and their correlation with environmental factors. We found b has a mean value of 6.01 ± 0.08. The asymmetric parameter b is small in dry regions (i.e., the desert ecosystem, 0.42 ± 0.02) and increases as the land surface wetness improves. The ecosystem mean air temperature and vapor pressure deficit have negative correlations with b (Pearson correlation coefficients are −0.57 and −0.52, respectively), and the mean soil water content has a positive correlation with b (0.69). Besides, the sigmoid function has a favorable capability in estimating evaporation no matter based on the site‐specific b values or the ecosystem mean b values. The ecosystem mean b values given in the current study also perform acceptably in the independent verifications, indicating these values can be applied extendedly for regional and global studies.
Subject(s)artificial neural network , atmospheric sciences , biology , computer science , covariance , ecology , ecosystem , eddy covariance , environmental science , evaporation , geology , geotechnical engineering , humidity , hydrology (agriculture) , machine learning , mathematics , meteorology , physics , potential evaporation , sigmoid function , soil science , statistics , wind speed
Language(s)English
SCImago Journal Rank1.863
H-Index217
eISSN1944-7973
pISSN0043-1397
DOI10.1029/2019wr026570

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