Chin J Plant Ecol ›› 2022, Vol. 46 ›› Issue (3): 300-310.DOI: 10.17521/cjpe.2021.0292

Special Issue: 生态学研究的方法和技术 生态系统碳水能量通量

• Research Articles • Previous Articles     Next Articles

Temporal and spatial variation characteristics and different calculation methods for the key parameter αe in the generalized complementary principle of evapotranspiration

HUANG Ying, CHEN Zhi, SHI Zhe, XIONG Bo-Wen, YAN Chun-Hua*(), QIU Guo-Yu*()   

  1. School of Environment and Energy, Peking University Shenzhen Graduate School, Shenzhen, Guangdong 518055, China
  • Received:2021-08-12 Accepted:2021-10-30 Online:2022-03-20 Published:2022-01-05
  • Contact: YAN Chun-Hua,QIU Guo-Yu
  • Supported by:
    Sichuan Science and Technology Program(2021YFH0082);National Natural Science Foundation of China(42001022)


Aims The generalized complementary principle of evapotranspiration is one of the important methods to estimate evapotranspiration when the observed data are scarce. In implementing this method, an accurate estimation of parameter αe is critical. The temporal and spatial variation of αe and the applicability of different methods for calculating αe were investigated at eight flux stations under different climatic conditions and ecosystem types in China.
Methods Firstly, the annual and monthly values of αe were calibrated based on the measured data. The spatiotemporal variability of αe was investigated and the influence of αe with different temporal scales on the calculation accuracy of the generalized complementarity principle model were compared. Considering that αe can not be calibrated without measured evapotranspiration data, the applicability of two statistical models of annual αe values based on aridity index (AI)(Liu method and Brutsaert method) were evaluated to determine whether αe can be determined using AI. Finally, the error sources of each calculation method were analyzed.
Important findings αe value varies with season, and the monthly variations of αe differ among different flux stations. In terms of spatial variation, the annual values of αe at humid sites were larger than those at arid sites. The αe calculated by Liu method and Brutsaert method were close to the calibrated values. In applying the generalized complementary principle model, high simulation accuracy can be achieved by using the calibrated annual αe, and the accuracy can be further improved by using the monthly αe. Two AI-based methods also achieved accurate simulation results, which further confirmed the potential of predicting αe based on AI in the absence of observed data. The generalized complementary principle model can simulate the annual variation trend of evapotranspiration when using annual αe, but the estimated value were biased in some months. The evapotranspiration calculated by Liu method and Brutsaert method were underestimated in summer months of the drought sites, which may be caused by the fact that the AI was overestimated in summer months when rainfall was concentrated. The results further demonstrate the potential of the generalized complementary principle in estimating evapotranspiration in a wide range of natural environments.

Key words: actual evapotranspiration, aridity index, complementary principle, eddy covariance, parameter calculation method