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Advances in Earth Science >

2023 , Vol. 38 >Issue 9: 931 - 942

DOI: https://doi.org/10.11867/j.issn.1001-8166.2023.052

Optimization of the Stomatal Conductance Slope in the Conductance-Photosynthesis Model and Improved Estimation of Transpiration in Evergreen Forests

  • Jiaxin JIN ,
  • Fengyan ZHANG ,
  • Han WANG ,
  • Ying LIU ,
  • Weiye HOU ,
  • Yulong CAI ,
  • Xiaolong PAN ,
  • Ying WANG ,
  • Qiuan ZHU ,
  • Xiuqin FANG ,
  • Yiqi YAN ,
  • Liliang REN
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  • 1.The National Key Laboratory of Water Disaster Prevention, Hohai University, Nanjing 210098, China
    2.College of Hydrology and Water Resourses, Hohai University, Nanjing 210024, China
    3.Department of Earth System Science, Tsinghua University, Beijing 100084, China
    4.Tourism and Social Administration College, Nanjing Xiaozhuang University, Nanjing 211171, China
    5.Hydrology Bureau of Yellow River Conservancy Commission, Zhengzhou 450004, China
    6.National Earth System Science Data Center, National Science & Technology Infrastructure of China, Beijing 100101, China
JIN Jiaxin, Associate professor, research area includes eco-hydrological remote sensing. E-mail: jiaxinking@hhu.edu.cn
REN Liliang, Professor, research area includes basic theory of water cycle. E-mail: RLL@hhu.edu.cn

Received date: 2023-05-15

  Revised date: 2023-07-29

  Online published: 2023-09-25

Supported by

the National Natural Science Foundation of China(U2243203)

Fold

Abstract

The conductance-photosynthesis (gs-A) model is widely used for estimating the transpiration rate (ETc). The stomatal conductance slope (g1) in the gs-A model is crucial, and is usually parameterized with a PFT-specific g1. However, because there is seasonal variation in g1, the scheme of constantizing g1 results in large potential uncertainties in ETc estimation. Therefore, optimizing the parameterized scheme of g1 is key for improving the estimation of ETc. Previous studies have shown that the dynamic parameterization of g1 using remote sensing-based Leaf Area Index (LAI) can effectively improve the accuracy of the estimated ETc in deciduous forests. However, it is still not clear whether this method is applicable to evergreen forests, in which the canopy shows slight seasonal variability. In addition, the First-Principles Theory indicates that g1 can be expressed as a function of temperature. Hence, whether temperature can be used to simulate g1 with a better performance than that when using LAI requires further investigations. In view of the above questions, six FLUXNET evergreen forest sites were selected to investigate the relationships between g1, and LAI and air temperature, and the ETc simulation results under the two parameterization schemes were compared. Results showed that g1 varied with both LAI and temperature at each site, showing a significant negative correlation, while the results of the dynamic parameterization scheme (DYN) were better than those of the constant g1 scheme (FIX). This showed that the simulation of g1 using LAI is still effective in evergreen forests, but the explanation of temperature on g1 (R2=0.45±0.12) is stronger than that of LAI (R2=0.28±0.23). Further comparing the gs and ETc obtained using the two schemes, we found that the scheme based on temperature performed better than that of LAI at the daily scale, with the Root Mean Square Error (RMSE) of the ETc estimation being significantly reduced (26.0%±24.4%). This study emphasizes the feasibility of the temperature-based parameterized scheme of g1 and helps to fundamentally improve the simulation of gs and ETc in evergreen forest landscapes.

Key words： Conductance-photosynthesis model; Stomatal conductance slope; Air temperature; Leaf area index; Evergreen forests

Cite this article

Jiaxin JIN , Fengyan ZHANG , Han WANG , Ying LIU , Weiye HOU , Yulong CAI , Xiaolong PAN , Ying WANG , Qiuan ZHU , Xiuqin FANG , Yiqi YAN , Liliang REN . Optimization of the Stomatal Conductance Slope in the Conductance-Photosynthesis Model and Improved Estimation of Transpiration in Evergreen Forests[J]. Advances in Earth Science, 2023 , 38(9) : 931 -942 . DOI: 10.11867/j.issn.1001-8166.2023.052

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