Improvement of transpiration estimation based on a two-leaf conductance-photosynthesis model with seasonal parameters for temperate deciduous forests

Jiaxin Jin; Ying Liu; Weiye Hou; Yulong Cai; Fengyan Zhang; Ying Wang; Xiuqin Fang; Lingxiao Huang; Bin Yong; Liliang Ren

doi:10.3389/fpls.2023.1164078

Improvement of transpiration estimation based on a two-leaf conductance-photosynthesis model with seasonal parameters for temperate deciduous forests

Front Plant Sci. 2023 May 8:14:1164078. doi: 10.3389/fpls.2023.1164078. eCollection 2023.

Authors

Jiaxin Jin^{1

2

3}, Ying Liu¹, Weiye Hou¹, Yulong Cai¹, Fengyan Zhang¹, Ying Wang⁴, Xiuqin Fang¹, Lingxiao Huang⁵, Bin Yong^{1

2}, Liliang Ren¹

Affiliations

¹ College of Hydrology and Water Resources, Hohai University, Nanjing, China.
² Key Laboratory of Water Big Data Technology of Ministry of Water Resources, Hohai University, Nanjing, China.
³ National Earth System Science Data Center, National Science & Technology Infrastructure of China, Beijing, China.
⁴ Tourism and Social Administration College, NanJing XiaoZhuang University, Nanjing, China.
⁵ State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China.

Abstract

Introduction: Conductance-photosynthesis (G_s-A) models, accompanying with light use efficiency (LUE) models for calculating carbon assimilation, are widely used for estimating canopy stomatal conductance (G_s) and transpiration (T_c) under the two-leaf (TL) scheme. However, the key parameters of photosynthetic rate sensitivity (g_su and g_sh) and maximum LUE (ϵ_msu and ϵ_msh) are typically set to temporally constant values for sunlit and shaded leaves, respectively. This may result in T_c estimation errors, as it contradicts field observations.

Methods: In this study, the measured flux data from three temperate deciduous broadleaved forests (DBF) FLUXNET sites were adopted, and the key parameters of LUE and Ball-Berry models for sunlit and shaded leaves were calibrated within the entire growing season and each season, respectively. Then, the estimations of gross primary production (GPP) and T_c were compared between the two schemes of parameterization: (1) entire growing season-based fixed parameters (EGS) and (2) season-specific dynamic parameters (SEA).

Results: Our results show a cyclical variability of ϵ_msu across the sites, with the highest value during the summer and the lowest during the spring. A similar pattern was found for g_su and g_sh, which showed a decrease in summer and a slight increase in both spring and autumn. Furthermore, the SEA model (i.e., the dynamic parameterization) better simulated GPP, with a reduction in root mean square error (RMSE) of about 8.0 ± 1.1% and an improvement in correlation coefficient (r) of 3.7 ± 1.5%, relative to the EGS model. Meanwhile, the SEA scheme reduced T_c simulation errors in terms of RMSE by 3.7 ± 4.4%.

Discussion: These findings provide a greater understanding of the seasonality of plant functional traits, and help to improve simulations of seasonal carbon and water fluxes in temperate forests.

Keywords: ball-berry model; light use efficiency; seasonal variability; stomatal conductance; temperate deciduous forests; transpiration; two-leaf scheme.

Grants and funding

This work was supported by the National Natural Science Foundation of China (U2243203, 41971374 and 41807173).