Jin Jiaxin, Liu Ying, Hou Weiye, Cai Yulong, Zhang Fengyan, Wang Ying, Fang Xiuqin, Huang Lingxiao, Yong Bin, Ren Liliang
College of Hydrology and Water Resources, Hohai University, Nanjing, China.
Key Laboratory of Water Big Data Technology of Ministry of Water Resources, Hohai University, Nanjing, China.
Front Plant Sci. 2023 May 8;14:1164078. doi: 10.3389/fpls.2023.1164078. eCollection 2023.
Conductance-photosynthesis (G-A) models, accompanying with light use efficiency (LUE) models for calculating carbon assimilation, are widely used for estimating canopy stomatal conductance (G) and transpiration (T) under the two-leaf (TL) scheme. However, the key parameters of photosynthetic rate sensitivity (g and g) and maximum LUE (ϵ and ϵ) are typically set to temporally constant values for sunlit and shaded leaves, respectively. This may result in T estimation errors, as it contradicts field observations.
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 were compared between the two schemes of parameterization: (1) entire growing season-based fixed parameters (EGS) and (2) season-specific dynamic parameters (SEA).
Our results show a cyclical variability of ϵ across the sites, with the highest value during the summer and the lowest during the spring. A similar pattern was found for g and g, 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 simulation errors in terms of RMSE by 3.7 ± 4.4%.
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.
伴随用于计算碳同化的光利用效率（LUE）模型的电导 - 光合作用（G - A）模型，在双叶（TL）方案下被广泛用于估算冠层气孔导度（G）和蒸腾作用（T）。然而，光合速率敏感性的关键参数（g和g）以及最大LUE（ϵ和ϵ）通常分别被设置为阳叶和阴叶的时间常数。这可能导致T估算误差，因为它与实地观测结果相矛盾。
在本研究中，采用了来自三个温带落叶阔叶林（DBF）FLUXNET站点的实测通量数据，分别在整个生长季节和每个季节内对阳叶和阴叶的LUE和Ball - Berry模型的关键参数进行了校准。然后，比较了两种参数化方案下总初级生产力（GPP）和T的估算值：（1）基于整个生长季节的固定参数（EGS）和（2）特定季节的动态参数（SEA）。
我们的结果表明，各站点的ϵ呈现周期性变化，夏季最高，春季最低。g和g也有类似模式，夏季下降，春季和秋季略有增加。此外，相对于EGS模型，SEA模型（即动态参数化）能更好地模拟GPP，均方根误差（RMSE）降低约8.0±1.1%，相关系数（r）提高3.7±1.5%。同时，SEA方案在RMSE方面将T模拟误差降低了3.7±4.4%。
这些发现有助于更深入地理解植物功能性状的季节性，并有助于改进温带森林季节性碳和水通量的模拟。
