Zhang Zhenyu, Li Xiaoyu, Ju Weimin, Zhou Yanlian, Cheng Xianfu
International Institute of Earth System Science, Nanjing University, Nanjing 210023, China; School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China; State Key Laboratory of Subtropical Silviculture, Zhejiang A&F University, Hangzhou 311300, Zhejiang, China; Jiangsu Center for Collaborative Innovation in Geographic Information Resource Development and Application, Nanjing, Jiangsu 210023, China.
State Key Laboratory of Subtropical Silviculture, Zhejiang A&F University, Hangzhou 311300, Zhejiang, China.
Sci Total Environ. 2022 Sep 10;838(Pt 2):156172. doi: 10.1016/j.scitotenv.2022.156172. Epub 2022 May 23.
Accurate estimation of terrestrial gross primary productivity (GPP) is essential for quantifying the net carbon exchange between the atmosphere and biosphere. Light use efficiency (LUE) models are widely used to estimate GPP at different spatial scales. However, difficulties in proper determination of maximum LUE (LUE) and downregulation of LUE into actual LUE result in uncertainties in GPP estimated by LUE models. The recently developed P model, as a LUE-like model, captures the deep mechanism of photosynthesis and simplifies parameterization. Site level studies have proved the outperformance of P model over LUE models. However, the global application of the P model is still lacking. Thus, the effectiveness of 5 water stress factors integrated into the P model was compared. The optimal P model was used to generate a new long-term (1981-2020) global monthly GPP dataset at a spatial resolution of 0.1° × 0.1°, called PGPP. Validation at globally distributed 109 FLUXNET sites indicated that PGPP is better than three widely-used GPP products. R between PGPP and observed GPP equals to 0.75, the corresponding root mean squared error (RMSE) and mean absolute error (MAE) equal to 1.77 g C m d and 1.28 g C m d. During the period from 1981 to 2020, PGPP significantly increased in 69.02% of global vegetated regions (p < 0.05). Overall, PGPP provides a new GPP product choice for global ecology studies and the comparison of various water stress factors provides a new idea for the improvement of GPP model in the future.
准确估算陆地总初级生产力（GPP）对于量化大气与生物圈之间的净碳交换至关重要。光能利用效率（LUE）模型被广泛用于估算不同空间尺度下的GPP。然而，难以正确确定最大LUE（LUE）以及将LUE下调为实际LUE导致LUE模型估算的GPP存在不确定性。最近开发的P模型作为一种类似LUE的模型，捕捉了光合作用的深层机制并简化了参数化。站点层面的研究已证明P模型优于LUE模型。然而，P模型仍缺乏全球应用。因此，比较了纳入P模型的5个水分胁迫因子的有效性。使用最优P模型生成了一个新的长期（1981 - 2020年）全球月度GPP数据集，空间分辨率为0.1°×0.1°，称为PGPP。在全球分布的109个FLUXNET站点进行的验证表明，PGPP优于三种广泛使用的GPP产品。PGPP与观测GPP之间的R等于0.75，相应的均方根误差（RMSE）和平均绝对误差（MAE）分别等于1.77 g C m² d⁻¹和1.28 g C m² d⁻¹。在1981年至2020年期间，全球69.02%的植被区域PGPP显著增加（p < 0.05）。总体而言，PGPP为全球生态学研究提供了一种新的GPP产品选择，并且对各种水分胁迫因子的比较为未来GPP模型的改进提供了新思路。
