Exploring complex water stress–gross primary production relationships: Impact of climatic drivers, main effects, and interactive effects

环境科学 初级生产 蒸汽压差 土壤水分 大气科学 相互作用 优势(遗传学) 生态系统 生态学 数学 土壤科学 统计 蒸腾作用 物理 化学 生物 生物化学 光合作用 基因
作者
Huan Wang,Shijie Yan,Philippe Ciais,Jean‐Pierre Wigneron,Laibao Liu,Yan Li,Zheng Fu,Hongliang Ma,Ze Liang,Feili Wei,Yueyao Wang,Shuangcheng Li
出处
期刊:Global Change Biology [Wiley]
卷期号:28 (13): 4110-4123 被引量:97
标识
DOI:10.1111/gcb.16201
摘要

The dominance of vapor pressure deficit (VPD) and soil water content (SWC) for plant water stress is still under debate. These two variables are strongly coupled and influenced by climatic drivers. The impacts of climatic drivers on the relationships between gross primary production (GPP) and water stress from VPD/SWC and the interaction between VPD and SWC are not fully understood. Here, applying statistical methods and extreme gradient boosting models-Shapley additive explanations framework to eddy-covariance observations from the global FLUXNET2015 data set, we found that the VPD-GPP relationship was strongly influenced by climatic interactions and that VPD was more important for plant water stress than SWC across most plant functional types when we removed the effect of main climatic drivers, e.g. air temperature, incoming shortwave radiation and wind speed. However, we found no evidence for a significant influence of elevated CO2 on stress alleviation, possibly because of the short duration of the records (approximately one decade). Additionally, the interactive effect between VPD and SWC differed from their individual effect. When SWC was high, the SHAP interaction value of SWC and VPD on GPP was decreased with increasing VPD, but when SWC was low, the trend was the opposite. Additionally, we revealed a threshold effect for VPD stress on GPP loss; above the threshold value, the stress on GPP was flattened off. Our results have important implications for independently identifying VPD and SWC limitations on plant productivity, which is meaningful for capturing the magnitude of ecosystem responses to water stress in dynamic global vegetation models.
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