Spatial variability of water use efficiency in China's terrestrial ecosystems - Zhu, Xian-Jin; Yu, Gui-Rui; Wang, Qiu-Feng; Hu, Zhong-Min; Zheng, Han; Li, Sheng-Gong; Sun, Xiao-Min; Zhang, Yi-Ping; Yan, Jun-Hua; Wang, Hui-Min; Zhao, Feng-Hua; Zhang, Jun-Hui; et al. (Global and planetary change, 2015)
Water use efficiency (WUE) reflects the coupling of carbon and water cycles. Analyzing the spatial variability of WUE can improve our understanding on the interaction between carbon and water cycles at a large scale, which also provides a basis for improving the regional carbon budget assessment. Based on China's eddy covariance measurements, we examined the spatial variation of China's WUE and its affecting factors. WUE showed a decreasing trend with the increasing altitude, which was the result of ecosystem type distribution resulting from the climatic gradient. After fully considering the vertical variation of WUE, we found that not only mean annual air temperature (MAT), mean annual precipitation (MAP), and mean leaf area index (MLAI) but also mean annual total photosynthesis active radiation (MAR) affected the spatial variation of WUE. With the increasing MAT, MAP, and MLAI, WUE increased significantly but the increasing MAR decreased WUE. The spatial variation of WUE could be directly depicted by MLAI and altitude, the equation including which explained 65% of the spatial variation of WUE. The effects of MAT and MAP on the spatial variation of WUE may be achieved through altering MLAI, while the mechanism underlying the effect of MAR on the spatial variation of WUE was still unclear, which should be the subject of future investigations. This study reveals the vertical variation of WUE and provides a new approach to generate the spatial variation in WUE, which will benefit the regional carbon budget assessment. •A dataset containing 37-site WUE data from China is constructed.•WUE decreased with the increasing altitude, which resulted from the climatic gradient.•Not only MAT, MAP, and MLAI but also MAR affected the spatial variation of WUE.•The equation containing MLAI and altitude explained 65% of WUE spatial variation.