Abstract Ecosystem water use efficiency (WUE) and carbon use efficiency (CUE) are critical indicators for understanding the coupling of water and carbon cycles, particularly sensitive to moisture conditions in drylands. However, the multifaceted responses of WUE and CUE to atmospheric, vegetation, and soil moisture factors remain unclear in drylands. In this study, we examined the spatial and temporal dynamics of WUE and CUE in the northern drylands of China during 2003–2018, and employed spatial random forest model to explore the nonlinear responses of WUE and CUE to vapor pressure deficit (VPD), vegetation optical depth (VOD), and soil moisture in vegetation greening and browning areas. Regionally, WUE significantly increased (0.01 gC kg −1 H 2 O yr −1 ), while CUE slightly declined. Vegetation greening areas showed rising WUE and declining CUE, whereas browning areas exhibited the opposite trends. The VPD was the most important factor explaining WUE variability, while the trends of VOD and VPD were almost equally important in explaining CUE variability across the drylands. In significant greening areas, higher VPD (>5), sufficient soil moisture, and moderate VOD (0.3–0.4) promoted increases in WUE but intensified declines in CUE. Conversely, in significant browning areas (less than 3% of study area), WUE displayed a complex nonlinear response to different moisture conditions, and higher VPD and a stronger soil wetting trend enhanced CUE, although higher VOD moderated this effect. These findings revealed the divergent nonlinear characteristics of WUE and CUE to moisture variability under different vegetation dynamics, offering insights into water‐carbon coupling processes and supporting dryland ecosystem management.
Building similarity graph...
Analyzing shared references across papers
Loading...
Yue Wang
Guangyao Gao
Yanzhang Huang
Journal of Geophysical Research Biogeosciences
Chinese Academy of Sciences
University of Chinese Academy of Sciences
Research Center for Eco-Environmental Sciences
Building similarity graph...
Analyzing shared references across papers
Loading...
Wang et al. (Sun,) studied this question.
www.synapsesocial.com/papers/698d6e055be6419ac0d535be — DOI: https://doi.org/10.1029/2025jg009271