Since 2000, the vegetation cover in the Yellow River Basin (YRB) has significantly increased. However, the responses of carbon and water cycles to large-scale vegetation recovery in the basin and their driving mechanisms remain unclear. This study employs methods such as Sen’s slope trend test, partial correlation analysis, residual analysis, and interpretable machine learning models to investigate the variations in gross primary productivity (GPP), evaporation (ET), and water use efficiency (WUE) in the YRB. It aims to reveal the spatial differentiation mechanisms that drive GPP, ET, and WUE. The results indicate the following: (1) From 2001 to 2020, significant increasing trends were observed in GPP, ET, and WUE across the YRB (p < 0.05), with the most pronounced vegetation recovery observed in the middle reaches. (2) GPP, ET, and WUE are most strongly correlated with the Leaf Area Index, with median values of 0.78, 0.30, and 0.70, respectively. (3) On average, climate change contributes spatially 24.8%, 35.6%, and 24.3% to GPP, ET, and WUE, respectively, while human activities contribute, on average, 75.2%, 64.4%, and 75.7%. (4) Regarding their synergistic evolution, GPP changes predominantly drive WUE changes in the YRB relative to ET. (5) The contributions of NDVI changes to WUE, GPP, and ET changes are 60.4%, 73.1%, and 14.9%, respectively. Overall, NDVI changes dominate the changes in GPP and, by extension, in WUE. This research sheds light on the pathways toward ecological restoration and sustainable development in the YRB.
Cao et al. (Thu,) studied this question.