Dynamics of Carbon and Water Fluxes over Cropland and Agroforest Ecosystems on the Southern Chinese Loess Plateau

Studies on the spatiotemporal dynamics in ecosystem carbon and water exchanges are essential in predicting the effects of climate change on regional carbon and energy budgets. Using the eddy covariance technique, carbon and water fluxes were observed in a typical winter wheat ecosystem (WWE) and an agroforest ecosystem (AFE) in the southern Loess Plateau from 2004 to 2010. The seasonal and inter-annual variability in gross primary productivity (GPP), net ecosystem exchange (NEE), evapotranspiration (ET), and water use efficiency (WUE) were examined and the main influencing factors were identified using the Pearson correlation. The results indicate that the seasonal GPP and NEE showed a bimodal distribution in WWE, while this was unimodal in AFE. The sinusoidal function did well in the characterization of seasonal ET dynamics for both ecosystems, with the determination coefficients being 0.85 and 0.94, respectively. In WWE and AFE, the annual mean GPP were 724.33 and 723.08 g C m−2 a−1, respectively, and the corresponding ET were 392.22 and 410.02 mm a−1. However, the difference in NEE between the two ecosystems was obvious, NEE were −446.28 and −549.08 g C m−2 a−1, respectively, showing a stronger carbon sink in AFE. There were strong coupling relationships between the GPP and ET of both ecosystems; the overall slopes were 1.71 and 1.69, respectively. The seasonal trend of WUE was bimodal in WWE, with peak values of 3.94 and 3.65 g C kg−1 H2O, occurring in November and April, respectively. However, the monthly WUE in AFE had one single peak of 4.07 g C kg−1 H2O in January. Photosynthetically active radiation (PAR) and soil temperature (Ts) were most positively correlated with GPP, net radiation (Rn) and Ts were the major factors influencing ET, while vapor pressure deficit (VPD) and soil water content (SWC) were the major influencing factors for WUE. These results provide observational support for regional carbon neutrality simulations.