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Urban wetlands, an important part of the urban ecosystem, play an important role in regional carbon cycles and the carbon balance. To analyze the CO2 source and sink effects of plateau urban wetlands, based on the data measured by an eddy covariance instrument, the temporal and spatial characteristics of CO2 flux and their influencing factors in the urban wetland of Xining City in the Qinghai Province of China during a warm season (July to September 2020) were studied. The results show that: (1) On the daily scale, the CO2 flux exhibited an obvious “U”-type variation, characterized by strong uptake in the daytime and weak emission at night, with an average daily flux of −0.05 mg·m−2·s−1. The CO2 uptake peak of the wetland took place at 13:00 (−0.62 mg·m−2·s−1), and the emission peak occurred at 23:30 (0.34 mg·m−2·s−1); (2) on the monthly scale, the CO2 flux of the wetland in the study period showed a net uptake each month. The flux increased month by month, and the maximum value occurred in September (−142.82 g·m−2·month−1); (3) from a spatial point of view, the river area showed a weak CO2 uptake (−0.07 ± 0.03 mg·m−2·s−1), while the artificial wetland area showed a strong CO2 uptake (−0.14 ± 0.03 mg·m−2·s−1). The former was significantly lower than the latter (p &lt; 0.01); (4) the regression analysis results show that the CO2 flux was significantly correlated with PAR, VPD, Tsoil, and SWC (p &lt; 0.01). The relationships between the flux and PAR, Tsoil, and SWC were rectangular hyperbola (y = 0.2304 − 2 × 10−3x/(0.9037 + 0.0022x), R2 = 0.64), exponential (y = 0.046exp(0.091x), R2 = 0.88), and quadratic (y = −0.0041x2 + 0.1784x − 1.6946, R2 = 0.83), respectively. Under the joint action of various environmental factors, the urban wetland ecosystem in plateau displayed a strong carbon sink function in warm seasons. This study can establish a data scaffold for the accurate estimation of carbon budget of this type of ecosystem.

Temporal and Spatial Characteristics of CO2 Flux in Plateau Urban Wetlands and Their Influencing Factors Based on Eddy Covariance Technique