Subtropical lakes are important source of atmospheric methane (CH₄). This study aims to investigate spatial variations of CH₄ flux in Lake Taihu, a large (area 2400 km²) and shallow (mean depth 1.9 m) eutrophic lake in Eastern China. The lake exhibited high spatial variations in pollution level, macrophyte vegetation abundance, and algal growth. We measured the diffusion CH₄ flux via the transfer coefficient method across the whole lake. In addition, data obtained with the flux gradient and the eddy covariance methods were used in conjunction with the data on the diffusion flux to estimate the contribution by ebullition. Results from 3 years' measurements indicated high spatial variabilities in the diffusion CH₄ flux. The spatial pattern of the diffusion CH₄ emission was correlated with water clarity, dissolved oxygen concentration, and the spatial distributions of algal and submerged vegetation. In comparison to the transfer coefficient method, the eddy covariance and the flux gradient method observed a lake CH₄ flux that was 3.39 ± 0.58 (mean ± 1 standard deviation) and 1.95 ± 0.36 times higher in an open‐water eutrophic zone and in a habitat of submerged macrophytes, respectively. The result implied an average of 71% and 49% ebullition contribution to the total CH₄ flux in the two zones. The annual mean diffusion CH₄ flux of the whole lake was 0.54 ± 0.30 g m⁻² yr⁻¹. Our CH₄ emission data suggest that the average CH₄ emission reported previously for lakes in Eastern China was overestimated.

There remains significant uncertainty in the estimation of anthropogenic CH₄ emissions at local and regional scales. We used atmospheric CH₄ and CO₂ concentration data to constrain the anthropogenic CH₄ emission in the Yangtze River Delta one of the most populated and economically important regions in China. The observation of atmospheric CH₄ and CO₂ concentration was carried out from May 2012 to April 2017 at a rural site. A tracer correlation method was used to estimate the anthropogenic CH₄ emission in this region, and compared this “top-down” estimate with that obtained with the IPCC inventory method. The annual growth rates of the atmospheric CO₂ and CH₄ mole fractions are 2.5 ± 0.7 ppm year⁻¹ and 9.5 ± 4.7 ppb year⁻¹, respectively, which are 9% and 53% higher than the values obtained at Waliguan (WLG) station. The average annual anthropogenic CH₄ emission is 4.37 (± 0.61) × 10⁹ kg in the YRD (excluding rice cultivation). This “top-down” estimate is 20–70% greater than the estimate based on the IPCC method. We suggest that possible sources for the discrepancy include low biases in the IPCC calculation of emission from landfills, ruminants and the transport sector.

Lakes are important natural sources of atmospheric methane (CH₄), which should be considered in global CH₄ budget estimations. However, the CH₄ emissions from lakes can be highly variable, and the emissions from the river inlet region were less studied, which seriously hamper our understanding of CH₄ budget estimates of lakes. Here, field measurements from over six years (2012 to 2017) at Lake Taihu, a large eutrophic shallow lake with a complicated river network, were conducted to address the issue. Results show that the river inlet region of the lake was a hot spot of CH₄ emission. The CH₄ emission at the river inlet region with an annual mean value of 0.183 mmol m⁻² d⁻¹ was seven times higher than that at pelagic region (0.028 mmol m⁻² d⁻¹). Peak CH₄ emission occurred in warm seasons, and the lowest in cold seasons at the pelagic region, but peak emission occurred in cold seasons at the river inlet region. The seasonal CH₄ emission at the pelagic region can be explained by water temperature; however, less temperature dependency of CH₄ emission at the river inlet region was found. The variability of CH₄ emission was linked to pollution loadings, and CH₄-rich water in the inflowing river likely sustained the large CH₄ emission of river inlet region.

A daily-scale flux variance (FV) method, which employs low-frequency air temperature measurements, was assessed against eddy covariance (EC) measurements of sensible and latent heat fluxes at four sites representing grassland and cropland ecosystems. The sensible heat flux was estimated using two daily-scale FV approaches: M1 (separating daytime and nighttime data) and M2 (integrating daily data), both derived from conventional formulations. The latent heat flux was extracted as a residual of the energy balance closure with the FV-estimated sensible heat flux and additional measurements of net radiation and soil heat flux. The results showed that the FV method performed poorly in estimating sensible heat flux across all four sites, primarily due to the negative flux values from cropland sites. In contrast, latent heat flux estimation showed reasonable agreement with EC measurements. Notably, upscaling the FV method from a half-daily (M1) to a daily (M2) scale did not improve the accuracy of sensible and latent heat flux estimations for most sites. The best performance for latent heat flux was achieved with M1 at a cropland site (YF), yielding a slope of 0.98, determination coefficient of 0.88, and root mean square error of 13.13 W m⁻². Overall, the daily-scale FV method—requiring only low-frequency air temperature data from microclimate systems—offers a promising approach for evapotranspiration monitoring, particularly at basic meteorological stations lacking high-frequency instrumentations.