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• Of the three gaseous fluxes, the CH 4 flux suffered the largest high frequency loss. • The aerodynamic model yielded the best results for the CO 2 and CH 4 fluxes. • The footprint with flux-gradient method was smaller than that of the eddy covariance. • A hybrid scheme is the most appropriate flux observation method for small ponds. Despite their small overall area, small ponds play a large role in the greenhouse gas budgets of inland water bodies. This study aims to evaluate the performance of the flux-gradient (FG) and the eddy covariance (EC) method for measuring the fluxes of CO 2 , CH 4 , and H 2 O at two small fish ponds (fetch < 120 m) in subtropical climate conditions. The EC fluxes were subject to two sources of error: high frequency flux loss and footprint contamination. Of the three gaseous fluxes, the CH 4 flux suffered the largest high frequency loss (18%) due to a combination of low EC instrument height and long optical path of the CH 4 analyzer. Despite the low measurement height, the EC fluxes were influenced by sources outside the boundary of the target fish ponds, with the footprint contamination most severe on the CO 2 flux and least severe on the CH 4 flux. With regards to the FG method, one major uncertainty lies in the eddy diffusivity calculation. Of the three eddy diffusivity models evaluated [the aerodynamic (AE) model deploying the full Obukhov stability correction, the modified Bowen-ratio model using H 2 O as a tracer, and the wind profile model for neutral stability], the AE model yielded the best results for the CO 2 and CH 4 fluxes. Our results support Horst's (1999, Boundary-Layer Meteorology 90, 171) theoretical prediction that the footprint of the AE flux based on a two-level concentration profile measurement should be much smaller than that of the gradient flux footprint and the EC flux footprint at the geometric mean of the two heights. We conclude that the most appropriate micrometeorological method for measuring fluxes from small water bodies is a hybrid scheme, whereby an EC system is deployed to measure the eddy diffusivity and a precision gas analyzer is used to obtain the concentration gradient of the target gas. [ABSTRACT FROM AUTHOR]

Eddy covariance data are widely used for the investigation of surface–air interactions. Although numerous datasets exist in public depositories for land ecosystems, few research groups have released eddy covariance data collected over lakes. In this paper, we describe a dataset from the Lake Taihu eddy flux network, a network consisting of seven lake sites and one land site. Lake Taihu is the third-largest freshwater lake (area of 2400 km 2) in China, under the influence of subtropical climate. The dataset spans the period from June 2010 to December 2018. Data variables are saved as half-hourly averages and include micrometeorology (air temperature, humidity, wind speed, wind direction, rainfall, and water or soil temperature profile), the four components of surface radiation balance, friction velocity, and sensible and latent heat fluxes. Except for rainfall and wind direction, all other variables are gap-filled, with each data point marked by a quality flag. Several areas of research can potentially benefit from the publication of this dataset, including evaluation of mesoscale weather forecast models, development of lake–air flux parameterizations, investigation of climatic controls on lake evaporation, validation of remote-sensing surface data products and global synthesis on lake–air interactions. The dataset is publicly available at https://yncenter.sites.yale.edu/data-access (last access: 24 October 2020) and from the Harvard Dataverse (10.7910/DVN/HEWCWM; Zhang et al., 2020). [ABSTRACT FROM AUTHOR]

Eddy covariance data are widely used for the investigation of surface-air interactions. Although numerical datasets exist in public depositories for upland ecosystems, few research groups have released eddy covariance data collected over lakes. In this paper, we describe a dataset from the Lake Taihu Eddy Flux Network, a network consisting of seven lake sites and one land site. Lake Taihu is the third largest freshwater lake (area 2,400 km2) in China, under the influence of subtropical climate. The dataset spans the period from June 2010 to December 2018. Data variables are recorded at half-hourly intervals and include micrometeorology (air temperature, humidity, wind speed, wind direction, rainfall, and water/soil temperature profile), the four components of surface radiation balance, friction velocity, and sensible and latent heat fluxes. Except for rainfall and wind direction, all other variables are gap-filled, with each datapoint marked by a quality flag. Several areas of research can potentially benefit from the publication of this dataset, including evaluation of mesoscale weather forecast models, development of lake-air flux parameterizations, investigation of climatic controls on lake evaporation, validation of remote sensing surface data products, and global synthesis on lake-air interactions. The dataset is publicly available at https://yncenter.sites.yale.edu/data-access and from Harvard Dataverse (doi: 10.7910/DVN/HEWCWM) [ABSTRACT FROM AUTHOR]