Abstract
Abstract. 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 km2) 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 (https://doi.org/10.7910/DVN/HEWCWM; Zhang et al., 2020).
Highlights
Inland lakes and reservoirs are a vital freshwater resource for the society
We describe a dataset from the Lake Taihu eddy flux network, a network consisting of seven lake sites and one land site
We describe the dataset from the Lake Taihu eddy flux network (Lee et al, 2014)
Summary
Inland lakes and reservoirs are a vital freshwater resource for the society. Globally, there are more than 27 million water bodies with size greater than 0.01 km, occupying a total of 3.5 % of the earth’s land surface area (Downing et al, 2006; Verpoorter et al, 2014). Z. Zhang et al.: Dataset from the Lake Taihu eddy flux network pers or are rarely archived in public data depositories accessible by the broader scientific community. We describe the dataset from the Lake Taihu eddy flux network (Lee et al, 2014). 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. Lee et al (2014) gave an overview of the Lake Taihu eddy flux network. We have used the locally calibrated bulk parameterizations of Xiao et al (2013) to gap-fill the flux variables
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