A dataset of microclimate and radiation and energy fluxes from the Lake Taihu eddy flux network

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).

Estimation of Water Surface Energy Partitioning With a Conceptual Atmospheric Boundary Layer Model

“…In addition to sea surface data sets, eddy covariance (EC) measurements over an inland lake (Lake Taihu) were also collected (Lee et al., 2014; Z. Zhang et al., 2020). Lake Taihu, located in the Yangtze River Delta, is the third‐largest freshwater lake in China with an area of approximately 2,400 km 2 and an average depth of 1.9 m. The Taihu EC network, developed by Lee et al.…”

Sigmoid Generalized Complementary Equation for Evaporation Over Wet Surfaces: A Nonlinear Modification of the Priestley‐Taylor Equation

“…The information of data quality measures employed during the field monitoring, the gap-filling methods, HAN ET AL. and data quality flags, as well as the results of post field evaluation of the data quality are given by Zhang et al (2020).…”

“…The data uncertainty may affect the different performances of the four wet surface evaporation equations. The uncertainty of key meteorological variables at half-hourly intervals from the Lake Taihu eddy flux network is summaries by Zhang et al (2020). The instrument uncertainties of the measured air temperature, relative humidity, and wind speed are ±0.2°C, ±2%, and ±0.3 m s −1 , respectively.…”

A benchmark dataset of diurnal- and seasonal-scale radiation, heat, and CO2 fluxes in a typical East Asian monsoon region

“…The EC technique can derive direct observation of the land-atmosphere carbon, water, and energy fluxes exchanges, and is regarded as the best and the most trustworthy measurement of turbulent fluxes (Baldocchi, 2003). With the development of the EC tool, there are more than 500 flux towers distributed in various climate zones (Lee et al, 2020;Kang and Cho, 2021). Under the same climate regions, the radiation budget and energy partitioning were mainly modulated by the surface properties (e.g., albedo, roughness length) and subsequently influence air/surface temperature, humidity, and precipitation (Feddema et al, 2005;Jin and Roy, 2005;Li and Wang, 2019).…”

“…This dataset is provided in a XLSX format which can be easily accessed and used by the atmosphere, hydrology, ecology and cryosphere communities, aiming to facilitate the coherence and continuity in scientific understanding of the interactions among the multi-sphere coupled systems in the YRD. These data will be valuable for validating remote-sensing data products, evaluating meteorological and air-quality models (Tsai et al, 2007), and improving boundary-layer parameterization schemes (Bian et al, 2002;Zhang et al, 2020).…”