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
(10.7910/DVN/HEWCWM; Zhang et al., 2020).
Introduction
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 km2, occupying a total of 3.5 % of the earth's land surface area
(Downing et al., 2006; Verpoorter et al., 2014). Accurate observation of the
lake microclimate and lake–air interactions will help to better manage this
water resource and to better predict how it may be affected by environmental
changes. Towards that end, an increasing number of studies have employed the
eddy covariance (EC) methodology to monitor physical state (temperature,
wind, humidity) and process variables (momentum flux and radiation and
energy fluxes) in the lake environment (Vesala et al., 2006; Blanken et al.,
2011; Nordbo et al., 2011; Wang et al., 2014; Li et al., 2015; Yusup and
Liu, 2016; Du et al., 2018; Hamdani et al., 2018; Xiao et al., 2018; Wang et
al., 2019). Unlike EC studies in land ecosystems, however, data from these
lake studies are rarely published as data papers or are rarely archived in public data depositories accessible by the broader scientific community. For
example, of the nearly 500 sites that have contributed EC and
micrometeorological data to AmeriFlux, a public data depository
(https://ameriflux.lbl.gov/data/data-availability/, last access: 24 October 2020), none are a lake site.
Although a few scientific groups have provided data supplements to their
scientific papers on lake–air fluxes (e.g., Charusombat et al., 2018; Franz
et al., 2018; Zhao and Liu, 2018), we are not aware of a data paper devoted
to systematic description and archival of EC lake observations.
In this paper, we describe the dataset from the Lake Taihu eddy flux network
(Lee et al., 2014). Established in 2010, the network currently consists of
six active lake sites, one inactive lake site and one active land site.
Lake Taihu is the third-largest freshwater lake (area of 2400 km2) in
China. Data variables are recorded at half-hourly intervals, and the
measurement has continued for over 8 years. 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.
This paper is organized as follows. Section 2 is a brief overview of the
sites and the instruments used by the network. This is followed, in Sect. 3, with a description of data quality measures employed during the field
monitoring. Section 4 provides the essential information about the dataset,
including data variables, gap-filling methods and data quality flags.
Results of postfield evaluation of the data quality are given in Sect. 5.
Users of this dataset may be interested in the relevant papers published by
our group. Lee et al. (2014) gave an overview of the Lake Taihu eddy flux
network. Using the data collected at a subset of the sites and during the
early phase of the network. Wang et al. (2014) investigated the spatial
variability of energy and momentum fluxes across the lake. Xiao et al. (2013) improved the bulk parameterizations of heat, water and momentum
fluxes for shallow lakes. Deng et al. (2013) and Hu et al. (2017) modified
the Community Land Model (CLM) lake simulator (Subin et al., 2012) to improve its prediction of the
lake evaporation. Wang et al. (2017) and X. Zhang et al. (2019) evaluated the
performance of two mesoscale models of the lake–land breeze. More recently,
Xiao et al. (2020) investigated drivers of the
interannual variability of the lake evaporation observed at one of the lake
sites (Bifenggang). The value of the dataset is enhanced by these peer-reviewed
publications because they have helped us to continuously improve our
measurement and data-processing protocols. For example, we have used the
locally calibrated bulk parameterizations of Xiao et al. (2013) to gap-fill
the flux variables.
A list of sites in the Lake Taihu eddy flux network.
Table 1 shows the basic site information, and Fig. 1 is a map that gives
the relative position of Lake Taihu in China and locations of the EC
measurement sites. Also shown in Fig. 1 are World Meteorological Organization (WMO) baseline weather stations
around the lake, whose data can be obtained from the National Meteorological
Information Center in China (http://data.cma.cn/site/index.html, last access: 24 October 2020). The lake,
located between the latitudinal range of 30∘5′40′′ to
31∘32′58′′ N and longitudinal range of 119∘52′32′′ to
120∘36′10′′ E, has a total area of 2400 km2 and an average
depth of 1.9 m. The climate is subtropical monsoon, with an annual mean
temperature of 16.2 ∘C and annual total precipitation of 1122 mm. The
lake is ice-free throughout the year.
Map showing locations of Lake Taihu, eddy covariance
sites (red bubbles) and WMO weather stations (yellow triangles). City names
are shown in blue. DS is a land site, and MLW, MLW2, DPK, PTS, XLS, BFG and
DTH are lake sites. The background is a natural color image from Landsat 8
without correction for atmospheric interference.
The EC network consists of seven lake sites and one land site. The lake
sites (Meiliangwan, MLW; Dapukou, DPK; Bifenggang, BFG; Xiaoleishan,
XLS; Pingtaishan, PTS; Dongtaihu, DTH; Meiliangwan2, MLW2) are
distributed according to biological characteristics and across
eutrophication gradients of the lake. The MLW site, located in Meiliangwan
Bay near the northern shore of Lake Taihu, was the first site in operation; the
measurement began in June 2010 and was replaced by MLW2 in 2018, 10 km
southwest of MLW. Both MLW and MLW2 sites are located in the lake eutrophic
zone. BFG is located in the eastern part of Lake Taihu in relatively clean
water inhabited by submerged vegetation with a growth season from April to
November. DTH is located in the shallow water (mean depth of 1.3 m) in the
southeastern part of the lake. After more than 20 years of crab aquaculture,
this zone was returned to an unmanaged state in December 2018 in order to
improve water quality. The observation at DTH enables the examination of
lake–air exchange processes in the transition from human management to a
natural state. PTS is situated in the middle of Lake Taihu, where occasional
algal blooms occur, and no aquatic vegetation is present. DPK is located near
the western shore, in a relatively deep (depth 2.5 m) super eutrophic zone due
to heavy influence of agricultural and urban runoffs. XLS is located in the
relatively clean and vegetation-free zone in the southeast. Finally, DS is a
land site surrounded by rice agriculture, serving as a land reference for
the lake sites. The MLW site is situated at a distance of 200 m from the
northern shore of the lake. All the other lake sites in the lake are at a
distance of more than 1 km away from the land.
Water depth at the eddy covariance sites.
Percent of data coverage. The percentage represents the
proportion of high-quality original measurement.
Variable typeMLWDPKBFGXLSPTSDTHMLW2DSMicrometeorology93.381.197.697.097.598.190.391.7Radiation flux85.590.896.997.498.698.298.282.7Water or soil temperature83.481.394.091.190.387.722.498.4Eddy flux73.361.882.779.180.685.785.582.8
The lake water level is monitored daily by the Taihu Basin Authority at five
locations around the lake (http://www.tba.gov.cn/, last access: 24 October 2020). Using the
water level time series, we have constructed the water depth for our eddy
covariance sites (Fig. 2).
Instrumentation
Each site is equipped with an EC system for long-term, continuous monitoring
of the surface momentum, sensible heat, latent heat and carbon dioxide
fluxes. The EC system consists of a sonic anemometer and thermometer (Model
CSAT3A; Campbell Scientific, Logan, UT, USA) and a CO2/H2O
infrared gas analyzer (Model 7500A, LI-COR, Inc., Lincoln, NE, USA at DS,
MLW, MLW2 and DPK; Model EC150, Campbell Scientific, at other sites). The EC
system is at a height of 3.5 to 9.4 m above the water surface at the lake
sites and at a height of 20 m above the ground at the land site. Other
measurements include air humidity and air temperature (Model HMP45D/HMP155A;
Vaisala, Inc, Helsinki, Finland) as well as wind speed and wind direction (Model
03002; R. M. Young Company, Traverse City, MI, USA) and four components of
the net radiation (Model CNR4; Kipp & Zonen B.V., Delft, the
Netherlands). At the lake sites, water temperature profile was measured with
temperature probes (Model 109-L; Campbell Scientific) at water depths of
20, 50, 100 and 150 cm and in the sediment at about 5 cm below the bottom of
the water column. The top four temperature sensors were tied to a nylon rope
hanging from a buoy to ensure that they were at the designed depths
regardless of water level fluctuations. At the DS land site, soil
temperature profile was measured with the same type of probes at depths
of 5, 10 and 20 cm. The MLW and the DS sites are supported by AC power, and
other sites are powered by battery packs connected to solar panels.
Measurements at the lake sites were made on fixed platforms. Readers are
referred to Lee et al. (2014) and Xiao et al. (2017) for photographs of the
platform and the instruments.
All the variables are reported as 30 min averages. The EC data are expressed
in the natural coordinate system (Lee et al., 2004). In this coordinate
system, the longitudinal coordinate axis is aligned with the 30 min mean
velocity vector so that the 30 min mean lateral and vertical velocity
components are 0, the magnitude of the mean velocity is equal to the
mean longitudinal component, and the covariance between the lateral and the
vertical velocity components is 0. Additionally, a small density
correction has been applied to the water vapor flux according to Webb et al. (1980).
Data quality control during field monitoring
Every site in the Lake Taihu eddy flux network is equipped with a wireless
transmission module for real-time monitoring and for data transmission. Time
series of all 30 min variables are examined weekly, and abnormal behaviors
are flagged for site operators. Each site is visited every 1 to 2 months
to perform instrument repair and maintenance and to download 10 Hz EC data.
The data coverage rates are summarized in Table 2, where the percentage
values represent the proportions of data with quality flag 0, which
indicates high-quality original measurement (Table 3).
A list of data quality flags
FlagData quality description0Original data1Gap-filled with time interpolation2Gap-filled with spatial interpolation3Gap-filled with bulk relationship4NAN
The four-way net radiometers at MLW and XLS were compared in the field
against a laboratory standard of the same type in the summer of 2018 to
check their long-term stability (Fig. 3). These two sites were chosen
because they have been in operation for more than 5 years. Additionally,
the radiometer at MLW was relocated to MLW2 after MLW had been discontinued.
The laboratory standard, which had been calibrated at the manufacturer prior
to this performance evaluation, was mounted next to the field instrument for
about 10 d at each site, covering overcast to clear-sky conditions. The
mean bias error was smaller than 1 W m-2 for all the radiation
components. It was -0.81, -0.81, 0.79 and -0.44 W m-2 for the downward
shortwave, upward shortwave, downward longwave and upward longwave radiation
flux at MLW, respectively. The corresponding values were 0.91, 0.40, 0.69
and 0.77 W m-2 for XLS. (Comparison experiments are being planned for
the other sites.)
Comparison of four components of the radiation balance
between the original radiometer (horizontal axis) and a laboratory standard
(vertical axis) at MLW and XLS. Refer to Table 4 for variable definitions.
The EC gas analyzers were calibrated every 1 to 2 years. The zero-point
calibration was carried out with high-purity nitrogen gas, the CO2 span
calibration was made with standard carbon dioxide gases (in the
concentration range of 389 to 525 ppm) provided by the National Institute of
Metrology of China (NIM) and certified to an accuracy of 1 %, and the
H2O span calibration was made with a portable dew point generator
(LI-610; LI-COR, Inc.).
Gap-filling methods and data quality flags
We use a five-point moving average to screen outliers. If the deviation from
the moving average is greater than 2 standard deviations, the data point
is discarded. If a gap length is 30 min to 1 h, the gap is filled by linear
interpolation. Larger gaps in meteorological variables, radiation components
and water temperature are filled with linear regression involving
observation of the same variable at another site. This spatial interpolation
consists of three steps. First, linear correlation is calculated using the
valid data at the target site and at all other sites for the month during
which the data gap occurred. Second, the observation at the site with the
highest linear correlation is used to establish a linear-regression
equation. Third, the gap at the target site is filled with the linear
regression and the observation at that site.
Radiation data gaps at the DS land site require special treatment. The
radiometer at the DS eddy flux site ended in January 2013. Subsequent
measurements of the radiation component are provided by a radiometer
belonging to the Dongshan WMO weather station at a distance of 50 m from the
eddy covariance tower (Fig. 1). While large gaps in meteorological
variables (air temperature, relative humidity, wind speed and air pressure),
downward solar radiation and downward longwave radiation are filled with the
spatial interpolation method, large gaps in upward shortwave radiation and
upward longwave radiation cannot be filled with data from other lake sites
even with linear regression. In the case of the upward shortwave radiation,
the data gaps were filled using the relationship between downward shortwave
radiation and the monthly mean albedo. In the case of upward longwave
radiation, the data gaps were filled by a regression equation between the
upward longwave radiation and the fourth power of soil temperature at 5 cm
depth. Compared to the original data, the gap-filled data do not capture the
full diurnal variations because the 5 cm soil temperature has smaller
diurnal amplitudes than the soil surface temperature, but the daily mean
upward longwave radiation flux seems reasonable.
Large data gaps in the EC variables (sensible heat flux, latent heat flux
and friction velocity) are filled with a hybrid method. First, if
observations exist for the relevant state variable, the gap is filled with
the bulk transfer relationship using a transfer coefficient tuned locally
for each site (Xiao et al., 2013). For example, the relationship for filling
gaps in the sensible heat flux is
H=ρacpCHU(Ts-Ta),
where ρa is air density, cp is specific heat of air at
constant pressure, CH is the transfer coefficient for sensible heat,
Ta is air temperature, and Ts is water surface temperature. The
transfer coefficient CH is determined from the observed H and the state
variables (U, Ta and Ts) outside gap periods. The missing data on H are
then filled with the above relationship using the tuned CH and the observed
U, Ta and Ts. Second, if data for the state variables are missing, the
spatial interpolation method is used to fill the gaps in these EC variables.
A list of data columns and variable definitions.
ColumnDescriptionVariable nameUnit1YearYear–2MonthMonth–3DayDay–4HourHH–5MinuteMM–6Day of YearDOY–7Air pressurePkPa8Quality flag of air pressureP_flag9Air temperatureTa∘C10Quality flag of air temperatureTa_flag11Relative humidityRH%12Quality flag of relative humidityRH_flag13Wind speedWSm s-114Quality flag of wind speedWS_flag15Wind directionWD∘16Quality flag of wind directionWD_flag17RainfallRmm18Quality flag of rainfallR_flag19Upward shortwave radiationURW m-220Quality flag of upward shortwave radiationUR_flag21Downward shortwave radiationDRW m-222Quality flag of downward shortwave radiationDR_flag23Upward longwave radiationULRW m-224Quality flag of upward longwave radiationULR_flag25Downward longwave radiationDLRW m-226Quality flag of downward longwave radiationDLR_flag27Water temperature at 0.2 mTw_20∘C28Quality flag of water temperature at 0.2 mTw_20_flag29Water temperature at 0.5 mTw_50∘C30Quality flag of water temperature at 0.5 mTw_50_flag31Water temperature at 1.0 mTw_100∘C32Quality flag of water temperature at 1.0 mTw_100_flag33Water temperature at 1.5 mTw_150∘C34Quality flag of water temperature at 1.5 mTw_150_flag35Sediment temperatureTw_bot∘C36Quality flag of sediment temperatureTw_bot_flag37Friction velocityU∗m s-138Quality flag of friction velocityU∗_flag39Sensible heat fluxHW m-240Quality flag of sensible heat fluxH_flag41Latent heat fluxLEW m-242Quality flag of latent heat fluxLE_flag
Notes: (1) time marks end of a half-hourly observation in Beijing time
(UTC+8); (2) at the DS site, columns 27, 29 and 31 represent soil
temperature at 5, 10 and 20 cm, respectively; column 33 represents soil
heat flux G (W m-2) measured at 5 cm depth; and column 34 represents
quality flag of soil heat flux.
Complete gap-filled time series for selected variables
observed at BFG. Blue, black, cyan and red dots represent quality flag 0, 1,
2 and 3, respectively. Variable definitions are given in Table 4.
The spatial interpolation method described above occasionally causes a
sudden jump at the beginning or end of a data gap. To harmonize the data, we
apply a five-point moving average to the gap-filled time series. If a data
point deviates by 2 times the standard deviation from the moving average,
it is replaced by linear interpolation using the two adjacent data points.
Each data variable is assigned a quality flag to distinguish original
measurements and gap-filled values and gap-filling methods (Table 3). The
data flags employed here should not be confused with quality flags commonly
assigned to the EC methodology in the literature. Specifically, Flag 0
indicates high-quality original data. Other flag values indicate gap-filled
data or missing values. Flag 1 indicates that the data were filled by
temporal interpolation. Flag 2 indicates that the data were filled by the
spatial interpolation method. Flag 3 for the EC variables indicates that the
data were filled by the bulk relationship. We also use Flag 3 to mark the
upward shortwave and longwave radiation data filled with the albedo and the
surface temperature relationship, respectively, for the DS land site.
Missing values occur in some situations, which are marked with Flag 4.
Figure 4 is an example showing the gap-filled time series of several
variables at BFG along with the flag status.
Rainfall data have not been quantity-controlled or gap-filled. Because of the
episodic nature of rainstorms and high spatial variability of rainfall, it
is not appropriate to fill data gaps with the time interpolation or spatial-interpolation
method. The total rain amount is likely biased low because no wind screens
are used to protect the rain gages from the influence of wind which is much
higher on the lake than on land (Fig. 5 below). On several site visits,
the drain opening to the tipping bucket was found to be partially blocked by
debris. Rain amount at a constant and low rate and excessively long rain
duration are evidence of such blockage. The flag status of 0 for the
rainfall variable simply indicates that the field measurement is available,
but it does not guarantee high data quality.
Annual mean air temperature (a), relative humidity (b) and wind speed (c) observed at the eddy flux sites (symbols)
and at the four WMO weather stations around the lake (line). Error bars
represent the range of the annual means of the four WMO stations.
The data coverage begins from the start time of each site (Table 1) and ends
in December 2018. The time resolution is 30 min. The dataset includes
microclimate variables (air pressure, air temperature, relative humidity,
wind speed, wind direction and rainfall); radiation fluxes (upward and
downward shortwave radiation, upward and downward longwave radiation); water
temperature at depths of 0.2, 0.5, 1.0 and 1.5 m and in the 5 cm
sediment; and eddy fluxes (friction velocity, sensible heat and latent heat
fluxes; Table 4). The time stamp is Beijing time (UTC+8), given by data
columns 1 to 5 as year, month, day, hour and minute, and marks the end of
the observation period. For example, time stamp “2012, 1, 1, 12, 00”
indicates that the data acquisition period is from 11:30 to 12:00 UTC+8 on 1 January 2012.
Although the data table does not include the radiative surface temperature
Ts, the user can easily calculate it from the two longwave radiation
fluxes as
Ts=L↑-(1-ε)L↓εσ14,
where σ is the Stefan–Boltzmann constant; ε is
emissivity; and L↑and L↓ are upward and downward
longwave radiation flux, respectively. We use a value of 0.97 for lake
surface emissivity in this calculation (Deng et al., 2013; Wang et al.,
2014).
Data consistency evaluation
Figure 5 compares the annual mean air temperature, relative humidity and
wind speed at the Taihu eddy flux sites with those at the four WMO weather
stations (Wuxi, Liyang, Huzhou and Dongshan) around the lake (Fig. 1). The
error bars represent the maximum and minimum values among the four WMO
stations, and the lines represent the mean values of the four station
measurements. The annual mean air temperature at DTH is 0.3 ∘C
higher than the station mean. At other sites, air temperature is in close
agreement with the weather station data in terms of both magnitude and
interannual variability. The annual mean wind speed at MLW, a site near the
shoreline, is comparable with the station data. At other more exposed sites,
the wind speed is much higher than observed at the WMO stations. The annual
mean relative humidity (RH) shows a larger spread among the eddy flux sites
than among the WMO stations partly because the measurement height at the
eddy flux sites is not standardized (Table 1). The upward trends in RH over
time at DPK and XLS seem to be related more to aging of the sensor than to a
real interannual variability. We have not fully investigated this aging
problem, but it is possible to rectify it by doing a detailed regression
analysis against the station data.
Comparison of observed monthly latent heat flux
with Priestley–Taylor model prediction using the original α
coefficient of 1.26 and a modified coefficient of 1.03. Here Rn is net
radiation, G is heat storage in the water column, Δ is the slope of
the saturation vapor pressure curve, and γ is the psychrometric
constant.
The relationship between changes in observed annual mean
upward longwave radiation flux and annual mean air temperature (dots). Solid
lines represent the prediction of the Stefan–Boltzmann law.
Consistency of the energy flux variables can be evaluated with the energy
balance closure. Using observations made at a subset of the sites in the
earlier years of the flux network, Wang et al. (2014) reported a closure
rate of 70 % to 110 % on a monthly basis, meaning that the sum of
the measured monthly sensible and the latent heat flux H+λE
is 70 % to 110 % of the monthly available energy Rn-G, where Rn is net radiation, and G is heat storage in the water column. By
selecting days without data gaps, we found that the daily energy balance
closure is in the range between 66 % and 78 % for all the lake sites
and all the years. Such closure rates are typical of eddy covariance
observations (Tanny et al., 2008; Wilson et al., 2002).
We have shown that the monthly latent heat flux at the lake sites MLW, BFG
and DPK during July 2010 to August 2012 follows the Priestley–Taylor (PT)
model prediction with the original PT constant α
of 1.26 and that at the DS land site it is in agreement with the PT model if
the constant is lowered to 1.0 (Lee et al., 2014). Figure 6 demonstrates
that the same relationships hold for all the sites and all the observational
months, indicating the overall stability of our measurement systems and the
robustness of our gap-filling procedure. The reader is reminded that the
monthly latent heat flux in Fig. 6 has been adjusted to force energy
closure following the method recommended by Barr et al. (1994), Blanken et
al. (1997) and Twine et al. (2000). (The half-hourly flux data in the data
archive have not been adjusted for energy balance.)
Uncertainty of key measurement variables at half-hourly
intervals. Instrument uncertainty is provided by the manufacturers.
Performance uncertainty is 1 standard deviation of the difference between
measurements made by the field instrument and the validation instrument.
Environmental uncertainty is the spatial standard deviation of the variable
measured at the lake sites.
VariableUncertaintyPeriod of evaluationInstrument uncertainty P±0.3 hPaTa±0.2 ∘CRH±2 %WS±0.3 m s-1WD±3∘UR/DR< 5 %ULR/DLR< 10 %Tw±0.6 ∘CPerformance uncertainty UR±2.1 W m-229 June–8 July 2018,6–15 October 2018DR±8.0 W m-229 June–8 July 2018,6–15 October 2018ULR±0.5 W m-229 June–8 July 2018,6–15 October 2018DLR±1.3 W m-229 June–8 July 2018,6–15 October 2018U∗±0.06 m s-113 July–23 August 2020H±3.1 W m-213 July–23 August 2020LE±21.2 W m-213 July–23 August 2020Environmental uncertainty Water depth±0.06 m1 September 2017–31 August 2018Ta±0.50 ∘C1–31 July 2018DR±36.3 W m-21–31 July 2018
The Stefan–Boltzmann law offers another way for checking data consistency.
Because the lake surface emits longwave radiation like a blackbody and
because the annual mean air temperature and the surface water temperature
are nearly identical at this lake (Wang et al., 2014), the change in the
annual upward longwave radiation ΔL↑ can be expressed as
ΔL↑=4σTa3ΔTa,
where Ta is annual mean air temperature, and Δ is the difference
between the target year and the year with the lowest air temperature
observed at the site. All the five long-term lake sites show good
consistency between the longwave radiation and the air temperature
observations (Fig. 7).
Table 5 is a summary of the uncertainty of key measurement variables at
half-hourly intervals. The performance uncertainty is 1 standard deviation
of difference in a variable measured by the field instrument and the same
variable measured by a validation instrument (the closed-path EC in the case
of eddy fluxes and the laboratory standard radiometer in the case of the
radiation fluxes). The environmental uncertainty is 1 standard deviation
of spatial variation in a variable measured at multiple lake sites.
Data availability
All data are open-access and are available online for download and use 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).
Summary
The dataset described here consists of microclimate variables (air
temperature, air humidity, wind speed, wind direction, water or soil
temperature profile, and rainfall), four components of the radiation
balance, friction velocity, and sensible and latent heat fluxes observed at
seven lake sites and one land site. The period of coverage is from June 2010
to December 2018. The observation interval is 30 min. Except for rainfall
and wind direction, all other variables have been gap-filled. Every data
point is tagged with a data quality flag to help the user determine how to
best use the data.
Author contributions
XL, WX and MZ directed the field program; ZZ performed data gap-filling and
prepared the data for public release; CC, WW, CX, HC, JW, JZ, LJ, QL, WH,
WZ, YL, YX, YW, YP, YH, ZC and ZQ participated in field data
collection; and ZZ, XL and MZ wrote the manuscript.
Competing interests
The authors declare that they have no conflict of interest.
Financial support
This research has been supported by the National Key R&D Program of China (grant no. 2019YFA0607202), the National Natural Science Foundation of China (grant nos. 41575147, 41801093, 41475141), and the Priority Academic Program Development (PAPD) of Jiangsu Higher Education Institutions.
Review statement
This paper was edited by David Carlson and reviewed by two anonymous referees.
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