2.3 Turbulent Heat Fluxes
Sensible (H ) and latent heat fluxes (LE ) (both in W
m−2) were calculated using the covariance between the
vertical wind speed w (m s−1) and air
temperature T (K), as well as between w and the specific humidityq (kg kg−1), such that:
\(H=\ \rho_{a}\ c_{\text{pa}}\ \overset{\overline{}}{w^{{}^{\prime}}\ T^{{}^{\prime}}}\)(1)
\(\text{LE}=\ \lambda\rho_{a}\ \overset{\overline{}}{w^{{}^{\prime}}\ q^{{}^{\prime}}}\)(2)
where ρa is the humid air density (kg
m−3), cpa is the specific heat
of humid air (J kg−1 K−1) andλ is the latent heat of vaporization (J kg−1).
Here, primes denote fluctuations from the 30-min average, indicated by
an overbar.
Raw wind velocity data from the raft were first corrected for
wave-induced motion, using the accelerometer data, following the method
proposed by Miller et al. (2008). In short, the apparent wind measured
by the sonic anemometer was corrected to account for Euler angles,
angular velocities and linear accelerations monitored by the
accelerometer. Then, for the shore and raft flux towers, we processed
the turbulence data using the EddyPro® software, version 7.0 (LI-COR
Biosciences, USA). In doing so, we applied time-lag compensation, linear
detrending, double rotation approach (Baldocchi et al., 1988; Wilczak et
al., 2001), density fluctuation compensation (Webb et al., 1980), spike
removal (Papale et al., 2006), and other statistical tests (Vickers &
Mahrt, 1997). Poor-quality data were flagged (Mauder & Foken, 2011) and
removed. Data from the raft and shore stations were aggregated into one
dataset by favoring data with the best quality criteria (Mauder et al.,
2013). Note that shore data were retained only when winds originated
from the reservoir. To complete the final dataset, gap-filling was
implemented based on the method developed by Reichstein et al. (2005).
Over the whole study period (1447 days), 57 % of the turbulent flux
data had to be gap-filled due to the fact that the raft was only
deployed from June to October and the shore flux tower was frequently
exposed to winds from the surrounding land. We assessed flux errors by
applying the Finkelstein and Sims (2001) random uncertainty method.