4.1 | Strengths and weaknesses of PRELES
The key advantages of PRELES are that it is a compromise between
predictive accuracy and model complexity. It can be calibrated with a
few variables derived from EC measurements and hence run with a few
environmental variables. The required EC data are available from many
sites around the world (Baldocchi, 2003). PRELES has been reported to
work well in all boreal forests (Minunno et al., 2016, Tian et al.,
2020). Based on this assessment, we felt justified in using it, with
calibration from 2014-2017 and environmental data from 2012-2013, to
model carbon fluxes in 2012-2013.
Although the availability of EC data is an advantage for PRELES, EC data
must be viewed with caution. In particular, at our site, preliminary
analyses of the data revealed significant problems in the data despite
the flat ground surface, uniform canopy, and low leaf area index. A
careful study of the problem revealed significant decoupling of the
above- and below-canopy air masses, which often led to advection (Jocher
et al., 2018). It is common for EC studies to use a vertical wind speed
cutoff, the u* filter, to detect and remove such events (Aubinet et al.,
2001; Papale et al., 2006). We found that the u* filter was insufficient
and that a measurement relying on the comparison of below-canopy and
above-canopy vertical wind speeds was required (Jocher et al., 2018).
This concern was earlier raised in another boreal forest in Finland
(Alekseychik, Mammarella, Launiainen, Rannik, & Vesala, 2013). We used
a decoupling filter (Thomas, Martin, Law, & Davis, 2013), which is
still unusual in the EC community, to correct the EC data that were used
to parametrize PRELES in this study. Unless this correction was
performed, PRELES would have been parameterised incorrectly if we wished
to quantify total ecosystem fluxes; it would only have described the
decoupled fluxes.