4.2.1 | Combination of sap flux and isotopic
measurements.
The key advantage of the sap flux/isotopic approach is that it is
independent of eddy covariance. Moreover, it leans on two methods, sap
flux (Poyatos et al., 2007) and isotopic measurements (Bowling, Pataki,
& Randerson, 2008 and references therein) that have been widely used at
many sites by ecosystem ecologists. The sap flux/isotopic approach
combines them to estimate GPP at the tree scale, which can then be
scaled up to the stand. In simple stand structures, that scaling is
relatively easy. In sum, the sap flux/isotopic method provides a simple
method to estimate tree GPP that, in combination with measurements of
ground vegetation GPP, can provide an alternative estimate for
comparison with GPP estimated by EC.
An additional strength of the sap flux/isotopic approach is that it
provides more detail on the ecophysiological mechanisms underlying GPP
variation. For example, we showed here that \(\ g_{C\hat{\alpha}}\) was
very similar between plots whereas WUEi was different between the F and
the R plot. This led us to conclude that the photosynthetic activity
rather than stomatal conductance was the main driver of GPP variation
between plots.
One critical advantage of the sap flux/isotopic method for estimating
GPP is that its requirements for the terrain and atmospheric conditions
are less restrictive than for EC measurements. It thus provides an
empirical method that can be applied in hilly topography, complex canopy
structure, and non-turbulent atmospheres.
The sap flux/isotopic method also has several important limitations. In
particular, sap flux is a key variable in the GPPiso/SFapproach in order to obtain transpiration. Although the technique
describes temporal variation well, its use for quantitative estimates
requires accounting for several known sources of variation (Oren,
Phillips, Katul, Ewers, & Pataki, 1998). Examples include sap flux
trends radially in the stem (Cohen, Cohen, Cantuarias Aviles, &
Schiller 2008; Ford, McGuire, Mitchell, & Teskey, 2004; Phillips, Oren,
& Zimmermann, 1996; Renninger & Schäfer, 2012), azimuthally around the
stem (Cohen et al., 2008; Oren, Phillips, Ewers, Pataki, & Megonigal,
1999), with tree size (Schäfer, Oren, & Tenhunen, 2000), and with local
competition (Xiong et al., 2015). In addition, corrections are required
when probe length exceed the sapwood depth (Clearwater, Meinzer,
Andrade, Goldstein, & Holbrook, 1999). Finally, the probes often
require specific calibration (Steppe, De Pauw, Doody, & Teskey, 2010;
Sun, Aubrey, & Teskey, 2012). Some corrections have been proposed to
reduce uncertainties from random variation (Peters et al., 2018; Steppe
et al., 2010; Sun et al., 2012), yet some tree-to-tree variation remains
(Oren et al., 1998).
The model we used to estimate transpiration was carefully built to
account for these errors. Tor-Ngern et al. (2017) began with high
quality data based on careful accounting for radial and azimuthal
variations and baseline corrections. They used one of the genera that
Granier originally calibrated for. They recognised that the sensors were
not specifically calibrated for P. sylvestris , but the values
agreed well with previously reported results and were robust to the
errors induced by the probes (Lundblad, Lagergren, & Lindroth, 2001;
Poyatos et al., 2007). Likewise the data were carefully scaled up to the
stand using detailed descriptions of the allometric parameters and tree
sizes (Ford et al., 2004; Oren et al., 1998). Because the sap
flux/isotopic method is so dependent on quantitative sap flux data,
other users must also ensure that their sap flux data remove any bias
and thus are accurate as well as precise.
4.2.2 | High variability of VPD impacts on\(\mathbf{g}_{\mathbf{C}\hat{\mathbf{\alpha}}}\)
There was considerable variation in our estimate of\(g_{C\hat{\alpha}}\). Because \(g_{C\hat{\alpha}}\) was calculated as
the ratio between transpiration and VPD, low VPD caused high variability
and improbable \(g_{C\hat{\alpha}}\) results (Eqn. 6, Ewers, Oren,
Johnsen, & Landsberg, 2001; Ewers & Oren, 2000). This was especially
true in the early and late growing season. The same phenomenon occurred
sporadically during the thermal growing season. It forced us to apply a
filter and to replace the inconsistent data inside the thermal growing
season by predictions from a simple regression between
gC and\(\ \hat{\alpha}\). This filtering and replacement
is a common procedure, especially at high latitudes where VPD is low
(Emberson et al., 2000; Tarvainen et al., 2015). Although we are
satisfied with this solution for the moment, a better means of dealing
with low VPD should be sought.