1 | INTRODUCTION
Gross primary production (GPP) represents the highest flux in the carbon
(C) budget of a forest ecosystem. GPP has been commonly estimated using
many approaches, such as eddy covariance (EC), empirical models, and
upscaling ecophysiological measurements at stand scale (Baldocchi, 2003;
Beer et al., 2010; Peichl, Brodeur, Khomik, & Arain, 2010). However,
there are still some uncertainties in these GPP estimates (Campbell et
al., 2017). For example, accurate EC estimates are based on a set of
assumptions, such as homogeneous flat terrain and turbulent mixing of
air (e.g. Baldocchi, 2003). Because the assumptions are not always met,
the estimates are prone to ~20% uncertainty (Jocher et
al., 2017; Keenan et al., 2019; Wehr et al., 2016).
Assuming that the EC assumptions are met, a semi-empirical model such as
PRELES (PREdict Light-use efficiency, Evapotranspiration and Soil water)
can estimate GPP (GPPPRELES) in a given forest ecosystem
using the EC data for model parameterisation (Mäkelä et al. ,
2008; Peltoniemi, Pulkkinen, Aurela, Pumpanen, Kolari, & Mäkelä, 2015).
PRELES can subsequently be used for gap-filling the EC data that have
been filtered out or are otherwise missing. One of the advantages of
PRELES is that it estimates ecosystem fluxes (GPP and
evapotranspiration) by using routinely measured weather data. It means
that GPPPRELES can be estimated everywhere with no
additional measurement than weather conditions (Tian et al., 2020). This
approach allows one to go back in time for estimating GPP of the boreal
forest in years for which EC are not available (Minunno et al., 2016).
The weakness of GPP estimates from PRELES is that its estimates are
often unanchored by methods that are independent of EC. Previous studies
that compared between biometric/component fluxes and GPP from EC
(GPPEC) data have found that the GPP trends agreed
reasonably well over several years, but often failed to find the same
absolute values at annual scales (Curtis et al., 2002; Ehman et al.,
2002; Peichl, Khomik, & Arain, 2010). These studies underlined two main
kinds of errors, one due to EC measurements and the other due to the
allometric equations and component fluxes. Thus, neither PRELES, EC nor
biometric methods can be considered an absolute standard.
A third, alternative approach for estimating GPP is to scale up
tree-level ecophysiological measurements to the stand level. This
approach requires the scaling of component fluxes such as leaf
photosynthesis or sap flux. For example, the Conductance Constrained
Carbon Assimilation model (4C-A) combined sap flux-based stomatal
conductance with light-dependent photosynthetic parameters to produce
vertically explicit photosynthesis estimates in both single- and
multi-species stands (Kim, Oren, & Hinckley, 2008; Schäfer et al.,
2003). These parameters were used to estimate the vertically explicit
ratio between internal C concentration in the stomatal cavity,
(Ci) and atmospheric C concentration
(Ca) (Ci/Ca) or,
weighted by vertical leaf area distribution, a canopy-scale effective
Ci/Ca at diurnal resolution. Although it
described photosynthesis well (Schäfer et al., 2003), the method
required detailed information on canopy architecture and gas exchange
properties, which are not straightforward to obtain.
A simpler way forward is to infer intrinsic water use efficiency
(WUEi) from δ13C (Cernusak et al.,
2013; Ehleringer & Farquhar, 1993). WUEi represents the
ratio between net photosynthesis and the stomatal conductance
(gS) to water vapour (Flexas et al., 2016). It is also
equivalent to the CO2 diffusion gradient between the
atmosphere and the substomatal cavity when considering
gS for CO2 (Farquhar, O’Leary, & Berry,
1982). The WUEi can be estimated from
δ13C in phloem
(δ13Cp) contents, which estimates
WUEi, at the tree scale (Ubierna & Marshall, 2011;
Werner et al., 2012). Tree-scale WUEi can be upscaled to
the stand by measuring several trees representing the area of interest.
The δ13Cp measurement integrates the
signal from the whole canopy, and therefore improves on Hu, Moore,
Riveros‐Iregui, Burns, & Monson. (2010), who used a similar approach,
but based their δ13C estimates on sugar extracts from
foliage. The elimination of photosynthetic parameters, the phloem
sampling, and the long time-step reduce error propagation as we scale up
the whole tree measurements to the stand. The scale of the calculated
WUEi thus matches the scale of the transpiration
estimate.
Some studies using δ13C to estimate
WUEi (Seibt, Rajabi, Griffiths, & Berry, 2008; Wingate,
Seibt, Moncrieff, Jarvis, & Lloyd, 2007) and GPP (Hu et al., 2010;
Klein, Rotenberg, Tatarinov, & Yakir, 2016) have highlighted the
importance of mesophyll conductance (gm). The
gm describes the ease with which CO2 can
diffuse from the substomatal cavity to the chloroplasts, where carbon
assimilation actually occurs (Flexas, Ribas-Carbó, Diaz-Espejo, Galmès,
& Medrano, 2008; Warren & Adams, 2006). Because gm is
finite, assuming that it is infinite leads to an overestimation of
WUEi (Seibt et al., 2008; Wingate et al., 2007). There
is as yet no agreement about how to model gm, but it has
often been estimated from gS (Warren, 2008).
We present a new GPP model, hereafter called GPPiso/SF,combining sap flux, δ13Cp, and
mesophyll conductance based on approaches developed previously (Hu et
al., 2010; Kim et al., 2008; Klein et al., 2016; Schäfer et al., 2003),
and compare it to estimates from PRELES. We estimated
GPPiso/SF of whole trees at a daily time step and then
scaled it up to the stand level. The sap flow/isotopic method would,
however, only consider the tree contribution to the ecosystem GPP, in
contrast to PRELES, which considers the contribution of the whole
ecosystem, including understorey and overstorey species. The understorey
contribution from PRELES is in the process of being analysed. However,
understorey GPP represents rather little of ecosystem GPP in a
closed-canopy boreal forest (Kulmala et al., 2011; Palmroth et al.,
2019, Tian et al, under review). PRELES and the sap flow/isotopic method
should therefore give similar results. The GPPiso/SFmethod can also provide information on how GPPiso/SFresponds to fertilisation in terms of assimilation and
gS.
A boreal forest is particularly suited for such method comparison
because of its simple species composition (Hänninen, 2016; Högberg,
2007). Moreover, because this biome is strongly nitrogen (N)-limited (Du
et al., 2020) adding extra N induces a strong response in terms of
growth and C fluxes (From, Lundmark, Mörling, Pommerening, & Nordin,
2016; Högberg, 2007; Hyvönen et al., 2008; Kergoat, Lafont, Arneth, Le
Dantec, & Saugier, 2008; Nohrstedt, 2001; Tamm, 1991). These increases
should be captured by all methods. However, a positive N-fertilisation
effect on GPP was not always observed. At our site, previous studies
showed no effect of N supply on GPP (Lim et al., 2015; Tarvainen,
Räntfors, Näsholm, & Wallin, 2016) but Tian et al (under review) found
a higher GPP in the fertilised plot than in the reference plot.
The method we propose in this paper aims to provide an alternative
stand-scale estimate of GPP that is independent of eddy covariance. Our
first objective here was to compare estimates of GPP based on stable
isotopes and sap flux against GPP based on PRELES, a process-based model
parameterised with eddy covariance data. The second objective was to
determine how fertilisation treatment influenced the canopy GPP with the
sap flux/isotope method. Finally, the third objective explores
alternative methods for incorporating an empirical gmestimate and how these alternatives influence the GPP estimate.