Net CO2 fluxes, F, provided by flux-tower measurements represent the difference between two fundamental processes of gross photosynthesis, Pg, and ecosystem respiration, Re. In general, factors of photosynthesis and respiration at the stand level are not the same (though overlap), and the patterns of their response to a given factor are not identical. Decomposition of the F data into photosynthesis and respiration is an essential part of flux-tower data processing, required for understanding the dynamics of CO2 exchange and prediction of its response to climatic change and anthropogenic management. While in the earlier period of flux data analysis partitioning was based on night-time fluxes, derivation of day-time respiration from day-time measurements became a dominant approach later (Gilmanov et al 2005; Reichstein et al 2005; Lasslop et al 2010). A crucial feature of the daytime CO2 exchange utilized in partitioning algorithms based on daylight measurements is that the decrease of F is directly associated with the increase of respiration Re, which, in its turn, is closely related to temperature. The problem is that the decrease of F may also be caused by decreasing photosynthesis Pg, e.g. resulting from water stress. A number of methods to incorporate water-stress were proposed in the literature (e.g., Gilmanov et al 2003; Lasslop et al 2010). In this presentation we describe a physiologically based approach incorporating combined effects of photosynthetically active radiation (Q), soil temperature (Ts), and vapor pressure deficit (VPD) according Gilmanov (2011).
Analysis of the tower CO2 exchange data in a wide range of non-forest terrestrial ecosystems led us to the partition equation:
F(Q, Ts, VPD) = Pg(Q, VPD) - Re(Ts),
provided that for modeling the net flux F, only the one day 30-min data on radiation Q, soil temperature Ts, and vapor pressure deficit VPD are used. This assumption excludes the need to introduce factors slowly changing within a day such as soil water content, soil nutrients concentrations, and leaf area. To allow variation of the convexity of the light-response at the canopy level we have selected nonrectangular hyperbolic light-response function with convexity parameter q :
F(Q,Ts,VPD; a, Amax, q, r0, kT) = [a Q + Amax – SQRT((a Q + Amax)2 – 4 a Amax q Q)]
* f(VPD; VPDcr, s)/(2 q) – r0 Exp(kT Ts)
f(VPD; VPDcr, s) = If[VPD < VPDcr, 1, Exp(–((VPD – VPDcr)/s)2)]
where the normalized VPD-response function f(VPD; VPDcr, s) depends on two parameters: critical value VPDcr, below which water deficit doesn't affect photosynthesis (f = 1 for VPD < VPDcr), and the standard deviation parameter s (typically, 1 < s < 20) with lower values describing strong water-stress effect, and the higher values - weak effect. Though, generally speaking, the values of parameter VPDcr may vary between crops and ecosystems, following El-Sharkaway et al (1984), at this stage of analysis we assumed a general value VPDcr = 1 kPa and considered a one-parametric VPD-response function in the form f(VPD; s).
Because in non-forest ecosystems soil respiration is not directly linked to VPD, it was possible to describe ecosystem respiration by only temperature-dependent term r0 Exp(kT Ts), where r0 = Re(0), and kT is the temperature response coefficient.
Equations above were applied to a range of grassland and crop flux-tower data sets from several continents. Parameters a, Amax, q, s, r0, and kT were numerically estimated for every day of the season with available data using the optimization tools of the "Mathematica" system (Wolfram Research 2011). E.g., for DOY=182 of the year 2009 at the Brookings, SD, grassland site, the estimates (st. errors) were: a = 0.00128 (0.00014) mg CO2 μmol-1, Amax = 1.15 (0.092) mg CO2 m-2 s-1, q = 8.6 « 10-9 (0.0075) (dimensionless), r0, = 0.0383 (0.0298) mg CO2m-2s-1; kT = 0.068 (0.041) (°C)-1; s = 2.676 (1.483) kPa.
The method was quite robust when applied to the data from a wide range of non-forest terrestrial ecosystems, demonstrating convergence and generating consistent parameter estimates under different meteorological conditions. One of the reasons for the robustness is due to our choice of light, top-soil temperature and air VPD, as flux predictors. At the 30-min time scale this set of variables has a much lower multicollinearity, than light, air temperature and air VPD often used by other authors.
Knowing dynamics of the parameters for most of the growing season serves as an effective tool for gap-filling and reconstruction of the long-term dynamics of the CO2 exchange at the ecosystem level as illustrated by the 7-year data set for the Brookings grassland (Figure 1).
Figure 1. Seasonal and year-to-year dynamics of CO2 exchange at the Brookings grassland site, 2004-2010: Pg, – photosynthesis, Re – respiration, F – net CO2 exchange, iNEP – integrated net ecosystem production.
REFERENCES
El-Sharkaway MA et al (1984) Crop Science 24, 503-507.
Gilmanov TG (2011) http://www.nacarbon.org/meeting_ab_presentations/2011/2011_Poster_Gilmanov_A_12_21.pdf
Gilmanov TG et al (2010) Rangeland Ecology and Management 63:16-39.
Gilmanov TG et al (2003) Basic and Applied Ecology 4:167-183.
Gilmanov TG et al (2005) Global Ecology and Biogeography 14:271-292.
Lasslop G et al (2010) Global Change Biology 16:187 - 208.
Reichstein M et al (2005) Global Change Biology 11:1424-1439.
Wolfram Research (2011) Mathematica portal: http://www.wolfram.com/
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