Carbon dioxide and water vapor exchange in a warm temperate grassland

Abstract

Grasslands cover about 40% of the ice-free global terrestrial surface, but their contribution to local and regional water and carbon fluxes and sensitivity to climatic perturbations such as drought remains uncertain. Here, we assess the direction and magnitude of net ecosystem carbon exchange (NEE) and its components, ecosystem carbon assimilation (A _c) and ecosystem respiration (R _E), in a southeastern United States grassland ecosystem subject to periodic drought and harvest using a combination of eddy-covariance measurements and model calculations. We modeled A _c and evapotranspiration (ET) using a big-leaf canopy scheme in conjunction with ecophysiological and radiative transfer principles, and applied the model to assess the sensitivity of NEE and ET to soil moisture dynamics and rapid excursions in leaf area index (LAI) following grass harvesting. Model results closely match eddy-covariance flux estimations on daily, and longer, time steps. Both model calculations and eddy-covariance estimates suggest that the grassland became a net source of carbon to the atmosphere immediately following the harvest, but a rapid recovery in LAI maintained a marginal carbon sink during summer. However, when integrated over the year, this grassland ecosystem was a net C source (97 g C m⁻² a⁻¹) due to a minor imbalance between large A _c (−1,202 g C m⁻² a⁻¹) and R _E (1,299 g C m⁻² a⁻¹) fluxes. Mild drought conditions during the measurement period resulted in many instances of low soil moisture (θ<0.2 m³m⁻³), which influenced A _c and thereby NEE by decreasing stomatal conductance. For this experiment, low θ had minor impact on R _E. Thus, stomatal limitations to A _c were the primary reason that this grassland was a net C source. In the absence of soil moisture limitations, model calculations suggest a net C sink of −65 g C m⁻² a⁻¹ assuming the LAI dynamics and physiological properties are unaltered. These results, and the results of other studies, suggest that perturbations to the hydrologic cycle are key determinants of C cycling in grassland ecosystems.

Appendices

Appendix A: leaf-level assimilation model

According to Farquhar et al. (1980), as later modified by Collatz et al. (1991) and Campbell and Norman (1998), the net photosynthetic rate at the leaf scale depends on light, CO₂ concentration, and leaf temperature (T _l) and can be described as:

$$ A_{{\text{n}}} = \min {\left( {\begin{array}{*{20}c} {{J_{{\text{E}}} }} \\ {{J_{{\text{C}}} }} \\ \end{array} } \right)} - R_{{\text{d}}} $$

where J _E and J _C are the assimilation rates restricted by light-driven electron transport processes and ribulose bisphosphate (RuBP) carboxylase-oxygenase activity (Rubisco), respectively, and R _d is dark respiration. For leaf-level processes we adopt the ecophysiological convention of positive fluxes into the leaf. When these fluxes are scaled to the canopy we revert to the micrometeorological convention. J _E is given by:

$$ J_{{\text{E}}} = \alpha \times e_{{\text{m}}} \times Q_{{\text{p}}} \times \frac{{\overline{{C_{{\text{i}}} }} - \Gamma _{ * } }} {{\overline{{C_{{\text{i}}} }} + 2\Gamma _{ * } }} $$

where α is the leaf absorptivity [not to be confused with the apparent quantum efficiency (α _a)] for photosynthetically active radiation (PAR), e _m is the maximum quantum efficiency for leaf CO₂ uptake, Q _p is PAR irradiance on the leaf, and $\overline{{C_{{\text{i}}} }}$ is the mean intercellular CO₂ concentration. The values of all parameters are listed in Table 3. The photosynthetic CO₂ compensation point, Γ_*, is given by:

$$\Gamma _{ * } = {{{{\left[ {{{\rm O}}_{{{\rm 2}}} } \right]}}}\over {{2\tau }}}$$

where [O₂] is the oxygen concentration in air (210 mmol mol⁻¹), and τ is a ratio of kinetic parameters describing the partitioning of RuBP to the carboxylase or oxygenase reactions of Rubisco. J _c is computed from

$$J_{{{\rm c}}} = {{{Vc_{{\max }} (\overline{{C_{{{\rm i}}} }} - \Gamma _{ * } )}}\over {{\overline{{C_{{{\rm i}}} }} + K_{{{\rm c}}} {\left( {1 + {\left[ {{{\rm O}}_{{{\rm 2}}} } \right]}/K_{{{{\rm O}}_{2} }} } \right)}}}}$$

where Vc _max is the maximum catalytic capacity of Rubisco per unit leaf area (μmol m⁻²s⁻¹), and K _c and $K_{{{\text{O}}_{2} }}$ are the Michaelis constants for CO₂ fixation and O₂ inhibition with respect to CO₂, respectively. J _c increases linearly with increasing $\overline{{C_{{\text{i}}} }}$, but approaches a maximum under a high CO₂ concentration state rarely encountered under present conditions, though likely under future climate scenarios.

Temperature dependence of kinetic variables is computed following the equations in Campbell and Norman (1998). Five kinetic parameters are needed to adjust for temperature: K _c, $K_{{{\text{O}}_{2} }}$ , τ, Vc _max and R _d. For the first two parameters, a modified exponential temperature function of the form:

$$ k = k_{{25}} \times \exp {\left[ {\gamma (T_{L} - 25)} \right]} $$

is employed, where k is defined at the leaf surface temperature or T _l, k ₂₅ is the value of the parameter at 25°C, and γ is the temperature coefficient for that parameter. τ is assumed to be 1.3.

Vc _max and R _d are adjusted by:

$$ Vc_{{\max }} = \frac{{Vc_{{\max ,25}} \exp {\left[ {.088{\left( {T_{L} - 25} \right)}} \right]}}} {{1 + \exp {\left[ {.29{\left( {T_{L} - 41} \right)}} \right]}}} $$

and

$$ R_{{\text{d}}} = \frac{{R_{{{\text{d}},25}} \exp {\left[ {.069{\left( {T_{L} - 25} \right)}} \right]}}} {{1 + \exp {\left[ {1.3{\left( {T_{L} - 55} \right)}} \right]}}} $$

where Vc _max, 25 and R _d, 25 are values of Vc _max and R _d at 25°C, respectively (Campbell and Norman 1998).

Following Collatz et al. (1991), the dark respiration rate at 25°C (R _d, 25) can be estimated using

$$R_{{{{\rm d}},25}} = 0.015 \times Vc_{{\max ,25}} $$

Appendix B: the boundary line analysis

Stomatal conductance was modeled according to Oren et al. (1999), with the parameters m and g _ref generated from a boundary line analysis. The boundary line analysis sorts the measured conductance data into 10 bins characterized by increasing mean light levels. A logarithmic function relating conductance to VPD is generated for each light level (i) using data points falling above the mean plus one standard deviation, after removing outliers at each light level. The function is given by:

$$g_{{{{\rm s}}{{\rm ,i}}}} = a_{{{\rm i}}} \times \ln {\left( {VPD_{{{\rm i}}} } \right)} + b_{{{\rm i}}} $$

where i=1–10 (for ten light levels). The slope (a _i) and intercept (b _i) for each i were computed via regression analysis, and the parameter m is the ratio of these two vectors:

$$m = {a \over b}$$

in this study, m=0.64, which is consistent with the theoretical value of m=0.6 from Oren et al. (1999). We use the latter value in the model.

The parameter g _ref is a light-dependent function derived from fitting the intercept vector b as a logarithmic function of PAR. Here, we found that g _ref is

$$\eqalign{ g_{{ref}} = & 0.0922 \times \log {\left( {PAR} \right)} \cr & - 0.3985\;\mu {{\rm mol}}\;{{\rm m}}^{{ - 2}} \;{{\rm s}}^{{ - 1}}} $$

Appendix C: night-time atmospheric stability considerations

Correcting night-time eddy-covariance fluxes under conditions of low u _* with respiration models parameterized using night-time fluxes with high u _* is standard eddy-covariance methodology (Goulden et al. 1996; Aubinet et al. 2000; Falge et al. 2001a). We conducted a sensitivity analysis on the annual NEE estimate by varying u _*t between 0 and 0.3, and found that NEE did not vary appreciably for u _*t between 0.12 and 0.18. Hence, we first filtered the data with u _*t=0.12 m s⁻¹. NEE is highly sensitive to the u _* threshold value chosen (u _*t; Barford et al. 2001), but the exclusive use of u _*t has not been examined, and we propose additional meteorological constraints to filters used for night-time eddy-covariance data. Namely, we propose two additional constraints that only accept fluxes when atmospheric stability conditions are near-neutral and when the peak of the source-weight function (x _p) lies within the dimensions of the study site (here 150 m). The atmospheric stability parameter in the atmospheric surface layer is defined as ς=(z−d)/L, and near-neutral conditions are defined as |ς|<0.1. We define the near-neutral atmospheric stability threshold of 0.1 to be ς _n.

The importance of adding ς _n, to model night-time respiration is illustrated by considering the flux footprint at night for all atmospheric conditions, which exceeds 5 km (Fig. 8a). Adding u _*t alone results in a flux footprint that exceeds 2 km, an order of magnitude larger than the dimensions of our field (Fig. 8b). Filtering with both u _*t and ς _n (Fig. 8c) reduces the night-time flux footprint to ~1,000 m, which still exceeds field dimensions, so we further filter night-time flux measurements when the peak of the source-weight function (x _p) exceeds 150 m, guaranteeing that measured night-time fluxes originate from our field in a probabilistic sense.

Fig. 8a–c

The effect of u _* and atmospheric stability on the calculated night-time CO₂ flux footprint. The measurement tower is at the center of the polar plot. Radial lines represent wind directions (0°=North) and concentric lines represent radial distances from tower (m). a Points represent footprint distances from the tower for all night-time 30-minute runs. b same as a but implementing the friction velocity threshold (i.e., selecting only runs with u _*>u _*t, u _*t=0.12). c Same as a but implementing both friction velocity and atmospheric stability thresholds (i.e., selecting runs with u _*>0.12 and |(z−d)/L|<ς _n, ς _n=0.1)