Deriving the flux equation: the model

Figure 1. A chamber of volume v (m3) and surface area s (m2) sitting over the soil, which has CO2 efflux rate fc (mol m‑2 s‑1) and water evaporation flux rate fw (mol m‑2 s‑1).

At constant pressure, the total rate at which water evaporates into the chamber sfw (mol s-1) is balanced by a small flow rate of air out of the chamber u (mol s-1). The CO2 mole fraction of the air outside the chamber is ca, inside the chamber is cc, and in the soil is cs, all in mol mol-1. The chamber air water vapor mole fraction is wc (mol mol-1). The rate constant k (s-1) characterizes leaks (if any) due to diffusion of CO2 between the soil chamber and outside air. The chamber volume v includes the volume of the pump and measurement loop.

The mass balance equations for CO2, water vapor, and air take the form

1storage = flux in - flux out

We neglect the effects of leaks for now, but we will consider them later.

CO2 mass balance


H2O mass balance


Air mass balance


is the number density of CO2 in the chamber, is the number density of water vapor in the chamber, and is the total number density of air in the chamber (all in mol m-3); , where is the number density of dry air in the chamber.

The number density of air is given by the ideal gas law, , where R is the gas constant (8.314 Pa m3 K-1 mol-1), and TK is the absolute temperature (K). From equation 4, with ρ and TK constant, and sfw >> sfc,


Combining equations 3 and 5, and noting that , we find


Combining equations 2, 5 and 6 gives


Equation 7 has the same form as that used in the LI‑6400 Portable Photosynthesis System for soil respiration; however, it can be simplified by defining , which is the chamber CO2 mole fraction corrected for water vapor dilution. This is called Cdry (ppm) in the LI-8100A data output.

Differentiating we find,


Substituting this into equation 7 gives


Equation 9 has an important advantage over equation 7 because it is not necessary to estimate the rate of increase in water vapor mole fraction. In most measurements, the water vapor mole fraction increases in a highly non-linear fashion, and the rate is estimated with a linear function. Thus, in effect, equation 7 forces us to use average values for and . But with equation 9, the dilution correction is made point-by-point, and estimates of the initial values at time zero are used to estimate fc at the instant the chamber closed. This is both easier and more accurate than the procedure required to implement equation 7.

In order to use equation 9 the initial values must be known for ρ and TK (to compute ρc), as well as the initial values for wc and . After the chamber closes, the LI-8100A performs a linear regression with time on the first 10 values of each measured variable. The initial values of ρ, TK and wc are obtained from the time zero intercepts of these regressions; however, finding the initial value for requires a little more work.

To do this, fc is defined in terms of the CO2 mole fraction gradient across the soil- to-chamber interface and a transfer coefficient, to obtain


where cs is the CO2 mole fraction in the soil surface layer communicating with the chamber (mol mol-1), g is conductance to CO2 (m s-1), and ρc is the density of air (mol m-3). The soil and chamber must be isothermal for equation 10 to hold.

Combining equation 10 with equation 9, considering all variables except cc' to be constant, and rearranging, gives


where . When wc = ws , cs', gives the dilution-corrected CO2 mole fraction in the soil layer communicating with the chamber. We do not expect wc to equal ws exactly, but most of the time they will differ by less than 0.02 mol mol-1 or so, which introduces only a small uncertainty in cs'. If cs' is taken as a constant, then equation 11 can be integrated to give


where A = sg v-1 is a rate constant (s-1) and cc'(0) is the initial value of the dilution- corrected CO2 mole fraction when the chamber closes. The rate of change in cc'(t) at any time can be computed from the derivative of equation 12.


Calculating the flux from measured data

In the LI-8100A, equations 9, 11 and 12 are implemented in a form that presents the variables in more familiar and intuitive units. Equation 9 is computed as


where Fc is the soil CO2 efflux rate (μmol m-2 s-1), V is volume (cm3), P0 is the initial pressure (kPa), W0 is the initial water vapor mole fraction (mmol mol-1), S is soil surface area (cm2), T0 is initial air temperature (°C), and is the initial rate of change in water-corrected CO2 mole fraction (μmol mol-1).

Examine Figure 2 to see C'(t) vs t data that were obtained from a soil CO2 flux measurement with two observations. The data are marked to show when the chamber closed and when it opened.

Figure 2. Soil CO2 flux data were collected on bare soil in a tropical greenhouse near Lincoln, NE in February 2004. Two observations are shown. About 60% of the data from the first observation has been removed for clarity. For both observations, the observation length was 120 seconds, dead band was 30 seconds, and pre-purge was 120 seconds. The chamber begins to close at the end of the pre-purge and the first data point used in the analysis is collected after the chamber touches down; the difference represents the time required for the chamber to close. Observation #1: t0 =7.3s, C0' = 434 ppm, Cx' = 1016 ppm, flux = 6.4 μmol m-2 s-1; observation #2: t0 = 2.0s, C0' = 425 ppm, Cx' = 1145 ppm, flux = 6.0 μmol m-2 s-1.

The dead band is the time until steady chamber mixing is established, and typically lasts 10s to 30s. After mixing is stable, the data are fit with an empirical equation that has a form similar to equation 12:


where C'(t) is the instantaneous water-corrected chamber CO2 mole fraction, C0' is the value of C'(t) when the chamber closed, and Cx' is a parameter that defines the asymptote, all in μmol CO2 per mol dry air (µmol mol-1); a is a parameter that defines the curvature of the fit (s-1).

The initial value of C'(t), called C0' in equation 15, is computed from the intercept of a linear regression of the first 10 points after the chamber closes. This is used as a parameter in the non-linear regression that fits equation 9 to the C'(t) vs t data between the end of the Dead Band and the end of the observation. This regression yields values for the parameters Cx', a and t0. t = t0 represents the time when C'(t) in equation 15 equals its initial value when the chamber closes, or C'(t0) = C0'. The delay between the instant the chamber closed and t0 gives the time required to establish steady mixing. CO2 offsets or time delays can occur when the chamber closes, and these events can cause t0 to be positive or negative in value.

All the initial values needed to obtain the soil CO2 efflux rate, Fc, in equation 9 can now be computed. The initial values P0, T0 and W0 are all obtained from the intercepts of linear regressions of the first 10 measurements of P, T and W after the chamber closes. The rate of change of dilution-corrected chamber CO2 mole fraction can be computed at any time from


When t = t0,


Equation 17 gives an estimate of the rate of change in C' at the instant the chamber closed. This value must be estimated mathematically. It cannot be measured directly at any time during the measurement because imperfect mixing prevents an accurate estimate early in the measurement cycle, and later in the cycle, the increasing chamber CO2 concentration continuously reduces the gradient between soil and chamber. This suppresses the rate, as can be seen from equation 16 and also in Figure 2.

Relationship between the model and the empirical equation

The diffusion model provides an equation with a form that allows correction for the effect of changing gradients on the rate, which in turn, makes it possible to estimate the initial rate. It is worthwhile to distinguish between the model function given in equation 12 and the empirical function in equation 15. As just described, the units are different in the two expressions; but more important, for the parameters cs and A in equation 12 to have their defined meaning, the assumptions underlying the derivation must be true. By contrast, equations 15 through 17 are treated as empirical functions and are used only to estimate the CO2 rate of change, dC'/dt. The parameters Cx', a and t0 do not depend upon a specific theoretical interpretation, and may or may not provide reliable estimates of soil parameters.

Correcting for initial CO2 concentrations that differ between measurements

Different measurements may begin at different CO2 concentrations, which introduces variation into the data, because the flux rate changes with chamber CO2 concentration. Correcting the measurements to a common target CO2 concentration may reduce such variation. For a given curve fit, the CO2 rate of change can be computed at any CO2 concentration according to


This calculation is supported in the SoilFluxPro software, which is included with the LI-8100A.

Evaluation of other methods for computing soil CO2 efflux

Other approaches have been used for computing CO2 flux in transient measurements. One commonly used method is to fit a linear function to what is sometimes referred to as "the linear portion" of the curve. Unfortunately, there is no linear portion, as can be seen from careful inspection of Figure 2. The slope is meaningless in the initial phase before steady mixing is established, and after steady mixing is established, the extent of the non-linearity depends upon the soil surface-to-chamber volume ratio and the flux rate. During this time, CO2 vs time curves are always concave in a downward direction, meaning that linear regression over this portion of the data set will give an underestimate of the rate of change. In every case we have tested so far, the average rate measured by linear regression is less than the initial rate measured by non-linear regression. Nevertheless, linear regression is a robust numerical approach and the mean values for the CO2 efflux rate reported by the LI-8100A in Type 3 records are computed by this method. We recommend you use these only for comparison to the initial values, which are obtained by fitting equation 15 to the data using a non-linear regression method.

Another approach that has been used to estimate the initial rate is to fit a polynomial to the CO2 concentration vs time data. This approach is theoretically sound inasmuch as a power series can be generated from a Taylor series approximation to equation 15. Usually, the data are fit with a quadratic equation. We tested this approach and found that while it can be justified on theoretical grounds, it does not work very well in practice. The shape of even a second order polynomial is sensitive to small perturbations in the data. This makes initial rate calculations subject to much larger variations than when the same data are analyzed by nonlinear regression using equation 15.

Effects of high chamber CO2 concentrations

Finally, we consider the importance of choosing appropriate observation times and pre-purge times. We do not have experience on all soil types, and cannot give absolute recommendations for the best observation length in all situations.

Nevertheless, our experience so far suggests that 60s to 120s will often work well. This prevents large build-ups in chamber CO2 concentration at typical change rates such as 0.5 ppm s-1. We have found that optimal dead band length can vary from about 10s to 60s, with 30s being a good value to use as a first estimate.

Dead bands and observation times can be adjusted after the fact, using SoilFluxPro software (formerly FV8100). This program provides curve fit analysis tools that can be used to find the optimal dead band lengths and observation lengths. Therefore, it is not critical to choose the right dead band and observation length in the field; as long as the observation lengths are long enough, they can be optimized later, if necessary.

Long observations can have the effect of capping the soil and causing the CO2 concentration to build up in the soil under the chamber. This phenomenon can be observed by performing a sequence of observations in which the chamber concentration is allowed to increase several hundred ppm during each observation. When the pre-purge is set to be just long enough to allow the chamber atmosphere to come back to the ambient CO2 concentration, the initial rates in sequential observations can often be observed to increase as the soil CO2 concentration increases. This is expected according to equation 17, dCc'/dt = a(Cx' - Cambient'), if it is assumed that Cx' = Csoil'. Thus, long observation lengths may perturb the very process we wish to measure.

Another effect of high chamber CO2 concentrations is to promote leaks between the chamber and atmosphere. Leaks can be ignored when the gradient between the chamber atmosphere and ambient atmosphere are small. But when the gradient is not small, leaks cannot be neglected and it can be shown that the parameters in equation 12 are altered to become


where k and ca' are the leak rate time constant and water-corrected ambient CO2 concentration, respectively, and the expression for cx' replaces cs' in equation 12. Thus, when chamber CO2 concentrations are high, the rate constant and asymptote will reflect leaks from the system. Variables that are stored in data files are described in Table 4.