A comparison of regression methods for estimating soil–atmosphere diffusion gas fluxes by a closed-chamber technique

Continuous changes in methane (CH4) and carbon dioxide (CO2) concentrations inside a closed chamber were measured on the forest floor at three sites: a deciduous forest and a coniferous forest in Hokkaido, Japan, and a birch forest in West Siberia, Russian Federation. Flux estimations by three types of regression methods, exponential, nonlinear, and linear, were examined using field-collected concentration data. The pattern of change with time of the gas concentration in the headspace differed, mainly according to site but also, to a lesser extent, according to the gas. This was a function of both the chamber height and surface soil property relating to soil gas diffusion and the gas concentration profile. Flux estimations did not differ statistically between the exponential and nonlinear methods for either gas at any site, because both of those regression methods were based on diffusion theory. However, the flux values estimated by linear regression were significantly different from those estimated by the other two methods for both CH4 and CO2 at the deciduous forest site and for CO2 at the coniferous forest site. Shortening the chamber deployment period improved the linearity of the curve, but did not completely eliminate the error. Our results suggest that linear regression is not a good model of the change in headspace concentration with time.