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SOIL PHYSICS

The Joint Estimation of Soil Trace Gas Fluxes

School of Environment and Natural Resources, The Ohio State Univ., Columbus, OH 43210

^* Corresponding author (hossler.3{at}osu.edu).

Soil gas flux is commonly measured by monitoring the change in headspace gas concentration over time within a sealed compartment at the soil surface. Often, more than one trace gas is monitored at a time (e.g., CO₂ and CH₄), but the data fit separately. Flux estimates for CO₂ and CH₄ were obtained simultaneously by minimizing a weighted sum-of-squares error. The approximation of one model parameter for CH₄, through theoretical relationship to the respective CO₂ parameter, reduced the total parameter count by one and allowed for the joint estimation of one parameter using the combined CO₂ and CH₄ datasets. The method of joint optimization was compared with separate optimization for two nonlinear models, using both real and simulated data. The datasets were best fit with the jointly optimized models. Furthermore, the jointly optimized models more accurately estimated initial soil–air fluxes (simulated data only). The method of joint optimization is recommended as a means to apply better-fitting nonlinear models to typically small gas sample sets. This method is applicable to any number of trace gases monitored simultaneously.

Abbreviations: AIC, Akaike's information criterion • Exp, exponential model • Jnt, jointly fit optimization method • Lin, linear model • mn, squared-means weighting scheme • MOP, multi-objective optimization problem • NDFE and Ndfe, non-steady-state diffusive flux estimator • Sep, separately fit optimization method • ss, sum-of-squares error weighting scheme • SSE, sum-of-squares error • SSE_w, weighted sum-of-squares error