Recent advancements in empirical and model-based approaches for optimizing fit windows for closed-transient chamber-based soil trace gas flux and δ13C data

Soil trace gas flux rates derived from closed-transient chamber-based techniques rely on estimating the rate of change in gas concentrations prior to disturbance by the chamber. Several regression methods are available for estimating this rate of change, all of which require selecting a fitting window over which to apply the regression. Window selection has historically been subjective and reliant on expert knowledge, using information from only small subset of measurements to fit to a larger dataset. This somewhat arbitrary approach overlooks the influence of individual chamber and sampling location properties on the development of turbulence, as well as the unique transport characteristics of different trace gases. Here, we describe an empirical method for selecting the fitting window for an exponential regression model, based upon iterative analysis of within-chamber gas mixing dynamics. The method operates at a batch scale, incorporating data from every observation to determine the appropriate start and stop times for the fitting window of the larger dataset, using SoilFluxPro 5.2’s JavaScript console.

Chamber-based estimates of the stable carbon isotope ratio (δ¹³C) of soil-respired CO₂, which utilize the Keeling mixing model, are also sensitive to the chosen fitting window; however no clear empirical approach for window optimization has been proposed. We also present a potential model-based approach to reduce uncertainty in Keeling model-derived δ¹³C estimates, offering a comprehensive analysis using low-magnitude soil gas flux data from a desert ecosystem. Together these fitting window optimization strategies enhance the robustness of soil gas flux and δ¹³C estimates.