Physical soil properties create lags between temperature change and corresponding soil responses, which obscure true Q(10) (temperature sensitivity) values and other biophysical parameters such as depth of production. This study examines an inversion approach for estimating Q(10) and e-folding depth of CO2 production (Z(p)) using physically based soil models, constrained by observed high-frequency surface fluxes and/or concentrations. Our inversion strategy uses a one-dimensional (1-D) multi-layered soil model that simulates realistic temperature and gas diffusion. We tested inversion scenarios on synthetic data using a range of constraining parameters, time-averaging techniques, mechanisms to improve computational efficiency, and various methods of incorporating real data into the model. Overall, we have found that with carefully constrained data, inversion was possible. While inversions using exclusively surface-flux measurements could succeed, constraining the inversion using multiple shallow subsurface CO2 measurements proved to be most successful. Inversions constrained by these shallow measurements returned Q(10) and Z(p) values with average errors of 1.85 and 0.16% respectively. This work is a first step toward building a reliable framework for removing physical effects from high-frequency soil CO2 data. Ultimately, we hope that this process will lead to better estimates of biophysical soil parameters and their variability on short timescales.
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