A Physical Inversion Approach for Determining True Temperature Sensitivity of Respiration from High-frequency Soil CO2 Datasets

Abstract:

Physical soil properties create lags between temperature change and corresponding soil responses, which obscure true Q₁₀ values and other biophysical parameters such as depth of production. This study examines inversion approaches for estimating Q₁₀ and depth of production using physically based soil models, constrained by observed high-frequency surface fluxes and/or concentrations. Our inversion strategy uses, at its base, a 1-D multi-layered soil model that simulates realistic temperature and gas diffusion physics. We tested inversion scenarios on synthetic datasets 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 datasets, inversion was possible. While inversions using exclusively surface flux measurements could succeed, constraining the inversion using at least one shallow subsurface CO₂ measurement proved to be most successful. Precision in modeling soil profile temperature was found to be crucial to the inversion process, along with the frequency of temperature measurements. This was not a surprise, given that temperature is the known determinant of soil lags. This presentation will share the various lessons from this work, and will illustrate these concepts using synthetic and real data. This work is a first step toward building a reliable framework for removing physical effects from high frequency soil CO₂ datasets. Ultimately, we hope that this process will lead to better estimates of biophysical soil parameters and their variability on short timescales.