- 1Soil Physics, University of Göttingen, Germany
- 2Chair of Statistics, University of Göttingen, Germany
Gas fluxes between soil and atmosphere play a significant role as global greenhouse gas sinks or sources. Accurate estimation of these gas fluxes is challenging due to the heterogeneous nature of soil properties, dynamic environmental factors, and the complexity of measurement in soil systems. The flux-gradient method is a reliable approach for estimating CO2 gas fluxes. This method utilises Fick's first law of diffusion to calculate gas fluxes by analysing gas concentration profiles. The gas concentration gradient is multiplied with the apparent gas diffusion coefficient of the gas species in the soil. The estimation of the apparent gas diffusion coefficient is dependent on numerous parameters, including soil pore space and soil water content, which must be carefully measured or derived. These parameters typically cannot be measured at the exactly same location as not to interfere with the gas measurements. All these factors are subject to different uncertainties depending on the parameters and the measured soil location.
In order to address and comprehend the extent of these uncertainties, Bayesian inference was employed, as this methodology enables uncertainty to be measured through probability distributions with credible intervals as opposed to point estimates. Furthermore, Bayesian inference functions effectively with small datasets and permits the incorporation of prior knowledge, a factor which also benefits soil gas modelling.
We used a previously published dataset (Wordell-Dietrich et al. 2020) to estimate CO2 fluxes by using the flux-gradient method. The gas diffusion coefficient was derived through the use of a Bayesian inference model. The resulting data were then compared with chamber measurements and other modelling approaches.
Acknowledgment
Wordell-Dietrich, P.; Wotte, A.; Rethemeyer, J.; Bachmann, J.; Helfrich, M.; Kirfel, K.; Leuschner, C.; Don, A. (2020): Vertical partitioning of CO2 production in a forest soil. Biogeosciences, 17, 6341-6356. https://doi.org/10.5194/bg-17-6341-2020
How to cite: Schirrmeister, F., Maier, M., and Kneib, T.: Using a Bayesian inference approach to assess the uncertainty in flux-gradient derived soil CO2 flux estimates, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9268, https://doi.org/10.5194/egusphere-egu25-9268, 2025.
