Uncertainty Quantification in Soil Gas Fluxes: Reducing the user's Arbitrariness

Abstract

Chamber‐based methods are essential field techniques for monitoring soil greenhouse gas (GHG) worldwide, providing key quantitative insights into total carbon stock fluxes and are essential to assess the impacts of climate change. Both commercial chambers with high‐resolution gas analyzers and low‐cost alternative systems have been widely deployed. However, the operation of all systems introduces biases either during the monitoring, by local disturbances, or during the data postprocessing with user‐defined decisions that may contribute to uncertainties in flux estimates and limit comparability across studies. In this paper, we develop a new Flux Calculation Schema (FCS) specifically designed to minimize user arbitrariness by introducing flexible time parameters (deadband and cutoff) that are optimized using a Markov Chain Monte Carlo (MCMC) framework. This adaptive approach accounts for variations in chamber designs and environmental conditions, while quantifying system uncertainty. Using synthetic datasets, we demonstrate that the FCS reduces bias from user‐defined settings and provides robust uncertainty estimates under both commercial and low‐cost configurations. We further demonstrate the practical benefits of the approach using a field case application, where the FCS provides uncertainty‐aware flux estimates under heterogeneous conditions and highlights when instrument limitations or transient disturbances dominate the inferred signal. This enables transparent quality control, supports reproducible post‐processing across deployments, and improves comparability of flux time series collected with different monitoring solutions. By moving toward a stochastic workflow, this method improves uncertainty‐aware quantification of chamber‐based soil gas fluxes and offers scalable, reproducible post‐processing as high‐frequency, continuous measurements become more widely available.