Simultaneous numerical representation of soil microsite production and consumption of carbon dioxide, methane, and nitrous oxide using probability distribution functions

Debjani Sihi Eric A. Davidson Kathleen E. Savage Dong Liang

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Production and consumption of nitrous oxide (N2O), methane (CH4), and carbon dioxide (CO2) are affected by complex interactions of temperature, moisture, and substrate supply, which are further complicated by spatial heterogeneity of the soil matrix. This microsite heterogeneity is often invoked to explain non‐normal distributions of greenhouse gas (GHG) fluxes, also known as hot spots and hot moments. To advance numerical simulation of these belowground processes, we expanded the Dual Arrhenius and Michaelis–Menten model, to apply it consistently for all three GHGs with respect to the biophysical processes of production, consumption, and diffusion within the soil, including the contrasting effects of oxygen (O2) as substrate or inhibitor for each process. High‐frequency chamber‐based measurements of all three GHGs at the Howland Forest (ME, USA) were used to parameterize the model using a multiple constraint approach. The area under a soil chamber is partitioned according to a bivariate log‐normal probability distribution function (PDF) of carbon and water content across a range of microsites, which leads to a PDF of heterotrophic respiration and O2 consumption among microsites. Linking microsite consumption of O2 with a diffusion model generates a broad range of microsite concentrations of O2, which then determines the PDF of microsites that produce or consume CH4 and N2O, such that a range of microsites occurs with both positive and negative signs for net CH4 and N2O flux. Results demonstrate that it is numerically feasible for microsites of N2O reduction and CH4 oxidation to co‐occur under a single chamber, thus explaining occasional measurement of simultaneous uptake of both gases. Simultaneous simulation of all three GHGs in a parsimonious modeling framework is challenging, but it increases confidence that agreement between simulations and measurements is based on skillful numerical representation of processes across a heterogeneous environment.

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