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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
https://doi.org/10.5194/gmd-2017-223
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
Model description paper
10 Oct 2017
Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Geoscientific Model Development (GMD).
Modelling soil CO2 production and transport with dynamic source and diffusion terms: Testing the steady-state assumption using DETECT v1.0
Edmund Ryan1,2, Kiona Ogle2,3,4,5, Heather Kropp6, Kimberly E. Samuels-Crow3, Yolima Carrillo7, and Elise Pendall7 1Lancaster Environment Centre, Lancaster University, Lancaster, UK
2School of Life Sciences, Arizona State University, Tempe, Arizona, USA
3School of Informatics, Computing, and Cyber Systems, Northern Arizona University, Flagstaff, Arizona, USA
4Center for Ecosystem Science and Society, Northern Arizona University, Flagstaff, Arizona, USA
5Department of Biological Sciences, Northern Arizona University, Flagstaff, Arizona, USA
6Department of Geography, Colgate University, Hamilton, NY, USA
7Hawkesbury Institute for the Environment, Western Sydney University, NSW, Australia
Abstract. The flux of CO2 from the soil to the atmosphere (soil respiration, Rsoil) is a major component of the global carbon cycle. Methods to measure and model Rsoil, or partition it into different components, often rely on the assumption that soil CO2 concentrations and fluxes are in steady state, implying that Rsoil is equal to the rate at which CO2 is produced by soil microbial and root respiration. Recent research, however, questions the validity of this assumption. Thus, the aim of this work was two-fold: (1) to describe a non-steady state (NSS) soil CO2 transport and production model, DETECT, and (2) to use this model to evaluate the environmental conditions under which Rsoil and CO2 production are likely in NSS. The backbone of DETECT is a non-homogeneous, partial differential equation (PDE) that describes production and transport of soil CO2, which we solve numerically at fine spatial and temporal resolution (e.g., 0.01 m increments to 1 m, every 6 hours). Production of soil CO2 is simulated for every depth and time increment as the sum of root respiration and microbial decomposition of soil organic matter, both of which can be driven by current and antecedent soil water content and temperature, which can also vary by time and depth. We also analytically solved the ordinary differential equation (ODE) corresponding to the steady-state (SS) solution to the PDE model. We applied the DETECT NSS and SS models to the 6-month growing season period representative of a native grassland in Wyoming. Simulation experiments were conducted with both model versions to evaluate factors that could affect departure from SS: (1) varying soil texture; (2) shifting the timing or frequency of precipitation; and (3) with and without the environmental antecedent drivers. For a coarse-textured soil, Rsoil from the SS model closely matched that of the NSS model. However, in a fine-textured (clay) soil, growing season Rsoil was ~ 3 % higher under the assumption of NSS (versus SS). These differences were exaggerated in clay soil at daily time-scales whereby Rsoil under the SS assumption deviated from NSS by up to ~ 20 % in the 10 days following a major precipitation event. Moreover, incorporation of antecedent drivers increased the magnitude of Rsoil by 15 % to 37 % for coarse- and fine-textured soils, respectively. However, the responses of Rsoil to the timing of precipitation and antecedent drivers did not differ between SS and NSS assumptions. In summary, the assumption of SS conditions can be violated depending on soil type and soil moisture status, as affected by precipitation inputs, and the DETECT model provides a framework for accommodating NSS conditions to better predict Rsoil and associated soil carbon cycling processes.

Citation: Ryan, E., Ogle, K., Kropp, H., Samuels-Crow, K. E., Carrillo, Y., and Pendall, E.: Modelling soil CO2 production and transport with dynamic source and diffusion terms: Testing the steady-state assumption using DETECT v1.0, Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2017-223, in review, 2017.
Edmund Ryan et al.
Edmund Ryan et al.

Data sets

Inputs folder contents for DETECT model v1.0
E. Ryan, K. Ogle, H. Kropp, K. E. Samuels-Crow, Y. Carrillo, and E. Pendall
https://doi.org/10.5281/zenodo.926064

Model code and software

Source code for running DETECT model v1.0
E. Ryan, K. Ogle, H. Kropp, K. E. Samuels-Crow, Y. Carrillo, and E. Pendall
https://doi.org/10.5281/zenodo.927501
Edmund Ryan et al.

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Short summary
Our work evaluated the appropriateness of the common steady-state (SS) assumption, for example when partitioning soil respiration of CO2 into recently photosynthesized carbon (C) and older C. Using a new model of soil CO2 production and transport we found that the SS assumption is valid most of the time, especially in sand/silt soils. Non-SS conditions occurred mainly for the few days following large rain event in all soil types, but the non-SS period was prolonged and magnified in clay soils.
Our work evaluated the appropriateness of the common steady-state (SS) assumption, for example...
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