Modeling soil CO₂ production and transport with dynamic source and diffusion terms: testing the steady-state assumption using DETECT v1.0

Journal Article · Mon May 28 00:00:00 EDT 2018 · Geoscientific Model Development (Online)

DOI:https://doi.org/10.5194/gmd-11-1909-2018· OSTI ID:1459713

Ryan, Edmund M. ; Ogle, Kiona; Kropp, Heather; Samuels-Crow, Kimberly E.; Carrillo, Yolima; Pendall, Elise

The flux of CO₂ from the soil to the atmosphere (soil respiration,R_soil) is a major component of the global carbon (C) cycle.Methods to measure and model R_soil, or partition it intodifferent components, often rely on the assumption that soil CO₂concentrations and fluxes are in steady state, implying thatR_soil is equal to the rate at which CO₂ is produced by soilmicrobial and root respiration. Recent research, however, questions thevalidity of this assumption. Thus, the aim of this work was two-fold: (1) todescribe a non-steady state (NSS) soil CO₂ transport and productionmodel, DETECT, and (2) to use this model to evaluate the environmentalconditions under which R_soil and CO₂ production are likelyin NSS. The backbone of DETECT is a non-homogeneous, partial differentialequation (PDE) that describes production and transport of soil CO₂,which we solve numerically at fine spatial and temporal resolution (e.g.,0.01m increments down to 1m, every 6h). Production of soil CO₂ issimulated for every depth and time increment as the sum of root respirationand microbial decomposition of soil organic matter. Both of these factors canbe driven by current and antecedent soil water content and temperature, whichcan also vary by time and depth. We also analytically solved the ordinarydifferential equation (ODE) corresponding to the steady-state (SS) solutionto the PDE model. We applied the DETECT NSS and SS models to the six-monthgrowing season period representative of a native grassland in Wyoming.Simulation experiments were conducted with both model versions to evaluatefactors that could affect departure from SS, such as (1) varying soiltexture; (2) shifting the timing or frequency of precipitation; and (3) withand without the environmental antecedent drivers. For a coarse-textured soil,R_soil from the SS model closely matched that of the NSS model.However, in a fine-textured (clay) soil, growing season R_soil was~3% higher under the assumption of NSS (versus SS). Thesedifferences were exaggerated in clay soil at daily time scales wherebyR_soil under the SS assumption deviated from NSS by up to 35% on average in the 10 daysfollowing a major precipitation event. Incorporation of antecedent driversincreased the magnitude of R_soil by 15 to 37% for coarse- andfine-textured soils, respectively. However, the responses ofR_soil to the timing of precipitation and antecedent drivers didnot differ between SS and NSS assumptions. In summary, the assumption of SSconditions can be violated depending on soil type and soil moisture status,as affected by precipitation inputs. The DETECT model provides a frameworkfor accommodating NSS conditions to better predict R_soil andassociated soil carbon cycling processes.

• Research Organization: Univ. of Wyoming, Laramie, WY (United States)
• Sponsoring Organization: USDOE Office of Science (SC), Biological and Environmental Research (BER)
• Grant/Contract Number: SC0006973
• OSTI ID: 1459713
• Alternate ID(s): OSTI ID: 1511012
• Journal Information: Geoscientific Model Development (Online), Journal Name: Geoscientific Model Development (Online) Vol. 11 Journal Issue: 5; ISSN 1991-9603
• Publisher: Copernicus Publications, EGU
• Country of Publication: Germany
• Language: English

Related Subjects

54 ENVIRONMENTAL SCIENCES