Modeling soil CO<sub>2</sub> production and transport with dynamic source and diffusion terms: testing the steady-state assumption using DETECT v1.0
Abstract
The flux of CO2 from the soil to the atmosphere (soil respiration,Rsoil) is a major component of the global carbon (C) cycle.Methods to measure and model Rsoil, or partition it intodifferent components, often rely on the assumption that soil CO2concentrations and fluxes are in steady state, implying thatRsoil 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 Rsoil 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-statemore »
- Publication Date:
- Research Org.:
- Univ. of Wyoming, Laramie, WY (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Biological and Environmental Research (BER)
- OSTI Identifier:
- 1459713
- Alternate Identifier(s):
- OSTI ID: 1511012
- Grant/Contract Number:
- SC0006973
- Resource Type:
- Published Article
- Journal Name:
- Geoscientific Model Development (Online)
- Additional Journal Information:
- Journal Name: Geoscientific Model Development (Online) Journal Volume: 11 Journal Issue: 5; Journal ID: ISSN 1991-9603
- Publisher:
- Copernicus Publications, EGU
- Country of Publication:
- Germany
- Language:
- English
- Subject:
- 54 ENVIRONMENTAL SCIENCES
Citation Formats
Ryan, Edmund M., Ogle, Kiona, Kropp, Heather, Samuels-Crow, Kimberly E., Carrillo, Yolima, and Pendall, Elise. Modeling soil CO<sub>2</sub> production and transport with dynamic source and diffusion terms: testing the steady-state assumption using DETECT v1.0. Germany: N. p., 2018.
Web. doi:10.5194/gmd-11-1909-2018.
Ryan, Edmund M., Ogle, Kiona, Kropp, Heather, Samuels-Crow, Kimberly E., Carrillo, Yolima, & Pendall, Elise. Modeling soil CO<sub>2</sub> production and transport with dynamic source and diffusion terms: testing the steady-state assumption using DETECT v1.0. Germany. https://doi.org/10.5194/gmd-11-1909-2018
Ryan, Edmund M., Ogle, Kiona, Kropp, Heather, Samuels-Crow, Kimberly E., Carrillo, Yolima, and Pendall, Elise. Mon .
"Modeling soil CO<sub>2</sub> production and transport with dynamic source and diffusion terms: testing the steady-state assumption using DETECT v1.0". Germany. https://doi.org/10.5194/gmd-11-1909-2018.
@article{osti_1459713,
title = {Modeling soil CO<sub>2</sub> production and transport with dynamic source and diffusion terms: testing the steady-state assumption using DETECT v1.0},
author = {Ryan, Edmund M. and Ogle, Kiona and Kropp, Heather and Samuels-Crow, Kimberly E. and Carrillo, Yolima and Pendall, Elise},
abstractNote = {The flux of CO2 from the soil to the atmosphere (soil respiration,Rsoil) is a major component of the global carbon (C) cycle.Methods to measure and model Rsoil, or partition it intodifferent components, often rely on the assumption that soil CO2concentrations and fluxes are in steady state, implying thatRsoil 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 Rsoil 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,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). Thesedifferences were exaggerated in clay soil at daily time scales wherebyRsoil 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 Rsoil by 15 to 37% for coarse- andfine-textured soils, respectively. However, the responses ofRsoil 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 Rsoil andassociated soil carbon cycling processes.},
doi = {10.5194/gmd-11-1909-2018},
journal = {Geoscientific Model Development (Online)},
number = 5,
volume = 11,
place = {Germany},
year = {Mon May 28 00:00:00 EDT 2018},
month = {Mon May 28 00:00:00 EDT 2018}
}
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https://doi.org/10.5194/gmd-11-1909-2018
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