Modeling Microbial Decomposition in Real 3D Soil Structures Using Partial Differential Equations

Nguyen-Ngoc, Doanh; Lèye, Babacar; Monga, Olivier; Garnier, Patricia; Nunan, Naoise

doi:10.4236/ijg.2013.410a003

Cited by 13 publications

(9 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Finally, the simulated domain is small compared to an actual soil sample, but we regard the number of simulated grid cells (10 4 ) as representative of the range of variation occurring in larger, similarly idealized samples. In other studies, more complex micro-scale models based on nonlinear reactive and diffusive fluxes have been implemented (Monga et al, 2008;Nguyen-Ngoc et al, 2013;Monga et al, 2014); however, their spatial upscaling would require volume averaging of the coupled transport and reaction equations, making the problem mathematically intractable when aiming for analytical solutions (Whitaker, 1999;Valdés-Parada et al, 2009;Porter et al, 2011;Lugo-Méndez et al, 2015). The two pool micro-scale model with initial heterogeneous distributions of substrate and microorganisms as described in this study offers a simplified way of simulating reaction-diffusion systems.…”

Section: Limitations Of the Upscaling Approachmentioning

confidence: 99%

Dynamic upscaling of decomposition kinetics for carbon cycling models

Chakrawal¹,

Herrmann²,

Koestel³

et al. 2019

Preprint

Self Cite

View full text Add to dashboard Cite

Abstract. The distribution of organic substrates and microorganism in soils is spatially heterogeneous at the micro-scale. Most soil carbon cycling models do not account for this micro-scale heterogeneity, which may affect predictions of carbon (C) fluxes and stocks. In this study, we hypothesize that the mean respiration rate R at the soil-core scale (i) is affected by the micro-scale spatial heterogeneity of substrate and microbes and (ii) depends upon the degree of this heterogeneity. To assess theoretically the effect of spatial heterogeneities on R, we contrast highly heterogeneous conditions with isolated patches of substrate and microbes versus spatially homogeneous conditions equivalent to those assumed in most soil C models. Moreover, we distinguish between biophysical heterogeneity, defined as the non-uniform spatial distribution of substrate and microbes, and full heterogeneity, defined as the non-uniform spatial distribution of substrate quality (or accessibility) in addition to biophysical heterogeneity. Three commonly used formulations for decomposition kinetics (linear, multiplicative and Michaelis-Menten) are considered in a coupled substrate-microbial biomass model valid at the micro-scale. We start with a 2D domain characterized by a heterogeneous substrate distribution and numerically simulate organic matter dynamics at each cell in the domain. To interpret the mean behavior of this spatially-explicit system, we propose an analytical scale transition approach in which micro-scale heterogeneities affect R through the second order spatial moments (spatial variances and covariances). It was not possible to capture the mean behavior of the heterogeneous system when the model assumed spatial homogeneity, because the second order moments cause the heterogeneous system to deviate from the behavior attained under homogeneous conditions. Consequently, R in the heterogeneous system can be higher or lower than the respiration of the homogeneous system, depending on the sign of the second order spatial moments. This effect of the spatial heterogeneities appears in the upscaled nonlinear decomposition formulations, whereas the upscaled linear decomposition model deviates from homogeneous conditions only when substrate quality is heterogeneous. Thus, this study highlights the inadequacy of applying at the macro-scale the same decomposition formulations valid at the micro-scale, and proposes a scale transition approach as a way forward to capture micro-scale dynamics in core-scale models.

show abstract

Section: Limitations Of the Upscaling Approachmentioning

confidence: 99%

Dynamic upscaling of decomposition kinetics for carbon cycling models

Chakrawal¹,

Herrmann²,

Koestel³

et al. 2019

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Soil organic substrates and microorganisms are heterogeneously distributed in the soil medium (Nunan et al, 2002;Peth et al, 2014;Raynaud and Nunan, 2014;Rawlins et al, 2016). The importance of this heterogeneous distribution in soil organic matter (SOM) dynamics has been shown both experimentally and in modeling studies.…”

Section: Introductionmentioning

confidence: 99%

Dynamic upscaling of decomposition kinetics for carbon cycling models

et al. 2020

Self Cite

View full text Add to dashboard Cite

Abstract. The distribution of organic substrates and microorganisms in soils is spatially heterogeneous at the microscale. Most soil carbon cycling models do not account for this microscale heterogeneity, which may affect predictions of carbon (C) fluxes and stocks. In this study, we hypothesize that the mean respiration rate R‾ at the soil core scale (i) is affected by the microscale spatial heterogeneity of substrate and microorganisms and (ii) depends upon the degree of this heterogeneity. To theoretically assess the effect of spatial heterogeneities on R‾, we contrast heterogeneous conditions with isolated patches of substrate and microorganisms versus spatially homogeneous conditions equivalent to those assumed in most soil C models. Moreover, we distinguish between biophysical heterogeneity, defined as the nonuniform spatial distribution of substrate and microorganisms, and full heterogeneity, defined as the nonuniform spatial distribution of substrate quality (or accessibility) in addition to biophysical heterogeneity. Four common formulations for decomposition kinetics (linear, multiplicative, Michaelis–Menten, and inverse Michaelis–Menten) are considered in a coupled substrate–microbial biomass model valid at the microscale. We start with a 2-D domain characterized by a heterogeneous substrate distribution and numerically simulate organic matter dynamics in each cell in the domain. To interpret the mean behavior of this spatially explicit system, we propose an analytical scale transition approach in which microscale heterogeneities affect R‾ through the second-order spatial moments (spatial variances and covariances). The model assuming homogeneous conditions was not able to capture the mean behavior of the heterogeneous system because the second-order moments cause R‾ to be higher or lower than in the homogeneous system, depending on the sign of these moments. This effect of spatial heterogeneities appears in the upscaled nonlinear decomposition formulations, whereas the upscaled linear decomposition model deviates from homogeneous conditions only when substrate quality is heterogeneous. Thus, this study highlights the inadequacy of applying at the macroscale the same decomposition formulations valid at the microscale and proposes a scale transition approach as a way forward to capture microscale dynamics in core-scale models.

show abstract

“…The production of greenhouse gases is not always modelled (27 references), and when it is, the majority of studies deal only with CO 2 (e.g., Nguyen-Ngoc, Leye, Monga, Garnier, & Nunan, 2013), whereas a minority also calculate denitrification and emission of N 2 O (Ebrahimi & Or, 2015Laudone et al, 2011) or CH 4 (Ebrahimi & Or, 2017.…”

Section: Microbial Metabolismmentioning

confidence: 99%