Long residence times of rapidly decomposable soil organic matter: application of a multi-phase, multi-component, and vertically resolved model (BAMS1)  to soil carbon dynamics

Riley, William J.; Maggi, Federico; Kleber, Markus; Torn, M. S.; Tang, Jinyun; Dwivedi, Dipankar; Guerry, N.

doi:10.5194/gmd-7-1335-2014

Cited by 108 publications

(140 citation statements)

References 130 publications

Supporting

Mentioning

139

Contrasting

Order By: Relevance

“…This type of kinetics was used by Riley et al (2014), Wieder et al (2014) and Wang et al (2014). These two models make different assumptions about the rate-limiting step in carbon decomposition.…”

Section: Model Descriptionmentioning

confidence: 99%

“…A number of nonlinear models have been developed that explicitly account for the dynamics of the soil microbial community (Parnas, 1978;Smith, 1979;Schimel and Weintraub, 2003;Wutzler and Reichstein, 2008;Allison et al, 2010;Grant, 2014;Riley et al, 2014;Tang and Riley, 2014). Parnas (1979) explored the mechanism of priming using a nonlinear soil microbial model that included both soil carbon and nitrogen dynamics.…”

Section: Y-p Wang Et Al: Responses Of Two Nonlinear Microbial Modementioning

confidence: 99%

“…The nonlinear soil carbon models described above have subsequently been used in a variety of studies: to explore different the fundamental mechanisms controlling soil carbon decomposition (Schimel and Weintraub, 2003, for example), to investigate the sensitivity of soil carbon and other biogeochemical processes to warming (Grant, 2014;Tang and Riley, 2014), to investigate the response of soil carbon to a small perturbation, such as priming (Wutzler and Reichstein, 2013), and to predict soil carbon responses to global change (Wieder et al, 2013;Sulman et al, 2014). Some studies have explored the mathematical properties of these nonlinear models in detail (for example, Manzoni et al, 2004;Manzoni and Porporato, 2007;Raupach, 2007;Wang et al, 2014).…”

Section: Y-p Wang Et Al: Responses Of Two Nonlinear Microbial Modementioning

confidence: 99%

“…These two simple formulations are amenable to analytic approximations, whereas the formulations with more general kinetics, such as the equilibrium chemistry approximations, are not. We only represent three soil carbon pools with each model and ignore abiotic processes for simplicity, despite these being potentially important under certain conditions (see Tang and Riley, 2014 for an example). In comparing the two nonlinear microbial models, we use the standard mathematical technique to analyse their responses to a small perturbation (see Wang et al, 2014), such as a step change in soil temperature or carbon input, or whether two models exhibit oscillatory behaviour under certain conditions, and how the analytic approximations to the exact model solutions differ between the two nonlinear mod-Y.-P. Wang et al: Responses of two nonlinear microbial models to warming and increased carbon input 889 els.…”

Section: Y-p Wang Et Al: Responses Of Two Nonlinear Microbial Modementioning

confidence: 99%

See 3 more Smart Citations

Responses of two nonlinear microbial models to warming and increased carbon input

et al. 2016

View full text Add to dashboard Cite

Abstract.A number of nonlinear microbial models of soil carbon decomposition have been developed. Some of them have been applied globally but have yet to be shown to realistically represent soil carbon dynamics in the field. A thorough analysis of their key differences is needed to inform future model developments. Here we compare two nonlinear microbial models of soil carbon decomposition: one based on reverse Michaelis-Menten kinetics (model A) and the other on regular Michaelis-Menten kinetics (model B). Using analytic approximations and numerical solutions, we find that the oscillatory responses of carbon pools to a small perturbation in their initial pool sizes dampen faster in model A than in model B. Soil warming always decreases carbon storage in model A, but in model B it predominantly decreases carbon storage in cool regions and increases carbon storage in warm regions. For both models, the CO 2 efflux from soil carbon decomposition reaches a maximum value some time after increased carbon input (as in priming experiments). This maximum CO 2 efflux (F max ) decreases with an increase in soil temperature in both models. However, the sensitivity of F max to the increased amount of carbon input increases with soil temperature in model A but decreases monotonically with an increase in soil temperature in model B. These differences in the responses to soil warming and carbon input between the two nonlinear models can be used to discern which model is more realistic when compared to results from field or laboratory experiments. These insights will contribute to an improved understanding of the significance of soil microbial processes in soil carbon responses to future climate change.

show abstract

Section: Model Descriptionmentioning

confidence: 99%

Section: Y-p Wang Et Al: Responses Of Two Nonlinear Microbial Modementioning

confidence: 99%

Section: Y-p Wang Et Al: Responses Of Two Nonlinear Microbial Modementioning

confidence: 99%

Section: Y-p Wang Et Al: Responses Of Two Nonlinear Microbial Modementioning

confidence: 99%

See 2 more Smart Citations

Responses of two nonlinear microbial models to warming and increased carbon input

et al. 2016

View full text Add to dashboard Cite

show abstract

“…For predictions that are more accurate, also within the framework of climate predictions, SOM models should consider temporarily and spatially variable abiotic factors such as temperature and moisture, nutrient dynamics, microbial activity, solid-water interactions, and transport processes of solid and dissolved organic matter and/or inorganic carbon (e.g., Schmidt et al (2011), Tang andRiley (2015)). Reactive transport models should account for these factors and effects, such as illustrated in recent studies by Tang et al (2013) and Riley et al (2014).…”

Section: Introductionmentioning

confidence: 99%

The HPx software for multicomponent reactive transport during variably-saturated flow: Recent developments and applications

Jacques

Šimůnek

Mallants

et al. 2018

Journal of Hydrology and Hydromechanics

View full text Add to dashboard Cite

HPx is a multicomponent reactive transport model which uses HYDRUS as the flow and transport solver and PHREEQC-3 as the biogeochemical solver. Some recent adaptations have significantly increased the flexibility of the software for different environmental and engineering applications. This paper gives an overview of the most significant changes of HPx, such as coupling transport properties to geochemical state variables, gas diffusion, and transport in two and three dimensions. OpenMP allows for parallel computing using shared memory. Enhancements for scripting may eventually simplify input definitions and create possibilities for defining templates for generic (sub)problems. We included a discussion of root solute uptake and colloid-affected solute transport to show that most or all of the comprehensive features of HYDRUS can be extended with geochemical information. Finally, an example is used to demonstrate how HPx, and similar reactive transport models, can be helpful in implementing different factors relevant for soil organic matter dynamics in soils. HPx offers a unique framework to couple spatial-temporal variations in water contents, temperatures, and water fluxes, with dissolved organic matter and CO 2 transport, as well as bioturbation processes.

show abstract

Explicitly representing soil microbial processes in Earth system models

Wieder

Allison

Davidson

et al. 2015

Global Biogeochemical Cycles

340

304

View full text Add to dashboard Cite

Microbes influence soil organic matter decomposition and the long‐term stabilization of carbon (C) in soils. We contend that by revising the representation of microbial processes and their interactions with the physicochemical soil environment, Earth system models (ESMs) will make more realistic global C cycle projections. Explicit representation of microbial processes presents considerable challenges due to the scale at which these processes occur. Thus, applying microbial theory in ESMs requires a framework to link micro‐scale process‐level understanding and measurements to macro‐scale models used to make decadal‐ to century‐long projections. Here we review the diversity, advantages, and pitfalls of simulating soil biogeochemical cycles using microbial‐explicit modeling approaches. We present a roadmap for how to begin building, applying, and evaluating reliable microbial‐explicit model formulations that can be applied in ESMs. Drawing from experience with traditional decomposition models, we suggest the following: (1) guidelines for common model parameters and output that can facilitate future model intercomparisons; (2) development of benchmarking and model‐data integration frameworks that can be used to effectively guide, inform, and evaluate model parameterizations with data from well‐curated repositories; and (3) the application of scaling methods to integrate microbial‐explicit soil biogeochemistry modules within ESMs. With contributions across scientific disciplines, we feel this roadmap can advance our fundamental understanding of soil biogeochemical dynamics and more realistically project likely soil C response to environmental change at global scales.

show abstract

Long residence times of rapidly decomposable soil organic matter: application of a multi-phase, multi-component, and vertically resolved model (BAMS1) to soil carbon dynamics

Cited by 108 publications

References 130 publications

Responses of two nonlinear microbial models to warming and increased carbon input

Responses of two nonlinear microbial models to warming and increased carbon input

The HPx software for multicomponent reactive transport during variably-saturated flow: Recent developments and applications

Explicitly representing soil microbial processes in Earth system models

Contact Info

Product

Resources

About