Articles | Volume 13, issue 3
https://doi.org/10.5194/gmd-13-1399-2020
https://doi.org/10.5194/gmd-13-1399-2020
Development and technical paper
 | 
23 Mar 2020
Development and technical paper |  | 23 Mar 2020

Dynamic upscaling of decomposition kinetics for carbon cycling models

Arjun Chakrawal, Anke M. Herrmann, John Koestel, Jerker Jarsjö, Naoise Nunan, Thomas Kätterer, and Stefano Manzoni

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Cited articles

Abramoff, R., Xu, X., Hartman, M., O’Brien, S., Feng, W., Davidson, E., Finzi, A., Moorhead, D., Schimel, J., Torn, M., and Mayes, M. A.: The Millennial model: in search of measurable pools and transformations for modeling soil carbon in the new century, Biogeochemistry, 137, 51–71, https://doi.org/10.1007/s10533-017-0409-7, 2018. a
Albertson, J. D. and Montaldo, N.: Temporal dynamics of soil moisture variability: 1. Theoretical basis, Water Resour. Res., 39, 1–14, https://doi.org/10.1029/2002WR001616, 2003. a
Aleklett, K., Kiers, E. T., Ohlsson, P., Shimizu, T. S., Caldas, V. E., and Hammer, E. C.: Build Your Own Soil: Exploring Microfluidics to Create Microbial Habitat Structures, ISME J., 12, 312–319, https://doi.org/10.1038/ismej.2017.184, 2018. a
Allison, S. D.: Cheaters, diffusion and nutrients constrain decomposition by microbial enzymes in spatially structured environments, Ecol. Lett., 8, 626–635, https://doi.org/10.1111/j.1461-0248.2005.00756.x, 2005. a
Allison, S. D.: A trait-based approach for modelling microbial litter decomposition, Ecol. Lett., 15, 1058–1070, https://doi.org/10.1111/j.1461-0248.2012.01807.x, 2012. a
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Short summary
Soils are heterogeneous, which results in a nonuniform spatial distribution of substrates and the microorganisms feeding on them. Our results show that the variability in the spatial distribution of substrates and microorganisms at the pore scale is crucial because it affects how fast substrates are used by microorganisms and thus the decomposition rate observed at the soil core scale. This work provides a methodology to include microscale heterogeneity in soil carbon cycling models.
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