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Biogeosciences An interactive open-access journal of the European Geosciences Union
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https://doi.org/10.5194/bg-2020-150
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/bg-2020-150
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  21 Jul 2020

21 Jul 2020

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This preprint is currently under review for the journal BG.

Simulation of soil carbon dynamics in Australia under a framework that better connects spatially explicit data with Rᴏᴛʜ C

Juhwan Lee1, Raphael A. Viscarra Rossel1, Zhongkui Luo2, and Ying Ping Wang3 Juhwan Lee et al.
  • 1School of Molecular and Life Sciences, Curtin University, GPO Box U1987, Perth WA 6845, Australia
  • 2College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, Zhejiang, China
  • 3CSIRO Oceans and Atmosphere, Private Bag 1, Aspendale, VIC 3195, Australia

Abstract. We simulated soil organic carbon (C) dynamics across Australia with the Rothamsted carbon model (Rᴏᴛʜ C) under a framework that connects new spatially-explicit soil measurements and data with the model. Doing so helped to bridge the disconnection that exists between datasets used to inform the model and the processes that it depicts. Under this framework, we compiled continental-scale datasets and pre-processed, standardised and configured them to the required spatial and temporal resolutions. We then calibrated Rᴏᴛʜ C and run simulations to predict the baseline soil organic C stocks and composition in the 0–0.3 m layer at 4,043 sites in cropping, modified grazing, native grazing, and natural environments across Australia. The Rᴏᴛʜ C model uses measured C fractions, the particulate, humus, and resistant organic C (POC, HOC and ROC, respectively) to represent the three main C pools in its structure. The model explained 97–98 % of the variation in measured total organic C in soils under cropping and grazing, and 65 % in soils under natural environments. We optimised the model at each site and experimented with different amounts of C inputs to predict the potential for C accumulation in a 100-year simulation. With an annual increase of 1 Mg C ha−1 in C inputs, the model predicted a potential soil C increase of 13.58 (interquartile range 12.19–15.80), 14.21 (12.38–16.03), and 15.57 (12.07–17.82) Mg C ha−1 under cropping, modified grazing and native grazing, and 3.52 (3.15–4.09) Mg C ha−1 under natural environments. Soils under native grazing were the most potentially vulnerable to C decomposition and loss, while soils under natural environments were the least vulnerable. An empirical assessment of the controls on the C change showed that climate, pH, total N, the C:N ratio, and cropping were the most important controls on POC change. Clay content and climate were dominant controls on HOC change. Consistent and explicit soil organic C simulations improve confidence in the model's predictions, contributing to the development of sustainable soil management under global change.

Juhwan Lee et al.

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Juhwan Lee et al.

Juhwan Lee et al.

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We performed Rᴏᴛʜ C simulations across Australia. Consistent prediction was enabled by a framework that connects Rᴏᴛʜ C with spatial data. Initialised with measured C fractions, Rᴏᴛʜ C explained 97–98 % of the variation in C under cropping and grazing and 65 % under natural environments. A 100-years simulation showed that the potential for C sequestration and the vulnerability to C loss is least in natural environments, larger in cropping and modified grazing, and greatest in native grazing.
We performed Rᴏᴛʜ C simulations across Australia. Consistent prediction was enabled by a...
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