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BG | Articles | Volume 17, issue 15
Biogeosciences, 17, 4043–4057, 2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
Biogeosciences, 17, 4043–4057, 2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 10 Aug 2020

Research article | 10 Aug 2020

A Bayesian approach to evaluation of soil biogeochemical models

Hua W. Xie et al.

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Cited articles

Allison, S. D., Wallenstein, M. D., and Bradford, M. A.: Soil-carbon response to warming dependent on microbial physiology, Nat. Geosci., 3, 336–340,, 2010. 
Anderson, T.-H. and Domsch, K. H.: Ratios of microbial biomass carbon to total organic carbon in arable soils, Soil Biol. Biochem., 21, 471–479,, 1989. 
Beskos, A., Pillai, N., Roberts, G., Sanz-Serna, J. M., and Stuart, A.: Optimal tuning of the hybrid Monte Carlo algorithm, Bernoulli, 19, 1501–1534,, 2013. 
Betancourt, M.: Diagnosing Suboptimal Cotangent Disintegrations in Hamiltonian Monte Carlo, arXiv [preprint], arXiv:1604.00695, 3 April 2016. 
Betancourt, M.: A Conceptual Introduction to Hamiltonian Monte Carlo, arXiv [preprint], arXiv:1701.02434, 10 January 2017. 
Publications Copernicus
Short summary
Soil biogeochemical models (SBMs) are needed to predict future soil CO2 emissions levels, but we presently lack statistically rigorous frameworks for assessing the predictive utility of SBMs. In this study, we demonstrate one possible approach to evaluating SBMs by comparing the fits of two models to soil CO2 respiration data with recently developed Bayesian statistical goodness-of-fit metrics. Our results demonstrate that our approach is a viable one for continued development and refinement.
Soil biogeochemical models (SBMs) are needed to predict future soil CO2 emissions levels, but we...
Final-revised paper