A Bayesian approach to evaluation of soil biogeochemical models

Xie, Hua W.; Romero-Olivares, Adriana L.; Guindani, Michele; Allison, Steven D.

doi:https://doi.org/10.5194/bg-17-4043-2020

Articles | Volume 17, issue 15

https://doi.org/10.5194/bg-17-4043-2020

© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/bg-17-4043-2020

© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 17, issue 15

Research article

|

10 Aug 2020

Research article |

| 10 Aug 2020

A Bayesian approach to evaluation of soil biogeochemical models

Hua W. Xie, Adriana L. Romero-Olivares, Michele Guindani, and Steven D. Allison

Supplement

https://doi.org/10.5194/bg-17-4043-2020-supplement

Data sets

A Bayesian Approach to Evaluation of Soil Biogeochemical Models Meta-analysis Data and R and Stan Code Hua Xie, Adriana Romero-Olivares, and Steven D. Allison https://doi.org/10.17605/OSF.IO/7MEY8

Model code and software

A Bayesian Approach to Evaluation of Soil Biogeochemical Models Meta-analysis Data and R and Stan Code Hua Xie, Adriana Romero-Olivares, and Steven D. Allison https://doi.org/10.17605/OSF.IO/7MEY8

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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.