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Biogeosciences An interactive open-access journal of the European Geosciences Union
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BG | Articles | Volume 17, issue 15
Biogeosciences, 17, 4043–4057, 2020
https://doi.org/10.5194/bg-17-4043-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
Biogeosciences, 17, 4043–4057, 2020
https://doi.org/10.5194/bg-17-4043-2020
© 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.

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.
Soil biogeochemical models (SBMs) are needed to predict future soil CO2 emissions levels, but we...
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