Articles | Volume 17, issue 15
https://doi.org/10.5194/bg-17-4043-2020
https://doi.org/10.5194/bg-17-4043-2020
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

Viewed

Total article views: 2,748 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
1,901 802 45 2,748 312 48 43
  • HTML: 1,901
  • PDF: 802
  • XML: 45
  • Total: 2,748
  • Supplement: 312
  • BibTeX: 48
  • EndNote: 43
Views and downloads (calculated since 19 Feb 2020)
Cumulative views and downloads (calculated since 19 Feb 2020)

Viewed (geographical distribution)

Total article views: 2,748 (including HTML, PDF, and XML) Thereof 2,394 with geography defined and 354 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 29 Jun 2024
Download

The requested paper has a corresponding corrigendum published. Please read the corrigendum first before downloading the article.

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.
Altmetrics
Final-revised paper
Preprint