Articles | Volume 12, issue 5
Geosci. Model Dev., 12, 2009–2032, 2019
https://doi.org/10.5194/gmd-12-2009-2019
Geosci. Model Dev., 12, 2009–2032, 2019
https://doi.org/10.5194/gmd-12-2009-2019

Methods for assessment of models 23 May 2019

Methods for assessment of models | 23 May 2019

Bayesian inference and predictive performance of soil respiration models in the presence of model discrepancy

Ahmed S. Elshall et al.

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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Ming Ye on behalf of the Authors (14 Mar 2019)  Author's response    Manuscript
ED: Referee Nomination & Report Request started (15 Mar 2019) by Christoph Müller
RR by Anonymous Referee #2 (27 Mar 2019)
RR by Anonymous Referee #1 (02 Apr 2019)
ED: Publish subject to minor revisions (review by editor) (08 Apr 2019) by Christoph Müller
AR by Ming Ye on behalf of the Authors (16 Apr 2019)  Author's response    Manuscript
ED: Publish subject to technical corrections (23 Apr 2019) by Christoph Müller
AR by Ming Ye on behalf of the Authors (24 Apr 2019)  Author's response    Manuscript
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Short summary
The assumptions that the residuals are independent, identically distributed, and have constant variance tend to simplify the underlying mathematics of data models for Bayesian inference. We relax these three assumptions step-wise, resulting in eight data models. Using three mechanistic soil respiration models with different levels of model discrepancy, we discuss the impacts of data models on parameter estimation and predictive performance, and provide recommendations for data model selection.