Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 12, issue 5
Articles | Volume 12, issue 5
https://doi.org/10.5194/gmd-12-2009-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-12-2009-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 12, issue 5
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, Ming Ye, Guo-Yue Niu, and Greg A. Barron-Gafford

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Latest update: 04 Nov 2024
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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.
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