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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Latest update: 08 Apr 2021
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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.