A Bayesian approach to evaluation of soil biogeochemical models
<p>To make predictions about the carbon cycling consequences of rising global surface temperatures, Earth system scientists rely on mathematical soil biogeochemical models (SBMs). However, it is not clear which models have better predictive accuracy, and a rigorous quantitative approach for co...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2020-08-01
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| Series: | Biogeosciences |
| Online Access: | https://bg.copernicus.org/articles/17/4043/2020/bg-17-4043-2020.pdf |
| Summary: | <p>To make predictions about the carbon cycling consequences of
rising global surface temperatures, Earth system scientists rely on
mathematical soil biogeochemical models (SBMs). However, it is not clear
which models have better predictive accuracy, and a rigorous quantitative
approach for comparing and validating the predictions has yet to be
established. In this study, we present a Bayesian approach to SBM comparison
that can be incorporated into a statistical model selection framework. We
compared the fits of linear and nonlinear SBMs to soil respiration data
compiled in a recent meta-analysis of soil warming field experiments. Fit
quality was quantified using Bayesian goodness-of-fit metrics, including the
widely applicable information criterion (WAIC) and leave-one-out
cross validation (LOO). We found that the linear model generally
outperformed the nonlinear model at fitting the meta-analysis data set.
Both WAIC and LOO computed higher overfitting risk and effective numbers of
parameters for the nonlinear model compared to the linear model,
conditional on the data set. Goodness of fit for both models generally
improved when they were initialized with lower and more realistic steady-state soil organic carbon densities. Still, testing whether linear models
offer definitively superior predictive performance over nonlinear models on
a global scale will require comparisons with additional site-specific data
sets of suitable size and dimensionality. Such comparisons can build upon
the approach defined in this study to make more rigorous statistical
determinations about model accuracy while leveraging emerging data sets,
such as those from long-term ecological research experiments.</p> |
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| ISSN: | 1726-4170 1726-4189 |