A flexible Bayesian model for describing temporal variability of N2O emissions from an Australian pasture

Huang, Xiaodong, Grace, Peter, Rowlings, David, & Mengersen, Kerrie (2013) A flexible Bayesian model for describing temporal variability of N2O emissions from an Australian pasture. Science of The Total Environment, 454-455, pp. 206-210.

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Abstract

Soil-based emissions of nitrous oxide (N2O), a well-known greenhouse gas, have been associated with changes in soil water-filled pore space (WFPS) and soil temperature in many previous studies. However, it is acknowledged that the environment-N2O relationship is complex and still relatively poorly unknown. In this article, we employed a Bayesian model selection approach (Reversible jump Markov chain Monte Carlo) to develop a data-informed model of the relationship between daily N2O emissions and daily WFPS and soil temperature measurements between March 2007 and February 2009 from a soil under pasture in Queensland, Australia, taking seasonal factors and time-lagged effects into account. The model indicates a very strong relationship between a hybrid seasonal structure and daily N2O emission, with the latter substantially increased in summer. Given the other variables in the model, daily soil WFPS, lagged by a week, had a negative influence on daily N2O; there was evidence of a nonlinear positive relationship between daily soil WFPS and daily N2O emission; and daily soil temperature tended to have a linear positive relationship with daily N2O emission when daily soil temperature was above a threshold of approximately 19°C. We suggest that this flexible Bayesian modeling approach could facilitate greater understanding of the shape of the covariate-N2O flux relation and detection of effect thresholds in the natural temporal variation of environmental variables on N2O emission.

Impact and interest:

4 citations in Scopus
2 citations in Web of Science®
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ID Code: 62672
Item Type: Journal Article
Refereed: Yes
Keywords: Bayesian model selection, Bayesian modeling, Environmental variables, Multiple imputation, Piecewise polynomials, Reversible jump Markov chain Monte Carlo, RJMCMC, Soil temperature measurements
DOI: 10.1016/j.scitotenv.2013.03.013
ISSN: 00489697
Divisions: Current > Schools > School of Earth, Environmental & Biological Sciences
Past > Institutes > Institute for Sustainable Resources
Current > Schools > School of Mathematical Sciences
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Deposited On: 19 Sep 2013 23:02
Last Modified: 22 Sep 2013 22:25

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