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Restricting the nonlinearity parameter in soil greenhouse gas flux calculation for more reliable flux estimates
The static chamber approach is often used for greenhouse gas (GHG) flux measurements, whereby the flux is deduced from the increase of species concentration after closing the chamber. Since this increase changes diffusion gradients between chamber air and soil air, a nonlinear increase is expected....
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6062054/ https://www.ncbi.nlm.nih.gov/pubmed/30048522 http://dx.doi.org/10.1371/journal.pone.0200876 |
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author | Hüppi, Roman Felber, Raphael Krauss, Maike Six, Johan Leifeld, Jens Fuß, Roland |
author_facet | Hüppi, Roman Felber, Raphael Krauss, Maike Six, Johan Leifeld, Jens Fuß, Roland |
author_sort | Hüppi, Roman |
collection | PubMed |
description | The static chamber approach is often used for greenhouse gas (GHG) flux measurements, whereby the flux is deduced from the increase of species concentration after closing the chamber. Since this increase changes diffusion gradients between chamber air and soil air, a nonlinear increase is expected. Lateral gas flow and leakages also contribute to non linearity. Several models have been suggested to account for this non linearity, the most recent being the Hutchinson–Mosier regression model (hmr). However, the practical application of these models is challenging because the researcher needs to decide for each flux whether a nonlinear fit is appropriate or exaggerates flux estimates due to measurement artifacts. In the latter case, a flux estimate from the linear model is a more robust solution and introduces less arbitrary uncertainty to the data. We present the new, dynamic and reproducible flux calculation scheme, kappa.max, for an improved trade-off between bias and uncertainty (i.e. accuracy and precision). We develop a tool to simulate, visualise and optimise the flux calculation scheme for any specific static N(2)O chamber measurement system. The decision procedure and visualisation tools are implemented in a package for the R software. Finally, we demonstrate with this approach the performance of the applied flux calculation scheme for a measured flux dataset to estimate the actual bias and uncertainty. The kappa.max method effectively improved the decision between linear and nonlinear flux estimates reducing the bias at a minimal cost of uncertainty. |
format | Online Article Text |
id | pubmed-6062054 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-60620542018-08-03 Restricting the nonlinearity parameter in soil greenhouse gas flux calculation for more reliable flux estimates Hüppi, Roman Felber, Raphael Krauss, Maike Six, Johan Leifeld, Jens Fuß, Roland PLoS One Research Article The static chamber approach is often used for greenhouse gas (GHG) flux measurements, whereby the flux is deduced from the increase of species concentration after closing the chamber. Since this increase changes diffusion gradients between chamber air and soil air, a nonlinear increase is expected. Lateral gas flow and leakages also contribute to non linearity. Several models have been suggested to account for this non linearity, the most recent being the Hutchinson–Mosier regression model (hmr). However, the practical application of these models is challenging because the researcher needs to decide for each flux whether a nonlinear fit is appropriate or exaggerates flux estimates due to measurement artifacts. In the latter case, a flux estimate from the linear model is a more robust solution and introduces less arbitrary uncertainty to the data. We present the new, dynamic and reproducible flux calculation scheme, kappa.max, for an improved trade-off between bias and uncertainty (i.e. accuracy and precision). We develop a tool to simulate, visualise and optimise the flux calculation scheme for any specific static N(2)O chamber measurement system. The decision procedure and visualisation tools are implemented in a package for the R software. Finally, we demonstrate with this approach the performance of the applied flux calculation scheme for a measured flux dataset to estimate the actual bias and uncertainty. The kappa.max method effectively improved the decision between linear and nonlinear flux estimates reducing the bias at a minimal cost of uncertainty. Public Library of Science 2018-07-26 /pmc/articles/PMC6062054/ /pubmed/30048522 http://dx.doi.org/10.1371/journal.pone.0200876 Text en © 2018 Hüppi et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Hüppi, Roman Felber, Raphael Krauss, Maike Six, Johan Leifeld, Jens Fuß, Roland Restricting the nonlinearity parameter in soil greenhouse gas flux calculation for more reliable flux estimates |
title | Restricting the nonlinearity parameter in soil greenhouse gas flux calculation for more reliable flux estimates |
title_full | Restricting the nonlinearity parameter in soil greenhouse gas flux calculation for more reliable flux estimates |
title_fullStr | Restricting the nonlinearity parameter in soil greenhouse gas flux calculation for more reliable flux estimates |
title_full_unstemmed | Restricting the nonlinearity parameter in soil greenhouse gas flux calculation for more reliable flux estimates |
title_short | Restricting the nonlinearity parameter in soil greenhouse gas flux calculation for more reliable flux estimates |
title_sort | restricting the nonlinearity parameter in soil greenhouse gas flux calculation for more reliable flux estimates |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6062054/ https://www.ncbi.nlm.nih.gov/pubmed/30048522 http://dx.doi.org/10.1371/journal.pone.0200876 |
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