A strategy for modelling heavy-tailed greenhouse gases (GHG) data using the generalised extreme value distribution: Are we overestimating GHG flux using the sample mean?

Dhanoa, M. S.; Louro, Aránzazu; Cardenas, L.M.; Shepherd, A.; Sanderson, R.; López, Secundino; France, J.; Biotechnology and Biological Sciences Research Council (UK)

A strategy for modelling heavy-tailed greenhouse gases (GHG) data using the generalised extreme value distribution: Are we overestimating GHG flux using the sample mean?

8 páginas, 6 figuras, 8 tablas.

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Bibliographic Details
Main Authors:	Dhanoa, M. S., Louro, Aránzazu, Cardenas, L.M., Shepherd, A., Sanderson, R., López, Secundino, France, J.
Other Authors:	Biotechnology and Biological Sciences Research Council (UK)
Format:	artículo biblioteca
Published:	Elsevier 2020
Subjects:	Nitrous oxide, Carbon dioxide, Generalised extreme value, Finney correction, Heavy-tailed data, Skewness correction,
Online Access:	http://hdl.handle.net/10261/220544 http://dx.doi.org/10.13039/501100000268
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id	dig-igm-es-10261-220544
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spelling	dig-igm-es-10261-2205442022-04-18T11:53:34Z A strategy for modelling heavy-tailed greenhouse gases (GHG) data using the generalised extreme value distribution: Are we overestimating GHG flux using the sample mean? Dhanoa, M. S. Louro, Aránzazu Cardenas, L.M. Shepherd, A. Sanderson, R. López, Secundino France, J. Biotechnology and Biological Sciences Research Council (UK) López, Secundino [0000-0001-6267-683X] Nitrous oxide Carbon dioxide Generalised extreme value Finney correction Heavy-tailed data Skewness correction 8 páginas, 6 figuras, 8 tablas. In this study, we draw up a strategy for analysis of greenhouse gas (GHG) field data. The distribution of GHG flux data generally exhibits excessive skewness and kurtosis. This results in a heavy tailed distribution that is much longer than the tail of a log-normal distribution or outlier induced skewness. The generalised extreme value (GEV) distribution is well-suited to model such data. We evaluated GEV as a model for the analysis and a means of extraction of a robust average of carbon dioxide (CO) and nitrous oxide (NO) flux data measured in an agricultural field. The option of transforming CO flux data to the Box-Cox scale in order to make the distribution normal was also investigated. The results showed that average CO estimates from GEV are less affected by data in the long tail compared to the sample mean. The data for NO flux were much more complex than CO flux data due to the presence of negative fluxes. The estimate of the average value from GEV was much more consistent with maximum data frequency position. The analysis of GEV, which considers the effects of hot-spot-like observations, suggests that sample means and log-means may overestimate GHG fluxes from agricultural fields. In this study, the arithmetic CO sample mean of 65.6 (mean log-scale 65.9) kg CO–C ha d was reduced to GEV mean of 60.1 kg CO–C ha d. The arithmetic NO sample mean of 1.038 (mean log-scale 1.038) kg NO–N ha d was substantially reduced to GEV mean of 0.0157 kg NO–N ha d. Our analysis suggests that GHG data should be analysed assuming a GEV distribution of the data, including a Box-Cox transformation when negative data are observed, rather than only calculating basic log and log-normal summaries. Results of GHG studies may end up in national inventories. Thus, it is necessary and important to follow all procedures that contribute to minimise any bias in the data. The work was supported by the Biotechnology and Biological Sciences Research Council (BB/P01268X/1, BBS/E/C/000I0320). 2020-09-30T07:30:26Z 2020-09-30T07:30:26Z 2020 2020-09-30T07:30:27Z artículo http://purl.org/coar/resource_type/c_6501 Atmospheric Environment 237 (2020) 1873-2844 http://hdl.handle.net/10261/220544 10.1016/j.atmosenv.2020.117500 http://dx.doi.org/10.13039/501100000268 http://dx.doi.org/10.1016/j.atmosenv.2020.117500 Sí none Elsevier
institution	IGM ES
collection	DSpace
country	España
countrycode	ES
component	Bibliográfico
access	En linea
databasecode	dig-igm-es
tag	biblioteca
region	Europa del Sur
libraryname	Biblioteca del IGM España
topic	Nitrous oxide Carbon dioxide Generalised extreme value Finney correction Heavy-tailed data Skewness correction Nitrous oxide Carbon dioxide Generalised extreme value Finney correction Heavy-tailed data Skewness correction
spellingShingle	Nitrous oxide Carbon dioxide Generalised extreme value Finney correction Heavy-tailed data Skewness correction Nitrous oxide Carbon dioxide Generalised extreme value Finney correction Heavy-tailed data Skewness correction Dhanoa, M. S. Louro, Aránzazu Cardenas, L.M. Shepherd, A. Sanderson, R. López, Secundino France, J. A strategy for modelling heavy-tailed greenhouse gases (GHG) data using the generalised extreme value distribution: Are we overestimating GHG flux using the sample mean?
description	8 páginas, 6 figuras, 8 tablas.
author2	Biotechnology and Biological Sciences Research Council (UK)
author_facet	Biotechnology and Biological Sciences Research Council (UK) Dhanoa, M. S. Louro, Aránzazu Cardenas, L.M. Shepherd, A. Sanderson, R. López, Secundino France, J.
format	artículo
topic_facet	Nitrous oxide Carbon dioxide Generalised extreme value Finney correction Heavy-tailed data Skewness correction
author	Dhanoa, M. S. Louro, Aránzazu Cardenas, L.M. Shepherd, A. Sanderson, R. López, Secundino France, J.
author_sort	Dhanoa, M. S.
title	A strategy for modelling heavy-tailed greenhouse gases (GHG) data using the generalised extreme value distribution: Are we overestimating GHG flux using the sample mean?
title_short	A strategy for modelling heavy-tailed greenhouse gases (GHG) data using the generalised extreme value distribution: Are we overestimating GHG flux using the sample mean?
title_full	A strategy for modelling heavy-tailed greenhouse gases (GHG) data using the generalised extreme value distribution: Are we overestimating GHG flux using the sample mean?
title_fullStr	A strategy for modelling heavy-tailed greenhouse gases (GHG) data using the generalised extreme value distribution: Are we overestimating GHG flux using the sample mean?
title_full_unstemmed	A strategy for modelling heavy-tailed greenhouse gases (GHG) data using the generalised extreme value distribution: Are we overestimating GHG flux using the sample mean?
title_sort	strategy for modelling heavy-tailed greenhouse gases (ghg) data using the generalised extreme value distribution: are we overestimating ghg flux using the sample mean?
publisher	Elsevier
publishDate	2020
url	http://hdl.handle.net/10261/220544 http://dx.doi.org/10.13039/501100000268
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A strategy for modelling heavy-tailed greenhouse gases (GHG) data using the generalised extreme value distribution: Are we overestimating GHG flux using the sample mean?

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