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Methodological challenges and new perspectives of shifting vegetation phenology in eddy covariance data

While numerous studies report shifts in vegetation phenology, in this regard eddy covariance (EC) data, despite its continuous high-frequency observations, still requires further exploration. Furthermore, there is no general consensus on optimal methodologies for data smoothing and extracting phenol...

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Autores principales: Panwar, Annu, Migliavacca, Mirco, Nelson, Jacob A., Cortés, José, Bastos, Ana, Forkel, Matthias, Winkler, Alexander J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10449856/
https://www.ncbi.nlm.nih.gov/pubmed/37620417
http://dx.doi.org/10.1038/s41598-023-41048-x
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author Panwar, Annu
Migliavacca, Mirco
Nelson, Jacob A.
Cortés, José
Bastos, Ana
Forkel, Matthias
Winkler, Alexander J.
author_facet Panwar, Annu
Migliavacca, Mirco
Nelson, Jacob A.
Cortés, José
Bastos, Ana
Forkel, Matthias
Winkler, Alexander J.
author_sort Panwar, Annu
collection PubMed
description While numerous studies report shifts in vegetation phenology, in this regard eddy covariance (EC) data, despite its continuous high-frequency observations, still requires further exploration. Furthermore, there is no general consensus on optimal methodologies for data smoothing and extracting phenological transition dates (PTDs). Here, we revisit existing methodologies and present new prospects to investigate phenological changes in gross primary productivity (GPP) from EC measurements. First, we present a smoothing technique of GPP time series through the derivative of its smoothed annual cumulative sum. Second, we calculate PTDs and their trends from a commonly used threshold method that identifies days with a fixed percentage of the annual maximum GPP. A systematic analysis is performed for various thresholds ranging from 0.1 to 0.7. Lastly, we examine the relation of PTDs trends to trends in GPP across the years on a weekly basis. Results from 47 EC sites with long time series (> 10 years) show that advancing trends in start of season (SOS) are strongest at lower thresholds but for the end of season (EOS) at higher thresholds. Moreover, the trends are variable at different thresholds for individual vegetation types and individual sites, outlining reasonable concerns on using a single threshold value. Relationship of trends in PTDs and weekly GPP reveal association of advanced SOS and delayed EOS to increase in immediate primary productivity, but not to the trends in overall seasonal productivity. Drawing on these analyses, we emphasise on abstaining from subjective choices and investigating relationship of PTDs trend to finer temporal trends of GPP. Our study examines existing methodological challenges and presents approaches that optimize the use of EC data in identifying vegetation phenological changes and their relation to carbon uptake.
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spelling pubmed-104498562023-08-26 Methodological challenges and new perspectives of shifting vegetation phenology in eddy covariance data Panwar, Annu Migliavacca, Mirco Nelson, Jacob A. Cortés, José Bastos, Ana Forkel, Matthias Winkler, Alexander J. Sci Rep Article While numerous studies report shifts in vegetation phenology, in this regard eddy covariance (EC) data, despite its continuous high-frequency observations, still requires further exploration. Furthermore, there is no general consensus on optimal methodologies for data smoothing and extracting phenological transition dates (PTDs). Here, we revisit existing methodologies and present new prospects to investigate phenological changes in gross primary productivity (GPP) from EC measurements. First, we present a smoothing technique of GPP time series through the derivative of its smoothed annual cumulative sum. Second, we calculate PTDs and their trends from a commonly used threshold method that identifies days with a fixed percentage of the annual maximum GPP. A systematic analysis is performed for various thresholds ranging from 0.1 to 0.7. Lastly, we examine the relation of PTDs trends to trends in GPP across the years on a weekly basis. Results from 47 EC sites with long time series (> 10 years) show that advancing trends in start of season (SOS) are strongest at lower thresholds but for the end of season (EOS) at higher thresholds. Moreover, the trends are variable at different thresholds for individual vegetation types and individual sites, outlining reasonable concerns on using a single threshold value. Relationship of trends in PTDs and weekly GPP reveal association of advanced SOS and delayed EOS to increase in immediate primary productivity, but not to the trends in overall seasonal productivity. Drawing on these analyses, we emphasise on abstaining from subjective choices and investigating relationship of PTDs trend to finer temporal trends of GPP. Our study examines existing methodological challenges and presents approaches that optimize the use of EC data in identifying vegetation phenological changes and their relation to carbon uptake. Nature Publishing Group UK 2023-08-24 /pmc/articles/PMC10449856/ /pubmed/37620417 http://dx.doi.org/10.1038/s41598-023-41048-x Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Panwar, Annu
Migliavacca, Mirco
Nelson, Jacob A.
Cortés, José
Bastos, Ana
Forkel, Matthias
Winkler, Alexander J.
Methodological challenges and new perspectives of shifting vegetation phenology in eddy covariance data
title Methodological challenges and new perspectives of shifting vegetation phenology in eddy covariance data
title_full Methodological challenges and new perspectives of shifting vegetation phenology in eddy covariance data
title_fullStr Methodological challenges and new perspectives of shifting vegetation phenology in eddy covariance data
title_full_unstemmed Methodological challenges and new perspectives of shifting vegetation phenology in eddy covariance data
title_short Methodological challenges and new perspectives of shifting vegetation phenology in eddy covariance data
title_sort methodological challenges and new perspectives of shifting vegetation phenology in eddy covariance data
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10449856/
https://www.ncbi.nlm.nih.gov/pubmed/37620417
http://dx.doi.org/10.1038/s41598-023-41048-x
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