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. 2020 Jul 9;7(1):225.
doi: 10.1038/s41597-020-0534-3.

The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data

Gilberto Pastorello  1 Carlo Trotta  2 Eleonora Canfora  2   3 Housen Chu  4 Danielle Christianson  5 You-Wei Cheah  5 Cristina Poindexter  6 Jiquan Chen  7 Abdelrahman Elbashandy  5 Marty Humphrey  8 Peter Isaac  9 Diego Polidori  2   3 Markus Reichstein  10 Alessio Ribeca  2   3 Catharine van Ingen  5 Nicolas Vuichard  11 Leiming Zhang  12 Brian Amiro  13 Christof Ammann  14 M Altaf Arain  15 Jonas Ardö  16 Timothy Arkebauer  17 Stefan K Arndt  18 Nicola Arriga  19   20 Marc Aubinet  21 Mika Aurela  22 Dennis Baldocchi  23 Alan Barr  24   25 Eric Beamesderfer  15 Luca Belelli Marchesini  26   27 Onil Bergeron  28 Jason Beringer  29 Christian Bernhofer  30 Daniel Berveiller  31 Dave Billesbach  32 Thomas Andrew Black  33 Peter D Blanken  34 Gil Bohrer  35 Julia Boike  36   37 Paul V Bolstad  38 Damien Bonal  39 Jean-Marc Bonnefond  40 David R Bowling  41 Rosvel Bracho  42 Jason Brodeur  43 Christian Brümmer  44 Nina Buchmann  45 Benoit Burban  46 Sean P Burns  34   47 Pauline Buysse  48 Peter Cale  49 Mauro Cavagna  26 Pierre Cellier  48 Shiping Chen  50 Isaac Chini  26 Torben R Christensen  51 James Cleverly  52   53 Alessio Collalti  2   54 Claudia Consalvo  2   55 Bruce D Cook  56 David Cook  57 Carole Coursolle  58   59 Edoardo Cremonese  60 Peter S Curtis  61 Ettore D'Andrea  54 Humberto da Rocha  62 Xiaoqin Dai  12 Kenneth J Davis  63 Bruno De Cinti  64 Agnes de Grandcourt  65 Anne De Ligne  21 Raimundo C De Oliveira  66 Nicolas Delpierre  31 Ankur R Desai  67 Carlos Marcelo Di Bella  68 Paul di Tommasi  54 Han Dolman  69 Francisco Domingo  70 Gang Dong  71 Sabina Dore  72 Pierpaolo Duce  73 Eric Dufrêne  31 Allison Dunn  74 Jiří Dušek  75 Derek Eamus  52 Uwe Eichelmann  30 Hatim Abdalla M ElKhidir  76 Werner Eugster  45 Cacilia M Ewenz  77 Brent Ewers  78 Daniela Famulari  54 Silvano Fares  79   80 Iris Feigenwinter  45 Andrew Feitz  81 Rasmus Fensholt  82 Gianluca Filippa  60 Marc Fischer  83 John Frank  84 Marta Galvagno  60 Mana Gharun  45 Damiano Gianelle  26 Bert Gielen  19 Beniamino Gioli  85 Anatoly Gitelson  86 Ignacio Goded  20 Mathias Goeckede  87 Allen H Goldstein  23 Christopher M Gough  88 Michael L Goulden  89 Alexander Graf  90 Anne Griebel  18 Carsten Gruening  20 Thomas Grünwald  30 Albin Hammerle  91 Shijie Han  92   93 Xingguo Han  50 Birger Ulf Hansen  82 Chad Hanson  94 Juha Hatakka  22 Yongtao He  12   95 Markus Hehn  30 Bernard Heinesch  21 Nina Hinko-Najera  96 Lukas Hörtnagl  45 Lindsay Hutley  97 Andreas Ibrom  98 Hiroki Ikawa  99 Marcin Jackowicz-Korczynski  16   51 Dalibor Janouš  75 Wilma Jans  100 Rachhpal Jassal  33 Shicheng Jiang  101 Tomomichi Kato  102   103 Myroslava Khomik  15   104 Janina Klatt  105 Alexander Knohl  106   107 Sara Knox  108 Hideki Kobayashi  109 Georgia Koerber  110 Olaf Kolle  87 Yoshiko Kosugi  111 Ayumi Kotani  112 Andrew Kowalski  113 Bart Kruijt  114 Julia Kurbatova  115 Werner L Kutsch  116 Hyojung Kwon  94 Samuli Launiainen  117 Tuomas Laurila  22 Bev Law  94 Ray LeuningYingnian Li  118 Michael Liddell  119 Jean-Marc Limousin  120 Marryanna Lion  121 Adam J Liska  32 Annalea Lohila  22   122 Ana López-Ballesteros  123 Efrén López-Blanco  51 Benjamin Loubet  48 Denis Loustau  40 Antje Lucas-Moffat  44   124 Johannes Lüers  125   126 Siyan Ma  23 Craig Macfarlane  127 Vincenzo Magliulo  54 Regine Maier  45 Ivan Mammarella  122 Giovanni Manca  20 Barbara Marcolla  26 Hank A Margolis  59 Serena Marras  3   128 William Massman  84 Mikhail Mastepanov  51   129 Roser Matamala  57 Jaclyn Hatala Matthes  130 Francesco Mazzenga  131 Harry McCaughey  132 Ian McHugh  18 Andrew M S McMillan  133 Lutz Merbold  134 Wayne Meyer  110 Tilden Meyers  135 Scott D Miller  136 Stefano Minerbi  137 Uta Moderow  30 Russell K Monson  138 Leonardo Montagnani  137   139 Caitlin E Moore  140 Eddy Moors  141   142 Virginie Moreaux  40   143 Christine Moureaux  21 J William Munger  144   145 Taro Nakai  146   147 Johan Neirynck  148 Zoran Nesic  33 Giacomo Nicolini  2   3 Asko Noormets  149 Matthew Northwood  150 Marcelo Nosetto  151   152 Yann Nouvellon  65   153 Kimberly Novick  154 Walter Oechel  155   156 Jørgen Eivind Olesen  157   158 Jean-Marc Ourcival  120 Shirley A Papuga  159 Frans-Jan Parmentier  16   160 Eugenie Paul-Limoges  161 Marian Pavelka  75 Matthias Peichl  162 Elise Pendall  163 Richard P Phillips  164 Kim Pilegaard  98 Norbert Pirk  16   165 Gabriela Posse  166 Thomas Powell  4 Heiko Prasse  30 Suzanne M Prober  165 Serge Rambal  120 Üllar Rannik  122 Naama Raz-Yaseef  4 Corinna Rebmann  167 David Reed  168 Victor Resco de Dios  163   169 Natalia Restrepo-Coupe  138 Borja R Reverter  170 Marilyn Roland  19 Simone Sabbatini  2 Torsten Sachs  171 Scott R Saleska  138 Enrique P Sánchez-Cañete  113   172 Zulia M Sanchez-Mejia  173 Hans Peter Schmid  105 Marius Schmidt  90 Karl Schneider  174 Frederik Schrader  44 Ivan Schroder  175 Russell L Scott  176 Pavel Sedlák  75   177 Penélope Serrano-Ortíz  172   178 Changliang Shao  179 Peili Shi  12 Ivan Shironya  115 Lukas Siebicke  106 Ladislav Šigut  75 Richard Silberstein  29   180 Costantino Sirca  3   128 Donatella Spano  3   128 Rainer Steinbrecher  105 Robert M Stevens  181 Cove Sturtevant  182 Andy Suyker  86 Torbern Tagesson  16   82 Satoru Takanashi  183 Yanhong Tang  184 Nigel Tapper  185 Jonathan Thom  186 Michele Tomassucci  2   187 Juha-Pekka Tuovinen  22 Shawn Urbanski  188 Riccardo Valentini  2   3 Michiel van der Molen  189 Eva van Gorsel  190 Ko van Huissteden  69 Andrej Varlagin  115 Joseph Verfaillie  23 Timo Vesala  122 Caroline Vincke  191 Domenico Vitale  2   3 Natalia Vygodskaya  115 Jeffrey P Walker  192 Elizabeth Walter-Shea  86 Huimin Wang  12 Robin Weber  23 Sebastian Westermann  166 Christian Wille  171 Steven Wofsy  144   145 Georg Wohlfahrt  91 Sebastian Wolf  45 William Woodgate  193   194 Yuelin Li  195 Roberto Zampedri  26 Junhui Zhang  93 Guoyi Zhou  196 Donatella Zona  155   197 Deb Agarwal  5 Sebastien Biraud  4 Margaret Torn  4 Dario Papale  198   199
Affiliations

The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data

Gilberto Pastorello et al. Sci Data. .

Erratum in

  • Author Correction: The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data.
    Pastorello G, Trotta C, Canfora E, Chu H, Christianson D, Cheah YW, Poindexter C, Chen J, Elbashandy A, Humphrey M, Isaac P, Polidori D, Reichstein M, Ribeca A, van Ingen C, Vuichard N, Zhang L, Amiro B, Ammann C, Arain MA, Ardö J, Arkebauer T, Arndt SK, Arriga N, Aubinet M, Aurela M, Baldocchi D, Barr A, Beamesderfer E, Marchesini LB, Bergeron O, Beringer J, Bernhofer C, Berveiller D, Billesbach D, Black TA, Blanken PD, Bohrer G, Boike J, Bolstad PV, Bonal D, Bonnefond JM, Bowling DR, Bracho R, Brodeur J, Brümmer C, Buchmann N, Burban B, Burns SP, Buysse P, Cale P, Cavagna M, Cellier P, Chen S, Chini I, Christensen TR, Cleverly J, Collalti A, Consalvo C, Cook BD, Cook D, Coursolle C, Cremonese E, Curtis PS, D'Andrea E, da Rocha H, Dai X, Davis KJ, De Cinti B, de Grandcourt A, De Ligne A, De Oliveira RC, Delpierre N, Desai AR, Di Bella CM, di Tommasi P, Dolman H, Domingo F, Dong G, Dore S, Duce P, Dufrêne E, Dunn A, Dušek J, Eamus D, Eichelmann U, ElKhidir HAM, Eugster W, Ewenz CM, Ewers B, Famulari D, Fares S, Feigenwinter I, Feitz A, Fensholt R, Filippa G, Fischer M, Frank J, Galvagno M, Gharun M, Gianelle D, Gielen B, Gioli B, Gitelson A, Goded I, Goeckede M, Goldstein AH, Goug… See abstract for full author list ➔ Pastorello G, et al. Sci Data. 2021 Feb 25;8(1):72. doi: 10.1038/s41597-021-00851-9. Sci Data. 2021. PMID: 33633116 Free PMC article. No abstract available.

Abstract

The FLUXNET2015 dataset provides ecosystem-scale data on CO2, water, and energy exchange between the biosphere and the atmosphere, and other meteorological and biological measurements, from 212 sites around the globe (over 1500 site-years, up to and including year 2014). These sites, independently managed and operated, voluntarily contributed their data to create global datasets. Data were quality controlled and processed using uniform methods, to improve consistency and intercomparability across sites. The dataset is already being used in a number of applications, including ecophysiology studies, remote sensing studies, and development of ecosystem and Earth system models. FLUXNET2015 includes derived-data products, such as gap-filled time series, ecosystem respiration and photosynthetic uptake estimates, estimation of uncertainties, and metadata about the measurements, presented for the first time in this paper. In addition, 206 of these sites are for the first time distributed under a Creative Commons (CC-BY 4.0) license. This paper details this enhanced dataset and the processing methods, now made available as open-source codes, making the dataset more accessible, transparent, and reproducible.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Map of 206 tower sites included in this paper from the 212 sites in the February 2020 release of the FLUXNET2015 dataset. The size of the circle indicates the length of the data record. The color of the circles represents the ecosystem type based on the International Geosphere–Biosphere Programme (IGBP) definition. When overlapping, locations are offset slightly to improve readability. Numbers in parentheses indicate the number of sites in each IGBP group. The inset shows the distribution of data record lengths. See also Supplementary Fig. SM4 for continental scale maps of Australia, Europe, and North America.
Fig. 2
Fig. 2
The logic of the data processing steps for FLUXNET2015 (details about the different steps and meaning of abbreviations in the text).
Fig. 3
Fig. 3
To identify and remove data collected under low turbulence conditions, under which advective fluxes could lead to an underestimation of fluxes, filtering based on the USTAR threshold was used. In order to estimate the uncertainty in the USTAR threshold calculation, a bootstrapping approach was implemented, with a selection of values representative of the distribution included in the final data products. From the (up to) 200 thresholds from the combined bootstrapping of the two methods, 40 percentiles are extracted. All the subsequent steps of the pipeline are applied to all 40 versions. For each of the final output products (e.g., NEE, as illustrated here), seven percentiles representative of the distribution are included.
Fig. 4
Fig. 4
Example of the distribution of USTAR thresholds calculated for each year using the MP method in blue and CP method in green for the US-UMB site (dark green where they overlap). All these thresholds were pulled together to extract the CUT final 40 thresholds, while for the VUT thresholds, each year was pulled with the two immediately before and after (e.g., 2005 + 2006 + 2007 to extract the 40 thresholds to be used to filter 2006). Note that the level of agreement between methods and between subsequent years is variable, justifying the approach that propagates this variability into uncertainty in NEE.
Fig. 5
Fig. 5
Ranked USTAR thresholds based on median threshold and error bars showing 25th to 75th percentiles of the 40 thresholds calculated with the Constant USTAR Threshold (CUT) method – only computed for sites with 3 or more years, so only 177 sites out of the 206 are shown. Colors show different ecosystem classes based on the site’s IGBP.
Fig. 6
Fig. 6
Distribution of the yearly (a) net ecosystem exchange (NEE), (b) gross primary production (GPP), and (c) ecosystem respiration (RECO) in FLUXNET2015. Only data with QC flag (NEE_VUT_REF_QC) higher than 0.5 are shown here. The values are reference NEE, GPP, and RECO based on the Variable USTAR Threshold (VUT) and selected reference for model efficiency (REF). GPP and RECO are based on the nighttime partitioning (NT) method. The grey histogram (bin width 100 gC m−2 y−1) shows the flux distribution in 1224 of the available site-years; negative GPP and RECO values are kept to preserve distributions, see Data processing methods section for details. Black lines show the distribution curves based on published data,. The boxplots show the flux distribution (i.e., 25th, 50th, and 75th percentiles) for vegetation types defined and color-coded according to IGBP (International Geosphere–Biosphere Programme) definitions. Circles represent data points beyond the 1.5-times interquartile range (25th to 75th percentile) plus the 75th percentile or minus 25th percentile (whisker). Numbers in parentheses indicate the number of site-years used in each IGBP group. The NO-Blv site from the snow/ice IGBP group is not shown in the boxplots.

References

    1. Aubinet, M., Vesala, T. & Papale, D. (Eds.). Eddy Covariance: A Practical Guide to Measurement and Data Analysis (Springer Netherlands, Dordrecht, 2012).
    1. Aubinet, M. et al. Estimates of the Annual Net Carbon and Water Exchange of Forests: The EUROFLUX Methodology, in Advances in Ecological Research (ed. Fitter, A. H., Raffaelli, D. G.), pp. 113–175 (Elsevier, 1999).
    1. Law B. AmeriFlux Network aids global synthesis. Eos Trans. AGU. 2007;88:286–286. doi: 10.1029/2007EO280003. - DOI
    1. Novick KA, et al. The AmeriFlux network: A coalition of the willing. Agr. Forest Meteorol. 2018;249:444–456. doi: 10.1016/j.agrformet.2017.10.009. - DOI
    1. Yamamoto S, et al. Findings through the AsiaFlux network and a view toward the future. J. Geogr. Sci. 2005;15:142–148. doi: 10.1007/BF02872679. - DOI

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