Published August 4, 2020 | Version 0.1.5
Dataset Open

SAPFLUXNET: A global database of sap flow measurements

  • 1. CREAF
  • 2. Ghent University
  • 4. CREAF/Universitat Autònoma de Barcelona


Data collectors:

  • 1. University of Sydney
  • 2. University of Debrecen, Department of Botany
  • 3. CREAF
  • 4. Instituto de Botânica de São Paulo, Brazil
  • 5. ETH Zurich
  • 6. Instituto de Ecología A.C., Red de Ecología Funcional
  • 7. Smithsonian Conservation Biology Insitute - Conservation Ecology Center
  • 8. Texas A&M University
  • 9. McMaster University, Canada
  • 10. INIA
  • 11. University of New Hampshire
  • 12. School of Biological and Biomedical Sciences, University of Durham, UK
  • 13. McMaster University
  • 14. CNRS
  • 15. University of Minnesota
  • 16. Faculty of Regional and Environmental Sciences - Geobotany, University of Trier
  • 17. USDA Forest Service
  • 18. Ohio State University
  • 19. INRA, UMR EEF
  • 20. University of Florida
  • 21. Dpt. Plant Biology, Universidad de La Laguna (ULL), tenerife, Spain
  • 22. CATIE
  • 23. University of Twente
  • 24. US Forest Service
  • 25. Environmental Protection Agency of Aosta Valley (ARPA VdA)
  • 26. Chinese Academy of Forestry
  • 27. ISA/ULisboa
  • 28. INIAV IP
  • 29. Université Paris-Sud
  • 30. University of Wisconsin
  • 31. IRD
  • 32. CTU in Prague
  • 33. Duke University
  • 34. CSIR
  • 35. University of Alaska Fairbanks
  • 36. Alterra, Wageningen UR
  • 37. University of Campinas
  • 38. Western Sydney University
  • 39. Max Planck Institute for Biogeochemestry
  • 40. University of Wyoming
  • 41. Swiss Federal Research Institute for Forest, Snow and Landscape Research (WSL)
  • 42. MNCN – CSIC
  • 43. Swiss Federal Institute for Forest, Snow and Landscape
  • 44. Laboratório de Clima e Biosfera do IAG-USP, Brazil
  • 45. Institute for Sustainable Agriculture (IAS-CSIC)
  • 46. University of Twente, Enschede, The Netherlands
  • 47. INRA
  • 48. University of Edinburgh
  • 49. The University of Melbourne
  • 50. MECWE-Xiamen university
  • 51. Oak Ridge National Laboratory
  • 52. Swedish University of Agricultural Sciences (SLU)
  • 53. Helmholtz Centre Potsdam
  • 54. Instituto de recursos naturales y agrobiología de Sevilla (IRNAS)-CSIC
  • 55. Forest Sciences, University of Helsinki
  • 56. Universidad Nacional Autónoma de México, Centro de Ciencias de la Atmósfera
  • 57. Tsinghua
  • 58. Yellowstone Ecological Research Centre
  • 59. KKU
  • 60. ZALF Müncheberg
  • 61. Unicamp, Brazil
  • 62. University of Auckland
  • 63. Seoul National University
  • 64. Dept. of Earth Sciences, University of Gothenburg
  • 65. Colgate University
  • 66. Physical Geography and Ecosystem Science, Lund University
  • 67. University of Trier, Faculty of Regional and Environmental Sciences, Geobotany
  • 68. University at Albany, SUNY
  • 69. Universidad de Jaén
  • 70. University of Göttingen
  • 71. CEFE CNRS, Montpellier
  • 72. Universidad Pablo de Olavide
  • 73. Dept. of Physical Geography and Ecosystem Science, Lund University
  • 74. IDAEA-CSIC
  • 75. University of Córdoba
  • 76. Ohio State University, University of Texas at Austin
  • 77. Los Alamos National Laboratory (LANL), USA
  • 78. Smithsonian Environmental Research Center
  • 79. ANU / University of Edinburgh
  • 81. CSIRO
  • 82. Free University of Bolzano/Bozen
  • 83. Hokkaido Regional Breeding Office, Forest Tree Breeding Center, Forestry and Forest Products Research Institute
  • 84. University of Technology Sydney
  • 85. Indiana University Bloomington
  • 86. Botany University Innsbruck
  • 87. Eurac Research
  • 88. USDA Forest Service, Southern Research Station, Coweeta Hydrologic Laboratory
  • 89. Instituto Nacional de Tecnología Agropecuaria
  • 90. Department of Biology, University of New Mexico, USA
  • 91. WIS
  • 92. NA
  • 93. USP
  • 94. UERJ
  • 95. University of Exeter
  • 96. Sukachev Institute of forest SB RAS
  • 97. University of Barcelona
  • 98. University of Edinburgh / University of Helsinki
  • 99. Universidad Politécnica de Madrid
  • 100. Rutgers University Newark
  • 101. INRA, UMR Ecofog
  • 102. Global Change Research Institute CAS
  • 104. IH CAS
  • 105. Chulalongkorn University
  • 106. Mendel University in Brno
  • 107. University of Zurich, Zurich, Switzerland
  • 108. A.N. Severtsov Institute of Ecology and Evolution RAS
  • 109. University of Bonn
  • 110. Faculty of Regional and Environmental Sciences - Geobotany, University of Trier
  • 111. Universität Freiburg
  • 112. Dpt. Alpine Timberline Ecophysiology,BFW, Innsnruck, Austria
  • 113. WSL, Birmensdorf, Switzerland


General description

SAPFLUXNET contains a global database of sap flow and environmental data, together with metadata at different levels.
SAPFLUXNET is a harmonised database, compiled from contributions from researchers worldwide. 

The SAPFLUXNET version 0.1.5 database harbours 202 globally distributed datasets, from 121 geographical locations. SAPFLUXNET contains sap flow data for 2714 individual plants (1584 angiosperms and 1130 gymnosperms), belonging to 174 species (141 angiosperms and 33 gymnosperms), 95 different genera and 45 different families. More information on the database coverage can be found here:

The SAPFLUXNET project has been developed by researchers at CREAF and other institutions (, coordinated by Rafael Poyatos (CREAF,, and funded by two Spanish Young Researcher's Grants  (SAPFLUXNET, CGL2014-55883-JIN; DATAFORUSE, RTI2018-095297-J-I00 ) and an Alexander von Humboldt Research Fellowship for Experienced Researchers).


Compared to version 0.1.4, this version includes some changes in the metadata, but all time series data (sap flow, environmental) remain the same. 

  • For all datasets, climate metadata (temperature and precipitation, ‘si_mat’ and ‘si_map’) have been extracted from CHELSA (, replacing the previous climate data obtained with Wordclim. This change has modified the biome classification of the datasets in ‘si_biome’.
  • In ‘species’ metadata, the percentage of basal area with sap flow measurements for each species (‘sp_basal_area_perc’) is now assigned a value of 0 if species are in the understorey. This affects two datasets: AUS_MAR_UBD and AUS_MAR_UBW, where, previously, the sum of species basal area percentages could add up to more than 100%.
  • In ‘species’ metadata, the percentage of basal area with sap flow measurements for each species (‘sp_basal_area_perc’) has been corrected for datasets USA_SIL_OAK_POS, USA_SIL_OAK_1PR, USA_SIL_OAK_2PR.
  • In ‘site’ metadata, the vegetation type (‘si_igbp’) has been changed to SAV for datasets CHN_ARG_GWD and CHN_ARG_GWS.

Variables and units

SAPFLUXNET contains whole-plant sap flow and environmental variables at sub-daily temporal resolution. Both sap flow and environmental time series have accompanying flags in a data frame, one for sap flow and another for environmental
variables. These flags store quality issues detected during the quality control process and can be used to add further quality flags. 

Metadata contain relevant variables informing about site conditions, stand characteristics, tree and species attributes, sap flow methodology and details on environmental measurements. The description and units of all data and metadata variables can be found here: Metadata and data units.

To learn more about variables, units and data flags please use the functionalities implemented in the sapfluxnetr package ( In particular, have a look at the package vignettes using R:

# remotes::install_github(
#   'sapfluxnet/sapfluxnetr',
#   build_opts = c("--no-resave-data", "--no-manual", "--build-vignettes")
# )
# to list all vignettes
# variables and units
vignette('metadata-and-data-units', package='sapfluxnetr')
# data flags
vignette('data-flags', package='sapfluxnetr')

Data formats

SAPFLUXNET data can be found in two formats: 1) RData files belonging to the custom-built 'sfn_data' class and 2) Text files in .csv format. We recommend using the sfn_data objects together with the sapfluxnetr package, although we also provide  the text files for convenience. For each dataset, text files are structured in the same way as the slots of sfn_data objects; if working with text files, we recommend that you check the data structure of 'sfn_data' objects in the corresponding vignette.

Working with sfn_data files

To work with SAPFLUXNET data, first they have to be downloaded from Zenodo, maintaining the folder structure. A first level in the folder hierarchy corresponds to file format, either RData files or csv's. A second level corresponds to how sap flow is expressed: per plant, per sapwood area or per leaf area. Please note that interconversions among the magnitudes have been performed whenever possible. Below this level, data have been organised per dataset. In the case of RData files, each dataset is contained in a sfn_data object, which stores all data and metadata in different slots (see the vignette 'sfn-data-classes'). In the case of csv files, each dataset has 9 individual files, corresponding to metadata (5), sap flow and environmental data (2) and their corresponding data flags (2).

After downloading the entire database, the sapfluxnetr package can be used to:
- Work with data from a single site: data access, plotting and time aggregation.
- Select the subset datasets to work with.
- Work with data from multiple sites: data access, plotting and time aggregation.

Please check the following package vignettes to learn more about how to work with sfn_data files:

Quick guide

Metadata and data units

sfn_data classes

Custom aggregation

Memory and parallelization

Working with text files

We recommend to work with sfn_data objects using R and the sapfluxnetr package and we do not currently provide code to work with text files. 

Data issues and reporting

Please report any issue you may find in the database by sending us an email:

Temporary data fixes, detected but not yet included in released versions will be published in SAPFLUXNET main web page ('Known data errors').

Data access, use and citation

This version of the SAPFLUXNET database is open access and corresponds to the data paper submitted to Earth System Science Data in August 2020.

When using SAPFLUXNET data in an academic work, please cite the data paper, when available, or alternatively, the Zenodo dataset (see the ‘Cite as’ section on the right panels of this web page).


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