Global transpiration data from sap ﬂow measurements: the … · 2021. 6. 14. · 2608 R. Poyatos...

Earth Syst Sci Data 13 2607ndash2649 2021httpsdoiorg105194essd-13-2607-2021copy Author(s) 2021 This work is distributed underthe Creative Commons Attribution 40 License

Global transpiration data from sap flow measurementsthe SAPFLUXNET database

Rafael Poyatos12 Viacutector Granda13 Viacutector Flo1 Mark A Adams45 Balaacutezs Adorjaacuten6 David Aguadeacute1Marcos P M Aidar7 Scott Allen8 M Susana Alvarado-Barrientos9 Kristina J Anderson-Teixeira1011Luiza Maria Aparecido1213 M Altaf Arain14 Ismael Aranda15 Heidi Asbjornsen16 Robert Baxter17

Eric Beamesderfer1819 Z Carter Berry20 Daniel Berveiller21 Bethany Blakely22 Johnny Boggs23Gil Bohrer24 Paul V Bolstad25 Damien Bonal26 Rosvel Bracho27 Patricia Brito28 Jason Brodeur29

Fernando Casanoves30 Jeacuterocircme Chave31 Hui Chen32 Cesar Cisneros3334 Kenneth Clark35Edoardo Cremonese36 Hongzhong Dang37 Jorge S David38 Teresa S David3839 Nicolas Delpierre4041Ankur R Desai42 Frederic C Do43 Michal Dohnal44 Jean-Christophe Domec4546 Sebinasi Dzikiti47

Colin Edgar48 Rebekka Eichstaedt49 Tarek S El-Madany50 Jan Elbers51 Cleiton B Eller52Eugeacutenie S Euskirchen48 Brent Ewers53 Patrick Fonti54 Alicia Forner5556 David I Forrester54Helber C Freitas5758 Marta Galvagno36 Omar Garcia-Tejera59 Chandra Prasad Ghimire3460

Teresa E Gimeno6162 John Grace63 Andreacute Granier64 Anne Griebel6566 Yan Guangyu32Mark B Gush67 Paul J Hanson68 Niles J Hasselquist69 Ingo Heinrich70

Virginia Hernandez-Santana71 Valentine Herrmann72 Teemu Houmllttauml73 Friso Holwerda74James Irvine63 Supat Isarangkool Na Ayutthaya75 Paul G Jarvis63 Hubert Jochheim76

Carlos A Joly7778 Julia Kaplick7980 Hyun Seok Kim818283 Leif Klemedtsson84 Heather Kropp8586Fredrik Lagergren87 Patrick Lane88 Petra Lang89 Andrei Lapenas90 Viacutector Lechuga91 Minsu Lee81

Christoph Leuschner92 Jean-Marc Limousin93 Juan Carlos Linares94 Maj-Lena Linderson87Anders Lindroth87 Pilar Llorens95 Aacutelvaro Loacutepez-Bernal96 Michael M Loranty97

Dietmar Luumlttschwager76 Cate Macinnis-Ng80 Isabelle Mareacutechaux98 Timothy A Martin99Ashley Matheny100 Nate McDowell101 Sean McMahon102 Patrick Meir63103 Ilona Meacuteszaacuteros6

Mirco Migliavacca50 Patrick Mitchell104 Meelis Moumllder105 Leonardo Montagnani106107Georgianne W Moore108 Ryogo Nakada109 Furong Niu110111 Rachael H Nolan65 Richard Norby112

Kimberly Novick113 Walter Oberhuber114 Nikolaus Obojes115 A Christopher Oishi116Rafael S Oliveira52 Ram Oren117118 Jean-Marc Ourcival93 Teemu Paljakka119

Oscar Perez-Priego50120 Pablo L Peri121122123 Richard L Peters54124 Sebastian Pfautsch125William T Pockman126 Yakir Preisler127 Katherine Rascher128 George Robinson129

Humberto Rocha130 Alain Rocheteau43 Alexander Roumlll111 Bruno H P Rosado131 Lucy Rowland132Alexey V Rubtsov133 Santiago Sabateacute1134 Yann Salmon119135 Roberto L Salomoacuten136137

Elisenda Saacutenchez-Costa138 Karina V R Schaumlfer139 Bernhard Schuldt140 Alexandr Shashkin141Cleacutement Stahl142 Marko Stojanovic143 Juan Carlos Suaacuterez144145 Ge Sun23 Justyna Szatniewska143

Fyodor Tatarinov127 Miroslav Tesar146 Frank M Thomas147 Pantana Tor-ngern148149150Josef Urban133151 Fernando Valladares56152 Christiaan van der Tol153 Ilja van Meerveld154Andrej Varlagin155 Holm Voigt156 Jeffrey Warren157 Christiane Werner158 Willy Werner159Gerhard Wieser160 Lisa Wingate161 Stan Wullschleger162 Koong Yi163164 Roman Zweifel165

Kathy Steppe137 Maurizio Mencuccini1166 and Jordi Martiacutenez-Vilalta12

1CREAF E08193 Bellaterra (Cerdanyola del Vallegraves) Catalonia Spain2Universitat Autogravenoma de Barcelona E08193 Bellaterra (Cerdanyola del Vallegraves) Catalonia Spain

3Joint Research Unit CREAF-CTFC Bellaterra Catalonia Spain4Faculty of Science Engineering and Technology Swinburne University of Technology

Hawthorn Vic 3122 Australia5School of Life and Environmental Sciences University of Sydney Camperdown NSW Australia

Published by Copernicus Publications

2608 R Poyatos et al Global transpiration data from sap flow measurements the SAPFLUXNET database

6Department of Botany University of Debrecen Faculty of Science and TechnologyEgyetem teacuter 1 4032 Debrecen Hungary

7Plant Physiology and Biochemistry Institute of Botany Satildeo Paulo Brazil8Department of Natural Resources and Environmental Science University of Nevada Reno NV USA

9Red Ecologiacutea Funcional Instituto de Ecologiacutea AC Xalapa Mexico10Center for Tropical Forest Science-Forest Global Earth Observatory Smithsonian Tropical Research Institute

Panama Republic of Panama11Conservation Ecology Center Smithsonian Conservation Biology Institute Front Royal VA USA

12Department of Ecosystem Science and Management Texas AampM University College Station TX USA13School of Earth and Space Exploration Arizona State University Tempe AZ USA

14School of Earth Environment amp Society and McMaster Centre for Climate ChangeMcMaster University Hamilton Ontario Canada

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16Department of Natural Resources and the Environment University of New Hampshire Durham NH USA17Department of Biosciences University of Durham Durham UK

18School of Geography and Earth Sciences and McMaster Centre for Climate ChangeMcMaster University Hamilton Ontario Canada

19School of Informatics Computing amp Cyber Systems Northern Arizona University Flagstaff AZ USA20Schmid College of Science and Technology Chapman University Orange CA 92866 USA

21Universiteacute Paris-Saclay CNRS AgroParisTech Ecologie Systeacutematique et Evolution 91405 Orsay France22University of Illinois at Urbana-Champaign Urbana-Champaign IL USA

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Earth Syst Sci Data 13 2607ndash2649 2021 httpsdoiorg105194essd-13-2607-2021

R Poyatos et al Global transpiration data from sap flow measurements the SAPFLUXNET database 2609

44Czech Technical University in Prague Faculty of Civil EngineeringThakurova 7 16629 Prague Czech Republic

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47Department of Horticultural Science University of Stellenbosch Stellenbosch South Africa48University of Alaska Fairbanks Institute of Arctic Biology Fairbanks AK 99775 USA

49Faculty of Regional and Environmental Sciences ndash Geobotany University of TrierBehringstraszlige 21 54296 Trier Germany

50Max Planck Institute for Biogeochemistry Hans-Knoumlll-Str 10 Jena Germany51Wageningen University and Research Water Systems and Global Change Group

PO Box 47 6700AA Wageningen the Netherlands52Department of Plant Biology University of Campinas Campinas 13083-862 Brazil

53Department of Botany University of Wyoming Laramie WY USA54Swiss Federal Institute for Forest Snow and Landscape Research WSL

Zuercherstrasse 111 8903 Birmensdorf Switzerland55Departamento de Ecologiacutea Vegetal Centro de Investigaciones sobre Desertificacioacuten (CSIC-UVEG-GV)

Carretera Moncada ndash Naquera km 45 Moncada 46113 Valencia Spain56Laboratorio Internacional de Cambio Global (LINCGlobal) Departamento de Biogeografiacutea y Cambio

Global Museo Nacional de Ciencias Naturales MNCN CSIC CSerrano 115 dpdo 28006 Madrid Spain57Satildeo Paulo State University (Unesp) School of Sciences Bauru Brazil

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Edifici Fruitcentre 25003 Lleida Spain60AgResearch Lincoln Research Centre Private bag 4749 Christchurch 8140 New Zealand

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65Hawkesbury Institute for the Environment Western Sydney University Sydney NSW Australia66School of Ecosystem and Forest Sciences The University of Melbourne 500 Yarra Boulevard Richmond

Vic 3121 Australia67Science amp Collections Division Royal Horticultural Society Wisley Woking Surrey GU23 6QB UK68Environmental Sciences Division Oak Ridge National Laboratory Oak Ridge Tennessee 37831 USA

69Department of Forest Ecology and Management Swedish University of Agricultural SciencesUmearing Sweden

70Section Climate Dynamics and Landscape Evolution Helmholtz Centre Potsdam GFZ German ResearchCentre for Geosciences 14473 Potsdam Germany

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University of Helsinki Helsinki Finland74Centro de Ciencias de la Atmoacutesfera Universidad Nacional Autoacutenoma de Meacutexico Mexico City Mexico

75Department of Horticulture Faculty of Agriculture Khon Kaen University Khon Kaen Thailand76Leibniz Centre for Agricultural Landscape Research (ZALF) Eberswalder Str 84

15374 Muumlncheberg Germany77Brazilian Platform of Biodiversity and Ecosystem ServicesBPBES Campinas Brazil

78Departamento de Biologia Vegetal Instituto de Biologia Universidade Estadual de CampinasCampinas Satildeo Paulo Brazil

79Head Office of Forest Protection Brandenburg State Forestry Center of Excellence16225 Eberswalde Germany

80School of Biological Sciences University of Auckland Auckland New Zealand81Department of Forest Sciences Seoul National University Seoul Republic of Korea

82National Center for Agro Meteorology Seoul Republic of Korea83Research Institute for Agriculture and Life Sciences Seoul National University Seoul Republic of Korea

httpsdoiorg105194essd-13-2607-2021 Earth Syst Sci Data 13 2607ndash2649 2021

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90Department of Geography and Planning University at Albany Albany NY USA91Department of Animal Biology Vegetal Biology and Ecology University of Jaeacuten Jaeacuten Spain

92Plant Ecology University of Goettingen 37073 Goumlttingen Germany93CEFE Univ Montpellier CNRS EPHE IRD Univ Paul Valeacutery Montpellier 3 Montpellier France

94Department of Physical Chemical and Natural Systems University Pablo de Olavide 41013 Seville Spain95Surface Hydrology and Erosion group Institute of Environmental Assessment and Water Research CSIC

Barcelona Spain96Departamento de Agronomiacutea Universidad de Coacuterdoba 14071 Coacuterdoba Spain

97Department of Geography Colgate University Hamilton NY USA98AMAP Univ Montpellier CIRAD CNRS INRAE IRD 34000 Montpellier France

99University of Florida School of Forest Resources and Conservation 136 Newins-Ziegler Hall GainesvilleFL 32611 USA

100Department of Geological Sciences Jackson School of Geosciences University of Texas at Austin AustinTX USA

101Pacific Northwest National Laboratory Richland WA USA102Center for Tropical Forest Science-Forest Global Earth Observatory Smithsonian Environmental Research

Center Edgewater MD 21307 USA103Research School of Biology Australian National University ACT 2601 Australia

104CSIRO Agriculture and Food Sandy Bay Tas 7005 Australia105Dept of Physical Geography and Ecosystem Science University of Lund Lund Sweden

106Faculty of Science and Technology Free University of Bolzano Piazza Universitagrave 5 Bolzano Italy107Forest Services Autonomous Province of Bolzano Bolzano Italy

108Department of Ecology and Conservation Biology Texas AampM University College Station TX USA109Hokkaido Regional Breeding Office Forest Tree Breeding Center Forestry and Forest Products Research

Institute Ebetsu Hokkaido Japan110School of Natural Resources and the Environment University of Arizona Tucson AZ 85721 USA

111Tropical Silviculture and Forest Ecology University of GoettingenBuumlsgenweg 1 37077 Goumlttingen Germany

112Department of Ecology amp Evolutionary Biology University of Tennessee Knoxville TN USA113OrsquoNeill School of Public and Environmental Affairs Indiana University-Bloomington

Bloomington IN USA114University of Innsbruck Department of Botany Sternwartestrasse 15 6020 Innsbruck Austria

115EURAC Research Institute for Alpine Environment Viale Druso 1 Bolzano Italy116USDA Forest Service Southern Research Station Coweeta Hydrologic Laboratory Otto NC USA

117Department of Forest Sciences University of Helsinki PO Box 27 00014 Helsinki Finland118Division of Environmental Science amp Policy Nicholas School of the Environment and Department of Civil

amp Environmental Engineering Pratt School of Engineering Duke University Durham NC USA119Institute for Atmospheric and Earth System Research (INAR)Forest University of Helsinki 00014

Helsinki Finland120Biological sciences department Macquarie University Sydney NSW Australia

121National Institute of Agricultural Technology (INTA) CC 332 CP 9400Riacuteo Gallegos Santa Cruz Argentina

122National Scientific and Technical Research Council of Argentina (CONICET) Riacuteo Gallegos Santa CruzArgentina

123National University of Southern Patagonia (UNPA) Riacuteo Gallegos Santa Cruz Argentina124Laboratory of Plant Ecology Faculty of Bioscience Engineering Ghent University

Coupure links 653 9000 Ghent Belgium

125Urban Studies School of Social Sciences Western Sydney UniversityLocked Bag 1797 Penrith NSW 2751 Australia

126Department of Biology University of New Mexico Albuquerque NM USA127The Earth and Planetary Science Department Weizmann Institute of Science Rehovot Israel

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130Laboratorio de Clima e Biosfera Instituto de Astronomia Geofisica e Ciencias AtmosfericasUniversidade de Sao Paulo Satildeo Paulo Brazil

131Department of Ecology IBRAG Universidade do Estado do Rio de Janeiro (UERJ)R Satildeo FranciscoXavier 524 PHLC Sala 220 CEP 20550900 Maracanatilde Rio de Janeiro RJ Brazil

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Barcelona Spain139Earth and Environmental Science Department Rutgers University Newark

195 University Av Newark NJ 07102 USA140University of Wuumlrzburg Julius-von-Sachs-Institute for Biological Sciences Chair of Ecophysiology and

Vegetation Ecology Julius-von-Sachs-Platz 3 97082 Wuumlrzburg Germany141Sukachev Institute of Forest of the Siberian Branch of the RAS Krasnoyarsk Russian Federation

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143Global Change Research Institute of the Czech Academy of SciencesBelidla 4a 60300 Brno Czech Republic

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Florencia Caquetaacute Colombia145Universidad de la Amazonia Programa de Ingenieriacutea Agroecoloacutegica Facultad de Ingenieriacutea Florencia

Caquetaacute Colombia146Institute of Hydrodynamics Czech Academy of Sciences Prague Czech Republic

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151Department of Forest Botany Dendrology and Geobiocenology Faculty of Forestry and Wood TechnologyMendel University in Brno Zemedelska 3 61300 Brno Czech Republic

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153University of Twente Faculty ITC PO Box 217 7500 AE Enschede the Netherlands154Department of Geography Hydrology and Climate University of Zurich

Winterthurerstrasse 190 8057 Zurich Switzerland155AN Severtsov Institute of Ecology and Evolution Russian Academy of Sciences 119071 Leninsky pr33

Moscow Russia

156ZEF Center for Development Research University of Bonn Genscherallee 3 53113 Bonn Germany157Environmental Sciences Division Oak Ridge National Laboratory Oak Ridge TN 37831 USA

158Ecosystem Physiology University of Freiburg 79098 Freiburg Germany159Geobotany Department University of Trier 54286 Trier Germany

160Division of Alpine Timberline Ecophysiology Federal Research and Training Centre for Forests NaturalHazards and Landscape (BFW) Rennerg 1 6020 Innsbruck Austria

161INRAE UMR ISPA 1391 33140 Villenave DrsquoOrnon France162Environmental Sciences Division Oak Ridge National Laboratory Oak Ridge TN 37831 USA163Department of Environmental Sciences University of Virginia Charlottesville VA 22904 USA

164OrsquoNeill School of Public and Environmental Affairs Indiana University BloomingtonBloomington IN 47405 USA

165Swiss Federal Institute for Forest Snow and Landscape Research WSL Birmensdorf Switzerland166ICREA Barcelona Catalonia Spain

previously published under the name Rebekka Boegeleindeceased

Correspondence Rafael Poyatos (rpoyatoscreafuabcat)

Received 5 August 2020 ndash Discussion started 9 October 2020Revised 29 April 2021 ndash Accepted 10 May 2021 ndash Published 14 June 2021

Abstract Plant transpiration links physiological responses of vegetation to water supply and demand with hy-drological energy and carbon budgets at the landndashatmosphere interface However despite being the main landevaporative flux at the global scale transpiration and its response to environmental drivers are currently notwell constrained by observations Here we introduce the first global compilation of whole-plant transpirationdata from sap flow measurements (SAPFLUXNET httpssapfluxnetcreafcat last access 8 June 2021) Weharmonized and quality-controlled individual datasets supplied by contributors worldwide in a semi-automaticdata workflow implemented in the R programming language Datasets include sub-daily time series of sap flowand hydrometeorological drivers for one or more growing seasons as well as metadata on the stand charac-teristics plant attributes and technical details of the measurements SAPFLUXNET contains 202 globally dis-tributed datasets with sap flow time series for 2714 plants mostly trees of 174 species SAPFLUXNET hasa broad bioclimatic coverage with woodlandshrubland and temperate forest biomes especially well repre-sented (80 of the datasets) The measurements cover a wide variety of stand structural characteristics andplant sizes The datasets encompass the period between 1995 and 2018 with 50 of the datasets being at least3 years long Accompanying radiation and vapour pressure deficit data are available for most of the datasetswhile on-site soil water content is available for 56 of the datasets Many datasets contain data for speciesthat make up 90 or more of the total stand basal area allowing the estimation of stand transpiration in di-verse ecological settings SAPFLUXNET adds to existing plant trait datasets ecosystem flux networks andremote sensing products to help increase our understanding of plant water use plant responses to droughtand ecohydrological processes SAPFLUXNET version 015 is freely available from the Zenodo repository(httpsdoiorg105281zenodo3971689 Poyatos et al 2020a) The ldquosapfluxnetrrdquo R package ndash designed toaccess visualize and process SAPFLUXNET data ndash is available from CRAN

1 Introduction

Terrestrial vegetation transpires ca 45 000 km3 of water peryear (Schlesinger and Jasechko 2014 Wang-Erlandsson etal 2014 Wei et al 2017) a flux that represents 40 ofglobal land precipitation and 70 of total land evapotran-spiration (Oki and Kanae 2006) and is comparable in mag-nitude to global annual river discharge (Rodell et al 2015)For most terrestrial plants transpiration is an inevitable wa-ter loss to the atmosphere because they need to open stom-

ata to allow CO2 diffusion into the leaves for photosyn-thesis Latent heat from transpiration represents 30 ndash40 of surface net radiation globally (Schlesinger and Jasechko2014 Wild et al 2015) Transpiration is therefore a keyprocess coupling landndashatmosphere exchange of water car-bon and energy determining several vegetationndashatmospherefeedbacks such as land evaporative cooling or moisture re-cycling Regulation of transpiration in response to fluctuat-ing water availability andor evaporative demand is a keycomponent of plant functioning and one of the main deter-

minants of a plantrsquos response to drought (Martin-StPaul etal 2017 Whitehead 1998) Despite its relevance for earthfunctioning transpiration and its spatiotemporal dynamicsare poorly constrained by available observations (Schlesingerand Jasechko 2014) and not well represented in models(Fatichi et al 2016 Mencuccini et al 2019) An improvedunderstanding of transpiration and its regulation along envi-ronmental gradients and across species is thus needed to pre-dict future trajectories of land evaporative fluxes and vege-tation functioning under increased drought conditions drivenby global change

Conceptually transpiration can be quantified at differentorganizational scales leaves branches and whole plantsecosystems and watersheds In practice transpiration is rel-atively easy to isolate from the bulk evaporative flux evapo-transpiration when measuring in a dry canopy at the leaf orthe plant level However in terrestrial ecosystems evapotran-spiration includes evaporation from the soil and from water-covered surfaces including plants Transpiration measure-ments on individual leaves or branches with gas exchangesystems are difficult to upscale to the plant level (Jarvis1995) Likewise transpiration measurements using whole-plant chambers (eg Peacuterez-Priego et al 2010) or gravimet-ric methods (eg weighing lysimeters) in the field are stillchallenging At the ecosystem scale and beyond evapotran-spiration is generally determined using micrometeorologicalmethods catchment water budgets or remote sensing ap-proaches (Shuttleworth 2007 Wang and Dickinson 2012)In some cases isotopic methods and different algorithms ap-plied to measured ecosystem fluxes can provide an estima-tion of transpiration at the ecosystem scale (Kool et al 2014Stoy et al 2019)

Transpiration drives water transport from roots to leavesin the form of sap flow through the plantrsquos xylem pathway(Tyree and Zimmermann 2002) and this sap flow affectsheat transport in the xylem Taking advantage of this ther-mometric sap flow methods were first developed in the 1930s(Huber 1932) and further refined over the following decades(Cermaacutek et al 1973 Marshall 1958) to provide operationalmeasurements of plant water use These methods have be-come widely used in plant ecophysiology agronomy andhydrology (Poyatos et al 2016) especially after the devel-opment of simple easily replicable methods (eg Granier1985 1987) Whole-plant measurements of water use ob-tained with thermometric sap flow methods provide estimatesof water flow through plants from sub-daily to interannualtimescales and have been mostly applied in woody plants al-though several studies have measured sap flow in herbaceousspecies (Baker and Van Bavel 1987 Skelton et al 2013) andnon-woody stems (eg Lu et al 2002) Xylem sap flow canbe upscaled to the whole plant obtaining a near-continuousquantification of plant water use keeping in mind that stemsap flow typically lags behind canopy transpiration (Schulzeet al 1985) Multiple sap flow sensors can be deployed inalmost any terrestrial ecosystem to determine the magnitude

and temporal dynamics of transpiration across species en-vironmental conditions or experimental treatments All sapflow methods are subject to methodological and scaling is-sues which may affect the quantification of absolute wateruse in some circumstances (Cermaacutek et al 2004 Koumlstner etal 1998 Smith and Allen 1996 Vandegehuchte and Steppe2013) Nevertheless all methods are suitable for the assess-ment of the temporal dynamics of transpiration and of its re-sponses to environmental changes or to experimental treat-ments (Flo et al 2019)

The generalized application of sap flow methods in eco-logical and hydrological research in the last 30 years hasthus generated a large volume of data with an enormouspotential to advance our understanding of the spatiotem-poral patterns and the ecological drivers of plant transpi-ration and its regulation (Poyatos et al 2016) Howeverthese data need to be compiled and harmonized to enableglobal syntheses and comparative studies across species andregions Across-species data syntheses using sap flow datahave mostly focused on maximum values extracted frompublications (Kallarackal et al 2013 Manzoni et al 2013Wullschleger et al 1998) Multi-site syntheses have focusedon the environmental sensitivity of sap flow using site meansof plant-level sap flow or sap-flow-derived stand transpira-tion (Poyatos et al 2007 Tor-ngern et al 2017) Becausedata sharing is only incipient in plant ecophysiology sap flowdatasets have not been traditionally available in open datarepositories Open data practices are now being implementedin databases which fosters collaboration across monitoringnetworks in research areas relevant to plant functional ecol-ogy (Falster et al 2015 Gallagher et al 2020 Kattge et al2020) and ecosystem ecology (Bond-Lamberty and Thom-son 2010) The success of the data sharing and data re-usepolicies within the FLUXNET global network of ecosystem-level fluxes has shown how these practices can contribute toscientific progress (Bond-Lamberty 2018)

Here we introduce SAPFLUXNET the first globaldatabase of sap flow measurements built from individualcommunity-contributed datasets We implemented this com-pilation in a data structure designed to accommodate time se-ries of sap flow and the main hydrometeorological drivers oftranspiration together with metadata documenting differentaspects of each dataset We harmonized all datasets and per-formed basic semi-automated quality assurance and qualitycontrol (QC) procedures We also created a software pack-age that provides access to the database allows easy visu-alization of the datasets and performs basic temporal ag-gregations We present the ecological and geographic cover-age of SAPFLUXNET version 015 (Poyatos et al 2020a)followed by a discussion of potential applications of thedatabase its limitations and a perspective of future devel-opments

2 The SAPFLUXNET data workflow

21 An overview of sap flow measurements

The main characteristics of sap flow methods have been re-viewed elsewhere (Cermaacutek et al 2004 Smith and Allen1996 Swanson 1994 Vandegehuchte and Steppe 2013)Given the already broad scope of the paper here we onlyprovide a brief methodological overview without delvinginto the details of the individual methods Sap flow sensorstrack the fate of heat applied to the plantrsquos conducting tis-sue or sapwood using temperature sensors (thermocouplesor thermistors) usually deployed in the plantrsquos main stemBoth heating and temperature sensing can be done either in-ternally by inserting needle-like probes containing electri-cal resistors (or electrodes for some methods) and tempera-ture sensors into the sapwood (Vandegehuchte and Steppe2013) or externally these latter systems are especially de-signed for small stems and non-lignified tissues (Clearwateret al 2009 Helfter et al 2007 Sakuratani 1981) Depend-ing on how the heat is applied and the principles underly-ing sap flow calculations sap flow sensors can be classifiedinto three major groups heat dissipation methods heat pulsemethods and heat balance methods (Flo et al 2019) Heatdissipation and heat pulse methods estimate sap flow per unitsapwood area and they have been called ldquosap flux densitymethodsrdquo (Vandegehuchte and Steppe 2013) heat balancemethods directly yield sap flow for the entire stem or for asapwood section Heat dissipation methods include the con-stant heat dissipation (HD Granier 1985 1987) the tran-sient (or cyclic) heat dissipation (CHD Do and Rocheteau2002) and the heat deformation (HFD Nadezhdina 2018)methods Heat pulse methods include the compensation heatpulse (CHP Swanson and Whitfield 1981) heat ratio (HRBurgess et al 2001) heat pulse T-max (HPTM Cohen etal 1981) and sapflow+ (Vandegehuchte and Steppe 2012)methods Heat balance methods include the trunk sector heatbalance (TSHB Cermaacutek et al 1973) and the stem heat bal-ance (SHB Sakuratani 1981) methods The suitability ofa certain method in a given application largely depends onplant size and the flow range of interest (Flo et al 2019) butheat dissipation and compensation heat pulse are the mostwidely used (Flo et al 2019 Poyatos et al 2016) Apartfrom these different methodologies within each sap flowmethod sensor design (Davis et al 2012) and data process-ing (Peters et al 2018) can vary resulting in relatively highlevels of methodological variability comparable to those inother areas of plant ecophysiology

The output from sap flow sensors is automaticallyrecorded by data loggers at hourly or even higher temporalresolution This output relates to heat transport in the stemand needs to be converted to meaningful quantities of wa-ter transport such as sap flow per plant or per unit sapwoodarea How this conversion is achieved varies greatly acrossmethods with some relying on empirical calibrations and

others being more physically based and requiring the es-timation of wood thermal properties and other parameters(Cermaacutek et al 2004 Smith and Allen 1996 Vandegehuchteand Steppe 2013) Depending on the method and the spe-cific sensor design sap flow measurements can be represen-tative of single points linear segments along the sapwoodsapwood area sections or entire stems Except for stem heatbalance methods which typically measure entire stems orlarge sapwood sections most sap flow measurements need tobe spatially integrated to account for radial (Berdanier et al2016 Cohen et al 2008 Nadezhdina et al 2002 Phillipset al 1996) and azimuthal (Cohen et al 2008 Lu et al2000 Oren et al 1999a) variation of sap flow within thestem to obtain an estimate of whole-plant water use (Cermaacuteket al 2004) At a minimum an estimate of sapwood areais needed to upscale the measurements to whole-plant sapflow rates Sap flow rates can thus be expressed per individ-ual (ie plant or tree) per unit sapwood area (normalizing bywater-conducting area) and per unit leaf area (normalizingby transpiring area)

Here we will use the term ldquosap flowrdquo when referring ingeneral to the rate at which water moves through the sap-wood of a plant and more specifically when we refer to sapflow per plant (ie water volume per unit time Edwards etal 1996) We acknowledge that the term ldquosap fluxrdquo has alsobeen proposed for this quantity (Lemeur et al 2009) butmore generally ldquosap flux densityrdquo (eg Vandegehuchte andSteppe 2013) or just ldquosap fluxrdquo are used to refer to ldquosapflow per unit sapwood areardquo Since here we include meth-ods natively measuring sap flow per plant or per sapwoodarea throughout this paper we will use the more general termldquosap flowrdquo and when necessary we will indicate explicitlythe reference area used ldquosap flow per (unit) sapwood areardquoldquosap flow per (unit) leaf areardquo or ldquosap flow per (unit) groundareardquo

22 Data compilation

SAPFLUXNET was conceived as a compilation of publishedand unpublished sap flow datasets (Appendix Table A1)and thus the ultimate success of the initiative critically de-pended on the contribution of datasets by the sap flow com-munity An expression of interest showed that a critical massof datasets with a wide geographic distribution could poten-tially be contributed and the results of this survey were usedto raise the interest of the sap flow community (Poyatos etal 2016) The data contribution stage was open betweenJuly 2016 and December 2017 although a few additionaldatasets were updated during the data quality control processand contain more recent data

All contributed datasets had to meet some minimum cri-teria before they were accepted in terms of both contentand format We required that all datasets contained sub-dailyprocessed sap flow data representative of whole-plant wa-ter use under different hydrometeorological conditions This

meant that both the processing from raw temperature data tosap flow quantities and the scaling from single-point mea-surements to whole-plant data had been performed by thedata contributor responsible for each dataset Time series ofsap flow data and hydrometeorological drivers were requiredto be representative of one growing season setting as a broadreference a minimum duration of 3 months Sap flow couldbe expressed as total flow rate either per plant or per unitsapwood area Contributors also needed to provide metadataon relevant ecological information of the site stand speciesand measured plants as well as on basic technical details ofthe sap flow and hydrometeorological time series Datasetshad to be formatted using a documented spreadsheet tem-plate (cf ldquosapfluxnet_metadata_templatexlsxrdquo in the Sup-plement) and uploaded to a dedicated server at CREAFSpain using an online form

23 Data harmonization and quality control QC1

Once datasets were received they were stored and entereda process of data harmonization and quality control (Fig 1Supplement Fig S1) This process combined automatic datachecks with human supervision and the entire workflowwas governed by functions and scripts in the R language(R Core Team 2019) including other related tools suchas R markdown documents and Shiny applications All Rcode involved in this QC process was implemented in thesapfluxnetQC1 package (Granda et al 2016) see the pack-age vignettes for a detailed description (httpsgithubcomsapfluxnetsapfluxnetQC1treemastervignettes last access8 June 2021) To aid in the detection of potential data issuesthroughout the entire process (Figs 1 S1) we implementedseveral elements of control (1) automatic log files trackingthe output of each QC function applied (2) automatic cre-ation and update of status files tracking the QC level reachedby each dataset (3) automatic QC summary reports in theform of R markdown documents (4) interactive Shiny appli-cations for data visualization (5) documentation of manualchanges applied to the datasets using manually edited textfiles (6) storage of manual data cleaning operations in textfiles and (7) automatic data quality flagging associated witheach dataset All these items ensure a robust transparent re-producible and scalable data workflow Example files for (2)(3) and (6) can be found in the Supplement

The first stage of the data QC (QC1) performed severaldata checks (Supplement Table S1) on received spreadsheetfiles and produced an interactive report in an R markdowndocument which signalled possible inconsistencies in thedata and warned of potential errors These data issues wereaddressed with the help of data contributors if needed Onceno errors remained the dataset was converted into an objectof the custom-designed ldquosfn_datardquo class (Fig S2 see alsoSect 25) which contained all data and metadata for a givendataset (Tables A2ndashA6 list all variable names and units) Dataand metadata belonging to all Level 1 datasets were further

Figure 1 Overview of the SAPFLUXNET data workflow Datafiles are received from data contributors and undergo severalquality-control processes (QC1 and QC2) Both QC1 and QC2 pro-duce an RData object of the custom-designed sfn_data S4 classstoring all data metadata and data flags for each dataset Theprogress and results of the QC processes are monitored through in-dividual reports and log files The final outcome is stored in a folderstructure with a either single RData file for each dataset or a set ofseven csv files for each dataset

visually inspected using an interactive R Shiny applicationand if no major issues were detected they were subjected tothe second QC process QC2

Datasets entering QC2 underwent several data cleaning anddata harmonization processes (Table S2) We first ran out-lier detection and out-of-range checks these checks did not

delete or modify the data but only warned about any suspi-cious observation (ldquooutlierrdquo and ldquorangerdquo warnings) The out-lier detection algorithm was based on a Hampel filter whichalso estimates a replacement value for a candidate outlier(Hampel 1974) For the range checks we defined minimumand maximum allowed values for all the time series variablesbased on published values of extreme weather records andmaximum transpiration rates (Cerveny et al 2007 Manzoniet al 2013) The outcome of outlier and range checks werevisually inspected on the actual time series being evaluatedusing an interactive R Shiny application (Fig S3) Followingexpert knowledge visually confirmed outliers were replacedby the values estimated by the Hampel filter Similarly we re-placed out-of-range values with ldquoNArdquo if the variable was outof its physically allowed range (Fig S3) Outlier and out-of-range ldquowarningsrdquo for each observation (eg for each variableand times step) were documented in two data flags tableswith the same dimensions as the corresponding data tables(Fig S2) Likewise those observations with confirmed prob-lematic values which were removed or replaced were alsoflagged further information can be found in the ldquodata flagsrdquovignettes in the ldquosapfluxnetrrdquo package (Granda et al 2019)

Final data harmonization processes in QC2 involved unittransformations and the calculation of derived variables (Ta-ble S2) When plant sapwood area was provided by data con-tributors we interconverted between sap flow rate per plantand per unit sapwood area If leaf area was supplied we alsocalculated sap flow per unit leaf area but note that this trans-formation does not take into account the seasonal variationin leaf area we document in the metadata for which datasetsthis information could be available from data contributorsIn QC2 we estimated missing environmental variables whichcould be derived from related variables in the dataset (Ap-pendix Table A6) We also estimated the apparent solar timeand extraterrestrial global radiation from the provided times-tamp and geographic coordinates using the R package ldquoso-laRrdquo (Perpintildeaacuten 2012) All estimated or interconverted obser-vations were flagged as ldquoCALCULATEDrdquo in the ldquoenv_flagsrdquoor ldquosap_flagsrdquo table (Fig S2)

25 Data structure

One of the major benefits of the SAPFLUXNET data work-flow is the encapsulation of datasets in self-contained R ob-jects of the S4 class with a predefined structure These ob-jects belong to the custom-designed sfn_data class whichdisplays different slots to store time series of sap flowand environmental data their associated data flags and allthe metadata (Fig S2) For further information please seethe ldquosfn_data classesrdquo vignette in the sapfluxnetr package(Granda et al 2019) The code identifying each dataset wascreated by the combination of a ldquocountryrdquo code a ldquositerdquocode and if applicable a ldquostandrdquo code and a ldquotreatmentrdquocode This means that several stands andor treatments canbe present within one site (Table S3)

At the end of the QC process we generated a folder struc-ture with a first-level storing datasets as either sfn_data ob-jects or as a set of comma-separated (csv) text files Withineach of these formats a second-level folder groups datasetsaccording to how sap flow is normalized (per plant sapwoodor leaf area) note that the same dataset expressing differentsap flow quantities can be present in more than one folder(eg ldquoplantrdquo and ldquosapwoodrdquo) Finally the third level containsthe data files for each dataset either a single sfn_data ob-ject storing all data and metadata or all the individual csvfiles More details on the data structure and units can befound in the ldquosapfluxnetr-quick-guiderdquo and ldquometadata-and-data-unitsrdquo vignettes respectively in the sapfluxnetr package(Granda et al 2019)

3 The SAPFLUXNET database

31 Data coverage

The SAPFLUXNET version 015 database harbours 202globally distributed datasets (Figs 2a and S4 Table S3)from 121 geographical locations with Europe the east-ern USA and Australia especially well represented Thesedatasets were represented in the bioclimatic space using theterrestrial biomes delimited by Whittaker (Fig 2b) but notethat as any bioclimatic classification it has its limitationsDatasets have been compiled from all terrestrial biomes ex-cept for temperate rain forests although some tropical mon-tane sites have been included Woodlandshrubland and tem-perate forest biomes are the most represented in the databaseadding up to 80 of the datasets (Fig 2b) However largeforested areas in the tropics and in boreal regions are still notwell represented (Fig 2a and b) Looking at the distributionby vegetation type (Fig 2c) evergreen needleleaf forest isthe most represented vegetation type (65 datasets) followedby deciduous broadleaf forest (47 datasets) and evergreenbroadleaf forest (43 datasets)

SAPFLUXNET contains sap flow data for 2714 individualplants (1584 angiosperms and 1130 gymnosperms) belong-ing to 174 species (141 angiosperms and 33 gymnosperms)95 different genera and 45 different families (SupplementTables S4ndashS5) All species but one ndash Elaeis guineensis apalm ndash are tree species Pinus and Quercus are the most rep-resented genera (Fig 3b) Amongst the gymnosperms Pi-nus sylvestris Picea abies and Pinus taeda are the threemost represented species with data provided on 290 178and 107 trees respectively (Fig 3a) For the angiospermsAcer saccharum Fagus sylvatica and Populus tremuloidesare the most represented species with 162 116 and 104trees respectively although most Acer saccharum data comefrom a single study with a very large sample size (Fig 3a)Some species are present in more than 10 datasets Pinussylvestris Picea abies Fagus sylvatica Acer rubrum Liri-odendron tulipifera and Liquidambar styraciflua (Fig 3aTable S4)

Figure 2 (a) Geographic (b) bioclimatic and (c) vegetation type distribution of SAPFLUXNET datasets In panel (a) woodland area fromCrowther et al (2015) is shown in green In panel (b) we represent the different datasets according to their mean annual temperature andprecipitation in a Whittaker diagram showing the classification of the main terrestrial biomes In panel (c) vegetation types are definedaccording to the International Geosphere-Biosphere Programme (IGBP) classification (ENF evergreen needleleaf forest DBF deciduousbroadleaf forest EBF evergreen broadleaf forest MF mixed forest DNF deciduous needleleaf forest SAV savannas WSA woody savan-nas WET permanent wetlands)

32 Methodological aspects

For more than 90 of the plants sap flow at the whole-plantlevel is available (either directly provided by contributors orcalculated in the QC process) this is important for upscalingSAPFLUXNET data to the stand level (cf Sect 42) Be-cause the leaf area of the measured plants is often not avail-able as metadata sap flow per unit leaf area was estimatedfor only 186 of the individuals (Fig 4) The heat dissi-pation method is the most frequent method in the database(HD 664 of the plants) followed by the trunk sector heatbalance (TSHB 164 ) and the compensation heat pulsemethod (CHP 84 ) (Fig 4) This distribution is broadlysimilar to the use of each method documented in the liter-ature although the TSHB method is overrepresented herecompared to the current use of this method by the sap flow

community (Flo et al 2019 Poyatos et al 2016) Somemethods especially those belonging to the heat pulse familyand the cyclic (or transient) heat dissipation (CHD) methodare mostly used in angiosperms while the TSHB and the heatfield deformation (HFD) methods are more frequently usedin gymnosperms (Fig 4)

Calibration of sap flow sensors and scaling from pointmeasurements to the whole-plant can be critical steps to-wards accurate estimates of absolute sap flow rates InSAPFLUXNET most of the sap flow time series have not un-dergone a species-specific calibration with the CHD methodshowing the highest percentage of calibrated time series (Ta-ble 1) This lack of calibrations may be relevant for the moreempirical heat dissipation methods (HD and CHD) whichhave been shown to consistently underestimate sap flow ratesby 40 on average (Flo et al 2019 Peters et al 2018

Figure 3 Taxonomic distribution of genera and species in SAPFLUXNET showing (a) species and (b) genera with gt 50 plants in thedatabase Total bar height depicts number of plants per species (a) or genus (b) Numbers on top of each bar show the number of datasetswhere each species (a) or genus (b) is present Colours other than grey highlight datasets with 15 or more plants of a given species (a)or genus (b) Bar height for a given colour is proportional to the number of plants in the corresponding dataset which is also shown inparentheses next to the dataset code

Steppe et al 2010) Radial integration of single-point sapflow measurements is more frequent than azimuthal integra-tion (Table 2) except for the CHD method For a large num-ber of plants measured with the HD method and all plantsmeasured with the HPTM method there was not any ra-dial integration procedure reported In contrast the CHPHR SHB and TSHB methods are those which more fre-

quently addressed radial variation in one way or another (Ta-ble 2) Azimuthal integration procedures are also more fre-quent when the TSHB method is used (Table 2)

Figure 4 Distribution of plants in SAPFLUXNET according tomajor taxonomic group (angiosperms gymnosperms) sap flowmethod (CHD cycling heat dissipation CHP compensation heatpulse HD heat dissipation HFD heat field deformation HPTMheat pulse T-max (HPTM) HRM heat ratio (HR) SHB stem heatbalance TSHB trunk sector heat balance) and reference unit forthe expression of sap flow (plant sapwood area leaf area) Com-binations of reference units imply that data are present in multipleunits

Table 1 Number of sap flow times series in SAPFLUXNET de-pending on whether they were calibrated (species-specific) or non-calibrated or whether this information was not provided for thedifferent sap flow methods cyclic (or transient) heat dissipation(CHD) compensation heat pulse (CHP) heat dissipation (HD)heat field deformation (HFD) heat pulse T-max (HPTM) heat ra-tio (HR) stem heat balance (SHB) and trunk sector heat balance(TSHB) The percentage of calibrated time series was expressedwith respect to the total number of sap flow time series for eachmethod

Method Calibrated Non-calibrated Not provided calibrated

CHD 6 13 0 316CHP 29 42 157 127HD 214 1491 98 119HR 3 55 47 29TSHB 7 433 4 16HFD 0 8 0 00HPTM 0 80 0 00SHB 0 27 0 00

33 Plant characteristics

Plant-level metadata are almost complete (995 of the in-dividuals) for diameter at breast height (DBH) while sap-wood area and sapwood depth important variables for sap

flow upscaling are not available or could not be estimatedfor 23 and 47 of the plants respectively Plant heightand plant age are missing for 42 and 62 of the indi-viduals respectively Sap flow data in SAPFLUXNET arerepresentative of a broad range of plant sizes (Fig 5a)The distribution of DBH showed a median of 250 and204 cm for gymnosperms and angiosperms respectivelywith a long tail towards the largest plants two Mortonio-dendron anisophyllum trees from a tropical forest in CostaRica that measured gt 200 cm (Fig 5a) The largest gym-nosperm tree in SAPFLUXNET (176 cm in DBH) is a kauritree (Agathis australis) from New Zealand The distributionof plant heights is less skewed with similar medians for an-giosperms (176 m) and gymnosperms (175 m) The tallestplants are located in a tropical forest in Indonesia where aPouteria firma tree reached 447 m Remarkably of the 16plants taller than 40 m over 60 are Eucalyptus speciesThe tallest gymnosperm (362 m) is a Pinus strobus from thenortheastern USA

Plant size metadata in SAPFLUXNET are complementedwith plant-level data of sapwood and leaf area which pro-vide information on the functional areas for water trans-port and loss (Fig 5a) Distributions of sapwood and leafarea show highly skewed distributions with long tails to-wards the largest values and slightly higher median val-ues for gymnosperms (262 cm2 and 330 m2 for sapwoodand leaf areas respectively) compared to angiosperms(168 cm2 and 299 m2) Accordingly median sapwood depthis also higher for gymnosperms (51 cm) compared to an-giosperms (37 cm) The largest trees (MortoniodendronPouteria Agathis) with deep sapwood (17ndash24 cm) are alsothose with largest sapwood areas Many large angiospermtrees from tropical (CRI_TAM_TOW IDN_PON_STEGUF_GUY_ST2 see Table S3 for dataset codes) and tem-perate forests (Fagus grandifolia USA_SMIC_SCB) alsoshow large sapwood areas (gt 5000 cm2) but the plant withthe deepest sapwood is a gymnosperm an Abies pinsapo inSpain with 307 cm of sapwood depth

34 Stand characteristics

Stand-level metadata include several variables associatedwith management vegetation structure and soil propertiesHalf of the datasets originate from naturally regenerated un-managed stands and 139 come from naturally regener-ated but managed stands Plantations add up to 322 andorchards only represent 4 of the datasets Reporting ofstructural variables is mixed with stand height age densityand basal area showing relatively low missingness (64 114 129 and 134 respectively) in contrast soildepth and leaf area index (LAI) are missing from 267 and337 of the datasets

SAPFLUXNET datasets originate from stands with di-verse structural characteristics Median stand age is 54 yearsand there are several datasets coming from gt 100-year-

Table 2 Number of plants in the SAPFLUXNET database using different radial and azimuthal integration approaches for the different sapflow methods cyclic (or transient) heat dissipation (CHD) compensation heat pulse (CHP) heat dissipation (HD) heat field deformation(HFD) heat pulse T-max (HPTM) heat ratio (HR) stem heat balance (SHB) and trunk sector heat balance (TSHB)

Azimuthal integration

Method Measured Sensor-integrated Corrected measured azimuthal variation No azimuthal correction Not provided

CHD 15 0 0 0 4CHP 61 0 0 167 0HD 216 0 520 1021 46HFD 0 0 0 8 0HPTM 0 0 0 80 0HR 7 0 2 88 8SHB 0 0 0 27 0TSHB 0 25 191 219 9

Radial integration

Method Measured Sensor-integrated Corrected measured radial variation No radial correction Not provided

old forests (Fig 5b) Stand height shows a similar rangeand distribution of values compared to individual plantheight (Fig 5a and b) The denser stands correspond tocoppiced evergreen oak stands from Mediterranean forests(FRA_PUE ESP_TIL_OAK) species-rich tropical forests(MDG_SEM_TAL) or relatively young temperate forests(eg FRA_HES_HE1_NON USA_CHE_MAP) The spars-est stands (lt 200 stems haminus1) correspond to treendashgrass sa-vanna systems (Spain Portugal Australia Senegal) drywoodlands (China) or oil palm plantations in Indone-sia (IDN_JAM_OIL) Stands with the largest basal ar-eas (gt 70 m2 haminus1) are mostly dominated by broadleafspecies except for a Picea abies plantation in Sweden(SWE_SKO_MIN)

The distribution of LAI shows a median of35 m2 mminus2 with the largest values observed in temperate(CZE_BIK USA_DUK_HAR HUN_SIK) and tropical(GUF_GUY_GUY COL_MAC_SAF_RAD) forests Thestands with the lowest LAI correspond to the sparse wood-lands from Mediterranean and semi-arid locations and alsothose from forests near altitudinal or latitudinal treelines(FIN_PET AUT_TSC) SAPFLUXNET datasets show amedian soil depth of 100 cm with only a dozen datasetsoriginated from sites with soils deeper than 10 m (Fig 5b)

The number of plants per dataset is highly variable withmost of the datasets (86 ) containing data for at least 4 treesand 46 of the datasets having data for at least 10 trees(Fig 6a see also Fig 9)

35 Temporal characteristics

The oldest datasets in SAPFLUXNET go back to 1995(GBR_DEV_CON GBR_DEV_DRO) while the most re-cent data reach up to 2018 (datasets from the ESP_MAJcluster of sites) Several multi-year datasets are present inSAPFLUXNET (Fig 6) with 50 of the datasets spanninga period of at least 3 years and some datasets being extraordi-narily long (16 years in FRA_PUE) Frequently the datasetsonly cover the ldquogrowing seasonrdquo periods or even shorter pe-riods for some sites which were eventually included becausethey improved the ecological and geographic coverage of thedatabase (eg ARG_MAZ ARG_TRE as representative ofdeciduous Nothofagus forest in southern Patagonia) In con-trast a few datasets show continuous records over multipleyears (Fig 6b) Amongst the longest datasets most of themcome from European or North American sites (Fig 6) exceptsome datasets from Israel (ISR_YAT_YAT 7 years) Rus-sia (RUS_FYO 7 years) South Korea (KOR_TAE cluster ofsites 6 years) or New Zealand (NZL_HUA_HUA 5 years)

SAPFLUXNET provides an unprecedented database tostudy the detailed temporal dynamics of plant transpirationacross species and sites globally Sub-daily records of sapflow (eg at least at hourly time steps) are available for ex-tended periods (Fig 6b) allowing both seasonal and diel pat-terns in water-use regulation by trees to be addressed as wellas how these temporal patterns change across species or yearsacross terrestrial biomes reflecting different phenologies and

Figure 5 Characteristics of trees and stands in the SAPFLUXNET database Panel (a) shows plant data and kernel density plots of the mainplant attributes coloured by taxonomic group (angiosperms and gymnosperms) diameter at breast height (DBH) plant height sapwoodarea sapwood depth and leaf area The inset in the sapwood area panel zooms in on values lower than 5000 cm2 Panel (b) shows stand dataand kernel density plots of the main stand attributes stand age stand height stem density stand basal area leaf area index (LAI) and soildepth

water-use strategies For instance in Mediterranean forestsevergreen species such as Quercus ilex Arbutus unedo andPinus halepensis show moderate sap flow the whole yearround while the deciduous Quercus pubescens shows highersap flow density during a shorter period and its water use isheavily reduced during a dry year (2012) (Fig 7a) Temper-ate forests without water availability limitations show rela-tively high flows during the growing season and similar dielsap flow patterns amongst species (Fig 7b) In contrast trop-ical forests show moderate to high sap flow rates during theentire year with different dynamics in the intradaily water-use regulation across species For example Inga sp in ahighly diverse wet tropical forest in Costa Rica reduced sapflow during mid-day hours compared to co-existing species(Fig 7c)

36 Availability of environmental data

All SAPFLUXNET datasets contain ancillary time seriesof the main hydrometeorological drivers of transpirationaccompanied by information on where these variables hadbeen measured (Fig 8a) Air temperature is available for alldatasets Although vapour pressure deficit (VPD) was origi-nally absent in 38 of the datasets (Fig 8a and b) we couldestimate it for those sites providing air temperature and rel-ative humidity data (QC Level 2 see Sect 23) and finallyonly 2 out of the 202 datasets have missing VPD informationFor radiation variables shortwave radiation was most oftenprovided compared to photosynthetically active and net radi-ation which were less provided only 8 out of 202 datasets donot have any accompanying radiation data Most of these en-vironmental variables were measured on site with precipita-tion being the variable most frequently retrieved from nearbymeteorological stations (48 of the datasets) (Fig 8a) Soil

Figure 6 (a) Measurement duration of SAPFLUXNET datasets expressed in number of days with sap flow data and coloured by the numberof plants measured on each day The 30 longest datasets are labelled For each dataset in panel (a) panel (b) shows its correspondingmeasurement period

water content measured at shallow depth typically between 0and 30 cm below the soil surface is provided for 56 of thedatasets while soil moisture from deep soil layers is avail-able for only 27 of the datasets

37 Uncertainty estimation and bias correction in sapflow measurements

Uncertainty for the main sap flow density methods couldbe obtained by using a recent compilation of sap flow cal-ibration data (Flo et al 2019) (Table B1) these calibra-tions generally covered the range of sap flow per sapwood

Figure 7 Fingerprint plots showing hourly sap flow per unit sapwood area (colour scale) as a function of hour of day (x axis) and day ofyear (y axis) for a selection of SAPFLUXNET sites with at least four co-occurring species Panel (a) shows data from a woodlandshrublandforest in NE Spain (ESP_CAN) for an average (2011) and a dry (2012) year Panel (b) shows data for a mesic temperate forest (USA_WVF)and panel (c) shows data for a tropical forest (CRI_TAM_TOW) For the latter site only 4 of the 17 measured species are shown and someof them were only identified at the genus level

area observed in SAPFLUXNET except for the CHP method(Fig B1) At low flows uncertainties were larger for HPTMand to a lesser extent for CHP while they were lowest forHR and HFD Uncertainties increased steeply with flow par-ticularly for the HPTM CHP and HR methods These pat-terns were evident when examining sub-daily sap flow mea-sured with the most represented sap flux density methods in

SAPFLUXNET (Fig B2) The analysis of calibration dataalso showed that HD the most represented method by farunderestimates water flow on average by 40 (Flo et al2019) when using the original calibration (Granier 19851987) Because plant-level metadata contain information thatdocument the conversion from raw to processed data a first-

Figure 8 Summary of the availability of different environmental variables in SAPFLUXNET datasets (a) Distribution of meteorologicalvariables according to sensor location (in brackets names of the variables in the database) (b) Distribution of soil moisture variablesaccording to the measurement depth (in brackets names of the variables in the database) (c) Venn diagram showing the number of datasetswhere each combination of different environmental variables are present grouping shortwave photosynthetic photon flux density (PPFD)and net radiation under ldquoradiationrdquo variables

order correction for data from uncalibrated HD probes canbe applied (Fig B3a)

Additional uncertainties and corrections by sapwood areaestimation and integration of sap flow radial variability mustalso be considered when upscaling to plant-level sap flowUncertainty from sapwood area estimation is expected tobe lower than methodological uncertainty given the gener-ally tight relationship between basal area and sapwood area(Fig B3b and c) Data without an explicit radial integrationof sap flow measurements can be adjusted using generic ra-dial sap flow profiles based on wood type (Berdanier et al2016) In this case assuming uniform sap flow along the sap-wood usually leads to sap flow overestimation for both ring-porous and diffuse-porous species (Fig B4)

4 Potential applications

41 Applications in plant ecophysiology and functionalecology

There are multiple potential applications of theSAPFLUXNET database to assess whole-plant water-use rates and their environmental sensitivity both acrossspecies (eg Oren et al 1999b) and at the intraspecific level(Poyatos et al 2007) SAPFLUXNET will allow disentan-gling the roles of evaporative demand and soil water contentin controlling transpiration at the plant level complementingrecent studies looking at how water supply and demandaffect evapotranspiration at the ecosystem level (Anderegg etal 2018 Novick et al 2016) The availability of global sap

flow data at sub-daily time resolution and spanning entiregrowing seasons will allow focusing on how maximum wateruse and its environmental sensitivity vary with plant-levelattributes such as stem diameter (Dierick and Houmllscher2009 Meinzer et al 2005) tree height (Novick et al 2009Schaumlfer et al 2000) hydraulic traits (Manzoni et al 2013Poyatos et al 2007) and other plant traits (Grossiord etal 2020 Kallarackal et al 2013) SAPFLUXNET thusprovides an unprecedented tool to understand how structuraland physiological traits coordinate with each other (Liuet al 2019) how these traits translate to whole-plantregulation of water fluxes (McCulloh et al 2019) and howthis integration determines drought responses (Choat et al2018) and post-drought recovery patterns (Yin and Bauerle2017) Analyses of the temporal dynamics of plant water usein response to specific drought events as recently assessedfor gross primary productivity (eg Schwalm et al 2017)can also help to quantify drought legacy effects If combinedwith water potential measurements sap flow data can beused to estimate whole-plant hydraulic conductance andstudy its response to drought (eg Cochard et al 1996)as well as the recovery of the plant hydraulic system afterdrought

SAPFLUXNET will allow new insights into within-daypatterns and controls in whole-plant water use which candisclose the fine details of its physiological regulation Cir-cadian rhythms can modulate stomatal responses to the en-vironment potentially affecting sap flow dynamics (eg deDios et al 2015) Hysteresis in diel sap flow relationshipswith evaporative demand and time lags between transpira-tion and sap flow are two linked phenomena likely aris-ing from plant capacitance and other mechanisms (OrsquoBrienet al 2004 Schulze et al 1985) that also influence dielevapotranspiration dynamics (Matheny et al 2014 Zhanget al 2014) A major driver of time lags is the use ofstored water to meet the transpiration demand (Phillips etal 2009) which can now be analysed across species plantsizes or drought conditions using time series analyses sim-plified electric analogies (Phillips et al 1997 2004 Wardet al 2013) or detailed water transport models (Bohrer etal 2005 Mirfenderesgi et al 2016) Night-time water usecan be substantial for some species (Forster 2014 Rescode Dios et al 2019) However available syntheses rely onstudy-specific quantification of what constitutes nocturnalsap flow and do not address possible methodological influ-ences (Zeppel et al 2014) SAPFLUXNET includes meta-data to identify methods (eg HRM Burgess et al 2001) anddata processing approaches (zero-flow determination methodin ldquopl_sens_cor_zerordquo Table A5) that can help identify suit-able datasets to quantify night-time fluxes

Sap flow data have been widely employed to assesschanges in tree water use after biotic (eg Hultine et al2010) or abiotic (Oren et al 1999a) disturbances Likewisesap flow data have been used to report changes in speciesand stand water use following experimental treatments in-

volving resource availability modifications (eg Ewers etal 1999) or density changes (ie thinning Simonin et al2007) The SAPFLUXNET database includes datasets withexperimental manipulations applied either at the stand or atthe individual level qualitatively documented in the meta-data (Table 3) The main treatments present are related tothinning water availability changes (irrigation throughfallexclusion) and wildfire impact (Table 3) potentially facil-itating new data syntheses and meta-analyses using thesedatasets (eg Grossiord et al 2018)

The combination of SAPFLUXNET with other ecophysi-ological databases can be informative as to the relative sen-sitivity of different physiological processes in response todrought for example those related to growth and carbonassimilation (Steppe et al 2015) Within-day fluctuationsof stem diameter can be jointly analysed with co-locatedsap flow measurements to study the dynamics of stored wa-ter use under drought and its contribution to transpiration(eg Brinkmann et al 2016) and to infer parameters ontree hydraulic functioning using mechanistic models of treehydrodynamics (Salomoacuten et al 2017 Steppe et al 2006Zweifel et al 2007) These analyses could be carried outfor a large number of species by combining SAPFLUXNETwith data from the DENDROGLOBAL database (http789020292streessdatabasesdendroglobal last access8 June 2021) there are at least 18 SAPFLUXNET datasetswith dendrometer data in DENDROGLOBAL This databaseand the International Tree-Ring Data Bank (S Zhao et al2019) could also be used with SAPFLUXNET to investigateat the species level the link between radial growth and wateruse including their environmental sensitivity (Moraacuten-Loacutepezet al 2014) and how these two processes comparatively re-spond to drought (Saacutenchez-Costa et al 2015) Moreovergiven the tight link between water use and carbon assimi-lation combining SAPFLUXNET with water-use efficiencyfrom plant δ13C data could potentially be used to estimatewhole-plant carbon assimilation (Hu et al 2010 Klein et al2016 Rascher et al 2010 Vernay et al 2020) a quantitythat is difficult to measure directly especially in field-grownmature trees

42 Applications in ecosystem ecology andecohydrology

SAPFLUXNET will provide a global look at plant waterflows to bridge the scales between plant traits and ecosys-tem fluxes and properties (Reichstein et al 2014) Vegeta-tion structure species composition and differential water-use strategies amongst and within species scale up to dif-ferent seasonal patterns of ecosystem transpiration with astrong influence on ecosystem evapotranspiration and its par-titioning Global controls on evaporative fluxes from vegeta-tion have been mostly addressed using ecosystem (Williamset al 2012) or catchment evapotranspiration data (Peel et al2010) These studies have described global patterns in evapo-

Table 3 Number of datasets plants and species by stand-level treatment in the SAPFLUXNET database

Treatment N sites N plants N species

Nonecontrol 155 2198 170Thinning 18 332 18Irrigation 9 36 4Post-fire 6 18 4CO2 fertilization 3 28 2Drought 3 9 2Soil fertilization 2 16 2Post-mortality 1 22 5Soil fertilization and pruning 1 12 1Soil fertilization and thinning 1 12 1Pruning and thinning 1 11 1Soil fertilization pruning and thinning 1 11 1Pruning 1 9 1

transpiration driven by different plant functional types or cli-mates but they cannot be used to quantify and to explain theenormous variation in the regulation of transpiration acrossand within taxa

The SAPFLUXNET database will provide a long-demanded data source to be used in ecohydrological re-search (Asbjornsen et al 2011) Upscaling individual mea-surements to the stand level (Cermaacutek et al 2004 Granieret al 1996 Koumlstner et al 1998) is necessary to quantita-tively compare sap-flow-based transpiration with evapotran-spiration and transpiration estimates at the ecosystem scaleand beyond Even though SAPFLUXNET was designed toaccommodate sap flow data at the plant level scaling to theecosystem level is possible for many datasets For a basic up-scaling exercise using SAPFLUXNET data (Poyatos et al2020b) whole-plant sap flow can be normalized by individ-ual basal area (as DBH is usually available in the metadatacf Sect 33) averaged for a given species and then scaled tostand-level transpiration using total stand basal area and thefraction of basal area occupied by each measured species (seestand metadata Table A3) For many datasets sap flow dataare available for the species comprising most of the standbasal area (often even 100 Fig 9) but species-based up-scaling may be unfeasible in many tropical sites (Fig 9b)where size-based scaling could be applied instead (eg daCosta et al 2018) Further refinements of the upscaling pro-cedure could be achieved by using trunk diameter distribu-tions of the sap flow plots (Berry et al 2018) This infor-mation however is not readily available in SAPFLUXNETand other data sources (eg forest inventories LIDAR data)or additional simplifying assumptions (ie applying the sizedistribution of measured individuals in the dataset) would beneeded

Stand-level transpiration estimates from a large numberof SAPFLUXNET sites can contribute to improve our un-derstanding of the role of forest transpiration in the contextof stand water balance and its components at the ecosystem

(eg Tor-ngern et al 2018) and catchment levels (Oishi etal 2010 Wilson et al 2001) Importantly SAPFLUXNETcan contribute to a better understanding of the global con-trols on vegetation water use (Good et al 2017) includ-ing the biological and climatic controls on evapotranspira-tion partitioning into transpiration and evaporation compo-nents (Schlesinger and Jasechko 2014 Stoy et al 2019)There is some overlap between the FLUXNET network andSAPFLUXNET (47 datasets from FLUXNET sites) Hencetranspiration from SAPFLUXNET can also be used as aldquoground-truthrdquo reference for transpiration estimates from re-mote sensing approaches (Talsma et al 2018) and from eddycovariance data (Nelson et al 2020) Extrapolating sap-flow-derived stand transpiration to large spatial scales can be chal-lenging due to landscape-scale variation in forest structure(Ford et al 2007) or topography (Hassler et al 2018) anddue to the low spatial representativeness of sap flow mea-surements (Mackay et al 2010) A promising research av-enue to help elucidate the role of vegetation in driving hy-drological changes across environmental gradients (Vose etal 2016) would be to combine species-specific stand tran-spiration data from SAPFLUXNET with stand structural andcompositional data from forest inventories (eg sapwoodarea index Benyon et al 2015)

Understanding the patterns and mechanisms underlyingspecies interactions with respect to water use within a com-munity is necessary to predict tree species vulnerabilityto drought (Grossiord 2020) Multispecies datasets fromSAPFLUXNET (Table S3) can be used to assess competi-tion for water resources amongst species for example byidentifying changes in seasonal water use across co-existingspecies and hence characterizing the spatiotemporal segre-gation of their hydrological niches (Silvertown et al 2015)By providing a detailed seasonal quantification of tree wateruse SAPFLUXNET could also complement isotope-basedstudies and contribute to interpreting the large diversity inroot water uptake patterns observed worldwide (Barbeta and

Figure 9 Potential for upscaling species-specific plant sap flow to stand-level sap flow using SAPFLUXNET datasets Datasets are shownusing an aggregated biome classification ldquodry and tropicalrdquo include ldquosubtropical desertrdquo ldquotemperate grassland desertrdquo ldquotropical forestsavannardquo and ldquotropical rain forestrdquo Each panel shows the percentage of total stand basal area that is covered by sap flow measurements foreach species in the dataset Datasets are also coloured by the number of species present Numbers on top of each bar depict the total numberof plants for a given dataset Empty bars show datasets for which sap flow data expressed at the plant level were not available

Pentildeuelas 2017 Evaristo and McDonnell 2017) and to ex-plaining the different seasonal origin of root-absorbed wa-ter across species and environmental gradients (Allen et al2019)

Plant water fluxes and hydrodynamics are amongst themost uncertain components of ecosystem and terrestrial bio-sphere models (Fatichi et al 2016 Fisher et al 2018) Thesemodels are now incorporating hydraulic traits and processesin their transpiration regulation algorithms (Mencuccini etal 2019) but multi-site assessments of these algorithms areusually performed against evapotranspiration from eddy fluxdata (Knauer et al 2015 Matheny et al 2014) Model val-idation against sap flow data has been carried out typicallyat only one (Kennedy et al 2019 Williams et al 2001) orfew (Buckley et al 2012) sites SAPFLUXNET can thuscontribute to assessing the performance of models simulat-ing transpiration of stands or species within stands (eg DeCaacuteceres et al 2021) for a large number of species and underdiverse climatic conditions

5 Limitations and future developments

51 Limitations

Sap flow data processing differs within and amongst meth-ods because different algorithms calibrations or parametersinvolved in sap flow calculations may be applied All of thesemethods contribute to methodological uncertainty (Looker etal 2016 Peters et al 2018) and this challenging method-ological variability precludes the implementation of a com-plete standardized data workflow from raw to processed datawithin SAPFLUXNET as it is done for eddy flux data (Vitaleet al 2020 Wutzler et al 2018) Commercial software forsap flow data processing from multiple methods is available(ie httpwwwsapflowtoolcomSapFlowToolSensorshtmllast access 8 June 2021) but it has not yet been widelyadopted Freely available data-processing software is onlyavailable for the HD method (Oishi et al 2016 Speckmanet al 2020 Ward et al 2017) Open-source software alsoallows a seamless integration of different data processingapproaches and the implementation of species-specific cal-ibrations which can contribute to obtaining more robust es-timations of sap flow and facilitate replicability (Peters et al2021)

Sap flow measured with thermometric methods providesa precise estimate of the temporal dynamics of water flowthrough plants (Flo et al 2019) However their performancein measuring absolute flows is mixed While some well-represented methods in SAPFLUXNET such as CHP yieldaccurate estimates (at least for moderate-to-high flows) theHD method the most represented method by far can signifi-cantly underestimate sap flow Our suggested bias correctionfor uncalibrated HD data (cf Sect 37) can be applied butgiven the unexplained high variability (ie by species andwood traits) in the performance of sap flow calibrations (Flo

et al 2019) these corrections should be applied with cau-tion

SAPFLUXNET has been designed to store whole-plantsap flow data and therefore sap flow measured at multiplepoints within an individual is not available in the databaseEven though this spatial variation could be useful to de-scribe detailed aspects of plant water transport (Nadezhdinaet al 2009) focusing on plant-level data greatly simplifiesthe data structure Hence SAPFLUXNET only includes dataalready upscaled to the plant level by the data contributorsThe main details of how this upscaling process was done foreach dataset are provided together with other plant metadata(Table A5) but these metadata show that within-plant varia-tion in sap flow is often not considered (Table 2) For thosedatasets without radial integration of point measurements weshow how to implement a radial integration based on genericwood porosity types (cf Sect 37 Appendix B) The im-pact of not accounting for radial and circumferential variabil-ity when scaling single-point measurements of sap flow tothe whole-plant level can be important (Merlin et al 2020)but the estimation of sapwood area can also cause large er-rors if it is not accurately determined (Looker et al 2016)SAPFLUXNET does not provide information on the methodemployed to quantify sapwood area (eg visual estimationwith or without the application of dyes indirect estimationthrough allometries at species or site levels) or on the accu-racy of sapwood area data This precludes uncertainty esti-mation at the individual level (Fig B3) Future developmentsin the SAPFLUXNET data structure could include this infor-mation as metadata to better document the sensor-to-plantscaling process Overall this first global compilation of sapflow data will allow addressing uncertainties in sap flow up-scaling in space and time in the same way that the develop-ment of FLUXNET stimulated the quantification and aggre-gation of uncertainties for eddy flux data (Richardson et al2012)

While SAPFLUXNET makes global sap flow data avail-able for the first time we note that spatial coverage is stillsparse and some forested regions are underrepresented inthe database (Fig 2a) We note especially the relativelysmall number of datasets for boreal and tropical foreststwo important biomes in terms of global water and car-bon fluxes (Beer et al 2010 Schlesinger and Jasechko2014) While many geographic gaps are caused by the ab-sence of sap flow studies from such areas some regionswhere sap flow studies have been conducted are still notrepresented in SAPFLUXNET For example the recent pro-liferation of Asian sap flow studies (Peters et al 2018)has not translated into a high representativity of Asiandatasets in SAPFLUXNET yet Similarly while the cover-age of taxonomic and biometric diversity is unprecedentedSAPFLUXNET lacks data for the extremely tall trees (Am-brose et al 2010) or for other growth forms such as shrubs(Liu et al 2011) lianas (Chen et al 2015) and other non-woody species (Lu et al 2002)

52 Outlook

The public release of SAPFLUXNET has set the stage forthe first generation of sap-flow-based data syntheses Thework on these syntheses will fuel new ideas and tools forfuture improvements of the database for example new com-puting approaches for the processing and analysis of sap flowdatasets One example would be the development of robustimputation algorithms to gap-fill time series of sap flow andenvironmental data which can take advantage of tools anddatasets already developed by the ecosystem flux commu-nity (Moffat et al 2007 Vuichard and Papale 2015) Thedissemination of SAPFLUXNET will encourage the use ofmachine learning algorithms only occasionally used to anal-yse sap flow datasets so far (eg Whitley et al 2013) Theseapproaches can also be used to identify the relative impor-tance of different hydrometeorological drivers of transpira-tion (W L Zhao et al 2019) or to produce global transpi-ration maps by combining SAPFLUXNET with other data(Jung et al 2019) This upscaling of stand transpiration tolarge areas will also allow addressing broader questions atthe regional and continental scale such as the role of transpi-ration in moisture recycling (Staal et al 2018)

The eventual success of this initiative in terms of enablingdata re-use and contributing to the understanding and mod-elling of tree water use at local to global scales will likelyencourage the sap flow community to contribute new datasetsto future updates of the database We expect that the devel-opment of open-source software for the processing of rawsap flow data (Peters et al 2021 Speckman et al 2020) itseventual widespread use by the sap flow community and theadoption of standardized calibration practices will increasethe quality and intercomparability of future sap flow datasetsThese new datasets will hopefully expand the temporal geo-graphical and ecological representativity of SAPFLUXNETwhen new data contribution periods can be opened in the fu-ture

6 Data availability access and feedback

In this paper we present SAPFLUXNET version 015 (Poy-atos et al 2020a) which contains some small metadataimprovements on version 014 the first one to be madepublicly available in March 2020 Both versions supersedeversion 013 which was initially released to data contrib-utors in March 2019 The entire database can be down-loaded from its hosting web page in the Zenodo reposi-tory (httpsdoiorg105281zenodo3971689 Poyatos et al2020a) In this repository we provide the database as sep-arate csv files and as RData objects see Sect 24 fordetails on data structure Together with the initial publica-tion of SAPFLUXNET in March 2019 we also releasedthe sapfluxnetr R package available on CRAN to enableeasy access selection temporal aggregation and visualiza-tion of SAPFLUXNET data Feedback on data quality issues

can be forwarded to the SAPFLUXNET initiative email ad-dress sapfluxnetcreafuabcat All the information aboutSAPFLUXNET including the publication of new calls fordata contribution can be found on the project website httpsapfluxnetcreafcat (last access 8 June 2021)

7 Code availability

The code to reproduce the figures in this paperis available in the following Zenodo repositoryhttpsdoiorg105281zenodo4727825 (Poyatos et al2021)

8 Conclusions

The SAPFLUXNET database provides the first global per-spective of water use by individual plants at multipletimescales with important applications in multiple fieldsranging from plant ecophysiology to Earth system scienceThis database has been built from community-contributeddatasets and is complemented with a software package tofacilitate data access Both the database and the softwarehave been implemented following open science practices en-suring public access and reproducibility Data sharing hasbeen a key component of the success of the FLUXNETnetwork of ecosystem fluxes (Bond-Lamberty 2018) andmany databases in plant and ecosystem ecology now of-fer open data (Bond-Lamberty and Thomson 2010 Falsteret al 2015 Gallagher et al 2020 Kattge et al 2020)SAPFLUXNET fully aligns with this philosophy We ex-pect that this initial data infrastructure will promote datasharing amongst the sap flow community in the future (Daiet al 2018) and will allow the continued growth of theSAPFLUXNET database

Appendix A References for individual datasets inSAPFLUXNET

Table A1 SAPFLUXNET dataset codes and DOIs (digital object identifiers) of the publications associated with each dataset Where no DOIwas available the bibliographic reference is shown Some datasets may have no associated publication (ldquounpublishedrdquo)

Site code DOI

ARG_MAZ httpsdoiorg101007s00468-013-0935-4ARG_TRE httpsdoiorg101007s00468-013-0935-4AUS_BRI_BRI UnpublishedAUS_CAN_ST1_EUC httpsdoiorg101016jforeco200907036AUS_CAN_ST2_MIX httpsdoiorg101016jforeco200907036AUS_CAN_ST3_ACA httpsdoiorg101016jforeco200907036AUS_CAR_THI_00F httpsdoiorg101016jforeco201111019AUS_CAR_THI_0P0 httpsdoiorg101016jforeco201111019AUS_CAR_THI_0PF httpsdoiorg101016jforeco201111019AUS_CAR_THI_CON httpsdoiorg101016jforeco201111019AUS_CAR_THI_T00 httpsdoiorg101016jforeco201111019AUS_CAR_THI_T0F httpsdoiorg101016jforeco201111019AUS_CAR_THI_TP0 httpsdoiorg101016jforeco201111019AUS_CAR_THI_TPF httpsdoiorg101016jforeco201111019AUS_ELL_HB_HIG httpsdoiorg101016jjhydrol201502045AUS_ELL_MB_MOD httpsdoiorg101016jjhydrol201502045AUS_ELL_UNB httpsdoiorg101016jjhydrol201502045AUS_KAR UnpublishedAUS_MAR_HSD_HIG httpsdoiorg101002eco1463AUS_MAR_HSW_HIG httpsdoiorg101002eco1463AUS_MAR_MSD_MOD httpsdoiorg101002eco1463AUS_MAR_MSW_MOD httpsdoiorg101002eco1463AUS_MAR_UBD httpsdoiorg101002eco1463AUS_MAR_UBW httpsdoiorg101002eco1463AUS_RIC_EUC_ELE httpsdoiorg1011111365-243512532AUS_WOM httpsdoiorg101016jforeco201612017 httpsdoiorg1010292019JG005239AUT_PAT_FOR httpsdoiorg101007s10342-013-0760-8AUT_PAT_KRU httpsdoiorg101007s10342-013-0760-8AUT_PAT_TRE httpsdoiorg101007s10342-013-0760-8AUT_TSC httpsdoiorg1010167jflora201406012BRA_CAM httpsdoiorg101093treephystpv001BRA_CAX_CON httpsdoiorg101111gcb13851BRA_SAN httpsdoiorg101016jagrformet201202002 httpsdoiorg101007s00468-015-1165-8 https

doiorg101007s00468-017-1527-5BRA_SAN httpsdoiorg101016jagrformet201202002 httpsdoiorg101007s00468-015-1165-8 https

doiorg101007s00468-017-1527-5CAN_TUR_P39_POS httpsdoiorg101016jagrformet201004008 httpsdoiorg101002hyp9315CAN_TUR_P39_PRE httpsdoiorg101016jagrformet201004008 httpsdoiorg101002hyp9315CAN_TUR_P74 httpsdoiorg101016jagrformet201004008CHE_DAV_SEE httpsdoiorg101007s10021-011-9481-3CHE_LOT_NOR httpsdoiorg101111pce13500CHE_PFY_CON httpsdoiorg101093treephystpp123CHE_PFY_IRR httpsdoiorg101093treephystpp123CHN_ARG_GWD httpsdoiorg101016jforeco201608049CHN_ARG_GWS httpsdoiorg101016jforeco201608049CHN_HOR_AFF httpsdoiorg105194bg-2017-69CHN_YIN_ST1 httpsdoiorg101016jforeco201608049CHN_YIN_ST2_DRO httpsdoiorg101016jforeco201608049CHN_YIN_ST3_DRO httpsdoiorg101016jforeco201608049

Table A1 Continued

Site code DOI

CHN_YUN_YUN httpsdoiorg105194bg-11-5323-2014COL_MAC_SAF_RAD UnpublishedCRI_TAM_TOW httpsdoiorg101002hyp10960CZE_BIK UnpublishedCZE_BIL_BIL UnpublishedCZE_KRT_KRT UnpublishedCZE_LAN Unpublished httpsdoiorg101098rstb20190518CZE_LIZ_LES httpsdoiorg102136vzj20120154CZE_RAJ_RAJ httpsdoiorg103832ifor1307-007CZE_SOB_SOB httpsdoiorg1014214sf1760CZE_STI UnpublishedCZE_UTE_BEE UnpublishedCZE_UTE_BNA UnpublishedCZE_UTE_BPO UnpublishedCZE_UTE_SPR UnpublishedDEU_HIN_OAK Unpublished httpsdoiorg102136vzj2018060116DEU_HIN_TER Unpublished httpsdoiorg102136vzj2018060116DEU_MER_BEE_NON httpsdoiorg1044320300-4112-86-83DEU_MER_BEE_THI httpsdoiorg1044320300-4112-86-83DEU_MER_DOU_NON httpsdoiorg1044320300-4112-86-83DEU_MER_DOU_THI httpsdoiorg1044320300-4112-86-83DEU_MER_MIX_NON httpsdoiorg1044320300-4112-86-83DEU_MER_MIX_THI httpsdoiorg1044320300-4112-86-83DEU_STE_2P3 httpsdoiorg101051forest2007020 httpsdoiorg103390f11050537DEU_STE_4P5 httpsdoiorg101051forest2007020 httpsdoiorg103390f11050537ESP_ALT_ARM httpsdoiorg101007s11258-014-0351-x httpsdoiorg101093treephystpy022 httpsdoiorg10

1016jenvexpbot201808006 httpsdoiorg101016jagwat201206024ESP_ALT_HUE httpsdoiorg101007s11258-014-0351-xESP_ALT_TRI Unpublished httpsdoiorg101007s10342-013-0687-0ESP_CAN httpsdoiorg101016jagrformet201503012ESP_GUA_VAL httpsdoiorg101093jxberw121 httpsdoiorg101093treephystpw029ESP_LAH_COM httpsdoiorg101007s00271-015-0471-7ESP_LAS httpsdoiorg101007s10342-014-0779-5 httpsdoiorg101016jagrformet201411008ESP_MAJ_MAI httpsdoiorg101016jagrformet201701009ESP_MAJ_NOR_LM1 httpsdoiorg101016jagrformet201701009ESP_MON_SIE_NAT httpsdoiorg101016jagwat201206024 httpsdoiorg1010160378-1127(96)03729-2

httpsdoiorg101007s004680050229 httpsdoiorg101016jactao200401003 httpsdoiorg101007s11258-004-7007-1 httpsdoiorg101093treephys2581041 httpsdoiorg101007s00468-007-0192-5 httpsdoiorg101111pce12103

ESP_RIN httpsdoiorg101016jforeco200803004ESP_RON_PIL httpsdoiorg103390f10121132ESP_SAN_A_45I httpsdoiorg101007s11104-013-1704-2 httpsdoiorg101016jagwat201206027 httpsdoiorg

101016jagrformet201511013ESP_SAN_A2_45I httpsdoiorg101007s11104-013-1704-2 httpsdoiorg101016jagwat201206027 httpsdoiorg

101016jagrformet201511013ESP_SAN_B_100 httpsdoiorg101007s11104-013-1704-2 httpsdoiorg101016jagwat201206027 httpsdoiorg

101016jagrformet201511013ESP_SAN_B2_100 httpsdoiorg101007s11104-013-1704-2 httpsdoiorg101016jagwat201206027 httpsdoiorg

101016jagrformet201511013ESP_TIL_MIX httpsdoiorg101111nph12278ESP_TIL_OAK httpsdoiorg103390f6082505 httpsdoiorg101111nph12278ESP_TIL_PIN httpsdoiorg103390f6082505 httpsdoiorg101111nph12278ESP_VAL_BAR httpsdoiorg101093treephys274537 httpsdoiorg105194hess-9-493-2005ESP_VAL_SOR httpsdoiorg105194hess-9-493-2005 httpsdoiorg101016jagrformet200705003ESP_YUN_C1 httpsdoiorg101016jforeco201710017 httpsdoiorg103390f10121132ESP_YUN_C2 httpsdoiorg101016jforeco201710017 httpsdoiorg103390f10121132

Table A1 Continued

Site code DOI

ESP_YUN_T1_THI httpsdoiorg101016jforeco201710017 httpsdoiorg103390f10121132ESP_YUN_T3_THI httpsdoiorg101016jforeco201710017 httpsdoiorg103390f10121132FIN_HYY_SME httpsdoiorg101007978-94-007-5603-8_9FIN_PET Unpublished httpsdoiorg101016jagrformet201202009FRA_FON httpsdoiorg101111nph13771FRA_HES_HE1_NON httpsdoiorg101051forest2008052FRA_HES_HE2_NON httpsdoiorg101051forest2008052FRA_PUE httpsdoiorg101111j1365-2486200901852xGBR_ABE_PLO httpsdoiorg101111j1365-3040200701647xGBR_DEV_CON httpsdoiorg101093treephys186393GBR_DEV_DRO httpsdoiorg101093treephys186393GBR_GUI_ST1 httpsdoiorg101007s00442-006-0552-7GBR_GUI_ST2 httpsdoiorg101007s00442-006-0552-7GBR_GUI_ST3 httpsdoiorg101007s00442-006-0552-7GUF_GUY_GUY httpsdoiorg101111j1365-2486200801610xGUF_GUY_ST2 httpsdoiorg101111j1744-7429201200902xGUF_NOU_PET httpsdoiorg1011111365-243513188HUN_SIK Meacuteszaacuteros et al (2011)IDN_JAM_OIL httpsdoiorg101016jagrformet201904017 httpsdoiorg101093treephystpv013IDN_JAM_RUB httpsdoiorg101016jagrformet201904017 httpsdoiorg101002eco1882IDN_PON_STE httpsdoiorg101007s13595-011-0110-2ISR_YAT_YAT httpsdoiorg101111nph13597ITA_FEI_S17 httpsdoiorg101111nph15348ITA_KAE_S20 httpsdoiorg101111nph15348ITA_MAT_S21 httpsdoiorg101111nph15348ITA_MUN httpsdoiorg101111nph15348ITA_REN UnpublishedITA_RUN_N20 httpsdoiorg101111nph15348ITA_TOR httpsdoiorg101007s00484-012-0614-y httpsdoiorg101007s00484-008-0152-9JPN_EBE_HYB UnpublishedJPN_EBE_SUG UnpublishedKOR_TAE_TC1_LOW httpsdoiorg101007s10310-014-0463-0KOR_TAE_TC2_MED httpsdoiorg101007s10310-014-0463-0KOR_TAE_TC3_EXT httpsdoiorg101007s10310-014-0463-0MDG_SEM_TAL UnpublishedMDG_YOU_SHO httpsdoiorg101093treephystpy004MEX_COR_YP httpsdoiorg101016jagrformet201311002 httpsdoiorg101016jagrformet201208004MEX_VER_BSJ UnpublishedMEX_VER_BSM UnpublishedNLD_LOO httpsdoiorg101016jagrformet201107020NLD_SPE_DOU httpsdoiorg1017026dans-zvq-dq4wNZL_HUA_HUA Unpublished httpsdoiorg101007s00468-015-1164-9PRT_LEZ_ARN httpsdoiorg101002hyp10097PRT_MIT httpsdoiorg101093treephys276793PRT_PIN httpsdoiorg101007s10021-011-9453-7RUS_CHE_LOW httpsdoiorg101002eco2132RUS_CHE_Y4 httpsdoiorg1010022016JG003709RUS_FYO Unpublished httpsdoiorg103402tellusbv54i516679RUS_POG_VAR httpsdoiorg101016jagrformet201902038 httpsdoiorg1017660ActaHortic2018122217SEN_SOU_IRR httpsdoiorg101093treephys28195SEN_SOU_POS httpsdoiorg101093treephys28195SEN_SOU_PRE httpsdoiorg101093treephys28195SWE_NOR_ST1_AF1 httpsdoiorg101016S0168-1923(99)00092-1SWE_NOR_ST1_AF2 httpsdoiorg101016S0168-1923(99)00092-1SWE_NOR_ST1_BEF httpsdoiorg101016S0168-1923(99)00092-1SWE_NOR_ST2 httpsdoiorg101016S0168-1923(99)00092-1SWE_NOR_ST3 httpsdoiorg101016S0168-1923(99)00092-1

Table A1 Continued

Site code DOI

SWE_NOR_ST4_AFT httpsdoiorg101016jforeco200712047SWE_NOR_ST4_BEF httpsdoiorg101016jforeco200712047SWE_NOR_ST5_REF httpsdoiorg101016jforeco200712047SWE_SKO_MIN httpsdoiorg101139cjfr-2016-0541SWE_SKY_38Y UnpublishedSWE_SKY_68Y UnpublishedSWE_SVA_MIX_NON httpsdoiorg105194hess-24-2999-2020THA_KHU httpsdoiorg101093treephystpr058USA_BNZ_BLA httpsdoiorg1010022014JG002683USA_CHE_ASP httpsdoiorg1010292007WR006272 httpsdoiorg1010292009WR008125 httpsdoiorg101029

2009JG001092 httpsdoiorg101111j1365-2435200901657xUSA_CHE_MAP httpsdoiorg101111j1365-2435200901657x httpsdoiorg1010292009WR008125 httpsdoi

org1010292010JG001377USA_DUK_HAR httpsdoiorg101016jagrformet200806013USA_HIL_HF1_POS httpsdoiorg101002hyp10474USA_HIL_HF1_PRE httpsdoiorg101002hyp10474USA_HIL_HF2 httpsdoiorg101002hyp10474USA_HUY_LIN_NON httpsdoiorg1023073858565USA_INM httpsdoiorg101016S0168-1923(00)00199-4 httpsdoiorg101046j1365-2486200200492xUSA_MOR_SF httpsdoiorg101093treephystpw126USA_NWH httpsdoiorg1010022015JG003208USA_ORN_ST1_AMB httpsdoiorg101093treephystpr002 httpsdoiorg101002eco173USA_ORN_ST2_AMB httpsdoiorg101093treephystpr002 httpsdoiorg101002eco173USA_ORN_ST3_ELE httpsdoiorg101002eco173USA_ORN_ST4_ELE httpsdoiorg101002eco173USA_PAR_FER httpsdoiorg101111j1469-8137201003245x httpsdoiorg101111j1365-3040200901981x

httpsdoiorg105849forsci11-051USA_PER_PER httpsdoiorg103390f7100214USA_PJS_P04_AMB httpsdoiorg101890ES11-003691USA_PJS_P08_AMB httpsdoiorg101890ES11-003691USA_PJS_P12_AMB httpsdoiorg101890ES11-003691USA_SIL_OAK_1PR httpsdoiorg101002hyp10104 httpsdoiorg101111j1365-2486200902037x httpsdoiorg10

1093treephystpt122USA_SIL_OAK_2PR httpsdoiorg101002hyp10104 httpsdoiorg101111j1365-2486200902037x httpsdoiorg10

1093treephystpt122USA_SIL_OAK_POS httpsdoiorg101002hyp10104 httpsdoiorg101111j1365-2486200902037x httpsdoiorg10

1093treephystpt122USA_SMI_SCB httpsdoiorg1011111365-243512470USA_SMI_SER Unpublished httpsdoiorg101002ece31117USA_SWH httpsdoiorg1010022015JG003208USA_SYL_HL1 httpsdoiorg1010292005JG000083USA_SYL_HL2 httpscuratendedushowhm50tq60r1c (last access 8 June 2021)USA_TNB httpsdoiorg101016S0168-1923(00)00199-4USA_TNO httpsdoiorg101016S0168-1923(00)00199-4USA_TNP httpsdoiorg101016S0168-1923(00)00199-4USA_TNY httpsdoiorg101016S0168-1923(00)00199-4USA_UMB_CON httpsdoiorg1010022014JG002804USA_UMB_GIR httpsdoiorg1010022014JG002804USA_WIL_WC1 httpsdoiorg101016jagrformet200406008USA_WIL_WC2 UnpublishedUSA_WVF httpsdoiorg101016S0168-1923(00)00199-4 httpsdoiorg101016S0168-1923(96)02375-1UZB_YAN_DIS httpsdoiorg101016jforeco200709005ZAF_FRA_FRA httpsdoiorg101016jforeco201511009ZAF_NOO_E3_IRR httpsdoiorg101016jagrformet201902042ZAF_RAD httpsdoiorg101016jagwat201806017 httpsdoiorg1017159wsa2020v46i28236ZAF_SOU_SOU httpsdoiorg101016jagwat201806017 httpsdoiorg1017159wsa2020v46i28236ZAF_WEL_SOR httpsdoiorg101016jforeco201705009

Table A2 Description of site metadata variables in SAPFLUXNET datasets

Variable Description Type Units

si_name Site name given by contributors Character Nonesi_country Country code (ISO) Character Fixed valuessi_contact_firstname Contributor first name Character Nonesi_contact_lastname Contributor last name Character Nonesi_contact_email Contributor email Character Nonesi_contact_institution Contributor affiliation Character Nonesi_addcontr_firstname Additional contributor first name Character Nonesi_addcontr_lastname Additional contributor last name Character Nonesi_addcontr_email Additional contributor email Character Nonesi_addcontr_institution Additional contributor affiliation Character Nonesi_lat Site latitude (ie 4236) Numeric Latitude decimal format (WGS84)si_long Site longitude (ie minus823) Numeric Longitude decimal format (WGS84)si_elev Elevation above sea level Numeric Metressi_paper Paper with relevant information on the dataset as DOI

links or DOI codesCharacter DOI link

si_dist_mgmt Recent and historic disturbance and management eventsthat affected the measurement years

Character Fixed values

si_igbp Vegetation type based on IGBP classification Character Fixed valuessi_flux_network Logical indicating if site is participating in the

FLUXNET networkLogical Fixed values

si_dendro_network Logical indicating if site is participating in the DEN-DROGLOBAL network

Logical Fixed values

si_remarks Remarks and commentaries useful to grasp some site-specific peculiarities

Character None

si_code Sapfluxnet site code unique for each site Character Fixed valuesi_mat Site annual mean temperature as obtained from

CHELSANumeric Celsius degrees

si_map Site annual mean precipitation as obtained fromCHELSA

Numeric Millimetres

si_biome Biome classification based on Whittaker (1970) basedon MAT and MAP obtained from CHELSA

Character SAPFLUXNET calculated

Table A3 Description of stand metadata variables in SAPFLUXNET datasets

st_name Stand name given by contributors Character Nonest_growth_condition Growth condition with respect to stand origin and management Character Fixed valuesst_treatment Treatment applied at stand level Character Nonest_age Mean stand age at the moment of sap flow measurements Numeric Yearsst_height Canopy height Numeric Metresst_density Total stem density for stand Numeric Stems per hectarest_basal_area Total stand basal area Numeric m2 haminus1

st_lai Total maximum stand leaf area (one-sided projected) Numeric m2 mminus2

st_aspect Aspect the stand is facing (exposure) Character Fixed valuesst_terrain Slope andor relief of the stand Character Fixed valuesst_soil_depth Soil total depth Numeric Centimetresst_soil_texture Soil texture class based on simplified USDA classification Character Fixed valuesst_sand_perc Soil sand content mass Numeric percentagest_silt_perc Soil silt content mass Numeric percentagest_clay_perc Soil clay content mass Numeric percentagest_remarks Remarks and commentaries useful to grasp some stand-specific

peculiaritiesCharacter None

st_USDA_soil_texture USDA soil classification based on the percentages provided bythe contributor

Table A4 Description of species metadata variables in SAPFLUXNET datasets

sp_name Identity of each mea-sured species

Character Scientific name without author abbreviation as accepted by The Plant List

sp_ntrees Number of trees mea-sured of each species

Numeric Number of trees

sp_leaf_habit Leaf habit of the mea-sured species

sp_basal_area_perc Basal area occupied byeach measured speciesin percentage over totalstand basal area

Numeric percentage

Table A5 Description of plant metadata variables in SAPFLUXNET datasets

pl_name Plant code assigned by contributors Character Nonepl_species Species identity of the measured plant Character Scientific name without

author abbreviation asaccepted by The PlantList

pl_treatment Experimental treatment (if any) Character Nonepl_dbh Diameter at breast height of measured plants Numeric Centimetrespl_height Height of measured plants Numeric Metrespl_age Plant age at the moment of measure Numeric Yearspl_social Plant social status Character Fixed valuespl_sapw_area Cross-sectional sapwood area Numeric cm2

pl_sapw_depth Sapwood depth measured at breast height Numeric Centimetrespl_bark_thick Plant bark thickness Numeric Millimetrespl_leaf_area Leaf area of each measured plant Numeric m2

pl_sens_meth Sap flow measurement method Character Fixed valuespl_sens_man Sap flow measurement sensor manufacturer Character Fixed valuespl_sens_cor_grad Correction for natural temperature gradients method Character Fixed valuespl_sens_cor_zero Zero flow determination method Character Fixed valuespl_sens_calib Was species-specific calibration used Logical Fixed valuespl_sap_units SAPFLUXNET-harmonized units for sap flow at the sapwood

leaf and plant levelCharacter Fixed values

pl_sap_units_orig Original sap flow units provided by the contributors Character Fixed valuespl_sens_length Length of the needles or electrodes forming the sensor Numeric Millimetrespl_sens_hgt Sensor installation height measured from the ground Numeric Metrespl_sens_timestep Sub-daily time step of sensor measures Numeric Minutespl_radial_int Integration of radial variation in sap flow along sapwood depth Character Fixed valuespl_azimut_int Integration of azimuthal variation of sap flow along stem cir-

cumferenceCharacter Fixed values

pl_remarks Remarks and commentaries useful to grasp some plant-specificpeculiarities

Character None

pl_code Sapfluxnet plant code unique for each plant Character Fixed value

Table A6 Description of environmental metadata variables in SAPFLUXNET datasets

env_time_zone Time zone of site used in the timestamps Character Fixed valuesenv_time_daylight Is daylight saving time applied to the original timestamp Logical Fixed valuesenv_timestep Sub-daily times step of environmental measurements Numeric Minutesenv_ta Location of air temperature sensor Character Fixed valuesenv_rh Location of relative humidity sensor Character Fixed valuesenv_vpd Location of vapour pressure deficit measurements Character Fixed valuesenv_sw_in Location of shortwave incoming radiation sensor Character Fixed valuesenv_ppfd_in Location of incoming photosynthetic photon flux density sensor Character Fixed valuesenv_netrad Location of net radiation sensor Character Fixed valuesenv_ws Location of wind speed sensor Character Fixed valuesenv_precip Location of precipitation measurements Character Fixed valuesenv_swc_shallow_depth Average depth for shallow soil water content measures Numeric Centimetresenv_swc_deep_depth Average depth for deep soil water content measures Numeric Centimetresenv_plant_watpot Availability of water potential values for the same measured

plants during the sap flow measurements periodCharacter Fixed values

env_leafarea_seasonal Availability of seasonal course of leaf area data Character Fixed valuesenv_remarks Remarks and commentaries useful to grasp some

environmental-specific peculiaritiesCharacter None

Appendix B Uncertainty estimation in sap flowmeasurements in the SAPFLUXNET database

Here we will show examples of uncertainty estimation forsap flow data in the SAPFLUXNET database We will ad-dress three main sources of uncertainty which affect plant-level estimates of sap flow (i) methodological uncertainty(ii) sapwood area uncertainty and (iii) radial integration un-certainty

Methodological uncertainty was estimated using the datain the global meta-analysis of sap flow calibrations by Floet al (2019) as published in Flo et al (2021) This esti-mation can be applied for the main sap flow density meth-ods We predicted the standard error (SE) of sub-daily sapflow density by fitting for each method linear mixed modelsof reference flow (ie using a gravimetric method or othersemployed as reference standards in calibration studies) as afunction of measured flow including the individual calibra-tion as a random intercept factor (Table B1 Fig B1) Thismodel shows that HPTM presents the highest uncertainty andthat this method and CHP are the ones showing larger uncer-tainties at low flows while HD and CHD show lower relativeuncertainty at high sap flow density (Figs B1 B2) We alsoshow in Fig B3a the effect of applying the bias correctionfactor for uncalibrated heat dissipation probes obtained fromthe meta-analysis by Flo et al (2019)

Uncertainty in the determination of sapwood area can arisewhen allometric relationships are used to estimate sapwoodarea because this area is then applied to upscale sap flowdensity values to the whole plant This uncertainty can beaccounted for if the original data employed to obtain the al-lometry are available Using these data for one of the datasetsin SAPFLUXNET (ESP_VAL_SOR) we first predicted sap-wood area together with the upper and lower bounds of its68 predictive interval (equivalent to 1 SE) Then we esti-mated the corresponding mean sap flow and its 68 uncer-tainty interval (Fig B3a) In this case methodological uncer-tainty was larger than that caused by sapwood area estimation(Fig B3b) Total combined uncertainty (ie methodologicaland sapwood) was obtained by adding their squared valuesand then taking the square root following error propagationtheory (Fig B3c)

In this example tree total uncertainty for instantaneousvalues is around 400ndash500 cm3 hminus1 resulting in a high uncer-tainty for low flows but low relative uncertainty for higherflows reaching 13 at peak flows on 6 June (Fig B3c)When expressed as daily means this uncertainty will be re-duced as temporal averaging decreases the uncertainty by afactor equal to the inverse of the root square of the num-ber of observations within a day (Richardson et al 2012)In the same example (Fig B3c) a day with high dailymean flow will also show lower relative uncertainty (6 June1589plusmn 45 cm3 hminus1 3 ) compared to one with lower dailymean flow (30 May 237plusmn 45 cm3 hminus1 19 )

Table B1 Fixed-effect coefficients from the linear mixed modelsfitting reference sap flow density as a function of measured sap flowdensity using the data from the global sap flow calibration meta-analysis by Flo et al (2019) Models for each method included theindividual calibration as a random intercept Sap flow methods areranked according to their presence in the SAPFLUXNET databasefrom most to least present HD (heat dissipation) CHP (compensa-tion heat pulse) HR (heat ratio) HPTM (heat pulse T-max) CHD(cyclic heat dissipation) and HFD (heat field deformation)

Method Intercept Slope

HD 149 001CHP 265 003HR 076 003HPTM 775 004CHD 203 001HFD 105 001

Finally when no information on the variation of sap flowalong the sapwood is available radial integration of pointmeasurements of sap flow density and associated uncertaintycan be obtained by applying generic radial profiles accord-ing to wood porosity (Berdanier et al 2016) as implementedin the R package ldquosapfluxrdquo (httpsgithubcomberdanierasapflux last access 8 June 2021) An example applicationof this procedure shows how different uncertainty boundscan be obtained depending on wood anatomy (Fig B4) Inaddition this application shows how assuming a uniform ra-dial profile for ring-porous or diffuse-porous species can leadto substantial underestimation of whole-plant sap flow com-pared to a lower impact for tracheid-bearing species

Figure B1 Methodological uncertainty estimation in sap flow den-sity measurements based on the data from the global meta-analysisof sap flow calibrations in Flo et al (2019) The main panel showspredicted standard error based on method-specific linear mixedmodels of reference flow as a function of measured flow includingthe individual calibration as a random intercept factor The span ofthe horizontal lines below the main panel corresponds to the max-imum sap flow density in SAPFLUXNET (estimated as the 99 quantile of sub-daily measurements) for that specific method

Figure B2 Sub-daily time series of sap flow and methodologicaluncertainty estimations (1 standard error) according to the model inFig B1 for 10 d periods in trees measured with (a) heat dissipation(b) compensation heat pulse and (c) heat ratio sensors Data forpanel (a) from a Pinus sylvestris tree in dataset ESP_VAL_SORdata for panel (b) from a Pinus sylvestris tree in GBR_DEV_CONand data for panel (c) from a Eucalyptus victrix tree in AUS_KAR

Figure B3 An example of sap flow uncertainty estimation and biascorrection for a Pinus sylvestris tree (ESP_VAL_SOR_Js_Ps_12)measured using heat dissipation sensors Panel (a) shows sap flowdensity HD measurements with and without the application of thebias correction reported in Flo et al (2019) together with thecorresponding uncertainty estimated from the model in Fig B1Panel (b) shows corrected sap flow data comparing methodolog-ical uncertainty with that derived from the 68 predictive inter-val of sapwood area estimation Panel (c) shows corrected sap flowdata together with the combined methodological and sapwood un-certainty

Figure B4 Effects of a generic radial integration on sap flow dataoriginally supplied without any radial integration procedure Ra-dial integration and uncertainty estimation (blue bands show the68 prediction interval based on 100 bootstrap samples) wereapplied using the wood-type-specific radial profiles provided inBerdanier et al (2016) for a ring-porous species (a) a tracheid-bearing species (b) and a diffuse-porous species (c) all belongingto the USA_UMB_CON dataset

Supplement The supplement related to this article is availableonline at httpsdoiorg105194essd-13-2607-2021-supplement

Author contributions VG RP VF and JMV designed and builtthe database RP VG and VF summarized the database and draftedthe manuscript with the contribution of JMV MM and KS Therest of the co-authors contributed data to the database and editedthe manuscript

Competing interests The authors declare that they have no con-flict of interest

Acknowledgements This data compilation would have not beenpossible without the contribution of all the people who supportedthe construction and maintenance of measurement infrastructureshelped with field data collection and participated in data process-ing of individual datasets We would also like to acknowledge thesupport of Agustiacute Escobar Roberto Molowny-Horas Marie Sirotand Guillem Bagaria in building the data infrastructure We wouldalso like to thank Stan Schymanski and Rob Skelton for their usefulfeedback during the review process This paper is dedicated to thememory of our colleague and co-author Niles J Hasselquist

Financial support This research was supported by the Minis-terio de Economiacutea y Competitividad (grant no CGL2014-55883-JIN) the Ministerio de Ciencia e Innovacioacuten (grant no RTI2018-095297-J-I00) the Ministerio de Ciencia e Innovacioacuten (grantno CAS1600207) the Agegravencia de Gestioacute drsquoAjuts Universitaris ide Recerca (grant no SGR1001) the Alexander von Humboldt-Stiftung (Humboldt Research Fellowship for Experienced Re-searchers (RP)) and the Institucioacute Catalana de Recerca i EstudisAvanccedilats (Academia Award (JMV)) Viacutector Flo was supported bythe doctoral fellowship FPU1503939 (MECD Spain)

Review statement This paper was edited by Sibylle K Hasslerand reviewed by Robert Skelton and Stan Schymanski

References

Allen S T Kirchner J W Braun S Siegwolf R TW and Goldsmith G R Seasonal origins of soil wa-ter used by trees Hydrol Earth Syst Sci 23 1199ndash1210httpsdoiorg105194hess-23-1199-2019 2019

Ambrose A R Sillett S C Koch G W Van Pelt RAntoine M E and Dawson T E Effects of height ontreetop transpiration and stomatal conductance in coast red-wood (Sequoia sempervirens) Tree Physiol 30 1260ndash1272httpsdoiorg101093treephystpq064 2010

Anderegg W R L Konings A G Trugman A T Yu K Bowl-ing D R Gabbitas R Karp D S Pacala S Sperry J SSulman B N and Zenes N Hydraulic diversity of forests regu-lates ecosystem resilience during drought Nature 561 538ndash541httpsdoiorg101038s41586-018-0539-7 2018

Asbjornsen H Goldsmith G R Alvarado-Barrientos M SRebel K Osch F P V Rietkerk M Chen J Gotsch SToboacuten C Geissert D R Goacutemez-Tagle A Vache K andDawson T E Ecohydrological advances and applications inplant-water relations research a review J Plant Ecol 4 3ndash22httpsdoiorg101093jpertr005 2011

Baker J M and Van Bavel C H M Measurement of mass flow ofwater in the stems of herbaceous plants Plant Cell Environ 10777ndash782 httpsdoiorg1011111365-3040ep11604765 1987

Barbeta A and Pentildeuelas J Relative contribution of groundwa-ter to plant transpiration estimated with stable isotopes SciRep-UK 7 10580 httpsdoiorg101038s41598-017-09643-x 2017

Beer C Reichstein M Tomelleri E Ciais P Jung M Carval-hais N Rodenbeck C Arain M A Baldocchi D Bonan GB Bondeau A Cescatti A Lasslop G Lindroth A LomasM Luyssaert S Margolis H Oleson K W Roupsard OVeenendaal E Viovy N Williams C Woodward F I and Pa-pale D Terrestrial Gross Carbon Dioxide Uptake Global Dis-tribution and Covariation with Climate Science 329 834ndash838httpsdoiorg101126science1184984 2010

Benyon R G Lane P N J Jaskierniak D Kuczera G and Hay-don S R Use of a forest sapwood area index to explain long-term variability in mean annual evapotranspiration and stream-flow in moist eucalypt forests Water Resour Res 51 5318ndash5331 httpsdoiorg1010022015WR017321 2015

Berdanier A B Miniat C F and Clark J S Predictive mod-els for radial sap flux variation in coniferous diffuse-porousand ring-porous temperate trees Tree Physiol 36 932ndash941httpsdoiorg101093treephystpw027 2016

Berry Z C Looker N Holwerda F Aguilar G Rodrigo L Or-tiz Colin P Gonzaacutelez Martiacutenez T and Asbjornsen H Whysize matters the interactive influences of tree diameter distri-bution and sap flow parameters on upscaled transpiration TreePhysiol 38 263ndash275 httpsdoiorg101093treephystpx1242018

Bohrer G Mourad H Laursen T A Drewry D Avis-sar R Poggi D Oren R and Katul G G Finite ele-ment tree crown hydrodynamics model (FETCH) using porousmedia flow within branching elements A new representa-tion of tree hydrodynamics Water Resour Res 41 W11404httpsdoiorg1010292005WR004181 2005

Bond-Lamberty B Data Sharing and Scientific Impact in Eddy Co-variance Research J Geophys Res-Biogeo 123 1440ndash1443httpsdoiorg1010022018JG004502 2018

Bond-Lamberty B and Thomson A A global databaseof soil respiration data Biogeosciences 7 1915ndash1926httpsdoiorg105194bg-7-1915-2010 2010

Brinkmann N Eugster W Zweifel R Buchmann N andKahmen A Temperate tree species show identical re-sponse in tree water deficit but different sensitivities in sapflow to summer soil drying Tree Physiol 12 1508ndash1519httpsdoiorg101093treephystpw062 2016

Buckley T N Turnbull T L and Adams M A Simple modelsfor stomatal conductance derived from a process model cross-validation against sap flux data Plant Cell Environ 35 1647ndash1662 httpsdoiorg101111j1365-3040201202515x 2012

Burgess S S O Adams M A Turner N C Beverly CR Ong C K Khan A A H and Bleby T M An im-

proved heat pulse method to measure low and reverse ratesof sap flow in woody plants Tree Physiol 21 589ndash598httpsdoiorg101093treephys219589 2001

Cermaacutek J Deml M and Penka M A new method of sapflowrate determination in trees Biol Plantarum 15 171ndash178 1973

Cermaacutek J Kucera J and Nadezhdina N Sap flow measurementswith some thermodynamic methods flow integration within treesand scaling up from sample trees to entire forest stands Trees18 529ndash546 httpsdoiorg101007s00468-004-0339-6 2004

Cerveny R S Lawrimore J Edwards R and Landsea C Ex-treme Weather Records B Am Meteorol Soc 88 853ndash860httpsdoiorg101175BAMS-88-6-853 2007

Chen Y-J Cao K-F Schnitzer S A Fan Z-X ZhangJ-L and Bongers F Water-use advantage for lianas overtrees in tropical seasonal forests New Phytol 205 128ndash136httpsdoiorg101111nph13036 2015

Choat B Brodribb T J Brodersen C R Duursma R ALoacutepez R and Medlyn B E Triggers of tree mortality underdrought Nature 558 531ndash539 httpsdoiorg101038s41586-018-0240-x 2018

Clearwater M J Luo Z Mazzeo M and Dichio B An ex-ternal heat pulse method for measurement of sap flow throughfruit pedicels leaf petioles and other small-diameter stems PlantCell Environ 32 1652ndash1663 httpsdoiorg101111j1365-3040200902026x 2009

Cochard H Breacuteda N and Granier A Whole tree hydraulic con-ductance and water loss regulation in Quercus during droughtevidence for stomatal control of embolism Ann Sci Forest53 197ndash206 1996

Cohen Y Fuchs M and Green G C Improvement ofthe heat pulse method for determining sap flow in treesPlant Cell Environ 4 391ndash397 httpsdoiorg101111j1365-30401981tb02117x 1981

Cohen Y Cohen S Cantuarias-Aviles T and SchillerG Variations in the radial gradient of sap velocity intrunks of forest and fruit trees Plant Soil 305 49ndash59httpsdoiorg101007s11104-007-9351-0 2008

Crowther T W Glick H B Covey K R Bettigole C May-nard D S Thomas S M Smith J R Hintler G DuguidM C Amatulli G Tuanmu M-N Jetz W Salas C StamC Piotto D Tavani R Green S Bruce G Williams S JWiser S K Huber M O Hengeveld G M Nabuurs G-JTikhonova E Borchardt P Li C-F Powrie L W FischerM Hemp A Homeier J Cho P Vibrans A C Umunay PM Piao S L Rowe C W Ashton M S Crane P R andBradford M A Mapping tree density at a global scale Nature525 201ndash205 httpsdoiorg101038nature14967 2015

da Costa A C L Rowland L Oliveira R S Oliveira A AR Binks O J Salmon Y Vasconcelos S S Junior J AS Ferreira L V Poyatos R Mencuccini M and Meir PStand dynamics modulate water cycling and mortality risk indroughted tropical forest Global Change Biol 24 249ndash258httpsdoiorg101111gcb13851 2018

Dai S-Q Li H Xiong J Ma J Guo H-Q Xiao X and ZhaoB Assessing the Extent and Impact of Online Data Sharing inEddy Covariance Flux Research J Geophys Res-Biogeo 123129ndash137 httpsdoiorg1010022017JG004277 2018

Davis T W Kuo C-M Liang X and Yu P-S Sap Flow Sen-sors Construction Quality Control and Comparison Sensors12 954ndash971 httpsdoiorg103390s120100954 2012

De Caacuteceres M Mencuccini M Martin-StPaul N Limousin J-M Coll L Poyatos R Cabon A Granda V Forner AValladares F and Martiacutenez-Vilalta J Unravelling the effectof species mixing on water use and drought stress in Mediter-ranean forests A modelling approach Agr Forest Meteorol296 108233 httpsdoiorg101016jagrformet20201082332021

de Dios V R Roy J Ferrio J P Alday J G Landais D MilcuA and Gessler A Processes driving nocturnal transpiration andimplications for estimating land evapotranspiration Sci Rep-UK 5 10975 httpsdoiorg101038srep10975 2015

Dierick D and Houmllscher D Species-specific tree wa-ter use characteristics in reforestation stands in thePhilippines Agr Forest Meteorol 149 1317ndash1326httpsdoiorg101016jagrformet200903003 2009

Do F and Rocheteau A Influence of natural temperaturegradients on measurements of xylem sap flow with ther-mal dissipation probes 2 Advantages and calibration of anoncontinuous heating system Tree Physiol 22 649ndash654httpsdoiorg101093treephys229649 2002

Edwards W R N Becker P and Cermaacutek J A unified nomencla-ture for sap flow measurements Tree Physiol 17 65ndash67 1996

Evaristo J and McDonnell J J Prevalence and magni-tude of groundwater use by vegetation a global sta-ble isotope meta-analysis Sci Rep-UK 7 44110httpsdoiorg101038srep44110 2017

Ewers B E Oren R Albaugh T J and Dougherty P M Carry-over effects of water and nutrient supply on water use of Pi-nus taeda Ecol Appl 9 513ndash525 httpsdoiorg1018901051-0761(1999)009[0513COEOWA]20CO2 1999

Falster D S Duursma R A Ishihara M I Barneche D RFitzJohn R G Varingrhammar A Aiba M Ando M AntenN Aspinwall M J Baltzer J L Baraloto C Battaglia MBattles J J Bond-Lamberty B van Breugel M Camac JClaveau Y Coll L Dannoura M Delagrange S Domec J-C Fatemi F Feng W Gargaglione V Goto Y Hagihara AHall J S Hamilton S Harja D Hiura T Holdaway R Hut-ley L S Ichie T Jokela E J Kantola A Kelly J W GKenzo T King D Kloeppel B D Kohyama T KomiyamaA Laclau J-P Lusk C H Maguire D A le Maire GMaumlkelauml A Markesteijn L Marshall J McCulloh K Miy-ata I Mokany K Mori S Myster R W Nagano M NaiduS L Nouvellon Y OrsquoGrady A P OrsquoHara K L Ohtsuka TOsada N Osunkoya O O Peri P L Petritan A M PoorterL Portsmuth A Potvin C Ransijn J Reid D Ribeiro SC Roberts S D Rodriacuteguez R Saldantildea-Acosta A Santa-Regina I Sasa K Selaya N G Sillett S C Sterck F Tak-agi K Tange T Tanouchi H Tissue D Umehara T UtsugiH Vadeboncoeur M A Valladares F Vanninen P Wang JR Wenk E Williams R de Ximenes F A Yamaba A Ya-mada T Yamakura T Yanai R D and York R A BAADa Biomass And Allometry Database for woody plants Ecology96 1445ndash1446 2015

Fatichi S Pappas C and Ivanov V Y Modeling plant-water interactions an ecohydrological overview from

the cell to the global scale WIREs Water 3 327ndash368httpsdoiorg101002wat21125 2016

Fisher R A Koven C D Anderegg W R L Christoffersen BO Dietze M C Farrior C E Holm J A Hurtt G C KnoxR G Lawrence P J Lichstein J W Longo M Matheny AM Medvigy D Muller-Landau H C Powell T L Serbin SP Sato H Shuman J K Smith B Trugman A T ViskariT Verbeeck H Weng E Xu C Xu X Zhang T and Moor-croft P R Vegetation demographics in Earth System Models Areview of progress and priorities Global Change Biol 24 35ndash54 httpsdoiorg101111gcb13910 2018

Flo V Martinez-Vilalta J Steppe K Schuldt B andPoyatos R A synthesis of bias and uncertainty in sapflow methods Agr Forest Meteorol 271 362ndash374httpsdoiorg101016jagrformet201903012 2019

Flo V Martiacutenez-Vilalta J Steppe K Schuldt B and Poy-atos R Sap flow methods calibrations [data set] Zenodohttpsdoiorg105281zenodo4559497 2021

Ford C R Hubbard R M Kloeppel B D and Vose J M Acomparison of sap flux-based evapotranspiration estimates withcatchment-scale water balance Agr Forest Meteorol 145 176ndash185 2007

Forster M A How significant is nocturnal sap flow Tree Phys-iol 34 757ndash765 httpsdoiorg101093treephystpu051 2014

Gallagher R V Falster D S Maitner B S Salguero-GoacutemezR Vandvik V Pearse W D Schneider F D Kattge J Poe-len J H Madin J S Ankenbrand M J Penone C FengX Adams V M Alroy J Andrew S C Balk M A BlandL M Boyle B L Bravo-Avila C H Brennan I CartheyA J R Catullo R Cavazos B R Conde D A ChownS L Fadrique B Gibb H Halbritter A H Hammock JHogan J A Holewa H Hope M Iversen C M JochumM Kearney M Keller A Mabee P Manning P McCor-mack L Michaletz S T Park D S Perez T M Pineda-Munoz S Ray C A Rossetto M Sauquet H Sparrow BSpasojevic M J Telford R J Tobias J A Violle C WallsR Weiss K C B Westoby M Wright I J and Enquist BJ Open Science principles for accelerating trait-based scienceacross the Tree of Life Nature Ecology amp Evolution 4 294ndash303 httpsdoiorg101038s41559-020-1109-6 2020

Good S P Moore G W and Miralles D G A mesic max-imum in biological water use demarcates biome sensitivityto aridity shifts Nature Ecology amp Evolution 1 1883ndash1888httpsdoiorg101038s41559-017-0371-8 2017

Granda V Poyatos R Flo V Sirot M and BagariaG sapfluxnetQC1 R package with functions related tosapfluxnet project SAPFLUXNET available at httpsgithubcomsapfluxnetsapfluxnetQC1 (last access 21 September 2017)2016

Granda V Poyatos R Flo V Nelson J and Team S Csapfluxnetr Working with ldquoSapfluxnetrdquo Project Data avail-able at httpsCRANR-projectorgpackage=sapfluxnetr lastaccess 14 May 2019

Granier A Une nouvelle meacutethode pur la mesure du flux de segravevebrute dans le tronc des arbres Ann Sci Forest 42 193ndash2001985

Granier A Evaluation of transpiration in a Douglas-fir stand bymeans of sap flow measurements Tree Physiol 3 309ndash3201987

Granier A Biron P Breacuteda N Pontailler J Y and Saugier BTranspiration of trees and forest standsshort and long-term mon-itoring using sapflow methods Global Change Biol 2 265ndash2741996

Grossiord C Having the right neighbors how tree species diver-sity modulates drought impacts on forests New Phytol 228 42ndash49 httpsdoiorg101111nph15667 2020

Grossiord C Sevanto S Limousin J-M Meir P Men-cuccini M Pangle R E Pockman W T Salmon YZweifel R and McDowell N G Manipulative experimentsdemonstrate how long-term soil moisture changes alter con-trols of plant water use Environ Exp Bot 152 19ndash27httpsdoiorg101016jenvexpbot201712010 2018

Grossiord C Christoffersen B Alonso-Rodriacuteguez A MAnderson-Teixeira K Asbjornsen H Aparecido L M TCarter Berry Z Baraloto C Bonal D Borrego I Burban BChambers J Q Christianson D S Detto M FaybishenkoB Fontes C G Fortunel C Gimenez B O Jardine K JKueppers L Miller G R Moore G W Negron-Juarez RStahl C Swenson N G Trotsiuk V Varadharajan C War-ren J M Wolfe B T Wei L Wood T E Xu C andMcDowell N G Precipitation mediates sap flux sensitivity toevaporative demand in the neotropics Oecologia 191 519ndash530httpsdoiorg101007s00442-019-04513-x 2019

Hampel F R The Influence Curve and its Role in Ro-bust Estimation J Am Stat Assoc 69 383ndash393httpsdoiorg10108001621459197410482962 1974

Hassler S K Weiler M and Blume T Tree- stand- and site-specific controls on landscape-scale patterns of transpiration Hy-drol Earth Syst Sci 22 13ndash30 httpsdoiorg105194hess-22-13-2018 2018

Helfter C Shephard J D Martiacutenez-Vilalta J Mencuc-cini M and Hand D P A noninvasive optical systemfor the measurement of xylem and phloem sap flow inwoody plants of small stem size Tree Physiol 27 169ndash179httpsdoiorg101093treephys272169 2007

Hu J Moore D J P Riveros-Iregui D A Burns SP and Monson R K Modeling whole-tree carbon as-similation rate using observed transpiration rates and needlesugar carbon isotope ratios New Phytol 185 1000ndash1015httpsdoiorg101111j1469-8137200903154x 2010

Huber B Beobachtung und Messung pflanzlicher SartstroumlmeBer Deut Bot Ges 50 89ndash109 httpsdoiorg101111j1438-86771932tb00039x 1932

Hultine K R Nagler P L Morino K Bush S E Burtch KG Dennison P E Glenn E P and Ehleringer J R Sapflux-scaled transpiration by tamarisk (Tamarix spp) before dur-ing and after episodic defoliation by the saltcedar leaf beetle(Diorhabda carinulata) Agr Forest Meteorol 150 1467ndash1475httpsdoiorg101016jagrformet201007009 2010

Jarvis P G Scaling processes and problems Plant CellEnviron 18 1079ndash1089 httpsdoiorg101111j1365-30401995tb00620x 1995

Jung M Koirala S Weber U Ichii K Gans F Camps-VallsG Papale D Schwalm C Tramontana G and ReichsteinM The FLUXCOM ensemble of global land-atmosphere en-ergy fluxes Sci Data 6 74 httpsdoiorg101038s41597-019-0076-8 2019

Kallarackal J Otieno D O Reineking B Jung E-YSchmidt M W T Granier A and Tenhunen J D Func-tional convergence in water use of trees from differentgeographical regions a meta-analysis Trees 27 787ndash799httpsdoiorg101007s00468-012-0834-0 2013

Kattge J Boumlnisch G Diacuteaz S Lavorel S Prentice I CLeadley P Tautenhahn S Werner G D A Aakala T AbediM Acosta A T R Adamidis G C Adamson K Aiba MAlbert C H Alcaacutentara J M Aacutelcazar C C Aleixo I AliH Amiaud B Ammer C Amoroso M M Anand M An-derson C Anten N Antos J Apgaua D M G AshmanT-L Asmara D H Asner G P Aspinwall M Atkin OAubin I Baastrup-Spohr L Bahalkeh K Bahn M BakerT Baker W J Bakker J P Baldocchi D Baltzer J Baner-jee A Baranger A Barlow J Barneche D R Baruch ZBastianelli D Battles J Bauerle W Bauters M BazzatoE Beckmann M Beeckman H Beierkuhnlein C BekkerR Belfry G Belluau M Beloiu M Benavides R Beno-mar L Berdugo-Lattke M L Berenguer E Bergamin RBergmann J Carlucci M B Berner L Bernhardt-Roumlmer-mann M Bigler C Bjorkman A D Blackman C BlancoC Blonder B Blumenthal D Bocanegra-Gonzaacutelez K TBoeckx P Bohlman S Boumlhning-Gaese K Boisvert-MarshL Bond W Bond-Lamberty B Boom A Boonman C C FBordin K Boughton E H Boukili V Bowman D M J SBravo S Brendel M R Broadley M R Brown K A Bru-elheide H Brumnich F Bruun H H Bruy D Buchanan SW Bucher S F Buchmann N Buitenwerf R Bunker D EBuumlrger J Burrascano S Burslem D F R P Butterfield B JByun C Marques M Scalon M C Caccianiga M CadotteM Cailleret M Camac J Camarero J J Campany CCampetella G Campos J A Cano-Arboleda L Canullo RCarbognani M Carvalho F Casanoves F Castagneyrol BCatford J A Cavender-Bares J Cerabolini B E L Cervel-lini M Chacoacuten-Madrigal E Chapin K Chapin F S ChelliS Chen S-C Chen A Cherubini P Chianucci F ChoatB Chung K-S Chytryacute M Ciccarelli D Coll L CollinsC G Conti L Coomes D Cornelissen J H C CornwellW K Corona P Coyea M Craine J Craven D CromsigtJ P G M Csecserits A Cufar K Cuntz M da Silva A CDahlin K M Dainese M Dalke I Fratte M D Dang-Le AT Danihelka J Dannoura M Dawson S de Beer A J Fru-tos A D Long J R D Dechant B Delagrange S DelpierreN Derroire G Dias A S Diaz-Toribio M H Dimitrakopou-los P G Dobrowolski M Doktor D Drevojan P Dong NDransfield J Dressler S Duarte L Ducouret E DullingerS Durka W Duursma R Dymova O E-Vojtkoacute A EcksteinR L Ejtehadi H Elser J Emilio T Engemann K ErfanianM B Erfmeier A Esquivel-Muelbert A Esser G EstiarteM Domingues T F Fagan W F Faguacutendez J Falster DS Fan Y Fang J Farris E Fazlioglu F Feng Y Fernan-dez-Mendez F Ferrara C Ferreira J Fidelis A Finegan BFirn J Flowers T J Flynn D F B Fontana V Forey EForgiarini C Franccedilois L Frangipani M Frank D Frenette-Dussault C Freschet G T Fry E L Fyllas N M Mazzo-chini G G Gachet S Gallagher R Ganade G Ganga FGarciacutea-Palacios P Gargaglione V Garnier E Garrido J Lde Gasper A L Gea-Izquierdo G Gibson D Gillison A NGiroldo A Glasenhardt M-C Gleason S Gliesch M Gold-

berg E Goumlldel B Gonzalez-Akre E Gonzalez-Andujar J LGonzaacutelez-Melo A Gonzaacutelez-Robles A Graae B J GrandaE Graves S Green W A Gregor T Gross N Guerin G RGuumlnther A Gutieacuterrez A G Haddock L Haines A Hall JHambuckers A Han W Harrison S P Hattingh W HawesJ E He T He P Heberling J M Helm A Hempel SHentschel J Heacuterault B Heres A-M Herz K Heuertz MHickler T Hietz P Higuchi P Hipp A L Hirons A HockM Hogan J A Holl K Honnay O Hornstein D HouE Hough-Snee N Hovstad K A Ichie T Igic B Illa EIsaac M Ishihara M Ivanov L Ivanova L Iversen C MIzquierdo J Jackson R B Jackson B Jactel H JagodzinskiA M Jandt U Jansen S Jenkins T Jentsch A JespersenJ R P Jiang G-F Johansen J L Johnson D Jokela E JJoly C A Jordan G J Joseph G S Junaedi D Junker RR Justes E Kabzems R Kane J Kaplan Z Kattenborn TKavelenova L Kearsley E Kempel A Kenzo T KerkhoffA Khalil M I Kinlock N L Kissling W D Kitajima KKitzberger T Kjoslashller R Klein T Kleyer M Klimešovaacute JKlipel J Kloeppel B Klotz S Knops J M H KohyamaT Koike F Kollmann J Komac B Komatsu K Koumlnig CKraft N J B Kramer K Kreft H Kuumlhn I KumarathungeD Kuppler J Kurokawa H Kurosawa Y Kuyah S LaclauJ-P Lafleur B Lallai E Lamb E Lamprecht A LarkinD J Laughlin D Bagousse-Pinguet Y L le Maire G leRoux P C le Roux E Lee T Lens F Lewis S L Lhot-sky B Li Y Li X Lichstein J W Liebergesell M LimJ Y Lin Y-S Linares J C Liu C Liu D Liu U Liv-ingstone S Llusiagrave J Lohbeck M Loacutepez-Garciacutea Aacute Lopez-Gonzalez G Lososovaacute Z Louault F Lukaacutecs B A Lukeš PLuo Y Lussu M Ma S Pereira CMR Mack M MaireV Maumlkelauml A Maumlkinen H Malhado A C M Mallik AManning P Manzoni S Marchetti Z Marchino L Marcilio-Silva V Marcon E Marignani M Markesteijn L Martin AMartiacutenez-Garza C Martiacutenez-Vilalta J Maškovaacute T MasonK Mason N Massad T J Masse J Mayrose I McCarthyJ McCormack M L McCulloh K McFadden I R McGillB J McPartland M Y Medeiros J S Medlyn B Meerts PMehrabi Z Meir P Melo F P L Mencuccini M MeredieuC Messier J Meacuteszaacuteros I Metsaranta J Michaletz S TMichelaki C Migalina S Milla R Miller J E D MindenV Ming R Mokany K Moles A T Molnaacuter A MolofskyJ Molz M Montgomery R A Monty A Moravcovaacute LMoreno-Martiacutenez A Moretti M Mori A S Mori S MorrisD Morrison J Mucina L Mueller S Muir C D Muumlller SC Munoz F Myers-Smith I H Myster R W Nagano MNaidu S Narayanan A Natesan B Negoita L Nelson AS Neuschulz E L Ni J Niedrist G Nieto J NiinemetsUuml Nolan R Nottebrock H Nouvellon Y Novakovskiy ANystuen K O OrsquoGrady A OrsquoHara K OrsquoReilly-Nugent AOakley S Oberhuber W Ohtsuka T Oliveira R Oumlllerer KOlson M E Onipchenko V Onoda Y Onstein R E Or-donez J C Osada N Ostonen I Ottaviani G Otto S Over-beck G E Ozinga W A Pahl A T Paine C E T PakemanR J Papageorgiou A C Parfionova E Paumlrtel M PataccaM Paula S Paule J Pauli H Pausas J G Peco B Penue-las J Perea A Peri P L Petisco-Souza A C Petraglia APetritan A M Phillips O L Pierce S Pillar V D PisekJ Pomogaybin A Poorter H Portsmuth A Poschlod P

Potvin C Pounds D Powell A S Power S A PrinzingA Puglielli G Pyšek P Raevel V Rammig A Ransijn JRay C A Reich P B Reichstein M Reid D E B Reacutejou-Meacutechain M de Dios V R Ribeiro S Richardson S RiibakK Rillig M C Riviera F Robert E M R Roberts S Ro-broek B Roddy A Rodrigues A V Rogers A RollinsonE Rolo V Roumlmermann C Ronzhina D Roscher C RosellJA Rosenfield M F Rossi C Roy D B Royer-Tardif SRuumlger N Ruiz-Peinado R Rumpf S B Rusch G M RyoM Sack L Saldantildea A Salgado-Negret B Salguero-GomezR Santa-Regina I Santacruz-Garciacutea A C Santos J SardansJ Schamp B Scherer-Lorenzen M Schleuning M SchmidB Schmidt M Schmitt S Schneider JV Schowanek S DSchrader J Schrodt F Schuldt B Schurr F Garvizu G SSemchenko M Seymour C Sfair J C Sharpe J M Shep-pard C S Sheremetiev S Shiodera S Shipley B ShovonT A Siebenkaumls A Sierra C Silva V Silva M Sitzia TSjoumlman H Slot M Smith N G Sodhi D Soltis P SoltisD Somers B Sonnier G Soslashrensen M V Sosinski E ESoudzilovskaia N A Souza A F Spasojevic M SperandiiM G Stan A B Stegen J Steinbauer K Stephan J GSterck F Stojanovic D B Strydom T Suarez ML Sven-ning J-C Svitkovaacute I Svitok M Svoboda M Swaine ESwenson N Tabarelli M Takagi K Tappeiner U TarifaR Tauugourdeau S Tavsanoglu C te Beest M TedersooL Thiffault N Thom D Thomas E Thompson K Thorn-ton P E Thuiller W Tichyacute L Tissue D Tjoelker M GTng D Y P Tobias J Toumlroumlk P Tarin T Torres-Ruiz J MToacutethmeacutereacutesz B Treurnicht M Trivellone V Trolliet F Trot-siuk V Tsakalos J L Tsiripidis I Tysklind N UmeharaT Usoltsev V Vadeboncoeur M Vaezi J Valladares F Va-mosi J van Bodegom P M van Breugel M Cleemput E Vvan de Weg M van der Merwe S van der Plas F van derSande M T van Kleunen M Meerbeek K V Vanderwel MVanselow K A Varingrhammar A Varone L Valderrama M YV Vassilev K Vellend M Veneklaas E J Verbeeck H Ver-heyen K Vibrans A Vieira I Villaciacutes J Violle C VivekP Wagner K Waldram M Waldron A Walker A P WallerM Walther G Wang H Wang F Wang W Watkins HWatkins J Weber U Weedon J T Wei L Weigelt P Wei-her E Wells A W Wellstein C Wenk E Westoby M West-wood A White P J Whitten M Williams M Winkler DE Winter K Womack C Wright I J Wright S J WrightJ Pinho B X Ximenes F Yamada T Yamaji K Yanai RYankov N Yguel B Zanini K J Zanne A E Zelenyacute DZhao Y-P Zheng Jingming Zheng Ji Zieminska K ZirbelC R Zizka G Zo-Bi I C Zotz G Wirth C TRY plant traitdatabase ndash enhanced coverage and open access Global ChangeBiol 26 119ndash188 httpsdoiorg101111gcb14904 2020

Kennedy D Swenson S Oleson K W Lawrence DM Fisher R Lola da Costa A C and Gentine PImplementing Plant Hydraulics in the Community LandModel Version 5 J Adv Model Earth Sy 11 485ndash513httpsdoiorg1010292018MS001500 2019

Klein T Rotenberg E Tatarinov F and Yakir D Associationbetween sap flow-derived and eddy covariance-derived measure-ments of forest canopy CO2 uptake New Phytol 209 436ndash446httpsdoiorg101111nph13597 2016

Knauer J Werner C and Zaehle S Evaluating stomatal modelsand their atmospheric drought response in a land surface schemeA multibiome analysis J Geophys Res-Biogeo 120 1894ndash1911 httpsdoiorg1010022015JG003114 2015

Kool D Agam N Lazarovitch N Heitman J L SauerT J and Ben-Gal A A review of approaches for evapo-transpiration partitioning Agr Forest Meteorol 184 56ndash70httpsdoiorg101016jagrformet201309003 2014

Koumlstner B Granier A and Cermaacutek J Sapflow measurements inforest stands methods and uncertainties Ann Sci Forest 5513ndash27 httpsdoiorg101051forest19980102 1998

Lemeur R Fernaacutendez J E and Steppe K Sym-bols SI units and physical quantities within thescope of sap flow studies Acta Hortic 846 21ndash32httpsdoiorg1017660ActaHortic20098460 2009

Liu B Zhao W and Jin B The response of sap flow in desertshrubs to environmental variables in an arid region of ChinaEcohydrology 4 448ndash457 httpsdoiorg101002eco1512011

Liu H Gleason S M Hao G Hua L He P Goldstein Gand Ye Q Hydraulic traits are coordinated with maximumplant height at the global scale Science Advances 5 eaav1332httpsdoiorg101126sciadvaav1332 2019

Looker N Martin J Jencso K and Hu J Contribution ofsapwood traits to uncertainty in conifer sap flow as estimatedwith the heat-ratio method Agr Forest Meteorol 223 60ndash71httpsdoiorg101016jagrformet201603014 2016

Lu P Muumlller W J and Chacko E K Spatial variations in xylemsap flux density in the trunk of orchard-grown mature mangotrees under changing soil water conditions Tree Physiol 20683ndash692 httpsdoiorg101093treephys2010683 2000

Lu P Woo K C and Liu Z T Estimation of whole-transpirationof bananas using sap flow measurements J Exp Bot 53 1771ndash1779 httpsdoiorg101093jxberf019 2002

Mackay D S Ewers B E Loranty M M and Kruger E L Onthe representativeness of plot size and location for scaling tran-spiration from trees to a stand J Geophys Res-Biogeo 115G02016 httpsdoiorg1010292009JG001092 2010

Manzoni S Vico G Katul G Palmroth S Jackson R B andPorporato A Hydraulic limits on maximum plant transpirationand the emergence of the safety-efficiency trade-off New Phy-tol 198 169ndash178 httpsdoiorg101111nph12126 2013

Marshall D C Measurement of sap flow in conifers by heat trans-port Plant Physiol 33 385ndash396 1958

Martin-StPaul N Delzon S and Cochard H Plant resistanceto drought depends on timely stomatal closure Ecol Lett 201437ndash1447 httpsdoiorg101111ele12851 2017

Matheny A M Bohrer G Stoy P C Baker I T Black AT Desai A R Dietze M C Gough C M Ivanov V YJassal R S Novick K A Schaumlfer K V R and VerbeeckH Characterizing the diurnal patterns of errors in the pre-diction of evapotranspiration by several land-surface modelsAn NACP analysis J Geophys Res-Biogeo 119 1458ndash1473httpsdoiorg1010022014JG002623 2014

McCulloh K A Domec J Johnson D M SmithD D and Meinzer F C A dynamic yet vulnerablepipeline Integration and coordination of hydraulic traitsacross whole plants Plant Cell Environ 42 2789ndash2807httpsdoiorg101111pce13607 2019

Meinzer F C Bond B J Warren J M and WoodruffD R Does water transport scale universally with treesize Funct Ecol 19 558ndash565 httpsdoiorg101111j1365-2435200501017x 2005

Mencuccini M Manzoni S and Christoffersen B Modellingwater fluxes in plants from tissues to biosphere New Phytol222 1207ndash1222 httpsdoiorg101111nph15681 2019

Merlin M Solarik K A and Landhaumlusser S M Quantifi-cation of uncertainties introduced by data-processing proce-dures of sap flow measurements using the cut-tree methodon a large mature tree Agr Forest Meteorol 287 107926httpsdoiorg101016jagrformet2020107926 2020

Meacuteszaacuteros I Kanalas P Fenyvesi A Kis J Nyitrai B SzollosiE Olaacuteh V Demeter Z Lakatos Aacute and Ander I Diurnaland seasonal changes in stem radius increment and sap flow den-sity indicate different responses of two co-existing oak species todrought stress Acta Silvatica et Lignaria Hungarica 7 97ndash1082011

Mirfenderesgi G Bohrer G Matheny A M Fatichi Sde Moraes Frasson R P and Schaumlfer K V R Tree levelhydrodynamic approach for resolving aboveground water stor-age and stomatal conductance and modeling the effects of treehydraulic strategy J Geophys Res-Biogeo 121 1792ndash1813httpsdoiorg1010022016JG003467 2016

Moffat A M Papale D Reichstein M Hollinger D Y Richard-son A D Barr A G Beckstein C Braswell B H ChurkinaG Desai A R Falge E Gove J H Heimann M Hui DJarvis A J Kattge J Noormets A and Stauch V J Compre-hensive comparison of gap-filling techniques for eddy covariancenet carbon fluxes Agr Forest Meteorol 147 209ndash232 2007

Moraacuten-Loacutepez T Poyatos R Llorens P and Sabateacute S Effects ofpast growth trends and current water use strategies on Scots pineand pubescent oak drought sensitivity Eur J Forest Res 133369ndash382 httpsdoiorg101007s10342-013-0768-0 2014

Nadezhdina N Revisiting the Heat Field Deformation (HFD)method for measuring sap flow iForest 11 118ndash130httpsdoiorg103832ifor2381-011 2018

Nadezhdina N Cermaacutek J and Ceulemans R Radial patterns ofsap flow in woody stems of dominant and understory speciesscaling errors associated with positioning of sensors Tree Phys-iol 22 907ndash918 2002

Nadezhdina N Steppe K De Pauw D J W Bequet R CermaacutekJ and Ceulemans R Stem-mediated hydraulic redistributionin large roots on opposing sides of a Douglas-fir tree followinglocalized irrigation New Phytol 184 932ndash943 2009

Nelson J A Peacuterez-Priego O Zhou S Poyatos R Zhang YBlanken P D Gimeno T E Wohlfahrt G Desai A R Gi-oli B Limousin J-M Bonal D Paul-Limoges E Scott RL Varlagin A Fuchs K Montagnani L Wolf S DelpierreN Berveiller D Gharun M Marchesini L B Gianelle DŠigut L Mammarella I Siebicke L Black T A Knohl AHoumlrtnagl L Magliulo V Besnard S Weber U CarvalhaisN Migliavacca M Reichstein M and Jung M Ecosystemtranspiration and evaporation Insights from three water flux par-titioning methods across FLUXNET sites Global Change Biol26 6916ndash6930 httpsdoiorg101111gcb15314 2020

Novick K Oren R Stoy P Juang J-Y Siqueira M and KatulG The relationship between reference canopy conductance and

simplified hydraulic architecture Adv Water Resour 32 809ndash819 httpsdoiorg101016jadvwatres200902004 2009

Novick K A Ficklin D L Stoy P C Williams C A BohrerG Oishi A C Papuga S A Blanken P D NoormetsA Sulman B N Scott R L Wang L and Phillips R PThe increasing importance of atmospheric demand for ecosys-tem water and carbon fluxes Nat Clim Change 6 1023ndash1027httpsdoiorg101038nclimate3114 2016

OrsquoBrien J J Oberbauer S F and Clark D B Whole tree xylemsap flow responses to multiple environmental variables in a wettropical forest Plant Cell Environ 27 551ndash567 2004

Oishi A C Oren R Novick K A Palmroth S and Katul GG Interannual Invariability of Forest Evapotranspiration and ItsConsequence to Water Flow Downstream Ecosystems 13 421ndash436 httpsdoiorg101007s10021-010-9328-3 2010

Oishi A C Hawthorne D A and Oren R Baseliner Anopen-source interactive tool for processing sap flux datafrom thermal dissipation probes SoftwareX 5 139ndash143httpsdoiorg101016jsoftx201607003 2016

Oki T and Kanae S Global hydrological cycles and world waterresources Science 313 1068ndash1072 2006

Oren R Phillips N Ewers B E Pataki D and Megonigal J PSap-flux-scaled transpiration responses to light vapor pressuredeficit and leaf area reduction in a flooded Taxodium distichumforest Tree Physiol 19 337ndash347 1999a

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Peters R L Pappas C Hurley A G Poyatos R FloV Zweifel R Goossens W and Steppe K Assimilateprocess and analyse thermal dissipation sap flow data us-ing the TREX r package Methods Ecol Evol 12 342ndash350httpsdoiorg1011112041-210X13524 2021

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Phillips N Nagchaudhuri A Oren R and Katul G Time con-stant for water transport in loblolly pine trees estimated fromtime series of evaporative demand and stem sapflow Trees 11412ndash419 httpsdoiorg101007s004680050102 1997

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Phillips N G Scholz F G Bucci S J Goldstein G andMeinzer F C Using branch and basal trunk sap flow mea-surements to estimate whole-plant water capacitance commenton Burgess and Dawson (2008) Plant Soil 315 315ndash324httpsdoiorg101007s11104-008-9741-y 2009

Poyatos R Martiacutenez-Vilalta J Cermaacutek J Ceulemans RGranier A Irvine J Koumlstner B Lagergren F MeiresonneL Nadezhdina N Zimmermann R Llorens P and Mencuc-cini M Plasticity in hydraulic architecture of Scots pine acrossEurasia Oecologia 153 245ndash259 2007

Poyatos R Granda V Molowny-Horas R Mencuccini MSteppe K and Martiacutenez-Vilalta J SAPFLUXNET towardsa global database of sap flow measurements Tree Physiol 361449ndash1455 httpsdoiorg101093treephystpw110 2016

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Whitley R Taylor D Macinnis-Ng C Zeppel M Yunusa IOrsquoGrady A Froend R Medlyn B and Eamus D Developingan empirical model of canopy water flux describing the commonresponse of transpiration to solar radiation and VPD across fivecontrasting woodlands and forests Hydrol Process 27 1133ndash1146 httpsdoiorg101002hyp9280 2013

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Wild M Folini D Hakuba M Z Schaumlr C Seneviratne SI Kato S Rutan D Ammann C Wood E F and Koumlnig-Langlo G The energy balance over land and oceans an assess-ment based on direct observations and CMIP5 climate modelsClim Dynam 44 3393ndash3429 httpsdoiorg101007s00382-014-2430-z 2015

Williams C A Reichstein M Buchmann N Baldocchi DBeer C Schwalm C Wohlfahrt G Hasler N BernhoferC Foken T Papale D Schymanski S and Schaefer KClimate and vegetation controls on the surface water bal-ance Synthesis of evapotranspiration measured across a globalnetwork of flux towers Water Resour Res 48 W06523httpsdoiorg1010292011WR011586 2012

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a model and sap flow data from ponderosa pine Plant Cell Envi-ron 24 679ndash690 2001

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Wutzler T Lucas-Moffat A Migliavacca M Knauer JSickel K Šigut L Menzer O and Reichstein MBasic and extensible post-processing of eddy covarianceflux data with REddyProc Biogeosciences 15 5015ndash5030httpsdoiorg105194bg-15-5015-2018 2018

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Zweifel R Steppe K and Sterck F J Stomatal regulation by mi-croclimate and tree water relations interpreting ecophysiologicalfield data with a hydraulic plant model J Exp Bot 58 2113ndash2131 httpsdoiorg101093jxberm050 2007

Abstract
Introduction
The SAPFLUXNET data workflow
- An overview of sap flow measurements
- Data compilation
- Data harmonization and quality control QC1
- Data structure
- - The SAPFLUXNET database
  - - Data coverage
    - Methodological aspects
    - Plant characteristics
    - Stand characteristics
    - Temporal characteristics
    - Availability of environmental data
    - Uncertainty estimation and bias correction in sap flow measurements
    - - Potential applications
      - Applications in plant ecophysiology and functional ecology
        
        Applications in ecosystem ecology and ecohydrology
        
        Limitations and future developments
        
        Limitations
        
        Outlook
        
        Data availability access and feedback
        
        Code availability
        
        Conclusions
        
        Appendix A References for individual datasets in SAPFLUXNET
        
        Appendix B Uncertainty estimation in sap flow measurements in the SAPFLUXNET database
        
        Supplement
        
        Author contributions
        
        Competing interests
        
        Acknowledgements
        
        Financial support
        
        Review statement
        
        References

Page 2: Global transpiration data from sap ﬂow measurements: the … · 2021. 6. 14. · 2608 R. Poyatos et al.: Global transpiration data from sap ﬂow measurements: the SAPFLUXNET database