Linking Flux Network Measurements to Continental Scale Simulations

28 Pages·2008·1.2 MB·English

Linking Flux Network Measurements to
Continental Scale Simulations: Ecosystem Carbon
Dioxide Exchange Capacity under Non-Water-
Stressed Conditions
Citation
Owen, Katherine E., John Tenhunen, Markus Reichstein, Quan Wang, Eva Falge, Ralf Geyer,
Xiangming Xiao, et al. 2007. Global Change Biology 13(4): 734-760.
Published Version
http://dx.doi.org/10.1111/j.1365-2486.2007.01326.x
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http://nrs.harvard.edu/urn-3:HUL.InstRepos:2757766
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GlobalChangeBiology(2007)13, 734–760,doi:10.1111/j.1365-2486.2007.01326.x
Linking flux network measurements to continental scale
simulations: ecosystem carbon dioxide exchange capacity
under non-water-stressed conditions
KATHERINE E. OWEN*, JOHN TENHUNEN*, MARKUS REICHSTEINw, QUAN WANGz,
EVA FALGE*, RALF GEYER*, XIANGMING XIAO§, PAUL STOY}, CHRISTOF AMMANNk,
ALTAF ARAIN**, MARC AUBINETww, MIKA AURELAzz, CHRISTIAN BERNHOFER§§,
BOGDAN H. CHOJNICKI}}, ANDRE´ GRANIERkk, THOMAS GRUENWALD§§,
JULIAN HADLEY***, BERNARD HEINESCHww, DAVID HOLLINGERwww,
ALEXANDER KNOHLw, WERNER KUTSCHw, ANNALEA LOHILAzz, TILDEN MEYERSzzz,
EDDY MOORS§§§, CHRISTINE MOUREAUXww, KIM PILEGAARD}}},
NOBUKO SAIGUSAkkk, SHASHI VERMA****, TIMO VESALAwwww andCHRIS VOGELzzzz
*DepartmentofPlantEcology,UniversityofBayreuth,95440Bayreuth,Germany,wMax-Planck-InstituteforBiogeochemistry,
Hans-Kno¨ll-Strasse10,07745Jena,Germany,zInstituteofSilviculture,FacultyofAgriculture,ShizuokaUniversity,Ohya836,
Shizuoka422-8529,Japan,§ComplexSystemsResearchCenter,InstitutefortheStudyofEarth,OceansandSpace,Universityof
NewHampshire,Durham,NH03824,USA,}NicholasSchooloftheEnvironmentandEarthSciences,DukeUniversity,A328
LSRC,Durham,NC27708-0328,USA,kSwissFederalResearchStationforAgroecologyandAgricultureofZurich-Reckenholz,
Reckenholzstrasse191,8046Zu¨rich,Switzerland,**SchoolofGeographyandEarthSciences,McMasterUniversity,1280Main
StreetWest,Hamilton,ON,CanadaL8S4K1,wwUnite´ dePhysiquedesBiosyste`mes,Faculte´ universitairedesSciences
agronomiquesdeGembloux,B-5030Gembloux,Belgique,zzFinnishMeteorologicalInstitute,ClimateandGlobalChangeResearch,
POBox503,FI-00101Helsinki,Finland,§§TechnischeUniversita¨tDresden,IHM-Meteorologie,Piennerstrasse9,01737Tharandt,
Germany,}}DepartmentofAgrometeorology,AgriculturalUniversityofPoznan˜,60-637Poznan˜,Poland,kkINRA,
Unite´ d’EcophysiologieForestie`re,F-54280Champenoux,France,***HarvardUniversity,HarvardForest,POBox68,
324N.MainStreet,Petersham,MA01366,USA,wwwUSDAForestService,NorthernResearchStation,271MastRd,POBox640,
Durham,NH03824,USA,zzzNOAA/ARL,AtmosphericTurbulenceandDiffusionDivision,POBox2456,
456SouthIllinoisAvenue,OakRidge,TN37831-2456,USA,§§§Alterra–CentreforWaterandClimate,WageningenUniversity,
6700AAWageningen,TheNetherlands,}}}Ris(cid:1)NationalLaboratory,BiosystemsDepartment,POBox49,DK-4000Roskilde,
Denmark,kkkNationalInstituteofAdvancedIndustrialScienceandTechnology,16-1Onogawa,Tsukuba,Ibaraki305-8569,Japan,
****SchoolofNaturalResources,UniversityofNebraska–Lincoln,244L.W.ChaseHall,POBox830728,Lincoln,NE68583-0728,
USA,wwwwDepartmentofPhysicalSciences,UniversityofHelsinki,FIN-00014,Helsinki,Finland,zzzzNOAAAirResources
Laboratory,CanaanValleyInstitute,POBox673,Davis,WV26260,USA
Abstract
This paper examines long-term eddy covariance data from 18 European and 17 North
American and Asian forest, wetland, tundra, grassland, and cropland sites under non-
water-stressedconditionswithanempiricalrectangularhyperboliclightresponsemodel
and a single layer two light-class carboxylase-based model. Relationships according to
ecosystem functional type are demonstrated between empirical and physiological para-
meters,suggestinglinkagesbetweeneasilyestimatedparametersandthosewithgreater
potential for process interpretation. Relatively sparse documentation of leaf area index
Correspondence:KatherineE.Owen,fax[phone],e-mail:[email protected]
Dataadditionallyprovidedby:ChristianBernhofer/ThomasGruenwald/BarbaraKoestner;EddyMoors/JanElbers/WilmaJans;TimoVesala/
Michael Boy/Nuria Altimir; Tuomas Laurila/Mika Aurela/Juha-Pekka Tuovinen/Annalea Lohila; Marc Aubinet/Bernard Heinesch/
Christine Moureaux; Riccardo Valentini/Giorgio Matteucci/Nicola Arriga/Mazzenga Francesco/Paolo Stefani; Andre´ Granier/Bernard
Longdoz;CorinnaRebmann/WernerKutsch/AlexanderKnohl;LiseLotteSoerensen/AndreasIbrom/KimPilegaard;BogdanChojnicki/
JanuszOlejnik/MarekUrbaniak;ChristofAmmann/JuergFuhrer;GabrielKatul/DavidEllsworth/RamOren/PaulStoy;AltafArain;David
Hollinger/EricDavidson/BobEvans/MikeGoltz/MoniqueLeclerc/JohnLee/KevinTu;T.AndrewBlack/MichaelNovak;HiroakiKondo/
Nobuko Saigusa/Shohei Murayama; Steven Wofsy/Julian Hadley/Patrick Crill/David Fitzjarrald/Michael Goulden/Kathleen Moore/
J.(Bill)Munger;WalterOechel/JoeVerfaillie,Jr./GeorgeVourlitis/RommelZulueta;TildenMeyers/ChrisVogel;LawrenceFlanagan;Shashi
Verma/ToddSchimelfenig/AndrewSuyker.
r2007TheAuthors
734 Journalcompilationr2007BlackwellPublishingLtd
LINKING FLUX NETWORK MEASUREMENTS TO CONTINENTAL SCALE SIMULATIONS 735
dynamicsatfluxtowersitesisfoundtobeamajordifficultyinmodelinversionandflux
interpretation.Therefore,asimplificationofthephysiologicalmodeliscarriedoutfora
subset of European network sites with extensive ancillary data. The results from these
selectedsitesareusedtoderiveanewparameterandmeansforcomparingempiricaland
physiologically based methods across all sites, regardless of ancillary data. The results
from the European analysis are then compared with results from the other Northern
Hemisphere sites and similar relationships for the simplified process-based parameter
were found to hold for European, North American, and Asian temperate and boreal
climatezones.Thisparameterisusefulforbridgingbetweenfluxnetworkobservations
and continental scale spatial simulations of vegetation/atmosphere carbon dioxide
exchange.
Keywords: carbondioxideexchange,crops,eddycovariance,forest,grassland,grossprimaryproduc-
tion,modelinversion,netecosystemexchange,up-scaling,wetland
Received23August2006;revisedversionreceived4October2006andaccepted11October2006
linking observations at flux tower sites with process-
Introduction
based models for estimation of carbon exchange at
Networks of eddy covariance sites have been estab- regional to global scales, including leaf area index
lished worldwide to observe the long-term character- (LAI), average carboxylase capacity of the leaves
isticsofcarbondioxide(CO ),watervapourandenergy (Vc ), average leaf light utilization efficiency (alpha),
2 max
fluxes associated with different ecosystem types along length of the active season for carbon uptake, and
climate gradients (Baldocchi et al., 1996, 2001). With sensitivity of stomata to changes in soil water avail-
respecttoCO ,suchstudiesallowonlydirectmeasure- ability can be used to express the potential of an
2
mentsandcomparisonsofnetecosystemCO exchange ecosystem to acclimate to changing constraints. While
2
(NEE).Nevertheless,consensusviewsonprocessingof variousstrategieshavebeenappliedtodescribespatial
data from such networks are being developed to (1) and temporal variation in these parameters at large
provide estimates of the flux components associated scales(Potteretal.,1993;Running&Hunt,1993;Bonan,
withcanopyphotosynthesis[grossprimaryproduction 1995;Sellersetal.,1996;Liuetal.,1997,1999;Chenetal.,
(GPP)]andecosystemrespiration(Reco)(cf.Falgeetal., 1999), consistent or standardized methodologies based
2001, 2002a; Reichstein et al., 2005), (2) reveal seasonal onfluxnetworkobservationshavenotyetbeendemon-
changesinCO exchangepotentials(Falgeetal.,2002b; strated.
2
Reichstein et al., 2002; Gilmanov et al., 2003), and (3) This paper describes results from the first of two
support the derivation of model parameters for use in analyses focused on comparative derivation of critical
spatial generalizations of vegetation/atmosphere CO ecosystem carbon exchange parameters along a Eur-
2
exchange(Reichstein etal.,2003b;Wangetal.,2003). opean transect from Mediterranean to Arctic climatic
Parametersthatdefinethecapacityofecosystemsfor zones, using both empirical and physiologically based
carbon uptake (GPP) and loss (Reco) are key compo- models. NEE data from all major ecosystem types
nentsinmodelsofcarbondynamics,andbothempirical studied within the network project CarboEurope
andphysiologicallybasedparametershavebeenexam- (www.carboeurope.org)areanalysed,butthisfirstsum-
ined.Empiricalanalysesbasedonlightresponsecurves marydescribesvegetationresponseinsituationswhere
for NEE (Tamiya, 1951; Gilmanov et al., 2003) are soil water availability is nonlimiting. A subsequent
advantageous, especially for those focusing research paper focuses on the additional complexity confronted
attention on additional data analysis problems (e.g. withtheoccurrenceofwaterstress,(e.g.whererelation-
remote sensing), since seasonal patterns in the direct ships between relative extractable water in the rooting
observationsarerevealed, results are rapidlyobtained, zone(Granieretal.,2006)and/ormeasuredmeteorolo-
and no other ecosystem structural information from gical variables (i.e. latent heat and air temperature) at
fluxtowersitesisrequired. eddycovariancetowersitesallowcleardefinitionofthe
Physiologically based analyses of GPP, on the other waterstressperiod).
hand, attempt to identify process components that Our goals are to (1) demonstrate relationships be-
regulatefluxes,withthehopethatthesemaybelinked tween calculated empirical and process-based CO ex-
2
inmodelling toan overallunderstandingofecosystem change parameters obtained for different ecosystem
physiology and biogeochemistry. Critical parameters types, (2) examine patterns in CO uptake or loss as
2
r2007TheAuthors
Journalcompilationr 2007 BlackwellPublishingLtd, GlobalChangeBiology,13, 734–760
736 K. E. OWEN et al.
related to climate factors, LAI, and canopy and leaf ditionsusingafrictionvelocityðu(cid:1)Þ-thresholdcriterion
physiology, (3) determine whether convergence occurs according to Reichstein et al. (2005). The same proce-
inCO uptakecharacteristicsoftheecosystemsstudied, dure of gap filling, using the marginal distribution
2
(i.e.whether‘functionalecosystemtypes’maybeiden- sampling method, and partitioning of the observed
tified), and (4) searchfor ways in which process-based NEE into GPP and Reco, using a short-term tempera-
descriptions may be simplified for continental scale turedependentmethodbasedonLloyd&Taylor(1994),
spatial simulations of vegetation/atmosphere CO ex- wasapplied at all sites usingthe method of Reichstein
2
change.Weconductasensitivityanalysisoncriticalleaf etal.(2005).Onemodificationofthepublishedmethod
level and canopy structure parameters to obtain accu- was made. In order for R , the reference ecosystem
ref
ratesimplificationsformodelapplications.Insummary, respiration at 151C, to be comparable with remote
we attempt to reduce a relatively complex physiologi- sensing indices, it was estimated using a window of 8
callybasedmodeltothelevelofempiricaldescriptions, days with a 4-day time step (i.e. 4 days of overlap).
at least in terms of the number of critical parameters, Errors and uncertainties introduced in the process of
while maintaining the capacity for response to factors gapfillingandfluxpartitioninghavebeendiscussedby
suchasremotelysensedLAI,fertilization,management, Reichsteinetal.(2005).Whileourobjectivewastousea
and canopy water use. The resulting model may be singlemethodacrossmanysites,adangerremainsthat
viewed as a prototype for bridging between observa- important individual site characteristics may be over-
tions within flux tower networks and simulations of looked,andproblemswithreportedmeasurementsare
carbonexchangeatcontinentalscale. difficult to assess. Furthermore, the annual sums of
GPP,Reco,andNEEfromthisstandardizedprocessing
may differ from other published values for the same
Materials and methods
sites (Barford et al., 2001; Hadley & Schedlbauer, 2002;
Lohilaetal.,2004;Stoyetal.,2006).
Site descriptions and eddy covariance fluxes
Theresearchprogramme EUROFLUX(‘Long-term car-
Hyperbolic light response model
bondioxideandwatervaporfluxesofEuropeanforests
and interactions with the climate system’) was estab- Empirical description of the measured daytime NEE
lished as a network of 15 forest sites to examine NEE fluxes was accomplished via a nonlinear least squares
along European continental gradients from western fit of the data to the hyperbolic light response model,
oceanic to eastern continental zones, and from boreal also known as the rectangular hyperbola or the
to Mediterranean climates (Baldocchi et al., 1996; Ten- Michaelis–Mententypemodel(Tamiya,1951;Gilmanov
hunen et al., 1998, 1999; Papale & Valentini, 2003). The etal.,2003):
network has been extended in the current European
abQ
Union Integrated Project CarboEurope to include mea- NEE¼(cid:2) þg; ð1Þ
aQþb
surement sites within croplands, grasslands, wetlands,
and additional Mediterranean woodland and shrub- whereNEEisungap-filledNEE(mmolCO m(cid:2)2s(cid:2)1),ais
2
landvegetationformations.Eddycovariancefluxmea- the initial slope of the light response curve and an
surements from 18 sites consisting of 27 years of data approximationofthecanopylightutilizationefficiency
including the major ecosystem types of Central and (mmolCO m(cid:2)2s(cid:2)1)/(mmolphotonm(cid:2)2s(cid:2)1),bisthemaxi-
2
Northern European vegetation and distributed along a mumCO uptakerateofthecanopy(mmolCO m(cid:2)2s(cid:2)1),
2 2
north–south transect were selected for analysis (Table Q is the PPFD (mmolphotonm(cid:2)2s(cid:2)1), g is an estimate
2),includingfourconiferous,onemixedandfivedecid- of the average daytime ecosystem respiration occurr-
uousforests,twowetlands,threenongrazedgrasslands, ing during the observation period (mmolCO m(cid:2)2s(cid:2)1),
2
and four cropland sites. To examine generality of the (a/b)istheradiationrequiredforhalf-maximaluptake
derived approaches, similar analysis of data from 17 rate, and (b1g) is the theoretical maximum uptake
NorthAmericanandAsiansiteswithatotalof37years capacity (mmolCO m(cid:2)2s(cid:2)1) as sometimes the rectan-
2
of observations was later carried out for comparison gularhyperbolasaturatesveryslowlyintermsoflight.
(Table6). abQ/(aQ1b) evaluated at a reasonable level of high
Half-hourlyaveragedglobalradiationandphotosyn- light(Q52000mmolm(cid:2)2s(cid:2)1isusedinthisstudy)isan
theticphotonfluxdensity(PPFD);airtemperatureand approximation of GPP and can be thought of as the
humidity; rainfall; and wind speed and direction were average maximum canopy uptake capacity, notated
used together with eddy covariance fluxes of CO and here as (b1g) . Ungap-filled NEE data were used
2 2000
H O (Aubinet et al., 2000), accounting for correction of to avoid effects of the gap-filling routine on the para-
2
CO storageandfilteringforlow-turbulencenightcon- meters.Theparametersa,b,andgwereestimatedwith
2
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LINKING FLUX NETWORK MEASUREMENTS TO CONTINENTAL SCALE SIMULATIONS 737
daytimedataforthreeperiodsineachmonth,including radiation above the canopy. Diffuse radiation reaching
data from days 1 through 10, 11 through 20, and 21 to the ground below the canopy is calculated with a
the end of the month. At least five half-hourly data simple exponential extinction, modified to consider
points with high-quality ungap-filled NEE were re- theinfluenceofclumping (O)
quiredtofittheparametersineachperiod.Parameters Sdif;under ¼Sdif expð(cid:2)G O PAI=cos (cid:2)yÞ; ð4Þ
werenotincludedinfurtheranalysiswhentherelative
where (cid:2)y is a representative zenith angle for diffuse
standarderrorwas>0.6orwhenparametervalueswere
negativeorabove0.17fora,100forb,or15forg.These radiation transmission, dependenton canopyelements
ðcos(cid:2)y¼0:537þ0:025PAIÞ.Owasassumedtobe0.5for
thresholdswereprimarilyusedduringdormantwinter
coniferousforests,0.7fordeciduousforests,and0.9for
months to eliminate periods when NEE is near zero
grasslands, wetlands, and crops (Chen & Cihlar, 1995;
and, correspondingly, the rectangular hyperbola is not
Chen, 1996a; Chen et al., 2003). Multiple scattered
anappropriatemodel.
radiationisbasedonNorman(1982)
S ¼0:07OS ð1:1(cid:2)0:1 LAIÞexpð(cid:2)cosyÞ: ð5Þ
scat dir
Carboxylase-based model
The physiologically based model applied to describe The average proportion of sunlit leaves, A , is
sunlit
light interception and leaf gas exchange is single calculatedas
layered and defines sun and shade light classes for
A ¼cosy=Gð1(cid:2)expð(cid:2)G O LAI=cosyÞÞLAI=PAI:
canopy foliage. Thus, it is similar to several other sunlit
ð6Þ
models developed for crop and forest stands (e.g.
Williams et al., 1996; dePury & Farquhar, 1997; Wang
&Leuning,1998). ThetermLAI/PAIcorrectsfortheeffectsofstems.
Lightinterception.Radiationdistributionontosunlitand Canopygasexchange.Thelightinterceptionofthesunlit
shaded leaves is described according to Chen et al. and shaded leaves is used along with absorption and
(1999). The equations for light interception in Chen emission of long-wave radiation, convective heat loss
et al. (1999), Eqns (21–24) were based on LAI only. As and latent heat loss through transpiration to calculate
stems and branches also intercept light, we expanded the energy balance of leaves in two classes (sunlit and
alloccurrencesofLAItoplantareaindex(PAI)withthe shaded). Owing to the iterative process of solving for
assumptionthatthestemareaindex(SAI)is14%ofthe stomatalconductanceandleaftemperature,theenergy
LAI and that PAI5SAI1LAI. Needle leaves were balance is calculated separately for sunlit and shaded
modelled with projected leaf area. Total shortwave leaves,whicharesummedtoobtaintotalcanopyfluxes.
radiation on sunlit leaves (S ) is the sum of direct The simulation of gross photosynthesis follows
sunlit
(S ), sky diffuse (S ), and multiple scattered Farquhar & von Caemmerer (1982) as modified for
beam sky
radiation (S ). Total shortwave radiation on shaded field applications by Harley & Tenhunen (1991).
scat
leaves (S ) includes only the sum of S and S . Model inversions for parameter estimation are based
shade sky scat
Direct radiation on leaves is calculated according to on Ribulose-1,5-bisphosphate-carboxylase-oxygenase
Norman(1982) (Rubisco) enzyme reactions where the rate of CO
2
S ¼S G=cosy; ð2Þ fixation is limited by either the regeneration of
beam dir
Ribulose-1,5-biphosphate (RuBP) (at low light
where Sdir is the direct component of global solar intensity and/or high internal CO2 concentration) or
radiation above the canopy, y is the solar zenith angle, by Rubisco activity and CO /O -concentration (at
2 2
andGisthefoliageorientationfunction(G50.5,i.e.the saturated light and low internal CO concentration;
2
cosineofthemeanleaf-sunanglef).Forcanopieswith Reichstein,2001).
sphericalleafangledistributionf5601(Norman,1979); Netphotosynthesis,P ,isobtainedusing
net
thiswasfoundtobeagoodapproximationforcanopies (cid:1) G(cid:1)(cid:2)
when y ranges between 30 and 601 (Chen, 1996b). Pnet ¼ 1(cid:2) c minðwc; wjÞ(cid:2)0:5 Rd; ð7Þ
Hence, this relationship was used for all sites in this i
study.Skydiffuseradiationonleavesiscalculatedfrom whereG(cid:1) istheCO2compensationpointintheabsence
theaverageoftotalinterceptedskydiffuseradiationfor of mitochondrial respiration, wc is the carboxylation
thetotalPAI rate supported by the Rubisco enzyme expressed by
Eqn (10), w is the carboxylation rate supported by the
Ssky ¼ðSdif(cid:2)Sdif;underÞ=PAI; ð3Þ j
actualelectrontransportrateexpressedbyEqn(12),R
d
where S is the diffuse component of global solar is the respiration occurring in mitochondria without
dif
r2007TheAuthors
Journalcompilationr 2007 BlackwellPublishingLtd, GlobalChangeBiology,13, 734–760
738 K. E. OWEN et al.
light,andc istheinternalCO concentrationexpressed Table1 Values used for leaf physiological parameters and
i 2
byEqn(8)basedonFick’sLawformoleculardiffusion their definitions that determine the temperature-dependent
ofCO throughthestomataandboundarylayer. responseofleavesaswellasstomatalconductance
2
1:6 P
c ¼c (cid:2) net; ð8Þ Conifer Deciduous Grasslands
i s gs Parameter forests forests andcrops Unit
where cs is the CO2 concentration at the surface of the DHa(Jmax) 47170 47170 40000 Jmol(cid:2)1
leafandgsisthestomatalconductanceforwatervapour DHd(Jmax) 245000 200000 200000 Jmol(cid:2)1
accordingtoBalletal.(1987;cf.Eqn(9)). DS(J ) 643 643 655 Jmol(cid:2)1K(cid:2)1
max
E (R ) 63500 63500 58000 Jmol(cid:2)1
ðP þ0:5R ÞrH a d
gs ¼gs:minþgfac net c d ; ð9Þ Ea(KO) 36000 36000 35900 Jmol(cid:2)1
s f(K ) 159.597 159.597 248 —
O
where gs:min is the minimum stomatal conductance for Ea(Kc) 65000 65000 59500 Jmol(cid:2)1
f(K) 299.469 299.469 404 —
water vapour, gfac is a proportionality constant c
DH (Vc ) 75750 75750 69000 Jmol(cid:2)1
evaluated in chamber experiments, R is the a max
d DH (Vc ) 200000 200000 198000 Jmol(cid:2)1
mitochondrial dark respiration, and rH is the relative d max
DS(Vc ) 656 656 660 Jmol(cid:2)1K(cid:2)1
humidityattheleafsurface. max
f(t) 2339.53 2339.53 2339.53 —
wcisthecarboxylationratesupportedbytheRubisco E (t) (cid:2)28990 (cid:2)28990 (cid:2)28990 Jmol(cid:2)1
a
enzymeataspecifictemperature gfac 9.8 9.8 12 —
Vc c
w ¼ i ; ð10Þ
c c þK ð1þO=K Þ
i c O
DH Activationenthalpyforenzymaticreactions
where Vc is the maximum rate of carboxylation, K is a
c DH Deactivationenthalpyforenzymaticreactions
the Michaelis constant for carboxylation, K is the d
O DS Entropytermforthedeactivationofenzymes
Michaelis constant for oxygenation, and O is the
J Maximumrateofelectrontransport
oxygen concentration of the air [210cm3O2(L air)(cid:2)1]. Emaax Activationenergy
The temperature dependency of carboxylation is R RateofCO evolutionfromprocessesother
d 2
describedas thanphotorespiration
K Michaelis–Mentenconstantforoxygenation
Vc¼VcmaxeDHaðTK(cid:2)298Þ=298RTK(cid:3)1þeð298DS(cid:2)DHdÞ=298R(cid:4); f O Scalingfactor
1þeðDSTK(cid:2)DHdÞ=RTK K Michaelis–Mentenconstantforcarboxylation
c
ð11Þ Vc Maximumrateofcarboxylation
max
t Enzymespecificityfactor
where Vc is the average leaf carboxylation capacity
max gfac Bell–Berrystomatalconductancefactor
at251C,DH isactivationenthalpyofcarboxylation,T
a K
is the estimate of the leaf temperature in the current Parameter values are generalized from (Harley et al., 1986;
modeliteration,Ristheuniversalgasconstant,Sisan Harley & Tenhunen, 1991; Falge et al., 1996; Ryel et al., 2001;
entropy term for deactivation and DH is the Falgeetal.,2003;Flecketal.,2004).Acclimationovertheseason
d
deactivationenthalpyofcarboxylation. fortheparameterslistedisnotconsidered.
w is the carboxylation rate supported by the actual
j
electrontransportrate
P c Parameterestimation – complex model. Leaf physiological
w ¼ m i ; ð12Þ
j c þ2:0 G(cid:1) parameters determining the temperature dependent
i
response of leaves and stomates were held constant at
where P is the maximum potential rate of RuBP
m generalized values established in leaf gas exchange
production and is calculated using the Smith equation
studies (under conditions without water limitation) as
(cf.Tenhunenetal.,1976)
shown in Table 1. Seasonal variation of alpha, the
alpha I average leaf light utilization efficiency without photo-
Pm ¼qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi; ð13Þ
1þðalpha2I2=P2 Þ respiration, and Vcmax, the average leaf carboxylation
ml capacity at 251C, were estimated via model inversion
where alpha is the average leaf light utilization studies with individual site GPP data (cf. Wang et al.,
efficiency without photorespiration, I is the incident 2003). We used the Levenberg–Marquardt method for
PPFD, and P is the CO and light saturated minimizing our objective functions to calculate these
ml 2
temperature dependent potential RuBP regeneration criticalparameters.ThecapacityforRuBPregeneration,
rateasdescribedin Falge(1997). c, and the capacity for leaf respiration, d, were
r2007TheAuthors
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LINKING FLUX NETWORK MEASUREMENTS TO CONTINENTAL SCALE SIMULATIONS 739
considered proportional to the leaf carboxylation thesumoftheabsolutevalueof theresiduals
capacity at 251C, Vc , as in Eqns (14) and (15) X
max o¼ jrj: ð17Þ
i
(Wilsonetal.,2000).
Vc
c¼ max; ð14Þ Parameter estimation – simplified model. Given the
2:1
difficulty of describing spatial variation of multiple
parameters for a process-based model at continental
d¼0:025Vcmax: ð15Þ scales, we tested two simplifying assumptions with
respect to critical parameters to which the models are
sensitive:(1)eliminating seasonalvariation inLAI and
Parameter determinations also require information
assumingLAIequaltotheobservedmaximum,and(2)
with respect to seasonal variation in LAI (described in
assuming alpha to be proportional to Vc [i.e. like c
detail with respect to the presented results below), max
and d in Eqns (14) and (15)] rather than independent.
except in the case of evergreen and coniferous forests
WhileVc intermsofthecomplexmodelisdefinedin
where LAI was considered constant. The parameters max
relation to the carboxylation capacity of the average
alphaandVc wereestimated,asinthecasewiththe
max
leaf, it is, in fact, influenced by experimental errors in
empirical hyperbolic light response model, for three
the measurement of NEE, assumptions made in the
periods in each month. Parameters were not included
estimation of GPP, our inability to obtain detailed
infurtheranalysiswhentherelativestandarderrorwas
spatial estimates of LAI and associate these with
>0.6orwhenparametervalueswerenegativeorabove
variations in NEE, the lack of information on potential
0.17foralphaor350forVc .
max
time-dependent changes in LAI, particularly in conifer
The total estimated canopy assimilation obtained
stands,andassumptionsofthemodels,suchaslackof
with first guess parameter estimates was compared
acclimationalonglightgradientsandnonoccurrenceof
with the gap-filled GPP in an iterative process to
water stress (see Wang et al., 2003 for additional
determine best parameter values. As GPP contains
discussion). Hence, estimates of Vc from the
many uncertainties, such as periods of unusually high max
simplified inversions are subject to interpretation as it
or low carbon uptake due to gaps in measurements
becomes a lumped parameter. To call attention to this,
(further discussed below) and/or effects from flux
partitioning and gap filling, parameters were first we refer to the derived values as Vcuptake2(cid:1) when alpha
estimated with 0% and 10% of GPP values trimmed. andVcmaxareindependent,andasVcuptake1(cid:1) whenalpha
dependsonVc .Additionally,wedefinewhetherLAI
Acomparativeanalysis(notshown)showedthatbetter max
parameter estimation with higher r2 values, fewer variesorisheldconstant.InthecaseofVcuptake1(cid:1),LAIis
alwaysconstantattheobservedseasonalmaximum.
outliers and lower root-mean-square errors occurred
when GPP data were trimmed. Therefore, a nonlinear
least-trimmed squares regression technique was used Results
(Stromberg, 1997; Reichstein et al., 2003a) that seeks to
minimize the sum of squared residuals as ordinary Ecosystem fluxes
nonlinear regression, but with exclusion of the largest
ResultsofthefluxseparationfortheEuropeansitesare
10% of residuals that are assumed to be due to
illustrated in Fig. 1 and summarized for all sites and
contaminated data or due to data inconsistent with
yearsinTable3.ReducedCO uptakeduringwinterat
the model. The technique is able to objectively 2
coniferous forest sites is influenced by the degree of
identifyoutliers,or more preciselydata points that are
continentality (cf. Tharandt) as well as latitude influ-
inconsistent with the model assumptions (Reth et al.,
ences on growing season length (cf. Hyytia¨la¨ and So-
2005).Theobjectivefunctionthatwasminimizedisthe
dankyla¨), where the long cold periods in northern
trimmedsumofsquarederrors(TSSE):
X Europe lead to strong dormancy (Suni et al., 2003b).
TSSE¼ r2; ð16Þ
i The Loobos maritime coniferous site remains active
i(cid:3)0:9N
throughout the year. In the case of deciduous forest
where r is the ith smallest residual, N is the total sites, there is a general decrease in annual GPP from
i
number of data points, and 0.9 is the fraction of south to north in response to growing season length
residuals to be kept. Recent analyses (Hollinger & withtheexceptionofCollelongo(1560ma.s.l.),whichis
Richardson, 2005; Richardson et al., 2006) indicate that influenced by altitude. Further details of forest site
fluxdatameasurementerrorsarenotGaussian,butare comparisons have been discussed for earlier periods
instead characterized by a peaked distribution with in Papale & Valentini (2003) and Valentini (2003). The
long tails. An alternative objective function would be uptake of CO by wetlands, crops, and grasslands is
2
r2007TheAuthors
Journalcompilationr 2007 BlackwellPublishingLtd, GlobalChangeBiology,13, 734–760
740 K. E. OWEN et al.
e p
50MeadowFinland40 30 20 10 0240300360 50BarleyFinland40 30 )120–s 2–10m 2O0C240300360 lom50µMeadow( 0Switzerland04002)(cid:2)+30(cid:1)(cid:1)( ,(cid:1)20 10 0 240300360 50Sugar beetBelgium40 30 20 10 0 240300360 gpersolidline,and,th hearrowsindicatecro
p T
20Jokioinen200215 10 5 0060120180 20Jokioinen200115 10 5 0 060120180 20Oensingen200415 10 5 0060120180 20Lonzee200415 10 5 0 060120180 1bgmeters(),theu2000etalReichstein.(2005).
50FenFinland40 30 20 10 0 180240300360 50WetlandPoland40 30 20 10 0 180240300360 50MeadowGermany40 30 20 10 0 180240300360 50RapeseedGermany40 30 20 10 0 180240300360 observationsforthepara cles,derivedaccordingto
20Kaamanen200115 10 5 0060120 20Rzecin200415 10 5 0060120 20Grillenburg200415 10 5 0 060120 20Gebesee200415 10 5 0 060120 ofyear emCOexchange2shownasopencir
50205020BirchSodankyläPetsikkoPineforestforest20011996Finland40401515Finland303010102020 551010 0000060120180240300360060120180240300360 50205020SpruceforestTharandtHyytiäläforestPineGermany20012002Finland40401515 303010102020 551010 0000060120180240300360060120180240300360 50502020PineforestLoobosMixedforestVielsalmNetherlands2001Belgium200240401515 303010102020 551010 0000060120180240300360060120180240300360 50205020HesseforestBeechCollelongoBeechforest2002France2001Italy40401515 303010102020 551010 0000 060120180240300360060120180240300360 Day 1Resultsofinvertingthesimplehyperboliclightresponsemodelbasedonnetecosyst rsolidline,superimposedonthedailyobservationsofgrossprimaryproduction(GPP),estorgrasslandcutdates.
)1–yad 2–m C g( PPG yliaD Fig. loweharv
r2007TheAuthors
Journalcompilationr 2007 BlackwellPublishingLtd, GlobalChangeBiology,13, 734–760
LINKING FLUX NETWORK MEASUREMENTS TO CONTINENTAL SCALE SIMULATIONS 741
quitevariablewithobviousresponsestoseasonlength, regression (also known as the Reduced Major Axis or
nutrientavailability(comparemaximumdailyratesfor the Model II regression) which considers the errors in
wetlandswiththeintensivelyfertilizedgrasslandOen- both x and y (Sokal & Rohlf, 1995). The slopes of the
singen), and management measures (crop rotation regressions shown for different ecosystem types in
schemes and harvests). Annual NEE was near zero or Fig. 2 are similar. The importance of maintaining the
positive (i.e. source of carbon) at the subarctic fen in suggested grouping is discussed further below in rela-
Kaamanen, depending on snow melt (Laurila et al., tiontophysiological modelinversions.
2001), at the Jokioinen site with both barley and grass- The parameter g provides an estimate of average
landduetohighrespiratoryfluxesfromtheunderlying observed daytime ecosystem respiration during 10-
peat soil, and at the Sodankyla¨ pine forest. Latent heat day periods as obtained from the fitting of the canopy
exchangeestimatedateachsiteissummarizedinTable3 lightresponsecurveandonthebasisofhalf-houreddy
forreference. flux measurements. In the process of flux partitioning,
the value for R is derived and similarly provides an
ref
estimate of seasonal changes in ecosystem respiratory
Hyperbolic light response parameters
capacity based on the evaluation of temperature re-
All sites and years were analysed with respect to the sponse during night-time. g and R are related with
ref
reported results (not shown), but in order to enhance r2 values between 0.46 and 0.86 (Fig. 3). Thus, the
thereadabilityofthegraphsandasresultsweresimilar, parameters provide a consistent picture of seasonal
only1yearpersiteisshownonthecomparisonfigures. influencesonRecoeventhoughdifferentdata(daytime
Theyearthatwaschosenprovidedthebestfit(highest vs.night-time)wereused.
r2 values). Results of inverting the simple hyperbolic
light response model based on NEE observations are
Process-based model parameters
shown for the parameters (b1g) and g superim-
2000
posedonthedailyobservationsofGPPinFig.1(gisthe Inversions to estimate the parameters alpha and
lower curve in each figure). As seen in the individual Vcuptake2(cid:1) for the physiological model were carried out
siteseasonalcourses inFig.1andin the regressionsin for those sites where confidence in estimates and mea-
Fig. 2, daily GPP is highly correlated with the average surements of LAI were best. This was especially of
maximumcanopyuptakecapacity,(b1g) ,observed concern for summer active ecosystems with strong
2000
for 10-day periods (r2 in Fig. 2 between 0.57 and 0.94) seasonal change in LAI. Thus, three pine sites, the
for all ecosystem types. Nevertheless, there are some Tharandt dense spruce forest, the Hesse beech forest,
periods, for example late in the season at Hyytia¨la¨, grasslands at Jokioinen and Grillenburg, and the crop
Loobos,andTharandt,wheretheapparentrelationship sitesLonzeeandKlingenbergwerestudiedasindicated
shifts.Thisshiftdependsontherelativelengthoftime in Fig. 4. Vcuptake2(cid:1) and alpha are negatively correlated
thecanopyperformsunderhighorlowlightconditions, with an upper quartile, average, and lower quartile of
which changes over the course of aseason. Such shifts (cid:2)0.29,(cid:2)0.54,and(cid:2)0.77,respectively,soacertaindegree
contributetothescatterinrelationshipsshowninFig.2 ofnoisewasassociatedwiththeseasonalcoursesforthe
and reduce the r2 values of the correlations. Other two parameters as they varied with respect to prefer-
differences stem from the comparison between the enceinthesearchforminimizingresidualerrors.Onthe
estimates of a, b, and g obtained using ungap-filled other hand, general seasonal trends were recognizable
NEEdataandthegap-filleddailyGPPdata.Vielsalm,a inallcasesandwerecompatiblewithtrendsfoundfor
mixed forest, was included in the analysis of both the themorerestrictedone-parameterfitsdiscussedbelow.
denseconiferousanddeciduousforests. Reasons for the sensitivity in parameterestimates may
Giventhesimplicityof thehyperboliclightresponse have to do with the change in importance of limiting
model, the inversion solutions are obtained in a very andhighlightconditionsduringindividualperiods,or
dependable fashion with few difficulties arising in the mayberelatedtotheimpositionofdefinedtemperature
statistical fitting of light response curves. a and b are response curves based on previous cuvette gas ex-
negatively correlated with an upper quartile, average, changeexperimentationthatarenotidealfordescribing
and lower quartile of (cid:2)0.73, (cid:2)0.51, and (cid:2)0.36, respec- overall gas exchange of the canopy, or may relate to
tively. a and g are very positively correlated with an time-dependentchange in measurement errors.Never-
upperquartile,average,andlowerquartileof0.88,0.82, theless,Vcuptake2(cid:1) asinthecaseof(b1g)2000waslinearly
and0.81,respectively.bandgarenotcorrelatedwithan relatedtoGPP(cf.Table4)eventhoughmorescattered.
upper quartile, average, and lower quartile of 0.17, The relationship of Vcuptake2(cid:1) to the parameter
(cid:2)0.01, and (cid:2)0.27, respectively. All regressions shown (b1g) from the hyperbolic light response model
2000
in the scatter plot figures use the geometric mean (noting both provide an estimate of total canopy CO
2
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742 K. E. OWEN et al.
Table2 EddycovariancefluxmeasurementsitesaccordingtovegetationtypeincludedinthecomparisonofGPP,Recoandmodel
parameters
Latitude Longitude
Sitelocationanddominantspecies (1N) (1E) Measurementmethods*
Coniferousforest
Tharandt,Germany 50.96 13.57 Bernhoferetal.(2003)
Piceaabies
Loobos,theNetherlands 52.17 5.74 Dolmanetal.(2002)
Pinussylvestris
Hyytia¨la¨,Finland 61.85 24.29 Ranniketal.(2004,2002),Sunietal.(2003a,b)
Pinussylvestris
Sodankyla¨,Finland 67.36 26.64 Aurela(2005),Sunietal.(2003b)
Pinussylvestris
Mixedforest
Vielsalm,Belgium 50.31 6.00 Aubinetetal.(2002,2001)
Pseudotsugamenziesii,Fagussylvatica
Deciduousforest
Collelongo,Italy 41.85 13.59 Valentinietal.(2000,1996)
Fagussylvatica
Hesse,France 48.67 7.07 Granieretal.(2002,2000a,b),Lebaubeetal.(2000)
Fagussylvatica
Hainich,Germany 51.08 10.45 Anthonietal.(2004),Knohletal.(2003)
Fagussylvatica
Soroe,Denmark 55.49 11.65 Pilegaardetal.(2003,2001)
Fagussylvatica
Petsikko,Finland 69.47 27.23 Aurelaetal.(2001b),Laurilaetal.(2001)
Betulapubescens
Wetlands
Rzecin,Poland 52.77 16.30 *
Scirpus,Carex,shrubsandmoss
Kaamanen,Finland 69.14 27.30 Aurelaetal.(2002,2001a),Laurilaetal.(2001)
Carexspp.,Betulanana,shrubsandmoss
Grasslandsandmeadows
Oensingen(intensivelymanaged),Switzerland 47.29 7.73 Ammannetal.(2007),Flechardetal.(2005)
Alopecuruspratensis,Lolliumperenne
Grillenburg,Germany 50.95 13.51 *
Festucapratensis,Alopecuruspratensis,Phleumpratensis
Jokioinen,Finland 60.90 23.51 Lohilaetal.(2004)
Poapratense,Festucapratensis
Crops
Gebesee,Germany 50.10 10.91 Anthonietal.(2004)
BrassicaNapusNapus
Lonzee,Belgium 50.55 4.75 Moureauxetal.(2006)
Betavulgarisl.
Klingenberg,Germany 50.89 13.52 *
Hordeumvulgaris
Jokioinen,Finland 60.90 23.51 Lohilaetal.(2004)
Hordeumvulgaris
*AllsitesexceptJokioinenandPetsikkoaredescribedintheCarboEuropedatabase.
uptakecapacity)isshowninFig.4(r2varyingbetween functional types exist among coniferous stands, espe-
0.48 and 0.70). The slope of the relationships are quite ciallyashigheractivityofneedlesofpineascompared
similarforallecosystemtypesexceptpineforestswhere withNorwayspruceinCentralEuropehasbeennoted
much higher Vcuptake2(cid:1) is predicted. The differences in previously (Ryel et al., 2001; Falge et al., 2003). How-
Vcuptake2(cid:1) inthepineforestscouldsuggestthatdifferent ever, inaccurate values of LAI may also contribute, as
r2007TheAuthors
Journalcompilationr 2007 BlackwellPublishingLtd, GlobalChangeBiology,13, 734–760