NPP = PAR fpar LUE T T W

Transcription

1 1 Supplemental Online Material 2 Model description 3 CASA model 4 CASA is a classic light use efficiency model that utilises satellite measurements to 5 estimate vegetation net primary production (Potter et al., 1993). It directly translates radiation into 6 Net Primary Production (NPP) based on the notion of light use efficiency (LUE), which is a 7 product of optimal efficiency and the regulatory functions of environmental factors (e.g., 8 9 temperature and water stress). The complete expression for NPP in the CASA is thus given by: NPP = PAR fpar LUE T T W (1) max s1 s2 s 10 where PAR is the incident photosynthetically active radiation per time period, fpar is the fraction 11 of PAR absorbed by the vegetation canopy, LUE max is the potential LUE (g C m -2 MJ -1 APAR) 12 without environment stress, T s1 and T s2 are two downward-regulation scalars for the effects of 13 temperature, W s is the downward-regulation scalar for the effects of moisture on LUE of 14 vegetation. 15 fpar is calculated as a linear function of the simple ratio (SR) according to Sellers et al. 16 (1993), 17 fpar = min{ SR / ( SR SR ) SR / ( SR SR ),0.95} (2) max min min max min 18 SR = (1 + NDVI) / (1 NDVI) (3) 19 where SR min represents SR for unvegetated land areas and is set to 1.08 for all grid cells. SR max 20 approximates the value at which all downwelling solar radiation is intercepted and corrects for 21 effects of canopy architecture and residual cloud contamination. NDVI is Normalized Difference 22 Vegetation Index. 1

2 23 T s1 serves to depress LUE max at very high and very low temperatures (T s1 ) and to depress 24 LUE max when the temperature is above or below the optimum temperature (T opt ), where T opt is 25 defined as the air temperature in the month when the NDVI reaches its maximum for the year. T s2 26 reflects the concept that the efficiency to light utilization should be depressed when plants are 27 growing at temperatures displaced from their optimum, has an asymmetric bell shape that falls off 28 more quickly at high than at low temperature. W s represents water stress on light use efficiency 29 using actual ecosystem evapotranspiration (ET) and potential evapotranspiration (PET). T s1, T s and W s are calculated as the following equations. Ts 1 = Topt Topt Topt (4) [0.2( Topt 10 T) [0.3( Topt 10 + T) Ts 2 = /{1 + e }/{1 + e } (5) W = ε ( ET / PET ) (6) CASA model simulates directly NPP, and an approximate conversion of 0.5 between 35 NPP and GPP is used in this study CFix model 38 C-Fix is a light use efficiency type parametric model driven by temperature (T), PAR 39 and fpar (Veroustraete et al., 2002). The model uses the following equations to estimate vegetation GPP on a daily basis: GPP = PAR fpar LUE ρ( T ) CO fert (7) wl 2 42 where LUE wl is light use efficiency by considering the impact of water stress. ρ(t) is the 43 normalised temperature dependency factor, defined according to Veroustraete et al. (1994); 44 CO 2 fert is the normalised CO 2 fertilisation factor, defined according to Veroustraete et al. (1994). 2

3 45 Verstraeten et al. (2006) integrated the impact of water limitation on light use efficiency 46 by considering two stomatal regulating factors from soil moisture deficit (F s ) and atmospheric 47 changes (F a ), which were simulated by soil moisture and evaporative fraction (EF) respectively. 48 Due to the difficulties at regional simulations of soil moisture, we only consider the impacts of 49 atmospheric changes on LUE in this study, and simplified the regulation equation of water limitation as following. LUE = ( LUE + F ( LUE LUE )) (8) wl min a max min 52 where LUE wl was delimited between a maximum (LUE max ) and minimum value (LUE min ) (g C 53 MJ -1 APAR). 54 CFix model uses a linear equation to describe the relationship between fpar and NDVI, and uses a set of empirical constants according to Myneni and Williams (1994): fpar = NDVI (9) 57 The temperature dependency factor p(t) is described by Wang (1996). CO 2 fertilisation 58 equation was defined by Veroustraete (1994) as the increase in carbon assimilation due to CO 2 59 levels above the atmospheric background level (or reference level) ρ( T ) = e 1+ e HaP, ( Cl ) R g T S T HdP, ( ) Rg T [ O ] [ CO2 ] CO 2 2fert = s [ O ] [ O ] [ CO ] K (1 + ) + [ CO ] [ O2 ] 2 ref Km (1 + ) + [ CO2 ] K0 ref m 2 2s K0 (10) (11) 62 where C l, ΔS, ΔH a,p, ΔH d,p, R g at the temperature response equation are 21.77, J K -1 mol -1, J mol -1, J mol -1, 8.31 J K -1 mol -1 according to Veroustraete et al. (2002); the 64 parameter values of s, K m, K o, [CO 2 ] ref are 2550, 948, 30 and 281 ppm respectively. In this study, 3

4 65 [O 2 ] was set to ppm, and [CO 2 ] was set to be annual mean global carbon dioxide 66 concentration using measurements of weekly air samples from the Cooperative Global Air 67 Sampling network ( CFlux model 70 The carbon flux model (CFlux) integrates data from multiple sources (Turner et al., ; King et al., 2011), and model inputs include daily meteorological data and satellite-derived 72 information on land cover, stand age and MODIS-fPAR product. A unique feature for CFlux 73 model is that GPP is influenced by stand age. The model can be represented as the following equations: GPP = PAR fpar LUE eg (12) LUE = LUE _ T min( W, S ) S (13) eg g base s s SWg SAg LUE = ( LUE LUE ) S + LUE (14) base max cs CI cs 78 The scalars for minimum temperature (T s ) and vapour pressure deficit (W s ) are formulated as in 79 MODIS-GPP product (i.e. equation (20, 21)) with a linear ramp (1 to 0) between a value when the 80 influence is at a minimum and a value when it is at a maximum (i.e. when LUE is reduced to 0). 81 The scalar for the influence of soil water (S SWg ) is based on the ratio of current soil water content 82 to soil water holding capacity (WHC). When the ratio is above a value of 0.5, S SWg is set to 1.0 (no 83 influence) and below a ratio of 0.5 there is a linear ramp from S SWg of 1 to an S SWg of 0 as the ratio 84 hits 0. We simplified the simulations of soil moisture and used evaporative fraction (EF) to 85 indicate S SWg. In the case of forest cover types, a scalar for the effect of stand age on GPP (S SAg ) is 86 implemented to reflect observations of reduced vegetation production in older stands (Van Tuyl et 4

5 87 al., 2005). S CI cloudiness index scalar that varies from 0 on clear days to 1 on fully overcast days 88 and is inferred from the ratio of PAR to potential PAR (Turner et al., 2006). LUE cs is initial LUE 89 for clear sky days. LUE max is initial LUE for overcast days. The age scalar (S Sag ) is equal to 1 for 90 non-forest vegetation types. Above a specified minimum age, S Sag declines asymptotically to a 91 value of , depending on forest type (Turner et al., 2006) EC-LUE model 94 Yuan et al. (2007, 2010) developed Eddy Covariance-Light Use efficiency (EC-LUE) 95 model to simulate daily vegetation GPP. The EC-LUE model is driven by only four variables: 96 normalised Difference Vegetation Index (NDVI), Photosynthetically Active Radiation (PAR), air temperature (T), and the Bowen ratio of sensible to latent heat flux. GPP = PAR fpar LUEmax Min( Ts, Ws) (15) fpar = 1.24 NDVI (16) 100 T s ( T Tmin ) ( T Tmax ) = (17) 2 (( T T ) ( T T ) ( T T ) ) min max opt 101 LE W s = (18) R n 102 where LUE max is the potential light use efficiency without environmental stress (g C m -2 MJ APAR). Min denotes the minimum values of T s and W s, and we assumed that the impacts of 104 temperature and moisture on LUE follow Liebig s Law (i.e., LUE is only affected by the most 105 limiting factor at any given time). T min, T max and T opt are the minimum, maximum and optimum air 106 temperatures ( ) for photosynthetic activity, respectively. If air temperature falls below T min or 107 increases beyond T max, T s is set to zero. In this study, T min and T max were set to 0 and 40, 5

6 108 respectively, while T opt was determined using nonlinear optimisation as 21 (Yuan et al., 2007). 109 LE is latent heat (MJ m -2 ), which is estimated by the revised RS-PM (Remote Sensing Penman 110 Monteith) model (Yuan et al., 2010). R n is net radiation (MJ m -2 ) MODIS-GPP product 113 MODIS-GPP algorithms (Running et al., 2000) rely heavily on the LUE approach, with 114 inputs from MODIS LAI/fPAR (MOD15A2), land cover, and biome-specific climatologic data 115 sources from NASA s Data Assimilation Office. Light use efficiency is calculated based on two 116 factors: the biome-specific maximum conversion efficiency LUE max, a multiplier that reduces the 117 conversion efficiency when cold temperatures limit plant function, and a second multiplier that 118 reduces the maximum conversion efficiency when vapour pressure deficit (VPD) is high enough 119 to inhibit photosynthesis. It is assumed that soil water deficit covaries with VPD and that VPD will account for drought stress. GPP = PAR fpar LUEmax Ts Ws (19) TMIN < TMIN TMIN TMIN = < < 1 TMIN > TMIN min Ts TMINmin TMIN TMINmax TMINmax TMINmin 0 VPD > VPD VPD VPD min Ws = VPDmin < VPD < VPDmax VPDmax VPDmin 1 VPD < VPD min max max min (20) (21) 124 where LUE max is the potential light use efficiency without environmental stress (g C m -2 MJ APAR). Based on the at-launch landcover product (MOD12), a set of biome-specific radiation use 126 efficiency parameters are extracted from the Biome Properties Look-Up Table (BPLUT) for each 6

7 127 pixel. There are five parameters used to calculate GPP VPM model 130 In the Vegetation Production Model (VPM) (Xiao et al., 2004), the potential LUE is 131 affected by temperature, land surface moisture condition and leaf phenology. The following is a brief description of the VPM model: GPP = PAR fpar LUEmax Ts Ws Ps (22) In the current version of the VPM model, fpar is assumed to be a linear function of EVI, and the 135 coefficient is simply set to be 1.0 (Xiao et al., 2004). T s, W s and P s are the down-regulation scalars 136 for the effects of temperature, water and leaf phenology on the light use efficiency of vegetation, 137 respectively. T s is estimated at each time step, using the equation developed for the Terrestrial 138 ecosystem Model (Raich et al., 1991) as shown at equation (17). 139 The VPM also utilizes the LSWI (Land Surface Water Index) (Xiao et al., 2004) to help 140 capture effects of water stress and phenology on plant photosynthesis: 141 LSWI ρ = ρ NIR NIR ρ + ρ SWIR SWIR (23) 142 where NIR refers to the nm band, and SWIR refers to nm. Water index was 143 presented as: 144 W s 1+ LSWI = (24) 1 + LSWI max 145 where LSWI max is the maximum LSWI within the plant growing season for individual pixels. P scalar 146 is included to account for the effect of leaf phenology (leaf age) on photosynthesis at the canopy 147 level. In this version of the VPM model, calculation of P s was dependent upon the longevity of 7

8 148 leaves (deciduous, versus evergreen). For a canopy that was dominated by leaves with a life 149 expectancy of 1 year (one growing season, e.g., deciduous trees), P s was calculated at two 150 different phases as a linear function of LSWI from bud burst to leaf full expansion (phase one) by 151 P s 1+ LSWI = (25) After leaf full expansion (phase two), P s was set to 1, and equation (26) was adopted 153 again during senescence (phase three). The dates for the three phases of phenology (bud burst, full 154 canopy, and senescence) were obtained using an EVI seasonal threshold similar to that of the 155 MODIS phenology product MOD12Q2 (Friedl et al., 2003). Thus for large-scale application of the 156 VPRM across North America, MOD12Q2 dates can be used directly VPRM model 159 Formulation of the VPRM starts from the Vegetation Photosynthesis Model (VPM) of 160 Xiao et al. (2004), which estimates GPP using satellite-based vegetation indices and 161 environmental data, adding a nonlinear function to account for the response of GPP to light. The 162 VPRM can be presented by GPP = PAR fpar LUEmax T P W (1 + PAR / PAR ) 0 s s s (26) 164 where PAR 0 is the half-saturation value. The other variables were calculated as the VPM models References 167 Friedl, M. A., McIver, D. K., Hodges, J. C. F., Zhang, X. Y., Muchoney, D., Strahler, A. H., 168 Woodcock, C.E., Gopal, S., Schneider, A., Cooper, A., Baccini, A., Gao, F., and Schaaf, C.: 169 Global land cover mapping from MODIS: algorithms and early results, Remote Sensing of 8

9 170 Environment, 83, , King, D. A., Turner, D. P., and Ritts, W. D.: Parameterization of a diagnostic carbon cycle model 172 for continental scale application, Remote Sensing of Environment, 115, , Myneni, R. B., and Williams, D. L.: On the relationship between FAPAR and NDVI, Remote 174 Sensing of Environment, 49, , Potter, C. S., Randerson, J. T., Field, C. B., Matson, P. A., Vitousek, P. M., Mooney, H. A., and 176 Klooster, S. A.: Terrestrial ecosystem production: a process model based on global satellite and 177 surface data, Global Biogeochemical Cycles, 74, , Raich, J. W., Rastetter, E. B., Melillo, J. M., Kicklighter, D. W., Steudler, P. A., Peterson, B. J., 179 Grace, A. L., Moore III, B., and Vorosmarty, C. J.: Potential net primary productivity in South 180 America: application of a global model, Ecological Applications, 1, , Running, S. W., Thornton, P. E., Nemani, R., and Glassy, J. M.: Global terrestrial gross and net 182 primary productivity from the Earth Observing System, Methods in ecosystem science, 44-57, Turner, D. P., Ritts, W. D., Styles, J. M., Yang, Z., Cohen, W. B., Law, B. E., and Thornton, P. E.: 185 A diagnostic carbon flux model to monitor the effects of disturbance and interannual variation 186 in climate on regional NEP, Tellus B, 585, , Van Tuyl S., Law B. E., Turner D. P., and Gitelman A. I.: Variability in net primary production and 188 carbon storage in biomass across Oregon forests-an assessment integrating data from forest 189 inventories, intensive sites, and remote sensing, Forest Ecology and Management, 209, , Veroustraete, F.: On the use of a simple deciduous forest model for the interpretation of climate 9

10 192 change effects at the level of carbon dynamics, Ecological modeling, 75-76, , Veroustraete, F., Sabbe, H., and Eerens, H.: Estimation of carbon mass fluxes over Europe using 194 the C-Fix model and Euroflux data, Remote Sensing of Environment, 833, , Verstraeten, W. W., Veroustraete, F., and Feyen, J.: On temperature and water limitation of net 196 ecosystem productivity: Implementation in the C-Fix model, Ecological Modelling, 199, 4-22, Xiao, X. M., Zhang, Q. Y., Braswell, B., Urbanski, S., Boles, S., Wofsy, S., Moore III, B., and 199 Ojima, D.: Modeling gross primary production of temperate deciduous broadleaf forest using 200 satellite images and climate data, Remote Sensing of Environment, 912, , Yuan, W. P., Liu, S. G., Yu, G. R., Bonnefond, J. M., Chen, J. Q., Davis, K., Desai, A. R., 202 Goldstein, A. H., Gianelle, D., Rossi, F., Suyker, A. E., and Verma, S. B.: Global estimates of 203 evapotranspiration and gross primary production based on MODIS and global meteorology data, 204 Remote Sensing of Environment, 1147, , Yuan, W. P., Liu, S. G., Zhou, G. S., Zhou, G. Y., Tieszen, L. L., Baldocchi, D., Bernhofer, C., 206 Gholz, H., Goldstein, A. H., Goulden, M. L., Hollinger, D. Y., Hu, Y., Lawn, B. E., Stoy, P. C., 207 Vesala, T., and Wofsy, S. C.: Deriving a light use efficiency model from eddy covariance flux 208 data for predicting daily gross primary production across biomes, Agricultural and Forest 209 Meteorology, 1433, ,

11 211 Table S1. Name, location, and other characteristics of the study sites used in this study. Vegetation Type Site Latitude Longitude DBF Ca-oas N W DBF De-hai N E DBF Dk-sor N E DBF Fr-fon N 2.78 E DBF Fr-hes N 7.06 E DBF It-col N E DBF It-non N E DBF It-pt N 9.06 E DBF It-ro N E DBF It-ro N E DBF It-vig N 8.85 E DBF Se-abi N E DBF Uk-ham N 0.86 W DBF Uk-pl N 1.27 W DBF Us-bar N W DBF Us-bn N W DBF Us-dk N W DBF Us-ha N W DBF Us-lph N W DBF Us-mms N W DBF Us-moz N W DBF Us-umb N W DBF Us-wcr N W DBF Us-wi N W DBF Us-wi N W EBF Au-tum S E EBF Au-wac S E EBF Br-ban 9.82 S W EBF Br-ma S W EBF Br-sa S W EBF Cn-bed N E EBF Cn-ku N E EBF Fr-pue N 3.60 E EBF Gf-guy 5.28 N W EBF Id-pag 2.35 N E EBF It-cpz N E EBF It-lec N E EBF Pt-mi N 8.00 W EBF Vu-coc S E ENF Ca-ca N W 11

12 ENF Ca-ca N W ENF Ca-ca N W ENF Ca-man N W ENF Ca-ns N W ENF Ca-ns N W ENF Ca-ns N W ENF Ca-ns N W ENF Ca-ns N W ENF Ca-obs N W ENF Ca-ojp N W ENF Ca-qcu N W ENF Ca-qfo N W ENF Ca-sf N W ENF Ca-sf N W ENF Ca-sj N W ENF Ca-sj N W ENF Ca-sj N W ENF Ca-tp N W ENF Ca-tp N W ENF Ca-tp N W ENF Ca-tp N W ENF Cz-bk N E ENF De-har N 7.60 E ENF De-tha N E ENF De-wet N E ENF Es-es N 0.32 W ENF Fi-hyy N E ENF Fi-sod N E ENF Fr-lbr N 0.77 W ENF Il-yat N E ENF It-lav N E ENF It-ren N E ENF It-sro N E ENF Nl-loo N 5.74 E ENF Ru-fyo N E ENF Ru-zot N E ENF Se-fla N E ENF Se-nor N E ENF Se-sk N E ENF Se-sk N E ENF Sk-tat N E ENF Uk-gri N 3.80 W ENF Us-blo N W 12

13 ENF Us-dk N W ENF Us-fmf N W ENF Us-fuf N W ENF Us-ho N W ENF Us-ks N W ENF Us-me N W ENF Us-me N W ENF Us-me N W ENF Us-me N W ENF Us-nc N W ENF Us-nr N W ENF Us-sp N W ENF Us-sp N W ENF Us-wi N W ENF Us-wi N W ENF Us-wi N W ENF Us-wi N W ENF Us-wrc N W GRA At-neu N E GRA Ca-let N W GRA Ch-oe N 7.73 E GRA Cn-du N E GRA Cn-ham N E GRA Cn-xi N E GRA Cn-xi N E GRA Cz-bk N E GRA De-gri N E GRA De-meh N E GRA Dk-lva N E GRA Es-vda N 1.45 E GRA Fi-sii N E GRA Fr-lq N 2.74 E GRA Fr-lq N 2.74 E GRA Hu-bug N E GRA Hu-mat N E GRA Ie-dri N 8.75 W GRA It-amp N E GRA It-be N E GRA It-mal N E GRA It-mbo N E GRA Nl-ca N 4.93 E GRA Nl-hor N 5.07 E GRA Pt-mi N 8.02 W 13

14 GRA Ru-ha N E GRA Ru-ha N E GRA Uk-ebu N 3.21 W GRA Us-arb N W GRA Us-arc N W GRA Us-aud N W GRA Us-bkg N W GRA Us-cav N W GRA Us-dk N W GRA Us-fwf N W GRA Us-goo N W GRA Us-ib N W GRA Us-var N W GRA Us-wkg N W GRA Ru-che N E MF Be-bra N 4.52 E MF Be-jal N 6.07 E MF Be-vie N 6.00 E MF Ca-gro N W MF Cn-cha N E MF Jp-tef N E MF Jp-tom N E MF Us-pfa N W MF Us-syv N W DBF: deciduous broadleaf forest; EBF: evergreen broadleaf forest; ENF: evergreen needleleaf forest; GRA: grassland; MF: mixed forest

15 Figure S1. The location of sites in this study. DBF: deciduous broadleaf forest; EBF: evergreen broadleaf forest; ENF: evergreen needleleaf forest; GRA: Grassland; MF: Mixed forest. 15