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Target Journal: Remote Sensing of Environment
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Global Comparison of Light Use Efficiency Models for Vegetation Gross Primary
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Production based on Eddy Covariance Towers Data
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Wenping Yuan1, Wenwen Cai1, Jiquan Chen2, Shuguang Liu3,4, Wenjie Dong1, Dan Liu1,
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Jiangzhou Xia1, Yang Chen1, other coauthors
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1State
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Change and Earth System Science, Beijing Normal University, Beijing 100875, China;
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2Department
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3United
Key Laboratory of Earth Surface Processes and Resource Ecology, College of Global
of Environmental Sciences, University of Toledo, Toledo, OH 43606, USA;
States Geological Survey, Earth Resources Observation and Science Center, Sioux Falls,
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South Dakota 57198, USA;
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4
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South University of Forestry and Technology, Changsha, Hunan 410004, China;
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Abstract
State Engineering Laboratory of Southern Forestry Applied Ecology and Technology, Central
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Predicting the gross primary productivity (GPP) of terrestrial ecosystems has been a
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major challenge in quantifying the global carbon cycle. Among all the predictive methods, the
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light use efficiency (LUE) model may have the most potential to adequately address the spatial
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and temporal dynamics of GPP because of its theoretical basis and practicality. Many different
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LUE models have been developed recently, but our understanding of the relative merits of
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different models is poor. Using carbon flux measurements data from 155 eddy covariance sites, we
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assessed the ability of seven LUE models and compared the major model algorithms.
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Comparisons between modeled and observed GPP showed that the model performance
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substantially differed among ecosystem types. In generally, high model performance was found
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over the deciduous broadleaf forests and mixed forests, and low performance was observed over
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the evergreen broadleaf forests and shrublands. Except CFlux model, over the cloudy and overcast
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days, other six models showed a significant underestimation due to ignoring the impacts of diffuse
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radiation on light use efficiency. Among seven models, CFlux and EC-LUE showed the better
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performance than averaged level at the 76% and 75% sites. All models were examined for
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simulating the interannual variability of GPP observations, and the higher simulation accuracy was
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found at CFlux and EC-LUE models. Paired comparisons showed the models differences majorly
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resulted from the differences of environmental regulations equations compared with the fraction of
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PAR absorbed by the vegetation canopy, and especially water stress equations substantially
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differed among seven models.
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Key words
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Gross primary production; Light use efficiency; CASA; C-Fix; CFlux; EC-LUE;
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MODIS; VPM; VPRM;
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1. Introduction
Terrestrial gross primary productivity (GPP) is the largest component flux of the global
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carbon cycle, and is about 20 times greater than the amount of carbon from anthropogenic sources
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(Canadell et al., 2007). Thus, even small fluctuations in GPP can cause large changes in the
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airborne fraction of anthropogenic carbon and influence future climate warming scenarios
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(Raupach et al., 2008). Terrestrial GPP also provides important societal services through provision
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of food, fiber and energy. Regular monitoring of terrestrial GPP is therefore required to understand
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and assess dynamics in the global carbon cycle, forecast future climate, and ensure long term
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security in services provided by terrestrial ecosystems (Bunn and Goetz, 2006; Schimel, 2007).
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Numerous of ecosystem models have been widely developed as a means of quantifying
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spatial-temporal variations in GPP at large scales. However, different ecosystem models are
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inconclusive regarding the magnitude and spatial distribution of GPP at the regional and global
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scales. Recently, the model comparison, using standardized data from the North American Carbon
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Program, showed none of the models consistently reproduce observed interannual variability
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within measurement uncertainty because models can not represent the variability in spring
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phenology, soil thaw and snowpack melting, and lagged response of ecosystems to extreme
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climatic events (Keenan et al., 2012). Another study evaluated simulated daily average GPP from
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26 models against estimated GPP at 39 eddy covariance flux tower sites across the United States
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and Canada, and showed none of the models in this study match estimated GPP within the range
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of uncertainty of observed fluxes, indicating the poor model performance (Schaefer et al., 2012).
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These conclusions were supported by the previous comparison of 16 dynamic global vegetation
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models that suggested the lowest estimation of global NPP (39.9 Pg C) by the Hybrid model was
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approximately 50% smaller compared to what was estimated by the TURC model (80.5 Pg C)
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(Cramer et al., 19999).
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The light use efficiency (LUE) model based on the satellite data may have the most
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potential to adequately address the spatial and temporal dynamics of GPP because of its
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theoretical basis and practicality (Running et al., 2000). Independently and as a part of integrated
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ecosystem models, the LUE approach has been used to estimate GPP and net primary production
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(NPP) at various spatial and temporal scales (Potter et al., 1993; Prince and Goward, 1995;
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Landsberg and Waring, 1997; Running et al., 2000; Xiao et al., 2004; Coops et al., 2005).
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Numerous of studies have validated LUE models at regional and global scales in a variety of
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major ecosystem types (Potter et al., 1993; Turner et al., 2006).
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LUE models are often developed based on the unique assumptions driving by different
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environmental variables, and formulate the processes controlling vegetation production in
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different ways. Thus, there is diversity in both the complexity of the LUE model structure and
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formulation though all of them follow the light use efficiency principle. Each model, therefore, is
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a complex combination of scientific hypotheses and choices, and their estimates depend on these
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inherent assumptions (Beer et al., 2010). Recent studies showed the large model uncertainties
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within LUE models. For example, using satellite-based models, estimated GPP for North America
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vary considerably between 12.2 and 18.7 Pg C yr-1 (Huntzinger et al., 2012). Available individual
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model validations, however, are not sufficient to identify the sources of model differences and
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shortcomings due to differences at validation datasets and driving variables. Therefore, in order to
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move towards more robust estimates of vegetation production dynamics, it is necessary to first
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compare estimates from a variety of LUE models, as well as evaluate estimates against consistent
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and extensive measurements that are available (Running et al., 2004; Heinsch et al., 2006).
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In this study, we evaluated how well seven different satellite-based models capture
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spatio-temporal variations of GPP. The overarching goals of this study are to (1) examine the
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model performance at the numerous of eddy covariance sites and (2) compare the temperature and
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water response curves among the seven LUE models.
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2. Model and data
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2.1 Light use efficiency model
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The LUE model is built on two fundamental assumptions (Running et al., 2004): (1) the
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ecosystem GPP is directly related to absorbed photosynthetically active radiation (APAR) through
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LUE, where LUE is defined as the amount of carbon produced per unit of APAR, and (2) realised
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LUE may be reduced below its theoretical potential value by environmental stresses, such as low
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temperatures or water shortages (Landsberg et al., 1986). The general form of the LUE model is:
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GPP  PAR  fPAR  LUEmax  f (Ts ,Ws , )
(1)
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where PAR is the incident photosynthetically active radiation (MJ m-2) per time period (e.g., day or
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month), fPAR is the fraction of PAR absorbed by the vegetation canopy, LUEmax is the potential
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LUE (g C m-2 MJ-1 APAR) without environment stress, f is a scalar varying from 0 to 1 that
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represents the reduction of potential LUE under limiting environmental conditions, Ts and Ws are
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temperature and water downward regulation scalars, and the multiplication of LUEmax and f is
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realised LUE.
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In this study, seven LUE models were selected to conduct the global comparison of
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model performance, including CASA (Potter et al., 1993), CFix (Veroustraete et al., 2002), CFlux
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(Turner et al., 2006; King et al., 2011), EC-LUE (Yuan et al., 2007, 2010), MODIS-GPP (Running
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et al., 2000), VPM (Xiao et al., 2004) and VPRM model (Mahadevan et al., 2008). The detailed
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model introduction and model operation can be found at Supplemental Online Material (SOM).
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2.2 Data and method
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LaThuile FLUXNET dataset was used in this study (http://www.fluxdata.org). Totally,
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155 eddy covariance (EC) towers were included in this study, from six major terrestrial biomes:
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evergreen broadleaf forest (EBF), deciduous broadleaf forest (DBF), mixed forest (MIF),
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evergreen needleleaf forest (ENF), shrubland (SHR) and grassland (GRA) (Table S1; Figure S1).
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Detailed information on data processing and site information (i.e. vegetation, climate and soils)
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are available at the LaThuile FLUXNET Internet sites.
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We examined the model performance using calibrated parameter values. Eighty percent
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sites were selected to calibrate model parameters for each vegetation type, and other 20% sites
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were used to validate models. This parameterization was repeated by 100 times, and the calibrated
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parameter values were collected within Table 1. Annual simulated and observed GPP were
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calculated in order to investigate the model performance on interannual variability of GPP. If
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missing daily data was 20% of entire year data, the value of this year was indicated as missing.
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For a site to be included for evaluating interannual variability, it had to have minimum of 3 years
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of GPP observations and simulations. Based on this criterion, 100 sites consisting of 462 years
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were included into the analysis (Table S1). We calculated the standard deviations of annual
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averaged GPP observations and simulations for each site, and examined the correlations through
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all 100 sites. Moreover, cloudiness index (CL), which was calculated by the ratio of PAR and
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potential PAR, was used to indicate the friction of cloud cover. The days when CL is less than 0.3
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were indicated as clear days, the CL ranges 0.3 to 0.6 for cloudy days and more than 0.6 were
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indicated as overcast days. Similarly, water stress was separated into three levels (i.e. drought,
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normal and wet conditions) based on the water stress scalars of seven models as the following
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equations
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Drought, Ws < Wsmin +
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Normal , (Wsmin +
{ Wet
Wsmax −Wsmin
3
Wsmax −Wsmin
, Ws > Wsmin +
) < Ws <
3
2×(Wsmax −Wsmin )
(Wsmin +
2×(Wsmax −Wsmin )
3
(2)
3
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where Wsmin and Wsmax are the minimum and maximum values of Ws at each site.
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<<Table 1>>
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)
Two pairwise comparisons were conducted on the model components in order to
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investigate the differences of model structure. First, we identified the impacts of fraction of PAR
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absorbed by the vegetation canopy on GPP simulations by comparing the two correlations:
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(a) the correlation of simulated GPP among seven models;
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(b) the correlation of potential light energy use (PLUE) (i.e. PAR×fPAR×LUEmax);
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Then, we diagnosed the primary environment variables by the second pairwise
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comparison:
(a) the correlation of realized light energy use only considering temperature stress
(RLUEtem) (i.e. PAR×fPAR×LUEmax×Ts);
(b) the correlation of realized light energy use only considering water stress (RLUEwater)
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(i.e. PAR×fPAR×LUEmax×Ws).
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2.3 Statistical analysis
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The nonlinear regression procedure (Proc NLIN) in the Statistical Analysis System
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(SAS, SAS Institute Inc., Cary, NC, USA) was applied to optimize the model parameters of seven
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LUE models across the calibration sites. Four metrics were used to evaluate the performance of
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the LUE models in this study, including coefficient of determination (R2), root mean square error
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(RMSE), mean predictive error (PE, difference between mean observations and simulations), and
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relative predictive error (RPE, the ratio between PE and mean observations).
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3. Results and discussion
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3.1 Comparison of model performance
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All of seven LUE models showed the substantial difference of model performance
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among various ecosystem types according to the R2, RMSE, PE and RPE (Figure S1). Over the
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shrublands and evergreen broadleaf forests, almost all models showed obvious low performance
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with low R2 and high RMSE. The highest model performance was observed over deciduous
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broadleaf forests within seven LUE models. Parameter calibration significantly improved the
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model performance over almost all ecosystem types (Figure 1; Table 1). Similarly, all of seven
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calibrated models showed the highest model performance over the deciduous broadleaf forests,
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intermediate over the evergreen needleleaf forests, mixed forests and grasslands, lowest at
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shrublands and evergreen broadleaf forests (Figure 1). For a given vegetation type, models
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performance differed among seven models. For example, CFlux and EC-LUE models were found
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higher R2 and lower RMSE compared with other five models (Figure 1).
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<<Figure 1>>
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From the spatial scales, EC-LUE and CFlux models explained higher GPP variations
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with determination coefficient of 0.55 and 0.44 respectively (Figure 2). All models appeared the
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overestimation at the low GPP regions, and underestimation at the high GPP regions (Figure 2).
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Moreover, we calculated the mean R2 and RMSE values of seven models at each site, and
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compared the R2 and RMSE of individual model with means of seven models. On average, at 80%
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and 75% sites, EC-LUE and CFlux models showed higher R2 compared with mean level of seven
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models, while they showed the lower RMSE at 76% and 75% sites respectively (Figure 3).
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<<Figure 2>>
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<<Figure 3>>
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Seven LUE models, expect CFlux model, significantly underestimated GPP at the
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overcastting and cloudy days (Figure 4). For example, the averaged predictive errors of CASA
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model was about -1.12 g C m-2 day-1 at overcastting days, however, the predictive errors was 0.15
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g C m-2 day-1 at clear days. Previous of studies have found that increased fraction of diffuse
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radiation at the cloudy days enhanced the plant photosynthesis (Gu et al., 2002, 2003; Urban et al.,
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2007; Alton et al., 2007). For example, Gu et al (2003) reported increase in diffuse radiation
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because of volcanic aerosols alone enhanced noontime photosynthesis of a deciduous forest by 23%
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in 1992 and 8% in 1993. This finding contributed to the temporary increase of terrestrial
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ecosystems carbon sink after the eruption of Mount Pinatubo (15.1ºN, 121.4ºE) on 15 June 1991
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(Ciais et al., 1995; Bousquet et al., 2000; Battle et al., 2000). Besides volcanic, cloud reduces the
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global solar radiation but increases the relative proportion of diffuse radiation at the Earth surface
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too.
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<<Figure 4>>
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It is a case that increased fraction of diffuse radiation can be the cause of changes in
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many atmospheric factors such as temperature, moisture, and latent heating etc. These factors all
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have direct or indirect influences on terrestrial ecosystem carbon dynamics (Gu et al., 1999).
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Therefore some researchers emphasized decreases in the respiration of sunlit leaves due to
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reduced leaf temperature and vapor pressure deficit (Baldocchi, 1997). However, direct impacts of
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diffuse radiation had been found: (1) Diffuse radiation results in higher light use efficiencies by
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plant canopies (Gu et al., 2002; Alton et al., 2007). (2) Diffuse radiation penetrates to lower depths
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of the canopy more efficiently than does the direct radiation (Matsuda et al., 2004). This increased
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the potential leaf area available for photosynthesis. (3) An increase of blue/red light ratio may lead
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to higher photosynthesis rates per unit leaf area with increasing faction of diffuse radiation
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(Matsuda et al., 2004).
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Among the investigated seven LUE models, CFlux is only one model to integrate the
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impacts of diffuse radiation on plant photosynthesis (Turner et al., 2006). CFlux model assumed a
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maximum potential LUE at the overcast condition and minimum LUE value at the clear days, and
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then a linear decreased trend with cloud cover (Turner et al., 2006). The results showed the simply
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linear equation successfully reflected the impacts of diffuse radiation. The latest study developed a
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two-leaf light use efficiency (TL-LUE) model based on the MOD17 algorithm, which separates
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the canopy into sunlit and shaded leaf groups and calculates GPP separately for them with
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different maximum light use efficiencies (He et al., 2013). The newly developed TL-LUE model
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shows lower sensitivity to sky conditions than the MOD17 algorithm.
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3.2 Comparison of interannual variability
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The interannual variability of vegetation production exists at different scales from global,
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regional to plot/stand levels and has been linked with the interannual variability of global carbon
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balance as well as atmospheric CO2 concentration (Goulden et al., 1996; Bousquet et al., 2000;
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Zhao et al., 2010). Previous study indicated interannual variability of the global vegetation
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production is a major driver of the interannual CO2 growth rate (Zhao et al., 2010). Therefore,
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understanding the cause and degree of interannual variability is important for both ecological
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theory and global carbon cycle.
In this study, the ability of simulating interannual variability was investigated at 100
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sites with more than three-year observations. The results showed the poor ability of seven models
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to identify the interannual variability. The correlation coefficient (R2) of standard deviation
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between simulations and observations through all sites ranged from 0.14 to 0.54, and EC-LUE,
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CFlux and CASA models showed the highest R2 (Figure 5).
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<<Figure 5>>
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Our result confirmed a previous study that modeling interannual variation in GPP has
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proven challenging (Richardson et al., 2007). Keenan et al (2012) assessed the performance of 16
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terrestrial biosphere models and 3 remote sensing products against long-term measurements of
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biosphere-atmosphere CO2 exchange made with eddy-covariance flux towers at 11 forested sites
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in North America. The results showed none of the models consistently reproduce observed
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interannual variability within measurement uncertainty. Compared with the process-based models,
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the remote sensing GPP products performed comparably to the average process-based model when
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assessed against interannual variability (Keenan et al., 2012). Although the response of terrestrial
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ecosystems to mean climatic drivers is relatively well captured, sensitivity to the impact of
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variability in climatic drivers may not be, leading to the accumulation of high frequency model
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error (Dietze et al., 2011) over longer time scales (Schwalm et al., 2010). Although estimates of
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GPP based on remote sensing have been used to evaluate process-based models, results herein
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suggest that estimates of interannual variability from both approaches are subject to similar
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magnitudes of error (Poulter et al., 2011).
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The possible causes of the errors for modeling interannual variability of GPP could be:
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(1) Incomplete integration of environmental regulations. Most of LUE models only integrate the
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impacts of temperature, water and radiation. Few LUE models consider the impacts of stand age,
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phenology and CO2 fertilization on vegetation production, however, previous of studies have
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showed the significant regulations of those environmental variables to vegetation production
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(White et al., 1999; DeLucia and Thomas, 2000). (2) Limited understands on the key
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physiological processes. Braswell et al. (1997) showed that climate induced physiological changes
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are greater than the direct effect of climatic variability on the carbon cycle. Hui et al. (2003) used
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a sum-of-squares approach to separate interannual variability in carbon cycle into four different
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sources: functional change, interannual climatic variability, seasonal climatic variability and
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random error, and seasonal climatic variability can explain mostly interannual variability in
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carbon cycles. However, current LUE models do not integrated any responses of physiological
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processes to environment changes.
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3.3 Comparison of model structure
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Pairwise comparison showed higher correlations of PLUE (i.e. PAR×fPAR×LUEmax)
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among seven models compared with those of GPP simulations (Fig.6). For example, the
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correlations of CASA model and other six models on PLUE ranged from 0.75 to 0.96, however,
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correlations of GPP simulations were found from 0.37 to 0.43 (Fig.6). Generally, the pairwise
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comparison of PLUE and GPP simulations can essentially indicate the contributions of fPAR and
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environment regulation scalars to the differences of GPP simulations. The result implied larger
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contribution of environment stress equations on model differences compared with the fraction of
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PAR absorbed by the vegetation canopy.
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<<Figure 6>>
Within LUE models, the fraction of solar radiation intercepted by terrestrial vegetation
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(fPAR) is calculated from remote sensing data, which is a critical variable for estimating vegetation
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production. Various methods were developed for calculating fPAR among the LUE models. At the
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CASA model, fPAR was calculated as a linear function of the simple ratio (SR), which was derived
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from NDVI (Potter et al., 1993). CFlux and MODIS models directly utilized MODIS-fPAR
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products. Other four models calculated fPAR based on vegetation index. CFix and EC-LUE
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models used linear equation with NDVI to estimate fPAR (Veroustraete et al., 2002; Yuan et al.,
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2007), and VPM and VPRM directly used EVI to indicate fPAR (Xiao, et. al., 2004). CASA, CFix
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and EC-LUE applied the equation with NDVI to calculate fPAR and showed the high consistence
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through most sites (Fig.6). Low correlations were observed among MODIS-fPAR, EVI and
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NDVI-based fPAR (Fig.6)
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The second pairwise comparison indicated the larger impacts of water stress equation on
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GPP simulations than that of temperature stress equation (Fig.7). On average, the correlations of
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RLUEtem between two models were 0.80±0.08, while the mean value of correlations of RLUEwater
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was 0.65±0.12. In generally, temperature and water response equations were the important two
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down-regulation factors for LUE models.
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<<Figure 7>>
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We compared the consistence of water stress derived from seven models within sites.
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Water stress scalars of seven models were separated into three levels: drought, normal and wet.
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The results showed the more than 50% water stress levels were not consistent among models
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(Table2). The largest inconsistence of water levels was found between MODIS and EC-LUE/CFix
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with 65% differences (Table 2). Moreover, results also showed large friction of inconsistent
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identifying drought and wet conditions. As shown at Table 2, on average among all models, more
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than 15% days were wrongly identified between drought and wet days (Table 2). Comparison of
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water stress equations among LUE models showed the substantial differences through the almost
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all sites (Fig.8). Moisture availability of CASA showed the largest correlation with that of
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EC-LUE model through the sites, and the weak relationship with that of MODIS-GPP. Among
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other four models, moisture availability hardly showed significant relationship. For example,
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EC-LUE models indicated the site with similar moisture availability, on the contrary, MODIS-GPP
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product showed the large difference (Fig.8). The moisture response curve of VPM and VPRM is
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unique and showed the different spatial distribution compare with those of other models.
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<<Figure 8>>
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Significant differences of model performance were found among seven LUE models at
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the three water stress condition (i.e. drought, normal and wet) (Fig.9). Except CFlux, VPRM and
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VPM, other four models showed low R2 at the drought days. For example, CFix model explained
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about 50±25% of the variation of GPP estimated at the wet conditions averagely, however, only
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explained 22±15% variation at the drought days (Fig. 9). Moreover, no consistent predictive errors
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were found under the different water stresses among the various models. CASA model tended to
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underestimate GPP at the drought days, on the contrary, CFix model showed obvious
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overestimation of GPP (Fig. 9).
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<<Figure 9>>
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Defining a function for quantifying the control of moisture availability on plant
photosynthesis has long been a challenge. The effects of water on plant photosynthesis have been
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estimated as a function of soil moisture, evapotranspiration friction and water vapor pressure
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deficit (VPD) in a number of LUE models (Field et al., 1995; Prince and Goward, 1995; Running
301
et al., 2000). For instance, in the EC-LUE model, water stress was estimated using the ratio of
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actual evapotranspiration to net shortwave radiation energy. This ratio was considered to be a very
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good indicator of soil or vegetation moisture conditions because decreasing amounts of energy
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partitioned into latent heat flux suggests a stronger moisture limitation (Kurc and Small, 2004;
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Zhang et al., 2004; Suleiman and Crago, 2004). Other of models, such as VPM and VPRM, used a
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satellite-derived water index (Land Surface Water Index) to estimate the seasonal dynamics of
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water stress (Xiao et al., 2004).
308
These variables, which have been used into LUE models, had their weaknesses. For
309
example, it is difficult to characterize soil moisture conditions over large areas from either
310
modeling or remote sensing. This limits the predictive power of any spatial GPP model that relies
311
on soil moisture. VPD is not a good indicator of the spatial heterogeneity of soil moisture
312
conditions across the landscape (e.g., slope versus valley) and it is not likely to be linearly related
313
to soil water availability for which it is often used as a proxy. Moreover, evapotranspiration
314
friction needs an ET model for simulating ecosystem evapotranspiration, and any uncertainties
315
within ET models will reduce the model performance of LUE models.
316
4. Summary
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We evaluated seven satellite-driven light use efficiency models against 155 eddy
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covariance sites globally including six major biomes. All seven models showed similar model
319
performance over the vegetation types. The best model performance was observed at deciduous
15
320
broadleaf forests and mixed forests, intermediate at grasslands and evergreen needleleaf forests,
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lowest at evergreen broadleaf forests. From the spatial respective, CFlux and EC-LUE models
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showed higher correlations between site-averaged GPP observations and simulations, and were
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represented with higher performance to simulate interannual variability of GPP. fPAR, temperature
324
curves and water stress equations largely differed among the seven LUE models. Comparably,
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water stress equations differed largestly which was the major cause for GPP simulations
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difference.
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328
329
Acknowledgments
This study was supported by the National Natural Science Foundation of China
330
(41201078), the National High Technology Research and Development Program of China (863
331
Program) (2013AA122003), Program for New Century Excellent Talents in University
332
(NCET-12-0060) and the Fundamental Research Funds for the Central Universities.
333
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Table 1 Calibrated model parameter values for seven models at the two parameterization
520
schemes.
Vegetation Type
Parameter
CSH
DBF
EBF
ENF
GRA
MIF
CASA
ɛ0
0.62±0.20
1.22±0.43
0.87±0.15
0.85±0.18
0.78±0.17
1.04±0.36
0.55±0.26
1.27±0.07
1.33±0.15
1.09±0.06
1.14±0.09
1.16±0.13
ɛmax
1.12±0.28
3.07±0.25
3.02±0.18
2.29±0.12
2.53±0.15
2.53±0.25
ɛcs
0.66±0.05
1.17±0.07
1.12±0.10
0.95±0.05
1.08±0.08
1.05±0.13
Tmin
-13.76±6.54
-4.35±2.13
-14.46±4.35
-14.20±2.40
-20.12±7.06
-14.26±4.17
Tmax
13.04±8.79
13.08±3.23
20.00±0.00
8.25±1.82
9.55±5.87
13.79±3.74
VPDmin
0.33±0.34
0.11±0.01
0.12±0.07
0.11±0.00
0.12±0.01
0.12±0.02
VPDmax
3.42±0.58
2.99±0.32
2.56±0.32
2.79±0.13
3.23±0.47
2.44±0.32
1.28±0.40
1.71±0.19
1.70±0.11
1.85±0.20
1.59±0.41
1.72±0.31
ɛ0
0.66±0.28
1.77±0.19
1.68±0.10
1.36±0.08
1.52±0.16
1.64±0.22
Tmin
-13.76±6.54
-4.35±2.13
-14.46±4.35
-14.20±2.40
-20.12±7.06
-14.26±4.17
Tmax
13.04±8.79
13.08±3.23
20.00±0.00
8.25±1.82
9.55±5.87
13.79±3.74
VPDmin
0.33±0.34
0.11±0.01
0.12±0.07
0.11±0.02
0.12±0.01
0.12±0.02
VPDmax
3.42±0.58
2.99±0.32
2.56±0.32
2.79±0.13
3.23±0.47
2.44±0.32
1.25±0.43
2.11±0.11
2.17±0.16
2.17±0.10
1.92±0.12
2.03±0.24
ɛ0
4.42±1.88
8.63±1.14
10.88±2.17
14.89±2.10
7.87±1.08
10.16±3.04
PAR0
4.47±1.22
3.15±0.44
2.37±0.75
1.61±0.24
3.07±0.52
2.50±0.81
CFix
ɛ0
CFlux
EC-LUE
ɛ0
MODIS
VPM
ɛ0
VPRM
521
522
523
CSH, DBF, EBF, ENF, GRA, MIF: calibrated parameter values within shrubland, deciduous broadleaf
forest, evergreen broadleaf forest, evergreen needleleaf forest, grassland, mixed forest respectively.
Parameters were introduced at the Supplemental Online Material.
26
Table 2 Comparison of water stress levels derived from seven LUE models
CASA
CFix/EC-LUE
CFlux
MODIS
VPRM/VPM
-
0.49±0.12
0.15±0.28
0.57±0.15
0.56±0.13
CFix/EC-LUE
0.09±0.04
-
0.52±0.12
0.65±0.11
0.58±0.11
CFlux
0.12±0.05
0.06±0.06
-
0.50±0.15
0.54±0.12
MODIS
0.16±0.09
0.19±0.09
0.18±0.09
-
0.61±0.12
VPRM/VPM
0.16±0.10
0.22±0.14
0.13±0.09
0.17±0.10
-
CASA
27
524
Figure caption
525
Figure 1 Model performance of seven Light Use Efficiency Models with calibrated parameters at
526
various vegetation types.
527
Figure 2 Observed vs. the simulated GPP over the 155 EC sites with calibrated parameters. The
528
long dash line is 1:1 line and the solid line is linear regression line.
529
Figure 3 Percentage of eddy covariance sites with higher domination coefficient and lower RMSE
530
for individual model compared the mean values of seven models.
531
Figure 4 The model performance at clear, cloudy and overcast days for seven models.
532
Figure 5 Correlation between standard deviations of simulated interannual variability of GPP.
533
Figure 6 Pairwise comparisons of correlations of GPP simulations and potential light energy use
534
(PLUE) among seven LUE models.
535
Figure 7 Pairwise comparisons of correlations of realized light energy use only considering
536
temperature stress (RLUEtem) and water stress (RLUEwater) among seven LUE modes.
537
Figure 8 Comparison of water stress curves among seven LUE models.
538
Figure 9 The model performance at drought, normal and wet days for seven models.
539
28
540
541
Figure 1 Model performance of seven Light Use Efficiency Models with calibrated parameters at
542
various vegetation types.
29
543
544
Figure 2 Observed vs. the simulated GPP over the 155 EC sites with calibrated parameters. The
545
long dash line is 1:1 line and the solid line is linear regression line.
30
546
547
Figure 3 Percentage of eddy covariance sites with higher domination coefficient and lower RMSE
548
for individual model compared the mean values of seven models.
31
549
550
Figure 4 The model performance at clear, cloudy and overcast days for seven models.
32
551
552
Figure 5 Correlation between standard deviations of simulated interannual variability of GPP.
33
553
554
Figure 6 Pairwise comparisons of correlations of GPP simulations and potential light energy use
555
(PLUE) among seven LUE models.
34
556
557
Figure 7 Pairwise comparisons of correlations of realized light energy use only considering
558
temperature stress (RLUEtem) and water stress (RLUEwater) among seven LUE modes.
35
559
560
Figure 8 Comparison of water stress curves among seven LUE models.
36
561
562
Figure 9 The model performance at drought, normal and wet days for seven models.
37