## 4 Discussion  
### Parameters of the SWH Model  
The values of the optimized parameters were consistent with previously-published results. For example, the slope coefficient in the Ball-Berry model (Equation (2)) was estimated as 7.5 to 10.3, consistent with the values in Community Land Model(CLM)4.0 [43]. The estimated maximum light-use efficiency values (0.0011 mg-CO\({}_{2}\cdot\upmu\)mol\({}^{-1}\) PPFD for forest and grassland and 0.0022 mg-CO\({}_{2}\cdot\upmu\)mol\({}^{-1}\) PPFD for cropland) are located in the middle range of \(\varepsilon_{\text{max}}\) variations in the meta-analyses of [PERSON] et al. [44] and [PERSON] et al. [45]. Note that we derived \(\varepsilon_{\text{max}}\) using the NDVI data to estimate FPAR (Equation (3)). A higher \(\varepsilon_{\text{max}}\) would have been obtained if the Enhanced Vegetation Index (EVI) had been used to derive this parameter due to the lower magnitude of EVI compared to NDVI. Our results indicated that the maximum light-use efficiency is two-fold higher in cropland than in forest and grassland, most likely due to the intensive management of cropland through techniques such as irrigation and nutrient manipulation.  
Our results indicated large site-to-site variability for most of the estimated parameters, which implies that the use of the look-up table approach for each biome type will introduce uncertainties into the model. For better region-scale performance of the model, we originally expected to identify some empirical relationships between the parameters and climate or biotic variables for application to the estimation of parameters at other sites. Unfortunately, we failed to identify any regression correlation between the parameters and climatic- or vegetation-related variables. The factors affecting the spatial  
Figure 5: Spatial patterns of ET, GPP, water-use efficiency (WUE: GPP/ET) and the ratio of soil water evaporation to ET (E/ET) in China from 2001 to 2010.  
variations in the parameters may be too complex to use a simple regression method to depict the spatial pattern. Alternatively, the number of sites in this study may not be sufficient to clarify the mechanism that controls the spatial patterns of the parameters. Data from more sites may clarify the spatial patterns of the parameters. In contrast to most parameters, the estimated maximum light-use efficiency, \(\epsilon_{\text{max}}\), was relatively constant across sites (Table 2). This finding is consistent with a recent study by [PERSON] et al. (2016), who reported that the use of a constant \(\epsilon_{\text{max}}\) introduces only a minor bias in the estimation of GPP.  
The data of observations may introduce some uncertainties for parameterization due to the effect of gap-filling at some flux sites. However, this uncertainty is minor and has quite little impact on the estimate of biome-specific parameters. First, the quality of the FLUXNET dataset had been comprehensively assessed and controlled before release. The very bad data were assigned the value of \(-9999\), which was excluded for parameterization in our study. Second, we have excluded the sites with poor calibration performance, which could be possibly due to the uncertainty from observations (see the Methods). In addition, assuming that the gap-filled observation data exert large uncertainties on model simulation, one can expect that the model performance would be very poor, and the parameterization results with data from different groups of sites would be much different and the simulated ET inconsistent. However, our results indicate that the model illustrated reliable performance, implying that the estimated parameter sets were reliable. Furthermore, using the data of 32 sites and 64 sites to estimate the parameters, respectively, we found that the two methods yielded very close values of biome-specific parameters, and the simulated ETs with the two parameter sets were nearly identical (data not shown).