## 5 Conclusions  
The SWH model prediction of ET and GPP agreed well with the observations recorded at global flux tower sites. In general, the model performance was better for woody ecosystems than for herbaceous ecosystems. Using interpolated climate data for China as the input climate forcing, the model simulations were also consistent with the measurements. Based on the simulation, high ET, GPP, WUE and E/ET values were observed in southern and eastern China, where forest and cropland are the dominant ecosystem types. The opposite pattern was observed in northern and western China, where grassland is the dominant ecosystem type. Notably, the model performance was poorerfor evergreen broadleaf forest and grassland. Uncertainties in the GPP model, the remote sensing product and key parameters are likely the sources of the biased estimation of ET in those ecosystems. These aspects should be targeted in future improvements of the model.  
**Supplementary Materials:** The following are available online at www.mdpi.com/2072-4292/9/1/54/s1, Table S1: Site Information for the eddy covariance (EC) flux sites in this study, including plant functional type (PFT), site identifier (Site ID), latitude (Lat), longitude (Lon), data period, country, and references. Figure S1: Performances of the SWH model in estimating ET (a,b) and GPP (c,d) at 32 sites, which were used for calibration (a,c) and the remaining sites for validation (b,d). Figure S2: Comparisons of eight-day ET (a) and GPP (b) derived from eddy covariance and estimated by the regionalized SWH model, which was driven by the 10-km interpolated meteorological products. Figure S3: Correlation between the model performance on GPP simulation and ET simulation. R2_GPP and R2_ET are the determinant coefficient of the relationship between modeled and measured GPP and ET, respectively.  
**Acknowledgments:** This work is financially supported by the Natural Sciences Foundation of China (41301043, 31570437), the Funding for talented young scientists of Institute of Geographic Sciences and Natural Resources Research (2013 RC203), and the Youth Innovation Promotion Association, Chinese Academy of Sciences (2015037), and the National Key Technology R & D Program (2013 BAC03B03). We thank all site investigators, their funding agencies, the various regional flux networks (Afriklu, AmeriFlux, AsiaFlux, CarboAfrica, CarboEurope-IP, ChinaFlux, Fluncut-Canada, KoFlux, LBA, NECC, OzFlux, TCOS-Siberia, USCCC), the Office of Science (BER), the U.S. Department of Energy (for funding the development of measurement and data submission protocols) and the FLUXNET project, whose work and support were essential for obtaining the measurements, without which the analyses conducted in this study would not be possible. The authors appreciate the very constructive suggestions and comments from four anonymous reviewers.  
**Author Contributions:** [PERSON] conceived and designed the experiments; [PERSON], [PERSON], and [PERSON] performed the experiments; [PERSON], [PERSON] and [PERSON] analyzed the data; [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] contributed reagents/materials/analysis tools; [PERSON] wrote the paper.  
**Conflicts of Interest:** The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.