); [EMAIL_ADDRESS] (X.Z. ); [EMAIL_ADDRESS] (H.Z. ); [EMAIL_ADDRESS] (L.Z. ); [EMAIL_ADDRESS] (X.S. ); [EMAIL_ADDRESS] (G.Y.) Leiming Zhang  
1 Synthesis Research Center of Chinese Ecosystem Research Network, Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; [EMAIL_ADDRESS] (G.W. ); lisg@iThe Penman-Monteith model (P-M model) [8] and the Shuttleworth-Wallace model (S-W model) [9] are favored because they capture the physical process understanding to the best of our current knowledge. At the site scale, since most input variables can be measured directly or estimated from meteorological and biotic measurements, these two models are widely used [10; 11; 12; 13; 14]. At the regional scale, however, the P-M and S-W models are seldom used because some key variables, particularly the canopy stomatal conductance, are difficult to derive using remote sensing data. [PERSON] et al. [15] developed a remote sensing ET algorithm using the P-M scheme in which canopy stomatal conductance was estimated as a function of the Normalized Difference Vegetation Index (NDVI) and Leaf Area Index (LAI). Based on [PERSON]'s algorithm, [PERSON] et al. [16; 17] developed a new version of a global remote sensing ET model. Compared with measurements from eddy covariance (EC) systems, [PERSON]'s model generally illustrated good performance for many FLUXNET sites [16]. The S-W model is a two-source model developed from the P-M model. This model simulates the water vapor flux from plants (i.e., plant transpiration) and soil (i.e., soil evaporation) separately. The S-W model has been widely used because of its simple and accurate consideration of hydrological processes and good performance [11; 12; 13; 14; 18; 19]. Most studies indicate that the performance of S-W for diverse ecosystems is superior to that of other ET models, particularly in terms of partitioning [10; 20]. If the variables and parameters are available, the S-W model could be promising for modeling and partitioning ET at the regional scale. Using the Ball-Berry model [21] to estimate canopy stomatal resistance and a soil moisture function to estimate soil surface conductance, we previously deployed the S-W model to simulate ET with high accuracy in four grassland ecosystems [14]. By further adopting a light-use efficiency-based gross primary productivity (GPP) model, we developed a model, SWH, for estimating and partitioning ET using meteorological data and remote sensing products [22]. Compared to EC measurements, the SWH model produced satisfactory estimates of ET and GPP in a temperate forest and alpine grassland. However, two features of the current version of the SWH model prevent its application at the regional scale. First, key parameters that were optimized at individual sites prior to model development are not available for regional application. Second, soil water content, an input variable, is measured directly with soil moisture sensors, which are difficult to access at the regional scale. Therefore, to apply the SWH at the regional scale, parameterization and soil water content estimation must be addressed. Accordingly, the objectives of this study are: (1) to optimize key parameters of the model by using the in situ measurements from FLUXNET towers across the globe; (2) to estimate soil water content with a bucket system model; (3) to evaluate the model performances on ET and GPP simulation in different biome types with the FLUXNET measurements; (4) using China as a case study region, to assess the spatial patterns of ET, GPP, the fraction of soil evaporation (E/ET) and water use efficiency (WUE, GPP/ET) based on the outputs of the new model.