## 4 Discussion
### Reliability and Merits of the SWH Model  
Compared with the EC observations, the SWH model yielded an average \(R^{2}\) of ca. 0.8 and a RMSE of ca. 0.7 mm\(\cdot\)day\({}^{-1}\) at all 63 sites (Table 3). This performance is comparable to the performance achieved using models driven by climatic and remote sensing data. Mostly, models yielded an \(R^{2}\) close to or less than that of SWH, e.g., 0.71 and 0.68 for the Remote Sensing Penman Monteith (RS-PM) model and EC-LUE model ([PERSON] et al., 2015), 0.64 for the modified PM model ([PERSON] et al., 2015) and 0.75 for the Breathing Earth System Simulator (BESS) model ([PERSON] et al., 2015). In addition, [PERSON] et al. (2016) evaluated the performances of three process-based models, including a single source energy budget model, a Penman-Monteith-based approach and a Priestley-Taylor-based approach at the global scale. Their intercomparison of model estimates with 12 EC towers yielded a mean correlation of 0.57. [PERSON] et al. (2016) reported mean \(R^{2}\) and RMSE values of 0.5 to 0.8 and 0.45 to 0.75 mm\(\cdot\)day\({}^{-1}\), respectively, for eight ET models at 23 EC sites. Compared with previous model reports, our estimated mean annual ET in China (408.7 mm) is within the range of the global average, i.e., 310 to 547 mm ([PERSON] et al., 2016). This value is slightly higher than the estimate of [PERSON] et al. (2016), who reported a mean annual ET in China of 364.9 mm. Recently, [PERSON] et al. (2016) estimated ET in China using eight ET models; the annual ET ranged from 535 to 852 mm among the models. The variation in these results may be the result of differences among model structures ([PERSON] et al., 2016). In addition, the different sources and spatial resolutions of the input climate data used in the different models may also underlie this variation ([PERSON] et al., 2016). Our estimated E/ET (0.55) is consistent with the estimated global average based on ten process models, which yielded values ranging from 0.36 to 0.75, with an average of 0.58 ([PERSON] et al., 2016). However, a recent meta-analysis by [PERSON] and [PERSON] (2016) indicated that global E/ET is 39% (\(\pm\)15% SD), suggesting that the average E/ET in ecosystems in China, when estimated using the SWH model, may be higher than the global average. This higher estimate may be partly due to the high proportion of grassland in China (ca. 40% of the country). In addition, the model's underestimation of GPP may also contribute to the higher E/ET. The SWH model simulated that the ecosystems with high ET were mainly distributed in southern and eastern China, whereas low-ET ecosystems were mainly distributed in northern and western China. Similarly, [PERSON] et al. (2016) and [PERSON] et al. (2016) also reported that ET was highest in the humid tropics and sub-tropics, intermediate in temperate regions and lowest in both cold and arid regions. Water-use efficiency (WUE) exhibited a spatial pattern similar to the patterns observed for GPP and ET, with high values in the forest ecosystems in southern and eastern China and low values in the grasslands in northern China. This result is consistent with the results of inter-site comparisons based on EC measurements [14; 53]. This spatial pattern may be primarily due to the lower E/ET in ecosystems with dense canopies, which may allow these ecosystems to use water more efficiently [54]. As a simplified water-carbon coupling model, the SWH model represents a compromise between biogeochemical models that involve many detailed processes and conventional remote sensing ET models that only involve several empirical functions. By using the scheme of the S-W model, the SWH model estimates the water vapor fluxes in ecosystems with a mechanistic approach. However, by introducing the stomatal conductance model and the light-use efficiency-based GPP model, the SWH model largely reduces the processes and parameters required to simulate the photosynthesis process (Table 4).