## 3 Results and Discussion
### Spatial and Temporal Patterns of ET  
The temporal patterns and magnitudes of ET are well simulated through the DLM-Ecohydro model, which were able to explain 82% (RMSE = 1.66 mm/day) and 83% (RMSE = 1.53 mm/day) of the variability in 2013 and 2014, respectively. The model agilely captured the seasonal patterns of ET losses at the Arou station of both years: With a fluctuated rise during the early summer until up to the top magnitude (around 5 mm/day) at mid-summer and then decrease to lower than 1 mm/day within September and October (as shown in Figure 6). Although the model could capture most of the variation during a year, there was a general underestimation especially in the winter and early summer. One possible reason for winter ET underestimation was the snow sublimation might cause an increase of latent heat measured through the EC tower. Snowfall is a crucial part of the ecosystem water cycle in the Tibetan Plateau of China [40], which has quite a number of accumulated snowpack during winter. According to the findings of [PERSON] [41], there could be a rapid response of the EC tower to detect the intercepted snow sublimation from the upper canopy. This contributed to a higher observed ET that the model underestimated. At the same time, the only water input of the DLM-Ecohydro model was precipitation which converts to rainfall or snowpack accumulation depending on air temperature. However, above 4500 m a.s.l. alpine region, ice and snowpack covered all year, which induced an increased snowmelt runoff during spring and summer. [PERSON] and [PERSON] [42], utilizing snowmelt runoff model (SRM), demonstrated that May and June are major melting months, and the melting runoff can take more than 50% of the total runoff in the Upper Heihe watershed. The negligence of melting runoff from high altitude snow/ice pack might contribute to the considerable underestimation during early summer. One the other hand, the model calculated plant stomatal conductance with the ambient air temperature, while considering the freezing-thawing process in alpine cold regions, the change of soil temperature usually lagging behind that of air temperature, with a relatively gently periodic variation [43]. It might cause an uncertainty in simulation of stomatal conductance and further cause the discrepancy of ET estimation. For this problem, [PERSON] [34] provided a rectified Jarvis algorithm, including a soil temperature scalar as an environmental factor to calculate stomatal conductance. For further development of the DLM-Ecohydro model, it is necessary to take this modification into consideration. Figure 7 reveals simulated sub-components of Arou station ET in two years. Because the underlying surface of the station is grassland (understory LAI was set as 0), only overstory canopy was took into consideration. In both years, canopy transpiration was the most important component, and took around 65% and 64% of ecosystem ET for 2013 and 2014, respectively. Previous research also demonstrated that ET loss from transpiration can reach 60-80%,  
Figure 5: Temporal dynamics of simulated subsurface baseflow (SSB) and surface overland flow (SOLF) and precipitation in 2013 and 2014 at Arou Station. especially during the growing season [44]. The flowing loss forms were soil evaporation and canopy intercepted evaporation, accounting for 22-24% and 11-14% in both study years, respectively. The findings of the simulation showed that the annual ET of the Upper Heihe watershed was around from 14.3 to 885 mm in two study years (Figure 8). Theoretically, the differences in copphysiological properties (e.g., \(g_{\text{smax}}\), \(\Omega\) and vegetation height) between vegetation functional types might highly govern the ET losses distribution through their effects of transpirational loss [45]. At the same time, the architectures of different vegetation canopies would affect the over/understory radiation transfer mechanisms [46].