## 3 Results and Discussion
### Spatial-Temporal Patterns of GPP  
Figure 9 reveals the comparison between simulated and observed daily GPP at the Arou footprint site in 2013 and 2014. The model fairly captured seasonal variations of GPP and explained 71% with RMSE = 5.25 gC/m\({}^{2}\)/day in 2013 and 81% with 3.38 gC/m\({}^{2}\)/day in 2014. The model had a good performance in the estimation in GPP magnitudes of two study years. Nevertheless, comparing with measurements, there was a general overestimated GPP in spring and autumn while during June and July there was an underestimation in both years. [PERSON] (2014) explored the spatiotemporal variations of water use efficiency (WUE), which represents the ratio of CO\({}_{2}\) assimilation to vegetation transpiration (2014) in the Heihe River Basin, and pointed that especially in the upper watershed, summer has the highest value of WUE, which is around 1.0-1.2 gC/mm/H\({}_{2}\)O/m\({}^{2}\) comparing with the other seasons (0-0.4 gC/mm/H\({}_{2}\)O/m\({}^{2}\)). It validated temporal congruency between ET and GPP in the underestimated summer period when the melting runoff was possibly underestimated. From Figure 8b we can see there was a general overestimation of GPP within two years' estimation. This discrepancy might be mainly  
Figure 8: Simulated spatial patterns of annual total ET for 2013 (**a**) and 2014 (**b**) in the Upper Heihe watershed. attributed to the inexact representation of vegetation physiological characteristics consequent on the soil moisture statuses. As discussed above, due to the lack of simulating the soil freezing-thawing process, the model treated each soil texture in the watershed with constant hydraulic properties and remained in the optimal soil moisture condition whole year round. It triggered an overestimated GPP through two major reasons. One is the unrealistic higher stomatal conductance which would speed up photosynthetic process [49]. According to the [PERSON] algorithm [50] embedded in DLM-Ecohydro, VSWC has a positive relationship with plant stomatal conductance when it is between the field capacity and permanent wilting point. Actually, for both frozen and thawing period, the infiltrated rate would not be as high as the model representation and quite a number of precipitation or snow melting water probably concentrate on the soil surface and drain out as lateral water fluxes, which would contribute to an increase in soil surface evaporation. This might explain why there was rare overestimation for simulated total ET although it is under the same g\({}_{\text{s}}\) as the GPP calculation. The experiment from [PERSON] [51] also manifested that compared with GPP, there was less sensitivity for ET in different soil moisture conditions. Another possible reason for the bias of modeled GPP was an inaccurate representation of ecosystem nitrogen (N) availability. Via the overestimation of g\({}_{\text{s}}\), the model exaggerated local photosynthesis, which would raise the soil respiration and soil carbon decomposition and further created a conducive status for increasing biomass and N mineralization. It synergistically promoted photosynthesis and constituted a regional feedback mechanism [52; 53; 54]. On the other hand, the overestimated soil moisture also positively affected the leaf N content and further overstated GPP through the increased maximum carboxylation rate [55]. The simulated GPP was between 0 and 2119 gC m\({}^{2}\)/year in 2013 and from 0 to 2174 gC m\({}^{2}\)/year in 2014 (Figure 10). Generally, the location near a stream had a higher annual photosynthetic efficiency and the highest GPP were assembled in the north-eastern part of the Upper Heihe watershed where the terrain is flatter. And the location with higher elevation (higher than 3900 m a.s.l.) in the north-western and central part had the lowest efficiency of photosynthesis for both years. The photosynthetic efficiency highly depended on the biophysical characters of different plant function types.