## 4 Results and Discussion  
### Assessment of Daily ET, GPP, and WUE at Footprint Scale  
Overall, the validation results indicate that daily ET, GPP, and WUE can be reliably produced over the midstream oasis agricultural areas in the HRB for the considered multi-year study period (ET: NSE 0.84, MAPE 26.59%, RMSE 0.75 mm/day, R = 0.93; GPP: NSE0.70, MAPE 36.26%, RMSE 1.13 gC/m\({}^{2}\), R = 0.86; WUE: NSE 0.28, MAPE 39.83%, RMSE 0.50 gC/kgH\({}_{2}\)O, R = 0.63) (Table 2). We integrated the ET, GPP, and WUE estimates at the 100 m scale to the same spatial representation as the EC measurements based on the EC flux source area. We then contrasted the integrated daily ET, daily GPP, and daily WUE estimates with the EC measurements at the Daman superstation (DMS) (Figure 3a-c). The results show that the daily estimated value is in good agreement with the EC measurements (Figure 3). The best performance was for estimated daily ET from the SEBS model. This is evidenced by the high NSE (>0.84), low RMSE (<0.75 mm/day), and low bias, as shown in Table 2. The errors were significantly lower than those reported by [PERSON] et al. (2019, 2020), with an RMSE ranging from 1.11 to 1.81 mm/day and an approximate MAPE from 20.8 to 26.6% for the irrigated field. The second best was for the estimated daily GPP using the MODIS GPP product algorithm (NSE 0.70), and the third best was for the estimated daily WUE (NSE 0.28). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Type** & **Site** & **Variables** & **NSE** & **MBE** & **MAPE** & **RMSE** & **R** \\ \hline \multirow{4}{*}{Cropland (maize)} & & Unit & - & mm/day & \% & mm/day & - \\  & & Daily ET & 0.84 & \(-\)0.06 & 26.59 & 0.75 & 0.93 \\  & & Unit & - & gC/m\({}^{2}\) & \% & gC/m\({}^{2}\) & - \\  & & Daily GPP & 0.70 & 0.21 & 36.62 & 1.13 & 0.86 \\  & & Unit & - & gC/kgH\({}_{2}\)O & \% & gC/kgH\({}_{2}\)O & - \\  & & Daily WUE & 0.28 & \(-\)0.05 & 39.83 & 0.50 & 0.63 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Statistics for model validations to estimate daily ET, GPP, and WUE for cropland (maize) cover types from Daman superstation in oasis midstream area. NSE, Nash–Sutcliffe efficiency coefficient; MBE, mean bias error; RMSE, root mean square error; MAPE, mean absolute percent error; R, correlation coefficient. Figure 3: _Cont._  
As WUE was calculated based on the ratio of GPP to ET, which have similar magnitudes, WUE exhibited relatively large fluctuations and likely contained significant uncertainties. When the average ET was small, small perturbations also had a large impact on WUE, which resulted in reduced accuracy, such as when it was measured outside of the growing season, as shown in Figure 4. Therefore, the MAPE of WUE was greater than that of ET and GPP. Based on the progress of research on WUE, [PERSON] et al. [93; 92] proposed a new calculation method for potential WUE (uWUE = GPP\(\cdot\)VPD\({}^{0.5}\)/ET) by considering the nonlinear effect of VPD. Because it takes into account the response of stomatal conductance to VPD, it can better reflect the physiological regulation of the water-carbon exchange in plants, and has better stability and accuracy. Therefore, new methods could be considered to reduce the uncertainty of WUE in future studies. The accuracy of the available meteorological datasets showed marked differences both spatially and temporally [67]. Uncertainties related to the use of coarse spatial resolution meteorological elements and multi-satellite remote sensing data can have different degrees of influence on the results [94]. Here, meteorological forcing data, such as temperature, relative humidity, wind speed, and radiation, are required as SEBS and GPP model inputs, which is bound to cause errors in the estimation results. However, it is very difficult to obtain large-scale and fine-resolution meteorological data at present, and new techniques for observing meteorological variables could provide a solution in the future.