## 5 Discussion
In our study, we also found a similar downward trend within the study area since 2000 due to a warmer and wetter climate. Based on MODIS products, the results presented here are similar to those reported by [PERSON] _et al._[49] who applied the SEBS within a forest site located in the middle of the Korean peninsula, with \(R^{2}\) = 0.80 (for this case) _versus_ 0.88 and with RMSE = 0.21 mm-day\({}^{-1}\) (for this case) _versus_ 0.22 mm-day\({}^{-1}\). [PERSON] _et al._[91] incorporated the TM using diverse compositions of forcing and _in situ_ meteorological data in order to estimate daily ETs using the SEBS over the Coleambally Irrigation Area, located in the southwestern portion of New South Wales, Australia. Their resultsyielded a higher \(R^{2}\) (up to 0.95) than we report here that can be ascribed to high-resolution remotely sensed data and other high-resolution and accurate inputs for homogenous irrigated croplands. However, the RMSE of their results was up to 0.74 mm\(\cdot\) day\({}^{-1}\), larger than those we report here. The result can be explained by understanding that due to abundant irrigation daily ETs over croplands are much larger than daily forest ETs for the QMs. In a similar manner, when applying Chinese satellite HJ-images (with a 30 m visible, 150m near-infrared, and 300 m thermal infrared bands), the same WRF estimates as those used in this study, the SEBS generated comparable results for irrigated cropland sites located within the middle reaches of the HRB [92] (with \(R^{2}\) = 0.79 and RMSE = 0.58 mm\(\cdot\) day\({}^{-1}\) at the vegetable site; \(R^{2}\) = 0.81 and RMSE = 1.29 mm\(\cdot\) day\({}^{-1}\) at the maize site; and \(R^{2}\) = 0.86 and RMSE = 1.25 mm\(\cdot\) day\({}^{-1}\) at the orchard site). However, due to the heterogeneity of the vegetation, the performance of the SEBS was reduced (with \(R^{2}\) = 0.53 and RMSE = 0.67 mm\(\cdot\) day\({}^{-1}\)) at the wetland site. In general, the SEBS model could perform well in areas with low vegetation or bare soil [38]. Nevertheless, the SEBS also showed inferiority in forested areas, especially for heterogeneous forests [93, 94]. As [PERSON] _et al._[95] indicated, correction terms that adjust the effects of the roughness sub-layer could contribute to improve the SEBS's performance in areas containing complex vegetation structural scenarios [72]. Further improvement of ET can be obtained using more accurate meteorological inputs, although in our study meteorological inputs have been verified to be reliable. Some improvements of wind speed and downwelling long-wave radiation estimates are still possible [62]. As indicated by the HiWATER team, other improvements for long-term MODIS LST inputs can also be expected [96]. Using unreliable meteorological inputs and MODIS LST products for the SEBS would result in deterioration for the agreement of both \(H\) and \(\lambda E\), and, thus, ET and EC measurements [97, 98]. The SEBS model has been proven to be reliable for multi-scale ET estimations. However, only a few studies have focused on short-term simulations within forests [65, 70]. Due to complicated heat transfer mechanisms within forests and their surroundings, the SEBS cannot consistently capture the ET process. For cold and arid forests with high heterogeneity, such as the ones analyzed in this study, climate and environmental factors were found to enhance the complexity of the ET process. For example, melting snow within the canopy and underground possibly has a large impact on the ET process. Some sharply increasing daily ET values were determined for EC measurements during the late spring and early summer, indicating that this is the case (Figure 3). Further improvements should be expected if soil moisture simulations and snow melt monitoring modules are embedded into the SEBS algorithm [99]. Moreover, the appropriate parameterization of \(z_{\text{0m}}\) is also critical for improving the behavior of the SEBS [38, 89].