## 4 Discussion  
### The ET Variations among the Five Products  
The results show that there are obvious differences in the annual and monthly ET values among these five ET products. The possible reasons for these ET variations could be explained as follows.  
(1) Different estimation approach. GLDAS ET is the simulation from a land surface model which is upscaling of space- and ground-based observations with data assimilation techniques [27]. MODIS ET is the terrestrial ET based on the logic of the Penman-Monteith equation, which includes inputs of daily meteorological reanalysis data from the Global Modelling and Assimilation Office of NASA along with MODIS remotely sensed data products [28]. TerraClimate ET is developed using a water balance model that incorporates reference evapotranspiration, precipitation, temperature, and interpolated plant extractable soil water capacity, while the AWAP ET is also developed using a water balance model, and their results are close except that there is a larger difference in grassland. This is because the TerraClimate ET uses a simplified one-dimensional Thormthwaite-Mather water balance model while AWAP employs a two-layer model to simulate the changes in the shallow (thickness 0-0.7 m) and deep (0.2-1.5 m) soil layers, and the latter can depict the loss of the deep soil water [24; 29]. Compared with the above three ET products, the CSIRO ET is similar to the AWAP ET, which is because they share many basic input data in this catchment [24; 26]. CSIRO ET uses an observation-driven Penman-Monteith-Leuning (PML) model, supported with meteorological forcing data and along with satellite derived vegetation forcing data, land cover data, emissivity, and albedo. This is similar with the MODIS ET using the Penman-Monteith method and remotely sensed data, but their results are different in the interannual change, annual change, average value of the ET, and the ET of each land use type. This might be explained as the input datasets applied in CSIRO ET better represent the characteristics of Australian territory [26; 28].  
Figure 8: The relationships between yearly GPP and ET and the water-use efficiency of forest and grassland: (**a**,**b**) forest, and (**c**,**d**) grassland.  
(2) Different inputs. There are obvious differences among inputs, such as meteorological forcing data, vegetation forcing data, and land surface data. For example, the precipitation, a key factor determining water availability and a basic component of the hydrological cycle, is different from each other in these ET products. GLDAS ET uses the space- and ground-based precipitation observations with data assimilation techniques, MODIS ET uses the daily meteorological reanalysis data, while AWAP ET uses a gridded daily rainfall dataset, which can directly influence the ET [24; 27; 28].  
(3) Different spatial-temporal scales. The spatial scale mismatch between finer vegetation data and coarser meteorological forcing could result in large variations in ET products [11; 35]. The spatial resolutions of these five ET products are different from each other and range from 500 m of MODIS ET to 2.5\({}^{\circ}\) of the TerraClimate ET. Generalisation of surface features (such as land use/cover and terrain) leads to the decreases of the resolution, thus the loss of details of surface features. The temporal resolutions of these five ET products vary from 3 h to one month, which could result in the representation of different meteorological processes and vegetation physiological processes in these ET products [14].  
(4) The different expression of dynamics in vegetation index-based data could also cause differences among ET products. Some ET estimation methods, based on remotely sensed data like MODIS, can timely reflect the vegetation changes, but others cannot. If the vegetation growth process cannot be truly expressed, the evapotranspiration of vegetation, as well as its photosynthesis and other related hydrological processes (such as canopy interception and infiltration) will be affected [14].