## Methods and Data
### Comparison among These Five Evapotranspiration (ET) Products  
The variations of ET among the products were quantified by comparing the four ET products (comparison value) with the AWAP ET component (reference value). The percent bias (PBIAS) and mean absolute error (MAE) were used [31] to assess the difference between the reference and comparison ET values. PBIAS measures the average tendency of comparison values. Positive values show overestimation and negative values indicate underestimation; low-magnitude values of PBIAS are preferred and the optimal value is 0. MAE means the average value of the absolute errors between the reference and comparison ET values and can better reflect the actual level of the comparison value error. The smaller the MAE, the better the comparison value. They are calculated as follows:  
\[\text{PBIAS}=\frac{\sum_{i=1}^{n}(Q_{i}^{sim}-Q_{i}^{obs})}{\sum_{i=1}^{n}Q_{ i=1}^{obs}}\times 100\% \tag{1}\]  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Data Name** & **Sources** & \begin{tabular}{c} **Spatial** \\ **Resolution** \\ \end{tabular}  & **Spatial Extent** & \ \begin{tabular}{c} **Temporal** \\ **Resolution** \\ \end{tabular}  & **Time Span** & **Provider** \\ \hline \begin{tabular}{c} Precipitation, \\ runoff, and water \\ storage changes \\ \end{tabular}  & AWAP a & 0.05\({}^{\circ}\) & Australian & Monthly & 1900–2017 & CSIRO \\ \hline \multirow{4}{*}{Evapotranspiration (ET)} & 1. AWAP & 0.05\({}^{\circ}\) & Australian & Monthly & 1900–2017 & CSIRO \\  & 2 CSIRO b & 0.5\({}^{\circ}\) & Global & Monthly & 1981–2012 & CSIRO \\  & 3. GLDAS c & 0.25\({}^{\circ}\) & Global & 3 h & 2000–August & NASA \\  & & & Global (–60\({}^{\circ}\) to & & \\  & 4. MODIS d & 500 m & 80\({}^{\circ}\) Latitude; & 8 days & 2001–September & NASA LP DAAC at the USGS EROS \\  & & & -180\({}^{\circ}\) to 180\({}^{\circ}\) & 2018 & Center \\  & 5. & & Longitude) & & \\  & TerraClimate & 2.5\({}^{\circ}\) & Global & Monthly & 1958–2017 & University of Idaho \\ \hline \multirow{2}{*}{Irrigation} & \multirow{2}{*}{ABARE e} & Catchment and & & & & \\  & & administrative area & Australian & Yearly & 2005–2017 & ABARE \\ \hline \multirow{2}{*}{GPP f} & \multirow{2}{*}{MOD17A3055} & \multirow{2}{*}{1000 m} & \multirow{2}{*}{Global} & \multirow{2}{*}{Yearly} & \multirow{2}{*}{2000–2014} & NASA LP DAAC at the USGS EROS \\  & & & & & & Center \\ \hline Land cover & DLDv2.1 b & 250 m & Australian & Yearly & 2001–2015 & www.ga-gov.au \\ \hline \multirow{2}{*}{Note: a [FOOTNOTE:a]Footnote a: Australian Water Availability Project (AWAP), b [FOOTNOTE:b]Footnote a: Australian Water Availability Project (AWAP), b [FOOTNOTE:b]Footnote b: Commonwealth Scientific and Industrial Research Organisation (CSIRO), c [FOOTNOTE:c]Footnote c: Global Land Data Assimilation System (GLDAS), d [FOOTNOTE:d]Footnote d: Moderate Resolution Imaging Spectroradiometer  
\[\text{MAE}=\frac{1}{n}\sum_{i=1}^{n}\left|Q_{i}^{sim}-Q_{i}^{obs}\right| \tag{2}\]  
The root mean square error (RMSE) and root mean square error observation standard deviation ratio (RSR) were then used to derive statistical goodness of fit of the ET comparison values and to evaluate the performance of the data product against reference values. RMSE is very sensitive to the large errors in a group of data. RSR is used to standardise RMSE and integrate the advantages of error statistics, and RSR ranges from 0 to 1. They are computed using the following equations:  
\[\text{RMSE}=\sqrt{\frac{\sum_{i=1}^{n}\left(Q_{i}^{sim}-Q_{i}^{obs}\right)^{2 }}{n}} \tag{3}\]  
\[\text{RSR}=\frac{\sqrt{\sum_{i=1}^{n}\left(Q_{i}^{sim}-Q_{i}^{obs}\right)^{2 }}}{\sqrt{\sum_{i=1}^{n}\left(Q_{i}^{obs}-Q_{avg}^{obs}\right)^{2}}} \tag{4}\]  
Following on, the coefficient of determination (\(R^{2}\)) value was computed to evaluate the linear relationship between reference and comparison ET data.