# Evaluation of Remote Sensing-Based Evapotranspiration
## 4 Results and Discussion
### Multi-Variable Calibration (Scheme II)
#### 4.3.2 Hydrological Signature Analysis
Furthermore, these conclusions are also confirmed by other two hydrological signatures, as presented in Table 5, namely L1 (mean of lowest annual daily flow divided by mean annual daily flow averaged across all years) and H1 (mean of highest annual daily flow divided by mean annual daily flow averaged across all years) [30; 53; 54]. Based on the result of hydrological signature analysis, PML can be applied to multi-variable calibration in drought forecasting. FLUXCOM and GLEAM are better choices for multi-variable calibration in flood forecasting. This study was only conducted in a humid region. Whether similar conclusions can be obtained in other regions, including semi-humid regions, semi-dry regions, and dry regions, is not certain. However, [PERSON] et al. [21] applied a remote sensing-based ET dataset to multi-variable calibration in a semi-humid region in North Dakota, United States, and the results revealed that the integration of the ET dataset noticeably increased the accuracy in both ET and runoff simulations. Moreover, [PERSON] et al. [2] only used remote sensing-based ET to calibrate model parameters in a semi-humid study area and their results showed that the KGE for individual land use types increased from \(-0.6\) to \(0.6\). Furthermore, [PERSON] et al. [55] applied several multi-variable calibration scenarios with remote sensing-based ET datasets in the Volta River Basin, shared among six countries of West Africa, and the multi-variable calibration scenarios led to higher model performance in all climatic zones of the study area, including humid, semi-humid, semi-arid, and arid zones. In addition, their results demonstrated that the model performance was better in the intermediate climatic regions than in the driest and wettest regions, which is not consistent with [PERSON] et al. [24]. [PERSON] et al. [24] used remote sensing-based ET to calibrate two lumped conceptual hydrological models in 222 catchments across Australia and their results revealed that the model performed better in wetter catchments than in drier catchments. Therefore, we can conclude that the integration of remote sensing-based ET datasets into multi-variable calibration is able to improve model performance in semi-humid regions, semi-dry regions, and dry regions, but whether better performance occurs in wetter or in drier regions requires further study. This is subject to many factors, including the selection of hydrological models, study areas, remote sensing-based ET datasets, calibration strategies, etc. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Periods**} & \multirow{2}{*}{**Calibration Scheme**} & \multicolumn{2}{c}{**Hydrological Signature**} \\ \cline{3-4}  & & **L1** & **H1** \\ \hline \multirow{4}{*}{Calibration (2004–2008)} & Observed runoff & 0.020 & 13.6 \\  & Scheme I & 0.088 & 9.6 \\  & Scheme II (PML) & 0.057 & 10.2 \\  & Scheme II (FLUXCOM) & 0.056 & 12.3 \\  & Scheme II (GLEAM) & 0.056 & 12.0 \\ \hline \multirow{4}{*}{Validation (2009–2010)} & Observed runoff & 0.034 & 8.2 \\  & Scheme I & 0.070 & 6.5 \\  & Scheme II (PML-V2) & 0.049 & 6.8 \\  & Scheme II (FLUXCOM) & 0.039 & 7.6 \\  & Scheme II (GLEAM) & 0.040 & 7.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The other two hydrological signatures (L1 and H1) in the calibration and validation periods based on Scheme I and Scheme II.