# Evaluation of Remote Sensing-Based Evapotranspiration
## 5 Conclusions  
This study evaluated the accuracy of three remote sensing-based ET datasets, namely PML, FLUXCOM, and GLEAM, and further investigated the utility of these three datasets in improving the hydrological model performance of DHSVM in the Jinhua River Basin, East China. Firstly, the accuracy of these three ET datasets was assessed against in situ observations of pan evaporation. Afterwards, these ET datasets, together with runoff observations, were utilized for the multi-variable calibration of the DHSVM model. The DHSVM model results calibrated with only runoff were used as a reference. The key findings of this study are summarized below.  
1. All three ET datasets have good performance compared with pan evaporation at the Jinhua River Basin. Among them, FLUXCOM and GLEAM have higher accuracy than PML.
2. Runoff simulation based on Scheme I shows good performance, with a KGE value of 0.87 in the calibration period and a KGE value of 0.89 in the validation period. The PBIAS values in the calibration and validation period are 3.45% and 4.28%, respectively. However, similar good performance cannot be found in ET simulation.
3. Scheme II is able to enhance ET simulation and maintain good runoff simulation. Among the three ET datasets, PML performs the best in simulating runoff, with a KGE of 0.87 and a PBIAS of 1.46% in the calibration period and a KEG of 0.91 and a PBIAS of 3.38% in the validation period, and ET simulation is improved with a great reduction in PBIAS (95.85%) and ME (96.72%) in the calibration period. FLUXCOM and GLEAM also show comparable performance in the calibration period.
4. The DHSVM model calibrated with Scheme II is able to generate reasonable low flow, intermediate flow, and high flow. Compared to Scheme I, Scheme II enhances the performance of extreme flow simulations (including extremely low flow and extremely high flow). PML can be applied to multi-variable calibration in drought forecasting based on the better improvement in extremely low flow simulation. FLUXCOM and GLEAM are good choices for multi-variable calibration in flood forecasting on the basis of the better improvement in extremely high flow simulation.  
Conceptualization, S.P. and Y.-P.X.; Data curation, H.G.; Formal analysis, S.P.; Funding acquisition, S.P.; Investigation, H.G. and W.X.; Methodology, S.P., H.G., B.Y. and W.X.; Project administration, Y.-P.X.; Resources, Y.-P.X.; Software, S.P., H.G., B.Y. and W.X.; Visualization, B.Y.; Writing--original draft, S.P.; Writing--review & editing, Y.-P.X. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Nature Science Foundation of China (51909233), the Zhejiang Natural Science Foundation (LY22E090010), and the Key Project of Zhejiang Natural Science Foundation (LZ20E090001).  
The meteorological data are provided by the National Climate Center of the China Meteorological Administration and Zhejiang Provincial Metrological Administration. The hydrological data are provided by Zhejiang Provincial Hydrology Administration Center. The ET datasets are provided by GLEAM ([[https://www.gleam.eu/](https://www.gleam.eu/)]([https://www.gleam.eu/](https://www.gleam.eu/)), accessed on 30 November 2020), PML ([[https://data.tpdc.ac.cn/zh-hans/data/48](https://data.tpdc.ac.cn/zh-hans/data/48) cl6a8d-d307-4973-abab-972e9449627c/]([https://data.tpdc.ac.cn/zh-hans/data/48](https://data.tpdc.ac.cn/zh-hans/data/48) cl6a8d-d307-4973-abab-972e9449627c/), accessed on 11 December 2020), and FLUXCOM ([[http://www.fluxcom.org](http://www.fluxcom.org)]([http://www.fluxcom.org](http://www.fluxcom.org)), accessed on 21 December 2020).  
The valuable comments and suggestions from the editor and three anonymous reviewers are greatly appreciated.  
The authors declare no conflict of interest.