# Evaluation of Remote Sensing-Based Evapotranspiration
## 1 Introduction  
Hydrological models are a critical tool for hydrological processes' simulation and their behavior prediction, which are very important for water resource planning and management. Hydrological models, especially physically-based ones, contain dozens and even hundreds of parameters [1]. However, it is difficult to determine some parameters in hydrological models, owing to the complex actual environment and limited data [2, 3]. Thus, parameter calibration is a necessary step in hydrological model implementation [4, 5]. Hydrological models are usually calibrated against runoff observations at a few locations or only at the basin outlet. Calibration with only runoff does not guarantee acceptablesimulation for other hydrological components, such as evapotranspiration and soil moisture [6; 7; 8]. Moreover, calibration with only runoff often results in parameter equifinality [9]. One way to address the aforementioned issues is to calibrate the model with multiple variables, such as runoff, evapotranspiration, soil moisture, and land surface temperature within the basin [10; 11]. Among the abovementioned variables, evapotranspiration is a good choice to calibrate the hydrological models together with runoff, due to its importance in the water cycle, carbon cycle, and energy cycle [12; 13]. However, the accurate observation of evapotranspiration remains lacking, owing to the complex terrain environment. Fortunately, remote sensing technology has greatly promoted the estimation of evapotranspiration in the past few decades [14; 15; 16]. The emergence of various regional or global evapotranspiration datasets is critical for ungauged and poorly gauged regions [17]. For example, the spatial-temporal characteristics of evapotranspiration over the Amazon River Basin remain largely uncertain, due to the sparse observation network and relatively short observational period of eddy covariance data. Thus, [PERSON] et al. [18] assessed the reliability of five global remote sensing ET datasets (JUN10, ZEN14, ZHA15, ZHA16, and GLEAM) (besides GLEAM, the names of the datasets are composed of the name of the first author and the publication year of the references) over the Amazon River Basin, and the results revealed that JUN10 has the best performance for the magnitude at both site level and basin scale. Moreover, [PERSON] et al. [19] merged six global ET datasets (Atmosphere-Land Exchange Inverse Model (MOD16), CSIRO Moderate-Resolution Imaging Spectroradiometer (MODIS) Reflectance-Based Evapotranspirationpiration (CMRSET), Operational Simplified Surface Energy Balance (SSEBop), Surface Energy Balance System (SEBS), Atmosphere-Land Exchange Inverse (ALEXI), and GLEAM) to obtain an ensemble prediction of evapotranspiration rates for the complex and inaccessible environment of the Amazon River Basin. With the rapid development of remote sensing-based ET datasets, it is worthwhile to investigate whether hydrological model performance can be improved when evapotranspiration is incorporated with runoff in multi-variable calibration. Recently, some studies have assessed the utility of remote sensing-based ET datasets in improving hydrological model performance [20]. [PERSON] et al. [8] used the GLEAM ET dataset and measured the streamflow at four stations to calibrate the Soil and Water Assessment Tool (SWAT) for Chindiwn Basin, Myanmar. The result shows that reasonable simulations for both streamflow and evapotranspiration were obtained in multi-variable calibration, but good performance could only be found in the corresponding variable (streamflow/ET) in single-variable calibration, and not for the other variable (ET/streamflow). [PERSON] et al. [7] adopted two remote sensing-based ET datasets (SSEBop and ALEXI) into multi-variable calibration together with runoff, and the result revealed that the performance of ET simulation was improved, and runoff simulation was maintained in multi-variable calibration. [PERSON] et al.