| Summary: | Hydrological models are usually calibrated against observed streamflow (Q<sub>obs</sub>), which is not applicable for ungauged river basins. A few studies have exploited remotely sensed evapotranspiration (ET<sub>RS</sub>) for model calibration but their effectiveness on streamflow simulation remains uncertain. This paper investigates the use of ET<sub>RS</sub> in the hydrological calibration of a widely used land surface model coupled with a source−sink routing scheme and global optimization algorithm for 28 natural river basins. A baseline simulation is a setup based on the latest model developments and inputs. Sensitive parameters are determined for Q<sub>obs</sub> and ET<sub>RS</sub>-based model calibrations, respectively, through comprehensive sensitivity tests. The ET<sub>RS</sub>-based model calibration results in a mean Kling−Gupta Efficiency (KGE) value of 0.54 for streamflow simulation; 61% of the river basins have KGE > 0.5 in the validation period, which is consistent with the calibration period and provides a significant improvement over the baseline. Compared to Q<sub>obs</sub>, the ET<sub>RS</sub> calibration produces better or similar streamflow simulations in 29% of the basins, while further significant improvements are achieved when either better ET or precipitation observations are used. Furthermore, the model results show better or similar performance in 68% of the basins and outperform the baseline simulations in 90% of the river basins using model parameters from the best ET<sub>RS</sub> calibration runs. This study confirms that with reasonable precipitation input, the ET<sub>RS</sub>-based spatially distributed calibration can efficiently tune parameters for better ET and streamflow simulations. The application of ET<sub>RS</sub> for global scale hydrological model calibration promises even better streamflow accuracy as the satellite-based ET<sub>RS</sub> observations continue to improve.
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