How Remote-Sensing Evapotranspiration Data Improve Hydrological Model Calibration in a Typical Basin of Qinghai-Tibetan Plateau Region

Abstract. Many rivers in the East Asian Monsoon region originates from the Qinghai-Tibet Plateau (QTP), which provide huge amount of fresh water resources for downstream counties. As a region characterized by high altitude and cold weather, distributed hydrological modelling provide valuable knowledge about water cycle and cryosphere of the QTP. However, the lack of streamflow data restricts the application of hydrological models in this data-sparse region. Previous studies have demonstrated the possibility of using remote sensing evapotranspiration (RS-ET) data to improve modelling. However, in the QTP, the mechanisms driving such improvements haven’t been understood thoroughly. In this study, such driving mechanisms were explored through the rainfall-runoff modelling of the Soil and Water Assessment Tool (SWAT) in the Yalong River Basin of the QTP. Three experiments of model calibrations were conducted using streamflow data at the basin outlet, basins averaged RS-ET data of the Global Land Evaporation Amsterdam Model (GLEAM), and the combination of the both data, under the framework of the Generalized Likelihood Uncertainty Analysis (GLUE). The results show that compared with calibration using streamflow data solely, the Nash-Sutcliffe Efficiency of simulated streamflow at 50% quantiles for the calibration using both of streamflow and RS-ET data increased from 0.71 to 0.81 in the calibration period, while in the validation period improved from 0.75 to 0.84, and more observations are embraced by the uncertainty bands. Similar improvements are also found for the ET estimates. Comparison of parameter posterior distributions among the three experiments demonstrated that calibration using both types of observations could increase the number of parameters that posterior distributions are different from assumed uniform prior distribution, indicating the degree of equifinality was reduced. A more comprehensive parameter sensitivity analysis by the Sobol' method were also conducted for reasoning the differences among the three calibrations. Although the number of the detected sensitive parameters are almost same, the sensitive parameter detected based on both types of observations covers surface runoff generation, snow-melting, soil water movement and evaporation processes, while using single type of observations, the identified sensitive parameters are only the ones related the hydrological processed quantified by the observations. From the aspects of model performance and parameter sensitivity, it is demonstrated that not only the model output performs better, but also the characteristics of water cycle are captured more effectively, highlighting the necessity of incorporating RS-ET data for hydrological model calibration in the QTP. Moreover, adopting observations or information about soil property or snow-melting processes to make more reasonable estimates of parameter distribution could further reduce simulation uncertainty under the calibration strategies proposed in this study.