Application of Meteorological Element Combination-Driven SWAT Model based on Meteorological Datasets in Alpine catchment: A case study in the Yellow River Source Region (China)

Abstract

Reanalysis meteorological datasets have been widely used for hydrological simulation research in areas where meteorological stations are scarce. However, most of them focus on the applicability of datasets to basin or hydrological model and pay little attention to the influence of meteorological elements of dataset on hydrological modeling. In this study, the precipitation, temperature and solar radiation from three meteorological datasets, gauge dataset (GD), the China Meteorological Assimilation Driving Datasets for the Soil and Water Assessment Tool (SWAT) model (CMADS), and Climate Forecast System Reanalysis (CFSR), were cross-combined with multiple scenarios to drive SWAT models in Yellow River Source Region (YRSR). After a comprehensive comparison of all the scenarios, the main conclusions are as follows: (1) replacing precipitation data has a large impact on streamflow simulation of SWAT model, and using observed precipitation from sparse stations consistently yielded better performance than using precipitation from CMADS and CFSR. (2) In the scenarios adopting observed precipitation as input, using temperature from CMADS and CFSR datasets yielded better performance than using observed temperature. (3) replacing solar radiation has slight impact on the streamflow simulation, and the solar radiation of CFSR is more suitable for hydrological simulation than that of CMADS in YRSR. (4) the SWAT model driven by different meteorological datasets shows that the runoff simulation of GD with CFSR solar radiation data (S6) is optimal with "very good" performance, while the simulation performance of CMADS and CFSR are poor with clearly underestimation for CMADS and significantly overestimation for CFSR, especially in the dry season. These result indicated that the element combination method of the meteorological dataset has been proven to be useful in YRSR, which provides a new insight for hydrological simulation research in areas where meteorological stations are scarce.