Enhancing streamflow simulation accuracy in ungauged catchments via parameter calibration with triple collocation-based merged evapotranspiration and streamflow features

Abstract

Estimating model parameters for accurate hydrological simulations poses a challenge in ungauged catchments. Evapotranspiration (ET) data derived from remote sensing (RS) holds promise for parameter calibration in ungauged catchments. Nonetheless, uncertainties inherent in raw RS-based ET data can pose challenges in the calibration process. This study evaluates two calibration methods mitigating the impact of this uncertainty. The first, a single-variable calibration, utilizes merged ET data based on the triple collocation technique, while the second incorporates streamflow features alongside ET data for multi-variable calibration. Applying the variable infiltration capacity model to 49 Chinese catchments revealed the superiority of the merged ET-based calibration scheme over the three raw RS ET-based calibrations, with improvements noted in the Nash-Sutcliffe efficiency (NSE) and Kling-Gupta efficiency (KGE) coefficients for simulated streamflow. On average, the NSE and KGE for the simulated streamflow demonstrate improvements ranging from 0.023 to 0.080 and 0.026 to 0.061, respectively. The multi-variable calibration schemes exhibit a significant enhancement in the reliability of streamflow simulations compared to the ET-based single-variable calibration schemes. On average, the NSE and KGE for the simulated streamflow exhibit improvements of 0.054 to 0.133 and 0.062 to 0.122, respectively. The performance of these calibrations was generally higher in humid catchments but showed more pronounced improvements in arid catchments when compared to raw RS ET-based calibrations. In conclusion, the proposed calibrations using streamflow features and/or merged ET data offer viable solutions for parameter calibration in ungauged catchments.