Quantitative Evaluation of Runoff Simulation and Its Driving Forces Based on Hydrological Model and Multisource Precipitation Fusion

Abstract

The hydrological cycle across the source regions of the Yellow River (SRYR) affects water supply for 324 million people across the Yellow River basin (YRB), and the scarcity of meteorological stations leads to great challenges for the estimation of hydrologic and energy balance. Therefore, our work employs multisource precipitation products across the YRB to develop a new integrated precipitation product with the optimized Bayesian mean algorithm (OBMA). It investigates the performance and hydrological utility of the optimal Bayesian integrated precipitation product (OBIPP). This study found that the OBIPP improved by 14.08% in overall performance relative to the optimal precipitation product across the SRYR, respectively. Meanwhile, the variable infiltration capacity (VIC) model, driven by daily OBIPP, can drastically improve the accuracy of runoff simulation compared with other precipitation products across the SRYR. According to the VIC model driven by daily OBIPP, the average precipitation and runoff depth across the SRYR were approximately 621 mm and 64 mm from 2001 to 2019, respectively, showing a spatial trend increasing from northwest to southeast. Overall, OBIPP is characterized by smaller uncertainty of simulation and higher simulation performance across the SRYR, which should provide a scientific basis for accurate prediction and assessment of water resources in areas where meteorological data are scarce.

Keywords

• multisource precipitation fusion
• Bayesian Mean Algorithm
• VIC model
• runoff
• source region of the Yellow River