Effect of sensitivity analysis on parameter optimization: Case study based on streamflow simulations using the SWAT model in China

Abstract

Parameter optimization is an essential step in hydrological simulations, especially for solving practical problems. However, parameter optimization is usually intractable for complex models with a large number of parameters. In this study, a parameter optimization system based on Sensitive Parameter Combinations (SPCs) was developed, which comprised four parameter sensitivity analysis (SA) methods and a sensitive parameter optimization method. In particular, parameter SA was used to screen out the relatively sensitive parameters with significant impacts on the model output, and instead of using All Parameter Combinations (APCs), the SPCs were optimized with a global optimization method. This system was applied to the Soil and Water Assessment Tool (SWAT) model for daily streamflow simulation and monthly evaluation in four watersheds of China. The results showed that no more than 10 sensitive parameters were identified from 27 adjustable parameters for each watershed. In particular, four parameters (CN2, SOL_K, ALPHA_BNK, and SLSUBBSN) were relatively sensitive in all watersheds. Compared with optimizing APCs, despite the number of parameters was reduced by almost 2/3 in the optimization of SPCs, the accuracy was still very close (the maximum Nash–Sutcliffe coefficient (NSE) difference was 0.024 and the minimum difference was 0.002) and the optimization speed was doubled. In the comparison of monthly streamflow optimization, the SPCs were in good agreement with the APCs and had an obvious improvement for the default simulation. The NSE values of the SPCs optimization were greater than 0.88 during the calibration period in all watersheds and greater than 0.83 during the validation period in three watersheds. These findings indicate that optimizing the sensitivity parameters can greatly reduce the computational costs of SWAT streamflow simulations while ensuring their accuracy.