Hydrological Simulation Program-Fortran (HSPF) model has many parameters and complex interactions. The traditional parameter optimization is insensitive to the optimization parameters and the optimization algorithm is easy to trap into local problems

which affects the precision and efficiency of optimization. In this paper

a new optimization approach is explored by integrating Qinglong River watershed

parameter sampling

sensitivity analysis

and parameter optimization. Response surface optimization software Design Expert was applied to sample the parameters of 9 HSPF models

and 130 sets of parameter sets were obtained. Multiple quadratic regression models were used to establish the nonlinear relationship between the parameter sets and the efficiency coefficient of nash-sutcliffe (NSE)

and the optimal parameters and their dense value ranges were identified by contour lines and response surface. The NSE mean value

maximum value

and minimum value of the response surface optimization parameters as well as the optimized interval reduction rate were all superior to the orthogonal range analysis method. LZSN

INFILT

and AGWRC were extremely sensitive parameters

while DEEPFR was sensitive parameters. The interactions between LZSN and INFILT

INFILT and AGWRC

INFILT and UZSN

and INFILT and IRC had significant impacts on the results. The dense value range of parameters were optimized as follows: LZSN[2.0

2.65]

INFILT[0.0

0.475]

AGWRC[0.0

0.885]

DEEPFR[0.1

0.176]

BASETP[0.1

0.120]

AGWETP[0

3

0.120]

CEPSC[0.6

0.244]

UZSN[0.3

1.22]

IRC[0.3

0.63]. The response surface method synthesized three aspects

i.e.

parameter sampling

parameter sensitivity analysis

and parameter optimization

which considers the nonlinear relationship of parameters

the interaction of parameters

and the optimization accuracy and efficiency

thus opening up a new way for parameter optimization of HSPF model in Qinglong River watershed.