In order to evaluate the impact of rainfall input on the simulation results of BASINs/HSPF model in Qinglong River Watershed and improve its simulation accuracy

200 sets of rainfall simulation series were obtained using random modeling procedures such as trend component modeling

periodic component modeling

dependent random component modeling

and independent random modeling (white noise) procedures

as well as the Monte Carlo simulation method. The Nash-Sutcliffe efficiency coefficient (ENS) was taken as the evaluation criterion of the model simulation

and the following conclusions were drawn: 1) Using trend component

periodic component

ARMA modeling

and normal random simulation to obtain rainfall stochastic simulation series is a suitable method for the quantification of randomness of rainfall input. 2) Under the condition of model parameter optimization

the variation range of ENS value of HSPF simulation was [71.09%

74.96%]

and the fluctuation range was 3.87%

suggesting that the randomness of rainfall input had a significant impact on the HSPF simulation results. 3) When considering the annual daily rainfall extremes

the variation range of ENS value was [75.35%

78.81%]

the fluctuation range was 3.46%

and the results were better than those of the stochastic simulation series which ignored the daily rainfall extremes. It revealed that the extremes of rainfall time series had a significant impact on HSPF simulation results. 4) With the increasing number of extremes considered in the simulated rainfall time series

the HSPF simulation results showed a downward trend

indicating that more attention should be paid to the impact of some critical extremes. 5) The uncertainty of rainfall input is a crucial source of the uncertainty of HSPF simulation results

so randomness and extremes of rainfall time series should be taken into consideration to improve the simulation effects of HSPF. This study provides reference for quantifying the impact of rainfall input on HSPF model simulation and optimizing rainfall scenarios for HSPF simulation.