By now,there are still no traditional optimization methods being capable of providing enough steady and efficient algorithm to determine the prior-calibrated modeling parameters in conceptual model for rainfall- runoff simulations.The most knotty problem for this issue mainly attributes to the local convergence of the traditional optimization methods,the genetic algorithm capable of yielding accurate and stable optimization of the modeling parameters in conceptual models,therefore,has attract much attention and has rapidly developed with the computing advances.However,most of the efforts on genetic algorithm applications for modeling parameter calibration are concentrated on flooding simulations,few attempt has been made to modeling parameter optimizations with genetic algorithm in daily stream-flow simulations.Bearing this in mind,this study presents an approach to systematically automate-optimize the calibrated parameters in daily stream-flow simulations by using the Xinanjiang hydrological model.For resolving the frequently occurred problem of rather low computation efficiency in genetic algorithm applications,a set of simplified method in parameter optimization based on hierarchy principle of the model parameters and model sensitivity analyses is proposed.An experimental study on model parameter calibration by using the Xinanjiang hydrological model is conducted to a 2 341.6 km2 watershed located on the upper stream of the Hanjiang River Basin.The preliminary study shows that the proposed approach is feasible and of high computation efficiency,and can be transferred to model parameter calibrations for conceptual hydrological models in the similar categories.