分布式水文模型的优势在于还原水文过程的时空变异性,可以很好地模拟和反映各种水文要素和下垫面因素的时空分布不均匀性。由此也导致模型参数过多,在子流域过多的情况下,人工调节参数繁琐复杂,应用优化算法实现参数自动调节成为首选。本文选取石羊河流域九条岭站1988-2005年实测径流资料,分别应用SCE-UA算法、遗传算法(GA)和粒子群算法(PSO)对分布式水循环模型(时变增益模型)进行参数率定,对比3种算法的收敛速度、所需迭代次数和算法稳定性。结果表明:通过SCE-UA、GA和PSO的优化,模型水平衡系数都控制在0.0左右,而相关系数和效率系数分别能达到0.90和0.84以上,模拟精度较好。但粒子群算法的全局搜索能力和收敛速度优于SCE-UA和遗传算法,所需迭代次数最少,初值敏感性小,更适合时变增益模型的参数寻优,有很高的扩展性和改进潜力。 The advantage of the distributed hydrological model lies in the reduction of the temporal and spatial variability of hydrological processes, which can well simulate and reflect the spatiotemporal distribution heterogeneity of various hydrological and underlying factors. As a result, the model parameters are too much. In the case of too many sub-basins, the manual adjustment parameters are complicated and complicated, and the application of the optimization algorithm to automatically adjust the parameters becomes the first choice. In this paper, the measured runoff data of Jiuziling station in Shiyanghe river basin from 1988 to 2005 were selected and the parameters of the distributed water cycle model (time-varying gain model) were calculated using SCE-UA algorithm, genetic algorithm (GA) and particle swarm optimization (PSO) Set and compare the convergence speed of the three algorithms, the number of iterations required and the stability of the algorithm. The results show that the model water balance coefficient is controlled to about 0.0 by the optimization of SCE-UA, GA and PSO, while the correlation coefficient and efficiency coefficient can reach 0.90 and 0.84, respectively, and the simulation accuracy is better. However, the global search capability and the convergence speed of PSO are better than those of SCE-UA and GA. The PSO algorithm needs the least number of iterations and the initial value sensitivity. It is more suitable for optimization of time-varying gain model parameters, Improve potential.