中文摘要：

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。

英文摘要：

The distributed hydrological model is so advantageous that the spatial and temporal variability of hydrological processes can be restored and hydrological elements and in- homogeneities of spatial and temporal distribution can be simulated. However, there are many parameters in model simulation, especially in cases with many sub-basins, and manually adjusting parameters is cumbersome and complex. Optimization algorithms, which can automatically adjust parameters, are a preferred approach to counter these issues. After selecting and analyzing runoff data from 1988 to 2005 for the Jiutiaoling Station, Shiyang River, distributed time-varying gain model parameters （Distribute Time Variant Gain Model, DTVGM） were calibrated by applying the SCE-UA algorithm, genetic algorithm （GA） and particle swarm optimization （PSO） algorithm. We compared performance by looking at convergence rates, iteration number and the stability of the three algorithms. We found that after optimization using the three algorithms, the water balance coefficients were well controlled at around 0.0; the correlation coefficient reached 0.90, and the efficiency factor was more than 0.84. All model simulation results had much better precision and accuracy; however, the global search ability and convergence rates of PSO are superior to the SCE-UA and genetic algorithms. The PSO algorithm had the best performance in the minimum number of iteration processes and showed little sensitivity to initial values. This algorithm is more suitable for parameter optimization of the time-varying gain model.