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中文摘要：
由于目前已有很多比较成熟的流域水文模型，因此我们可以选用几个流域水文模型进行并行运算，来同时模拟流域降雨一径流关系。在相同的降雨输入情况下，不同模型得到的模拟流量必然会有所不同，模型效率系数和模拟精度也会不同。因此，如何将不同模型的模拟结果进行综合以进一步提高流量模拟精度是一个关键问题。本文选用自适应神经模糊推理系统（ANFIS）作为水文模型综合平台，以牧马河流域为试验区域，对两个并行运算水文模型（三水源新安江模型和总径流响应模型）的结果进行综合处理，得到了更稳健的流量模拟结果，大大提高了模型效率和模拟精度。该方法值得在实践中借鉴。
英文摘要：
As there have been many widely used watershed hydrological models, it now becomes possible to select a number of different hydrological models in parallel running for rainfall-runoff simulation. Under the condition of the same rainfall inputs, however, different models will produce various runoff estimates, and the model efficiency and accuracy will also differ from each other. Thus, the very crucial problem is how to combine the simulation results from a number of different simultaneously running hydrological models for improving the model efficiency and estimate accuracy, In this paper, the adaptive-network-based fuzzy inference system （ANFIS） is chosen as a combination framework, which has been applied to combine the simulation results of two hydrological models, one is the three-source Xinanjiang model and the other is the simple linear model （SLM）, The combination results have been demonstrated more robust and accurate than those of either of the two hydrological models. It is recommended that ANFIS be used in practical runoff simulation and forecasting.
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Historical temporal trends of hydro-climatic variables and runoff response to climate variability an
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