陈昕 鱼京善

摘要：为降低水文模型的不确定性对流域水文过程模拟的影响，优化模型的实际应用效果，选取四种常见的水文模型：SWAT模型、BTOPMC模型、VIC模型和DTVG模型在中国西南的雅砻江流域分别建模，采用一套统一的模型输入数据与模拟时间范围，再次运用四个水文模型进行径流计算，并运用北京师范大学水科学研究院自主开发的基于人工神经网络方法的多模型输出集合系统对四个模型的模拟结果进行集合计算，得到集合计算的流量过程线及误差水平，与各水文模型计算结果相比较。研究结果表明，多模型集合计算的确定性系数和纳什效率系数均达到了090，相比单一水文模型的计算精度有大幅提高，且计算结果较稳定，与实际径流过程具有很好的一致性，说明多模型集合模拟在该流域具有很好的适用性。

关键词：水文模型;人工神经网络;水文过程;多模型集合;雅砻江流域

中图分类号：TV121.1文献标志码：A文章编号：

16721683（2018）02007407

Abstract：

In order to reduce the influence of the uncertainty of hydrological models on hydrological simulation and improve the actual application effect of the models，we took the Yalong River basin as an example，and constructed four commonly used hydrological models： SWAT model，BTOPMC model，VIC model，and DTVG model.We conducted independent simulation using these models with the same input data and simulation time range.Then，we calculated the simulation results of the four models using the Multimodel Ensemble Output System independently developed by Beijing Normal University based on the artificial neural network method to obtain the flow hydrograph and error，and compared them with the results of the four models.The results indicated that the correlation coefficient and Nash efficiency coefficient of the multimodel ensemble simulation were both above 090，which was a great improvement in accuracy than the independent models.The results were stable and consistent with the actual runoff process.These indicated that the multimodel ensemble hydrological simulation had good applicability in this river basin.

Key words：

hydrological model;artificial neural network;hydrological process;multimodel ensemble;Yalong River Basin

水文模型的不確定性主要来源于模型输入、模型结构、模型参数和模型输出四个方面。其中，模型结构的不确定性属于系统不确定性，没有任何一个模型相比于其他模型具有绝对优势;此外，面向某种空间尺度进行开发与设计的水文模型都具有特定的适用空间尺度。因此，应用单一模型得到的模拟结果无法避免由模型结构带来的不确定性[1]，也无法模拟多空间尺度下的水文过程，容易影响模拟效果与预报精度。……