中小尺度流域洪水模型模拟比较研究
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摘要
中小尺度流域洪水模拟是水文预报和防洪减灾的重要基础工作,选择适宜的水文模型对制定水文预报方案具有重要意义。以长江上游支流沿渡河流域为研究对象,对比分析了3种不同类型的水文模型(新安江模型、TOPMODEL、人工神经网络模型)对场次暴雨洪水过程的模拟效果及适用性。结果表明:各模型在模拟场次和验证场次的平均NSE效率系数均超过0.7,平均径流深误差均低于12%,可见3种模型在沿渡河这一湿润地区典型中小尺度流域均有较好的适用性。在验证期,新安江模型模拟的径流深相对误差均未超出许可误差20%的范围,且NSE系数均值达到0.826,然而Topmodel和BP模型模拟下各场次洪水的NSE系数虽均大于0.6,但个别场次结果精度较低。此外,新安江和BP模型的实测与模拟流量点群更接近1∶1线,在流量模拟方面更好,Topmodel的流量模拟整体偏大。总的来说,新安江模型在流域的适用性更好,Topmodel和BP模型次之。
Flood simulation in small and medium-scale basins is an important basic work for hydrological forecasting,flood control and disaster mitigation. It is of significance to choose an appropriate hydrological model for developing hydrological forecasting schemes. Taking Yandu River catchment,a tributary of upper Yangtze River,as a study case,three hydrological models( e. g. Xin ' anjiang model,TOPMODEL,artificial neural network model) are used to simulate event floods and compare the applicability. The results indicate that the average NSE efficiency coefficients of the simulations exceed 0.7 and the average runoff depth errors are all below 12% in the simulation and verification fields. It can be seen that the three models have good applicability in Yandu River catchment,the typical small and mediumscale catchment in the wet area. During the verification period,the relative error of the runoff depth simulated by the Xin'anjiang model does not exceed the allowable error( 20%),and the mean value of the NSE coefficient reaches 0.826. The NSE coefficients of the floods in the Topmodel and BP models are all more than 0.6,but the results of few floods are less accurate. The points of the recorded discharges against the simulated discharges by the BP model and Xin ' anjiang model approach to 1 ∶ 1 line,which do well in discharge simulation. The simulation of Topmodel is overall larger. Generally speaking,the Xinanjiang model has better applicability in the catchment,followed by the Topmodel and BP neural network model.
Flood simulation in small and medium-scale basins is an important basic work for hydrological forecasting,flood control and disaster mitigation. It is of significance to choose an appropriate hydrological model for developing hydrological forecasting schemes. Taking Yandu River catchment,a tributary of upper Yangtze River,as a study case,three hydrological models( e. g. Xin ' anjiang model,TOPMODEL,artificial neural network model) are used to simulate event floods and compare the applicability. The results indicate that the average NSE efficiency coefficients of the simulations exceed 0.7 and the average runoff depth errors are all below 12% in the simulation and verification fields. It can be seen that the three models have good applicability in Yandu River catchment,the typical small and mediumscale catchment in the wet area. During the verification period,the relative error of the runoff depth simulated by the Xin'anjiang model does not exceed the allowable error( 20%),and the mean value of the NSE coefficient reaches 0.826. The NSE coefficients of the floods in the Topmodel and BP models are all more than 0.6,but the results of few floods are less accurate. The points of the recorded discharges against the simulated discharges by the BP model and Xin ' anjiang model approach to 1 ∶ 1 line,which do well in discharge simulation. The simulation of Topmodel is overall larger. Generally speaking,the Xinanjiang model has better applicability in the catchment,followed by the Topmodel and BP neural network model.
引文
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[5]王璐,叶磊,吴剑,等.山丘区小流域水文模型适用性研究[J].中国农村水利水电,2018(2):78-84,90.
[6]廖如婷,胡珊珊,杜龙刚,等.基于HEC-HMS模型的温榆河流域水文模拟[J].南水北调与水利科技,2018,16(6):15-20.
[7]孙娜,周建中,张海荣,等.新安江模型与水箱模型在柘溪流域适用性研究[J].水文,2018,38(3):37-42.
[8]刘佩瑶,郝振纯,王国庆,等.新安江模型和改进BP神经网络模型在闽江水文预报中的应用[J].水资源与水工程学报,2017,28(1):40-44.
[9]张汉辰,李致家,晁丽君,等.CASC2D模型和新安江模型的应用比较[J].水力发电,2015,41(8):23-25,61.
[10]王思媛,孙利敏,胡高辉,等.HBV模型与新安江模型在黄河源区的应用比较[J].水电能源科学,2016,34(12):41-45,14.
[11]宋晓猛,孔凡哲.新安江模型和人工神经网络的耦合应用[J].水土保持通报,2010,30(6):135-138,144.
[12]阚光远,刘志雨,李致家,等.新安江产流模型与改进的BP汇流模型耦合应用[J].水科学进展,2012,23(1):21-28.
[13]徐宗学.水文模型[M].北京:科学出版社.2009.
[14]刘青娥,左其亭.TOPMODEL模型探讨[J].郑州大学学报(工学版),2002,23(4):82-86.
[15]赵振宁.模糊理论和神经网络的基础与应用[M].北京:清华大学出版社,1996.
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