文章摘要
李 瑞, 张士锋.河北雨洪模型在半干旱区的应用与研究Journal of Water Resources and Water Engineering[J].,2017,28(2):19-25
河北雨洪模型在半干旱区的应用与研究
The application and research of Hebei Flood model in semi-arid area
  
DOI:10.11705/j.issn.1672-643X.2017.02.04
中文关键词: 河北雨洪模型  半干旱区  参数率定  目标函数  洪水模拟
英文关键词: Hebei Flood model  semi-arid area  parameter calibation  objective function  flood simulation
基金项目:中国科学院重点部署项目(KFZD-SW-301);国家自然科学基金项目(41641001)
Author NameAffiliation
LI Rui1,2,3,ZHANG Shifeng1,2 (1.中国科学院 地理科学与资源研究所北京 100101 2.中国科学院 陆地水循环及地表过程重点实验室北京 100101 3.中国科学院大学 北京 100049) 
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中文摘要:
      河北雨洪模型是一种兼具蓄满和超渗产流机制的概念性水文模型。为了探索河北雨洪模型在半干旱区的应用效果,并研究目标函数对模型模拟结果的影响,以黄河上游小流域为研究区,对研究区1994-2010年间的10场洪水过程进行模拟,并分别以峰值加权均方根误差、加权误差及效率系数为目标函数,研究了不同目标函数对模型模拟结果的影响。结果表明:河北雨洪模型对西北半干旱区的洪水过程模拟有良好的适用性;不同目标函数对模型模拟效果的影响各有侧重,但模拟结果都很接近,在实际问题中,应根据关注的重点恰当选取目标函数而避免一定的盲目性;选取峰值加权均方根误差作为目标函数时,模型对副峰的模拟效果较好。
英文摘要:
      Hebei Flood model is a conceptual hydrological model with both storage-excess runoff and infiltration-excess runoff mechanisms. In order to investigate the application effects of Hebei Flood model in semi-arid areas and of objective function on the simulation results, in this paper 10 floods were simulated from 1994 to 2010 based on Hebei Flood model for the study area which located in the upstream of the Yellow River areas. The effects of different objective functions (by using the peak-weighted root mean square error, weighted error and efficiency coefficient, respectively) on the simulation results were studied. The results showed that: Hebei Flood model has good applicability to simulate the flood process in the semi-arid areas of northwest China, the effects of different objective functions on the simulation results of the model have different emphases, but the simulation results were very close, so in practical respects, the objective function should be selected considering the appropriate focus of attention to avoid certain blindness, when the peak-weighted root mean square error was chosen as the objective function, the simulation of the secondary peak was better.
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