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首页> 外文期刊>Journal of hydrologic engineering >New Multisite Cascading Calibration Approach for Hydrological Models: Case Study in the Red River Basin Using the VIC Model
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New Multisite Cascading Calibration Approach for Hydrological Models: Case Study in the Red River Basin Using the VIC Model

机译:水文模型的新的多站点级联标定方法:使用VIC模型的红河流域案例研究

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摘要

A novel multisite cascading calibration (MSCC) approach using the shuffled complex evolution-University of Arizona (SCE-UA) optimization method, developed at the University of Arizona, was employed to calibrate the variable infiltration capacity (VIC) model in the Red River Basin. Model simulations were conducted at 35 nested gauging stations. Compared with simulated results using a priori parameters, single-site calibration can improve VIC model performance at specific calibration sites; however, improvement is still limited in upstream locations. The newly developed MSCC approach overcomes this limitation. Simulations using MSCC not only utilize all of the available streamflow observations but also better represent spatial heterogeneities in the model parameters. Results indicate that MSCC largely improves model performance by decreasing the number of stations with negative Nash-Sutcliffe coefficient of efficiency (NSCE) values from 69% (66%) for a priori parameters to 37% (34%) for single-site calibration to 3% (3%) for MSCC, and by increasing the number of stations with NSCE values larger than 0.5 from 9% (9%), to 23% (23%) to 34% (29%) during calibration (and validation) periods across all sites.
机译:亚利桑那大学开发的一种使用经过改组的复杂演化的新型多站点级联校准(MSCC)方法-亚利桑那大学(SCE-UA)优化方法用于校准红河流域的可变渗透能力(VIC)模型。在35个嵌套测量站上进行了模型仿真。与使用先验参数的模拟结果相比,单点校准可以改善特定校准点的VIC模型性能。但是,上游地区的改进仍然有限。新开发的MSCC方法克服了此限制。使用MSCC进行的模拟不仅利用了所有可用的流量观测值,而且还更好地表示了模型参数中的空间异质性。结果表明,通过将负Nash-Sutcliffe效率系数(NSCE)值从负先验参数的69%(66%)减少到单点校准的37%(34%),MSCC大大提高了模型性能。 MSCC为3%(3%),并且在校准(和验证)期间,将NSCE值大于0.5的站点数量从9%(9%)增加到23%(23%)到34%(29%)所有网站的期间。

著录项

  • 来源
    《Journal of hydrologic engineering》 |2016年第2期|05015019.1-05015019.9|共9页
  • 作者单位

    School of Civil Engineering and Environmental Sciences, Univ. of Oklahoma, Norman, OK 73019 Hydrometeorology and Remote Sensing Laboratory and Advanced Radar Research Center, National Weather Center, Norman, OK 73072;

    Cooperative Institute for Mesoscale Meteorological Studies, Univ. of Oklahoma, Norman, OK 73072;

    School of Civil Engineering and Environmental Sciences, Univ. of Oklahoma, Norman, OK 73019 Dept. of Hydraulic Engineering, Tsinghua Univ., Beijing 100084, China;

    NOAA/National Severe Storms Laboratory, Norman, OK 73072;

    Chickasaw Nation Division of Commerce, Ada, OK 74820;

    Dept. of Geography and Environmental Sustainability, Univ. of Oklahoma, Norman, OK 73019 University, South Central Climate Science Center, Norman, OK 73072;

    School of Civil Engineering and Environmental Sciences, Univ. of Oklahoma, Norman, OK 73019 Hydrometeorology and Remote Sensing Laboratory and Advanced Radar Research Center, National Weather Center, Norman, OK 73072;

    Dripping Springs, TX 78620;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Hydrological model; Multisite calibration; Optimization; Shuffled complex evolution method-University of Arizona (SCE-UA);

    机译:水文模型;多站点校准;优化;改组复杂进化方法-亚利桑那大学(SCE-UA);

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