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PARAMETER UNCERTAINTY ANALYSIS OF A STORAGE FUNCTION MODEL USING BOOTSTRAP METHOD FOR AN URBAN WATERSHED

机译:基于BOOTSTRAP方法的城市流域存储函数模型参数不确定度分析。

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Parameter uncertainty analysis of rainfall-runoff models is very important especially in urban watersheds due to the high flood risk in these areas. Among the different methods available for uncertainty analysis, bootstrap method gained popularity in view of its flexibility. Hence, this study aims to conduct the parameter uncertainty analysis of the urban storage function (USF) model, a storage function model specifically developed for the urban watersheds, using the model-based bootstrap method. We successfully evaluated the uncertainty of USF model parameters and the results exhibited that the 95% confidence interval of all parameters is wide compared with the search range during parameter estimation except for two parameters. Moreover, the parameters with the highest and least uncertainties were identified. Further, model simulation efficiency using the estimated parameters was found to be high with a Nash-Sutcliffe Efficiency value of 97%. Lastly, the effect of parameter uncertainty on model simulation uncertainty was analysed and found that the SCE-UA method along with the model-based bootstrap method can predict, on an average, 68% of observed data within the simulation uncertainty range of USF model.
机译:降雨径流模型的参数不确定性分析非常重要,特别是在城市流域,因为这些地区洪水风险很高。在可用于不确定性分析的不同方法中,自举方法因其灵活性而广受欢迎。因此,本研究旨在使用基于模型的自举法对城市存储功能(USF)模型进行参数不确定性分析,该模型是专门为城市流域开发的存储功能模型。我们成功地评估了USF模型参数的不确定性,结果表明,除两个参数外,所有参数的95%置信区间都比参数估计期间的搜索范围宽。此外,确定了具有最高和最小不确定性的参数。此外,发现使用估计参数的模型仿真效率很高,纳什-舒特克里夫效率值为97%。最后,分析了参数不确定性对模型仿真不确定性的影响,发现SCE-UA方法和基于模型的自举方法可以平均预测USF模型仿真不确定性范围内观测数据的68%。

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