Uncertainty Analysis Due to the Application of Different Infiltration Methods on the Performance of HEC-HMS Model Using GLUE Algorithm

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Roodaki S.; AZIZIAN A.

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Journal: IRAN-WATER RESOURCES RESEARCH Year:2020 | Volume:16 | Issue:2 Page(s): 50-66

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Title

Uncertainty Analysis Due to the Application of Different Infiltration Methods on the Performance of HEC-HMS Model Using GLUE Algorithm

Author(s)

Roodaki S. | AZIZIAN A. | Issue Writer Certificate

Keywords

Hydrological ModelQ1

UncertaintyQ2

GLUE AlgorithmQ1

Flood RoutingQ1

Effective RainfallQ1

Abstract

Quantifying the Uncertainty contribution of important factors on the performance of rainfall-runoff models has always been one of the major challenges for researchers and hydrologists. The main problems of applying these models especially in calibration period are the large number of required parameters and the lack of physical understanding for some of them. In contrast of previous studies that have been carried out on the assessment of infiltration methods in the field scale and point measurements, this research addressed the Uncertainty contribution of different infiltration methods (Green-Ampt, SCS-CN, Exponential, Smith-Parlange, Initial-Constant and Deficit-Constant) on the performance of HEC-HMS model using GLUE Algorithm in the Chehel chai river basin. Results showed that using each of infiltration methods imposes different Uncertainty bounds on the simulated flood hydrograph by HEC-HMS. Findings indicate that SCS-CN and Smith-Parlange owing to have the higher P-factor (0. 78 and 0. 72) and lower ARIL (0. 39 and 0. 40) values, enforce minimum Uncertainty on the model output. In addition, these methods have the fewer sensitive parameters and then outperform other methods. In contrast, the Uncertainty of applying Initial-Constant and Deficit-Constant methods for simulation of flood hydrograph is relatively high and the smaller percentage of observed data fall into the 95% Uncertainty bounds. Moreover, sensitivity analysis of the parameters of each of the infiltration methods using the nonparametric Kolmogorov– Smirnov (D) test showed that parameters with the sharp and peaked distributions indicate well-identifiable parameters, while flat and spread distributions indicate uncertain parameters. Overall, the outcomes of this study prove that GLUE Algorithm has high ability to determine the optimal range of rainfall-runoff model parameters and the prediction Uncertainty bounds.

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APA: Copy

Roodaki, S., & AZIZIAN, A.. (2020). Uncertainty Analysis Due to the Application of Different Infiltration Methods on the Performance of HEC-HMS Model Using GLUE Algorithm. IRAN-WATER RESOURCES RESEARCH, 16(2 ), 50-66. SID. https://sid.ir/paper/383890/en

Vancouver: Copy

Roodaki S., AZIZIAN A.. Uncertainty Analysis Due to the Application of Different Infiltration Methods on the Performance of HEC-HMS Model Using GLUE Algorithm. IRAN-WATER RESOURCES RESEARCH[Internet]. 2020;16(2 ):50-66. Available from: https://sid.ir/paper/383890/en

IEEE: Copy

S. Roodaki, and A. AZIZIAN, “Uncertainty Analysis Due to the Application of Different Infiltration Methods on the Performance of HEC-HMS Model Using GLUE Algorithm,” IRAN-WATER RESOURCES RESEARCH, vol. 16, no. 2 , pp. 50–66, 2020, [Online]. Available: https://sid.ir/paper/383890/en

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