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Showing 3 results for Flood Routing
Comparison of Flood Routing by Muskingum and Muskingum-Cunge Methods in Section of Lighvan River
H.r. Moradi, M. Vafakhah , A. Akbari Baviel,
Volume 11, Issue 42 (1-2008)
Abstract

  Discharge routing as a mathematic process to forecast the changes of greatness, speed and form of flood wave is function of time in one or more points along drainage, canal or reservoir. Hydrologic and hydraulic methods are used to accomplish the flood routing. Although hydrologic method is less accurate than hydraulic methods but it is simpler to use in flood control and designing works with acceptable confidence. This study has been conducted for flood routing in Lighvan River from Lighvam hydrometric station to Hervy hydrometric station in eastern Azerbaijan Province in a distance the 12 Km. The discharge for different return periods (2, 10, 20, 50 and 100 years) was calculated by using upstream stations data. Then routing of every flood discharge was studied with different return periods by Muskingum and Muskingum-Cunge methods. Results showed significant difference between calculated discharges routing by two methods with discharge values to relate that return period in Hervy hydrometric station. The reasons of exist this different, is numerous for example mountain location this area and to exist sub drainage in between two stations and etc.


Using Nonlinear Programming for Flood Routing and Comparing the Results with those of Dynamic Wave Hydraulic and Muskingum Hydrological Methods
R. Ghobadian, M. Zare, S. M. Kashefipour,
Volume 16, Issue 60 (7-2012)
Abstract
Development of precise and simple methods in flood simulation has greatly reduced financial damage and life loss. Various methods and procedures have been implemented based on Saint-Venant's one-dimensional equation governing unsteady flows. To simplify the solution for these flows, analytical and numerical methods have been used. In the present study, a new method that provides the optimal outcome is introduced using non-linear programming. Penalty function has also been used to convert nonlinear programming (NLP) constrained problems into unconstrained optimal issues. To verify the accuracy of decision variables, the study covered 60 cross-sections of Gharasu River and 25-year flood hydrographs. After determining the model correctness, the 50 and 100-year flood hydrograph were routed in 18 Kilometers. The results were statistically compared with hydraulic and Muskingum hydrological methods. To sum up the routed hydrographs introduced by NLP method were very close to the hydrographs produced by dynamic wave method. The R2 of calculated discharge of routed hydrograph by NLP and dynamic wave method were 0.948, 0.990, and 0.989, respectively, with the return period of 25, 50 and 100-year flood being 0.989. It can be concluded that NLP method is more accurate than Muskingum method, especially when predicting the peak discharge of flood hydrograph.
Flood Routing in Rivers Using Two-Dimensional HEC-RAS Software and its Comparison with the Non-Linear Muskingum Method
M. Amiri, E. Fazel Najafabadi, M. Shayannejad,
Volume 28, Issue 3 (10-2024)
Abstract
One of the important issues in river engineering is flood trends. In general, two types of methods are used to determine the flood trends in rivers. The first group of hydraulic methods, such as the dynamic wave method, is based on solving continuity and momentum equations or Saint-Venant equations. The second category is hydrological methods like the non-linear Muskingum method. In this research, both methods have been used to determine the trends of flood hydrographs in the Plasjan River, one of the main tributaries of the Zayandehrud River. The coefficients of the non-linear Muskingum method were obtained by optimizing and solving the related equation with the fourth-order Runge-Katai numerical method using MATLAB software and the dynamic wave method using the two-dimensional HEC-RAS software. In this study, four flood events were used. In the non-linear Muskingum method, the first event was used for model calibration and the other three events were used for validation. The error rate in this method for the second, third, and fourth events was 84.23, 6.6, and 7.96 percent, respectively, and the error rate in the dynamic wave method for these four events was 17.58, 87.3, 5.4, and 6.21 percent, respectively. Therefore, the dynamic wave method is more accurate in estimating the output hydrograph. However, the non-linear Muskingum method has acceptable accuracy and is recommended in terms of cost, required information, simplicity, and speed of calculation in situations where sufficient information is not available.

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