Efficiency of the Linear Muskingum Method in Flood Routing of Dual Rockfill Detention Dams

Document Type : Research Article

Authors

1 Ph.D. Candidate of Hydraulic Structures, Department of Civil Engineering, Faculty of Engineering, University of Zanjan, Zanjan, Iran

2 Associate Professor, Department of Civil Engineering, Faculty of Engineering, University of Zanjan, Zanjan, Iran

Abstract

One of the most important applications of rock-fill dams is to control flood by reducing the peak discharge of inflow. It is of great importance to study how much of the inflow to the rock-fill dam reservoir is transferred to the downstream under unsteady flow conditions. In the present study, flood routing in dual detention rock-fill dams was studied using four experimental data samples, linear Muskingum method, and particle swarm optimization (PSO) algorithm and the effect of rock-fill dam length, distance between two dams, and aggregate size was studied on the K coefficient of linear Muskingum method. The results showed that the mean relative error (MRE) of the four experiments used in the present study were equal to 4.9, 3.4, 4.35 and 3.55%, respectively, and the relative error of the peak discharge (DPO) of the mentioned experiments were calculated as 1.58, 0.47, 2.86 and 1.78%, respectively, which indicates the high accuracy of the linear Muskingum method in estimating the outflow hydrograph. The results also showed that, by increasing the distance between the inflow and outflow hydrographs, the K coefficient increased and by increasing the aggregates size, the flow velocity increased and consequently, the K coefficient decreased.

Keywords

References

[1]. McWhorter, D. B. Sunada, D. K. and Sunada, D. K. Ground-water hydrology and hydraulics. Water Resources Publication.‏LLC. U.S.Library. 1977
[2]. Hansen, D. Garga, V. K. and Townsend, D. R. Selection and application of a one-dimensional non-Darcy flow equation for two-dimensional flow through rockfill embankments. Canadian Geotechnical Journal. 1995; 32(2): 223-232.‏
[3]. Forchheimer, P. Wasserbewagung Drunch Boden, Z.Ver, Deutsh. Ing. 1901; 45: 1782-1788.
[4]. Leps, T. M. Flow through rockfill, Embankment-dam engineering casagrande volume edited by Hirschfeld, RC and Poulos, SJ.‏ 1973.
[5]. Stephenson, D. J. Rockfill in hydraulic engineering. Elsevier scientific publishing compani.‏ Distributors for the United States and Canada. 1979.
[6]. Subramanya. K, Engineering hydrology. 1994; 2nd Ed.
[7]. Tsai CW. Flood routing in mild-sloped rivers—wave characteristics and downstream backwater effect. Journal of Hydrology. 2005; 308 (1-4):151-67.
[8]. Hosseini, S. M. Joy, D. M. Development of an unsteady model for flow through coarse heterogeneous porous media applicable to valley fills. International Journal of River Basin Management. 2007; 5(4): 253-265.
[9]. Nagesh Kumar, D., & Janga Reddy, M. Multipurpose reservoir operation using particle swarm optimization. Journal of Water Resources Planning and Management, 2007; 133(3), 192-201.‏
[10]. Meraji, S. H. Optimum design of flood control systems by particle swarm optimization algorithm (Doctoral dissertation, M. Sc. thesis, Iran University of Science and Technology).‏ 2004.
[11]. Afshar, A., Kazemi, H., & Saadatpour, M. Particle swarm optimization for automatic calibration of large scale water quality model (CE-QUAL-W2): Application to Karkheh Reservoir, Iran. Water resources management, 2011; 25(10), 2613-2632.
[12]. Lu, W. Z., Fan, H. Y., Leung, A. Y. T., & Wong, J. C. K. Analysis of pollutant levels in central Hong Kong applying neural network method with particle swarm optimization. Environmental monitoring and assessment, 2002; 79(3), 217-230.‏
[13]. Chau, K. A split-step PSO algorithm in prediction of water quality pollution. In International Symposium on Neural Networks, 2005; (pp. 1034-1039). Springer, Berlin, Heidelberg.
[14]. Chu, H. J., & Chang, L. C. Applying particle swarm optimization to parameter estimation of the nonlinear Muskingum model. Journal of Hydrologic Engineering, 2009; 14(9), 1024-1027.‏
[15]. Moghaddam, A., Behmanesh, J., & Farsijani, A. Parameters estimation for the new four-parameter nonlinear Muskingum model using the particle swarm optimization. Water resources management, 2016; 30(7), 2143-2160.‏
 
[16]. Bazargan, J., & Norouzi, H. Investigation the Effect of Using Variable Values for the Parameters of the Linear Muskingum Method Using the Particle Swarm Algorithm (PSO). Water Resources Management, 2018; 32(14), 4763-4777.‏
[17]. Norouzi, H. & Bazargan, J. Flood routing by linear Muskingum method using two basic floods data using particle swarm optimization (PSO) algorithm. Water Science and Technology: Water Supply. 2020; 20(5): 1897-1908.
[18]. Ergun, S. Fluid Flow through Packed Columns. Chemical Engineering Progress. 1952; 48: 89–94.
[19]. Ward, J. C. Turbulent flow in porous media. Journal of the hydraulics division. 1964; 90(5): 1-12.‏
[20]. Ahmed, N. and Sunada, D. K. Nonlinear flow in porous media. Journal of the Hydraulics Division, 1969; 95(6): 1847-1858.‏
[21]. Sidiropoulou, M. G., Moutsopoulos, K. N., & Tsihrintzis, V. A. Determination of Forchheimer equation coefficients a and b. Hydrological Processes. 2007; 21(4), 534-554.‏ https://doi.org/10.1002/hyp.6264.
[22]. Sadeghian, J. Khayat Kholghi, M. Horfar, A. and Bazargan, J. Comparison of binomial and power equations in radial non-darcy flows in coarse porous media. Journal of Water Sciences Research. 2013; 5(1): 65-75.‏
[23]. Sedghi-Asl, M. Ansari, I. Adoption of extended dupuit–Forchheimer assumptions to non-darcy flow problems. Transport in Porous Media. 2016; 113(3): 457-469.‏
[24]. Di Nucci, C. Unsteady free surface flow in porous media: One-dimensional model equations including vertical effects and seepage face. Comptes Rendus Mécanique. 2018; 346(5): 366-383.
[25]. Mohan, S. Parameter estimation of nonlinear Muskingum models using genetic algorithm. Journal of hydraulic engineering, 1997; 123(2), 137-142.‏
 
[26]. Barati, R. Parameter estimation of nonlinear Muskingum models using Nelder-Mead simplex algorithm. Journal of Hydrologic Engineering, 2011; 16(11), 946-954.‏
[27]. Hirpurkar, P., & Ghare, A. D. Parameter estimation for the nonlinear forms of the Muskingum model. Journal of Hydrologic Engineering, 2014; 20(8), 04014085.‏
[28]. Niazkar, M., & Afzali, S. H. Parameter estimation of an improved nonlinear Muskingum model using a new hybrid method. Hydrology Research, 2016; DOI: 10.2166/nh.2016.089.
[29]. Zhang, S., Kang, L., Zhou, L., & Guo, X. A new modified nonlinear Muskingum model and its parameter estimation using the adaptive genetic algorithm. Hydrology Research, 2017; 48(1), 17-27.‏ DOI: 10.2166/nh.2016.185.
[30]. Kalagar Naftchali, B. Comparison between mathematical model and experimental data for flood routing in reservoirs of multiple detention rockfill dams. M. Sc. Thesis, Iran University of Tarbiat Modarres. 2003.
[31]. McCarthy G. T. The unit hydrograph and flood routing. New London. Conference North Atlantic Division. US Army Corps of Engineers. New London. Conn. USA. 1938.
[32]. Chow, Vente. open channel hydraulics, Newyork;Macgraw-Hill book company. 1959.
[33]. Eberhart, R. and Kennedy, J. A new optimizer using particle swarm theory. In Micro Machine and Human Science, 1995. MHS'95., Proceedings of the Sixth International Symposium on. 1995; 39-43.
[34]. Shi, Y. and Eberhart, R. A modified particle swarm optimizer. In Evolutionary Computation Proceedings, 1998. IEEE World Congress on Computational Intelligence., The 1998 IEEE International Conference on. 1998; 69-73.
[35]. Di Cesare, N. Chamoret, D. and Domaszewski, M. A new hybrid PSO algorithm based on a stochastic Markov chain model. Advances in Engineering Software. 2015; 90: 127-137.
Iranian journal of Ecohydrology
Volume 7, Issue 4
January 2021
Pages 1061-1070

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History

  • Receive Date: 31 July 2020
  • Revise Date: 06 November 2020
  • Accept Date: 06 November 2020

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