Investigating the Effect of Basic Flood Characteristics on Flood Routing Accuracy in Karun River Using Hydrological Routing Methods

Document Type : Research Article


1 Ph.D. Student of Water Engineering and Hydraulic Structures, Department of Civil Engineering, Faculty of Engineering, University of Zanjan, Zanjan, Iran

2 MSc. Student of Water Engineering and Hydraulic Structures, Department of Civil Engineering, Faculty of Engineering, University of Zanjan, Zanjan, Iran

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


Unsteady flow analysis is performed using hydraulic and hydrological routing methods. Hydrological routing methods, while accurate, are much simpler and less expensive than hydraulic routing methods, and for flood routing calculations, only data related to hydrographs (flow changes over time) recorded at upstream and downstream hydrometric stations of the study area are needed. In the present study, the effect of the basic flood on the accuracy of flood routing in the Karun River was investigated using hydrological routing methods including linear Muskingum method, Working Values, Convex, and modified Att-Kin method. In other words, using the Particle Swarm Optimization (PSO) Algorithm and the 4 observational floods data recorded at the Mollasani (upstream) and Ahwaz (downstream) hydrometric stations of the Karun River, for each flood, separately, the linear Muskingum method parameters (X, K, ) Optimized and used to calculate the outflow hydrograph of all floods. The results show that due to the effect of rivers flooding extent on the mentioned parameters, if the range of inflow of basic flood changes is closer to the inflow of calculated flood, the accuracy of hydrological routing methods in outflow hydrograph estimation increases.


[1]. Vafaei F, Harati AN. Strategic management in decision support system for coastal flood management. 2010; 4(1): 169-176.
[2]. Raghunath HM. Hydrology: principles, analysis and design. New Age International; 1997.
[3]. Weinmann PE, Laurenson EM. Approximate flood routing methods: A review. Journal of the Hydraulics Division. 1979;105(12):1521-1536.
[4]. Chow, Vente. Open channel hydraulics, Newyork;Macgraw-Hill book company. 1959.
[5]. SHAW, EM. Hydrology in Practice. T.J. Press (Pads tow) LTD , Cornwall, UK. 1994.
[6]. Yadav B, Perumal M, Bardossy A. Variable parameter McCarthy–Muskingum routing method considering lateral flow. Journal of Hydrology. 2015. 489-499.
[7]. Tsai CW. Flood routing in mild-sloped rivers—wave characteristics and downstream backwater effect. Journal of Hydrology. 2005; 308(1-4):151-167.
[8]. Farahani NN, Farzin S, Karami H. Flood routing by Kidney algorithm and Muskingum model. Natural Hazards. 2018:1-19.
[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 WZ, Fan HY, Leung AY, Wong JC. 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. International Symposium on Neural Networks. 2005; 1034-1039.
[14]. Chu HJ, Chang LC. 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]. Abdolshahnejad, A. Comparison of different methods hydraulic and hydrologic in flood routing (Case Study: Part of Karoun river), M.Sc. Thesis, University of Tehran. 1997. 230 pp. [Persian].
[18]. Dehghani, M. The Efficiency Assessment of Flood Routing Methouds in Tidal Zohre River, M.Sc. Thesis, Tarbiat Modarres University. 2004. 104 pp. [Persian].
[19]. Ghasemieh, H. Investigation of Muskingum and Modified Att-Kin Methoud Efficiency in river Flood Routing (Case Study, Babolroud River), M.Sc. Thesis, University of Mazandaran. 2005. 136 pp. [Persian].
[20]. Barati, R. & Akbari, GM. Comparison of Flood Routing Hydrology Models in Rivers. Iranian Water Researches Journal, 2012. 105-114. [Persian].
[21]. Abbasizadeh, M. & Mahdavi. M. & Salajeghe. A. Evaluation of Flood Routing Methods Efficiency (Case Study: Dez River). 2010. 63-76. [Persian].
[22]. Manavi Amiri. S.M, & Malekian. A, & Shahedi. K, & Motamed Vaziri. B. Evaluation of Muskingum and Modified Att-Kin Methods Efficiency in Flood Routing (Case Study: Talar Watershed, Mazandaran
Province). 2013. 106-119. [Persian].
[23]. McCarthy GT. The unit hydrograph and flood routing, Conference of North Atlantic Division. US Army Corps of Engineers, New London, CT. US Engineering. 1938.
[24]. Hamedi, MH. Open Channel Hydraulics, Khaje Nasir University. Second edition. 2011. (In Persian).
[25]. Mahdavi, M. Applied hydrology. Tehran University. Second edition. 2013. [Persian].
[26]. Eberhart R, Kennedy J. A new optimizer using particle swarm theory.. In MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science. 1995.39-43
[27]. Shi Y, Eberhart R. A modified particle swarm optimizer. In1998 IEEE international conference on evolutionary computation proceedings. IEEE world congress on computational intelligence (Cat. No. 98TH8360) 1998. 69-73.
[28]. Di Cesare N, Chamoret D, Domaszewski M. A new hybrid PSO algorithm based on a stochastic Markov chain model. Advances in engineering software. 2015. 127-137.
Volume 7, Issue 3
October 2020
Pages 609-618
  • Receive Date: 01 April 2020
  • Revise Date: 23 May 2020
  • Accept Date: 23 May 2020
  • First Publish Date: 22 September 2020