Investigation Optimal Input using Gamma Test in Rainfall-Runoff Modeling of Northern Karun Watershed

Document Type : Research Article


1 Ph.D. Candidate, Watershed Management Engineering, Faculty of Natural Resources, Sari Agricultural Sciences and Natural Resources University. Sari, Iran

2 Professor, Watershed Management Engineering, Faculty of Natural Resources, Sari Agricultural Sciences and Natural Resources University, Sari, Iran

3 Associated Professor, Watershed Management Engineering, Faculty of Natural Resources, Sari Agricultural Sciences and Natural Resources University. Sari, Iran

4 Professor, Faculty of Civil and Environmental Engineering, Warsaw University of Life Sciences, Warsaw, Poland


Rainfall-Runoff (R-R) is one of the most complicated processes in hydrology. The aim of this study was to select the effective parameter and optimum combination for R-R modeling using Gamma test in the WinGamma software for Northern Karun watershed. For this purpose, daily precipitation and discharge data in 1997-2017 was used. Lake data was removed and homogeny of data done using Run-Test. Statistical corrections were corrected by correlation method and data homogeneity test was performed by Run-Test method. The coefficients of auto-correlation, partial auto-correlation and cross-correlation in the R Studio software environment were used to determine rainfall and discharge delays. Optimum inputs and combinations were also obtained using the Gamma test in WinGamma software environment. The results showed that 9 optimum input parameters include precipitation today (Pt), precipitation with a delay of one day (Pt-1), two days (Pt-2), three days (Pt-3) and four days (Pt- 4), As well as discharge the day before (Qt-1), discharge with a delay of two days (Qt-2), three days (Qt-3) and four days (Qt-4) had a significant level of confidence at the level of 5%, which for the creation of 130 suitable combinations was identified and based on the lowest Gamma (Γ) statistics, a suitable composition was determined for each sub-watershed. Finally, for the whole basin, the optimum inputs include the parameters Pt, Pt-1, Qt-1, Qt-2 and Qt-4, and the best combinations are Pt, Pt-1, Qt-1, Qt-2, Qt-3. In general, in all sub-watersheds and the whole watershed precipitation of the current day, the precipitation of one or two days ago as well as the discharge of the previous day and two days ago have a significant effect on the run-off entering the in river basin.


[1]. Haghizadeh A, Mohammadlou M, Noori F. Simulation of rainfall-runoff process using multilayer perceptron and adaptive neuro-fuzzy interface system and multiple regression (Case Study: Khorramabd Watershed). Iranian Journal of Ecohydrology. 2015; 2(2): 233-243. [Persian].
[2]. Kumari P, Kumar P, Singh PV. Rainfall-runoff modelling using artificial neural network and adaptive neuro-fuzzy inference system. Indian Journal of Ecology. 2018; 45(2): 281-285.
[3]. Dehghani N, Vafakhah M, Bahremand A. Rainfall-runoff modeling using artificial neural network and neuro-fuzzy inference system in kasilian watershed. Journal of Watershed Management Research. 2016; 7(13): 128-137. [Persian].
[4]. Nourani V. An Emotional ANN (EANN) approach to modeling rainfall-runoff process. Journal of Hydrology. 2017; 544: 267-277.
[5]. Salajegheh A, Fathabadi A, Mahdavi M. Investigation on the efficiency of neuro-fuzzy method and statistical models in simulation of rainfall-runoff process. Journal of Range and Watershed Management. Iranian Journal of Natural Resources. 2009; 62: 65-79. [Persian].
[6]. Simonovic S P, Ahmad S. An artificial neural network model for generating hydrograph from hydro-meteorological parameters. Journal of Hydrology. 2005; 315: 236-251.
[7]. Napiórkowski JJ. Application of volterra series to modeling of rainfall-runoff systems and flow in open channels. Hydrological Sciences Journal. 1986; 31(2): 187-203.
[8]. Nourani V, Komasi M. A geomorphology-based ANFIS model for multi-station modeling of rainfall–runoff process. Journal of Hydrology. 2013; 490: 41–55.
[9]. Rezaei E, Shahidi A, Khashei Siuki A, Riahi Madvar H. Application of least squares support vector machine model for water table simulation (Case study: Ramhormoz plain). Iranian Journal of Irrigation & Drainage. 2013; 7(4): 510-520. [Persian].
[10].            Besalatpour AA, Hajabbasi MA, Ayoubi Sh. Use of Gamma test technique for choosing the optimum input variables in modeling of soil shear strength using artificial neural networks. Journal of Water and Soil Conservation. 2013; 20(1): 97-114. [Persian].
[11].            Moghaddamnia A, Ghafari Gousheh M, Piri J, Amin S, Han D. Evaporation estimation using artificial neural networks and adaptive neurofuzzy inference system techniques. Advanced Water Resources. 2009; 32: 88-97.
[12].            Jones AJ, Margetts S, Durrant P. The winGamma User Guide. University of Wales, Cardiff; 2001.
[13].            Kemp SE, Wilson ID, Ware JA. A Tutorial on the Gamma Test. International Journal of Simulation: Systems, Science and Technology. 2004; 6(1-2): 67-75.
[14].            Sharifi AR, Dinpashoh Y, Fakheri-Fard A, Moghaddamnia AR. Optimum combination of variables for runoff simulation in amameh watershed using gamma test. Soil and Water Science. 2013; 23(4): 59-72. [Persian].
[15].            Akhoni Pourhosseini F, Darbandi S. Sofichay river runoff modeling using support vector machine and artificial neural network. Journal of Watershed Management Research. 2018; 9(17): 57-66. [Persian].
[16].            Singh VK, Kumar P, Singh BP. Rainfall-runoff modeling using artificial neural networks (ANNs) and multiple linear regression (MLR) techniques. Indian Journal of Ecology. 2016; 43(2): 436-442.
[17].            Koncar N. Optimisation strategies for direct inverse neurocontrol. (Doctoral dissertation, Imperial College London (University of London)); 1997.
[18].            Durrant PJ. Win-GammaTM: A non-linear data analysis and modeling tool with applications to flood prediction. PhD Thesis, Department of Computer Science, Cardiff University, Cardiff, Wales, UK; 2001.
[19].            Tsui APM. Smooth data modelling and stimulus-response via stabilization of neural chaos. Ph.D. thesis, Department of Computing, University of London; 1999.
[20].            Entesari MR, Norouzi M, Salamat AS, Ehsani MR, Tavakoli AS. Comparison Penman- Montith with other recommended methods for calculating potential evapotranspiration (ET0) in several different regions of Iran. Eighth roceedings seminar on National Committee of Irrigation and Drainage. 2007; 237-221. [Persian].
[21].            Varkeshi B, ZareAbyaneh H, Marufi A, Sabziparvar F, Soltani M. Simulation of reference evapotranspiration using artificial neural method and empirical methods and comparison with experimental Lysimeter data in cold semi-arid climate of Hamedan. Journal of Soil Water Conservation. 2010; 16(4): 79-100. [Persian].
[22].            Jones AJ, Tsui A, De Oliveira AG. Neural models of arbitrary chaotic systems: Construction and the role of time delayed feedback in control and synchronization. Complexity international. 2002; 9(2002): 1-9.
[23].            Jones AJ. New tools in non-linear modeling and prediction. Computational Management Science. 2003; 1(2): 109-149.
[24].            Ghaderi K, Motamedvaziri B, Mahmudi P. Simulation of rainfall-runoff process using artificial neural network in kurkursar watershed, nowshahr. First international comprehensive competition conference on engineering sciences in Iran. Anzali, Iran. 2016; 1-13. [Persian].
Volume 7, Issue 4
January 2021
Pages 967-979
  • Receive Date: 12 July 2020
  • Revise Date: 21 September 2020
  • Accept Date: 21 September 2020
  • First Publish Date: 01 December 2020
  • Publish Date: 21 December 2020