Uncertainty Analysis of the Water Level of the Karun and Dez Dam Reservoirs Due to the Inflow Hydrograph Uncertainty

Document Type : Research Paper


1 Department of Water Engineering, Shahrekord University, Shahrekord Iran, Iran.

2 Department of Water Engineering, Shahrekord University, Shahrekord, Iran.

3 Department of Water Science & Engineering, Islamic Azad University, Ahvaz Branch, Iran.



The Real-time flood control operation of a reservoir system can greatly reduce human and financial losses. In this study, a model for real-time flood control operations in reservoirs under the occurrence of floods in April 2019 and the uncertainty of hydrographs of inflows is presented. The presented model includes three modules: the flood that occurred in April 2019, Monte Carlo-HEC-ResSim simulation, and uncertainty analysis. The considered uncertainty factor is the hydrograph of the input to the reservoirs and side flows to the river. A Monte Carlo-HEC-ResSim simulation was performed according to the real flood of April 2018. In order to quantify the uncertainty of the HEC-ResSim model in the simulation of the water level of the dam reservoir, two 95 Percent confidence band factors (P-factor) and the band width factor index (d-factor) were used. Based on the results of the Monte Carlo simulation, the uncertainty in the water level of Dez, Karun and Gotvand dams due to the uncertainty of the hydrograph of the inflow was 0.037, 0.107 and 0.034 Percent, respectively. Therefore, the highest and lowest uncertainty in the water level due to the uncertainty of the inflow hydrograph is related to the Karun 1 and Upper Gotvand dams, respectively. In addition, the uncertainty bandwidth of the HEC-ResSim model in simulating the water level in Dez, Karun 1, and Upper Gotvand reservoirs was 0.151, 0.407, and 0.808, respectively. Considering that the maximum allowable amount of the accepted bandwidth factor is equal to one, the uncertainty bandwidth obtained in all the parameters (including the input and output values from the reservoirs, the volume of the reservoirs and the control points downstream of the dams) is accepted. This indicates the low uncertainty of the HEC-ResSim model in reservoir exploitation operations. The 95ppu values of the observational data in the 95 Percent confidence band for the water level of the reservoirs in the three studied dams were 100 Percent. A high 95ppu value indicates that the model has a strong physical and theoretical basis. For other parameters, the 95ppu values were low due to the low uncertainty of the parameters.


Main Subjects

  1. Abbaspour, K.C., Yang, J., Maximov, I., Siber, R., Bogne, K., Mieleitne, J., Zobrist, J., & Srinivasan, R. (2007). Modeling hydrology and water quality in the pre-alpine.alpine Thur watershed using SWAT. Journal of Hydrology, 333, 413-430
  2. Akbari, M., Mirabbasi, R., & Bagheri, M. H. (2021). Simulating the operation of Surak dam reservoir using HEC-ResSim model. Scientific Journal of Water Sciences and Engineering, 11(31), 23-34 (In Persian).
  3. Alizadeh, A., Eizad, A., Davar, K., Ziaie, A.N., Akhavan, S., & Hamidi, Z. (2013). Estimation of actual evapotranspiration at regional-annual scale using SWAT. Iranian Journal of Irrigation and Drainage, 2, 243-254. (In Persian).
  4. Bates, B.C., & Townley, L.R. (1988). Nonlinear, discrete flood event models, 3. Analysis of prediction uncertainty. Journal of Hydrology, 99, 91-101.
  5. Behroz, M., Alimohammadi, S., Attari, J., & Akbarian Aghdam, A. (2013). Sensitivity analysis of hydrological, hydraulic and economic uncertainties in the design of flood control systems, 5th Iran Water Resources Management Conference, Shahid Beheshti University, Iran Water Resources Science and Engineering Association, Tehran, 29-30 February. (In Persian).
  6. Belay, G. W., Azeze, M., & Melesse, A. M. (2019). Reservoir operation analysis for Ribb reservoir in the Blue Nile basin. In Extreme Hydrology and Climate Variability, Monitoring, Modelling, Adaptation and Mitigation, (pp. 191-211). Elsevier.
  7. Biazar, M., Ghorbani, M., & Shahidi, K. (2017). Uncertainty of artificial neural network in estimation of daily evaporation (Case study: Rasht and Manjil stations). Journal of Watershed Management Research, 10(19), 1-12 (In Persian).
  8. Chen, J., Zhong, P. A., An, R., Zhu, F., & Xu, B. (2019). Risk analysis for real-time flood control operation of a multi-reservoir system using a dynamic Bayesian network. Environmental Modelling and Software, 111, 409-420.
  9. Dalledonne, G., Kopmann, R., & Brudy-Zippelius, T. (2019). Uncertainty analysis of floodplain friction in hydrodynamic models. Hydrology and Earth System Sciences, 23(8), 3373-3385.
  10. Faraj, D. M., Abdulrahman, K. Z., & Al-Ansari, N. A. (2022). The impact of the Tropical Water Project on the operation of Darbandikhan dam. Journal of King Saud University-Engineering Sciences. Learning Gaussian networks, Uncertainty Proceedings, 738, 235-243.
  11. Farokhnia, A. & Morid, S. (2010). Uncertainty analysis of artificial neural networks and neuro-fuzzy models in river flow forecasting. Journal of Iran Water Resources Research, 3, 14-27. (In Persian).
  12. Hamraz, B., Akbarpour, A., & Pourza Bilandi, M. (2015). Parametric uncertainty analysis of MODFLOW model by GLUE method (Case study of Darsht Birjand). Water and Soil Research Journal, 22(6), 61-79 (In Persian).
  13. Heydari, N., & Abbasi, F., (2016). Optimization of design and management parameters of border irrigation: A case study of Ramshir irrigation and Drainage network, Journal of Applied Research in Irrigation and Drainage Structures Engineering, 17(66), 55-70 (In Persian).
  14. Huang, K., Ye, L., Chen, L., Wang, Q., Dai, L., Zhou, J., ... & Zhang, J. (2018). Risk analysis of flood control reservoir operation considering multiple uncertainties. Journal of Hydrology, 565, 672-684.
  15. Ilkhchi, R. (2002). Hajilar Chai Reservoir Dam Project Report. First and ninth volumes, East Azerbaijan Regional Water Joint Stock Company. (In Persian).
  16. Kaheh, M., Javadi, S., & Rozbahani, A. (2018). Uncertainty analysis of hydraulic conductivity parameter in MODFLOW model by Monte Carlo and PREM method (case study: Aliabad plain of Qom). Iran's Water Resources Research, 14(2), 23-35 (In Persian).
  17. Mansouri, N. (2011). Analysis of the risk of water crossing in the dam by considering hydraulic and hydrological aspects (case study: Vanak Dam). Master's thesis. Faculty of Technical Engineering. Department of Civil Engineering (Water). Isfahan University of Technology. (In Persian).
  18. Mohammadi, M., But, M., & Samani, H. (2012). Optimizing the step method of valve maneuver using genetic algorithm. Master's thesis. School of Agriculture. Department of Water Engineering. Shahid Chamran University of Ahvaz. (In Persian).
  19. Nourali, M., Hero, B., Pourza Bilandi, M., & Davari, K. (2015). Uncertainty estimation of HEC-HMS flood simulation model using Markov chain Monte Carlo algorithm. Watershed Management Research, 15, 235-249 (In Persian).
  20. Nourani, V., & Fard, M.S. (2012). Sensitivity analysis of the artificial neural network outputs in simulation of the evaporation process at different climatologic regimes. Advance Engineering Software, 47, 127-146.
  21. Riyahi, H., & Fakuri, B. (2021). Uncertainty analysis of the results of HEC-RAS model in the simulation of hydraulic parameters of Karun River flow with Monte Carlo approach. Journal of Hydraulic, 16(1), 1-22 (In Persian).
  22. Schuol, J., & Abbaspour, K.C. (2006). Calibration and uncertainty issues of a hydrological model SWAT applied to West Africa. Advances in Geoscience, 9, 137- 143.
  23. Shakri, H. (2015). Analysis of overpass risk in the dam considering the uncertainty of the effective parameters (case study: Golpayegan Dam). Master's thesis. Faculty of Technical Engineering. Department of Civil Engineering (Water). Yasouj University. (In Persian).
  24. Sharifi, A., Dinpazhouh, Y., Fakheryfard, A., & Moghadamni, A. (2012). Optimum combination of variables for run off simulation in Amameh watershed using gamma test. Journal of Knowledge of Soil and Water, 13, 76-79 (In Persian).
  25. Tung, Y. K., & Wong, C. L. (2013). Assessment of design rainfall uncertainty for hydrologic engineering applications in Hong Kong, Stochastic Environment a Research and Risk Assessment, 28(3), 583-592.
  26. US Army Corps of Engineers. (2021). HEC-ResSim Reservoir System Simulation. User’s Manual. Version 3.3. February 2021.
  27. Uysal, G., Akkol, B., Topcu, M. I., Sensoy, A., & Schwanenberg, D. (2016). Comparison of different reservoir models for short term operation of flood management. Procedia Engineering, 154, 1385-1392.
  28. Wang, D. (2009). Rethinking risk analysis: the risks of risk analysis in water issues as the case. Human and Ecological Risk Assessment: An International Journal, 15(6), 1079-1083.
  29. Yan, B. W., Guo, S. L., & Chen, L. (2014). Estimation of reservoir flood control operation risks with considering inflow forecasting errors. Stochastic Environmental Research and Risk Assessment, 28(2), 359-368.
  30. Zargar, M. (2015). Simulation-optimization model of integrated flood risk management in a chain system of series and parallel reservoirs. Doctoral thesis in Water Resources Management Engineering. Faculty of Engineering. Shahid Chamran University of Ahvaz. Iran. (In Persian).