Traditional and Numerical Approaches for Estimating Reservoir Routing Parameters

Zainab Ali Omran

doi:10.2478/cee-2025-0100

Abstract

Reservoir routing is a strategy that calculates the outflow hydrograph from a reservoir based on the inflow hydrograph, taking into consideration the elevation, storage, and discharge characteristics of the reservoir and spillways. The conservation of mass equation is solved assuming that outflow discharge and storage volume are precisely proportionate. According to this paper, the conventional approaches for reservoir routing, parameter analysis, and evaluation require solving a transcendental equation at each time step. A differential equation’s numerical solution is reservoir routing. Any numerical technique can be applied, and they are all easier to understand than the old way. The study also shows that there are benefits to using reservoir surface elevation to define the governing equation rather than the conventional approach of using storage capacity. A situation is shown in which reservoir outflow can be controlled by gates or sluiceways. Reservoir routing using numerical techniques are evaluated using Math lab program version (7.12) and compared with conventional approach for reservoir routing. The inflow from one of Iraq’s dams was analyzed, and the outflow was estimated, with the results demonstrating that the numerical methods are identical, simpler, and more reliable than conventional method.

References

AKAN A. O.: 2-Energy and momentum principles. Open channel hydraulics. Oxford: Butterworth-Heinemann, 2006, pp. 24–66.
Search in Google Scholar Back to article
VALIZADEH N. - El-Shafie A. : Forecasting the level of reservoirs using multiple input fuzzification in ANFIS. Water Resour. Manag., vol. 27, no. 9, 2013, pp. 3319–3331.
Search in Google Scholar Back to article
AHMED J.A. - A. Sarma K. : Artificial neural network model for synthetic streamflow generation. Water Resour. Manag., vol. 21, no. 6, 2007, pp. 1015–1029.
Search in Google Scholar Back to article
KISI O., et al.: Forecasting daily lake levels using artificial intelligence approaches. Comput. Geosci., vol. 41, 2012, pp. 169–180.
Search in Google Scholar Back to article
OGBONNA D., et al.: Application of Flood Routing Model for Flood Mitigation in Orashi River, South-East Nigeria. J. Geosci. Environ. Prot., vol. 5, no. 03, 2017, p. 31.
Search in Google Scholar Back to article
BAGATUR T.- Onen F.: Development of predictive model for flood routing using genetic expression programming. J. Flood Risk Manag., vol. 11, 2018, pp. S444–S454.
Search in Google Scholar Back to article
ZHOU Y., et al.: Methodology that improves water utilization and hydropower generation without increasing flood risk in mega cascade reservoirs. Energy, vol. 143,2018, pp. 785–796.
Search in Google Scholar Back to article
KADHIM M.A., et al.: Evaluation of Flood Routing Models and Their Relationship to The Hydraulic Properties of the Diyala River Bed. in IOP Conference Series: Earth and Environmental Science, IOP Publishing, 2022, p. 12058.
Search in Google Scholar Back to article
HAMEDI F., et al. : Parameter estimation of extended nonlinear Muskingum models with the weed optimization algorithm. J. Irrig. Drain. Eng., vol. 142, no. 12, 2016, p. 4016059.
Search in Google Scholar Back to article
BARATI R., et al. :Discussions and Closures. J. Irrig. Drain Eng, vol. 144, no. 1, 2018, p. 7017021.
Search in Google Scholar Back to article
Reggiani P., et al. : On mass and momentum conservation in the variable-parameter Muskingum method. J. Hydrol., vol. 543, 2016, pp. 562–576.
Search in Google Scholar Back to article
YOO C., et al. : Parameter estimation of the Muskingum channel flood-routing model in ungauged channel reaches. J. Hydrol. Eng., vol. 22, no. 7, 2017, p. 5017005.
Search in Google Scholar Back to article
NIAZKAR M. - Afzali S. H. : New nonlinear variable-parameter Muskingum models. KSCE J. Civ. Eng., vol. 21, no. 7, 2017, pp. 2958–2967.
Search in Google Scholar Back to article
MAZZOLENI M., et al.: Real-time assimilation of streamflow observations into a hydrological routing model: effects of model structures and updating methods. Hydrol. Sci. J., vol. 63, no. 3, 2018, pp. 386–407.
Search in Google Scholar Back to article
VATANKHAH A. R.: Discussion of ‘assessment of modified honey bee mating optimization for parameter estimation of nonlinear muskingum models’ by Majid Niazkar and Seied Hosein Afzali. J. Hydrol. Eng., vol. 23, no. 4, 2018, p. 7018002.
Search in Google Scholar Back to article
LEE E. H., et al.: Development and application of advanced Muskingum flood routing model considering continuous flow. Water, vol. 10, no. 6, 2018 p. 760.
Search in Google Scholar Back to article
LEE E.H., “Development of a New 8-Parameter Muskingum Flood Routing Model with Modified Inflows. Water, vol. 13, no. 22, 2021, p. 3170.
Search in Google Scholar Back to article
ZHANG S., et al.: Parameter estimation of nonlinear Muskingum model with variable exponent using adaptive genetic algorithm. Environ. Conserv. Clean Water, Air Soil (CleanWAS); IWA Publ. London, UK, 2017, pp. 231–237.
Search in Google Scholar Back to article
COSTABILE P., et al.: Performances and limitations of the diffusive approximation of the 2-d shallow water equations for flood simulation in urban and rural areas. Appl. Numer. Math., vol. 116, 2017, pp. 141–156.
Search in Google Scholar Back to article
PERUMAL M., et al.: Evaluation of a physically based quasi-linear and a conceptually based nonlinear Muskingum methods. J. Hydrol., vol. 546, 2017, pp. 437–449.
Search in Google Scholar Back to article
AKBARIFARD S., et al.: Parameter estimation of the nonlinear muskingum flood-routing model using water cycle algorithm. J. Watershed Manag. Res., vol. 8, no. 16, 2018, pp. 34–43.
Search in Google Scholar Back to article
CAI X., et al.: Bat algorithm with triangle-flipping strategy for numerical optimization. Int. J. Mach. Learn. Cybern., vol. 9, no. 2, 2018, pp. 199–215.
Search in Google Scholar Back to article
HADDAD O.B., et al.: Application of a hybrid optimization method in Muskingum parameter estimation. J. Irrig. Drain. Eng., vol. 141, no. 12, 2015, p. 4015026.
Search in Google Scholar Back to article
WANG L., et al.: Accuracy of the Muskingum-Cunge method for constant-parameter diffusion-wave channel routing with lateral inflow. arXiv Prepr. arXiv1802.04429, 2018.
Search in Google Scholar Back to article
JAMIL M., et al.: Multimodal function optimization using an improved bat algorithm in noise-free and noisy environments. in Nature-Inspired Computing and Optimization, Springer, 2017, pp. 29–49.
Search in Google Scholar Back to article
NIAZKAR M. - Afzali S.H. : Assessment of modified honey bee mating optimization for parameter estimation of nonlinear Muskingum models. J. Hydrol. Eng., vol. 20, no. 4, 2015, p. 4014055.
Search in Google Scholar Back to article
MOGHADDAM A., et al.: Parameters estimation for the new four-parameter nonlinear Muskingum model using the particle swarm optimization. Water Resour. Manag., vol. 30, no. 7, 2016, pp. 2143–2160.
Search in Google Scholar Back to article
KANG L. - Zhang S. : Application of the elitist-mutated PSO and an improved GSA to estimate parameters of linear and nonlinear Muskingum flood routing models. PLoS One, vol. 11, no. 1, 2016, p. e0147338.
Search in Google Scholar Back to article
AL SHAIKHLI H., et al.: Using Flow 3D Simulation, Multiple Nonlinear Regression Approach, and Artificial Neural Network Approach Approaches to Study the Behavior of Vertical Drop Structures. Civil and Environmental Engineering, Volume 20, Issue 2, (December 2024), pp. 654 - 683.
Search in Google Scholar Back to article
JASSAM M. G., et al.: Effect of Initial Water Content on the Collapsibility of Natural and Cement-Treated Gypseous Soils. Civil and Environmental Engineering, Volume 19, Issue 2 (December 2023), pp. 610 - 617.
Search in Google Scholar Back to article

Traditional and Numerical Approaches for Estimating Reservoir Routing Parameters

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