Skip to main content
Log in

Near Real-Time Optimization of Multi-Reservoir during Flood Season in the Fengman Basin of China

  • Published:
Water Resources Management Aims and scope Submit manuscript

Abstract

This study aims to develop a multi-objective optimization model in a multi-reservoir system during flood season using Numerical Weather Predictions (NWPs) outputs (short forecast). The optimization model was coupled with the Water and Energy Budget-based Distributed Hydrological Model that was used to forecast the reservoir inflows. The model was forced by 8-day lead time global deterministic NWPs by Japan Meteorological Agency. The reservoir objective function was established by considering the reservoir and upstream safety, downstream safety and future water use. The model was applied to the Baishan-Fengman multi-reservoir system of Northeast China. The results have demonstrated the model with high efficiency in optimizing reservoir objectives for all of the reservoirs. The sensitivity of the system to lead time and decision time were investigated. With the decreasing of lead time, the dam release peaks decrease and the end water levels increase. This is mainly due to the fact that the model with longer lead time needs to keep storage capacity for detected floods during long lead time period. The variation amplitude of dam releases and water levels decrease with the increasing of decision time due to the smoothing of floods and dam releases during long decision period. The model is easy to operate and is able to be coupled with other hydrological models or earth system models.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  • Chang YT, Chang LC, Chang FJ (2005) Intelligent control for modeling of real-time reservoir operation, part II: artificial neural network with operating rule curves. Hydrol Process 19(7):1431–1444

    Article  Google Scholar 

  • Dessalegne T, Nicklow JW (2012) Artificial life algorithm for management of multi-reservoir river systems. Water Resour Manag 26(5):1125–1141. doi:10.1007/s11269-011-9950-7

    Article  Google Scholar 

  • Duan Q, Sorooshian S, Gupta VK (1992) Effective and efficient global optimization for conceptual rainfall-runoff models. Water Resour Res 28(4):1015–1031. doi:10.1029/91WR02985

    Article  Google Scholar 

  • Food and Agriculture Organization (2003) Digital soil map of the world and derived soil properties, land and water digital media series, Rev.1. United Nations Food and Agriculture Organization, CD-ROM

  • Goor Q, Halleux C, Mohamed Y, Tilmant A (2010) Optimal operation of a multipurpose multireservoir system in the Eastern Nile River Basin. Hydrol Earth Syst Sci 14:1895–1908. doi:10.5194/hess-14-1895-2010

    Article  Google Scholar 

  • Haddad OB, Afshar A, Marino MA (2008) Design-operation of multi-hydropower reservoirs:Hbmo approach. Water Resour Manag 22:1709–1722

    Article  Google Scholar 

  • Hajkowicz S, Collins K (2007) A review of multiple criteria analysis for water resource planning and management. Water Resour Manag 21:1553–1566

    Article  Google Scholar 

  • Han Y, Huang F, Wang G, Maqsood I (2011) A multi objective linear programming model with interval parameters for water resourses allocation in dalian city. Water Resour Manag 25(2):449–463

    Article  Google Scholar 

  • Higgins AJ, Archer A, Hajkowicz S (2008) A stochastic non-linear programming model for a multiperiod water resource allocation with multiple objectives. Water Resour Manag 22(10):1445–1460

    Article  Google Scholar 

  • Huntington TG (2006) Evidence for intensification of the global water cycle: review and synthesis. J Hydrol 319:83–95

    Article  Google Scholar 

  • Jairaj PG, Vedula S (2000) Multireservoir system optimization using fuzzy mathematical programming. Water Resour Manag 14:457–472

    Article  Google Scholar 

  • Kim T, Heo JH, Jeong CS (2006) Multireservoir system optimization in the Han River basin using multi-objective genetic algorithms. Hydrol Process 20:2057–2075. doi:10.1002/hyp.6047

    Article  Google Scholar 

  • Labadie JW (2004) Optimal operation of multireservoir system: State-of-the-Art review. J Water Resour Plan Manag 130:93–111. doi:10.1061/(ASCE)0733-9496(2004)130:2(93)

    Article  Google Scholar 

  • Myneni RB, Nemani RR, Running SW (1997) Algorithm for the estimation of global land cover, LAI and FPAR based on radiative transfer models. IEEE Trans Geosci Remote Sens 35:1380–1393

    Article  Google Scholar 

  • Nash JE, Sutcliffe JV (1970) River flow forecasting through conceptual models part I–A discussion of principles. J Hydrol 10(3):282–290. doi:10.1016/0022-1694(70)90255-6

    Article  Google Scholar 

  • Nayak RC, Panda RK (2001) Integrated management of a canal command in a river delta using multi-objective techniques. Water Resour Manag 15:383–401

    Article  Google Scholar 

  • Ngo LL, Madsen H, Rosbjerg D (2007) Simulation and optimization modelling approach for operation of the Hoa Binh reservoir, Vietnam. J Hydrol 336:269–281. doi:10.1016/j.jhydrol.2007.01.003

    Article  Google Scholar 

  • Niewiadomska-Szynkiewicz E, Malinowski K, Karbowski A (1996) Predictive methods for real-time control of flood operation of a multireservoir system: methodology and comparative study. Water Resour Res 32(9):2885–2895. doi:10.1029/96WR01443

    Article  Google Scholar 

  • Ostadrahimi L, Mariño MA, Afshar A (2012) Multi-reservoir operation rules: multi-swarm PSO-based optimization approach. Water Resour Manag 26(2):407–427. doi:10.1007/s11269-011-9924-9

    Article  Google Scholar 

  • Rani D, Moreira MM (2010) Simulation–optimization modeling: a survey and potential application in reservoir systems operation. Water Resour Manag 24(6):1107–1138. doi:10.1007/s11269-009-9488-0

    Article  Google Scholar 

  • Reddy MJ, Kumar DN (2006) Optimal reservoir operation using multi-objective evolutionary algorithm. Water Resour Manag 20(6):861–878

    Article  Google Scholar 

  • Reis LFR, Walters GA, Savic D, Chaudhry FH (2005) Multi-reservoir operation planning using hybridgenetic algorithm and linear programming (GA-LP): an alternative stochastic approach. Water Resour Manag 19(6):831–848

    Article  Google Scholar 

  • Roefs TG, Bodin LD (1970) Multireservoir operation studies. Water Resour Res 6(2):410–420. doi:10.1029/WR006i002p00410

    Article  Google Scholar 

  • Saavedra Valeriano OC, Koike T, Yang K, Graf T, Li X, Wang L, Han X (2010) Decision support for dam release during floods using a distributed biosphere hydrological model driven by quantitative precipitation forecasts. Water Resour Res 46:W10544. doi:10.1029/2010WR009502

    Article  Google Scholar 

  • Saito K, Ishida JI, Aranami K, Hara T, Segawa T, Narita M, Honda Y (2007) Nonhydrostatic atmospheric models and operational development at JMA. J Meteorol Soc Jpn 85B:271–304. doi:10.2151/jmsj.85B.271

    Article  Google Scholar 

  • Sellers PJ, Randall DA, Collatz GJ, Berry JA, Field CB, Dazlich DA, Zhang C, Collelo GD, Bounoua L (1996) A revised land surface parameterization (SiB2) for atmospheric GCMs, Part I: model formulation. J Clim 9(4):676–705

    Article  Google Scholar 

  • Unver OI, Mays LW (1990) Model for real-time optimal flood control operation of a reservoir system. Water Resour Manag 4(1):21–26

    Article  Google Scholar 

  • Wang YC, Yoshitani J, Fukami K (2005) Stochastic multiobjective optimization of reservoirs in parallel. Hydrol Process 19:3551–3567. doi:10.1002/hyp.5845

    Article  Google Scholar 

  • Wang L, Koike T, Yang K, Jackson TJ, Bindlish R, Yang DW (2009a) Development of a distributed biosphere hydrological model and its evaluation with the Southern Great Plains Experiments (SGP97 and SGP99). J Geophys Res 114, D08107. doi:10.1029/2008JD010800

    Article  Google Scholar 

  • Wang L, Koike T, Yang K, Yeh P (2009b) Assessment of a distributed biosphere hydrological model against streamflow and MODIS land surface temperature in the upper Tone River Basin. J Hydrol 377(1–2):21–34. doi:10.1016/j.jhydrol.2009.08.005

    Article  Google Scholar 

  • Wang F, Wang L, Zhou H, Saavedra Valeriano OC, Koike T, Li W (2012) Ensemble hydrological prediction based multi-objective reservoir real-time optimization during flood season in a semiarid basin with global numerical weather predictions. Water Resour Res 48, W07520. doi:10.1029/2011WR011366

    Google Scholar 

  • Wurbs RA (1993) Reservoir-system simulation and optimization models. J Water Resours Plan and Manag 119(4):455–472

    Article  Google Scholar 

  • Yang DW, Herath S, Musiake K (2002) A hillslope–based hydrological model using catchment area and width functions. Hydrol Sci J 47(1):49–65. doi:10.1080/02626660209492907

    Article  Google Scholar 

  • Yeh WWG (1985) Reservoir management and operations models: a state-of-the-art review. Water Resour Res 21(12):1797–1818. doi:10.1029/WR021i012p01797

    Article  Google Scholar 

  • Yeh WWG, Becker L (1982) Multiobjective analysis of multireservoir operations. Water Resour Res 18(5):1326–1336. doi:10.1029/WR018i005p01326

    Article  Google Scholar 

  • Yeniay Ö (2005) Penalty function methods for constrained optimization with genetic algorithms. Math Comput Appl 10(1):45–56

    Google Scholar 

  • Zhang A, Zhang C, Fu G, Wang B, Bao Z, Zheng H (2012a) Assessments of impacts of climate change and human activities on runoff with SWAT for the Huifa River Basin, Northeast China. Water Resour Manag 26(8):2199–2217

    Article  Google Scholar 

  • Zhang C, Peng Y, Chu J, Shoemaker CA, Zhang A (2012b) Integrated hydrological modelling of small- and medium-sized waterstorages with application to the upper Fengman Reservoir Basin of China. Hydrol Earth Syst Sci 16(11):4033–4047

    Article  Google Scholar 

  • Zhang C, Wang G, Peng Y, Tang G, Liang G (2012c) A Negotiation-based multi-objective, multi-party decision-making model for inter-basin water transfer scheme optimization. Water Resour Manag 26(14):4029–4038

    Article  Google Scholar 

Download references

Acknowledgments

We would like to thank the valuable comments and suggestions given by the anonymous reviewers. The authors gratefully acknowledge Kitsuregawa laboratory in Institute of Industrial Science, the University of Tokyo for providing JMA NWP data. The reservoir operation data were obtained from Fengman Hydropower Plant and State Grid Xinyuan Baishan Power Plant.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Fuxing Wang.

Appendix

Appendix

C, α and β :

constants for dynamic penalty function, C = 1, α = 0.5, β = 2.

f fc,r  , f fc,d , f wu :

objective function of reservoir and upstream flood control safety, downstream flood control safety, and future water use.

F :

the sum of objective function and penalty function.

H i,m :

water level during time period i for the m-th reservoir, unit: m.

H T , H target :

end and target reservoir water levels, unit: m.

H lmt :

limited reservoir water level, unit: m.

H dead :

dead reservoir water level, unit: m.

i :

the i-th time step.

k :

optimization algorithm’s current iteration number.

m :

the m-th reservoir.

MROS:

Multi-Reservoir Optimization System.

n :

total reservoir number.

p :

the total number of penalty function for each dam.

P :

dynamic penalty function.

Qin, Qout :

reservoir inflow and outflow, unit: m3/s.

Qloss :

reservoir water leakage, unit: m3/s.

Q min , Q max :

minimum and maximum reservoir release, unit: m3/s.

Q ability :

reservoir release ability, unit: m3/s.

Qinc :

interval coming water amount, unit: m3/s.

Qctl, Qctl max :

simulated and maximum river discharges at control point, unit: m3/s.

Qout init :

simulated dam release at initial, unit: m3/s.

Qout obs,0 :

observed dam release at previous day before optimization begins, unit: m3/s.

T :

total time step, unit: day.

T ld :

lead time.

V i , V i+1 :

initial and final reservoir storage volumes, unit: m3.

V dead , V lmt :

water volumes corresponding to H dead and H lmt  , unit: m3.

ΔQ :

dam release variation amplitude constraint between two time steps.

Δt :

reservoir operation time interval, unit: s.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Wang, F., Saavedra Valeriano, O.C. & Sun, X. Near Real-Time Optimization of Multi-Reservoir during Flood Season in the Fengman Basin of China. Water Resour Manage 27, 4315–4335 (2013). https://doi.org/10.1007/s11269-013-0410-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11269-013-0410-4

Keywords

Navigation