Deduction of reservoir operating rules for application in global hydrological models

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Coerver, Hubertus M. ; Rutten, Martine M. ; Giesen, Nick C. (2018)

<p>A big challenge in constructing global hydrological models is the inclusion of anthropogenic impacts on the water cycle, such as caused by dams. Dam operators make decisions based on experience and often uncertain information. In this study information generally available to dam operators, like inflow into the reservoir and storage levels, was used to derive fuzzy rules describing the way a reservoir is operated. Using an artificial neural network capable of mimicking fuzzy logic, called the ANFIS adaptive-network-based fuzzy inference system, fuzzy rules linking inflow and storage with reservoir release were determined for 11 reservoirs in central Asia, the US and Vietnam. By varying the input variables of the neural network, different configurations of fuzzy rules were created and tested. It was found that the release from relatively large reservoirs was significantly dependent on information concerning recent storage levels, while release from smaller reservoirs was more dependent on reservoir inflows. Subsequently, the derived rules were used to simulate reservoir release with an average Nash-Sutcliffe coefficient of 0.81.</p>
  • References (51)
    51 references, page 1 of 6

    Adam, J. C., Haddeland, I., Su, F., and Lettenmaier, D. P.: Simulation of reservoir influences on annual and seasonal streamflow changes for the Lena, Yenisei, and Ob' rivers, J. Geophys. Res.- Atmos., 112, D24114,, 2007.

    Aghakouchak, A., Norouzi, H., Madani, K., Mirchi, A., Azarderakhsh, M., Nazemi, A., Nasrollahi, N., Farahmand, A., Mehran, A., and Hasanzadeh, E.: Aral Sea syndrome desiccates Lake Urmia: Call for action, J. Great Lakes Res., 41, 307-311,, available at: S0380133014002688, 2015.

    Alsdorf, D. E., Rodríguez, E., and Lettenmaier, D. P.: Measuring surface water from space, Rev. Geophys., 45, RG2002,, 2007.

    Arnold, J. G., Srinivasan, R., Muttiah, R. S., and Williams, J. R.: Large area hydrologic modeling and assessment Part I: Model development1, J. Am. Water Resour. As., 34, 73-89,, 1998.

    Aström, K. J. and Wittenmark, B.: Computer-Controlled Systems: Theory and Design, Third Edition, Courier Corporation, Lund University Publications, 2011.

    Baumgartner, A. and Reichel, E.: The World Water Balance: Mean Annual Global, Continental and Maritime Precipitation Evaporation and Run-Off, Elsevier Science Inc, Elsevier, New York, available at:, 1975.

    Beck, H. E., van Dijk, A. I., de Roo, A., Miralles, D. G., McVicar, T. R., Schellekens, J., and Bruijnzeel, L. A.: Global-scale regionalization of hydrologic model parameters, Water Resour. Res., 52, 3599-3622,, 2016.

    Biemans, H., Haddeland, I., Kabat, P., Ludwig, F., Hutjes, R., Heinke, J., von Bloh, W., and Gerten, D.: Impact of reservoirs on river discharge and irrigation water supply during the 20th century, Water Resour. Res., 47, W03509,, 2011.

    Chang, F.-J. and Chang, Y.-T.: Adaptive neuro-fuzzy inference system for prediction of water level in reservoir, Adv. Water Resour., 29, 1-10,, available at: S0309170805001338, 2006.

    Chang, L.-C. and Chang, F.-J.: Intelligent control for modelling of real-time reservoir operation, Hydrol. Process., 15, 1621-1634,, 2001.

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