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Although a number of adaptation strategies for coping with the ever-growing problem of inflow nonstationarity have been proposed in the reservoir operation literature, the robustness of reservoir operating rules to inflow nonstationarity has largely been ignored. This present study fills this gap. Fuzzy inference system based reservoir operating rules (FIS-ORs) are developed by optimization-simulation and compared with linear, nonlinear, and artificial neural network rules under simulated stationary and nonstationary inflow conditions. Results are obtained for two case studies assuming, in each case, a single reservoir with water supply, flood control, and environmental flow allocation functions. The FIS-ORs are found to be most robust to inflow nonstationarity. Applying the FIS-ORs to 30 years of projected future inflows show their advantage to be enhanced when they are recalibrated every 10 years or so, as compared to when there is no recalibration. It is also observed the advantage of the FIS-ORs to be more significant when the weighting of the problem objectives is such that the overall objective function is more difficult to satisfy.
Yang et al. (Wed,) studied this question.