Abstract. Traditional chance-constrained programming (CCP) and simulationoptimization methods of incorporating input information uncertainty in pollution management models are unsuitable for complex river systems with several critical water quality segments. Using the CCP method, characterization of the joint probability distribution of coefficients of the management model is often difficult because stream information is limited and the model formulation is generally difficult to understand and solve. For the simulation-optimization method most of the solutions produced are inferior. The multiple realization model, which includes several scenarios of design conditions simultaneously in an optimization model, overcomes such weaknesses by not requiring the joint probability distribution of the stochastic model coefficients and by producing noninferior solutions. Heuristic and neural network techniques are developed to reduce the computational time required to solve the multiple realization model, through identification and utilization of only potentially important stream and water quality information that influence the optimal solution. These techniques are applied to develop trade-off relationships between waste treatment cost and reliability of achieving dissolved oxygen objectives for an example river basin. Results show that the heuristic technique is computationally efficient when <1000 realizations are included in the model, while the neural network method is suitable when several thousand realizations are needed to adequately represent the stochastic water quality system.
The design of seasonal water quality management programs that allow different waste discharge rates during different periods of the year may require reliable long‐term records of stream conditions. A modified regionalized sensitivity analysis (RSA) is applied for assessing the effect of unreliable stream records on the design of such programs. This approach is demonstrated for a seasonal waste discharge program that controls biochemical oxygen demand in a typical river basin. Here the uncertain input parameters include flow and temperature data at different times in the year and at different locations in the stream. An RSA is conducted for waste management scenarios that have different season length combinations and dissolved oxygen goals. Results indicate that the design of a seasonal waste discharge program for this case study is generally more sensitive to uncertainty in summer flow and temperature data than to uncertainty in winter data. Downstream flow data are shown to be more important than upstream data in low‐flow periods, while upstream flows are also important in high‐flow periods. In addition, the degree to which uncertain stream conditions affect the management model outcome depends on the water quality goals of the governing agency and the length of the seasons examined.
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