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Disaggregation models are perhaps the best available method for generating multiseasonal multsite streamflow sequences for use in water resource system simulations. They are the basis of two general packages: LAST and SPIGOT. Both packages use staged disaggregation procedures and condensed disaggregation models to avoid the excessive number of parameters required by the multisite monthly Valencia-Schaake model. This paper examines the model size issue and what is gained and lost by the use of condensed models. In particular, when modeling the normal transforms of streamflow volumes, generated seasonal flows generally fail to sum to the specified annual total. A Monte Carlo study demonstrates that the discrepancy can be quite large and that alternative correction schemes perform quite differently.

Successive linear programming, an optimal control algorithm, and a combination of linear programming and dynamic programming (LP‐DP) are employed to optimize the operation of multireservoir hydrosystems given a deterministic inflow forecast. The algorithm maximize the value of energy produced at on‐peak and off‐peak rates, plus the estimated value of water remaining in storage at the end of the 12‐month planning period. The LP‐DP algorithm is clearly dominated: it takes longer to find a solution and produces significantly less hydropower than the other two procedures. Successive linear programming (SLP) appears to find the global maximum and is easily implemented. For simple systems the optimal control algorithm finds the optimum in about one fifth the time required by SLP but is harder to implement. Computing costs for a two‐reservoir, 12‐month deterministic problem averaged about seven cents per run using optimal control and 37 cents using successive linear programming.

Hirsch's (1982) maintenance of variance extension technique for filling in missing observations or producing a unique extended streamflow sequence with a specified mean and variance is generalized to reproduce also the correlation between the variate of interest and other variables. The technique is used to extend several seasonal streamflow forecast series so that the generated forecasts have the appropriate mean, variance, and forecasting power. A multivariate model is developed which can extend several series simultaneously.

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