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Stochastic models have been widely used to forecast and generate synthetic monthly streamflow series for the definition of reservoir sizing and operation. The traditional periodic PAR (periodic autoregressive) and PARMA (autoregressive moving average) models are generally able to preserve seasonal and annual time series characteristics that present low annual and monthly dependencies. However, many monthly flow series present high dependencies that are not preserved by these models. In this sense, periodic autoregressive moving average models have been developed, called multiplicative PARMA multiplicative (p, q) x (P, Q) models, also known as PMIX (p, q, P, Q) , which present parameters relating consecutive monthly flows in the same YEar (short dependence) and monthly flows for the same month in consecutive YEars (long dependence). The present study sought to verify, through simulations and model fitting to generated synthetic series, whether a developed computer nonlinear optimization routine that utilizes the Powell algorithm to minimize the sum of squared errors of estimates, is able to adequately estimate complete and parsimonious PMIX model parameters. It is concluded that the computer routine allows the appropriate selection of models and is efficient in estimating model parameters. Consequently, the results of this study will enable significant gains in the definition of rules of operation and in the estimation of volumes of flow regulation reservoirs for many rivers with monthly streamflow series presenting high dependencies. Key-words: autoregressive models, stochastic models, reservoirs.

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Avaliação do Desempenho de Modelos Estocásticos Parma Multiplicativos na Geração de Séries Fluviométricas Mensais para Dimensionamento de Reservatórios de Regularização de Vazões