Distributions of observed monthly streamflows at many gaging stations exhibit slight, moderate, or sharp reverse curvatures that cannot be accommodated by the commonly used classical distributions such as the normal and gamma (Pearson type 3) distributions applied to flows or their logarithms. The two distribution, essentially a bimodal distribution, fits most of these observed distributions very well. Herein is presented a unique and versatile method for modeling and sequential generation of monthly streamflows. In order to assure the normality of the multivariate distribution of variables representing monthly flows the flows or their logarithms are converted to standardized normal deviates by suitable transforms. The distributions of monthly streamflows generated by the two-distribution method fit best the observed flow distributions. Annual flows obtained by summing generated monthly flows fit the observed annual flows very well. The use of the two-distribution method can obviate arbitrary adjustments in generated monthly flows to bring the annual flows obtained from them in line with observed annual flows.A historical hydrologic sequence or data series is a chance event that may never occur again in the same way. The single response obtained with this sequence is unlike!y*to be a satisfactory representation of future response of a system under consideration. A set of system responses can be obtained from simulated hydrologic sequences called synthetic sequences [Fiering, !966]. A number of these sequences of any desired length can be sequentially generated to aid in decision making with respect to system design, operation, and performance. These synthetic sequences must resemble the historical sequence in terms of certain properties that characterize the historical sequence [Matalas, 1967].An inspection of monthly flows of various streams and rivers indicates a tendency for high flows to follow high flows and low flows to follow low flows. This tendency is designated as hydrologic persistence and is attributed to storage processes in the atmosphere and/or in the drainage basin. Persistence is described by the correlations between various months of the year. Usually, the correlation between two consecutive monthly series is taken to be a measure of persistence. This assumption implies that the sequence is generated by a first-order Markov process.Stochastic models of streamflow generation are becoming more popular as a tool in hydrologic planning and design. A stochastic model is predicated to preserve meaningful statistical properties of a historical record in equally likely generated sequences that can be used to give a broad spectrum of response of the system under consideration. Thomas and Fiering [1962] presented a mathematical model for the sequential generation of nonhistorical monthly streamflows x. This model was formulated as a recursion relation of the type x,.i = •', q-b,(x•_•.i --.•_•) q-t•.is,(1 --ri2) ø'• (1) i= 2,3,-.-,12 and x,.• = •.• + b,(x•.•_• -5:•) + t•,•s• (1 -r,•) ø'• (2) in which subsc...