Inflow data plays an important role in water and energy resources planning and management. In general, due to the limited availability of historical inflow data, synthetic streamflow time series have been widely used for several applications such as mid-and long-term hydropower scheduling and the identification of hydrological processes. This paper explores the use of fuzzy inference systems for the identification of two hydrological processes, and its use in the generation of synthetic monthly inflow sequences. Experiments using Brazilian monthly records show that fuzzy systems provide a promising approach for synthetic streamflow time series generation. © 2011. The authors-Published by Atlantis Press.1110601065Sudheer, K., Neelakantan, T.R., Srinivas, V.V., A nonlinear data-driven model for synthetic generation of annual streamflows (2008) Hydrological Processes, 22, pp. 1831-1845Stedinger, J.R., Taylor, M.R., (1982) Synthetic Streamflow Generation: 1. Model Verification and Validation. Water Resour. Res., 18 (4), pp. 909-918García-Bartual, R., Ochoa-Rivera, J.C., Andreu, J., Multivariate synthetic streamflow generation using a hybrid model based on artificial neural networks (2002) Hydrology and Earth System Science, 6 (4)Ahmed, J., Sarma, A., Artificial neural network model for synthetic streamflow generation (2007) Water Resources Management, 21, pp. 1015-1029Luna, I., Soares, S., Lopes, J.E.G., Ballini, R., Verifying the use of evolving fuzzy systems for multi-step ahead daily inflow forecasting (2009) 15th International Conference on Intelligent System Applications to Power Systems-ISAP '09rfpag 1-6, pp. 1-6. , NovemberEvsukoff, A., Lima, B., Ebecken, N., Long-term runoff modeling using rain-fall forecasts with application to the iguaçu river basin (2010) Water Resources Management, pp. 1-23Akbari, M., Overloop, P., Afshar, A., Clustered k nearest neighbor algorithm for daily inflow forecasting (2010) Water Resources Management, pp. 1-17Chiu, S.L., A cluster estimation method with extension to fuzzy model identification (1994) Proceedings of the Third IEEE Conference on Fuzzy Systems, 2, pp. 1240-1245. , Orlando-Forida, USA, JunhoLuna, I., MacIel, L., Da Lanna Rodrigo, F., Ballini, R., Estimating the brazilian central bank's reaction function by fuzzy inference system (2010) Of Communications in Computer and Information Science, 81, pp. 324-333. , Eyke Hüllermeier, Rudolf Kruse, and Frank Hoffmann, editors, IPMU (2), SpringerSchwarz, G., Estimating the dimension of a model (1978) Ann. Statist., 6 (2), pp. 461-468Zambelli, M.S., Luna, I., Soares, S., Longterm hydropower scheduling based on deterministic nonlinear optimization and annual inflow forecasting models (2009) PowerTech, 2009 IEEE Bucharest, pp. 1-8. , Julho