This work describes the use of two hydrological simulation deterministic models to represent the rainfuJI-runnoff processo The Itapetiniga, Almas and Guarapiranga rivers basin, located in the State of São Paulo and the Guaporé river basin located in the State of Mato Grasso, are taken as a case study. Both deterministic models used parameters optimization, with a nonlinear and unconstrained structure: (a) SMAP - Soil Moisture Accounting Procedure, which uses a first order optimization procedure; (b) Artificial Neural Network (ARN) model, which uses a second order optimzation procedure. Both models assume a montly interval to account rainfall and river flow. Some tests include a mix structure between SMAP and ARN, aiming at an evaluation of ARN potential to replace physical parameters and typical processes of conceptual rainfall-runnoff models.
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