Rainfall-runoff models usually present good results, but parameter calibration sometimes is tedious and subjective, and in many cases it depends on additional data surveys in the field. An alternative to the conceptual models is provided by empirical models, which relate input and output by means of an arbitrary mathematical function that bears no direct relationship to the physical characteristics of the rainfall-runoff process. This category includes the artificial neural networks (ANNs), whose implementation is the main focus of this paper. This study evaluated the capacity of ANNs to model with accuracy the monthly rainfall-runoff process. The case study was performed in the Jangada River basin, Paraná, Brazil. The results of the three ANNs that produced the best results were compared to those of a conceptual model at monthly time scale, IPHMEN. The ANNs presented the best results with highest correlation coefficients and Nash-Sutcliffe statistics and the smallest difference of volume.

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Precipitation analysis is embedded in a range of important hydrological studies for hydraulic works construction and maintenance. However, flaws and limitations in records are obstacles often encountered by researchers. One feasible solution for overcoming these obstacles is to generate synthetic series. The main objective of this work is to structure and validate a model for generating synthetic rainfall series at a daily scale. A parametric model has been constructed, where the occurrences are determined by a stochastic Markov process and the cumulative rainfall quantities are computed using a mixed exponential probability distribution. Since no previous studies using the proposed probability distribution in La Plata Basin were found in the literature, several significance tests and relevant criteria were applied, in order to verify the model accuracy. The approach was studied in 11 rainfall stations inside Parana and Uruguay rivers basins, located in Brazilian South and Southeast regions, obtaining good results. Additional analyses of the model performance related to extreme events and droughts are also present.

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