This study used two models for stochastic generation. When the available evaporation observed data are often very short, then there is a need for stochastic generation of such data for better understanding of possible future values of evaporation. Generation of evaporation data helps in planning, operation, and management of water resources. Annual and monthly evaporation time series were generated for School River Basin Dam Lake in São João do Cariri, semi-arid region of Northeast Brazil. Evaporation data used in this work were obtained from Academic Unit Civil Engineering for period of 20 years. Tests of normality and homogeneity were performed and results showed that data was normally distributed and homogeneous. For generating annual time series, a First –order Markov or Autoregressive first-order model – AR(1) was used. Monthly evaporation time series were generated by using Fragments method. Hundred replicates for annual series and hundred replicates, each of length equal to the historical data record, were generated for each month. Models performance was evaluated by comparing the statistical parameters of the generated time series with those of the observed time series. Annual and monthly models were found to be satisfactory in preserving the statistical parameters of the historical time series. About 92 per cent of the tested values of the considered parameters were within the assigned confidence limits. Forecastings for monthly evaporation values were made. The models are recommended by authors for semi-arid region of Northeast Brazil that presents similar physical characteristics, similar hydrological regime and climate also.