Rainfall- runoff modeling is one of the key steps of hydrology. Among rainfall- runoff models, the conceptual

models are frequently used. However, calibration of the conceptual rainfall-runoff models has been

a challenge for hydrologists for decades. In this study, automatic calibration of ARNO conceptual rainfallrunoff

model is developed using genetic algorithm. This model is in the class of continuous semi-distributed

models and has been successfully used in different parts of the world. Using the employed automatic

calibration tool, the calibration step of the model can be performed rapidly and simply for a given basin,

without requiring an extensive knowledge of the model structure and parameters. The model was calibrated

automatically for rainfall-runoff simulation of a basin in southwest Iran. The calibration results for 5

years daily observed data showed that the values of efficiency coefficient and squared correlation coefficient

was 0.80 and 0.82, respectively and the validation results for 4 years daily observed data showed that

the values of efficiency coefficient and squared correlation coefficient was 0.82 and 0.83, respectively. The

results show that this model in conjunction with employed automatic calibration method can successfully

be used for daily rainfall- runoff simulation.