Automatic Calibration of the ARNO Conceptual Rainfall- Runoff Model

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.