A Comparative Study on Calibration Methods of Nash's Rainfall-Runoff Model; case study: Ammameh Watershed, Iran

Increasing importance of watershed management during last decades highlighted the need for sufficient data and accurate estimation of rainfall and runoff within watersheds. Therefore, various conceptual models have been developed with parameters based on observed data. Since further investigations depend on these parameters, it is important to accurately estimate them. This study by utilizing various methods, tries to estimate Nash rainfall-runoff model parameters and then evaluate the reliability of parameter estimation methods; moment, least square error, maximum likelihood, maximum entropy and genetic algorithm. Results based on a case study on the data from Ammameh watershed in Central Iran, indicate that the genetic algorithm method, which has been developed based on artificial intelligence, more accurately estimates Nash's model parameters.