Rainfall Runoff Modelling Using the Principle of Maximum Entropy(Case Study: Kasilian Watershed)

Accurate estimation of runoff for a watershed is a very important issue in water resources management. In this study, the monthly runoff was estimated using the rainfall information and conditional probability distribution model based on the principle of maximum entropy. The information of monthly rainfall and runoff data of Kasilian River basin from 1960 to 2006 were used for the development of model. The model parameters were estimated using the prior information of the watershed such as mean of rainfall, runoff and their covariance. Using the developed model, monthly runoff was estimated for different values of runoff coefficient,, return period,, at different probability levels of rainfall for the basin under study. Results showed that the developed model estimates runoff for all return periods satisfactorily if the runoff coefficient value is taken 0.6. Also, it is observed that at a particular probability level and runoff coefficient, the estimated runoff decreases as return period increases. However, the rate of change of runoff decreases slightly as return period increases.