Uncertainty analysis and statistical generation of storm pattern in Seymareh catchment

Random variables are the major reasons for uncertainty in flood modeling. Storm pattern is the most important random variable among the other variables in modeling of flood variability. Storm pattern include duration, depth and time distribution in a storm event. Accurate identification and uncertainty analysis of the effective variables on storm pattern and uncertainty analysis of storm pattern is useful for uncertainty analysis of flood modeling. In this study, combination of the rain data processor model (RDP) and the rain pattern generator model (RPG) models were used for identification and evaluation the variables affecting the rainfall patterns. Furthermore, random properties of storm pattern are evaluated in Seymareh catchment and various storm patterns were generated with acceptable accuracy. The results indicated that the RPG model has a good accuracy in hyetograph generation. Moreover, the comparison of sharpness index of Seymareh data revealed that rainfalls with less rainfall depth and long duration had less uncertainty.