Life Science Journal 2013;10(1) http://www.lifesciencesite.com 1575 Stochastic Generation of Storm Pattern A. Sharafati (Corresponding author), B. Zahabiyoun B. School of Civil Engineering, Iran University of Science and Technology, Narmak, Tehran, P.O. Box 16765-163,Iran Correspondence Tel.: ++9877451500; fax: ++9877240399. Abstract: Lack of storm patterns (storm hyetograph) in many catchments is an important issue in hydrological analysis. So, in many studies various methods are developed to generate storm pattern. There are uncertainties in generated storm patterns due to uncertainty of generating method (model uncertainty) and uncertainty of the variables affecting the storm patterns such as the total depth of rainfall, rainfall duration and dimensionless hyetograph (inherent uncertainty). This study developed the Rain Data Processor (RDP) and the Rain Pattern Generator (RPG) models to generate storm patterns based on Mass curve method with considering inherent and model uncertainty in ungauge catchments by using the Monte Carlo simulation and Bootstrap resampling. Methodology of this study is applied in Iran (Seymareh catchment).According to the statistics of generated peak intensity by the RPG model; there is an acceptable agreement between observed and generated hyetographs. Also, the RPG model is more accurate than triangular hyetograph model in generation of storm pattern. [A. Sharafati and B. Zahabiyoun. Stochastic Generation of Storm Pattern. Life Sci J 2013;10(1):1575-1583] (ISSN:1097-8135). http://www.lifesciencesite.com . 232 Key words: generation, Storm pattern, Seymareh catchment, RDP, RPG Introduction The storm pattern (storm hyetograph) is one of the most important variables in flood hydrographs simulation and peak discharge estimation. The storm pattern includes duration, depth and time distribution in a storm event and it’s recorded by synoptic and rain gauge station. Hence, there is no any storm pattern in many catchments. Therefore, many researchers and designers use the Soil Conservation Service (SCS) or storm pattern of other catchments for flood modeling in Iran. These approaches cause many errors in flood modeling. Many studies have pointed out various methods to establish storm pattern in the following four categories (Ellouze and Abida 2009): (I) Generation of storm pattern by using a single point of IDF curves for the specified duration (Chow et al. 1988). (II) Generation of storm pattern by using the entire IDF curve(USAE 1948, Keifer and Chu 1957, Bandyopadhyay 1972, Chen 1983, Veneziano and Villani 1999, Alfieri 2007). (III) Generation of non-dimensional storm pattern(Hershfield 1962, Huff 1967, Eagleson 1970, SCS 1972, Pilgrim and Cordery 1975, Tsubo et al. 2005, Powell et al. 2007). (IV) Generation of storm pattern by using stochastic models (Koutsoyiannis et al. 1993, Zarris et al. 1998, Cheng et al. 2001, Koutsoyiannis and Mamassis 2001, Lin et al. 2005, Wu et al. 2006, Grimaldi and Serinaldi 2006). In the first category; storm pattern is generated based on a specified return period, rainfall duration and an average value of rainfall intensity from the IDF curve. In the second category; whole set of duration- intensity values are used for a specified return period. In the third category (mass curve methods); observed storm events are converted to the dimensionless curves with cumulative fraction of rainfall duration on the horizontal axis, and cumulative fraction of total rainfall depth on the vertical axis. Comprehensive information of the above categories for establishing storm patterns can be found also in other papers (Yen and Chow 1983). In mass curve generation method, uncertainty in generated hyetograph is a main issue. Mass curve method has highly uncertainty because of the inherent uncertainty and randomness of the observed storm events (Veneziano and Villani 1999). Wu et al. (2006) used the Monte Carlo simulation and cluster analysis to quantify inherent variability of the observed storm events for generating probabilistically rainfall hyetographs of a particular pattern in Hong Kong Territory. Wu et al. (2006) classified all observed storm events in four basic types of storm patterns: advanced type, central-peaked type, delayed type, and uniform type. These categories have same concept as Huff rainfall mass curves. Another source of uncertainty in this method is incorrect connection between rainfall duration, total rainfall depth and generated dimensionless storm pattern and can be named model uncertainty(Yen et al. 1986). Uncertainty of storm event can strongly affect the flood generation and quantification of hydrograph variability has strong relation to quantify uncertainty of rainfall (Kusumastuti et al. 2007, Hong et al. 2006, Habib et al.