Development of Daily Rainfall Simulation Model by Using Markove Chain and Preserve Spatial Correlation (Case Study: Khozestan Province)

Water scarcity is a big problem in many areas, especially in arid and semi-arid regions. It is rising due to the demand growth driven by increased economic activity and population growth in developing countries. Since Iran is on the world's dry belt and it has rain equivalent to 1/3 of the rain world's average, it is considered a dry country. The rain trend indicates that Iran is going to drought, so plans and measures of water resources management should be developed accordingly (Samadi Broujeni and Ebrahimi, 2010). Also rainfall in Iran is one of the main variables for assessing of water resources, but its spatial and temporal distribution is very Non-uniform. For this reason, the water resources distribution of the country is not uniform, too. Preservation and water resources management are not only a function of rainfall but also depend on the variability of rainfall. If spatial change of rainfall be small, the water resources are more homogeneity and consistency (Mirmousavi and zohrehvandi, 2011) . Hence, the rainfall variations are important in assessing water resources of rivers and the relative study of local and regional water resources. Although various approaches have been proposed for modeling of rainfall, the use of single generators can not properly reproduce the spatial correlations between different meteorological variables. In this paper, was used the first-order Markov chain(MC1), the second-order Markov chain(MC2) and the third-order Markov chain(MC3) for the occurrence of daily precipitation. The Wilks method was used to simulate the occurrence of daily precipitation by preserving the spatial correlation between stations for four synoptic stations in Khozestan province of Iran, considering the importance of preserving the spatial correlation between adjacent stations in water and agricultural studies in daily scale, which has not been studied in Iran up to now.