Analysis of Spatial Patterns of Monthly Precipitation in West and Northwest of Iran Using Spatial Statistic Models

E Precipitation is a vital component in the hydrological cycle. Its spatio-temporal variations has great environmental an socioeconomic impacts. The spatial variation of rainfall is depending upon many factors. Some of this variation is due to synaptic systems and some others is formed by local physiographical characteristic of station such as elevation from sea level، slope، windward and leeward slopes، land cover and land use and etc…. if the rainfall is formed by widespread and pervasive synoptic system it can be exist a significant spatial similarity and homogeneity in amount of given rainfall in all over the region which is affected by synoptic system. But if the rainfall is dominated by the local factors the higher heterogeneity of given amount rainfall can be expected. In this study، we used the 20-years monthly average precipitation (1990-2010) for 42 synoptic stations، in the west and north western portion of iran which include 6 province namly: the East and West Azerbaijan، Kurdistan، Ilam، Kermanshah، Hamadan and Zanjan. We preparation this data as an long term average of monthly precipitation for each station and then import them to GIS by metric Orojected coordinate system (PCS). We used Moran،s Index as an Spatial statistic approach to investigate the spatial relations of monthly precipitation. This tool measures spatial autocorrelation (feature similarity) based on both feature locations and feature values simultaneously. Given a set of features and an associated attribute، it evaluates whether the pattern expressed is clustered، dispersed، or random. The tool calculates the Moran''s I Index value and both a Z score and p-value evaluating the significance of that index. In general، a Moran''s Index value near +1. 0 indicates clustering while an index value near -1. 0 indicates dispersion. However، without looking at statistical significance you have no basis for knowing if the observed pattern is just one of many، many possible versions of random. In the case of the Spatial Autocorrelation tool، the null hypothesis states that «there is no spatial clustering of the values associated with the geographic features in the study area». When the p-value is small and the absolute value of the Z score is large enough that it falls outside of the desired confidence level، the null hypothsis can be rejected. If the index value is greater than 0، the set of features exhibits a clustered pattern. If the value is less than 0، the set of features exhibits a dispersed pattern. The morans I Statisic for spatial autocorrelation is given as 1) Moran index 2) Where Zi is the diviation of an attribute for feature I frome its mean، wij is the spatial weight between feature i and j، n is total number of object and S0 is aggregate of al spatial weight. We found the amount of monthly given rainfall in the study region in cool season (November to February) reveal a significant positive autocorrelation. and on the other hand the spatial variation coefficient of rainfall in these month is smaller than other remaining month. the revealed Moran’s I indicated in the 4 mentioned months so strong significance that somebody cannot Assigning this spatial homogeneity to chance and randomness. In the cool season the study area which located in west and northwestern of Iran is dominated by westerly and following them the atmospheric synoptic systems entrance to country and affecting all of the country area then the rainfall is formed by widespread and pervasive synoptic system has significantly spatial similarity and homogeneity in all over the region and the strong positive autocorrelation is revealed in these months. In the warm season (July، September، August، October، and May) we find inverse condition. The Moran’s index in these months was very small and near to zero. We couldn’t detect any significant spatial autocorrelation in these months. In our study region the warm season especially summer season (July to September) is the dry period of year. The occurred rainfall in these months is usually sporadic and non-comprehensive. These rainfalls usually characterized by being showery which is formed by local atmospheric convective a cells. In this type of rainfall the different local physiographical characteristics such as elevation from sea level، slope، windward and leeward slopes، land cover and land use and etc… have a substantial roll in formation and spatial distribution of this rainfall. So that the difference physiographical characteristic of each region this local formed precipitation is not too similar. In the warm season absenting westerly in this region، the local physiographical characteristics determinant the occurred rainfall and due to this physiographical dissimilarity in the region، heterogeneity of given amount rainfall can be rise. The spatial variation coefficient of rainfall in warm season is very higher than col season. The revealed Moran’s I was not significant in 0. 95 confident level and there are no spatial pattern in this warm. Our finding indicated that only the cool season months including November، December، January and February reveal a significant spatial autocorrelation in 0. 95 and 0. 91 confident level.