Spatiotemporal Variations of Cold Period Precipitation in Iran(1950-2009)

Precipitation plays an important role in the global energy and water cycle. Knowing just the amount of precipitation reaching the ground water for assessment and management of land use، agriculture and hydrology، such as flood and drought risk reduction is so important. Assessment of climate change and its effects require long-term rainfall analysis in all spatial scales. Growing concern in the scientific community over whether there is a significant change in the amount of precipitation? (Nicholls and Alexander، 2007). 29% reduction in the daily maximum water flow caused by higher temperatures and increased evaporation with any change in rainfall causing drought in southern Canada during the years 1847 to 1996 (Zhang، Harvey، Hogg and Yuzyk، 2001). Recently summer drought caused by unusually dry heating in areas related to tropical West Pacific and Indian Ocean (Andreadis، Clark، Wood، Hamletand and Lettenmaier،2005; Pagano and Garen، 2005) including studies of climate change on global precipitation regimes. Germer (2008) has examined monthly variations in rainfall، floods، droughts and runoff in the Yangtze River Basin in China. In another study، Dao (2004) studied the daily variation of rainfall in semi-arid regions of northern China. Raziyy and Azizi (2008) said that topography and latitude are main factors of controlling the precipitation in the west of Iran. Also، Asakereh (1386) has investigated spatiotemporal variation in Iran precipitation. The results show that about 51. 4% of rainfall areas in the Iran have changed. The researcher suggested that factors share of rainfall Iran in precipitation changes be investigated. In this context، the author aims to explore the development of spatiotemporal changes of the rainfall pattern with access to the available and reliable database and the new approach to extract patterns and possible trends in the data. This paper investigated the spatial patterns of precipitation amount trends in cold period of year، for Iran between 1950 and 2009. We used of reanalysis monthly data of GPCC with 0. 5*0. 5 spatial resolution in Iran (From 44E until 63. 5E and 25N until 40N) and global & local Moran’s spatial autocorrelation methods. Global Precipitation Climatology Center data accuracy is measured by the geographical weighted regression (GWR) method. Spatial autocorrelation of precipitation data were extracted by global Moran Index. Global Moran index shows only the overall clustering of precipitation data. Therefore، to detect the different local patterns spatial autocorrelation، local Moran was used. The index measured spatial differences in rainfall amounts between each grid point and its neighboring points and evaluated its significant level. Trend analysis of spatial patterns configured based on Man Kendall’s τ nonparametric test. Geographically weighted regression between the global climatology center data and station data showed that gridded data has verified acceptable to replace the station data. Rainfall Gridded data and station data an average correlation of 76% has had. Global spatial autocorrelation results that precipitation data in all of the month shown significant positive spatial autocorrelation (or clustered pattern). Local spatial autocorrelation results that each month shown proprietary precipitation pattern and monthly precipitation entailed any significant trends. October shows the lowest average index values and dispersion in rainfall patterns has taken. Unlike the months of December rainfall patterns have shown concerted. A spatial clustering map shows، usually in October، a strong spatial cluster is formed on the southern coast of the Caspian Sea. However، during the months of November and December، a strong spatial cluster formed in Zagros on rain again. In February and March the Caspian cluster is reduced in proportion severity. Conclusion Temporal variations of the No، high - high points and No، low-low points in all months showed no significant change. This suggests a lack of space to expand or reduce the size of the cluster rainfall during the study period Comparison between pervasive drought events and Low value of Global Moran’s Index shown a strong relationship between them. Thereupon should be used of other variables for water defect researching. Comparing of general Moran index values and widespread drought in Iran showed that the low index values based on the years of drought. Results suggest that should evidence of dehydration in other climatic variables such as temperature and evaporation search.