Knowing of precipitation values in different regions is always of main and strategic issues of human which has important role in short- term and long-term decisions. In order to determine of precipitation model and forecasting it, there are different models, but given that the precipitation data have a spatial autocorrelation, the spatial statistic is a powerful tool to recognition of spatial behaviors. In this research, for determine of precipitation model and predicting of it with geographical factors e.g. altitude, slope and view shade and latitude- longitude by using spatial regressions analysis such as ordinary least squares (OLS) and geographical weighted regressions(GWR), 13 synoptic stations of Khuzestan province from establishment to 2010 were used. Results showed a powerful correlation between precipitations with geographical factors. Also results of modeling through OLS and GWR representative that forecasting of GWR is close to reality, so that in GWR, the sum of errors of residuals is less, the  is more and there aren't any spatial autocorrelation in residuals and the residuals are normal. The of OLS can only justify 75 percent of precipitation variations with spatial factors while in GWR this quantity is 82- 97 percent. Accordingly, it was found that, in east, northeast and north of province the altitudes, in east and northeast and Zagros Mountains the view shade and slope are the most important spatial factors, respectively.

Rainfall is one of the most important components of the water cycle and plays a very important role in the measurement of climate characteristic in any area. Limitations such as lack of sufficient information about the amount of rainfall in time and space scale and complexity of the relationship between meteorological elements related to rainfall, causes the calculation of these parameters using the conventional method not to be implemented. One method of evaluating and forecasting of rainfall in each region is time series models. In this research, to predict the average annual rainfall synoptic station at Mahabad, Uromiya and Mako in West Azarbayejan provience during 1984-2013, linear time series ARIMA was used. To investigate model static, Auto Correlation Function (ACF) and Partial Auto Correlation Function (PACF) was applied and with differencing method, the non-static data transformed to static data. In next step, stochastic models to estimate the annual rainfall average were used. With regard to the evaluation criterion such as T, P-VALUE < 0.05 and Bayesian Information Creterion (BIC), ARIMA (1,0,0), ARIMA (0,1,1) and ARIMA (0,1,1) models was determined as a suitable model for predicting annual rainfall in the three selected stations at Uromiya, Makoo and Mahabad. In the following, the annual rainfall for 3 (2013-2016) years is forecasted which based on rainfall data in that time, the adjusted model was acceptable.

Paleo-climate reconstructions based on multi-proxy such as sedimentology is current in climatology investigation. In this way, National Institute of Oceanography and Atmospheric Science cored 960cm from Arzhan wetland SW of Iran than we have investigated of sedimentology. Primarily the core was measured on magnetic sensibility per 1_cm and secondly was subsampled according to magnetic sensibility and were analyzed include TOC, CaCO₃, and Grading laser and etc. Finally four samples of pace of plant were selected and have been dated in carbon 14 dating laboratory of Poznan, Poland, and calibrated trough of core based on Calib rev. 7.1. Dating have determined that core had covered 15000 Cal. BP. According to this investigation, we obtained 5 period of climate conditions during Pleistocene to Holocene in this area. 15000-13000 Cal. BP. Late Pleistocene climate conditions were colder and wetter period that indicate end of the Ace age.  13000-11000 Cal. BP. Climate begin to warmer and dryer. 11000-9000Cal.BP. because of this period shows many fluctuations in all proxies we named this period to the period of transfer from ace age to interglacial period. 9000-7000Cal. BP there have been warm and cold conditions compare to late Holocene. 7000- Present indeed in this period radiantly modern pattern of atmospheric circulation has been performed due to all of proxies in late Holocene begin to fixed on the same pattern. So probably atmospheric circulation from meddle Holocene begin to get harmonic to present. Any way for determinate of paleo-climate in this area we need to use of multi proxy investigation in area.
 

owledge of spatial rainfall behavior in environmental, land planning is effective. These changes in the later place in the form of time later and in the climate of the area. The Target of this study was to reveal the presence or absence of precipitation trend in the ratio of the height of local precipitation behavior and identify province mazandarn. Therefore, the purpose of the rainfall data station 32 (Meteorological Agency and Department of energy), the statistical period 1988-2010. To get the regression analysis of precipitation process was used to identify the local behavior of precipitation, the method of spatial statistics were used. The results obtained from the behavior of precipitation, the existence of the process within the scope of the study and the emphasis is most consistent with the Be modified regression model at adjustment indicate. According to the regional behavior of precipitation, using local spatial statistics, spatial Moran well hot spots check this behavior. The results showed that precipitation in the province of Mazandaran has the pattern of clusters with high value. According to the local hot spots and methods Moran, West Coast up to a height of 700 m has positive z score and clusters with high value, 99% confidence level. This range includes 15% of the total of the province. The range of the Southern Highlands as well as the negative z score and clusters with low value with a confidence level shows 99%. This range is also about 20 per cent of the province's total. About 65 percent of the total area of the province as well as the lack of a significant trend show.