In this study, the effects of climate change on desertification process in Tabas was studied using the SDSM software and long-term statistics (30-year period) of synoptic stations to predict temperature and rainfall parameters by the data of HADCM3 Model for future periods (2010-2039, 2040-2069 and 2070-2099) under two scenarios A2 and B2. In addition, this process was studied in two 15-year periods and Domarten method was also used to determine the degree of rainfall area. The results of the predictions and the observation period were compared together. Due to the significant effect of decreasing temperature and rainfall as well as drought index on the desertification process, drought indices were also determined. Results indicated that the effects of climate change on the getting worse of conditions in the area and consequently, decreasing rainfall, increasing temperature and decreasing the drought index were significant. According to the results, it can be stated that although the desertification process is affected by several factors, but in the future climate changes, there are the probability of increasing the desertification process and expansion of desert areas in Tabas, due to the change in rainfall and increasing temperature. On the other hand, because of increasing temperature and decreasing rainfall, many agricultural lands will be released, and the conditions for soil degradation as a result of increasing desert areas will increase.

General circulation Models (GCMS) are only tools to predict future climate condition under climate change scenarios. An outstanding issue with the use of GCM output for regional and local application is the coarse spatial resolution. So there are various methods to predict, climate variables at regional, local and a station scale that all these methods are known downscaling. The Statistical Downscaling Model (SDSM) is one of the most used methods that identifies relationships between variable predictors (output GCM) and variable predictands (Temperature, precipitation, etc. in particular station) using multiple linear regression. Intergovernmental Panel on Climate Change (IPCC) has determined two periods (1961-1990) and (1971-2000) as baseline to compare the effects of climate change in future periods. As well as reanalysis data, that produced by the National Center for Environmental Prediction (NCEP) are important components for the structuring of the SDSM as they supply the predictor values for the calibration and validation of the Model. Type and period of reanalysis data can be effective in Model accuracy. In this study for downscaling temperature and precipitation variables the sensitivity of the SDSM Model was examined to type and reanalysis dada of NCEP in Kerman meteorological station. The mean absolute error (MAE) was used to determine the sensitivity of the Model. Result showed that the Model is sensitivity to both type and base period reanalysis data. The mean absolute error of the reanalysis CGCM Model data, for the average maximum, minimum and mean temperature variables equal to 11, 4.5 and 4.7 times the case that theHadCM3 Model data is used respectively. In the case of the base period, when data of (1961-1990) is used, MAE for the mentioned variables and daily precipitation equal to 3.5, 1.4, 3.5 and 1.4 times that the state which is used for the base period (2000-1970), respectively.

In this study downscaling of temperature was down in Tajan Plain located in the province of Mazandaran. The result of atmospheric general circulation Models was obtained with HadCM3 climate Model under scenario A2. Since the output of atmospheric general circulation Models has a low locative resolution, should be downscaled in the area or Basin level that it was conducted with statistical method. The statistical methods used included of downscaling SDSM 5.5. And artificial neural network Model. In this study, by using the average daily temperature data of Kordkheil Station during the30-year statistic Period (1971-2001) and the large-scale variables NCEP, as inputs to the neural network and SDSM Model, simulation and downscaling was down respectively of the maximum and minimum temperature in the last period to determine Models error. To this end were used of the features and functions available in the programming software MATLAB. Then To evaluate the performance of the Models, were used the statistical criteria including of correlation coefficient, coefficient of declaration and root mean square error between observed and predicted values of temperature. The obtained results show the appropriate performance of SDSM Model for downscaling temperature Than the ANN Model. So that the error percentage of SDSM Model is lower and the correlation coefficient is more than the ANN Model. The best Structure of neural network to simulate of maximum temperature is perceptron Model with four hidden layer with the 5-5-6-6 architecture and for the minimum temperature Variable is perceptron Model with three hidden layer with 5.3.1 architecture.

Global warming and its consequence which occurs as climate change are of the world's major problems in the current century. Climate change and the warming of the earth have adverse effects on resources such as water, forests, pastures, agricultural land, industry and ultimately human life. The initial effect of climate change is on the atmospheric elements, particularly on the precipitation and temperature. Through evaluating long-term temperature trends we can be provided with a better insight as to how to plan for the upcoming years.....

limate Modeling is one of the fundamental methods of simplifying the complexity of the climate that can increase our understanding of the system’ s behavior. Climate simulating through using the outputs of general circulation Models in order to be aware of the characteristics of the climate, will be required in the coming years. The achievements of general circulation Models cannot be used directly in regional and smaller-scale climate simulations. A common way to solve this problem is by statistically downscaling the output of general circulation Models. SDSM is one of the most practical Models in the mentioned fields. In this study, attempts are made to assess the ability of the SDSM in downscaling and simulating the temperature data of Urmia since the beginning of 1961 until the end of 2010 using National Emergency Communications Plan’ s re-analyzed data and the outputs of HadCM3 under A2 and B2 scenarios. To assess the adequacy of the Models obtained and the SDSM’ s ability to simulate, some statistical tests such as the Chow test, the standard error, Wilmot index compatibility and also monthly and annual diagrammed data have been used. The results of this study show that the greater the time period is, the more preferable and closer to reality the simulated mean temperature will be. However the SDSM Model’ s function is inadequate in simulating the maximums and minimums. Therefore the achievements of this Model are suitable only to obtain a general understanding of the characteristics of future climate and they cannot be used in precise projects.

Climate change impacts are very dependent on regional geographic features, local climate variability and socio-economic status. Therefore, impact assessment researches on climate change must be launched at the local or at the regional level so that the evaluation of consequences can take place. Climate scenarios are produced by Global Circulation Models for the entire Globe with spatial resolutions of several hundred kilometers. For this reason, downscaling methods are used to bridge the gap between the large-scale climate scenarios and the fine scale where local impacts happen. In order to overcome limited computing power and for catchments with limited data, statistical downscaling is the most feasible approach in obtaining climate data for future impact investigations. So a decision support Model named SDSM was used to downscale the data. Model errors and uncertainties were estimated using non-parametric statistical methods at the 95% confidence interval for precipitation, maximum temperature and minimum temperature for the mean and variance for a single site in Kermanshah in the western part of Iran. The comparison between the observed dataset and the simulations showed that the SDSM Model was able to better represent the minimum and maximum temperature while for precipitation simulations are slightly under-estimated but still acceptable according to statistical tools. It is also presented simulations for the A2 SRES scenario for the 2041-2069 periods showing that the method can produce similar general tendencies.

In recent decades, the increase of temperature has caused the disturbance of climatic balance of the earth and extensive climate changes which is called climate change. The aim of this study is to predict the climate changes using statistical downscaling Model (SDSM) based on A2 scenario over future periods. Daily precipitation, minimum and maximum temperature data of Kermanshah synoptic station, for two periods 2015-2040 and 2040-2065, were predicted and compared with the baseline period. The first 27 years of data (1988-1961) were used for calibration and the second 12 years (1989-2001) were used for validation of the Model as well. The results showed that based on the A2 scenario, in the periods of 2015-2040 and 2040-2065, the average annual precipitation decreases, the average minimum and maximum temperature increases compared to the baseline period in the Kermanshah synoptic station. Since the precipitation reduction and temperature increase are one of the main factors of desertification, so it is necessary for decision makers and planners in Kermanshah province to adopt necessary solutions for mitigation and adaptation with new climatic conditions.

Climate change especially global warming is the most problem in the 21st century. So investigation variability trend this problem is very important in global, regional and local scale. Newadays numerous general circulation Models (GCMs) have been designed to predicat future climat. An outstanding issu of output for regional and local applications is coarse spitial resoluation. To produce accurate predications of future climate variables at the regional and local scale various methods are suggested. Despit many studies this case, ufortunately, there is not a standard method for a specific rogion. Thus it is necessary that accurate predications of these methods are evaluated befor applaying in a certain region. One of th most widspread methods is Statistical DownScaling Model (SDSM). In this research efficiency of SDSM Model is evaluated to simulate temperture indexes in Kerman station, instance arid and semi- arid regions. Hence, SDSM is calibrated and validated by using kerman station observ tempertur and national center enviromental predication data. We used mean absolute criterium to evaluate Model. After obtaining confidence simulation accuracy. Temperture indexes (mean, absoluate maximmum and minimm temperture) are simulated by using two GCMs (CGCMand HadCM3 under A2 and B2 scenarios) until 2100-year.The result of this study is shown that SDSM Model has suitably to simulate temperture indexes also using HadCM3 Model data is beter than that of CGCM Model. Increasing mean annual temperture on base HadCM3 Model in (2010-2039), (2040-2069) and (2070-2100) periods relation to base period (1961-1990) is respectively 1.5, 2.8 and 4.5 degree of centigrade in Kerman station.

According to the fourth report from the IPCC, it was confirmed that climate change and its impacts on drought, floods, health problems and food shortages is real. Therefore, understanding of how climate change could be significant in the management of resources, especially water resources management. Atmosphere-Ocean Global Circulation Models (AOGCM) are tools for predicting the future climate variables and it must be downscaled its output for the studies on the local scale. In statistical downscaling methods, output of GCM grid was transferred to station. The accuracy of downscaling is dependent on location of weather stations in GCM grid. The main objective of this study was to predict temperature and precipitation by using the HadCM3Model under the A2 emission scenario and statistical downscaling Model (SDSM) to year 2099. Furthermore, relation between accuracy of SDSM downscaling Model in different station of KAN basin which is located in one grid was evaluated. The results showed at the station that mean of temperature and rain was closer than to mean of temperature and rain of HadCM3 grid, simulation were obtained with higher accuracy. Finally, temperature and precipitation for this three periods (2011-2040), (2041-2070) and (2071-2099) were predicted and compared with base period (1961-2001). The results showed temperature will increase and precipitation will decrease by 2099 in KAN watershed.