In this study, we have analyzed the climate of Gorgan by using CLIMATIC methods and then according to the trends of CLIMATIC elements during a 17-year period, the future climate of Gorgan has been studied. CLIMATIC elements have been predicted up to year 2015. To the study changes and the types of trends, lineal Regression was used during a 50 year statistical period (1956-2005). We used the software SPSS and EXCEL for statistical ANALYSIS. We also used WRPLOT View software for studying the wind speed. Temperature, precipitation, relativehumidity, frost, wind, Z-standard temperature and precipitation, and climate classification are the parameters that are studied in this paper. The results of analyzing the statistics are as followings: Gorgans temperature is almost steady and has little changes. Precipitation has a meaningfull decreasing trend in all seasons. Relative humidity has a meaning full increasing trend. Frost dose not have a ditinct trend. Western and south western winds are predominatnt winds of Gorgan and the wind speed is usually between11 to 17 knots. By analyzing Z-standard for temperature and precipitation, it became clear that Gorgan climate has changed in 1972. Also Gorgan climate is studied based on Koppen method, Mediterranean weather and Semi-Humid moderate Emberger method.

To doing this research, daily precipitation data from 188 synoptic, climatology and rain gauges stations in and out of Kurdistan province during 21/3/1961 to 19/1/2011 have been used. Daily precipitation interpolated on 6*6 km pixels by Kriging spatial statistic over Kurdistan province. One matrix with dimension 18203*811 has been created that located time (days) on the rows and column were pixels. For each pixels total monthly precipitation has been calculated. Data of ten teleconnection patterns extracted from NCEP/NCAR. Linear regression has been used to detection correlation between precipitation and teleconnection patterns. The correlation tested in 95% confidence level. The results showed that correlation between precipitation and teleconnection patterns is significant in 95% confidence level. The extent and intensity of significant correlation go to head in autumn season. The impact of South Oscillation Index (SOI), North Sea Caspian Pattern (NCP) and East Atlantic and West Russia (EAWR) is high in autumn. In cold seasons, SOI has negative correlation while NCP and EAWR have positive correlation with precipitation. In warm seasons two recent indices have negative correlation with precipitation. In total, increase sea level pressure and geopotential height in 500 hPa over Western European parts and decrease over eastern Mediterranean, Caspian Sea and north Caspian result in increase precipitation in Kurdistan province during cold seasons. In contrast, increase sea level pressure and geopotential height in 500 hPa over northern part of Caspian Sea result in increase precipitation during warm seasons.

Among the important characteristics of each lake, its water level, Understanding of how water vel fluctuations in an effective change-related issues including water storage changes – by shoreline construction and environmental issues. In this study, the effect of fluctuating lake water discharge and meteorological signals lake basin are discussed. The data used in this study due to high volume in the interval from 1986 to 2008 and from 1951 to 2011 at different stations. In this study, monthly data signals NAO - Nino1 +2 - Nino3 Nino4 Nino3.4 NOI-NP - PDO - SOI is used. All the above data were obtained from the NCEP. After explaining the relation and the prediction model using artificial neural networks for the same time interval, three months and six months respectively. The results of this model were evaluated and analyzed. Model output of the neural network software Neurosolutions6 that all stations in the state, ranging from the time delay of the quarter and half-year delay of the effective signal swing and lake water flows into the lake basin Order 1-NINO3 2 - NINO 3 +4 3 - NINO1 +2 4-NINO4 is the least of the order of 1 - NAO 2 - NOI 3 - PDO 4 - SOI 5 - NP is.

Concerning the drought being experienced recently and its effect on planning, and economical, agricultural fields and the demands on predicting and applying different models for decision making, Markov Chains model was applied in Khorasan at Mashhad, Torbat Heydarieh, Birjand and Bojnord stations. This model studies the phenomena which depend on the previous ones. Here, we have studied the possibility of the occurrence of dry and wet days (wetness threshold of 0.1 mm), the cold days (below 0o C) and warm days (above 25o C).Finally, two analyses were done using Markov Chains model. Also, for predicting 1 to 10 day periods, Xn=Pn-1 × q from statistical distribution was applied and the data was presented in the form of equiprobable maps. To analyze the provided data and maps, SPSS and SURFER softwares were applied.

Effect of CLIMATIC parameters in human comfort is very high. this research want to ANALYSIS weather of Tehran.the aim of this research recognize best region for human comfort in Tehran and because center of big city is polluted for this reason we only pay attention to this region. Parameters of human comfort that most effect rainful, temperature, humidity and speed of wind.we used census of 8 CLIMATIC station per day in this research and we use software for eduction abundance percent of parameters. in next stage we used A.H.P model and make matrix chart and we got best number of CLIMATIC parameters. When we ANALYSIS parameters and census we understand that in summer north of Tehran is better and in winter south of Tehran and 50%favourableness for this region is in north and decrease in south.

In this paper for the study of the amount of vulnerability of Iran to desertification we have used bioCLIMATIC index of UNEP (United Nation Environment Programme). First average annual temperature and total annual precipitation has been collected from 45 synoptic meteorological stations for a 30 years period (1976-2005) from the Meteorological Organization of Iran. Then UNEP index are computed for all of studied stations. Using Mann-Kendall graphical–statistical test we then investigated UNEP changes trends in Iran.On the basis of UNEP index we observed that most areas of Iran have arid and semi-arid climates. With respect to the desertification intensity class, these two kinds of climates have classes of severe and very severe conditions. After those two kinds of climates, ultra arid, dry sub-humid, very humid and sub-humid climates cover most areas in Iran respectively.Results of Mann-Kendall test show that in three stations of Birjand, Tabriz and Mashhad, UNEP index changes trend have signs of tendency from semi-arid climate to arid climate. Changes trend at two stations of Urmia and Khoy have a tendency from dry sub-humid climate to semi-arid climate. Changes trend at station of Sanandaj has a sign of replacement between semi-humid climate with semi-arid climate and Yazd station shows tendency to ultra-arid from arid climate.However, Zabol station at north of Sistan and Baluchistan province has had positive trend, whose changes trends have been from ultra-arid climate to arid climate. Changes trend of all of these stations at level of are significant.

This study was conducted to investigate the main factors affecting groundwater level fluctuations in the Najafabad plain. For this ANALYSIS, the 10-year period from 2004 to 2014 was considered and changes in temperature, precipitation, in river discharge between the beginning and end of the study area, groundwater withdrawal and in water flow in irrigation and drainage networks, were analyzed. In order to establish the relationship between each of the mentioned components with the changes in groundwater level in the study area, two general classifications were considered to determine the groundwater unit hydrograph. In the first classification, a groundwater unit hydrograph was prepared for all observation wells in the study area. In the second category, this hydrograph was prepared for observation wells around the river. Then, using multivariate regression ANALYSIS in SAS software, the best relationship between these components with changes in groundwater level was determined. Results showed that if the changes of groundwater unit hydrograph level are considered in the first group, changes in groundwater withdrawal with a share of 36.19%, had the maximum impact on changes in groundwater level. After that, the changes in the river flow with a share of 28.60%, had the greatest share in groundwater fluctuations. On the other hand, when the groundwater unit hydrograph is considered for the second group, changes in flow in the river with a share of 34.64% will have the maximum effect on changes in groundwater level and the share of groundwater withdrawal will reach to 28.53.

In this Survey, the CLIMATIC comfort conditions of Tabriz megalopolis have been examined with regard to the thermal sense of people. The evaluation of the thermal conditions has been made based on the calculations which are based on the human energy balance, and has been made for recognizing lack of comfort against the hot or cold weather for the people walking outdoors with suitable clothing. Hot and cold hours are defined depending on the extent and duration of thermal discomfort against the cold and warm conditions. Major methods used in this study are American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) standard 55-2004 Predicted Mean Vote (PMV) method and it's put out to Givoni psychometric diagram. This is possible using hourly data for analyzing of monthly, seasonal and total 44 yearly (1961-2004) study period. Achieved results shows that maximum hourly rate of CLIMATIC comfort centralizations located in August, July and September months and minimum hourly rate of CLIMATIC comfort is in November, December, January, February and March months. From consecutive Distribution of months of the highest CLIMATIC comfort point of view, thermal period of April to October to have highest rates of hourly CLIMATIC comfort and while November to March period have not CLIMATIC comfort hours. From the view point of seasonal distribution of CLIMATIC comfort hours, summer with total mean of 989 hours and 64 percent seasonal distribution has allocated itself the most CLIMATIC comfort hours of Tabriz, after that, spring with 437 hours has 28 percent of seasonal distribution of hourly CLIMATIC comfort. Autumn with 123 hours and 8 percent of the seasonal distribution of hourly CLIMATIC comfort is located in next Place and winter has no role in seasonal distribution of hourly CLIMATIC comfort in Tabriz and total hours of winter season of Tabriz are discomfort hours.The total mean of CLIMATIC comfort hours of Tabriz in Statistical period shows 1549 hours from total 8760 hours that it shows 17.7 percent relation of CLIMATIC comfort hours to CLIMATIC discomfort hours in Tabriz. This means that in 82.3 percent of cases, for Creating balanced climate with human physiological conditions, energy shall be consumed, which in addition to the Expense, requires professional issues.