نشریه جغرافیا و مخاطرات محیطی
پیاپی 16 (زمستان 1394)

Iran is one of the most disaster-prone countries in the world. As the statistics show, 31 cases out of 40 types of natural disasters occurring in the world happens in Iran. Such natural disasters in the country have placed Iran on the list of top ten countries of the world in terms of natural disasters in which the incidence and frequency of natural disasters such as earthquake, flood, and landslide show characteristics typical of it. The north parts of the province, including the mountainous regions of the country located in an area with high and medium hazard are subject to various hazards and natural disasters such as flood, earthquake, landslide, and mountain collapse due to the their ecological, seismic, and geological characteristics as well as their topographic conditions and their special climate. . According to the previous studies, almost every year a number of natural disasters occur in this area in which case the focus of influence of most hazards is related to the rural areas due to their large extent in the country.
Study Area :The central district of Faruj has an area of about 1023 square kilometers covering 4 percent of North Khorasan Province. The district is located on geographical coordinates of 58 degrees to 88 degrees and 55 minutes of east longitude and 36 degrees, 45 minutes to 37 degrees, 40 minutes of the north latitude, at an average elevation of 1500 meters above the sea level. The city leads to Esfarāyen from northwest, from east to Ghochan in Khorasan Razavi Province, and from north to the district of Khabvshanin in Faruj. In this district, currently 58 villages have been recorded; 56 villages are populated while the other two villages are deserted.
Material and The research method used in the study was descriptive-analytic following practical purposes. The theoretical foundations were established based on the studies of documentaries and library research. To select the effective criteria and prioritize them for locating optimal temporary housing base, 30 questionnaires were completed by the village administrators and managers, and experts in geography and rural planning. Because the locating process was a matter of making multiple trait, the cell models were used; the analytical hierarchy models included AHP .AHP is a logical framework to understand and analyze complex decisions by decomposing it into a hierarchical structure to makes it easy. Also in this study, to choose a suitable location for temporary housing crisis management database, a combination of AHP (AHP) and Geographic Information System (GIS) has been used.
The reason to pay attention to the environmental indicators of environmental hazards (earthquake, flood, and landslide) is that the location selected in the first phase should be away from these hazards. For this purpose, the hazards and effective factors should be identified in this phenomenon and the importance of the following criteria were evaluated. After preparing seismicity maps, flood, landslide and topography for the final drawings of environmental factors, and the beat raster maps to locate and integrate maps and effective layers in locating had to be standardized.That is, the layers become capable of being integrated with each other through the use of decision rules. For this purpose, the paired comparisons, product hierarchy, linear and hierarchical process were performed using the Analytic Hierarchy Process and Expert Choice software. According to the final map of locating the environmental factors, it can be concluded that almost 58.5 percent of the central district of Faruj benefits from good conditions and is perfectly suited for the location in which 95 percent of the villages (N=52) out of 55 villages are appropriate for the purpose of temporary housing base in terms of natural indicators and the avoidance of hazards. In addition, only three villages of Agh Cheshme, Armod Aghchi, and Hasht Markh are in poor areas which are relatively appropriate and at greater risk; therefore, they are not suitable for locating temporary housing base.
Through the process of locating emergency accommodation in the area, we came to the following conclusions. According to the environment criteria, earthquake (weight: 569/0) and landslide (weight: 228/0) have higher priority coefficient in locating. This means that villages that are suitable for locating temporary housing are away from environmental hazards. In general, only the village of Mayvan takes the highest scores for the physical location of temporary housing. In addition, the villages of Se Gonbad, Bash Mahale, Kharagh, and Yenge Ghale are placed at the next level benefiting from good conditions, whereas 26 villages under the study are not appropriate in terms of physical facilities and are thus not suitable to locate as temporary housing base.

Stability achieved when the rural areas of natural and socio economic crisis in a way that protects the natural environment and the rural community to interact with each other to act in good condition. This is one of the most important strategies to assess the sustainability of existing rural settlements. In the first approach to identify the critical points and critical factors and the control of natural and human crises in order to achieve sustainability and survival of their villages, For this reason, today, in the context of rural development and planning of terms such as diagnosis, assessment and comprehensive assessment of the status quo to be much. The study area villages Khawmyrabad of Marivan, Kurdistan province is located at the zero point of the border with Iraq. There are multiple crises of the remoteness of the center and morphological characteristics, climatic and tectonic rural parts of the sector has been unstable, with drain and depopulation. The questions raised in this connection are the most volatile crisis and rural areas that are part of the relationship between the distance from the city center and the sustainability of the rural population, which significant relationships are: Identify the major natural disasters, economic, social and critical points with priority sector by experts, which can provide further insight into the current situation in order to establish the groundwork for better planning and population decline in rural areas provide highly volatile. To prioritize hierarchical analysis or expert judgment (AHP) used as a multi-criteria decision making techniques. Note that this method compares favorably to the decision and provides various options; it can be a suitable method for this study.
Study area :Marivan Township one of the 10 township of Kurdistan province in the West of Iran, adjacent to Iraq and in longitude 45 degrees 58 minutes and 46 degrees 45 minutes east longitude and 35 degrees 02 minutes and 35 degrees 48 minutes north latitude located. This township including central district, Sarshiv and Khawvamyrabad district. Khavvmyrabad district with an area of 338 square kilometers has 32 villages inhabited and is located at the border with Iraq, It has 2763 households and a population of over 11407 people. In terms of coupons division in the Mediterranean climate placed. Marivan mountainous region in the immediate Sanandaj - Sirjan Zagros folds that have been strong and weak metamorphism. The study area is influenced on the one hand on the Sanandaj-Sirjan zone and high Zagros structure. Although the specific structures in Sanandaj -Sirjan is more, but cannot ignore the impacts of Zagros highlands in this region, the main symbol of this drift and faults are abundant in the South West and adjacent to the Zagros region in the study area. However, climate and tectonic besides being far away from the center have encountered the region with diverse socioeconomic and natural disasters.
Material and This study, in terms of objective, applied and methods of doing it, is descriptive - analytical. In this study, we evaluated the existing challenges of the data (Census, 1385 and 1390) and maps of the area by experts (4 MSc Geography of Rural Planning, 2 Master in Development Sociology, 1 person of Dehyari, 2 bachelors senior natural geography (geomorphology trends in environmental planning) was determined. The AHP method to prioritize rural or oral judgment of experts as one of multiple criteria decision making techniques and software were used Expert choice. Finally, the final weight was calculated for each of the villages in the Excel software. Preparation of base data layers and maps required utilizes Arc GIS software based on the map 1: 250,000 geological and topographic maps Bane- Marian 1:50,000 was followed to answer questions used Kruskal-Wallis test, analysis of variance and linear regression analysis and other research have been. First calculate the relative weight of the criteria according to experts in the couple were in a diagonal scale. Comparison in a matrix of n in n (in this study, 8 × 8) occurs. A gives pair wise comparison judgment matrix: A= aij represents , and the aij show the judge the planner and the excellence criterion I (row) with regard to measures j (column) according to the original target. Calculate the relative weight of the criteria by judging experts and using Expert Choice software (total coefficient is equal to one important criteria). then the weight of sub-criteria in compare to related criteria determine and finally in attention to defined levels of experts that for all criteria and sub criteria defined, relative weigh of each option determine against sub criteria or directly by criteria itself and the final weigh in the regard of sum multiple of options. Finally, the final weight for each option determines the position of the villages in the class of stable and unstable.
The calculations confirm the assumption research suggest that exposure to more rural regions (44%) is a semi-permanent class. unstable stair take about 22% of villages, While potentially stable class (top), 34% of the villages in the region to be included in the overall average of 3.45 that indicates the stability of the region in the semi-stable category. these result show that the planning which have done by now was not suitable. there is a necessary for change in planning for unstable villages especially for semi stable villages according to their instability level, population and capabilities. Orientation program that is associated with this group of villages (semi-permanent) takes place, it must be based on the combined vision and carefully done, Because of rapid changes, and regardless of the underlying cause of these villages is critical and unstable conditions. As expressed in research theory and the result of comparisons show, the main factors of crisis making in economic and social sects are unemployment, low level of services, reduced population rate, and high rate of illiteracy. Other research assumes that the significance of the relationship between population size and stability of the rural sector was confirmed to be stated that the allocation of resources, facilities and orientation programs to the more densely populated areas. This reduces the vulnerability of the rural economic and social crisis and increased stability level. In contrast, the distance between the degrees of stability of the villages near the city center, a significant relationship was found to assume that the impact of research and significant relationship between the city center and the sustainability of rural rejected. So emphasis on populated villages and developed as a pole and a center for organizing other low populated villages and improving services to marginal part could be helpful to decrease instability level.
Although natural factors are less important in terms of creating crisis, but the combination of these factors in rural areas also suffer the humanitarian crisis has multiplied deterioration (sherke, Benawchela, Mohammad, Gagel, Anjiran). For removing this problem in regard of similarities and closeness this group of villages to each other, by using resettlement strategy by emphasizing on aggregation and integration of villages is a logical solution. In overall summary Close to 65% of total of the villages’ has in class of unstable and metastable. So more attention and applied planning, in the short and medium term timeframe necessary to solve the immediate crisis.

Dust phenomenon which occurs in arid and semiarid lands of the world is closely related to climatic characteristics of the region. According to the world meteorological organization (WMO), whenever wind speed exceeds 15 meter per second and horizontal visibility reduces to less than 1 km in a station, dust storm is reported (Goudie & Mideleton, 2006). Great storms are created when long-term droughts occur and the soil is quite dry (Azimzadeh et al, 2002). Sometimes, dust particles affected by physical and chemical processes are combined with other pollutants in a long- distance transition and form new compounds (Zhao, 2010). Toxic pollutants which travel with dust clouds can be absorbed into the skin or entered into the respiratory tract and consequently cause skin irritations and respiratory illnesses (Goudie & Mideleton, 2006). The process of studying and analyzing storm events and tracing dust sources can be performed through different methods. Concerning the frequency of dust storms in the world, Engelstadler (2001) stresses the important role of dry bed lakes and African great desert as the major producers of dust, and considered Sahara to produce dust more than any other deserts in the world. Wang (2005) studied the formation of dust storms in northeast Asia from synoptical view point and found that dust storms in this region are always accompanied by a cyclone or a low-pressure system and the amount of dust is maximized in the warm sector of the cyclone. Each year, Iran suffers serious casualties and damage from natural disasters due to its great breadth, diversity of climate, and geographical conditions. Kutiel and Furman’s study (2003) indicates that the highest frequency of dust storm in the Middle East belongs to Iran, Sudan, Iraq and Saudi Arabia. Therefore, the aim of studying the dust events influencing the capital of Iran is to identify the areas prone to particle emission by using statistical-synoptic approaches and dust source tracing (HYSPLIT) model.
Study area :This study was conducted performed through statistical analysis on observational data from Tehran synoptical stations including: Karaj, Abali, Firoozkooh, North Tehran and Chytgr.
Material and Methods In this study, three different phases with statistical, synoptic and modeling approaches were adopted, respectively. The first phase began by analyzing the observational data collected from five synoptical stations, including Tehran, Firozkhoh, Chitgar, Karaj and Abali during the period of 1981-2005. The daily data of dust, the visibility of less than 2 km from Tehran meteorological organization, monthly, seasonal and annual frequency distribution of dust were calculated. The vegetation coverage and the topology of Tehran were studied in the second phase by using GLCC land use data and GTOPO elevation data adopted from the Abdus Salam international centre for theoretical physics (ICTP). Wind speed and atmospheric pressure systems play a major role in causing dust storms (Alijani, 1997); therefore, atmospheric condition at pressure levels of 500, 700 and 850 hPa was analyzed using atmospheric parameters, including zonal wind, meridional wind and geopotential height with a grid size of 2.5×2.5 degree at three pressure levels provided the national nenter for atmospheric research (NCAR/NCEP). In third phase, In order to identify the dust sources of the mentioned stations, backward movement of dust particles was traced by applying HYSPLIT software within 48 hours before entering the region during May 1-5, 2000 at pressure level with 100, 500, 1000 meters in elevation.
The results indicate that within the period of 1981-2005, the highest frequency of dust occurred in May, 2000. In spring, the highest frequency of dust was observed in all stations under study. The 24 year old dust frequency of five synoptic stations shows that Abali station had the highest number of events (109 days) and Chitgar station had the lowest number of events (12 day). Since in transition period of spring pressure systems of north latitudes are still active over Iran, the present study, conducted in May, was affected by these systems. The observations at the level of 700 hPa indicate that one of the pressure systems causing dust and determining its direction is the pressure system which influences Saudi Arabia with had little displacement during all dusty days, and it was thus considered as a permanent system in the region. Other effective pressure system in relation to Tehran’s dust storms was the low-pressure system in the north of Iran. This system affected the region along with high-pressure system over Saudi Arabia on May 1st and 2nd. However, on May 4th and 5th, due to the movement of the mentioned system toward the north, cut-off low-pressure system was formed over the north of Mediterranean, which partly affects pressure lines, speed and direction of flows.
Studying particles transition directions of HYSPLIT model outputs indicates that the main dust sources of tehran are generally located at 25N-37N latitudes within range of Iraq, Saudi Arabia and Syria. Surveying the elevation data of the emission particles shows that dust particles in higher layers flowed toward Iran and reached Tehran at lower levels. In exploring the pressure systems, firstly, it seems that dust particles were transmitted to the higher levels by low-pressure system, drawn to the high-pressure over Saudi Arabia and then were descended to the ground level. Overall, anti-cyclonic flows dominate this area.

Most of the warming over the last 50 years has been caused by emissions of carbon dioxide (CO2) and other greenhouse gases due to human activities. Observed changes in the climate due to increasing greenhouse-gas concentrations have made it essential to investigate these changes. The General Circulation Model (GCM) is the most current method of investigating climate change studies. Although they are imperfect and uncertain, these models are a key to understanding climate change. However, it still has serious difficulties in reproducing daily precipitation and temperature despite an increasing ability of GCMs to successfully model the presentday climate. Even when daily GCMs output is available, the coarse spatial resolution of GCMs and large uncertainty in their output on a daily scale, particularly for precipitation, indicates that the output is not appropriate for direct use with processbased models and the analysis of extreme events. Output of GCMs requires application of various downscaling techniques. One of the downscaling techniques to create daily site-specific climate scenarios is to makes use of a stochastic weather generator. Recently, weather generators have been used in climate change studies to produce daily site-specific scenarios of future climate. Two important reasons for using LARS-WG model include the provision of a means of simulating synthetic weather time-series with certain statistical properties which are long enough to be used in an assessment of risk in hydrological or agricultural applications and in providing the means of extending the simulation of weather timeseries to unobserved locations. In fact, LARS-WG has been used in various studies, including the assessment of the impacts of climate change which can be divided into three distinct steps: calibration model, validation model, and the generation of synthetic weather data. Changes in the climate variables are studied in Gilan Province located in the north of Iran. The output of two GCM models was compared with a stochastic weather generator. In this study LARS-WG and suitable GCM model were used to produce a climate change scenario.
Material and The study area is Gilan Province, which is situated in the north of Iran and located in the South of the Caspian Sea and has about extent areas of approximately14600 kilometers. The performance of the LARS-WG stochastic weather generator model was statistically evaluated by comparing the synthesized data with climatology period at 8 selected synoptic stations, based on 2 GCMs models (MPEH5, HADCM3) and 2 scenarios (A2, B1). In this study, it has shown the period of base data, including precipitation, minimum and maximum temperatures and solar radiation from 1992 to 2010. Firstly, LARS-WG model was performed based on the historical climate data obtained from 1992-2010 to verify the model. The model was performed after assessing the model ability in each station for all 4 states (2 GCMs models based on 2 scenarios). Then, the results were compared and the best model was chosen to evaluate the climate change in the study area.
Model validation is one of the most important steps of the entire process. The objective was to assess the performance of the model in simulating the climate at the chosen site to determinate whether or not it is suitable for using. Firstly, LARS-WG model was performed based on the historical climate data obtained from 1992-2010 to verify the model. A large number of years of simulated daily weather data were generated and were compared with the observed data through t-test. The monthly mean correlation of the precipitation, minimum and maximum temperature, and solar radiation was accepted at the 0.05 confidence level. Then, to select a suitable GCM model, the LARS-WG stochastic weather generator model considered for MPEH5 and HADCM3 models in A2 and B1 scenarios was compared with the mean of all the models. Of all these 4 states, MPEH5 model based on A2 scenario, which has the least difference with the mean of the models, was selected and used to predict the future climate. Finally, the produced data based on the selected model within the period of 2011-2030 was compared with the observed data within the period of 1992-2010 to evaluate the trend of changes between the two periods.
Research results have shown that the mean of precipitation in Gilan Province has decreased during 2011-2030. The mean of precipitation was estimated 15.2 mm. Likwise, precipitation has decreased in the most parts of the study area. The maximum decrease in precipitation has been related to Astara. In the south and west of Gilan Province, including Langerood, Amlash, Ramsar, Roodbar, and parts of Siahkal, Precipitation has increased, whereas the maximum increase has been in the boundary of Gilan with Mazandaran. Precipitation in other months has decreased except in February, March, August and November while the maximum decrease has been in September. The minimum temperature of the study area will increase in Anzali station most with 0.5 ºC. Generally, the mean of minimum temperature in study area has decreased within the period of 2011- 2030 having 0.4 ºC. Most changes have been in winter and spring when the minimum temperature has increased in these periods. The greatest increase happened in May with 1.9 ºC while the maximum temperature of Gilan Province was 0.4 ºC and the most changes have been in the east of the study area. Most changes have happened in April and May when the maximum temperature has increased in these months about 1.2 ºC. This has decreased wet day length (days with precipitation more than 0.1 mm) while the greatest decrease has happened in Rasht. This has been the minimum decrease in the mountainous areas, whereas it has been decreased the wet day length in the plain of Gilan more than mountainous areas. The amount of decrease in wet day length has been 11 days. The greatest decrease has happened in October with 4 days. This has increased the mean of the dry day length (days with precipitation less than 0.1 mm) that the amount of it has been 12 days. Most changes have happened in Rasht and the difference between the two periods has decreased toward the areas. This has also increased dry day length except in April, whereas the greatest increase is in October for 4 days. It has increased the mean of hot day length (days with maximum temperature more than 30 ºC), three days when the greatest increase has happened in Rasht. Except Anzali and Astara, hot day length has decreased in other places. The greatest increase has been in July for 2 days. This will decrease within the period of 2011- 2030; the mean of frost day length of Gilan Province (days with minimum temperature equal or less than 0 ºC) will decrease for 5 days as compared with the base period. The greatest decrease has happened in Astara, Rasht, Roodbar and the south of Talesh, whereas the fewest changes have been in Anzali. Despite the decrease in the average number of frost days in the whole province, in November and December the number of hot days increases. The number of frost days in other months will decrease while the greatest decrease will be in October for 3 days.

Rainfall intensity and duration are one of the most important factors in the occurrence of landslides; therefore, nowadays rainfall is recognized as the most common and triggering factor in the occurrence of landslides. Since raw data of landslide displacement is very important to researchers in order to study the deformation process and laws of landslide displacement, the Global Positioning System (GPS) and computer sciences can be used as a tool to measure the landslide displacement. So monitoring and determining the relations between the amount of landslide movement and characteristics of precipitation (such as type, intensity, duration) and the direction of the sliding mass using GPS can be a turning point in the sustainable management of this natural disaster.
Study Area :The study area refers to the Tavan landslide which occurred in 2010 covering an area of 40 ha in Qazvin Province in Iran.
Material and After identifying the sliding mass, a network of fixed points on the inside (17 points) and outside of it (3 points) was created in order to monitor the movement, and the exact position of the points was determined using a dual-frequency GPS at 5timescales. The precipitation data and their characteristics such as the amount of precipitation, maximum intensity of 10, 20, 30, 60 minutes, average of precipitation, height of snow, average and total duration of precipitation were also calculated for each timescale. Finally, the relationship and correlation between the amount of displacement and the characteristics of precipitation was investigated by determining the vertical and horizontal displacement at the 5 timescales and the characteristics of precipitation for each period of time.
In the total period of the study, 64 precipitation events happened in which the total of precipitation was more than 2 mm in 52 cases. Drawing displacement vectors of the points on the topographic map revealed that the direction of mass movement is consistent with the general slope of the region. The correlation between rainfall characteristics and the amount of horizontal and vertical displacement of the monitoring points at the five timescales are provided in Table 7. The results showed that among all the characteristics of precipitation, rainfall intensity features have more solidarity with the average of horizontal and vertical displacement of points at the five timescales. Moreover, no significant correlation was found between other characteristics of precipitation and the amount of displacement.
The analyses of displacement at the five timescales showed that the relative displacement has occurred at some points of the network. Although there have been slow vertical and horizontal displacement at all points in the network but the amount of movement is clearly visible at the points of 1, 2, 6 and 8. The total amount of horizontal displacement of moving points at the five timescales (511 days) was 1876mm with the monthly rate of 110 mm. The total amount of vertical displacement of moving points was also 898 mm with the monthly rate of 56 mm. The results also showed that among the different characteristics of precipitation, only the intensity of precipitation has a good correlation with the amount of landslide displacement. Previous studies have also achieved similar conclusions about the triggering role of precipitation and the relationship between the intensity of rainfall and the landslide occurrence. The highest coefficient of correlation was found between the average intensity of precipitation and the maximum rainfall of 30 minutes with the horizontal movement of the sliding mass. Generally, it can be concluded that several factors such as topography, soil, geology, land use and intensity of precipitation caused favorable conditions for the occurrence of Tavan sliding; however, in this landslide, rainfall intensity has played the role of a trigger.

The position of Iran in the subtropical region that has made its arid and semiarid climate and the topographic diversity changes this trend of climate. Therefore, climate hazards such as frost, flood, dust storm, drought, temperature anomalies, heavy rainfall, avalanches, snow, sleet, etc occur in Iran. The Northwest of Iran experiences the above conditions following the arrival of west winds, cool and humid polar air mass from the East Europe, and the complex topography (mountain nodes of Iran). The relationships among climate hazards, and the weather patterns and identifying climate anomaly patterns of activity can mitigate the effects of climate and forecasting atmospheric phenomena, which helps the planners and managers. Much research has been done in this regard, including the planetary-scale circulation anomalies compared with synopticscale anomalies associated with higher intensity of extreme weather events are in America (Konrad, 1995, p. 1067) and the atmospheric circulation patterns associated with heavy snowfall in Montana of America, with a deep trough in the west winds and the cold flow from northwest, and with temperatures below zero (Birkland & Mock, 1996, p. 281). The flow pattern in the Great Lakes Basin winter severity Lorentz in the period of 1950-1998 were shows to the prevailing winter circulation patterns in the three synoptic type associated with the winter cold, temperate and hot identified, each of them during the rule of a special circular pattern (Rodionov & Assel, 2000, p. 601). The daily circulation patterns causing heavy snowfall in Poland using principal component analysis are related to strong positive anomalies in the Nordic and North Atlantic sealevel pressure, with positive anomalies of Iceland and the Azores high pressure weak anomalies (Bednorz, 2008, p.133). The aim of this study is identified the atmospheric important index of heavy snow in the middle levels in Northwest of Iran.
Study Area : The Northwest of Iran locates in the East latitude of 36. 4 and 39. 2 and North longitude of 49. 2 and 44. 26. The mountains of Alborz and Zagros connected together and one of the largest lakes in the world is locate in this area. Also the cold and wet air masses from the Arctic, Northern Europe and Eastern Europe come to Iran from this area. With this feature the lowest temperatures throughout the year and most snow is falling in this region.
Material and In connect to aim of study, to determine of the Synoptic snow days, the daily data of precipitation and temperature since 1989-2010 of twelve synoptic stations received of Iran Meteorological Organization. The identify of heavy snowfall according to definition of the World Meteorological Organization, rainfall of 15 inches of snow in 24 hours, and considering that 15 inches of snow is equal to 12.5 mm of precipitation done (Alizadeh,1999). Because of the lack of long-term statistics height of fresh snow, the rain days with 12 mm of precipitation or more and the minimum temperature of zero or less is snow day. In order to more accurately in determination of snow day used snow coefficient (Bairoodain, 2003, p. 320). In order to determine of the activity centers associated with the heavy snowfall, first to receive of the digital data was selected the geographic window to the North latitude of 10-60 and East longitude of 15-80. Therefore the daily data for the height of 500 hPa, sea level pressure, Omega of 850 and temperature of 700 of the Centers for Environmental Prediction and Center for atmospheric research were received. The principal component analysis (PCA) was used to determine of the activity centers of heavy snow at level of 500 hPa.
The seven of action center was identified that account for 79% of the total variance in the original data. The first five Center of action are the most important, the first centers to 18% of the variance locate on the Arabian Sea and North Pole, this pattern shows westerly winds and the general circulation. The second center (14%) with two West- East of center represents meridional movements in Western Europe and Central Siberia. The third center (13%) locates on Eastern Europe and the northern Black Sea (Balkan). The fourth of action center (12%) is located on the central Asia. The fifth center (9%) is on the Anatolia area, Turkey and Northwest of Iran. The pattern of the atmosphere of first center is associated with the polar vortex from Western, Eastern Europe and the Mediterranean with height of 5480 m and the deep trough to cut off low locate on Turkey with height of 5450 meters. The pressure pattern of the surface is Siberian high pressure and southern Europe that combined in the region. The pattern of the second center is the deep trough in the northern Siberia. The strong blocking is located on the Mediterranean and Europe. The surface pattern is low pressure in the Arctic, North Asia and the Caspian Sea and the high pressure center is locate on the southern Europe and the extension to Iran and South Asia. The pattern of the third center is associated to ridge on the central Mediterranean, at the same time, cutoff low in the Caspian is caused deep trough in East Mediterranean. The cutoff low in Caucasus and ridge of European caused flows from northern Europe and Scandinavia to the Black Sea and Iran. Also at ground level, extreme high pressure on Europe combined to Siberian high pressure. In this pattern, isobar of 1035 hPa in Black Sea and isobar of 1023 and 1026 pass of the Northwest of Iran. The pattern of the fourth of action is connected to the west winds in southern Europe, the Middle East, Northern Europe and northern Asia. The ridge of Southern Europe and cutoff low locate on the Caucasus, caused deep trough in the westerly winds in the East Mediterranean. At the same time, Siberian high pressure andEurope to 1020 hPa is evident. The pattern of the fifth of action center is related to meridional flow and the ridge on Europe and height of 5700 meters is located on the north of the Mediterranean and southern Europe. In surface, the deep trough of westerly winds of the north-western Russia continues to southern Egypt. In this pattern, the polar vortex is located on the Scandinavia, Black Sea, Turkey, Iraq, north and Northwest of Iran. The pattern of the Earth's surface is connect to high pressure of western Mediterranean and high pressure of Siberia in East Iran and low pressure in northern Europe to Iran.
The climatic indices of heavy snow in the Northwest of Iran is locate in northern Siberia, Central Siberia, Balkan, Central Asia and the Anatolia. The of upper atmospheric patterns of these indicators are associate with meridional westerly winds, blocking of southern Europe, ridges in central Asia, eastern Europe, east Mediterranean sea, central Asia and cutoff low on Turkey. The blocking and long ridges related to air masses of the north Atlantic over Europe by crossing of the Black Sea and persistence of the cutoff low on Balkans is caused heavy snowfall. At ground level are a combined high pressure of Siberia and Europe. So that the Isobaric 1017 and 1020 hPa passes through the Northwest of Iran to the cold air masses in heavy snowfall days. The Siberian high pressure in the East and Northeast of Iran causes continued rainfall in northwestern of Iran. the lowest temperature in the middle levels of atmosphere experience in the days of heavy snow with -10 ° C and isotherm of zero is on the Persian Gulf in southern of Iran, Therefore, fluctuations in temperature in Iran is above 15 degrees Celsius.. Also local and geography phenomena, including high elevation and local high pressure are affective.

One of the six great challenges recognized by the World Climate Researches Program (WCRP) is the prediction and the characteristics of extreme events. The outcomes of various sources indicate that the extreme climate events have significantly increased over the last decades. Trend analysis of extreme temperature indicators is of crucial importance in estimating the trend of global warming. Temperature rise plays a decisive role in drought intensity leading to a more frequent occurrence of extreme drought events. Temperature rise also results in desertification, decline of water resources, and a decrease in agricultural products following the loss and decay of the products. Following the establishment of the Intergovernmental Panel on Climate Change (IPCC) in 1988 and the extensive research on it at a global scale, researchers began to realize that the average monthly temperature measure cannot indicate the trend changes of global warming. The results of the analysis of minimum and maximum average temperatures in the early 90's show that the minimum average temperatures have been rapidly increasing in relation to minimum average temperatures. At the beginning of the 2000s, IPCC published a special report on emissions scenarios (SRES) in the Third Assessment Report (TAR) and used them for the Assessment Report Four (AR4). With the beginning of the 2010s, the Coupled Model Intercomparison Project Phase 5 (CMIP5) suggested new scenarios called Representative Concentration Pathways (RCP), identified with values of 2.6, 4.5, 6.0, and 8.5 W/m2. The 4.5, 6.0 and 8.5 RCP scenarios roughly correspond to B1, A1B, and A2 scenarios. After the development of RCP scenarios, some researchers studied the effects of such scenarios on diverse issues such as water resources, agriculture, and so forth through the temperature change trend analysis.
Study Area : This research is an attempt to analyze the future climate conditions of Khuzestan Province till the 2050s according to the RCP scenarios. Located in the southwest of Iran, Khuzestan plays a vital role in Iran’s economy in terms of agriculture and industry.
Material and The method of the study on Khuzestan’s future climate conditions is based on RCP scenarios, dealing with both current and future conditions different stages. To analyze the future climate conditions, the following seven indices were selected out of the indices introduced by the Expert Team on Climate Change Detection and Indices ETCCDI: DTR (diurnal temperature range), TMAX mean (mean maximum temperature), TMIN mean (mean minimum temperature), TN10p (cold nights), TX10p (cold days), TN90p (warm nights), and TX90p (warm days to analyze the future conditions from 2013 to 2050, the 2.6, 4.5, and 6.0 RCP scenarios were chosen through the four proposed scenarios of the fifth assessment report of IPCC. The goal of all these scenarios is to predict the highest, lowest, and the mid-range of future climate changes. MarkSim model(Jones, 2012) was selected as the downscaled model of the study.First, to assess the accuracy of the simulation model of regional temperature changes, the following models were selected for the years from 2013 to 2015: GCM, HadGEM2-ES (1.2414× 1.875), MIROC5 (1.4063×1.4063) and MRI-CGCM3 (1.125×1.125). Then, downscaling and temperature indices modeling were performed and the output of different models was compared. Regarding the variance analysis, the model of MIROC5 was considered as an appropriate model for the study and the level of significance for temperature indices trend was analyzed (p=0.05).
In this paper, the spatial change pattern of temperature extreme indices of Khuzestan was compared and analyzed within the two statistical periods of 1983-2012 and 2013- 2050 using RCP 2.6, RCP 4.5, RCP 6.0, and RCP 8.5 scenarios. The results indicate that under different scenarios, maximum and minimum temperature indices, particularly those in percentile would depict different trends in different parts of Khuzestan. On the basis of the present situation, the northern, central and southern parts of Khuzestan represent a significantly increasing trend, whereas other areas of the province have just an increasing trend. The time series of the maximum and minimum temperature means in the present situation indicate that in reference to the present situation, the temperature minimums (.72°C) are increasing ata roughly more rapid pace in relation to temperature maximums (.2°C), which explains the increase in DTR index. In relation to other scenarios, on average the TMIN mean index will show 1°C increase under RCP 4.5 scenario which will be witnessed in most parts of Khuzestan. In relation to other scenarios, the trend of this index under RCP 6.0 is more homogenous throughout the province and barely southern parts of Khuzestan will show an increase of trend in relation to other areas. The TMAX mean index under RCP 2.6 will show a more increasing trend as compared with the two other scenarios. The highest increase occurs in western parts of Khuzestan while other parts represent a similar trend. In like manners, the analysis of percentile-based temperature indices such as the indices of cold events (TX10p, TN10p) and warm events (TN90p, TX90p) under miscellaneous scenarios indicate that the cold events indices depict a declining trend, whereas their warm equivalents depict an increasing trend. Of all the future scenarios, the RCP 4.5 and RCP 6.0 scenarios represent the highest spatial changes of the TN10p and TN90p indices, respectively. Under different scenarios, the TN90p index represents an increasing trend with almost the same linear slope that cold nights index curve declines. Under the RCP 2/6 scenario, the TN90p index indicates that if appropriate policies are taken to adjust to climate changes and bring them under control, the trend of warm nights will be controlled.
With a spatial-temporal analysis of temperature extreme indices under the present situation (from 1982-2012) and the RCP 2.6, RCP 4.5 and RCP 6.0 scenarios (2013- 2050), This article simulates the temperature changes of Khuzestan based on the obtained data, GCM and MIROC5 models. However, the results indicate that in the present situation, the temperature minimums are roughly increasing more rapidly than the temperature maximums to such an extent that they lead to the declining trend of the DTR index. The simulation of climate change trend based on the RCPs suggests that the increase in temperature trend is likely to maintain in the future. All in all, the results show that, the trend of cold and warm nights indices (TN90p, TN10p) are compatible with the TMIN mean index trend as well as the compatibility of the trend of cold and warm days indices (TX90p, TX10p) and the TMAX mean index trend in different parts of Khuzestan. In this sense, the indices of the cold period (cold days and nights) and the indices of the warm period (warm days and nights) will represent a declining and an increasing trend, respectively.

The long-term result of cooperation between environmental factors and circulation patterns determines the arrangement of type and manner in temperature heat islands in geographical areas. The knowledge about space dispersion in geographical areas provides the grounds for sound programming and proper environmental decision making. The information about time and place distribution of temperature is necessary to determine energy balance of earth, meteorology studies, and Eva transpiration; in fact, this is the reason why researchers approve of temperature studies. However, traditional trends of statistics have not clearly shown this fact. In environmental studies, we are dealing with observations which are interdependent; the dependence relates to the position and setting of the observations in the studied atmosphere. Thus, traditional trends of statistics should not be used in this type of observations because this kind of data has an interdependent structure in time and place. Therefore, this kind of data is called 'spatial data' in environmental studies and their studies need a normal approach to deal with the action of the data in time and place.
Study area :This study is carried out to identify the spatial-temporal autocorrelation of temperature heat islands of Khorasan Razavi Province.
Material and To reach the expressed goal of the study, the station of networking data of maximum and minimum temperature of Khorasan Razavi Province was established. The data homogeneity of the stations’ temperature with the Kolmogorov-Smirnov Test was applied to SPSS software and their homogeneity was also confirmed. Then, from the data of the station a statistical period of 30 years in a daily period from 1980/1/1 until 2010/12/31 is used as the base of the present research and a network in range of 15× kilometers have been spread over the location under study. In reviewing the changes of temperature heat islands of Khorasan Razavi Province during a year, modern spatial statistics methods such as Spatial Auto Correlation Global Moran, Local Insulin Moral Index and Hotspots (through GIS) and Matlab were used.
The conclusions showed that Global Moran Index for each of 12 month of the year is more than 0.98 while it is one for the four months of August, September, October, and November. This point indicates that temperature in Khorasan Razavi Province based on Global Moran within the period of the study has the cluster pattern as high of 95 and 99 percent; however, the highest index of Global Moran was in August with the scale of 1/006052. The Z statistics estimated for each 12 month of the study ranges between 9 and 99. According to Global Moran, it can thus be concluded that Khorasan Razavi Province follows a very high cluster pattern during the year. To assess the autocorrelation of spatial temperature change in heat islands of Khorasan Razavi Province, the local Moran island index along with the analysis of hotspots were used. According to the both indicators, the southwest areas such as Ferdows and Boshruyeh stations and northeast areas such as Tabas station play a significant role in forming the heat islands patterns with high cluster. As such, the areas of Khorasan Razavi under study have positive spatial autocorrelation. This occurs while regions have negative spatial autocorrelation. In other words, Cold Island in 12 month of a year is limited to the high regions. On the whole, a significant area of the province in all 12 months of the study lacks significant or disciplined pattern. In fact, they statistically lack sound virtual spatial autocorrelation. The results of this research showed the islands are formed over long periods of time under local and distributional elements playing different roles.
Generally, the geographical arrangement of heat islands is formed by regional factors, especially heights, latitude. In this way, the formation, structure and the role of latitude can be traced. However, we should not ignore the role of external factors in formation of heat islands because external factors including the general circulation atmosphere elements play a significant role in determining heat regime and temperature lapse. If we look at the temperature cluster of Khorasan Razavi Province, we see that the clusters in high and low levels are not the same. This contrast is due to the influence of circulation element factors. Therefore, generally we can say that heat islands are created and controlled by two systems, namely 1) Regional factors controlling the region (geographical arrangement of heat islands) and 2) external factors controlling the time (heat island regime).