نشریه جغرافیا و مخاطرات محیطی
پیاپی 20 (زمستان 1395)

IntroductionKhorasan Razavi province is located in northeast of Iran, and it had the population of 5,994,402 people in 2011 census. According to land use in Khorasan Razavi province, 71% of the total area of the province is in moderate to high hazard which 24 cities with a population of 2,900,000 and 1,063 villages with a population of 580,000 are in relatively high-risk zones. The province has more than 5,500 large and small faults detected that 60% area, 75% of towns and 35% of villages are in the privacy of these faults. This is when the fact that only 28% of houses in the province have a steel and concrete structure and 72% of houses also used other materials that will seriously damage in earthquake. The situation indicates that in the event of a large earthquake, the province of Khorasan Razavi will be faced with disaster; therefore, in this study we have tried to use the features of occurred earthquakes in Khorasan Razavi in the years 1900-2014, and the places that are more likely to have earthquake are identified and have prioritized planning.
Study AreaThe current research area is in Khorasan Razavi province in I. R. Iran which the following are the most important characteristics of earthquakes happened and the seismic situation with regard to faults privacy are pointed out.
• 40% of the area of Khorasan Razavi province is in the safe range and about 60% of its area is located near fault lines.
• Checking the focal depth of earthquakes occurred in the region shows that more than 90% of these earthquakes had focal depth of less than 40 kilometers long and 79% of them also have a focal depth between 10 and 40 km respectively.
• The least of depth of the earthquake occurred in 1 km surface of the earth, the maximum depth was 64 kilometers and an average depth was 20.96 km of surface of the earth.
Material and MethodsA model of neural networks that was used in the classification and clustering of used data was Self Organization Feature Maps (SOM or SOFM). This was first presented by Kohonen and is known as Kohonen model. The root of Kohonen learning rule dates back to 1962 and unsupervised clustering issues. The primary goal of unsupervised Kohonen self-organizing neural networks is converting the input pattern with optional dimensions into a discrete model with one or two dimensions. The reason for selecting Kohonen neural network is that these networks are capable of categorizing large amounts of input data simultaneously and in parallel and recognizing seismic patterns. Using Kohonen neural network is due to internal statistical models in algorithms and unlike most statistical methods does not require many assumptions. It should be noted that the data used in the study through the information available on the website of International Institute of Earthquake Engineering and Seismology have been extracted from the characteristics of earthquake location (latitude), hours after the earthquake, the earthquake magnitude and depth of the earthquake in Khorasan Razavi province since the beginning of 1900 until the end of 2013. As well as a large number of earthquakes occurred in the area between 4-8 on the Richter scale are 199. Also, to evaluate the accuracy of neural network model used in the study, the statistical methods root mean square error (RMSE), mean bias error (MAE) and mean absolute error (MAE) are used.
Results and DiscussionAfter spatial prediction of a possible earthquake with a neural network, the predicted data have been processed by statistical analysis. Therefore, the Gaussian Mixture Model (GMM) was used. From seismic zoning maps percent it can be concluded that the maximum possibility of earthquake is predicted in the central region toward the west and the south east region of Khorasan Razavi province with a probability of 30% higher than other regions. Based on the model predictions it was good in cities of Mashhad, Fariman, Khaf, Gonabad, Kashmar, Khalil Abad, Torbat Heydarieh, Rashtkhar, Sabzevar, and Ghouchan and in other cities it is average. However, the total RMSE error model for Khorasan Razavi province is 0.460 that represents a model is a good fit. The results of MBE and MAE also confirm the results of RMSE criteria. The total amount of MBE model error is 0.274 and MAE model is 0.418 models that represent a good forecast.
ConclusionThe findings show that 12% of the urban population and 10% of rural population in zones with relatively high risk and most possibility of earthquake occurrence is in the central region toward the west of Province (Kashmar city and southeast of Sabzevar), southeast of Bardeskan and in southeast region (Khaf city) with 30% more than other predicted regions, this is while in cities of Kashmar, Sabzevar, Bardaskan and Khaf, respectively 10, 16, 17 and 18 percent of houses have used steel and reinforced concrete materials and 89, 83, 82 and 80% of houses in the cities have used stone, brick, wood, clay and mud that in case of a possible earthquake could cause irreparable damages to life and property. The Kohonen neural network model could predict the probability of earthquakes in areas which areigher than other regions of the province.

IntroductionUsually the major human casualties caused by the earthquake, are the damages related to buildings and structures. According to estimates the more than 75 percent of deaths and casualties began to collapse (Lantana, 2008). Whereas the damage and human losses range from minor injuries to death. These two factors are important characteristics of intensity earthquake (Coburn et al., 2002). Earthquake vulnerability of buildings can be termed as mount of damage induced in the building due to earthquake. Vulnerability is expressed on a scale of 0 to 1, where 0 is no damage and 1 defines complete destruction. It can be expressed in various terms like vulnerability tables, fragility curve, response curves, etc. Vulnerability of a building is determined by factors like shape of building, type of building, its construction material, height, design and structure. A building behaves differently based on different intensities of ground motion.
Seismic simulations allow scientists to better understand the distribution of shaking and damage that can accompany earthquakes, including possible future "scenario" earthquakes. The simulations are only as valid as the elements going into the simulations, such as the source and subsurface models. Thus, the recent earthquake provides data to validate methods and models.
Materials and methodsRegarding the topic of research, area of study and complicated existence of city as a spatial and social system, use of different methods and techniques with title of Compound Method is essential. In this study, indicators were extracted by Delphi technique and the studying of records. After weighting of parameters by FUSSY-AHP, the selected indicators were converted to distances maps in GIS-SPATIAL ANALYSIS EXTENTION software. Finally, the final map of permeability area in Zone 1 of Ahwaz mega city Were prepared.
The objectives of the study include the following:To examine the nature and types of road closures in Zone 1 of Ahwaz.
To compute the level of withdrawal of public access in the enclosed neighborhoods of Zone 1 of Ahwaz facing with hazards.
The study area of research is Ahwaz. Ahwaz is a city in the south of Iran. In the 2006 census, its population was 1,432,965, in 796,239 families. Ahwaz has the world's worst air pollution according to a survey by the World Health Organization in 2011. Ahwaz is built on the banks of the Karun River and is situated in the middle of Khuzestan province, of which it is the capital and most populous city. The city has an average elevation of 20 meters above sea level. Ahwaz, being the largest city in the province, consists of two distinctive districts: the newer part of Ahwaz which is the administrative and industrial center, which is built on the right bank of the Karun river while residential areas are found in the old section of the city, on the left bank.
Result and discussionTo calculate the human toll, eight criteria were used in the fuzzy logic model. Based on the findings, District 4 is known as the most vulnerable region in the face of a possible earthquake. In this district, mortality potential of accessory buildings and structures loss was predicted about 2579 person.
An important aspect of preparedness for an earthquake is evaluating the building stock particularly in terms of structural vulnerability. While Iran has a National Building Code that takes into account earthquake resistance in the design of buildings, the vast majority of properties do not meet those standards, exposing the occupants to the risk of injury or death arising from the building collapsing in the event of a major earthquake. According to the research area 4 as the most vulnerable region in face of an earthquake is possible. In this area, mortality potential loss of structural buildings and equipment of 2,579 people have been predicted. Old, unstable materials and high density construction are of the most important reasons for this pattern of vulnerability. In this connection, District 5 with low density and new tissue has shown the least amount of casualties. Moreover, the distribution percentage of type of buildings out of 50 samples surveyed is then extrapolated to total number of buildings present in that particular ward.
ConclusionAccording to research findings in the fuzzy model a major way in which loss of life and injury can be reduced in a major earthquake is by undertaking a nonstructural vulnerability assessment. Based on the findings derived from the analysis of spatial and statistical modeling on the model Coburn, Earthquake Crisis management priorities for reducing the amount of probable losses in the earthquake area is obtained. This involves a visual inspection of each room of a property to identify furniture and fittings that could topple or break in the event of an earthquake and cause injury and/or restrict pressure on external resources. The results derived from the analysis of spatial and statistical modeling based on Coburn, priorities earthquake crisis management to reduce potential earthquake zone 1 of Ahwaz was obtained.

IntroductionThe concept of vulnerability is used widely in many different contexts, from medicine to the poverty and development literature. Vulnerability and resilience assessment in the field of environmental planning is one of the most remarkable discussions. The vulnerability is defined a degree to which a system, subsystem or component is likely to experience harm due to exposure to a hazard, either a perturbation or stress. In another word, vulnerability is a function of character, magnitude, and rate of climate change and variation to which a system is exposed, its sensitivity, and its adaptive capacity.
The widespread concern over the overwhelming effects of climate change, particularly in the agriculture sector, has become very serious. Climate variability impacts on agriculture sector have been mostly harmful. For instance, staggering impacts such as droughts and floods threaten the livelihood of rural people who are dependent on agriculture. The vulnerability in agriculture is not just limited to production losses, but also, it has major socioeconomic and environmental impacts. Agriculture is closely linked to climate change. However, vulnerability to climate change should be seen in the context of existing broader socio-economic and environmental conditions. Agriculture is the primary occupation in a rural area and a source of livelihood for one-third of the study area in the East Azerbaijan province.
Urmia lake is one of the most biotic and valuable ecosystems and it is registered as a national park in Iran. It is also, listed as a biosphere reserve by UNESCO. Despite the allegation for 25 years vision of Urmia Lake that has been mentioned in Integrated Management plan for Urmia Lake Basin, the lake will have an attractive landscape and rich biodiversity. The crisis during the recent years particularly in September of 2012 has become more serious and 70% of the lake has dried up. It is ironic that the collapse of Urmia Lake in the country like Iran the 1971 Ramsar Convention was signed. Continuation of the current trend of lake drying from now after a couple of years a vast amount of salt will be released into the surrounding region which means resulting in an ecological, agriculture, and social tragedy not only in the northwestern region of Iran but also in neighboring countries. Therefore, the main premise of this research is that agriculture sector is more sensitive and vulnerable due to Urmia lake crisis.
Material and MethodsIn this paper, the role of Urmia lake disaster on agriculture function and structure changes and resilience of rural communities in the eastern region have been analyzed. The method of this research is quantitative. For this propose, new approaches in vulnerability assessment have been applied. Based on the comprehensive methodology of this study to assess the vulnerability related to Urmia Lake disaster in agriculture system and rural community resilience, spatial analysis and GIS-based techniques by using remote sensing method were implemented. In this sense, in order to gain insights into the spatial distribution of Urmia lake disaster, vulnerability assessing of agriculture sector has been done. Criteria for vulnerability assessing were as follow in three main indices: exposure, sensitivity, and adaptive capacity. In the first section, lake surface change, temperature trend, precipitation, relative humidity and L.Q were selected. Sensitivity indices of this study include land use and land cover change and agriculture production change trend. Finally, in adaptive capacity section, NDVI, Tasseled Cap, salinity index, rural population density and the density of employees in rural areas were selected.
The selected case study sites Azarhsar, Osku, Ajabshir, Shabestar, Malekan and, Bonab counties are located beside the southeastern region of Lake Urmia and encompasses 22 rural administrative units. Also, 199 rural points are located in the study area among which Ajabshir county has the highest rural point density. The rural population of study area totally is 232925 people.
Results and DiscussionWe report the result of data analysis for agriculture of the study region in three parts, which are presented below focusing on exposure index of Urmia lake crisis then application of exposure indicator in the term of ecosystem changes particularly aquatic one indicate that Urmia lake disaster is a main driving force in agriculture function and structure changes. Moreover, land use and land cover change analysis have approved that Urmia Lake is inevitably more effective on agriculture section. Eventually, utilization of remote sensing index presents environmental capacity of agriculture section that tremendously is in the low rate.
Landsat image classification to obtain land use and land cover change detection by using multilayer perceptron was conducted. The finding from this section showed that there was a dramatic change especially in a water body, irrigation and dry farming classes in which these are strongly related to ecosystem changes. The issue of land degradation is further contributing to the loss of production. The statics shows that during the last decade horticultural products have been faced with negative growth rate. Finally, in the adaptive capacity analysis of agriculture sector utilization of remote sensing index present a spatial capability of agriculture resilience
ConclusionThe result of vulnerability assessment has been useful in generating planning measures which can increase the resilience of rural communities of eastern regions agriculture sector to impact Urmia lake crisis. The vulnerability process among index method identified the major drivers of vulnerability. The result of assessment process indicates that there is a top priority for the local government to generate measures reduce the vulnerability of agriculture sector. Finally, this study highlights resilience is not based on a single factor, nor is it related to climate change, environmental change or issues such as Urmia lake crisis separately. Rather, this analysis has suggested that the origin of success to deal with Urmia lake disaster is promoting of rural communities.

IntroductionA season is a certain time of a year that is distinguished by alternation in timing and intensity of the solar radiation and atmospheric conditions (Zolfaghari, 2005). A natural season is a certain time of the year which can be separated by homogeneous weather type (Alsop, 1989). Considering the importance of season’s determination, nowadays different methods have been using to determine the natural seasons by means of various parameters (Alijani, 1997). The methods are as follow:Astronomical This method is determined based on the apparent motion of the sun. The beginning of the season happens in a fraction of a second. Nowadays, in addition to the astronomical definition, other definitions based on weather and climate criteria are required for determining the seasons that are known as climatological method (Zolfaghari, Masoompour Samakoush, Jaliliyan, & Fathniya, 2013).
Climatological This method uses the climatic variables along with the apparent motion of the sun. According to this method, a season is a certain time of the year which a weather phenomenon is happening regularly (Alijani, 1997).
Jet stream is a permanent member of the general circulation of the atmosphere that makes rapid flow of air in the tropopause (Kington, 1999; Kraus, 1999; Phillips, 1999). Jet streams have been used as a valid indicator to determine the beginning and the end of the natural seasons in some parts of the world. Sheng (1986) expressed that the abrupt changes of the subtropical jet stream over the Eastern Asia can be an indicator for separating natural seasons in the China. Kutie and Kay (1992) showed that the abrupt changes in the behavior of subtropical jet stream over the Middle East can be the main factor for occurrence of summertime climate change over the whole area. Also, the structure of large-scale atmospheric circulation over the Southwest Asia during warm period of the year has been investigated by Mofidi and Zarrin (2013). They introduced three atmospheric indices to determine the beginning and the ends of summer season over the Middle East. They found that the summer season is starting around June 3rd and it ends around October 15th, based on the jet index (Mofidi & Zarrin, 2012).
Study AreaIn this study, the zonal wind component at 200 hPa from National Center for Environmental Prediction/National Centers for Atmospheric Research (NCEP/NCAR) reanalysis dataset at 2.5×2.5 degree global grids and 17 pressure levels for 30 years (1981-2010) have been employed. The data have been used for a 91-day period (April 16 to July 15) to clarify the behavior of upper troposphere jet stream over the Middle East in the early summer to identify the beginning of the summer season for the area.
For better and more accurate determining the position of the subtropical jet in the Middle East, with simple random sampling (random numbers table), 3 years were selected through 30 years to determine the scope of the study (1988, 1998, 2010). For study of each of these three years and the 91-days period, first, the day by day location of subtropical jet were pictured in an area larger than the Middle East (3×91=273 image). Then, the original location and preferred range of subtropical jet in the region were selected after evaluation. The beginning of the summer was the result of 27.5 °N-42.5 °N and 35 °E-65 °E to study of the behavior of the subtropical jet.
Material and MethodsThe criteria for assigning subtropical jet jump is that every year during the advancing of the warm season, jet core has a sudden north jump up to the 3 to 5 degrees of latitude and place on summer position, only in a few days (Mofidi, 2007; Mofidi & Zarrin, 2012). After that jet does not return to lower latitude and stays on the same higher latitude.
First, 30 separate script and output images were obtained for 91-days of each 30 years. Subtropical jet stream jump and the beginning of the summer of each year is determined by observing these images. Then, geostatistics functions were defined that gave us an output text files for study area. These output files displayed a series of numbers that tells us about maximum velocity, latitude and longitude of zonal wind components:Function amax: This function displays the greatest velocity in each script for that coordinates.
Amax (u, lon=35,lon=65, lat=27.5, lat=42.5)
Function amaxlocY: In this function, the Y-axis location of coordinates (latitude) is determined.
AmaxlocY (u1, lon=35, lon=65, lat=27.5, lat=42.5)
Other plots were made with these numerical outputs. The exact time of subtropical jet jump can be determined by using these plots and output images together.
Results and DiscussionAfter preparing the image and numeric outputs, the start and the end days for each year jet jump were converted to Julian day and the beginning of the summer were determined by using two A-The beginning of the summer season based on the time of start of the jet jump
In this way, the start time of the summer subtropical jet stream jump for each 30 years were converted to the Julian day and then were averaged. Finally, June 1 (3 weeks earlier than the date of astronomy) was determined for the beginning of the summer in the study area.
B-The beginning of the summer season based on the time of the end jet jump
In this way, in addition to the start time, the end time of jet jump for each year also was converted to Julian day and then was averaged. This method is more accurate because in addition to the start time, the end time also is taken into consideration. According to this method, the period between June 1 to June 9 was the most likely timeframe for the start of the summer season due to the behavior of subtropical jet. The middle of this time frame, that is June 5 (about 17 days earlier than the date of astronomy), was determined for the beginning of summer in the study area.
ConclusionResults of this study showed that the climatic average beginning time of the summer does not match with the date of astronomy (June 22). It occurs since about 21 days earlier, i.e. June 1 (based on the start time of the jump) until 17 days earlier, i.e. June 5 (based on the end time of the jump) in the Middle East region. Chi-square calculations showed that this start date was not incidental. Actually, large-scale atmospheric circulation in area is in such a way that the beginning of the summer is earlier than the date of astronomy in this region.

IntroductionRivers through production, movement and storage of sediments are one of the most important factors that modify the earth’s surface. Historically, some rivers have been selected as the boundary lines between the countries and have acquired additional importance. River channels, particularly alluvial bed rivers are continuously changing and this can cause many problems. In this study, lateral movement of the Aras River, 15 km away from west of Aslanduz city to exit of the river in Iran’s border, are investigated in three time periods, 1987, 2000 and 2014. This river has a great importance in relation to water supply in the northwestern parts of the country. Moreover, in the large distances, it forms Iran boundary line with the countries of Armenia and Azerbaijan. Therefore, research on the lateral changes of river becomes necessary.
Material and MethodsTopographic maps with scale of 1: 50,000, digital elevation model (DEM) with 27 m resolution, and satellite imagery (Landsat 7 ETM satellite sensor& Landsat 8 OLI satellite sensor) are most important materials in this research. Studied channel reach of the Aras River for three time periods, 1987, 2000 and 2014 were extracted by processing satellite images. Then, channel based on morphology and changes trend was divided into 17 transects, and quantitative indicators were calculated for each transect.
Results and DiscussionThe Aras river planform in the study is mainly meandering. According to the studies, meanders of the river channel are active, and the formation of new meanders, meanders migration as a result of erosion and creation of cutoff frequently occurs with a relatively high rate. Study of lateral migration of the Aras River have shown high change in late 26 years. Even lateral migration in some parts of the third reach has been 1.7 km. The average of channel migration rate in the study reach of the Aras River is about 8 meters per year, which is a significant value. By comparing the mean values of the central angle and rate of channel migration can be said, in transects that planform is the developed meandering river, the rate of channel migration is higher. But in a few transects where the river tends to be a straight pattern, in other words the central angle and sinuosity coefficient values are smaller, the amounts of movement was lower. In the study area, according to the past changes trend, channel changes have occurred due to three major reasons: (1) channel migration in the floodplain due to the erosion of concave banks of meander loops, (2) occur cutoffs through development and near the base of meanders, which its effects can be seen as an abandoned channel, and (3) occur avulsion in the parts of the river channel. In fact, large quantities and unusual migration rate in some transects were related to the avulsion. Most likely, the avulsion caused by the river flooding, especially in the spring and disturbances are due to the confluences. In some cases, the combination of these factors associated with intervening variables such as the effects of confluences have caused the channel movement to be very significant and unusual.
ConclusionThe modification of the Aras River route in the study area (From Aslandoz to Parsabad) in 11 different reach has been done in Ardabil regional water authority. These actions in reaches: old dyke, Muhammad Rezalvo reach, Ghara Daghlou reach, Salamn Kandi reach, Alireza Abad reach, Topraq Kandy reach, West Sarband reach, Haj Hassan Kandy reach, Ozone Tape Reach, 1/53 reach, Maghsoudlou reach which trigger the release of more than 420 hectares of coastal land in Iran and have a very important role in the modification of the Aras River route and prevent lateral movement of the Aras river during the past years.

IntroductionTropical cyclones(TC) are the important hazardous phenomena in tropical zones that impact on subtropical regions of both hemispheres. These storms originate from tropical sea and oceans where the sea surface temperature is at least 27°c.
The abnormal development of two tropical cyclones (Gonu and Yamin) in June 2007 concerns to the Arabian Sea more warming than to the Bay of Bengal. Generally, the tropical storms that are formed in Arabian sea tend to move to the west and north side and rarely were entered to the Gulf of Oman but the Indian Meteorological Department (IMD) recent publications showed that a few numbers of these strong storms entered the Gulf of Oman and can impact on the Iranian coastline in north of Gulf of Oman.
The aim of this study was to compare and analyze the tropical cyclones in Arabian and Gulf of Oman structurally in order to investigate the role of the atmospheric and oceanic parameters in determining the tracks of these cyclones.
Material and MethodsAt first, according to the statistics available in the Joint Typhoon Warning Center (JTWC), the characteristics of tropical cyclones including Gonu, Phet, Nilophar, Ashoba and Chapala were extracted and the principle components of these cyclones such as wind speeds, velocity and tracks from formation to vanishing were analyzed. Also, the directions of Tc were plotted by using ArcGIS and calculated angles of azimuth.
In the next step, by using reanalyzed data from the European Centre for Medium-Range Weather Forecasts (ECMWF), the variables such as sea level pressure, 850 hp Geopotential height, sea surface temperature and surface temperature for the life periods of cyclones were extracted and by using Grads software, the maps for 5-up to 40° north latitude and 40° to 80° East longitude, was prepared. Finally, the correlation coefficient between sea surface temperature and pressure were calculated and based on the “Traction and driving” rules, similarities and differences of these tropical cyclones were analyzed.
Results and DiscussionGonu on June 1, 2007, at azimuth 270° was formed in the east of Arabian Sea, then moved to the North West and by reaching to the coasts of Oman changed direction to northern coasts of the Gulf of Oman.
The maximum intensity was on June 4 that it turned to the form of a super cyclonic storm and with peak, 3-min sustained winds reaching 240 km/h (150 mph) and an estimated pressure of 920 HP. Gonu was a Category 5 tropical storm, according to the Saffir-Simpson scale.
Phet cyclone was formed as a tropical disturbance on May 31, 2010, at azimuth 320 °.
During the second and third days of its activity, the intensity of this tropical cyclone reached to peak and was a category 2 tropical storm, according to the Saffir-Simpson scale.
This system by changing its tracks converted to a tropical cyclone on 5 June and crossed southern coast of Pakistan near Karachi. The minimum sea level pressure (SLP) has occurred in northern and southern coasts of Oman and Pakistan and the maximum temperature in the West and South West Indian subcontinent has been recorded. The northward spread of high pressure in the southwest of Indian subcontinent to accompany with a strong ridge cause the Phet TC drive to west and northwest. In the next day’s subtropical high-pressure deployment in the southern part of the Arabian Sea and Arabian Peninsula have prevented the establishment of Phet TC on the Gulf of Oman.
Niloofar TC was formed on October 25, 2014, and moved at azimuth 320° to the north west. The intensity of this tropical cyclone reached to peak and on 29 October was a category 3 tropical storm, according to the Saffir-Simpson scale.
The highest sea surface temperature and the air temperature in the TC location are respectively 28.8°c and 27°c. The correlation coefficient between temperature and pressure was negative in the onset of Niloofar TC and increase to 0.8 in the peak of activity. The wide high-pressure system in the north of Afghanistan, Pakistan and the Caspian Sea prevented the transition of Niloofar TC to the northern latitudes.
A low-pressure system at the same time with the onset of southwest monsoon was formed in the Arabian Sea on June 6, 2015, and moved to the north and northwest after the intensification. Since the wind speed of Ashoba tropical storm was not too much, it was not in the category of the Saffir-Simpson scale. The coincidence of low-pressure areas and maximum temperature at the time of deployment of Ashoba tropical storm justifies the correlation between these two factors well. High-pressure systems in the south of the Arabian Sea, north of Iran and northern areas of Arabia prevented the northward transition of Ashoba tropical storm and caused the system to be concentrated in the south of Yemen and southern Oman.
Chapala on October 28, 2015, was originated in the form of a tropical disturbance from a low-pressure area in the Arabian Sea and by a movement towards the west on 30 October reached its maximum intensity and ranking in category 4, according to the Saffir-Simpson scale.
During the Chapala TC activity, in high latitudes, the high-pressure systems were prevailing and acted like an obstacle to control the location and movement of the cyclone to the north.
ConclusionAnalysis of tropical cyclonic tracks azimuth were showed except the Gonu TC that changes its direction depending on the sudden rising and decreasing of velocity, in other cyclones these changes do not follow certain trends.
Also, in Gonu and Ashoba TC minimum pressure regions coincident with maximum temperature and there was a significant correlation between sea surface temperature and pressure.
The analysis of sea level pressure showed that according to the laws of “attraction and driving”, the cyclones tracks have been influenced by the movement of low-pressure and high-pressure centers. The Niloofar and Chapala TC, due to differences in the season of formation and the prevailing of high-pressure systems, cannot move to northern latitudes.
Finally, the establishment of high-pressure systems in higher latitudes and season have important roles in displacement the tropical cyclones to the northern coasts of Oman and Gulf of Oman.

IntroductionThe interaction between mesoscale convective cells and the synoptic conditions results in heavy precipitation which causes flooding in certain regions. Deep and dump convection, which are product of interaction of processes in various time-zone scales are different. When the moist troposphere which is conditional unstable and unstable air masses which are able to climb a medium scale systems, in that place at the moment, we can expect formation of a deep moist convection which results in heavy rainfall. Heavy rainfalls can be one of the most dangerous risks in most parts of the world as we have seen floods that killed many people around the world. In Iran and also in this region, heavy rainfalls occur in different parts of the country. But according to the country's arid and semi-arid climates and nature destruction and manipulation by humankind, such phenomena face with severe reaction, leading to severe flooding and destruction is in the range of Iran.
The Study AreaChaharmahal-O Bakhtiari province with 533/16 square kilometers occupies 1% area of the country, and is a high area which has located in spread of Central Iranian Plateau and along the Zagros Mountains. In terms of geographical location it has located, between 31 degrees and 9 minutes to 32 degrees 48 minutes north latitude and 49 degrees 30 minutes to 51 degrees 26 minutes east longitude. Nearly 80 percent of this province is occupied with mountains and hills. This mountains has 16 peaks with more than 3,500 meters high. The highest one is the Zardkouh with 4548 high in north west of the country and 4548 meters in the north west and the lowest region of the country with 800 meters high, is located in exiting part of the Karoun river in joint of the Khorasan river to the Karoun. Precipitation in this area is as much as supplying 10 percent of the country's water which can feed central parts of Iran and Khuzestan plain as 4.7 billion cubic meters of water, in a year, exits from this province and is stored in Shahid Abbaspoor, Des and Zayandehroud dams.
Material and MethodsIn this study, three sets of data, data from ground stations, data from upper atmosphere, and TRMM satellite data have been used to analyze heavy rainfall wave of Chaharmahal-O Bakhtiari. This was an 8-day study from April 25, 2009 to May 2, 2009. Data used from the upper atmosphere re-analyzed data elements of geopotential height, Omega orbital wind, meridional wind, especial humidity, relative humidity, sea level pressure that have been obtained from website of the National Center for Environmental Prediction (NCEP ). Also, we used 3B42 data of TRMM satellite for satellite analyzing. TRMM 3B42 data of seventh version of this satellite were available for anyone to use from May 22, 2012. This version has spatial resolution of 0/25 degrees latitude and 0/25 degrees longitude and temporal resolution of 1 day and 3 hours. For this purpose, at first according to data obtained from upper levels, atmospheric maps were designed and analyzed. For detecting occurred rainfall and estimating amount of it, after getting TRMM satellite's data, we prepared a data base in Excel. For zoning TRMM satellite rainfall data, some small amounts of data were transferred to GIS software and then Geostatistics model and Kriging method for precipitation zone were used, which had less error zone compared with other methods for estimating TRMM rainfall data.
Results and DisscussionDrawing and analyzing atmospheric maps showed that the pressure gradient between anticyclone located on Central Europe, the North Caspian and West China, with cyclone located on the North West of Saudi Arabia, the Persian Gulf, the West Indies and North West Africa and on the other hand, domination of cyclonic conditions in last days of study in earth with blocking event on upper levels and stretching deep descending’s of them, on studied region in 500, 600, 700 and 850 HP, and ascendance of airflow in atmosphere (negative omega) which is indicator of air ascending and reinforcement of convection flows in mentioned levels that causes extreme divergence and instability. Atmospheric eddies with negative balance of 850, 925, 1000, HP has provided favorable conditions for the occurrence of heavy rainfall. In addition, supplying and feeding of humidity by the Red Sea at levels of 500, 600, 700 HP and the Persian Gulf at levels of 850, 925, 1000 HP, and at the end, existence of all mentioned conditions with domination of strong jet stream in most parts of Iran in period of study has caused rising atmospheric divergence and instability in studied region and resulted in 551 mm heavy rainfall. Considering precipitation estimated by TRMM satellite and comparing it with recorded values by observation stations, it is obvious that TRMM satellite does not have adequate accuracy for estimating rainfall in this region. And in most stations, the estimation was more than which has been observed. The correlation and coefficient determination between them is 22/0 and 05/0 percent.According to atmospheric maps analysis, we can say that pressure differences between anticyclone in the northern area and low pressure located in south of it, along with occurrence of blocking in the upper atmosphere and also locating the region in the East, along with this, domination of negative conditions of eddy and omega in atmosphere of studied area, with adequate moisture and with association atmospheric jet streams the proper condition for precipitation of heavy cloud has been prepared . TRMM satellite rainfall estimation results of the evaluation of this heavy precipitation cloud wave indicate not so good accuracy of this satellite in this area. TRMM satellite in most stations have had surplus estimation in comparison to other recorded data. Therefore, we can say that in complex topographical areas like Bakhtiari region due to being located in the Zagros mountainous region that has complex topography, we cannot trust estimated values of this satellite.

IntroductionNowadays, tourism is internationally considered as one of the greatest economic sectors. Certainly, tourism is one of economically growing activities. In tourism systems, factors such as tourists, domestic people, the route and destination depend on external factors and the environment (e.g. socio-cultural, economic and political systems). Hence, the route which a tourist goes through from the residence to destination, tourism destination attractions, road network of the region, hoteling and fuel supply systems, visa issuing and official systems and many other factors are wholly considered as part of tourism system. For instance, the iron wall frontier between the North and South Korea in recent years has essentially halted the development of tourism system between these countries. Devastation of an airport in an island due to earthquake or storm possibly affects the development of tourism system.
Tourism hazards are defined as the possibility of occurring unpleasant events during the trip and especially at the destination. Tourism crises are defined as any event that may threaten usual activities and tourism-related operations and impact believes of tourists from fame, safety, attraction and tranquility point of view. These happenings increase cost of living and decrease entrance of tourists so that cause tourism economic loss and disruption of business activities in tourism sector. In the process of tourism development program, it is necessary to recognize and assess harmful factors exactly, so that we can prevent harming factors that cause losses in tourism system. Developing tourism is one of the issues which basically requires correct planning and management. In recent years, crisis management in tourism industry is considered as a basic requirement due to the increase of natural and political disasters in many destinations. Although crisis management researches are almost used in management and business for about 40 years, in 1990s, researchers and managers in tourism started to discuss this issue and presented methods to react against crisis in tourism industry. Generally speaking, tourism activities happen in a less-known environment and are susceptible to any event. Especial geographical environment of Sarein town has made this region as an important tourism destination in Iran. Generally, it is possible that development of tourism in this region is threatened by the hazards similar to the other tourism destinations. As a result, the aim of present research is recognition of hazards that affect the development of tourism in Sarein town and also cause crisis for development of this sector and as a result it is necessary to make suitable plan for them.
Material and MethodsIn this research a questionnaire was used to gather the necessary data. Indices that were used for gathering necessary data from previous studies include the possibility of political, legal, economic-financial, socio-cultural, environmental, technological, structural-operational and safety hazards. For each index some questions were determined and then gathered in a questionnaire.
Results and DiscussionFrom tourist´s viewpoint, the average for political risks is 2.43, from authorities viewpoint is 2.34 and finally the average for this kind of risk is 2.38. The average for legal risks from tourist´s viewpoint is 2.78, from authorities viewpoint is 3.06 and finally the average for this kind of risk is 2.42. The average for economic and financial risks from tourist´s viewpoint is 3.41, from authorities viewpoint is 3.17 and the final average for this kind of risk is 3.29. The average for socio-cultural risks from tourist´s viewpoint is 3.01, from authorities viewpoint is 2.74 and the final average for this kind of risk is 2.87. The average for environmental and sanitary risks from tourist´s viewpoint is 3.69, from authorities viewpoint is 2.66 and the final average for this kind of risk is 3.17. The average for technology risks from tourist´s viewpoint is 2.85, from authorities viewpoint is 2.78 and the final average for this kind of risk is 2.81. The average for structural performance risks from tourist´s viewpoint is 2.67, from authorities viewpoint is 3.20 and the final average for this kind of risk is 2.93. The average for safety and security risks from tourist´s viewpoint is 2.46, from authorities viewpoint is 2.94 and the final average for this kind of risk is 2.70.
For analysis of tourists and authorities’ view point with regard to risk possibility test (T), two independent samples were used. The test shows that there is not a meaningful difference between tourists and authorities’ view point with regard to the sum of risk possibility in developing tourism system.
ConclusionNowadays, tourism is interpreted as a system which is composed of different parts such as corporations, tourists, society and environment and each part of it is in relation with others. There are various factors that can threaten one or some parts of the system. This itself unfortunately has a negative impact on the whole system and causes problem for it. These threatening factors can harm tourists or the destinations. Also, occurrence of natural and social hazards in tourist regions through a decrease in the number of tourists harms tourism industry and the development of touristic destination development. The result of this research represented that the possibility of sanitary and environmental hazards occurrence was high from tourists’ point of view. Also, the possibility of structural-operational hazards occurrence was high from authorities’ point of view active in tourism sector. However, calculation of total hazards possibility from both tourists and authorities’ point of view represents that financial-economic hazards possibility with a value of 3.29 is the highest among all hazard categories. Consequently, if financial-economic hazard vulnerability of destination rises, this damages destination and causes problem for the town tourism development. The second and third highest hazards possibility values are 3.17 and 2.93 which are related to sanitary-environmental and structural-operational hazards respectively. As a result, to reduce these damages, we must make careful planning and reduce the vulnerability of tourism system. To show the difference between authorities and tourists’ view point, T test of two independent samples were used. The results of this test indicate that there is a meaningful difference between tourists and authorities view point about the sum of hazards possibility in development of tourism system.