فصلنامه پژوهش های جغرافیای طبیعی
پیاپی 100 (تابستان 1396)

Being in vicinity of vast deserts, the west and southwest of Iran are characterized by high-levels of dust events. Western Iran located in the neighborhood of some important dust sources: the Tigris and Euphrates basin in Iraq as well as Syria to the west and the Arabian Peninsula to the south. These sources are among the most active in the dust belt, especially in recent years.
Overall, sand and dust storms are the most important atmospheric phenomena in arid and semi-arid regions that have been recognized as having a wide range of environmental and climate impacts including distractive effects upon air quality and human health, agricultural activities, land use and soil formation and they are also recognized as the factor of desertification. Dust particles are important components of the earth’s climate system as they affect the balance of solar radiation by scattering and absorption. These feedbacks have a direct link with the intensity and height of the column of dust in the troposphere. The aim of current study are understanding the vertical distribution patterns of Middle Eastern Dust Storms (MEDS) associate with atmospheric circulation patterns and topography in cold period of the year (November-May) in west of Iran.
Data and The horizontal and vertical distribution of dust aerosols simulated with chemistry/aerosol module of Weather Research Forecast system (WRF-CHEM). The WRF–Chem configured with the Goddard global ozone chemistry aerosol radiation and transport (GOCART) dust emission scheme to calculate the influx of dust into the atmosphere. The effect of the Zagros Mountains on vertical and horizontal distribution of dust emission examined by WRF model in an area between 16º–44ºN and 33º–65ºE with a 30 km horizontal grid spacing. The FNL re-analysis data set were used to provide the initial and lateral boundary conditions in a control run and in a simulation run by removing the Zagros Mountains.
The atmospheric circulation pattern is investigated to explain the mechanisms of dust emission in the Middle East and its vertical emission over this region. The hourly visibility and dust dataset of 34 synoptic stations located in the western part of Iran were obtained from the Iran Meteorological Organization (in 2004-2013 period) to extract dust events in the area of study. The NCEP/NCAR 6-hourly reanalysis dataset with 2.5°×2.5° horizontal resolution is used for mentioned period.
The atmospheric circulation patterns which lead to generation of dust events in the Arabian region classifying in two categories of frontal and non-frontal patterns.
In the frontal events of MEDS that occur in the cold period of the year, dust is created under the influence of emigrate systems of westerly winds setting in the Middle East region. So that, formation of a divergence system in mid-level of troposphere (500 hPa) leads to formation of a surface convergence center as well as frontogenesis, air uplift and atmospheric instability condition in the source areas of MEDS. In addition the Polar Jetstream position as one of the enhancing factors of instabilities and air uplift in the region has a key function in vertical distribution of MEDS. Generally, MEDS events which occur due to the frontal pattern are similar to the precipitation systems except the lack of humidity in case of dust generation in arid lands of the Middle East region. Frontal patterns are divided into two patterns including Trough and Blocking. These two patterns are the dominant patterns of dust generation in November to May in this region in cold period. In frontal pattern the vertical distribution of column dust divided to two category: in first pattern the maximum height of dust is above 7 km and in second pattern the maximum height is below 4 km. These patterns to be related to the position and strengthen of Polar Jetstream, the strength of mid-levels vorticity and upward motions of air flow. In first vertical distribution pattern there is upward motion to the 9 km of the troposphere where as in second pattern the upward motion is to the 5 km of the troposphere.
In non-frontal pattern neither frontogenesis happens nor there is a polar front Jetstream which causes instabilities in the Middle East dust storm sources. So that dust generation is due to the regional circulation system in the lower level of troposphere. In this pattern the concentration of dust load is less than frontal MEDS and the maximum height of column dust is below 3.5 km.
The results of impact of topography in vertical and horizontal distribution of MEDS reveals that the Zagros Mountains have a limit effect on the vertical and horizontal distribution of MEDS. However in the absence of the Zagros Mountains and the main factor which control the vertical and horizontal distribution pf dust storm is the strength of atmospheric systems.
Two main patterns of cold period of MEDS are frontal and non-frontal patterns. The vertical distribution of column dust in mentioned patterns are different. In frontal pattern the height of dust are varied between 4 to 7 km in the troposphere. The position and strengthen of Polar Jetstream, the strength of mid-levels vorticity, upward motions of air flow and divergence of moisture flux in MEDS sources are the most important factors which determine the strength and height of dust storm in the middle east in the cold period. In non-frontal pattern the concentration of dust in the troposphere is below 3.5 km. the result of this study reveals that the important of strength of atmospheric systems is more than topography barrier in vertical and horizontal transport of MEDS in west of Iran.

The variation in the rainfall regimeincluding the striking aspects of climate change. Reduce or increase the amount of rainfall on many environmental and climatic phenomena such as runoff, flooding, air temperature, air humidity as well as the many human activities such as agriculture, type of housing and its effects. On the other hand the growing need for understanding climatic features of the necessities of human life today.
The first long-term data to identify areas of precipitation in Iran Hour Precipitation 53 synoptic stations from 1984 to 2013 were collected from Meteorological Organization. First to obtain day precipitation and more precipitation 7 categories 1/0 days of rain, the daily and hourly to precipitation rainy days spread over 7 floors, 1-day precipitation, precipitation sequence, two days, three days or annual precipitation sequence 7 days and was extracted sequences reviews. To perform cluster analysis and zoning of the Euclidean distance and Ward method to minimize the sum of squared deviations in the relevant classification groups. In order to study the characteristics of both indices rainfall coefficient of variation and uniformity profile is used.
Results and Discussion In this study, using the characteristics apply cluster analysis showed that 7 days of rainfall and precipitation regions in the country. As the number of rainy day, rainfall, rainfall distribution is different in each of these areas. Region 1 area dry with sparse rainfall, storms and rainfall when the distribution is irregular density. This region has the highest spatial variability and the highest coefficient of variation is annual. Only in the summer because of the the summer monsoon rains, the coefficient of variation has experienced less rainfall distribution is appropriate. Unlike the summer, the autumn had the highest share of rainfall. The area of the temporal distribution of rainfall and uniformity rainfall, the lowest annual index is uniform. Region 1 includes stations Bandar Lengeh, Konarak, Chabahar,Zahedan, Bandar Abbas, Bam, Zabol Iranshahr and Kish. The second Region is a dense irregular rainfall. This region has the highest coefficient of variation is the summer season. In this Region due to low and sparse showers, rainfall has been high Temporal and spatial variations of rainfall. Region2, the area includes stations in Abadan, Yazd, Tabas, Fasa, Bushehr, Kerman, Birjand and parts of East, Central and parts of South West Iran is included. .The Third Region includes the coast of the Caspian Sea, which has the lowest coefficient of variation each period and yearly. Region 3 includes the Caspian Sea coast which has the lowest coefficient of variation each period and yearly. The temporal distribution of precipitation areas is almost uniform density. region 4, covering the North West and North East Iran, that includes the stations of Ardabil, Gorgan, Pars Abad, Khoy, Bojnord, Tabriz, Quchan and Shahrud. It has precipitation regions in three ten-year period when distribution is uniform. In the spring due to Azerbaijani and distribution of spring rains this season than the coefficient of variation is the lowest was seven. Region Rainfall 5 has a high coefficient equal to 146 and lack of uniformity is the uniformity index is equal to 52 which represents the precipitation average density. Region 6 Tehran stations, Dowshan tppeh, Maragheh, Karaj, Qazvin, Shahrekord, Mashhad, Orumiyeh, Zanjan included. This area equal to 111 and uniformity coefficient of variation is equal to 59 and indicates a lack of proper distribution when precipitation. In other words, is the average density areas. Region 7 West and South West regions of the country, mainly in the covers. Features irregularity of rainfall and precipitation medium density in this Region of the country. Region 7 includes stations Hamedan, Yasouj, Ilam, Khorramabad, Arak, Hamedan Nojeh, Kermanshah and is Saqez.
Seasonal investigation showed that the coefficient of variation of rainfall in the winter when precipitation system of the country to reach their maximum value and almost all parts of the country are included and have appropriate uniformly distributed rainfall. Coefficient of variation of summer, the rainy season in most areas of Iran has been discontinued, the reason why the coefficient of variation All areas have reached their maximum value. The coefficient of variation region1 declined to the central regions of Iran. By comparing rainfall areas in terms of spatial and temporal variability in three decades, the study found that, compared with the average coefficient of variation of the second decade of the first and third reduction. What consideration is being discussed, the increase mean coefficient of variation third decade. By comparing the uniformity index of rainfall Region in three decades, was found that changes in rainfall zones 5 and 4 were unchanged in three ten-year period. average uniformity precipitation index (H) All regions showed that in the second decade, index H, has increased compared to the first decade, but declined in the third decade of this index. This indicates that the temporal distribution of rainfall Iran has experienced in the third decade of focus. In other words, most rainfall areas, Showery, short-term and focused and limited to certain times of the year.

More than 30 percent of the Earth is covered by seasonal snow and about 10 percent of it by permanent glaciers. Approximately about 5 percent of global precipitation is snow. This amount to about 50 to 95 percent is in polar areas. Spatial-temporal distribution of snow is important. The estimation of snow cover area provides valuable information on snow-melting water in terms of runoff and water supply of the watershed in mountainous regions. Snow is an important geophysical factor for climate through its role in the Earth’s albedo and for hydrology. It has potentials for water storage, agriculture, hydropower generation, and flooding in local scales. Snow creates an insulating to keep plants from the cold weather in winter. Therefore, it is necessary to study the snow cover area, snow depth, and snow water equivalent. The snow cover area is affected by environmental factors, leading to different melting patterns which are important for assigning a deterministic model.
The importance of snow has been recently understood by scientists and watershed managers resulted in different snow studies. Applying remotely sensed data for such studies is cheaper faster and easier than traditional approaches. One can also study larger areas using these data which is more beneficial. Snow cover area is the most accurate factor of snow which can be estimated using remotely sensed data. Different sensors have been applied to study snow areas which have their own advantages and disadvantages. Earth Observing System (EOS)-Terra was launched with 5 mounted sensors on December 18, 1999. One of the 5 sensors in EOS is MODIS. The sensor was embedded on Aqua satellite launched on May 3, 2002. Terra is a sun-synchronous satellite, elevated at 705 Km, having polar orbit. Terra passes occur at roughly 11:00 – 12:00 AM and 10:00 – 11:00 PM local standard time each day. MODIS is the biggest sensor in EOS. Its mission is to measure temperature, ocean color, vegetation and deforestation, clouds, aerosols, and snow covers. Different ground resolution, the capability of distinguishing cloud from snow, and provide complete coverage of the Earth. Therefore, this sensor has very high potentials in snow cover studies. The sensor has a radiometric resolution of 12 bites, spectral resolution of 36 bands from 0.4 till 14.4 µ. It also has a high temporal resolution (Repeating cycle of 1 to 2 days), and moderate spatial resolution (250, 500, and 1000 m).
MODIS satellite images are used for estimating snow cover area in this study. In this research, two common techniques including Normalized Difference Snow Index (NDSI) and Linear Spectral Unmixing (LSU) were used. In order to determine the accurassy of NDSI and LSU approaches (MODIS images), the IRS images were selected since their spatial resolution is very high (24 m). The MODIS pixels were interpreted as snow using Snow map algorithm. A number of 11 similar sites on MODIS and IRS were selected to compare the results. The snow area of MODIS images (NDIS and LSU) were compared with the corresponding value on IRS images using t-student test and regression coefficients. A scatter plot of non-snow against snow was used. A regression model was established for the same purpose.
The scatter plots of snow areas produced by crossing IRS versus snow area estimated by NDSI and LSU approaches were separately studied. The regression model of each scatter plot was then calculated. The results show that both NDSI and LSU methods have high efficiency to compute snow cover area; however, the LSU method shows a little more efficiency than the NDSI method. Another comparative investigation over the NDSI and LSU methods was performed by t-student test with significant level of 5%. The t-student test indicated that the LSU method has a higher potential in estimating snow cover area in the study area than the NDSI method.
The use of remote sensing techniques, satellite images, GIS, and statistical methods for studying and monitoring ground features such as snow is very beneficial due to their lower expenses and ease of use. Among them, high temporal and spatial resolution images are preferred. Due to the importance of snow in the study area, the snow cover area was computed using MODIS and IRS satellite images to determine the best approach. The results showed that to use the methods which apply subpixels to calculate snow cover area is more appropriate. The study reveals that remote sensing techniques can provide reliable information on snow and can overcome the problems stemming from traditional approaches

With 6.8 percent oil reservoirs, Zagros is one of the most prolific oil sedimentary basins. The greater part of its hydrocarbon reservoirs are concentrated in anticlinal traps, which they are also the subsets of the structural traps. In addition to rich and vast hydrocarbon reservoirs, Zagros have been also considered well in terms of it's the Neotectonic activities. Studies of Neotectonic activities as an important factor in the control of landforms in tectonic regions, Apart from its social and economic interest, studies of active tectonics follow a multi disciplinary approach, integrating data from structural geology, geomorphology, stratigraphy, geochronology, seismology, and geodesy.The unrelenting competition between tectonic processes that tend to build topography and surface processes that tend to tear them down represents the core of tectonic geomorphology.Since so Most effective morphometric indices have been related to erosional and depositional processes associated with fluvial systems.Rivers are highly sensitive to subtle landscape fluctuations induced by tectonic activity and can assist in differentiating active segments of geologic structures. Because Drainage basins represent dynamic systems that may retain records of formation and progression since most tectono-geomorphic processes occur within its confines. Therefore, Morphometric analyses of river networks, drainage basins and relief using geomorphic indices, as well as geostatistical analyses of topographical data, have become useful tools for investigating landform evolution. In recent studies related to morphotectonics, a mixture of geomorphologic and morphometric analyses of landforms and topographic analyses are utilized to obtain active tectonics and they have been tested in different tectonically active areas and provide insight about particular areas that are subject to active tectonic deformation.Therefore, since so many of geomorphologic effects are highly susceptible to tectonic movements and their changes are happening at the same time, we should be looking for forms and shapes that have retained these changes over the years. With regard to the abovementioned matter and using geomorphologic indices, the current study attempts to consider Neotectonic activities and its impact on the positioning of the oil fields in the Jarahi and Zohre sub-basins.
In order to achieve the goals of this research, documentary information,1:50000-1:25000 topographic maps and 1:100000-1:250000 geological map, a digital elevation model (DEM) related to SRTM topographic data and landsat 8 satellite images have been the important research tools. For the analysis of Neotectonic activities in the case study area, have been used such geomorphologic indices as Stream Length-Gradient index, River Sinuosity, Relief Amplitude, Hypsometric Integral, Basin Shape Factor and Drainage Basin Asymmetry Factor Index.Arc GIS software was used to digitize the topographic maps and drawing of river networks for calculating these indices.
Results of the calculation of geomorphologic indices: The SL values in the study area range from 0 to 573, The S values in the study area range from `1.1 to 2.46, The RA values in the study area range from 31 to 3254, The HI values in the study area range from 0.04 to 0.56, TheB_Svalues in the study area range from 0.19 to 2.49 and The |AF–50| values in the study area range from -28.83 to 32.59. The classification used in this paper for each geomorphic index is calculated from El Hamdouni's method.According to Relative Tectonic Activity (lat) index, three class high active(1.6 ≤ LAT The obtained quantitative values from the results of the geomorphic indices in the 38 sub – basin led to divide the studied basin into three tectonic areas with low, medium, and high tectonic activity. It was also shown that the Neotectonic activity level in different parts of the basin is not the same and the forces act with greater intensity in the eastern half. This activity caused to more oil fields of Jarahi – Zohre basin, i.e. 61.6 percent located in the region with the lowest Neotectonic activity. In fact, being in the lowest Neotectonic Class acted as the factor to emerge the oil traps and to maintain the hydrocarbons. On the other hand, in the areas with the highest Class of Neotectonic, there was virtually no oil field. The results indicated that Neotectonic has important role in the running or migration of oil traps and the extent of tectonic is necessary to create small fractures to oil running and finally oil production. In fact, it can be attributed to Neotectonic both destructive and inhibiting role in addition to constructive and transferring hydrocarbons.

Nowadays, along with other social and cultural attractions, the geomorphological and climate ýattraction, curative waters and caves, and water stream have the special importance for ýeconomical studies and policy making. Geotourism is a new approach to explain the earth ýplanet and its natural capital. In addition to educational and scientific roles, it can cause to ýdevelopment of regional tourism and offer strategies for sustainable development in the ýgeotourism sites. Geotourism is one of the new area in tourism that follow tourism principles ýcompletely and compose of geology, geomorphology, natural landscapes, landforms, stones ýand minerals with emphasis on processes that create these shapes. This branch of tourism ýintroduce the phenomenon result from geology and geomorphology to tourists by observing ýthe international rules and standards along with keeping the local identity and also arrange and ýorganize this treasure observation and preventing from it’s destroying by human and pave the ýway for region developing. Therefore, it emphasize on a set of geographical, geology, bio–ýenvironmental, cultural and ancient in heritage characterizations and including every part of ýland surface that relate to better perception of land meaning and it’s history. It is necessary for ýdeveloping the geotourism of each region to identify the various geotourist attractions such as ýdesert, coastal, volcanic, mountainous regions and it’s development need to programming and ýcost spending that finally result in developing the region geotourism. This activity, not only ýhave economical, ecological and cultural – social benefits but also provide the employment of ýextensive range of students in mine, bio–environment , geography, geology areas and etc. ýGeomorphotourism is an approach that emphasizes on the use of geomorphological and ýgeological features and their ability with a focus on saving these features and forms and ýsustainable use of them (Reynard, 2008: 226). This approach also places an emphasis on ýmaintaining the geographical identity (Ranjbar, 1388) and referring to relationship between ýgeotrourism and historic - cultural signs and reminders (De Waele and Melis, 2009: 578ýþ þºPanizza and Piacente, 2008)ýþ þand also interactions between geomorphology and tourism .That ýeventually would be related to human activities and the history of the human life..So, ýgeomorphotourism is a combination of tourism goods, services, and foundations that are ýpromoted in the specific region and cultural elements which are related to them (Reynard, ýý2008: 225).Geomorphosite assessment is an issue that all geographers are interesting to ýfocusing on and developing it (Comanescu et al., 2011: 1164).The various studies have been ýcarried out in internal and world level about estimating the geomarphosites in two past decade ýand at the present they are doing with evolutional trend. In this research, the capability of ýlandforms result from salty diapirism in developing tourism in Semnan have been estimated. ýSemnan have been known as a salty tourism pole but unfortunately, there isn’t any research ýabout tourism result from salty diapirism in this city and even Iran.
Semnan province is located between 51◦ 51′ 51″ E and 57◦ 03′ 00″ E from prime meridian and ýý34◦ 13′ 00″ N and 37◦ 20′ 00″ from Equator. In this study, descriptive - analytical indicators ýand brilha method are used to analyze the data. Salty Geomorphosites of Semnan Province, ýwith using the satellite image processing and combined with topographical and geological ýmaps identified, then with help of library and field studies, geomorphological Properties of ýthe sites based on brilha method had been studied. The instrument used in this study consists ýof Digital Elevation Model (DEM), ETM Satellite Images, IRS Images, and Topography Maps ýin scale of 1:50000 produced by Iran Geographical Organization of the Armed Forces and ýGeological Maps in the Scale of 1:150000. In the first stepýþ þý35 cases of the most important ýattractions of geo-tourism features in the Semnan province was selected then value and ýcriteria according to brilha method determined and then each geomorphosite evaluated. Brilha ýmethod is a Quantitive technique to primary evaluation of geomorphosites from the view ýpoint of planning and sustainable management of natural heritage sites and turning them into ýtourist destinations. This method include 4 criteria such as Scientific, Educational, touristic ýand degredation risk and 37 indicators.ý
Landforming is one of the tectonical effects of salty diapiar in large and small scales. ýGeomorphological landform is a geomorphological event that have scientific, cultural – ýhistorical , geology and social – economical values according to human identification. In this ýresearch salty capabilities of Semnan province in order to geotourism development evaluated. ýFrom the salty landforms finally 35 geomorphosite in the semnan province in order to ýevaluation, selected. In order to select these features, some criteria such as ýRepresentativeness, rarity, integrity and Scientific Knowledge had been considered. Results ýshow that in the Scientific and Educational Criteria, Geomorphosite of southern of Semnan ýSalty dome get the highest value (3.8, 3.73) and then is placed on the first order, in the ýtouristic criteria, Geomorphosite of Kohdasht Kohan Salty dome get the highest value (3.63) ýand then is placed on the first order.ý
ý
In this research , we tried to estimating the capability of salty domes geomorphosite in the ýSemnan Province from geological and geomorphological point of view by using brilha ýmodel. Results show that salty geomorphosites have high scientific, protective and aesthetic ývalues but from the view point of tourism services and foundation are faced to several ýproblems and there isn’t enough facilities in this field. High protection level in this area is not ýrelated to administrative and scientific protection but is related to lack of awareness of these ýgeomorphosites and this means that officials and planners in the field of introducing the ýdesert geomorphosites of the Semnan province Limited efforts have been done.ý

Debris and rocky hillsides are one of the important and typical land forms in arid and semi- arid regions. In this study, the relation between the hardness of Aghajari sandstone with producing the debris has been surveyed by Selby Method (1981). Selby Method has 6 parameters including: Schmidt Hammer hardness, joint width, joint spacing, continuity joint, orientation of joint toward the slopes, and the degree of weathering. Each parameter is categorized to 5 classes as very loose, loose, medium resistant, resistant, and very resistant. Porosity factor was also added as the sixth factor in the revised method32 samples were collected from 4 transects (each consisted of 8 samples, A to H). Schmidt Hammer Model N and ISRM (1978) standard has been used and Debris landforms were prepared with 1:25000 topographic map, Arc/GIS and surfer software. The results show strong and direct relation between the formation of debris and Schmidt Hammer hardness, weathering and continuity of joint. There are also intermediate relation with conjunction joints and weak relation with joint spacing however no relation with joint width can be seen. Moreover, the layer A (The oldest one) has the lowest resistance and hardness in Aghajari Sand stone.
Relatively, sandstone rocks display landscape features obviously. Debris on the slopes is one of these landscapes, for example rock fall and rock topple are a kind of triggers for create these landscapes. Debris is characteristics of arid and semi-arid region. Geology and climate are the main mechanisms for generate of debris. Weathering, gravity, earthquake, joints and temperature fluctuation are another mechanism. To be used Selby (1980) method, in order to determine the affective factor on debris. Also to determining of resistance rocks, Schmidt rebound hardness (SRH) is so practical. So far In the Geomorphology and geology, More than half a century used of SRH in its researches. In addition to the Selby method, we used of porosity to determine of rock hardness in this paper. The aim of this study is the effect of rock hardness characteristics on generates of debris based on modified Selby method on Agha - Jari sandstone in southwest of Iran (Masjed - Soleyman).
Material and In this study, In order to obtain samples and estimate of SRH, we took samples along eight layers which are named A to H. Samples A1 to A4 were taken from the oldest layer and samples H1 to H4 belonged to the youngest one. Because the thickness of layers varied from place to place, the sampling interval changes from 50 to 150 meters. Landforms geomorphic map of the study area was prepared by Freehand software via using 1:25,000 topographic maps of Iranian National Survey Organization. Geological data, such as lithology and contacts of the Aghajari sandstone layers, were derived from 1:100,000 geological maps of Geological Survey of Iran. Also we estimate on the field some factors Selby method such as: Width of the joints, the spacing of joints, lateral or vertical continuity of the joints, Orientation of joints with respect to the slopes, State of weathering of the rocks. In following, thin section samples were prepared from fresh rock samples. After preparing thin sections, porosity was determined via point counting by counting 400 points in each sample.
For understanding the effect of each Selby factors with debris, we comparing all factors to amount of debris on each layer, B, C, D and F have the most debris in throughout layers and A layer don’t any debris. Results showed have directly relationship by increasing between SRH and amount of debris with a high correlation (89%). Another factor showed respectively: Width of the joints: non correlation, the spacing of joints: low correlation (21%), weathering: high correlation (87%), lateral or vertical continuity of the joints: high correlation (83%), Orientation of joints with respect to the slopes: almost high correlation (63%) and porosity with mid correlation (56%).
Results this paper showed respectively high relationship between debris and Schmidt rebound hardness (SRH), weathering and lateral or vertical continuity of the joints and low relationship between the spacing of joints and non-relationship between widths of the joints. Porosity and Orientation of joints with respect to the slopes have mid relationship with debris. Also A layer (the oldest layer) is weakest layer in through layers. B, C, D, E, G, F layers are resistances and H layer (the youngest) is mid hardness.

Spatial Analysis Degree Days cooling Iran in the coming decades
It into one of the most important issues of climate change and variability of atmospheric sciences, ocean and environment has become. Nowadays, many researchers have been attracted to global warming and climate change. One of the aspects of climate change is global warming. Today, evidence of global, including an increase in temperature measurement, fluctuations in energy input to Earth, increasing ocean temperatures, changes in the melting lakes and changes in settlement patterns of plants and animals are at a higher latitude .Modeling the past, present, and future weather parameters, especially the day of fundamental importance in relation to climate change and variability for more accurate algorithm parameters studied, RegCM4 regional climate model used for Downscaling data to lower resolution. Using local climate is thriving in the last two decades, these models based on dynamic relations atmosphere boundary layer, surface topography, atmospheric chemistry and aerosols, ocean current flow, aqueous coating, plants and soil surface Downscaling local climate data to their low resolution. One of these climatic parameters influencing the occurrence of global warming, temperature, especially the day. the average daily temperature is measured using the threshold at which the selected temperature thresholds for calculating cooling degree days, depending on the specific objectives are One of the methods of data analysis, spatial analysis.
First, the average daily temperature model, (EH5OM) was simulated. Given that this area of research later (Iran), the data in the fourth edition of the regional climate model (RegCM4) that are better suited for small scale micro-scale processes output Downscaling model with dimensions of 0/27 * 0/27. The latitude is about where the dimensions of 30 x 30 km area covering Iran. After the simulation, the average daily temperature for a period of 36 years (2050 -2015) was extracted by model. In this study, cluster analysis and analysis is used to study cooling degree days. Cluster analysis and the local Moran insulin index is also known, is a model optimized for displaying statistical distribution of phenomena in space.
that require cooling in warm months for the whole country increases, the size of these parameters has been in the country for neighboring units. But in the cold months cooling needs in the country is decreased significantly. In the months April, May and June (spring) this parameter is the highest merit. This is because almost similar matches leaps cooling degree days is the total area of the country. Moran spatial autocorrelation showed the world just sort of pattern is clear. In order to show the spatial distribution pattern of spatial distribution of cooling degree days during the period of the local Moran has been used. In the spring, the plains and the southern coast, Dasht , bar and pits Jazmurian South East (at a significance level of 99%) were positive correlation with the pattern of hot and humid or the points at April 20, May 28 and June 29% of the range across the country are low and warm, moist air of the need for this parameter in these areas is maximized. Bar mountainous, foothills and plains interior and north coast in April also 47, is a 43-June by 36% (at the 99% significance level) negative correlation with the pattern of dry and hot or requires less cooling than areas have the South. The outer foothills and desert in this season are no significant pattern.
The results showed that the method of global Moran Moran index for warm months (March, April, May, June, July, August, September) is higher than 90 per cent of this represent a broadly based index World Moran, cooling degree days in Iran during the study period in the warmer months of the year, with high cluster pattern on the surface of 90, 95 and 99 percent. While in the cold months down the cluster pattern indicative of the low index levels are mentioned. As the global Moran index only determines the type of pattern, so to change the spatial autocorrelation cooling patterns of the local Moran index () and analysis of hot spots () was used. It can be said that the country's mountainous strip in the first half year saw a negative correlation pattern (cluster down) and the plains and the southern coast has a pattern of positive correlation (positive cluster) are. In the cold months, the majority of regions of the country except the southern coastal areas are no specific pattern. In the spring and summer plain and the southern coast, , bar and pits Jazmurian South East (at a significance level of 99%) have a positive correlation pattern, which is hot and humid or hot and humid weather of the maximum width of the bottom and the need for this parameters are in the area. Bar mountainous, foothills and plains interior and north coast in April also 47, is a 43-June by 36% (at the 99% significance level) negative correlation with the pattern of dry and hot or requires less cooling than areas have the South. The results of this study could be a model for other climatic parameters of Space Studies. For spatial statistical studies before the new valves open .

Linking resolution global climate models with local scale is a micro climatic process that itself is a significant issue. Recently, attempts have been made by the climatology community to develop dynamics and statistical downscaling methods for expressing climate change has taken place at a local and regional scale. Two general techniques are used for downscaling of the output of general circulation models (GCM). The prior is using of statistical methods in which the output of a statistical model (MOS) and a planned approach to weather short-term numerical prediction is presented. The second is regional climate model (RCM), that same is limited GCM model in a subnet of the network global model and by dynamic method uses climatic conditions temporal changes according to GCM model. Both methods Play an important role in Determine the potential effects of climate change caused by increased greenhouse gas emissions. Much work is done to use this method for downscaling of the global model output in different areas In which the performance of the model is assessed and uncertainty analysis has been done on these methods or were compared by other statistical methods.
In this study, three approaches to statistical downscaling methods are provided. The first approach uses random generation of climate models based on time series and Fourier series delivers. LARS-WG statistical model(Rskv et al., 1991, 27) is one of the ways is built on the basis of this approach,. In this model, the empirical distribution of daily series of dry and wet precipitation and solar radiation use is desirable. The minimum and maximum daily temperatures as the daily stochastic process with mean and standard deviations are taken daily. Seasonal cycles by means of finite Fourier series are of the order of 3 models and model residuals (model errors) is approximated by a normal distribution.
The second approach is regression model or transfer function that is more used, which uses the relationship between atmospheric parameters and synoptic (predictor variables) and climatology Parameter that it is necessary to have a vision of the future(Instant predictor variable) is a transfer function is provided. One of the applications that combines these two approaches based on statistical downscaling model (SDSM) is. The meteorological station data required as input and output in seven steps GCM model on the basis of daily data in the area are downscaled.
The third downscaling model is artificial neural network (ANN), developed by Coulibaly et al., 2005. This model is a non-linear regression type in which a relationship is developed between a few selected large-scale atmospheric predictors and basin scale meteorological predictands. In developing that relationship a time lagged recurrent network is used in which inputs are supplied through tap delay line and the network is trained using a variation of backpropagation algorithm (Principle et al., 2000). A slightly different approach is used in selecting predictors for the case of neural network downscaling.
To compare data generated models and observations can be compared to an average of two non-parametric test Mann-Whitney society that is using correlation analysis. For the observed data and the model can be generated from correlation Spearman used. The basic correlation analysis based on linear correlation coefficient of the two variables. One of the important indicators that can be used for performance evaluation model, index model mean square error (RMSE) is defined as follows:The area North West of Iran, which includes the provinces of East and West Azerbaijan, Ardebil, Zanjan and a part of Kurdistan is the geographical coordinates '30 ˚49 '07 ˚44 to the East and the '00 ˚36 to '50 ˚39 North, is located. To study the effects of climate change in the region, using statistical models mentioned the need for a minimum period of 1961-1990 is based. In addition to the complete statistical period synoptic meteorological stations of old climate data confirmed the country's Meteorological Agency has been helping though some regional stations are multi-year statistical vacuum.
The results show that according to the Mann-Whitney test the performance of three models for minimum temperature in the study area are close. Spearman correlation test results for minimum temperature show that the number of correlation, in all stations for LARS-WG model is less than the other two and demonstrate low performance LARS-WG model is in this respect. The average number of months with significant correlation for ANN model with seven months of the year, the best performance among the three models in this respect. SDSM model with a four-month correlation table in the middle. In terms of RMSE index for the minimum temperature, LARS-WG and ANN models have average values are close together and show the error of sum of squares closer together the two models. RMSE values are less than the SDSM model and this shows the SDSM model is less than the other two models.
According to our evaluation, according to Mann-Whitney test data generated in which the difference between the observed and tested model placed, Parameters for minimum and maximum temperatures, three models have not different performance. But the results were somewhat different in different stations. Correlation data for SDSM and ANN models for maximum high temperature and minimum temperature for solidarity in SDSM model is less than ANN model. However, because the same structure prediction methods and large-scale use of such an outcome was not unexpected.
Mann-Whitney test for precipitation results show that significant differences observed and modeled data for ANN model is much more than the other two, which reflects the low performance of this model. SDSM and LARS-WG model and have similar good performance in this regard. The Spearman correlation test, all three models have a low correlation was observed and the model and represents the three models in the study area in this respect is low. According to the RMSE, the SDSM model is better than the other two models have average performance.

Global warming and climate change have been identified as the most important challenges of the current century. Methane as one of the most important greenhouse, accounted for about 18% of the total increase in radiative forcing due to long-lived greenhouse gases in the atmosphere. The average CH4 concentration (XCH4) was 1808 ppb in 2010, which represents an increase of 158% from approximately 700 ppb in the pre-industrial era. Satellite observations with continuous monitoring can be used to provide the extensive information on the temporal and spatial variations of atmospheric CH4 concentration. The Greenhouse Gases Observing Satellite (GOSAT) as the first satellite in orbit dedicated to observing greenhouse gases has provided extensive research opportunities for applications using space-based greenhouse gas measurement. The main objectives of this study are investigation of methane concentration trend changes and amplitude in XCH4 from 2009 to 2015 in Iran using GOSAT data and assessment the relationship between XCH4 and Meteorological parameters obtained from weather stations and MODIS products for the year 2013 on the study area.
Study area The study area IS IRAN which located in Middle East Asia between between 25°-40° N and 44°- 64° E, covering approximately 1645000 km2 .The location of the study area is shown in Figure1.
Data: The GOSAT was launched in January 2009 which is a joint effort of the Ministry of Environment (MOE), National Institute for Environmental Studies (NIES) and Japan Aerospace Exploration Agency (JAXA). It is equipped with two sensors: The Thermal and Near-infrared Sensor for Carbon Observation Fourier Transform Spectrometer (TANSO-FTS) and the Cloud and Aerosol Imager (TANSO-CAI). MODIS (Moderate Resolution Imaging Spectroradiometer) as a key instrument aboard the Terra and Aqua is one of the most reliable data sources at the global scale. Terra MODIS and Aqua MODIS are viewing the entire Earth's surface every 1 to 2 days, acquiring data in 36 spectral bands, or groups of wavelengths (see MODIS Technical Specifications).The meteorological parameters (temperature, humidity and precipitation) used in this study were obtained from the Islamic Republic of Iran Meteorological Organization (http://www.irimo.ir/).In this research, GOSAT TANSO-FTS level 2 data, MOD13Q1 and MOD11C3 products of MODIS satellite, meteorological parameters (Temperature, Precipitation and Humidity) for 2013 were used.
Statistical analysis: GOSAT data, MODIS products and meteorological parameters value were analyzed in SPSS statistical program. The correlation coefficient was calculated to investigation the relationships between CH4 concentration and used variable (temperature, precipitation, humidity, NDVI and LST). Analysis of Variance was applied for investigation of difference between XCH4 concentration in diffrerent years.
In this research, the CH4 concentrations value was calculated using TANSO-FTS sensor from 2009 to 2015 in whole of the study area. The results show a steady increase in the mean atmospheric XCH4 from 1788.36 ppb in the year 2009 to 1823.45 ppb in the year 2015 which illustrate an increase of about 35.09 ppb for a 6-year period. To assessment the monthly changes of CH4 concentration, the monthly average concentrations of this gas from 2009 to 2015 were calculated. The results reveal that CH4 concentration varied significantly between different months, with the highest concentration of XCH4 in October-September and its lowest concentration in March –April. According to the results, the coefficient of correlation between CH4 concentration and MODIS products showed that the correlation of this gas with NDVI and LST was negative and positive, respectively. As correlations coefficient for NDVI is -0.526, -0.138, -0.186 and -0.322 for spring, summer, autumn and winter, respectively. The correlation coefficient between XCH4 and LST is 0.6, 0.223, 0.458 and 0.634 for spring, summer, autumn and winter, respectively. Moreover, the coefficient of correlation between CH4 concentration and metrological parameters indicate that correlation of this gas with humidity and precipitation are negative (r humidity= -0.479, r precipitation= -0.505) and the correlation between this gas and temperature is positive (r=0.484). Its means that CH4 concentration will increases with increases in temperature and LST, and decrease in precipitation, humidity and NDVI.
The satellite monitoring of CH4 concentrations showed increase at about 35.09 ppb over time from 2009 to 2015 in the study area. We observed that the XCH4 varied significantly between different months, with the highest concentration of XCH4 in October-September and its lowest concentration in March –April. This amplitude is related to different source and sink of methane in different seasons. The correlation between this gas and NDVI, precipitation humidity was seen to be negative, and correlation between this gas and LST, temperature was positive. So it is necessary to conserve the natural ecosystems in whole of IRAN especially in arid and semi-arid regoins for decreasing CH4 concentartion.

In recent years due to the benefits of conservation tillage and also disadvantages of crop residue burning, extension and education of conservation tillage, has been highlighted on the agenda of agriculture policymakers. In this regard, for farmers who use conservation tillage or crop residue burning on their farms considered subsidies or crimes respectively. Lack of information, cost, and time consuming of information gathering from the farm using conventional methods, led to the poor performance of law enforcement. Therefore, present research was carried out to find an accurate and fast method for monitoring the residue management. In this research, the ability of Landsat-8 satellite imagery for monitoring of burned fields was evaluated using spectral indices and linear spectral unmixing analysis.
The present research was carried out in the Orzooiyeh region of Kerman province. For conducting the experiment, an area with approximate size of 25 square kilometers is considered and 10 farms (include burnt residue of wheat or corn) were selected randomly in this area as 10 replications of experimental plots. Images were downloaded from the landsat-8 website and all features were extracted from images using ENVI software. On the other hand, the data of real burned areas on the farm were collected using handheld GPS device and also the exact date of residue burning was recorded directly on the field. The maps of experimental farms were prepared using ArcGis software. The correlation between data of real burned area on the farms and ENVI extracted data of burned areas were studied and real burned areas were expressed as a function of burned area that extracted from satellite images by a linear regression curve. Finally, the accuracy of regression functions and correlation between real data and satellite data were calculated. For this purpose, spectral indices include; Normalized Difference of Vegetation Index (NDVI), Burned Area Index (BAI), Normalized Burn Ratio (NBR) and Normalized Burn Ratio Thermal (NBRT) was created for experimental lands and four soil surface condition as experimental plots were considered include; no planted field, residue covered field, green vegetation field and burned residue field.
In the present study, because of extracting the pure spectral data of soil and residue, directly from Landsat-8 images, spectral unmixing analysis was not sensitive to the spectral changes that caused by conditions such as moisture content of soil and plant residue (Pacheco and McNairn, 2011). The average value of BAI index obtained 88.39, 9.29, 4.20 and 6.87 for burned residue field, no planted field, residue covered field and green vegetation field respectively. As can be seen, the average value of BAI index for burned residue field is significantly higher than values for other soil surface conditions. This difference is because of the very low percentage of spectral reflectance of ash in the red and near-infrared bands (Alonso, et al., 2007). Therefore, BAI index was selected as an indicator to distinguish between burnt residue and other three surface conditions in the farm. The result showed, there is a significant difference between means in four soil surface conditions of studied indices. Also, the results showed that the BAI index can be used as a good indicator for separation of burned fields. By the BAI index, location and area of trial burned farms were determined with higher accuracy than other indices. The area average of burned fields that had been separated from other fields using BAI index had high correlation (R2=0.95) with ground-truth data. Also, the area of burned fields that had been estimated by linear spectral unmixing analysis had a good correlation (R2=0.89) with obtained data from the ground-based method.
According to the results, BAI index had most accuracy for estimating burned area of farms and BAI index proposed for separation and determining the area of burnt fields. However, there is a slight error in estimating burned area using spectral indicators and linear spectral unmixing analysis, due to pixel nature of satellite images, basically. Since there is only one spectral data for each pixel of satellite images, spectral data of pixels that are more than the threshold value, are considered as the burned pixels while it is possible, only the part of pixels has been burned, it would be overestimating the actual amount of burned area. And for spectral data of pixels that are less than the threshold value, are considered as unburned pixels while that may be part of pixels is burned, it would be estimating the burned area less than the actual amount.

In recent years the development of cities, have been created changes in the climate. Such changes, the sustainability of the natural environment and the rate people's health is affected, especially in cities. If the human body to be in an environment warmer than the the skin surface, begin to absorb the heat, and in the colder environment, gradually loses its heat. In addition, the air moisture has affecte on evaporation capacity and amount of cooling by evapotranspiration. At 20 to 25 degrees Celsius, the air humidity almost no effect on human and relative humidity of 30 to 85 percent, practically not felt. At more than 25 ° C, the effect of air humidity on the human being gradually increased. Because the hot and humid conditions evaporation and transpiration of Human body had reduces and lead to nervous tension. On the other hand dry air also creates problems for the respiratory mucosa (Kasmaee, 2103). Increasing duration of the heat also has a significant impact on the daily mortality (Laschewski, 2002). Duration of cold and heat also has affects on resonance of some diseases. So that in tropical climates, coronary heart disease during cold periods has shown a significant increase (Barnett et al., 2004). In the present study, Kermanshah bioclimatic conditions were identified using several indicators. Relationship between bioclimatic conditions that determined by each of those indices with cardiovascular disease admissions in Kermanshah was evaluated individually.
In this study, two types of data to assess the bioclimatic conditions and their relationship with Kermanshah cardiovascular admissions was used. In other words set of climate variables synoptic station of Kermanshah and cardiovascular admissions Imam Ali Hospital of Kermanshah, were selected for analysis as samples. Atmospheric variables, including average temperature (°C), wind speed (meters per second), relative humidity (percent), solar radiation (watt per square meter), height or sun angle (degrees) and dew point temperature (°C), cloudiness (Octa), water vapor pressure (hPa), as well as statistics about the number of daily admissions for cardiovascular patients were used. Although data on atmospheric variables Kermanshah station were available in the long term, but due to lack of information about the admissions cardiovascular disease, common period of the day September 9 2009 to April 30 2015 in order to determine the relationship between them was selected. The data related to cardiovascular patients acceptance Kermanshah Imam Ali Hospital were collected. A database of this information was provided during the period from September 9 2009 until the Day April 30 2015. As well as another database of meteorological variables (average temperature, relative humidity, wind speed, solar radiation, water vapor pressure and the height of the sun) on a daily basis was created for the same period. Based on this database, bioclimatic conditions of Kermanshah on a daily basis were identified. In this study, two software RayMan and BioKlima was used to determine bioclimatic conditions of Kermanshah. PET and PMV bioclimatic indices were calculated based software RayMan. Finally, the link between acceptance of cardiovascular disease and each bioclimatic conditions in Kermanshah individually through statistical tests (Levene test, Univariate Analysis of Variance, Scheffe and Games-Howell post hoc) were investigated using SPSS software.
Generally, Investigate the relationship between bioclimate indices and cardiovascular disease in the Kermanshah showed that acute climatic conditions are the most important factor in the increasing acceptance of cardiovascular disease in Kermanshah. In other words, under the cold, hot and sultry conditions, Admission cardiovascular patients in Kermanshah had significant increase compared to the climatic comfort condition. In general, based on the results of this study can be said that in each index, one or two different bioclimatic conditions on the hospital admissions of cardiovascular patients have been effective. For example, based on Tek index slightly sultry has been effective in increasing cardiovascular diseases. So that in the slightly sultry conditions, every day 20 people on average is referred to the Imam Ali hospital. Moreover, in cold conditions, an average of 19 people admitted with cardiovascular disease. Meanwhile at the thermal comfort condition the lowest rate of hospital admissions have been reported. In fact, at the confidence level 95 percent, there is significant difference in the number of cardiovascular patients in sultry and cold condition compared to thermal comfort. Therefore, based on Tek index cold and sultry conditions are effective in increasing hospital admissions and simultaneously with the occurrence of thermal comfort a significant reduction in the mean number of patients has been observed. In fact this index relationship between extreme conditions with an increase in cardiovascular admissions is approved. Among other indices, TE index showed that direct correlation between hot and warm conditions with increase hospital admissions. The results of PMV and PET indices also indicate that cold and cool bioclimatic conditions (generally tend to cold conditions) more than the warm and comfort conditions are effect on the acceptance of cardiovascular patients. As a conclusion we can say that extreme bioclimatic conditions (very cold or hot and sultry) are directly related with increase cardiovascular disease in Kermanshah. Also under comfort or close to the comfort condition the hospital admissions have been less.
The results of this study showed that in each index, one or two bioclimatic conditions have been effective in increasing acceptance of cardiovascular patients. For example, based on Tek index there are significant relationship between extreme conditions (very hot and very cold) with increase in cardiovascular admissions in the confident level 95 percent. But in the TE index, between warm and hot conditions, with increase in cardiovascular admissions in the confident level 95 percent, was seen a significant correlation. Based on the PMV and PET indices cool and cold bioclimatic conditions (in general tend to cold conditions) more than warm and comfort conditions are affected on the acceptance of cardiovascular patients. Finally the results of most indicators suggest that acute climatic conditions (very cold or hot and sultry) are directly related to increase of cardiovascular disease in Kermanshah.