جستجوی مقالات مرتبط با کلیدواژه "شاخص لکه داغ" در نشریات گروه "جغرافیا"

The aim of this study is to identify the spatial distribution of Vertically Integrated Moisture Flux Convergence (Vertically Integrated) Moisture Flux Convergence) on Iran’s atmosphere. To achieve this aim, the monthly ECMWF gridded data used during the period from 1/1979-12/2013. First, based on the specific humidity content in the atmosphere, troposphere divided into three layers (850-1000hPa), mid (700-775hPa) and upper (500-600hPa). In order to achieve VIMFC spatial variations on Iran, spatial self-correlation methods   of globular moron and hot spots used at 90, 95, 99 and 99/99 percent significance levels. The results of this study showed that the spatial distribution of VIMFC in Iran during the first layer of troposphere and especially during warm months of year has a high cluster pattern and in cold months of the year and in the third layer of troposphere cluster pattern decrease. Based on the hot spots index in the first layer of troposphere low height regions, in the second layer of troposphere the  high regions of the Alborz, zagros and central mountains and in the third layer of troposphere alpine regions of central and eastern Iranchr('39')s mountains has positive spatial self-correlation (hot spots). The results show that in winter and autumn during the second period (1999-2013), the range of hot spots of the VIMFC show a significant reduction compared to the first period (1979-1998) on Iran.

This study was fulfilled aiming at the identification of spatial and temporal changes of temperature islands in Isfahan province First, a data basis of the highest and lowest net-based temperature and a committee were founded in Isfahan. Using the data of the basis, a statistical period of 35 years from 1980 to 2014 was chosen as the basis of the study and a geo with dimensions of 18 by 18 kilometers was spread on the area understudy. In order to access the during-year changes of the temperature islands of Isfahan, modern methods of spatial statistics such as Moran's I autocorrelation and hotspots in GIS environment were used. The results of the study showed that the changes of spatial and temporal in temperature islands of Isfahan possess a high cluster pattern. Based on the local Moran’s indicator and hotspot, the temperature islands of the east and north of the province have a positive spatial autocorrelation while the west and southwest areas have negative spatial correlation. During the study 41.45 of the province has not had any meaningful pattern or spatial autocorrelation. It was also shown that the temperature islands are controlled and affected by the local factors which control the space as well as the outside factors which control the time.

This study aims to identify the spatial autocorrelation and spatial variation of sunshine hours in Iran. For this purpose, the sunshine hours to form a network database have been made in Iran. The data from the base of a 30-year period, the daily period from 1/01/1982 to 12/31/2012 AD to the present study, and intercellular dimensions of 15 × 15 km area stretching is studied. In order to achieve the sunshine hourly changes within a year, the sunshine of the Iran of spatial statistical methods, such as spatial autocorrelation global Moran, Moran's index of local Insulin, and hot spots was used by using the programming environment GIS. The results of this study showed that the spatial and temporal variation in sunshine hours in Iran is High-cluster pattern. In the meantime, based on local Moran and hot spots, South, South East and Central synoptic stations representing the provinces of Sistan and Baluchistan, Kerman, Shiraz, Isfahan and Yazd have positive spatial autocorrelation pattern, full sun pattern, and parts of North, North East and North West representing synoptic stations in Tabriz, Mazandaran, Mashhad and Semnan have a negative spatial autocorrelation, low sun pattern. In the study period, in most cases, a large part of the Iran, almost half of the total area, has had no significant pattern or spatial autocorrelation

In order to identify the changes of spatial autocorrelation between heat clusters in Fars province, the minimum and maximum temperatures have been recorded to form a network database in Fars province. Then a 33 year period was selected for this research from the afore-mentioned database, the selected daily period was from 1/01/1980 to 12/31/2012, and an area with the dimensions of 18×18 km was added to the region under study. In order to achieve the temperature changes of these heat clusters, the newest spatial statistical methods such as spatial autocorrelation (Global Moran’s I), Anselin Local Moran’s and hot spots by environment GIS were used. The results of this study showed that the pattern of spatial and temporal variations of heat clusters of Fars Province is high-cluster. However, based on local Moran and hot spots, heat clusters in the South, South West and South East have positive spatial autocorrelation pattern (heat clusters) and parts of North and North East have a negative spatial autocorrelation (cold clusters). In the study period, a large part of the province, in most cases, almost half of the total area had no pattern of spatial autocorrelation, significant or non-significant.

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

The consequence of cooperation between environmental factors and circulation patterns in a long time can determine the arrangement of type and manner in humidity in geographical area. The knowledge about space dispersion in geographical areas assists preparing sound programming and proper environmental decision making. Relative Humidity is the most commonly used measurements of moisture content in the air. The key to understand relative humidity is to understand that it is a measure of the ‘actual humidity’, relative to the maximum possible humidity at a given temperature. Let’s explain it a bit further. In this context a number of studies have been conducted to refer this. Some of these studies are Diffenbaugh and et al. (2008), Ohayon (2011), Jia and et al. (2011), Homar and et al. (2010), Chao-bing and et al. (2011), Allard and Soubeyrand (2012), Ageena and et al. (2013), Del Río and et al. (2013), Kim and et al (2014) and Bajat and et al. (2014). This research is fulfilled to detect the temporal and place spatial autocorrelation of humidity in Iran. In order to reach the expressed goal, the base of network data of relative humidity in Iran has been established. Similarity of data of the stations has been evaluated by the Kolmogorov-Smirnov Test in SPSS software and their similarity has been proved. Then, from the data of the stations a statistical period of 30 years in a daily period from 1982/1/1 until 2012/12/31 is used as the base of the present research and a network in range of 15×15 kilometer have been spread over the study area. In reviewing the changes of Transmittal Humidity of Iran during a year, modern spatial statistics method such as spatial auto correlation global moran, local insulin moral index and hot spots were used by using (GIS) and MATLAB. The results of this research showed that the global moran index for each 12 monthes of a year is one more than 0.90. This point indicates that in accordance to global moran, Transmittal Humidity of Iran in the study period has the high cluster pattern in 90, 95 and 99 level percent. Then, the highest index of global moran in scale of 0.97 is related to the February in winter. Z statics for every 12 monthes of a studied statistic period is high and between 247 and 263. Therefore, according to global moran it can be concluded that during a year in the index in Iran shows a very high cluster pattern. Alteration of spatial autocorrelation of Transmittal Humidity of Iran used the local moran index and analysis of hot spots. According to both the indicators, the north, north west, north east, west and south west areas like east Azarbaijan, west Azarbaijan, Ardabil, Zanjan, Guilan, Mazandaran, Ghorgan, Khorasan and Kermanshah stations plays a significant role in forming the Humidity patterns with high cluster. This is in a way that the named areas of Iran have positive spatial autocorrelation. This is while the regions have negative spatial auto correlation or in other words dry humidity in 12 months of a year limited to high regions. Totally, a considerable area of the province in all 12 months is without significant or disciplined pattern or they lack sound virtual spatial autocorrelation statistically. The results of this research showed the humidity pattern is formed through a long time period and under local and distributional elements with a different role. Generally, the geographical arrangement of humidity patterns are formed by regional factors specially heights, latitude and in a clearer explanation formation and structure and the role of latitude. This is while we should not ignore the role of outer factors in formation of humidity patterns. Outer factors or the general circulation atmosphere elements play a significant role in determination of a humidity regime and humidity lapse. If we look at the humidity cluster of Iran we see that the clusters in high and low level are not the same. This contrast is because of influence of circulation element factors.