Climatology
Seyed Hossein Mirmousavi; Zahara Taran
Abstract
Introduction
Dust is one of the most common climatic phenomena in arid and semi-arid regions of the world The phenomenon of dust is a natural occurrence and occurs in areas with vast areas of arid and desert areas, Lack vegetation and other surface coatings. Due to its presence in the arid and semi-arid ...
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Introduction
Dust is one of the most common climatic phenomena in arid and semi-arid regions of the world The phenomenon of dust is a natural occurrence and occurs in areas with vast areas of arid and desert areas, Lack vegetation and other surface coatings. Due to its presence in the arid and semi-arid belt of the world, Iran is constantly exposed to local and synoptic dust and dust systems. In recent years, the phenomenon of dust in the Middle East has been increasing, Because it is one of the five regions of the world that has the highest dust production . Long periods of drought and inappropriate interventions in nature can increase the likelihood of this phenomenon.
In recent years, the trend of dust events in the west and south of Iran, especially in the spring and summer, has increased dramatically .This phenomenon is affected by certain atmospheric conditions and its distribution can affect the temperature, temperature, precipitation and atmospheric circulation conditions of the area during the months of the year.
Materials and methods
In this study, data of 56 years old (during 1961-2016) precipitation, temperature and dust on daily scale from 30 synoptic stations in the west and southwest of Iran were obtained from the country's meteorological organization. In line with this study, MATLAB, ArcGIS and SURFER softwares have been used. In order to analyze the information, recognition of fluctuations and the relationship between dust, temperature and precipitation have been used.
Results and discussion
Recognition of fluctuations and the relationship between dust, temperature and precipitation are investigated using regression, spectral analysis and Pearson correlation coefficient. Then it is represented by trend maps, cycles, and correlation tables. The results for the West and Southwest of Iran have been obtained and explained in detail.
Conclusion
The study of the spatial distribution of the trend shows that most of the stations studied in the dust and rainfall have an increasing trend and have been in a decreasing trend temperature. Spectral analysis of dust, dry days, and temperature showed that short-cycle cycles in addition to the most frequent distribution, showed a higher probability of occurrence than long-term periods. In most of the stations studied, the correlation of dust with temperature and dry days has a positive and direct, relationship with the rainfall has a negative and inverse relationship. The local mororan analysis for the spatial autocorrelation of dust with dry days in the western, northwest, northern and parts of the east of the study area has shown a high value cluster pattern (positive spatial autocorrelation). The spatial autocorrelation of dust with precipitation in the northeastern, eastern, and small parts of the southeast and west of the study area has a high cluster pattern (positive spatial autocorrelation). The spatial autocorrelation of dust with temperature in the eastern, western, and small parts of the south of the range has a high cluster pattern (positive spatial autocorrelation).
Climatology
mehdi asadi; Ali Mohammad Khorshiddoust; Abbas Ali Dadashi Roudbari
Abstract
Introduction As the stations measuring precipitation continuously are not regularly available, the best solution should be to investigate the points without statistics using optimal methods. Among these methods, we can mention geostatistical methods. Geostatistical methods have been approved as appropriate ...
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Introduction As the stations measuring precipitation continuously are not regularly available, the best solution should be to investigate the points without statistics using optimal methods. Among these methods, we can mention geostatistical methods. Geostatistical methods have been approved as appropriate ways for studying precipitation data and estimating precipitation regions. Results of many studies have shown that geostatistical techniques are more accurate than conventional interpolation methods. Statistical context can also be used for precipitation variability. Accurate estimation of the spatial distribution of precipitation requires a dense and regular cell network. The spatio-temporal variation of precipitation is one of the most important issues of applied climatology, so the main purpose of this study is to monitor the spatio-temporal variation of precipitation in Iran in seasonal context by the application of the mentioned techniques. Data and Methods In this study, the common statistics of 125 synoptic stations in the country with the statistical period of 30 years (1980-2010) have been used. Also, the station data were generalized to the 15 km cell spaces using the Kriging interpolation method in ArcGIS 10.2.2 software. To speed up the computational process, the capabilities of GS + software were used to fit the variogram, and ArcGIS software was used to map the precipitation regions of the country. In order to study the pattern of precipitation, spatial autocorrelation techniques (local Moran and global Moran) were used. Also, the skewness coefficient (G1) and the peak degree coefficient (G2) were calculated separately for each of the months studied. Cluster and non-cluster analyses and hot spot method were used to study the patterns and spatio-temporal variations of precipitation. Cluster and non-cluster analysis, also known as Moran local Anselin index is an optimal model for showing the statistical distribution of phenomena in space (Anselin et al, 2009: 74). For cluster and non-cluster analyses for each complication in the layer, the value of the local Moran index score, which represents the significance of the calculated index, was also calculated. Results and Discussion The value of the global Moran index for all 4 studied seasons and the annual total is above 0.95, which indicates the pattern of high clusters of precipitation in the country at the level of 95 and 99%. However, the highest Moran index in the world with a value of 0.970356 is related to the winter. Statistics for each of the five decades studied are high, between 255 and 261. Therefore, based on global trends, it can be inferred that the annual changes in precipitation in the country follow a very high cluster pattern. Consequently, due to the high value and low value, the hypothesis of no spatial autocorrelation between data in each of the five decades can be rejected. If precipitation were to be normally distributed in space for different seasons in the country, the global Moran index would be -0.000139. Moran's spatial autocorrelation only determines the type of pattern. For this reason, to show the spatial distribution of the pattern governing the distribution of precipitation in Iran, local Moran has been used during the studied periods. In winter (36.56%) there was no pattern or in other words it lacked spatial autocorrelation. This amount increased by 1.14% for spring and reached 37.70. This amount has increased significantly in summer, so that it has increased by 47.04% compared to spring. It has reached areas with no spatial autocorrelation in autumn (41.92) and winter (36.56). LL precipitation patterns have been distributed in the five studied periods with values of 36.53, 0, 34.64, 35.31 and 38.29% in the country, respectively, and in the form of nationwide spots in the eastern, southeastern and central regions. Precipitation values with negative spatial correlation in summer had the highest value (84.74%) and the lowest annual average (35.06%). However, values with high rate or positive spatial autocorrelation in all five studied periods were limited to the northern regions of the country, the highlands of Alborz, Zagros and had significant fluctuations in some parts of the country. Local Moran Anselin statistics have been able to well determine the process of precipitation (Masoudian, 1390: 97) and the era of windbreak slopes as well as adjacent areas with climatic contrasts such as north-south slopes of Alborz and slopes of east-west Zagros. Due to the complexity of precipitation patterns in the country, spatial statistics can well explain precipitation patterns. The general results of this statistic (local Anselin Moran) indicate that the amount of rainy areas in the country has been reduced during five study periods. It should be noted that most of these reductions were related to the Zagros region, the southeast of the country and the northern regions of Khorasan. Conclusions Iran has special conditions in terms of precipitation due to its vastness with respect to latitude and longitude, the configuration of unevenness and exposure to air masses. The general structure of precipitation in Iran is affected by latitude, altitude and air masses, so that with the change of any of these factors, precipitation will also change. In other words, the general conditions of precipitation are a function of latitude and altitude, and other factors such as water areas and land cover, which are referred to as local factors, play a role in the formation of Iranian precipitation. In the present study, spatio-temporal analysis of Iranian precipitation has been done using a new method of spatial statistics. For this purpose, high and low clustering methods, local and global Moran, hot spots and cluster and non-cluster analyses have been used. The present study focuses on the assumption that precipitation in Iran follows a cluster pattern and the pattern of precipitation distribution is itself a function of internal and external conditions. To achieve this goal, the average seasonal and annual precipitation statistics of 125 synoptic stations in the country during the statistical period of 1980-2010 were used. Then, to apply the methods used in this research, the capabilities of GIS were used. The results of the global Moran method and the K-function of some distances showed that the annual changes in precipitation in Iran follow the pattern of high clusters. According to spatial autocorrelation analyses, the areas with negative spatial autocorrelation in all studied periods are related to the southeast, the coasts of the Oman Sea to Abadan and parts of the northeast of the country. Areas with positive spatial autocorrelation were often located on the southern shores of the Caspian Sea and the Zagros strip. In all the studied periods, less than one quarter of the country's area lacked a significant spatial autocorrelation pattern. Spatial analyses showed that Iran's precipitation patterns are divided into two precipitation spots of southern tabs (low precipitation spot LL), and Caspian coasts west and northwest (precipitation spot HH). The results also indicated that during the period under study, low precipitation spots (negative spatial autocorrelation) had much more frequency than precipitation spots.
Climatology
Golam Abbas Fallah Galharei; mehdi asadi
Volume 22, Issue 64 , September 2018, , Pages 229-246
Abstract
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 ...
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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
Urban Planning
Mohammad Hosein Yazdani; Ebrahim Firoozei Majande
Abstract
Uneven and unequal distribution of public land has been one of the consequences of libertine urban growth in the recent decades that has led to an unbalance in the distribution of urban public lands. This has had enormous implications, one of them being the lack of equal access to municipal services ...
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Uneven and unequal distribution of public land has been one of the consequences of libertine urban growth in the recent decades that has led to an unbalance in the distribution of urban public lands. This has had enormous implications, one of them being the lack of equal access to municipal services for citizens. Due to political and political developments in Ardabil during the recent decades this city achieved a massive physical growth so that this libertine physical growth has led to an unbalanced distribution of public land and caused forming a bipolar wealthy and deprived city. Accordingly this study aims at investigating and analyzing the public land distribution manner to evaluate the distribution of public utility and to grade and determine wealthy and deprived urban regions. This study employs descriptive-analytical method and essentially could have applied nature. To collect data the library method was used. To achieve study objectives two hypotheses were designed, in order to test the first one spatial autocorrelation analysis tools and the nearest neighbor index and to test the second one Kernel density function were used.
