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Hamed Haidari, Dariush Yarahmadi , Mostafa Karampour,
Volume 7, Issue 3 (11-2020)
Volume 7, Issue 3 (11-2020)
Abstract
Dust phenomenon is one of the climatic fronts that is often formed in the dry and desert regions of the world, and is known as a natural hazard. Occurrence of walnuts causes dust, damage to the environment and the occurrence and exacerbation of respiratory, cardiac, air traffic and threats of tourism, agriculture and so on. Also, in the health section of the compounds in calcium dust, more than 2.5 g of it causes the appearance of kidney stones, and blood vessels. Iron causes swelling of the conjunctiva and retinal inflammation, as well as the syndrome. Magnesium causes depression, depression and dizziness of the individual. Short-term breathing of aluminum leads to coughing and irritation of the lungs and prolonged breathing causes damage to the lungs. In recent years, the identification of dust source areas has attracted the attention of researchers in numerous studies, and have introduced various areas around the world as the main source of generous production. The country of Iran, and in particular the Western and Southwestern logic of Iran, is constantly experiencing the phenomenon of dust and its problems. In the west of Iran, desert areas are located in the deserts of southern Iraq, Saudi Arabia and far away from Syria and North Africa. The geographic location of the southwestern part of Iran and its proximity to these deserts have led to a frequent occurrence of the phenomenon of dwarfs, which are different throughout the year.
In this research, two categories of data were used: the first group is data on climatic elements or unstable elements.
The annual climatic layers of the region were used for a 30-year statistical period of 2016- 2016. Measurement data on the temperature of surface temperature was obtained from a MODIS sensor in a 17-year statistical period (2000-2016). The second group of data layers and information on the ground factors or factors were stable. The layers of these variables included:
In this research, two categories of data were used: the first group is data on climatic elements or unstable elements.
The annual climatic layers of the region were used for a 30-year statistical period of 2016- 2016. Measurement data on the temperature of surface temperature was obtained from a MODIS sensor in a 17-year statistical period (2000-2016). The second group of data layers and information on the ground factors or factors were stable. The layers of these variables included:
- The digital elevation layer of the area with a precision spacing of 30 meters from this layer was used as the elevation layer of the area.
2. The slope of the region, in percent, which is the layer derivative of the digital elevation model and derived from the same specifications of the DEM layer.
3. The surface layer of the surface layer that was taken from the MODIS surface coating product
4. Layer of vegetation on the surface of the earth, which was also taken from the 1 kilogram MODIS vegetation cover
5. Soil layer that was prepared by the country's water and soil organization
The method of conducting analytical and statistical research in which the main objective is the determination of areas conducive to the formation or expansion of dust cores. In this regard, the establishment of land-based and climate databases is the first stage of work, after forming the required databases, the formation of information layers These data are in the GIS environment. In order to form these layers, the interpolated models in GIS were used and the optimal model was selected in such a way that less error values were obtained. After forming the existing layers, we classified and weighed each layer based on the AHP weighting algorithm. Finally, due to the assigned weights, the overlaying of the weights of the layers in the GIS environment was obtained and finally a potential capability map of the formation of local dust collectors in Lorestan province was obtained.
The rainfall factor is the most important and most important factor in determining the areas susceptible to becoming dusty. The weight of this factor in determining and identifying areas susceptible to dusty cores was equal to 239%. Vegetation factor, which was prepared using the NDVI indicator of the MODIS product, was the second factor in the development of dust-prone areas with a weight of 199.99. Relative humidity factor is the third factor or component that influences the determination of suitable areas to become the local focus of dust. The weight of this factor is equal to 0.15. The wind speed factor is in the fourth place in terms of determining the areas susceptible to dust. The relative weight of this factor is estimated at 116.0. As shown in Table 14, slope and elevation factors are the least important factors that can play a role in the production and development of dusty centers. The weight of these factors in identifying areas susceptible to formation of dusts is 0.024 and 032.2, 0 is detected. The calculated incompatibility index for this weighing is as high as 4.8, as shown in Table 15, which indicates that the contradiction between the offered weights of indices relative to each other is less than the allowed threshold (12).
Neutbay expressed the highest concentration of areas susceptible to dust mites in the eastern region, especially the northeast of the province, which includes the cities of Azna and Aligudarz. There are also parts of this category in the southern regions of the study area, including the cities of Poldokhtar and Rumshagan. In the central regions of the province as well as in the northwest of the province including the Khorramabad, Delfan, Dynasty and Dorood districts this class is not observed. In the southern parts of the city of Kohdasht, small parts of the floor of the potential centers of dust are also observed. This flooring has the most risk of becoming a dusty focus. The power source of many of the province's dusty incidents can also be said to be areas where some of them are currently potential sources of dust. Since they have played a major role in the identification and detection of these areas, rainfall and vegetation cover, these areas are exactly in line with parts of the province, which, firstly, have a mean rainfall of less than 250 mm, and the density of vegetation is less than 2 / 0 (NDVI index), which represents a very poor vegetation and, in fact, a lack of viable vegetation. In terms of land use, these areas, or inferior land, or very rangelands, are very weak.
The rainfall factor is the most important and most important factor in determining the areas susceptible to becoming dusty. The weight of this factor in determining and identifying areas susceptible to dusty cores was equal to 239%. Vegetation factor, which was prepared using the NDVI indicator of the MODIS product, was the second factor in the development of dust-prone areas with a weight of 199.99. Relative humidity factor is the third factor or component that influences the determination of suitable areas to become the local focus of dust. The weight of this factor is equal to 0.15. The wind speed factor is in the fourth place in terms of determining the areas susceptible to dust. The relative weight of this factor is estimated at 116.0. As shown in Table 14, slope and elevation factors are the least important factors that can play a role in the production and development of dusty centers. The weight of these factors in identifying areas susceptible to formation of dusts is 0.024 and 032.2, 0 is detected. The calculated incompatibility index for this weighing is as high as 4.8, as shown in Table 15, which indicates that the contradiction between the offered weights of indices relative to each other is less than the allowed threshold (12).
Neutbay expressed the highest concentration of areas susceptible to dust mites in the eastern region, especially the northeast of the province, which includes the cities of Azna and Aligudarz. There are also parts of this category in the southern regions of the study area, including the cities of Poldokhtar and Rumshagan. In the central regions of the province as well as in the northwest of the province including the Khorramabad, Delfan, Dynasty and Dorood districts this class is not observed. In the southern parts of the city of Kohdasht, small parts of the floor of the potential centers of dust are also observed. This flooring has the most risk of becoming a dusty focus. The power source of many of the province's dusty incidents can also be said to be areas where some of them are currently potential sources of dust. Since they have played a major role in the identification and detection of these areas, rainfall and vegetation cover, these areas are exactly in line with parts of the province, which, firstly, have a mean rainfall of less than 250 mm, and the density of vegetation is less than 2 / 0 (NDVI index), which represents a very poor vegetation and, in fact, a lack of viable vegetation. In terms of land use, these areas, or inferior land, or very rangelands, are very weak.
Hamed Heidari, Darush Yarahmadi, Hamid Mirhashemi,
Volume 9, Issue 2 (9-2022)
Volume 9, Issue 2 (9-2022)
Abstract
Revealing surface reflection forcings of land cover in Lorestan province using MODIS sensor products
Introduction
Human interventions in natural areas as a change in land use have led to a domino effect of anomalies and then environmental hazards. These extensive and cumulative changes in land cover and land use have manifested themselves in the form of anomalies such as the formation of severe runoff, soil erosion, the spread of desertification, and salinization of the soil. The main purpose of this study is to reveal the temperature inductions of the land cover structure of Lorestan province and to analyze the effect of land use changes on the temperature structure of the province. In this regard, the data of land cover classes of MCD12Q2 composite product and ground temperature of MOD11A2 product of MODIS sensor were used. Also, in order to detect the temperature inductions of each land cover during the hot and cold seasons, cross-analysis matrix (CTM) technique was used. The results showed that in general in Lorestan province 5 cover classes including: forest lands, pastures, agricultural lands, constructed lands and barren lands could be detected. The results of cross-matrix analysis showed that in hot and cold seasons, forest cover (IGBP code 5) with a temperature of 48 ° C and urban and residential land cover (IGBP code 13) with a temperature of 16 ° C as the hottest land use, respectively. They count. In addition, it was observed that the thermal inductions of land cover in the warm season are minimized and there is no significant difference between the temperature structure of land cover classes; But in the cold season, the thermal impulses of land cover are more pronounced. The results of analysis of variance test showed that in the cold period of the year, unlike the warm period of the year, different land cover classes; Significantly (Sig = 0.026) has created different thermal impressions in the province. Scheffe's post hoc analysis indicated that this was the difference between rangeland cover classes and billet up cover.
materials and Method
In this study, to reveal the relationship between land cover levels and different land use classes, cross-information matrix analysis was used in the ARC-GIS software platform. Since one of the main objectives of the study was to investigate and reveal the albedo inductions of land cover classes in Lorestan province, so the relationship between these two factors was investigated by cross-matrix analysis technique. In this regard, two sets of data were used. The first set of data was related to land cover classes of MODIS sensor composite product with a spatial resolution of 1 km and hierarchical data format (MCD
12(Q2 (MCD product) which was obtained from the database of this sensor
Conclusion
Land cover classes or perhaps it can be said that land use is one of the most important shapers and determinants of climate near the earth. In this study, it was observed that in general, 5 major land cover classes in the province are separable, among which rangeland and forest lands account for 85% of the total land cover of the province. On the other hand, it was seen in this study that the average spatial albedo of the province in spring, autumn and winter is about 0.2, which is very close to the global value of this component, but in winter the average value of this index in the province reaches 0.3, which can be increased Shows attention. The five land cover classes in the province had their own unique albido induction in winter, which was separable and distinct from each other, but in spring, summer and autumn, no significant distinction of albido induction of these land cover was revealed.
Keywords: Land cover changes, Land surface temperature, Cross-information analysis matrix, Lorestan province
Zynab Dolatshahi, Mehry Akbari, Bohloul Alijani, Darioush Yarahmadi, Meysam Toulabi Nejad,
Volume 10, Issue 3 (9-2023)
Volume 10, Issue 3 (9-2023)
Abstract
This study was aimed at examining the types of inversion and their severity using the thermodynamic indices of the atmosphere such as SI, LI, KI and TT at Bandar Abbas Station for 2010-2020. In this study, Radioosvand data at the Bandar Abbas Station was obtained and used from the University of Wioming for the last 11 years (3.5 local) during the last 11 years (2010 to 2020). The results of the analysis showed that the average number of inversion phenomenon in Bandar Abbas was 501 cases per year, as in some days several types of inversion were observed at different altitude. Of these inversion, about 31.6 % are related to radiation temperature inversion, 4.3 % front, and another 64.1 % for subsidence inversion. Due to the air session underneath, the share of subsidence inversions is more than other types of inversion. In the meantime, the most severe inversion of subsidence was 1354 and the weakest inversions were with 29 cases and fronts. In general, the long -term average intensity coefficient of inversion of Bandar Abbas station with a coefficient of 0.062 indicates that the intensity of the city's inversion is mostly extremely severe, which can be very destructive effects both environmentally and physical health in the city's residents. Bandar Abbas follow. The correlation between the inversion elements also showed that by reducing the thickness of the inversion layer, the intensity of temperature inversion also increased.
Mrs Mozhgan Shahriyari, Dr Mostafa Karampoor, Dr Hoshang Ghaemi, Dr Dariush Yarahmadi, Dr Mohammad Moradi,
Volume 11, Issue 1 (5-2024)
Volume 11, Issue 1 (5-2024)
Abstract
Flash floods are one of the most dangerous natural events and often cause loss of life and damage to infrastructure and the environment. This research investigated the occurrence of the most intense continuous monthly floods (October-March) from 1989 to 2021. Precipitation data from 115 synoptic stations were selected. Then, the total rainfall of 1 to 9 days was sorted according to intensity. Using Minitab statistical software and the Andersen-Darling index, heavy rains were extracted based on the 95th percentile. Then, based on the criteria of the highest and lowest number of rainy days, the highest and lowest accumulated rainfall, the wettest and driest months were determined. Considering the three criteria of intensity, continuity, and rainfall coverage, the strongest storms in the wettest months were selected. The data used for synoptic analysis include the average sea level pressure data, the height and vertical component of the wind at 500 hPa, the wind and humidity field specific to the pressure levels 925, 850, and 700 hPa, and the horizontal moisture flux values specific to the pressure level 925, 850 and 700 hPa. The probability of the occurrence of atmospheric rivers was identified by the moisture flux extracted from the specific, meridional, and meridional wind components. The results showed that the storms of October 27-31, 2015, November 5-7, 1994, December 12-16, 1991, January 11-15, 2004, February 3-9, 1993, and March 13-15, 1996 were the strongest in the wettest months. During the storms of October, November, February, and March, moisture has been transported from the southwest of the Red Sea by atmospheric rivers to the western, southwestern, southern, and southeastern regions of Iran.
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