نشریه تحلیل فضایی مخاطرات محیطی
سال نهم شماره 3 (پاییز 1401)

In the Ethiopian-Sudan range forms the low pressure system without front in the cold and transition seasons that is affecting the climate of the adjacent regions by crossing the Red sea. Based on the evidence in the context of Iran, studying Sudan low was first begun by Olfat in 1968. Olfat refers to low pressures which are formed in northeastern Africa and the Red Sea and then pass Saudi Arabia and the Persian Gulf, enter Iran, and finally, cause rainfall. The most comprehensive research specifically examining Sudan low, was the work carried out by the Lashkari in 1996. While he studying the floods that occurred in southwestern of Iran, he was identified Sudan low by the most important cause of such flooding and he explained how they are formed, and how these low-pressure systems were deployed on the southwest of Iran.

The study period with long-term variations was considered from 9.5 to 11 years based on solar cycles. Precipitation data for 13 synoptic stations are considered above 5 mm in south and southwestern Iran. With three criteria were determined for the days of rainfall caused by each type of atmospheric system. The visual analysis of high and low altitude cores and geopotential height at 1000 hPa pressure level (El-Fandy, 1950a; Lashkari, 1996; 2002) were considered based on the aim of the study. Accordingly, the approximate locations of activity centers, as well as the range of the formation and displacement of the Sudan system were initially identified based on the location of the formation of low and high-pressure cores. Then, the rainy days due to the Sudan system in January were separated from the precipitation of the other atmospheric system.

According to the selected criteria in the forty-year statistical period, 507 precipitation systems were identified with different continuities that led to precipitation in the northern coast of the Persian Gulf. The pattern of independent Sudan low rainfall was responsible for 77% of the precipitation in the Persian Gulf. Decade frequency share of Sudan low was lower in the first decade (16%) compared to the next three decades. This system of rainfall was more activated during the second and third decades compared to the first decade. However, rainfall changes were not evident in the mid-decade. Independent Sudan low precipitation provide 25% and 27% of the cold season precipitation of the Persian Gulf during the second and third decades respectively. In accordance with the 24th solar cycle, at the end of the study period, the Sudan low was more effective on the Gulf coast than ever before. During this decade, 125 cases of Sudan low rainfall was recorded for the Persian Gulf. Thus, the frequency of Sudan low during the fourth decade was about 31%, which was higher than in the rest of the decade. Overall, the Sudan low rainfall was repeated 151 times for 2 days rainfall, during the statistical period studied. This Precipitation has increased over the last decades compared to other periods.

The severe variability of rainfall along the timing and location of the permanent Persian Gulf coasts can have a significant impact on the economic and agricultural behavior of the Gulf population in the three provinces of Ahwaz, Bushehr and Hormozgan.The purpose of this study was to evaluate the precipitation changes due to Sudan low in the Persian Gulf coastal region during the cold period. The results of this study showed that the role of integration patterns in influencing the precipitation of the Persian Gulf coast has decreased with the strengthening and further activation of the Sudan low system during the last two decades. That way, about 77percent of the region's rainfall is provided by independent Sudan low. At the end of the course (in accordance with 24th solar cycle activity) the Sudan low system was more active than before. Although the Sudan low activity was different at each station during the period studied, but in the historical passage incremental and decade's positive behavior of Sudan low was common to all stations. Evaluation of changes in rainfall duration shows that the pattern of precipitation with 2days duration is more frequent than the patterns of one to several days.

 Season is the natural pattern of change in nature, which is related to the movement of the sun, the temperature cycle, the life cycle of the earth (phenology) and human culture. In astronomical and climatic seasons, a year divided into four seasons, spring, and summer, autumn and winter (Alsop, 2005), (Trenberth, 1983). Season is a period of the year with a homogeneous climate (Alsop, 1989), that is difficult to determine exactly when to start and end. The methods of determining of the seasons are: change in the face of the earth (Cayan et al, 2001), (Wang et al., 2021), constant temperature threshold, (Jaagus et al, 2003; Kitowski et al, 2019; Ruosteenoja et al, 2019; Alijani,1998), Air Masses, (Lamb, 1950; Cheng et al, 1997; Pielke et al, 1987; Kalinicky,1987; Alpert et al, 2004). What is a natural constant sign is the key to determining change and starting a new season. Organisms react to the onset and end of natural seasons by changing their behavior. Naturally, plants and animals adjust and adapt their phonological stages to temperature changes and jumps (Sparks et al, 2002), Plants germinate and flower in spring,fruit in summer, reduced activity and leaf in autumn and in winter fall asleep (Menzel et al, 1999 Animals are also adapted to reproduction, nesting and childbirth, And their phonological period is also related to vegetation conditions. In other words, the life stages of living organisms are adapted and dependent on these natural changes (Schwartz et al, 2000). Some organisms also migrate in order to adapt (Smith et al, 2012). The genetic response of organisms to rapid climate change and seasons associated with winter warming across the north, the early onset of spring and a long growing season is a factor in impairing the physiological response (reproduction, dormancy or migration time) of species(Bradshaw et al, 2008). On the other hand, the sensible temperature of organisms is affected by radiation, wind, air temperature and humidity. As appearance temperature is an important heat factor (heat and cold) in nature, to which animals, plants and humans react. Ruosteenoja et al (2019), showed the length and onset seasons of European with thresholds of 0 and 10 ° C focusing on the scenario of a 2 ° increase in temperature, an increase in summer length and a decrease in winter compared to pre-industrialization. The length of summer increases by 1 degree, increases by 10 days, and the length of winters decreases by 10 to 24 days. Kitowski et al, (2019), showed the onset of summer earlier, the shorter autumn, the longer summer and the shorter winter in Poland with zero-, 5- and 15-degree temperature thresholds. Wang et al, (2021) change the onset time and length of natural and summer seasons from 78 days to 95 days, and spring, autumn and winter, 124 to 115, 87 to 82, and 76 to 73 days, respectively. Also, summer is halfway through the year and winter is less than two months to 2100 in the middle of the Northern Hemisphere. Dong (2009) showed that in most parts of China since 1950, summers have been longer and winters shorter, with the onset of summer 5.8 days earlier and the length of the season 9 days longer and the winter 5.6 days later and the length of the season 11 days. Changes in transition seasons are less. Season start, end and season length changes studied in Oregon and Washington (Alsop, 1989), in the United States (Barry and Perry, 1973), Europe (Jaagus et al, 2003), Estonia (Jaagus et al, 2000), South Korea (Choi et al, 2006), China (Ma et al, 2020), Xinjiang in northwestern China (Jiang et al, 2011; Cheng et al, 1997), Eastern Mediterranean (Alpert et al, 2004), Iran (Alijani ,1377). Therefore, with the increasing trend of temperature in different regions of Iran (Alijani et al, 2012), study of change of the start and end dates of natural seasons in connection with life in nature is necessary (Penuelas et al, 2002). The aim of this study is determine the time of onset, end and length of natural and significant seasons and its difference with astronomical and climatic seasons in Iran with highlands, inland and coastal lowlands in the north and south with a new approach based on biological physiology.

To determine the onset and end of natural seasons, daily data of relative humidity, water vapor pressure, and wind speed and air temperature over a 60-year period for 32 synoptic stations in Iran from 1959 to 2018 were used. Selected stations cover all areas of Iran (coastal, low and highlands). In the first step, the apparent daily temperature of each station was calculated (Formula 1). In the second stage, with the knowledge of the direct effect of atmospheric circulation factors in the occurrence of natural phenomena (Alijani, 2011) And rapid changes in temperature (season), the 4-day moving averages of apparent temperature (average life of cyclone and anticyclone) at each station were calculated and was the basis of study. The onset and end of the season are with a natural and biological approach related to the stages of bio phenology and the natural part's reaction to temperature changes. Therefore, the apparent temperature of zero and below zero with the reduction or cessation of biological activity in nature, is the onset of winter. On the other hand, the time required by nature to adapt to new temperature conditions, is at least 10 days (Joy, 2017). Therefore, the temperature of zero degrees and non-return to zero Up to at least the next 10 days, is the basis for the onset of winter. In fact, with the continuation of sub-zero temperatures for 10 days, the living part of nature receives the signal of change. If after that, for a period of less than 10 days, the temperature goes above zero, the situation will not return to the previous state (nature did not react and adaptation occurred). On the other hand, the best temperature for the growth period is from at least zero degrees to a maximum of 30 degrees in nature (Abrami, 1972). The second key indicator is the temperature of the onset of summer and the warm period. For the onset of the summer season, the temperature of 20 degrees was base with the previous conditions. Because at this temperature, the reproductive period in plants and animals has started, most animals and plants have children and humans also feel warm. As plants begin to fill grain at this temperature, including wheat (Jenner, 1991 and Dupont et al, 2003) as the world's oldest grain. Here, the same condiction as before, don’t return to 20 degrees for at least the next 10 days was the basis. So at the onset of both seasons, if the temperature returns to zero and 20 in the 10-day period, the season has not begun, and in that year the station does not have winter and summer, respectively. Then, the temperature of 19 degrees and less with the above conditions, the onset of autumn and the temperature of 1 degree and more with the above conditions, are the basis for the onset of spring.Formula 1: Calculate the apparent temperature AT = T + 0.33 PV - 0.7 WS – 4 T = air temperature in Celsius, PV = water vapor pressure in hPa, WS = wind speed in meters per second, AT = apparent temperature in Celsius

 The onset and end of natural seasons are different in the geographical and topographical location of Iran. Southern regions and the northern coasts are two seasons with a warm summer season and a transitional season (cool). Other parts of Iran, like the temperate regions of the globe, have four seasons, but the start, end and length varies. The longest winter in the northwest and the western heights and the length of winter to the east and south is short and vice versa, the longest summer in the south and center of Iran. Spring season in below 29 degrees orbit, Khuzestan and the shores of the Caspian Sea is not a separate season, but with the absence of winter, it merges with autumn. In other regions, spring begins in the south and northwest, respectively, from 31 January to 8 March. In most parts of Iran, the onset of spring coincides with the traditional date of Nowruz, after small chelleh of winter. This month coincides with the rise in temperature and the revival of nature and the introduction of the New Year. The end of spring in the central regions, 10 May and in the northwest, 18 June, and its length varies from 103 to 96 days in the northwest and northeast, respectively. In the temperate regions of Iran, it is about three months with a 10-day spatial fluctuation (Table 1). The onset of summer is with a new stage of phenology in nature. The onset of summer is from 15 April on the southern coasts with high tropical arrival and the latest onset of summer in the northwestern part is 19 June (Table 1). In the south of the orbit of 29 degrees and the region of Khuzestan, until 8 May, in the central and northeastern regions of Iran from 22 May to 29 May and the west and northwest region, from mid-June to the end of June. The end of summer, as opposed to the onset, is the earliest time of 17 September in the northwest, and in the southern regions of Iran, the end of 8 October is in the 29 degree orbit. The southern regions of Iran, the longest summer that shows the role of latitude and slower exit of the tropical system (Alijani, 1390). The length of the summer season in temperate regions varies from 90 to 139 days, approximately three to five months, respectively in the northwest and the 29-degree geographical orbit, respectively. Therefore, the spatial trend of summer length from east and south of Iran to north and northwest is decreasing and there are the shortest summers in northwest of Iran. Naturally, this spatial trend is related to the high-altitude inbound and outbound routes of the subcontinent and the western systems from the south and northwest, respectively. The month of October and November is the onset of autumn in Iran, in the northwest and northeast, with the arrival of cold atmospheric circulation from above, the angle of radiation and altitude, is 18 September. The latest start of autumn in Hormozgan is 12 November (Table 1). The end of autumn is the first of April to the first of June in the south and north coasts, respectively. In the northeast of Iran, 24 to 28 December, and in the central regions, 28 to 31 December, is the end of the autumn season. The earliest end in the northwestern regions of Iran at the end of December is 10-17 December. The length of the autumn season in temperate regions is 83 to 97 days, respectively, in the northwest and northeast, that’s an average of nearly three months. With the onset of winter, decreases in temperature (frost) and winter during the year below the 29 degree orbit are rare, but on the northern coast, with the influence of atmospheric systems, it is a coincidence. In other regions of Iran, northwest, west and east of Zagros and south of Alborz, above 29 degree orbit, from 11 December to 1 January, is the time of winter. Respectively, the earliest onset of winter is in the northwest, and the latest onset in the central regions (Table 1). As the westerly winds of the extraterrestrial latitudes with cyclones and anticyclones dominate the Iranian atmosphere, also, the angle of radiation and the amount of radiation received at the earth's surface at this time, reaches a minimum during the year. The end of winter in temperate regions is from 30 January in the 29 degree orbit to 7 March in the northwestern regions. Winter length reaches 86 days in northwestern Iran, 29 days in central regions (above 29 degree orbit) and 58 days in northeastern Iran, Therefore, there are only three winter months in northwestern Iran and in other parts of Iran, it is the shortest season during the year. Spatial trend of winter length from northwest of Iran to east and south is decreasing.

The time of the onset, end and length of natural seasons in Iran are different from astronomical and calendar seasons. The slow decreasing and increasing trend of temperature at the onset and end of the seasons is initially a function of the angle of radiation and the length of day and night, but the real onset of a season with temperature jumps associated with the migratory atmospheric system (cyclone and anticyclone), Siberian hypertension, It is from the north and high in the subtropics from the south. Areas below 29 degree orbit in the south of Iran and Khuzestan and the northern coasts, have only two seasons of autumn (cool) and summer (warm) and the temperature decreases to zero and less (occurrence of winter), in the southern regions, rare and on the northern coasts is accidental and short. The apparent temperature in these areas has been decreasing since late summer and in the middle of the cold period, it is decreasing to the maximum (lowest temperature during the year) and increasing again until the onset of summer. Therefore, the above areas are two periods, with a cool season and a hot and hot season. The southern coasts of Iran and Khuzestan have short cooling seasons and long hot and hot summers, and the northern coasts, on the contrary, have shorter summers and longer and cooler autumns, that The influence of water temperature, latitude, topography and atmospheric systems are effective in these differences. In other regions of Iran, except the mentioned regions, four natural seasons occur (spring, summer, autumn and winter). In connection with the role of latitude, altitude, the arrival of migratory and high pressure Siberian atmospheric systems, the time of onset, end and length of the season has a change of location. As the length of summer is more in the southern, eastern and central regions of Iran and decreases in the northwest and west of Iran, and the length of winter is the opposite. The length of the transitional seasons (autumn and spring) in the temperate regions of Iran is not different and the three months in the season are similar to the astronomical and calendar seasons. The most important spatial difference is during winter and summer. Winter decreases from three months in the northwest of Iran to the south and east of Iran and reaches a month in the 29 degree orbit. On the other hand, the length of summer, on the contrary, varies from five and three months from east and south of Iran to northwest of Iran. Therefore, in temperate regions of Iran, the length of natural seasons from the south and east of Iran to the west and northwest of Iran is more regular and approaches to three months in each season. This spatial trend indicates the climatic similarity of western and northwestern Iran with temperate regions of the globe in higher latitudes and but to the center, south and east of Iran, this similarity decreases and to hot and cold dry desert climate in the Middle East and central Asia region is similar, respectively. This indicates regularity and order in nature, which is related to the geographical principle of Tobler’s law, the spatial correlation of climates and the onset, end and length of their seasons. Therefore, if we consider three months in a season as a natural feature of the temperate regions of the earth and two seasons (climatic period) as a feature of the subtropical regions, Iran is in the transition zone of these two climates. As from three months, the length of each season in the northwest to less than a month in the range of orbit 29 degrees, and then the subtropical conditions with two seasons (warm and cool) appear. Therefore, from northwest to east and south of Iran, the climatic moderation decreases and its tropical sub-characteristic (longer summer and shorter winter) heat and dryness to heat and humidity in southern Iran is added. Naturally, in this spatial process, primarily large-scale atmospheric rotations and secondly, geographical phenomena (their shape and position) play a pivotal role. The Caspian Sea coast is an exception to this rule due to its higher latitude and complexity of geographical phenomena and the role of water, because the climate systems related to the Caspian climate are different from other regions of Iran.

Problem statement:

In recent years, Lake Urmia, the largest lake in Iran, has faced severe water shortages, which has raised concerns in terms of economic, social and environmental consequences in the surrounding communities, especially in rural areas. Livelihood dependence of rural community stakeholders, to the natural resources and agricultural products have caused the harmful effects of drying Urmia Lake to be more visible. The drying up of Lake Urmia is not limited to this lake, but human communities have also suffered a lot from their sphere of influence. Due to the human effects of the drying of Lake Urmia,  it is necessary to analyze the effects of this phenomenon from a human perspective in research. Identifying the adaptive capacity of rural community stakeholders makes it possible to adopt appropriate management strategies to reduce the damage caused by lake drying. Therefore, despite the importance of the subject of this research, it seeks to study the factors and forces affecting the adaptation capacity of rural settlements in the face of the drying crisis of Lake Urmia in the city of Miandoab and so on.

In terms of methodology, strategy and design, the present study is a combination of (mixed), sequential and explanatory exploratory, respectively. In this study, for a detailed study of community mentalities, a discourse on effective factors to increase the adaptive capacity of rural settlements in the face of drying or water retreat of Lake Urmia, the combined method of (Q) was selected. The research discourse community included local managers (governorate experts, heads and employees of government departments, districts, rural districts and Islamic councils) as well as local experts in the sample villages of Miandoab city. Targeted sampling method (snowball) was used to select the statistical sample. Q statements were also compiled using first-hand sources (expert opinions, local managers, field observations, etc.) and codified sources (books, articles, publications, etc.) using the library and field methods. The Q questionnaire was also used to assess the attitude of experts. In order to analyze the data of the Q (Q) method matrices, heuristic factor analysis based on the individual method (Stanfson method) was used.

 In reviewing the findings of the exploratory factor analysis model with KMO criterion, Bartlett test confirmed the sufficient number of samples and its appropriateness for the research. To investigate the most important influencing factors, the specific value and percentage of variance were calculated and the number of factors was determined by pebble diagram and Kaiser Guttman criterion. The results showed that the most important factors and forces affecting the increase of adaptation capacity to the drying of Lake Urmia in the sample villages of Miandoab are: 1) Increasing economic capital and the use of natural resources, 2) Increasing social capital and investment, 3) Developing infrastructure facilities and improving the skills of villagers, 4) Economic diversification and improving rural management .. Among these factors, the first factor with a specific value of 5.40 and a percentage of variance of 24.55 was recognized as the most important factor and effective force in increasing the adaptation capacity of the studied villages against the drying of Lake Urmia. Thus, economic and natural factors, as the most important assets of the villagers, are endangered at any time by the drying up and retreat of the water of Lake Urmia and have a direct impact on the livelihood of the villagers.

Rapid urbanization in recent decades has been a major driver of ecosystems and environmental degradation, including changes in agricultural land use and forests. Urbanization is rapidly transforming ecosystems into buildings that increase heat storage capacity. Loss of vegetation and increase in built-up areas may ultimately affect climate variability and lead to the creation of urban heat islands. The occurrence of natural disasters such as flood, earthquake … is one of the most effecting factors on the changes in intensity of urban heat islands. So far, a lot of research has been done on how it is affected by various types of natural disasters such as floods, earthquakes, droughts and tsunamis.Two major environmental challenges for many cities are preventing flooding after heavy rains and minimizing urban temperature rise due to the effects of heat islands. There is a close relationship between these two phenomena, because with increasing air temperature, the intensity of precipitation increases. Drought is also a phenomenon that is affected by rainfall, temperature, evapotranspiration, water and soil conditions. One of the major differences between drought and other natural disasters is that they occur over a longer period of time and gradually than others that occur suddenly. Another natural disaster is the tsunami, which increases the area of water by turning wetlands into lakes, thereby increasing the index of normal water differences, which has a strong negative relationship with surface temperature. Ecosystems in urban areas play a role in reducing the impact of urban heat islands. This is because plants and trees regulate the temperature of their foliage by evaporation and transpiration, which leads to a decrease in air temperature.Applying the locked down of the Covid-19 pandemic since the spring of 2020 has led to the global restoration of climatic elements such as air quality and temperature. In this study, the effects of Covid-19 locked down on the intensity of urban heat islands due to the limitations in industrial activities such as factories and power plants and the application of new laws to reduce traffic in Tehran were investigated. In this regard, the Landsat-8 satellite taken from a part of Tehran city has been used.

In order to investigate the effects of locked down in the spring of 2020 on the intensity of urban heat islands; the status of UHI maps in Tehran during the same period of locked down in three years before and one year after has been studied. The proposed method in this paper consists of two main steps. The first step is to generate UHI maps using land surface temperature (LST), normalized difference vegetation index (NDVI) and land use / land cover map analysis. In the second step, in order to analyze the behavioral changes in the intensity of urban heat islands during locked down and compare it with previous and subsequent years, changes in the intensity of UHIs are monitored.UHI maps consist of three classes of high, medium and low intensities urban heat islands, which are based on performing the rule based analysis on land surface temperature characteristics and normal vegetation difference index derived from Landsat-8 satellite images as well as land use / land cover map. LULC maps are produced by support vector machine classification method consisting of three classes of soil, building and vegetation. In order to calculate the spectral features used in the rule based analysis, atmospheric and radiometric corrections must first be made on the red, near-infrared, and thermal spectral bands of the image captured by the Landsat-8 satellite. Then, vegetation spectral indices including NDVI and PV indices are generated.

The capability of the proposed algorithm in this paper is first evaluated in the whole area covered by satellite images taken from the city of Tehran, and then in three areas including residential, industrial and green spaces. The data used inhis article are images taken by the OLI sensor of Landsat-8 satellite in the spring of 2017-2021.in the first step of the proposed method, maps of urban heat islands are generated based on multi-temporal satellite images of Landsat-8 taken in the years 2017to 2021 in the MATLAB programming software. Then, by comparing pairs of UHI maps in each of the residential, industrial and green space study areas, the trend of changes in the intensity of UHI is analyzed and the effects of locked down application in 2020 are evaluated.The results of changes detection in urban heat islands in the period under consideration in this study showed that the percentage of areas that are in the class of high UHI in 2020 due to locked down of pandemic Covid-19 compared to the average of three years before that is 55.71%, has a decrease of 17.61%. The percentage of areas in the class of medium UHI intensity in 2020 due to locked down compared to the average of three years ago, which is 39%, increased by 4.8%, and in 2021 this amount again has decreased to less than the average. Also, the percentage of low intensity UHI class in 1399 compared to the average of three years ago, which is 5.3%, has increased by 12.8%.

In this study, the effect of locked down application due to the Covid-19 virus pandemic, which was applied in Iran in the spring of 2020 is investigated on the intensity of  urban heat islands in a part of Tehran city and three selected areas with residential, industrial and green space. Detection of changes in the intensity of urban heat islands was done based on the post-classification method and on the UHI classification maps related to the years 2017 to 2021. In order to produce UHI maps, in addition to the land surface temperature, the amount of vegetation index and the type of land use / land cover class were also used in the form of a set of classification rules.Comparing the results of the study areas of residential, industrial and green spaces, it is important to note that the rate of reduction of the area of UHI with high intensity in the residential area is 5.25% more than the industrial area and 6.1% more than the green space. However, the reduction of locked down restrictions in 2021 had the greatest effect on the return of the area of ​​the high UHI class and caused the area of ​​this class to increase by 23% compared to 2020. These results indicate the fact that restrictions on the activities of industrial units such as factories and power plants and the application of new laws to reduce traffic, despite the same weather conditions in an area have been able to significantly reduce the severity of urban heat islands.

The temperature of the urban environment is one of the parameters that citizens are in contact with at any moment. Studies show that the global temperature is constantly increasing due to environmental changes. One of these parameters that affect the increase in temperature; The physical growth of the city and its consequent destruction and loss of vegetation. In this study, using Landsat satellite images for the years 2001, 2011 and 2021; and the implementation of the single-channel algorithm, the surface temperature of the ground in the Iraqi city of Halla was calculated and its changes were investigated and analyzed. On the other hand, the NDVI index was calculated as a vegetation index on the mentioned dates and its changes were analyzed with the temperature changes of the earth's surface. The general results of this research showed that the area of the city of Halle has doubled during the study period, and this has caused a decrease in the amount of vegetation and an increase in the temperature of the earth's surface. In the end, the correlation between the surface temperature and the NDVI index was calculated, which was equal to 46.92, 44.35 and 52.98% for the years 2001, 2011 and 2021, respectively. This issue shows the strong relationship between these two parameters and the effect of the reduction of vegetation on the increase in the temperature of the earth's surface.

This study, which was conducted in 8 urban gas areas of Mashhad; At first, descriptive statistics of the state of Mashhad urban gas regulators and different leakage modes were presented; In order to analyze the collected data and investigate the causes of leakage, the relationship between 5 variables and the amount of leakage from gas regulators was tested with the Statistical Package for the Social Sciences (SPSS) V.26 software; These 5 variables are: regulator equipment/connections, regulator operation age, regulator service type (domestic, industrial and commercial), urban area and different seasons of the year. The results of the analysis showed that there was a significant difference between the type of equipment/connections and leakage. (P-Value = 0.0001). Also, a significant difference was observed among other variables of the research (the operation age of the regulator, the type of regulator service (domestic, industrial and commercial), the urban area and different seasons of the year) with the leakage rate (P-Value=0.0001); The pressure drop due to the greater demand of gas consumption in the winter season has reduced the amount of leakage compared to other seasons; The influence of the age of distribution network equipment/connections due to wear and tear and longer life will aggravate the amount of methane gas leakage; Also, the amount of leakage in commercial places had a significant difference with other types of uses; Being in an urban area has also increased the amount of methane gas leakage compared to other areas; The type and quality of equipment and connections as the main and influential factor in methane gas leakage should be considered by managers and officials in this field of work.

Although natural hazards occur in all parts of the world, their incidence is higher in Asia than in any other part of the world. Natural phenomena are considered as natural hazards when they cause damage or financial losses to human beings. Iran is also one of the high-risk countries in terms of floods. Until 2002, about 467 floods have been recorded by the country's hydrometric stations. In addition to natural factors such as rainfall, researchers consider human impacts such as destruction of vegetation cover, soil destruction, inefficient management, destruction of pastures and forests, and encroachment on the river are the most important factors for the occurrence and damage of floods in the country. One of the most efficient and emerging tools in flood surveys is the use of radar images. SAR images and flood maps produced by radar images provide researchers valuable and reliable information. Moreover, maps obtained from SAR images help officials to manage the crisis and take preventive measures against floods. The Sentinel-1 satellite is part of the Copernicus program, launched by the European Space Agency, and is widely used in mapping flood-prone areas. The contribution of Sentinel-1 to the application of flood mapping arises from the sensitivity of the backscatter signal to open water. This study aims to determine high-risk and flood-prone areas along the Kashkan River using Sentinel-1 radar images.

 The study area includes a part of the Kashkan river from Mamolan city to the connection point of this river to Seymareh river, after Pol-dokhtar city. The average annual discharge of the Kashkan river is 33.2 cubic meters per second based on the data of the Pole-Kashkan Station. The length of the river in the study area is about 100 km. To investigate flood-prone areas, we applied pre-processing and image-processing steps to each flood event including SAR images belonging to March 25th, 2019, March 31st 2019, and April 2nd, 2019. SAR images were acquired from ESA Copernicus Open Access Hub. climatic data was downloaded from power.larc.nasa.gov. To create meander cross-sections, the Digital Elevation Model of the studied area was utilized. Cross-sections were created using QGIS software. Pre-processing steps include: applying orbit data, removing SAR thermal noise, calibration of SAR images, de-speckling and topographic correction. In image processing, we applied the Otsu thresholding method to distinguish water pixels from land pixels. In thresholding methods, the histogram of each image is divided into two parts according to the amount of gray composition. The higher the amount of gray (i.e., the pixel tends to be darker), the more pixels represent water, and conversely, the lighter-toned pixels (i.e., pixels that tend to whiten) represent land. The Otsu thresholding method is a commonly used method for water detection in SAR images. It uses an image histogram to determine the correct threshold. The most important feature of the Otsu method is that it is capable of determining the threshold automatically. The Otsu algorithm was applied to all images using MATLAB.

According to the flood maps, on March 25th, 6.51 percent of the study area was flooded, while on March 31th, only 3.96 percent was flooded. This is mainly due to less precipitation on the 31st. On March 25th the average daily precipitation was 47.46 mm while on 31st of March the average daily precipitation was 31.64 mm. On April 2nd, however, there was no rainfall, on the day before more than 63 mm of precipitation has occurred. This massive amount of precipitation on the previous day has led to more than 25km2 being flooded in the studied area.

Results showed that meanders and their surrounding areas are the most dangerous sections in terms of flooding. The meander's dynamic and the river's hydrologic processes are essential factors affecting flooding in those sections. Generally, various factors affect flooding and the damage caused by it. This study aimed to determine flooded and flood-prone areas (according to flooded areas in previous events) using new methods in a short time and with high accuracy to use this tool for more accurate zoning and efficient planning in the future. The results showed that radar images are practical, robust, and reliable tools for determining flooded areas, especially for rapid and near-real-time studies of flood events.

The statistical and spatial analysis of extreme rainfall is considered as one of the components of the management tool to prevent or control the risks caused by this phenomenon. The purpose of this research is to statistically investigate and spatially analyze the extreme precipitations in the Kashan Plain.The extreme rainfall of Kashan synoptic station were statistically analyzed in the period of 1971-2022 AD and the water year of 1350-1351 to 1401-1400 for a total of 18618 days.Then six cases of widespread extreme rainfall were selected and analyzed with the rainfall data of 13 synoptic stations and 11 rain gauge stations using geostatistics and spatial analysis methods.The extreme rainfall zonation maps of Kashan plain were prepared using by variogram models and kriging method.The results showed that the frequency of heavy and super heavy rains in winter and very heavy rains in spring is more than other seasons.The very high correlation of annual rainfall with the total and frequency of extreme rainfall shows that the volume of annual rainfall is more affected by the concentration of rainfall in short periods of a few days than by the distribution of rainfall throughout the year.Therefore, it was found that extreme precipitation plays an important role in the total precipitation and surface runoff, and as a result, the water balance of the region.The zoning maps showed that the rainfall of April 8, 2020, which is concentrated on the western belt and the heights of the basin, causes the erosion of the heights and causes floods in the foothills and low-lying areas of the plain. Also, rains such as the rains of March 8, 2019, which are most concentrated in the central areas, have a high potential to cause flooding.

Rainfall is the most important phenomenon or feature of the environment and so far many studies have been done about its causes. In any place, rainfall occurs when humid air and climbing cause are provided. Both of these conditions are provided by the circulation pattern. The study area is affected by some severe and sudden weather phenomena such as low annual rainfall, short rainfall period and rainfall in the form of heavy showers. Thus, it is possible that the limited and pervasive precipitation of the area is due to a different synoptic pattern. Because the relationship between circulation patterns and precipitation is significant, achieving acceptable results in the field of the relationship between these patterns with the limit and total rainfall of the studying area requires the analysis of synoptic maps. Therefore, the most important purpose of the present study is the synoptic analysis of heavy cloud rainfall of the studying area on Day 1398. Two sets of data were required for this study: A: Daily precipitation data of study stations on the day of heavy cloud rainfall on 21 Day (January 11, 2020) along with daily precipitation data in the days before the flood (96 hours before the flood) which was received from the main Meteorological Organization of the country. B: atmosphere data levels including: sea level (SLP), 850 and 500 hPa levels, vertical atmospheric velocity and wind flow levels of 1000, 850 and 500 hPa, specific humidity of 1000 and 700 hPa levels and 250 hPa surface flow winds for study days from the US National Center for Environmental Forecasting / National Atmospheric Research Center (NCEP/NCAR) were provided in the range of 0 to 60 degrees at north latitude and 0 to 80 degrees at east longitude, and finally, maps were drawn and prepared in Gardes software to provide the ability to interpret. The synoptic analysis of sea level showed that: on the day of the heavy cloud, a low-height closed center with a central core of 1,010 hPa in the northeast-southwest direction covered the entire study area. Then, the high-height with a central core equal to 1030 hPa is located at northwest of Iran, northwest of Europe and on Tibet. According to the location of high-pressure dams around Iran and the location of low-pressure centers on the study area and water resources in the south, a strong pressure has been created. Subsequently, with height increasing, low-height with central core equal to 1440 geopotential meters is located at northeast-southwest direction of entire study area. And the low height of northern Russia extends to the Persian Gulf and provides the conditions for severe ascent and instability in a very large area. The rear dams of Nave transferred the cold air of the high latitudes into the bottom of the Nave located on the study area and have intensified the instability. Also, the geopotential height of 500 hPa level of deep descent is located at the northeast-southwest direction of Iran and core of the Nave covers the Persian Gulf completely, that is the study area in the best condition and in front of the Nave, which is diverged by hot and humid weather. This deepening of the rotation and the penetration of the Nave to the lower latitudes caused the cold air to fall. The analysis of the 250-hectopascal-level flow-wind shows that the flow-wind with a core speed of 65 meters per second has covered the entire study area by crossing above the Persian Gulf, and compared to the previous days, the flow-wind is completely meridional. Synoptic analysis of the vertical velocity at the level of 1000 hPa shows that the maximum negative omega -0.2 to -0.15 Pascal per second in the northwest-southeast direction has covered the study area. The presence of negative omega index values ​​indicates the role of convection in intensifying precipitation in mentioned area and the dynamic ascent of air. The study map shows that compared to other countries in the study map, the maximum of negative omega is located on Iran, which is reduced along to the west of Iran. With increasing altitude, the maximum negative omega has increased to -0.3 Pascal per second and the core of the maximum negative omega is completely located on the study stations (Zahedan and Qeshm). Then, at the level of 500 hPa, the maximum negative omega has reached -0.6 Pascal per second and its value has doubled compared to the level of 850 hPa, which covers the northeast-southwest direction from Zahedan to the Strait of Hormuz. Cold air fall has increased with increasing of omega levels in the middle levels of the atmosphere.In other words, in the middle levels of the atmosphere, with increasing temperature difference between the earth's surface and the level of 500 hPa, the amount of precipitation has increased. Synoptic analysis of specific moisture level of 1000 hPa shows that the most moisture deposition was from south water sources to the study area, and the amount of moisture equal to 14 grams per kilogram has entered the study area from the Oman Sea and then its amount has been reduced crossing to other regions of Iran. Furthermore, at the level of 700 hPa, the maximum advection of hot and humid air is in front of the upper atmosphere of Nave from the Red Sea over the study area. There is a moisture strip from the southeast to the whole area under analysis. These suitable humidity conditions with the depth of the western wave have been able to cause heavy cloud rainfall. The maximum amount of moisture in the study area is equal to 7 grams per kilogram, which is a large amount compared to heavy rainfalls.

It is importance to note that Iranian oil company have to transfer this valuable enrage from one side to other side of country passing form several ridge and valley prone with several natural hazard. This is because the natural sources of oil and gas generally lied in south west part of Iran locally calling Manathegh Nafte Khize Jonoub (south oil field area). This area is closed to one of most active geological zone of Iran (Zakrose) covering thousands of kilometer from south east to north west. Supplying natural enrages to central port of country need to crossing from this zone which is suffering with several difficulties as well as neutral hazard. Out of neutral hazards can found to excite in this area, the landslide hazard is a main restriction for pipeline crossing over. The present research is dale with radar interferometry techniques applying for risk assessment and mapping over the oil and gas pipelines suffering to landslides hazard in the part of Central Zagros (Maroun-Esfahan). For this purpose, two individual radar dataset in C (ASAR) and L (PALSAR) band with deferent time were collected. Furthermore, the D-InSAR technique was applied for land surface movement and land displacement detection. The outcome map was showed the maximum rate of land displacement in this region is about 7.4 cm uplifted and 3.9 cm subsidence with duration of almost one year. this is due to shape of landslide over the area’s slop. Overlying the landslide map with the pipeline crossing route shown at lies three active landslides over the Maroun-Esfahan gas and oil pipelines. For investigation about this three landslide and damage estimation over the pipeline the field study has been done for accuracy assessment and land movement rat measuring and evaluation. Which, successfully identified and mapped 3 landslides were located across the pipeline and damage it. Furthermore, map surveying by DGPS in RTK method over the one of landslide shown that sliding transfer 20 m with falling 10 m over the length of 45 m of gas pipeline. moreover, the press of landslide made curvatures on straight pip hogging pipe 43 cm. continued this landslide activation and more pressing in close further can make a fracture and pessimistic pipe expulsion. With can a kind of disaster if the event be close to settlements are. The outcome landslide map shown the active landslide points (small area) very well, but the main think need to suffusion information about interred area. For this exigency have to convert points data map to area as prediction hazard. For this proses and to understanding the amplitude of landslide hazard in area the information value model was applied for hazard zonation and mapping. The landslide hazard map resulting from D-InSAR technique as inventory map along with 8 data set maps namely, lito-logy, soil, land cover, lineaments, faults, roads, derange pattern and slop, has been interred to model for zonation and hazard estimation over the area. Furthermore, this map was reclass in 5 individual hazard and risk class from low to high risk. The hazard map analyses and calculation was show about 20 percent of area study was marked as high and very high risk zone. This is mainly because of morphological and lito-logical exclusivity of area resulting by active tectonics. Crooning and overlaying the landslide hazard map with pipeline track has been shown 28.5 percent of line length crossing over the high and very high risk zone, where the 52 percent was prone with low and very low risk zone. This mine that near 1/3 of mention pipeline length suffering from hazardous area which can classified as high risk part of pipeline. Interpreting the hazardous classes on the prediction map is an important concern in landslide prediction models. For this purpose, the prediction-rate curve was generated using validation group of landslide locations to validate the prediction map obtained. This rate curve explains how well the model and factors predict the landslide. Results from the success-rate curve are very promising, since the 3% area predicted as the most hazardous, includes 42.35% of the total area affected by landslides, and this value grows to 90%, when about 25% area of highest susceptibility is considered. The prediction accuracy can be assessed qualitatively by calculation the area under cover. The total area equal to one means perfect prediction accuracy. In this model ratio area was 0.633 that means the prediction accuracy was 63.3%.

The expansion of the pole toward the tropical belt is thought to be due to climate change caused by human activities, in particular the increase in greenhouse gases and land use change. The variability of the tropical belt width to higher latitudes indicates the expansion of the subtropical arid region, which indicates an increase in the frequency of drought in each hemisphere. In order to change the width of the tropical belt of the Northern Hemisphere in the middle offerings, indices of  precipitation minus evaporation, wind vector orbital component, stream function, tropopause surface temperature, OLR, and SLP have been used. Findings showed that the expansion of tropical belt latitude with stream function to higher latitudes with 1° to 3° latitude and the effect of Hadley circulation subsidence has increased the amplitude of evaporation minus precipitation has shown that the fraction of precipitation minus evaporation 1° to 3° latitude geographically increased. The subtropical jet has increased the movement of the upper branches of troposphere from the Hadley circulation by 2° to 4° latitude, which can have a negative effect on transient humidification systems as well as on the amount of precipitation. The extension of the pole towards the tropical belt, which is a consequence of climate change and hazards, will lead to the displacement of the pole towards the tropical side of the river, thus providing dry tropical belts to the pole; Also, the long-wave radiation of the earth's output has increased by 1° to 2° latitude and has caused an increase in heat in the upper troposphere, which has increased the dryness and slightly reduced the clouds in the upper troposphere and also caused the tropical belt to expand to higher latitudes. Has been. In general, the research findings showed that most tropical belt indicators have been increasing since 1979.

Landslide risk assessment is essential for all landslide damage mitigation plans. The purpose of this research is to assess the risk of landslides in the Shahrood watershed of Qazvin province. First, the landslide susceptibility map was prepared using fuzzy operators. the landslide distribution map and also 11 effective factor layers including slope, slope direction, altitude, land use, lithology, distance to road, distance to stream, distance to fault, earthquake acceleration, precipitation, and maximum daily precipitation were first prepared. After determining the frequency ratio and fuzzy membership values for the map classes of different factors, the landslide susceptibility map was prepared using different gamma values. Then, after preparing the fuzzy map of vulnerability for different land use units, the amount of landslide risk was determined from the product of two maps of landslide susceptibility and vulnerability. In general, 104 landslides with a total area of 1401 hectares were recorded in this region, 70% of which were used for modeling (73 landslides with an area of 982 hectares) and the remaining 30% (31 landslides with an area of 418 hectares) were used to assess the accuracy. The evaluation results showed that the highest value of Qs index (equal to 1.34) belongs to the gamma equal to 0.93 and therefore this model has higher accuracy than other gamma values. The importance of features at risk ranges from 0.05 (no coverage) to 1 (residential and industrial areas). To deal with landslide damages, three general policies including suitable for development, prevention, and treatment were proposed, which should be applied based on the two factors of risk and vulnerability for different areas of landslide risk. Finally, in order to reduce landslide damages, suitable land uses for high-risk regions were introduced.