Journal of Spatial Analysis

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As 7 Stations include; Ardebil, Sarab, Shahrekord, Ahar, Takab, Zanjan, and Saghez were experiments on average every year less than 30 days with thermal stress. From these 7 stations, Ardebil and Sarab regions, having 3 and 7 days with thermal stress, respectively, have the least amount of days with heat stress. All the days with the heat stresses obtained for these stations have been the days of the first class of heat stress map, and all of them were randomly distributed over the warm period of the year.
But in contrast to this stations that had the fewest days of thermal stress, southern Iranian stations, especially those stationed at the Persian Gulf and the Gulf of Oman Sea coasts, were the most frequent days of heat stress.
The two Jask and Chabahar stations with the annual average of 304 and 301 days, with the highest thermal stress, were the most frequent regions of Iran. The lower latitudes, lower elevation, higher temperatures and relative humidity are factors that make the conditions for having the most frequencies of days with heat stress in this part of Iran.
The spatial pattern of five classes this index also show different patterns in comparison with each other so that as all stations in Iran experience at least 3 days of thermal stress in the first class during the year. But with increasing intensity classes, the number of stations that experience the conditions of these five classes over a year will be reduced. As for the second class, 16.2% of the stations, for the third class, 55.4% for the fourth class, 83.7 %, and finally for the fifth class, 90.5% of stations, do not experience comfort in any way during one year. Finally, with regard to the important role of the elevations in the spatial distribution, the relationship between the total frequency of days with thermal stress and elevation was modeled using classical linear regression model. The results of this model showed that per 100 meters above sea level, 9 days from the total frequency of days associated with Iran's thermal stress is reduced. This downward trend is such that there is no thermal stress in Iran at 2300 m above sea level. In other words, the height of 2300 meters is the elevation border between the occurrence and absence of days with thermal stress in Iran.
 

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Introduction
Changes, although low in temperature, change the occurrence of extreme phenomena such as droughts, heavy rainfall and storms (Varshavian et al., 2011: 169). Reducing the daily temperature variation has led to a reduction in the frequency of occurrence of temperature minima, especially in winter (Schiffinger et al., 2003, p. 51-41).
Material and method
The purpose of the present study was to investigate and predict the risk of monthly rainfed temperatures on horticultural and agricultural products in northern Iran. For this purpose, first, the data of the temperature of the whole station were obtained at a time interval of 30 years. Then, using Anfis's adaptive neural network model, data were collected for prediction and prediction for the next 6 years. Then, to measure the land suitability of the northern strip Iran was used for cultivating according to the predicted data using two models of Vikor and Topsis.
Conclusion:
In recent years, damage to agricultural and horticultural products has been increased due to temperature fluctuations. Accordingly, in this research, the prediction of the risk of monthly rainfed temperatures on horticultural and agricultural products in northern Iran has been investigated. Based on the predicted data, the minimum temperature of the Gorgan station was the lowest educational error with a value of 0.10 and at the maximum temperature, the lowest error was 0.015. Finally, in Golestan province, the maximum temperature And at least both are weak in the incremental state. Minimum and maximum temperature of Bandar Anzali station was the lowest educational error with the value (0.013, 0.10). In Gilan province, the maximum temperature peaks and at least both are in incremental conditions and the maximum temperature has a higher intensity. Be The minimum temperature of the Babolsar station was the lowest educational error with the value of 0.019 and at Ramsar maximum temperature, the lowest error was 0.016, and Mazandaran province exhibited maximum and minimum temperatures at both incremental and minimum levels Temperature showed greater intensity.
Results:
According to the findings of the study, with respect to the friction frain modeling, the maximum temperature showed the lowest defect compared to the minimum temperature. In Golestan province, the maximum temperature peaks and at least both are in weak increment, but in Gilan province, the maximum temperature peaks and at least both the maximum and maximum temperatures are higher. Mazandaran province showed maximum temperature and minimum temperature in both incremental and minimum temperature conditions.
 

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A new Approach to Urban livability, Thermal Comfort as the Primitive Condition to enhance the livability: Case study, District 22 of Tehran.
 
 
Ahmad Porahmad: Professor of Urban Geography and Planning, University of Tehran
Hossain Hataminezhad: Professor of Urban Geography and Planning, University of Tehran
keramatollah Ziyari: Professor of Urban Geography and Planning, University of Tehran
Saeideh Alijani^*: PhD candidate of Urban Geography and Planning, University of Tehran
 
The concept of urban livability is defined as the quality of life and wellbeing of urban residents. That is the interaction of people, environment and built environment. The residents can achieve happy life and well-being only when the nature surrounding them is happy and healthy. According to the range of welfare concept there is a spectrum of quantitative indicators that directly measure (human body temperature, heart rate, air temperature, wind speed ...) and qualitative indicators such as quality of life, pleasure and joy. The comfort and ease of environment are in the middle of the spectrum, in other words, the intrinsic concept of ambient comfort is environment. The inadequacy of natural environment will affect both indicators in the spectrum and lead to citizens' dissatisfaction and decline in social welfare and threaten the health of humans. Living in a salty marsh or very dry hot climate is never happy and satisfied. Accordingly, many concepts such as living quality, living environment, and quality of place, quality of life and sustainability are often used interchangeably with livability).
         This research is trying to weight the natural environment at least equal to the other two components of the sustainable development triangle. Among the components of natural environment, climate is playing the most important and significant role. Urban climate affects all aspects of city including building interiors, city architecture and open spaces. Thermal comfort of open spaces promote the social life and interrelations of residents. Therefore, in order to promote the social relations and economic activities especial consideration should be paid to open spaces. Accordingly, two types of data were measured for calculating the thermal comfort in the district 22. Subjective and objective evaluations which present qualitative and quantitative data. Objective data includes micrometeorological measurements with mobile instruments. Subjective data evaluated actual sensation vote or perception vote of thermal comfort by people using the urban open spaces. To this goal, questionnaires were prepared and scattered through space users simultaneously with micrometeorological measurements. Subjective data evaluated perceptual sensation vote of thermal comfort by people using the urban open spaces in three hot days of August 2018. Nine points are selected for site measuring and field survey which are representative of two types of urban open spaces in this research:1) Urban park and 2) street. Four cardinal points were chosen adjacent to the Shohadaye Khalije Fars Lake inside the park located in sidewalk pathway around the Lake.  Other five points were selected in streets with different orientation and aspect ratio through the district. The physiologically equivalent temperature (PET), mean radiant temperature (Tmrt), sky view factor (SVF) and aspect ratio (H/W) are the most important indicators in this research which were calculated for evaluating comfort in the district.
      Results showed that urban open spaces in the district are discomfort and expose people to the extreme heat stress; over 40°C. This determines that, the natural environment especially around the Shohadaye Khalije Fars is not comfort. The questionnaire also indicated that people felt warm and dissatisfied.
        There is a high linear correlation between thermal comfort and mean radiant temperature and globe temperature. Therefore, it is concluded that thermal comfort in the district, is directly affected by urban areas.  Also in the streets with low SVF and high aspect ratio, PET were calculated more comfortable than other streets. Point 5 at Naghibzade street, confirmed the effect of urban geometry on thermal comfort. Otherwise, the lack of tremendous trees for creating shade is visible especially around the lake. The high linear correlation between Tmrt and SVF around the lake confirmed the openness of the area and the high amount of solar radiation. Therefore, planting more trees for creating the shade effect is necessary. 
        The perceptual analysis of thermal comfort indicated that by increasing of PET, people felt warmer. However, in a city like Tehran, people are more resistance to the heat stress. In addition, the characteristics of human body strongly depends on psychology and individual features and is a hard issue to predict. Otherwise, the people who felt warm were more than those felt slightly warm which indicates dissatisfaction of people. To be noticed that, thermal comfort of above 40 °C in summer is an alarm to urban planner and designers to rethink about climate consideration and global warming as a most important urban challenge in the district seriously. Besides, the consideration of thermal comfort and urban geometry should be imbedded into the comprehensive plan. This research proved that the climatic consideration for improving the quality of life and livability is important and urban designers and planners should rethink and review the comprehensive plan of Tehran to make a livable and sustainable city in the future.
Keywords: urban livability, climate comfort, sustainable development, urban sustainability, urban geometry, physiologically equivalent temperature, district 22 of Tehran.
 
 
 
 
 
 
 
 

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Analysis of the Characteristics of Sultry Days in Iran
 
Extended Abstract
Sultry phenomenon occurs due to the combined effect of high temperature and humidity. Sultry intensity increases with increasing relative humidity and decreases with decreasing temperature. This phenomenon has a tremendous impact on comfort and other human activities. Various indices have been used to study this phenomenon in Iran and in the world. According to previous studies, and as far as information is concerned, there has not been a comprehensive study across Iran on the characteristics of sultry days based on degree of severity. Therefore, the purpose of this study is to investigate the frequency, duration and severity of sultry days and its temporal and spatial analysis throughout Iran.
To do this research, daily temperature, relative humidity and partial water vapor pressure of 101 synoptic stations were used for a 28-year period (1987-2014). In choosing the indices of sultriness, the goal was to select indices that show the sultry state on a daily scale. For this purpose, in the first stage, 16 empirical sultry or sultry-related indices were used, all of which used climatic parameters such as temperature, relative humidity, water vapor pressure and cloudiness to calculate the sultry state or comfort. Among them, 13 indices were eliminated because they surveyed the phenomenon on a monthly or annual basis or were not consistent with the objectives of this study. Finally, according to the objectives of the study, three indices were chosen: 1- Sultry Intensity Index (Lancaster-Carstone empirical equation), 2- Partial Water Vapor Pressure Index (partial water vapor pressure equal to or greater than 18.8 hPa), and 3- Heat Index (HI).
The results of this study showed that two indices of Sultry Intensity and Partial Water Vapor Pressure are suitable for explaining the conditions in Iran and their outputs are not significantly different. But Heat Index did not lead to desirable results. According to the results of the Sultry Intensity Index, the sultry phenomenon is not comprehensive in the country and is limited to 21 stations adjacent to the Caspian Sea coasts in the north (besides Parsabad Moghan Station) and the Persian Gulf coasts (besides Ahwaz station) and the Oman Sea coasts in the south. In other parts of the country, due to their internal and leeward position, being away from moisture sources, poverty or lack of vegetation and insufficient penetration of wet and rainy systems, there is no sultry condition and, on average, even one day is not seen with sultry circumstances. On the southern coasts, on average, sultry conditions begin on April 3 and end on November 16. Therefore, in this area, 7 months and 11 days of the year have sultry conditions. This is natural due to the lower latitude and the Azores high pressure sovereignty in the south. But on the northern coasts, the sultry period is shorter and with a 48-day delay compared with the southern coasts, the average sultry day begins on May 22 and ends on October 12. Therefore, the duration of the sultry period is on average 4 months and 19 days. In terms of the number of sultry days, the most frequencies belong to the southern coasts stations. The largest number of sultry days related to the Chabahar port on the coasts of the Oman Sea with 291 days, followed by Jask port with 264 days. The lowest number of sultry days is also from Ahwaz station with 1 day and then Mahshahr port with 42 days. Among the stations on the southern coasts, the Oman Sea stations compared with the Persian Gulf stations have more sultry days due to lower latitudes, Azores high pressure sovereignty and Southeast Asian monsoon moisture influence. In contrast, the number of sultry days on the northern coasts is much lower and averages 140 to 150 days a year. Sultry severity is also less, so that there are no extreme severe sultry days in any of the stations on the northern coasts. But the number of extreme sultry days is remarkable on the coasts of the South, to 160 days in the port of Chabahar and 111 days in the port of Jask. At Parsabad Moghan in the north and port of Mahshahr in the south, due to distance from the coast and lack of sufficient moisture, the duration and severity of sultry is much lower and there are basically no days of severe and extreme sultry states. The annual trend of the number of sultry days at any station is not significant.
 
Keywords: Sultry, Temperature, Relative Humidity, Sultry Indices, Iran
 
 

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Occurrence conditions for severe snow blizzard in the west-north of Iran
 
Abstract
The blizzard incident is one of the climatic hazards that occurs due to the combination of other climatic factors such as temperature (below zero), snow and wind (at 15 m/s). In this research, the conditions of blizzard in Northwest of Iran are carried out using statistical methods. By analyzing all the meteorological codes of the blizzard (36, 37, 38 and 39) during the statistical period from 1987 to 2016 for 11 synoptic stations of the study area, codes with severe blizzard (37, 39) were selected. Then, using the geopotential height, wind and Leveling temperature of 500 and 850 hpa, obtained from the NCEP/NCAR open source database, the synoptic patterns of blizzard incident analyzed. Statistical analysis of the relationship between the effects of geographic factors on severe blizzard has shown that the factor of height has the greatest effect on intensity, increase and incident spatial differences of this phenomenon. The study of the synoptic patterns of the incident of the blizzard phenomenon showed that five main patterns play a role in creating it in the region. The synoptic patterns of development include the formation of a low cut-off center, a long landing passage from Iran, the formation of a relatively deep and drawn Mediterranean East, The rectangular system is a rex-shaped system and is an umbilical bundle system. Among the patterns obtained, the patterns that were bundled were, the most important role in the survival and transfer of flows associated with cold, and other patterns, despite the frequency they had, were periodically of severity and weakness.
Keywords: Blizzard; North West; Wind speed; Temperatures below zero; Synoptic patterns
Human life is always affected by climatic phenomena, especially the hazards of the two variables of temperature and wind. One of the most important simultaneous phenomena of these two variables is the blizzard, which is caused by heavy snow, stormy winds, and very low temperatures. This climate risk can cause damage to various areas of horticulture, agriculture, urbanization, transportation, and so on. This phenomenon is present in regions such as Canada and North America with a cold weather wave that results from turbulence in the winters and damages the lawns in these areas. There are plenty of local storm in the polar regions and it lasts for a few days. For example, the wind in the Adelie land in the Antarctic is so severe that the area is known as the storm Land. In Iran, the most significant blizzard occurred in mid-February 1350, resulting in the deaths of more than 4,000 people across the country. In this research, considering the characteristics of the blizzard phenomenon at the time of occurrence (severity, continuity, expansion, and time of occurrence), the study has been conducted to determine the statistical synoptic patterns in the northwest region.
In this research, the studied area is northwest of Iran, which includes 6 provinces (Ardebil, West Azarbaijan, East Azarbaijan, Zanjan, Kurdistan, Kermanshah, Hamedan). In order to study, the days with the blizzard phenomenon in the form of 3 hours and in codes of this phenomenon (36, 37, 38 and 39), were obtained from the establishment of the stations studied by 2016. In the following, for precise examination, stations with 30 years of statistic from 1987 to 2016 were identified and the statistical (frequency, daily, monthly and annual frequency) codes 39 and 37 were studied. Finally, the relationship between blizzard with the latitude and elevation in the studied stations was determined. To assess the statistical results, the correlation coefficients (R) and coefficient of determination (R²) were used.
In the second part, the identification of synoptic patterns was done by Principal Component Analysis in MATLAB software and ocular method. The criterion for identifying synoptic patterns, the days where codes 37 and 39 are more than 1 time (3 hours) within 24 hours or two days behind each other at the stations studied. In order to determine the patterns, at first, the average geopotential data of the 500-hpa level from 1987 to 2016 were obtained from a range of 10-70 degrees north latitude and 0-80 degrees east longitude with a spatial resolution of 2.5 * 2.5 from the NCEP / NCAR data.
Statistical analyzes on the relationship between the effect of geographic factors on severe blizzard showed that the factor of height had the greatest effect on the intensity, magnitude and spatial differences of this phenomenon. In sum, the most important factor in the occurrence of this phenomenon is due to atmospheric conditions and synoptic patterns of the region. In this study, the most frequent occurrence of codes 37 and 39 in all stations studied was at Sardasht station and Khalkhal station, respectively. Also, the statistical study of the frequency of the annual and monthly occurrence of each code showed that code 39 in 1990 and code 37 in the years 1989 and 1990, as well as in January, had the highest frequency of each of the two codes.
Investigating the patterns of the occurrence of the blizzard phenomenon showed that five main patterns have contributed to its creation in the region, the first pattern due to the formation of a low cut-off center, which, with the cold weather in Central and Eastern Europe, has reduced the temperature in the northwest. The second pattern is due to the high landing passage from Iran, which has crossed the descent from a cold and cold weather zone from Europe to Iran. The third pattern is the location of the studied area in the relatively moderate, dragged, eastern Mediterranean wavelength, causing cold weather to fall to the northwest. The fourth pattern, with the formation of a Rex-type blockade on the Mediterranean, has led to the transfer of cold air from Eastern Europe, Kazakhstan, and high latitudes to Iran. The fifth pattern, with the formation of a blockade, has caused cold weather in northern Europe and Central Asia to enter the country from the north, causing a drop in temperature in the region.
Among the known patterns, the patterns that were blocked (pattern 4 and 5) played the most important role in the survival and transfer of cold fluxes and even drawn to lower latitudes. Other patterns, despite frequent periods, provide conditions for the occurrence of this phenomenon and, unlike the blocking patterns, have had severity and weakness.
 
Keywords: Blizzard; North West; Wind speed; Temperatures below zero; Synoptic patterns
 
 
 

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Abastract
Introduction
Road accidents is the outcome of driver behavior, road condition, vehicle status, and environmental factors. Therefore, identification and assessment of effective parameters on road accidents can be considered as an appropriate way to reduce the accident events, driving violations and increase the road safety. Determining the effects of meteorological factors on the road accident events has gained more attention in recent years.
  The The main objective of this study was to investigate the relationship between the number of road accidents and the meteorological variables in the intercity road of Grmi-Ardabil in the Barzand route.
Methodology:
In this regard, the effects of climatic factors (including rainfall amount, the minimum absolute temperature, and the number of frost days) on the frequency of perilous events were analyzed. The data of accident events (in recent 4 years) were obtained from the trooper department of Ardabil Province along with the meteorological parameters of Germi station through a 11-year period. The statistical tests were performed using R programming software through statistical analysis.
Findings and Discussion:
The results showed that the majority of accidents were occurred in winter season which is in consistent with the frequency of frost days and also corresponded to the absolute minimum temperature. According to the results, the highest significant positive correlation at (R²= 0.43) was observed between the number of injured people and frost days. In addition, the relationship between the absolute minimum temperature and the number of were identified as significant negative correlation.
Conclusion:
As a concluding remark, the poor road conditions caused by climate element can be considered increasing the frequency of accident events. Accordingly, the proper strategies related to behavior change could be
considered in setting the rules and regulations to reduce the accidents and the number of injuries.

Keywords: Climatic hazards, Correlation analysis, Frost days, Minimum absolute temperature, Germi-Ardabil road


 

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Introduction
Climate change is the greatest price society is paying for decades of environmental neglect. The impact of global warming is most visible in the rising threat of climate-related natural disasters. Globally, meteorological disasters more than doubled, from an average of forty-five events a year to almost 120 events a year (Vinod, 2017). Climate change refers to changes in the distributional properties of climate characteristics like temperature and precipitation that persist across decades (Field et al., 2014). Because precipitation is related to temperature, scientists often focus on changes in global temperature as an indicator of climate change. Valipour et al. (2021) reported the mean of monthly the global mean surface temperature (GMST) anomalies in 2000–2019 is 0.54 C higher than that in 1961–1990. Many studies have been done on climate change in Iran. These studies have mostly studied the mean and extreme temperature trends (Alijani et al., 2011; Masoudian and Darand, 2012). In general, the results of previous studies showed that the statistics of mean, maximum and minimum air temperature in most parts of the Iranian plateau have increased in recent decades. Also, the increase of minimum temperature is greater than maximum temperature.
A review of the research background shows that we need to understand more about regional climate change in Iran. Therefore, present study performs the climate change of 14 extreme temperature indices using multivariate statistical methods at the regional scale.

Data and methodology
Historical climate observations including daily maximum and minimum temperature were obtained from the Iranian Meteorology Organization for the period 1968 to 2017 at 39 stations. In this paper, 14 extreme temperature indices defined by ETCCDI were analyzed. The indices are as follows: (1) Annual maxima of daily maximum temperature (TXx); (2) Annual maxima of daily minimum temperature (TNx); (3) Annual minima of daily maximum temperature (TXn); (4) Annual minima of daily minimum temperature (TNn); (5) Cold nights (TN10p); (6) Cold days (TX10p); (7) Warm night (TN90p); (8) Warm day (TX90p); (9) Frost days (FD); (10) Icing days (ID); (11) Summer day (SU); (12) Tropical nights (TR); (13) The warm spell duration index (WSDI) and (14) the cold spell duration index (CSDI). The extreme temperature indices were extracted using R software environment, RclimDex extension. The Mann–Kendall Test and Sen’s Slope Method was employed to assess the trends in 14 extreme temperature indices. To identify homogeneous groups of stations with similar annual thermal regimes, Principal Component analysis (PCA) and Clustering (CL) was applied. Pearson correlation coefficient was used to investigate the relationship between height and trend slope.

Result
All the extreme temperature intensity indices (TXx, TNx, TXn, and TNn) showed increasing trends during 1968 to 2017. The increasing trends of TXx, TNx, TXn, and TNn were 0.2, 0.3, 0.44, and 0.5 ° C per decade, respectively. These results indicated that the extreme warm events increased and the extreme cold events decreased. The average of the extreme temperature frequency indices over Iran showed that the frequency of warm night (TN90p) and warm day (TX90p) significantly increased with a rate of 6.9 and 4.2 day per decade, respectively. Also, the frequency of cold night (TN10p) and cold day (TX10p) significantly fell with a decrease rate of 3.8 and 3.8 day per decade, respectively. The frequency of warm nights (TN90p) was higher than that of warm days (TX90p). The result indicated that the trend of nighttime extremes were stronger than those for daytime extremes. The average of frost days (FD) and icing days (ID) indices over Iran showed decreasing trends during 1968 to 2017 with rates of 3 and 1.1 d per decade, respectively. While, the averaged of summer days (SU) and tropical days (TR) indices over Iran showed increasing trends with rates of 4.4 and 6.4 day per decade, respectively. The warm spell duration index (WSDI) indices showed a clear increase, with a rate of 2.1 per decade. In contrast the cold spell duration index (CSDI) showed a significant decrease, with a rate of 1.7 per decade. In general, the cold indices displayed decreasing trends, whereas the warm indices displayed increasing trends over most of Iran. Pearson correlation coefficient between height and Sen’s Slope was estimated to be equal to -0.62 (p < 0.01). In general, the results of this study showed that there is a negative correlation between the elevation factor and the Sen’s Slope of warm extreme indices. That is, as the altitude decreases, the Sen’s Slope increases. Therefore, the stations located in low altitude have experienced stronger increasing trends than in high altitude. The area of ​​Iran was classified into four clusters using PCA and CL methods. Cluster 1 has experienced the strongest increasing trends. The average height of cluster 1 is 535 meters. Approximately 38% of the studied stations were located in cluster 1. Cluster 2 showed a moderate heating trends. 33% of the stations were located in cluster 2. Most of the stations of cluster 2 are located in the northwest and west of Iran. Cluster 3 showed a weak increasing trends compared to clusters 1 and 2. The stations of cluster 3 did not show a special geographical concentration and were scattered in all parts of Iran. 18% of the studied stations are located in cluster 3. The stations of Cluster 4, have experienced weak decreasing trends, which was different from the other three clusters

Conclusion
In this study we analyzed the climate change of extreme temperature indices in Iran. The result showed that the frequency of warm nights, warm days, summer days and tropical days increased. Also, the frequency of cold nights, cold days, Frost days and icing days decreased. The warm spell duration index showed a clear increase. In contrast the cold spell duration index showed a significant decrease. In general, the extreme warm events increased and the extreme cold events decreased over most of Iran. There is a negative correlation between the elevation factor and the Sen’s Slope of extreme warm indices (R = -0.62). Therefore, the stations located in low altitude have experienced stronger increasing trends than in high altitude. The area of ​​Iran was classified into four clusters using PCA and CL methods. Cluster 1 has experienced the strongest increasing trends. The average height of cluster 1 is 535 meters. Therefore, the most heating have occurred in Low-lying areas of Iran. Cluster 2 and Cluster 3 showed a moderate and weak heating trends, respectively. The stations of Cluster 4, have not experienced clear trends.

Key words: climate change; Extreme temperature; clustering; Iran



 

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The effect of climatic parameters on vegetation distribution in central Iran
Introduction
Climate or climate reflects the daily weather conditions in a particular place for a long time. Most climatic elements are closely related to ecological factors, which is why the analysis of the relationship between climate and plant distribution patterns has been discussed in scientific and research circles for many years. And in recent years, scientists have been using a combination of climatic characteristics with other environmental factors to describe vegetation around the world. Climate change and atmosphere condition will change the content and composition of many plant communities.

The Study Area
The geographic coordinates of the studied area are between latitudes 29°32’ to 33°59’ and 51°27’ to 55°5’. The position of the selected provinces of central Iran compared to the neighboring provinces are shown in Figure 1 The annual data of 8 stations have been analyzed during the stations period determined by the National Meteorological Organization. The stations characteristics including latitude, longitude, elevation and specific statistical period are shown in Table 3.

Data and research methods
In this study, the role of temperature changes and relative humidity on vegetation in Central Iran has been investigated using statistical models of analysis of the main components and hierarchical clustering. This research is applied and its method is slightly analytical. In order to investigate the climatic fluctuations of the center of Iran with respect to urban green space, statistical data related to average temperature and relative humidity during the 32-year period (1986 to 2018) selected central stations of Iran to come and statistical deficiencies such as Data loss was performed by reconstructing differential equations using SPSS software. The criterion for selecting stations is the availability of long-term statistics. Using statistical methods and Geographic Information System (GIS), vegetation classification was performed for Central Iran. ArcGIS, Minitab, SPSS and EXCEL software are used in this research. After identifying the stations, climatic variables including temperature and relative humidity were selected from the data of 8 meteorological stations and were analyzed using the techniques mentioned above. Then, using statistical regression analysis, the impact (topography, average temperature and average relative humidity) on how to distribute and distribute vegetation was investigated. Kendall-man non parametric test was used to investigate changes in the vegetation index trend.

Results and discussion
Analysis of temporal changes in climatic parameters and NDVI index
The results show that the distribution of relative humidity in Abadeh and Kerman stations has decreased by 3% and the temperature distribution in these stations has increased by more than one percent. Relative humidity changes in Kashan and Sirjan stations have a weak decreasing trend, while the relative humidity distribution in Isfahan station has decreased by about 2%.The temperature distribution of Shiraz and Yazd stations increased by 3%, Abadeh station increased by 2% and also Isfahan and Kerman stations increased by 1%. The distribution of vegetation in Yazd and Khor Biyabank stations has decreased by one percent, while the growth of vegetation in Isfahan, Abadeh and Sirjan stations is increasing by less than one percent.

Distribution of NDVI vegetation index in Central Iran using cluster analysis
The stations are located in three distinct areas in terms of distribution of vegetation, each group having the same climatic characteristics in the distribution of similar vegetation. Based on this, three climatic zones in the study area can be identified.

Conclusion
The aim of this study was to investigate the effect of climatic parameters (average temperature and relative humidity) on the distribution of vegetation in Central Iran using comparison of statistical models; by examining the distribution and density of vegetation, eight factors were identified. Among the factors, the first and second factors, with 81.57% of the total vegetation variance, have played the most important role in determining the climatic diversity of Central Iran. In total, these eight factors have justified about 100% of the vegetation behavior in the area Also, according to the analysis of images of Modis satellite measuring satellites from the vegetation situation in the last 5 years, Central Iran, the value of NDVI index in Central Iran varies between 0.2 to 0.64, the northwestern parts of Fars province have the highest vegetation density and The central parts of Isfahan, especially Yazd, lack vegetation. Based on the results, altitude has a direct and significant relationship with temperature distribution in plants, especially in the study area. However, the height of Iran's central regions has affected the distribution of vegetation.

Keywords:  climatic parameters, vegetation distribution, central Iran

 

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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












 

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Onset and End of Natural Seasons in Iran

Introduction:
 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.
Material and methods:
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

Results and discussion:
 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.
Figure1: Date of onset, end and duration of natural seasons in different regions of Iran

Fall

Summer

Spring

Winter

Season

Length

End

onset

Length

End

onset

Length

End

onset

Length

End

onset

83

10 Dec

18 Sep

227

17 Sep

15 Apr

100

10 may

31 Jan

86

30 Jan

11 Dec

Earlier

160

21 Apr

13 Nov

90

12 Nov

19 Jun

103

18 Jun

8 Mar

29

7 Mar

1 Jan

Later

77

133

56

137

55

65

3

39

36

57

36

21

Fluctuation

Conclusion:
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.

Key words: Natural Seasons, Apparent Temperature, Plant and Animal Phenology, Iran.


 

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Investigation of the effects of Covid-19 pandemic on UHI in residential, industrial and green spaces of Tehran

 Abstract
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.

Materials and Methods
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.

Disscussion of Results
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 in this 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%.

Conclusion
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.

 Keywords: Urban Heat Islands, Land Surface Temperature, Vegetation Index, Change Detection, Covid-19

 

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Changes in ground surface temperature in the city of Halle and its relationship with changes in the NDVI index
abstract
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.

Key words: Earth surface temperature, vegetation, NDVI, city growth, Halle city
 

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Introduction:
The growth rate of urbanization during the recent decades of metropolises has had many destructive effects on the urban environment, among which we can mention the change of temperature of surfaces and local climates. The increase in the urban population, the rapid growth of industrialization and the increase in the concentration of pollutants in the lowest level of the atmosphere have affected the severity of the city's heat islands. Land surface temperature (LST) is a key variable to control the relationship between radiant, latent and sensible heat flux. Analyzing and understanding the dynamics of LST and identifying the relationship between it and changes of human origin is necessary for modeling and predicting environmental changes. The heat of urban surfaces is affected by various characteristics of urban surfaces such as color, surface roughness, humidity level, possibility of chemical compounds, etc. In addition, the changes between LST in a city and its surrounding area are due to surface changes, heat capacity and topography. Since the surface temperature regulates the temperature of the lower layers of the atmosphere, it can be considered as a weather indicator and an important factor in the urban environment. Changes in land use by changing the features of the surface cover such as the shape of the constructed areas, the amount of heat absorption, building materials, surface albedo and the amount of vegetation lead to changes in the temperature of the earth's surface. Barren lands with soil cover, on the contrary, increase the surface temperature of the earth. Climatic characteristics at the time of satellite image imaging also play a role in the extent and intensity of urban cold islands, so that satellite imaging in the middle of hot summer days shows urban cold islands better. The innovation of the research is in the large area of the investigated area, which includes eight urban areas, in order to examine the pattern of temperature changes on a wider level.

Materials and methods
Considering the rapid development of urban and industrial areas in the Ardakan-Yazd plain in recent decades, this study aims to investigate changes in the surface temperature pattern using Landsat 7 and 8 satellite images for both winter and summer seasons. It was done in 2002 and 2019. In addition, the relationship between land use/land cover and surface temperature was also investigated. Geometrical correction of satellite images was done using topographic map 1/25000 of Mapping Organization and atmospheric correction using FLAASH method in ENVI software. Algorithms used to obtain land surface temperature for Landsat 7 images were single-window method and for Landsat 8 images, the Landsat Science Office model was used. Land use/land cover layers related to the years 2002 and 2019 were used, and central statistical profiles and LST distribution were extracted for pasture, agricultural land, blown sands, industrial areas, rock outcrops and cities. In addition to examining temperature changes in different uses, it is also possible to compare over time.

Results and discussion
The results of this study showed that the area of cold islands and thermal islands in winter and summer of 2002 is not much different, so that in winter 10.8 percent and in summer 10.4 percent of the area were cold islands and thermal islands in winter 9.02. It was 8.5% of the region in summer, while this difference is huge in 2019. Thus, 9.4% of the area in winter and 12.1% in summer are covered by cold islands, and thermal islands are 8.3% in winter and 1.6% in summer. Changing land use and increasing the size of urban and industrial areas and reducing agricultural land is one of the main reasons for the increase in cold islands. The survey of land use/land cover changes between these years showed that the extent of urban areas increased from 22,045 to 23,714 hectares, and industrial areas also grew by about two times, from 4,615 in 2002 to 8,187 hectares in 2019. However, during this period, the area of agricultural land has decreased from 1161 hectares in 2002 to 793 hectares in 2019. Also, the results show that the percentage of heat islands is higher in winter than in summer. The main reason for this can be the much less vegetation covers in the winter than in the summer, because the vegetation cover acts as a moderator of the earth's surface temperature. Cold islands are formed in the built-up areas in the winter and summer. From 2002 to 2019, the extent of cold islands decreased in winter and increased in summer, while the extent of thermal islands decreased in winter and summer. Also, the results of the validation section of the single-window method and the model of the Landsat Science Office in calculating LST showed that for both summer and winter seasons, Landsat 8 has a higher accuracy than Landsat 7, and the LST estimation model is based on the exclusive method of this The Landsat series of satellites (Landsat Office of Science model) has a higher efficiency than the single-window method.

Conclusion
The results showed that cities play an important role in changes in the temperature pattern of the earth's surface, and the phenomenon of urban cold islands is not exclusive to big cities in hot, dry and semi-arid regions, but also occurs in medium-sized cities. The temperature variability of eight cities located in the Ardakan-Yazd plain with the land use/cover of the suburbs also showed that the cities are colder than the suburbs in both winter and summer seasons. This study showed the role of vegetation in hot and dry areas in reducing LST and also provided evidence for the change in the degraded state of pastures in this area.

Keywords: Urban climate, Land use, Land surface temperature, surface urban cool island (SUCI), ‎ surface urban ‎heat islands (SUHI)‎


 

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Investigation of the Temporal and Spatial Variation of Maximum Soil Temperature in Iran

Extended Abstract
Introduction
The study of soil temperature in different depths of soil is important in climatology, hydrology, agrometeorology and water resource management. Different depths has a different temporal and spatial soil temperature variation. It represents the regional ground temperature regime. Furthermore, due to its rapid response to environmental changes, soil temperature is one of the most important indicators of climate change. The increase in soil temperature because of global warming can promotes disasters such as drought by increasing the water demand of agricultural products during the plant growth period. The increase in soil temperature also have a various consequences, include increasing evaporation from the soil surface, soil salinity in susceptible areas, which can lead to a decrease in soil yield and failure in plant growth. Therefore, knowledge of soil temperature changes in different environments is very important in climate studies. The aim of the current research is to analyze the spatial and temporal variations of soil temperature at different depths from five to 30cm of the ground and to investigate the existence of any kind of increasing or decreasing trend at different climates of Iran.
Methodology
Hourly soil temperature data (depths of 5, 10, 20 and 30 cm) were used in this research for the period of 1998-2017. The soil depth temperature is measured three times a day at 6:30 am, 12:30 pm, and 6:30 pm local time (3, 9, and 3 p.m. UTC). These data have been received for 150 synoptic stations of Iran on a daily basis from the Iran Meteorological Organization (IRIMO). IRIMO monitored the quality of soil temperature for data entry, data recording, and data reformatting errors. Data availability, discrepancies, errors, and outliers were identified during the second stage.
At the first step, temporal coefficient of variation were calculated for available soil temperature time series from five to 30 cm depths of each station. For this purpose, the average of three daily measurements of soil temperature was calculated and then the temporal coefficient of variation was obtained. In the next step, trend analysis of soil temperature has been investigated using the non-parametric Mann-Kendal test. The trend slope was calculated using Sen’s slope for each station in seasonal time scale. Trend analysis has been done for all three observations of the day.
Results and Discussion
The studied stations show significant spatial patterns in the temporal variability of soil temperature. In all four investigated depths, from five to 30 cm, the northwest parts of Iran, and some parts of Zagros and Alborz mountain ranges have high temporal coefficient of variation. In contrast, the stations located on the southern coasts and southern islands had the lowest temporal variability. In warm and cold seasons (summer and late autumn to mid-winter), the spatial changes of soil temperature at different depths are lower than spring and early autumn. However, in the warm period of the year, the soil temperature experiences lower spatial variations at different depths. Spring and autumn seasons, as the transition period from cold to warm and warm to cold seasons, show the most spatial temperature variations in Iran. Detected trends do not have significant differences among the three observations of the day. Soil temperature Trend analysis at different depths showed positive values for two seasons of summer and winter over most of the stations throughout Iran. Extreme trends are more frequent in the summertime of Zagros and Alborz mountainous regions, while in the winter season the stations located at the southern latitudes of Iran have experienced the most positive trends. In the summer season, higher trends with 99% confidence are more frequent in the mountainous areas. These positive trends in soil temperature have occurred in all studied depths. The negative trend at different depths is a distinct feature of the autumn season, which is significantly more prevalent than other seasons throughout Iran. The analysis of soil temperature trends in different depths shows that values above 1 degree Celsius often occur in 5 to 20 cm deeps. The increasing trend of soil temperature in winter shows a greater spatial expansion, which is indicate increasing annual minimum soil temperatures and the increasing trend of Iran's soil temperature.
Keywords: Soil Temperature, Spatiotemporal Variations, Man-Kendal Test, Sen's Slope, Iran

 

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Simulation of Zanjan temperature trends based on climate scenarios and artificial neural network method

Abstract
Severe climate changes (and global warming) in recent years have led to changes in weather patterns and the emergence of climate anomalies in most parts of the world. The process of climate change, especially temperature changes, is one of the most important challenges in the field of earth sciences and environmental sciences. Any change in the temperature characteristics, as one of the important climatic elements of any region, causes a change in the climatic structure of that region. The summary of the investigated experimental models on climate change shows that if the concentration of greenhouse gases increases in the same way, the average temperature of the earth will increase dangerously in the near future. More than 70% of the world's CO2 emissions are attributed to cities. It is expected that with the continuation of the urbanization process, the amount of greenhouse gases will increase. According to the fifth report of the International Panel on Climate Change, the average global temperature has increased by 0.85 degrees Celsius during 1880-2012. Therefore, knowing the temperature changes and trends in environmental planning based on the climate knowledge of each point and region seems essential. For this reason, the present study simulates the daily temperature (minimum, maximum and average) of Zanjan until the year 2100.

Research Methods
The method of conducting the research is descriptive-analytical and the method of collecting data is library (documents). To check the temperature of Zanjan city, the minimum, maximum and average daily temperature data from Hamdeed station of Zanjan city during the period of 1961-2021 were used. The data of general atmospheric circulation model was used to simulate climate variables (minimum, average and maximum temperature) using artificial neural network and climate scenarios in future periods. The output variables in this study are minimum, maximum and average daily temperature. Therefore, three neural network models were selected. For model simulation, model inputs (independent variables) need to be selected from among 26 atmospheric variables. Therefore, two methods of progressive and step-by-step elimination were chosen to determine the inputs of the model. In these methods, climate variables that have the highest correlation with minimum, maximum and average daily temperature were selected. By using RCP2.6, RCP4.5 and RCP8.5 scenarios, variables were simulated until the year 2100. Markov chain model was used to check the possibility of occurrence of extreme temperatures of the simulated values.

results
According to the RCP2.6, RCP4.5 and RCP8.5 scenarios and the simulation made by the neural network model, it is possible that on average the minimum temperature will be 3.6 degrees Celsius, the average temperature will be 3.3 degrees Celsius and the maximum temperature will be 2.7 degrees Celsius. Celsius will rise. The monthly review of the simulated data for all scenarios and the observed data of the studied variables shows that the average minimum, average and maximum temperatures in January and February, which are the coldest months of the year, will increase the most and become warmer. While the average minimum temperature in August, the average temperature in April and the maximum temperature in October will have the least increase. According to the simulated seasonal temperature table based on all scenarios, it was found that the average minimum, average and maximum temperature observed with the maximum simulated conditions were 6.9, 5.5 and 5.4 respectively in the winter season, and 3.3 in the spring season. 4, 2.3 and 3, in the summer season it increases by 3.3, 3.4 and 1.4 and in the autumn season it increases by 4.6, 4.5 and zero degrees. The frequency of extreme temperatures observed in all three variables of minimum, average and maximum temperature for the 25th and 75th quartiles is less than the number of occurrences of extreme temperatures simulated in all three scenarios. Based on this, all three variables will increase and there will be fewer cold periods. An increase in night temperature and average temperature in winter season and maximum temperature in summer season will occur more than other seasons. The difference between day and night temperature will be less in autumn and summer. Also, all seasons, especially the summer season, will be hotter and the occurrence of extreme temperatures is increasing for the coming years.

Keywords: climate scenarios, simulation, extreme temperatures, artificial neural network, Zanjan



 

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