نشریه پژوهش های اقلیم شناسی
پیاپی 42 (تابستان 1399)

Climatic conditions is one of the most important factors affecting different aspects of human life, especially in urban areas and affects social and economic sectors. Establishing a thermal balance between the human body and the environment is one of the primary needs for health and comfort. Human thermal comfort conditions can be evaluated by using various indices based either on simple empirical approaches or on more complex and reliable human-biometeorological approaches. Human thermal comfort conditions with a single parameter or thermal indices derived from experimental equations, cannot fully evaluate thermal comfort conditions. In this study, we tried to investigate the temporal distribution of heat and cold stress events based on the physiological equivalent temperature (PET) index of Tehran in 9th area.

Determining suitable weather conditions for outdoor presence (in terms of heat and cold stresses) can be an important factor in reducing mortality and providing human comfort in these areas. Therefore, the focus of this paper is to examine the weather conditions in Tehran 9 area. The establishment of Tehran's Mehrabad Airport in this area as well as the presence of the western terminus on the northwest side of Azadi Square has made it one of the busiest areas in Tehran. The evaluation was based on statistics and information from Tehran's Mehrabad Airport synoptic station located on the southwestern side of zone 9. In this paper, to determine the time range of occurrence of heat and cold stresses, temperature data, relative humidity, water vapor pressure, wind speed, and cloudiness on a scale of hourly during 2008 to 2017 were obtained from the Meteorological Organization  and PET index was calculated using RayMan Model and analyzed on annual, seasonal, monthly, daily and hourly scales.

The PET index for Tehran 9 area was calculated from January 2008 to December 2017 using data from Tehran Mehrabad Synoptic Station. The results showed that in this decade, the thermal comfort, frequency of PET index accounted for only 12.91% of the whole period. Much of the analyzed data belong to cold stress classes of less or higher, with 54.62% being the most abundant. The rest of the data are in the range that according to the PET index classification, are slightly warm to hot and 27% of the whole period is affected by different heat stresses. The results showed that extreme cold stresses began in the second decade of October and continued until the first decade of April. This class of cold stresses completely disappears between the second decade of April to the first decade of November. Initial surveys showed that the highest percentage of very severe cold stress was distributed in the first 10 days of January. This percentage is slowly declining as temperatures rise in the coming days, reaching their lowest level in the first decade of April. The percentage of severe, moderate and mild cold stresses decreased by 11% over the whole period from 23% to 12%. The range of severe cold stresses started from the second decade of October and was steadily stable in the study area until the third decade of April. Maximum and minimum percentages of severe cold stress were in the second decade of March (35%) and the first decade of October (2.5%). Moderate cold stresses began 20 days earlier than severe cold stresses and continued until the second decade of May. According to the figures obtained from the PET index, it peaked at 46% in the second decade of April. Slight cold stresses that started in the third decade of April and continued until the third decade of June. Extreme heat stress is distributed only in June, July, August and September. Frequency of heat stress increases significantly in July, especially in the second decade. The frequency of this stress in the first 10 days of June increased by 1.3% and increased to 37.5% in the second decade of July. The convergence frequency of the PET index is observed in different percentages between the third decade of March to the second decade of October.

Area 9 of Tehran, 86% of the total year was outside the range of thermal comfort conditions, with 59% related to cold stresses (PET <18 ° C) and 27% related to varying degrees of heat stress(PET>23C°). Extremely severe cold stresses in January, February and December from 6 pm to 6 am and severe to moderate heat stress from late May to late September between 9 am and 3 pm.

Precipitation is one of the most important climatic elements which has been changed over the last few decades. Changing in climatic elements, especially precipitation, could dramaticaly influancs natural, social and economic aspects of the environment. Change in precipitation turn up in different ways.The purpose of current study is to examine the changes in the decade of the monthly mean precipitation of the cold season over the Caspian coast of Iran territory in the last half century.

For this purpose, the daily database of precipitation which resulted from the interpolation of 385 stations, under Meteorological Organization and the Ministry of Energy's supervision have been used for the period of 2016-1966 (51 years) with a spatial resolution of 3 × 3 km.

Our finding showed that the precipitation gradiant transfered from the west to east and the areas with low precipitation increased in compare to the areas with high precipitation. This decreasing general trends emerged in a decadal- sequential oscelatory pattern.
As the Caspian coast precipitation atributed to thermal high pressure systems and Meditraniean cyclones, the changes in values of precipitation and the area, are considered as a result of these factors which are investigated in many researches previously.

In addition to significant environmental impacts, precipitation variations in the decade have an undeniable effect on the quantity and quality of water resources.The results show that in the last 11 years (2006 to 2016), the average monthly precipitation and its location from the southwest of the Caspian Sea to the Alborz and eastern altitudes have been shifted and the extent of ​​the low amount precipitation is increasing.Changes in precipitation during the cold months of the year in the Caspian Sea region have been attributed to changes in thermal high pressure, as well as to the variations in the frequency and direction of the Mediterranean Cyclones, as shown in previous studies.Since precipitation variations are very complex and requires comprehensive studies, it is suggested that in order to plan and make more precise decisions, it is recommended to identify the changes of the Synoptic systems in this area.

Precipitation as one of the most important parameters of meteorology and climate, is basic factor in water resource management. This factor has a direct relation with the regional climate. The accuracy of simulating this parameter is very important due to its wide variation. Observation data at Iran's first synoptic stations from 1330 (1951) is available at the Iranian Meteorological Organization website. Old and long-term temperature and monthly precipitation data in five cities of Iran Including Mashhad, measured by the Embassy of the United States and Britain from the Qajar period (around 1880) and recorded in World Weather records. Unfortunately, these data have missing. Monthly missing data are during World War II (1949-1949) and sporadically during the statistical period. Stations from neighboring countries due to the Parity criterion, solidarity and completeness of data in missing periods selected as base stations. Monthly precipitation of Ashgabat Station from Tajikistan and monthly rainfall of Sarakhs, Kooshkah, Bayram Ali, Kerki and Repetek from Turkmenistan were selected as independent variable in the making of Missing Rainfall in Mashhad. Three factors of distance to Mashhad station, correlation and existence of data in missing months were effective in selecting these stations. This research has fitted ten multiple regression models to monthly rainfall of Mashhad station and then the parameters of these patterns are optimized by genetic and Ant Colony algorithm. In statistical modeling, regression analysis is a set of statistical processes for estimating the relationships among variables. It includes many techniques for modeling and analyzing several variables, when the focus is on the relationship between a dependent variable and one or more independent variables (or 'predictors'). Genetic algorithm (GA) is a metaheuristic inspired by the process of natural selection that belongs to the larger class of evolutionary algorithms (EA). Genetic algorithms are commonly used to generate high-quality solutions to optimization and search problems by relying on bio-inspired operators such as mutation, crossover and selection. Ant colony optimization algorithm (ACO) is probabilistic technique for solving computational problems which can be reduced to finding good paths through graphs. This algorithm is a member of the ant colony algorithms family, in swarm intelligence methods, and it constitutes some metaheuristic optimizations. The repair of the monthly precipitation of Mashhad with these stations has been done with ten regression linear, semi-logarithmic and logarithmic regression models as follows. This was done with programming in the R-Studio environment. The parameters of the five selected patterns were optimized by evolutionary methods (genetic algorithm and anion colony algorithm). Simulation of these methods has been done with the help of MATLAB software 2017. The results showed that the genetic algorithm and Ant Colony methods Ratio of regression methods , dramatically increase the accuracy of estimating missing rain data. The lowest RMSE regression pattern is 9.79, which is optimized by genetic algorithm to 2.66 and by Ant Colony algorithm to 2.659. Precipitation as one of the most important parameters of meteorology and climate, is basic factor in water resource management. This factor has a direct relation with the regional climate. The accuracy of simulating this parameter is very important due to its wide variation. Observation data at Iran's first synoptic stations from 1330 (1951) is available at the Iranian Meteorological Organization website. Old and long-term temperature and monthly precipitation data in five cities of Iran Including Mashhad, measured by the Embassy of the United States and Britain from the Qajar period (around 1880) and recorded in World Weather records. Unfortunately, these data have missing. Monthly missing data are during World War II (1949-1949) and sporadically during the statistical period. Stations from neighboring countries due to the Parity criterion, solidarity and completeness of data in missing periods selected as base stations. Monthly precipitation of Ashgabat Station from Tajikistan and monthly rainfall of Sarakhs, Kooshkah, Bayram Ali, Kerki and Repetek from Turkmenistan were selected as independent variable in the making of Missing Rainfall in Mashhad. Three factors of distance to Mashhad station, correlation and existence of data in missing months were effective in selecting these stations. This research has fitted ten multiple regression models to monthly rainfall of Mashhad station and then the parameters of these patterns are optimized by genetic and Ant Colony algorithm. In statistical modeling, regression analysis is a set of statistical processes for estimating the relationships among variables. It includes many techniques for modeling and analyzing several variables, when the focus is on the relationship between a dependent variable and one or more independent variables (or 'predictors'). Genetic algorithm (GA) is a metaheuristic inspired by the process of natural selection that belongs to the larger class of evolutionary algorithms (EA). Genetic algorithms are commonly used to generate high-quality solutions to optimization and search problems by relying on bio-inspired operators such as mutation, crossover and selection. Ant colony optimization algorithm (ACO) is probabilistic technique for solving computational problems which can be reduced to finding good paths through graphs. This algorithm is a member of the ant colony algorithms family, in swarm intelligence methods, and it constitutes some metaheuristic optimizations. The repair of the monthly precipitation of Mashhad with these stations has been done with ten regression linear, semi-logarithmic and logarithmic regression models as follows. This was done with programming in the R-Studio environment. The parameters of the five selected patterns were optimized by evolutionary methods (genetic algorithm and anion colony algorithm). Simulation of these methods has been done with the help of MATLAB software 2017. The results showed that the genetic algorithm and Ant Colony methods Ratio of regression methods , dramatically increase the accuracy of estimating missing rain data. The lowest RMSE regression pattern is 9.79, which is optimized by genetic algorithm to 2.66 and by Ant Colony algorithm to 2.659.

Iran has an arid and semi-arid climate that 61% of the country receiving less than 250 mm of rainfall and only 4% of the country receiving more than 600 mm of rainfall. This fact indicates a very uneven distribution of precipitation in Iran. On the other hand, given the fact that Iran is located in a dry region, the time variability of precipitation is very high. 85% of Iran is in the arid territory. Drought and water scarcity its main characteristics of Iran's climate (Karimi et al. 2018). After the heat and humidity factors, the third factor is precipitation that one of the 6 most important factors in shaping Iran's climate which is associated with temporal and spatial fluctuations (Masoudian 2003). Increased greenhouse gas emissions, global warming and subsequent climate change, especially in recent years, have caused major changes in this important climate factor, with Iran increasing 0.5 mm in volume over the last half-century (Masoudian 2011). Increased greenhouse gas emissions, global warming and subsequent climate change, especially in recent years, have caused major changes in this important climate factor, with Iran increasing 0.5 mm in volume over the last half-century (Masoudian 2011). On the other hand, forecasts for an increase of 1.5 Celsius degrees in 2030 and 2050 will add to the probable severity of these changes in the future (IPCC 2018).

In this paper, for the first time, the temporal variations of the rainfall duration of the country during the wet season were considered. In order to achieve this goal, after extracting the beginning and ending days of three-day rainfall and more, the rainfall season duration at 108 stations studied was calculated and first, using hierarchical cluster analysis, zoning the duration precipitation regions of the country. Subsequently, the study of rainfall season fluctuations based on the 25-year time series (1991–2015) was carried out using the Yue & Wang modified Kendall method.

The results showed the duration of the rainy season in 5 regions (very long, long, short, short and very short). As it was noted, these areas, like the rainfall regions of the country, mostly followed latitude and had more orbital arrangement (Masoudian 2009). Investigating the trend of the duration of rainy season changes in the country showed that these changes decrease significantly in the southern latitudes relative to the northern latitudes as the dry and semi-arid regions of the country, which have the shortest rainfall season, experience the least significant changes. (Modares & Dasilva 2007). Except for the southwestern coastal area of the Caspian Sea and the northwest of the country, significant incremental changes occur during the rainy season, along the southern slopes of central Alborz and the eastern slopes of northern Zagros. It should be noted that the Caspian coastal region is dominated by precipitation and humidity and in the northwest of the country up to the southern slopes of central Alborz, the thunderstorms are dominated pattern. In addition to the eastern slopes of the northern Zagros are prevailing by the thermal radiation climate, and is one of the low rainfall areas of Iran (Masoudian 2009, 2005). Therefore, the duration of the rainy season is increasing in regions with different climates. In general, as we move from north to south and from highlands to plains, inland deserts and coastlines, the long-term changes in the rainy season also decrease significantly. The most significant changes in these changes are mainly seen in the western half of the country and the southern slopes of Alborz and scattered in the northeastern and southern heights of the country.

Regardless of the significant incremental changes mentioned above, in general, the trend of long-term changes in the country during the wet season indicates a significant decrease in most areas and regions of the country. In other words, the country's rainfall regime is more concentrated and the rainfall season is shorter. Considering the importance of the duration of the rainy season and its variations in the growing season, flowering, sprouting (Zhou 2019, Moor & Lauenroth 2017, etc.) and crop yields (Daewoo 2019, Karimi et al. 2018, Maddah et al. 2015, etc.). As well as in the storage and management of water resources, it is hoped that the results of the forthcoming research will be useful in taking steps to achieve water and food security in the coming years.

Rainfall modeling is essential in water resources and agriculture management, especially in arid and semi-arid climates. Problems in generalizing precipitation from a point to a region are the reasons for alternative methods to estimate precipitation, and one of these methods is the use of remote sensing. Across the globe, research is being conducted to evaluate the accuracy of remote sensing precipitation data. Accordingly, the aim of this study is to model the prediction of TRMM Multi-satellite Precipitation Analysis (TMPA-3B43) data in arid and semi-arid climates of Iran, using the SARIMA simulation model for the period of 1998-2017 in seasonal scale.  

For this study, monthly precipitation data from TMPA 3B43-v7 were selected from two different climates. To ensure the reliability of the results, a completely randomized selection of four regions was made from different provinces and topographic conditions. The TMPA satellite data (3B43-V7) was prepared by the NASA databases during the period of 1998-2017. These data have a time-stamping time resolution and spatial resolution of 0.25 degrees, covering from 50 degrees south to 50 degrees north of latitudes. Satellite pixel data was corrected using the corresponding synoptic station data. Monthly data after correction were aggregated to seasonally data. Spring data from April, May and June, the summer data from July, August, and September, autumn data from October, November, and December, and winter data were obtained from January, February, and March. Using the autocorrelation function (ACF) and partial autocorrelation function (PACF), time series models were determined. The normality of the residuals, the independence of the residuals and the constant of the variance of the residuals was checked out. A general model was fitted to the data to check the accuracy of the selected model. The result showed that the selected model has the least error compared to other models. After calibration and evaluation of the selected models, seasonal precipitation data was predicted based on the monthly scale for the period (2018-2021) and compared with the base period (1998-2017). The evaluation was measured using some statistical indices including AIC, R, MBE, MAE, and RMSE.  

The correlation coefficient based on monthly data changes from 0.81 to 0.9 in the studied areas which confirms the high accuracy of modeling based on SARIMA technique in monthly data for seasonal prediction. Best model for corresponding pixel of Behbahan station was SARIMA(0,0,1)(1,1,1)12 and SARIMA(0,0,0)(2,1,1)4, Zarghan station was SARIMA(2,0,0)(1,1,1)12 and SARIMA(0,0,0)(2,1,2)4, Kashan station SARIMA(0,0,0)(1,1,1)12 and SARIMA(0,0,1)(2,1,1)4 and Sirjan station SARIMA(1,0,1)(1,1,1)12 and SARIMA(0,0,0)(2,1,1)4 for monthly and seasonal modeling respectively. The mean bias error in the monthly and seasonal periods of the Kashan station corresponding pixel (0.2 and 0.5 respectively) and the monthly period of the Sirjan station corresponding pixel (-0.3) was obtained. Seasonal modeling based on monthly data was compared with seasonal prediction data. In the autumn, the same as the spring season, seasonal precipitation increases in three regions and decreases in one station. In the summer, it was observed relative stability in seasonal precipitation at all points. In winter, unlike the spring and autumn seasons, seasonal precipitation was decreased in three areas.  

The results showed that if the predicted monthly data turned into seasonal data, they would be more accurate for seasonal forecasting. Generally, based on annual precipitation, the studied areas will have a relative precipitation reduction of 1.8 mm per year. Regarding the average annual rainfall in the four studied areas, the average annual precipitation is estimated to be decreased by about 1 percent in the study period. Separately, annual precipitation changes are -1.3%, 1.1%, 1%, and 1.8% in Kashan, Sirjan, Behbahan, and Zarghan areas respectively.

Jazmurian wetland is located in an endorheic basin at the southern edge of the Dasht-e-Lut. Several factors such as high evaporation, over exploitation of groundwater, dam construction on the rivers feeding the wetland, and the effect of drought and climate changes have caused this wetland to dry out during the recent years. Investigation of dynamic monitoring of water surface area in past and its relation with climatological variation has an important role for reclamation and conservation of wetlands. This study investigated the water body of Jazmurian seasonal wetland from 1972 to 2017 by Landsat satellite images. The temporal monitoring of wetland water area was performed using Landsat Data Series (MSS, TM, ETM+, and OLI). Further, the relationship between wetland water area, rainfall, as well as inflow water the wetland in this period was investigated.

The meteorological data used included information of 3 evaporation stations for measuring evaporation and 16 rainfall stations for measuring precipitation. Due to lack of hydrometric stations surrounding the wetland, Kahnak Sheybani (Kahn) on Halil River and Bampur (Bamp) on Bampur River were used for measuring inflow water to the wetlands. Note that Kahn and Bamp are about 200 km and 150 km away from the wetland, respectively. In this study Landsat data series (MSS, ETM, ETM+ and OLI/TIRS) between 1987-2017 were downloaded from EarthExplorer. All study regions were within Path/Row 158/41. Geometric and radiometric corrections were done for all used images. After that Normalized difference water index (NDWI) was used to extract water bodies from remotely sensed imagery. NDWI values were derived using combinations of the NIR and green bands as (McFeeters 1996).  In this study an appropriate threshold for identifying water features was achieved through trial and error, with comparison to base maps made using visual commentary and field visit. A series of field surveys of water body was done at the same time as the satellite pass occurred using a Garmin GPS device on 9 March 2017, 10 April 2017, and 26 April 2017. Random sample points of the boundary of water body were identified for comparison with the results obtained by NDWI. For visual interpretation of water features, because of strong absorption of near-infrared spectrum by water and strong reflection of vegetation and dry soil.  Moreover the relation between wetland water body with climatological variables such as evaporation, precipitation and water inflow to the wetland was determined. Finally in the study period based on data series of observation (60 series) and using SPSS software an equation is presented. That can be used in planning, management and restoration of wetlands
 

The near-infrared (NIR) band visual interpretation as well as the optimized threshold value of -0.085 was applied to the NDWI image to discriminate between water and non-water surfaces. A number of control points were used to identify the optimized thresholds for NDWI reclassification in water/non-water. Which showed that the NDWI index has a good performance. Awareness of flooding and the drying trend of the wetland will help in its restoration. If we know which areas of the wetland are drying out earlier and the soil moisture is out of reach sooner, that is to say, they are more susceptible to dust generation.  According to field observations, it is clear that the slope is very gentle at the extreme end of the basin. As already mentioned, all the surface water drains towards the wetland. On the other hand, extreme floods in the past have led to considerable sediments moving toward the wetland, where fine-grained sediments have reduced the slope and permeability of the wetlands. In other words, even during a slight precipitation around the wetland, a noticeable surface area of the wetland will become wet and water will appear on the surface of the wetland. Obviously, the vast surface area and the low water level and the high potential of evaporation in the region are not favorable for water to remain on the wetland surface.  It also seems that there have been no significant changes in the topography of the eastern part of the wetland, with low rainfall mostly appearing in it.  Also the results shows there is a direct relationship between rainfall on the wetland and its water area. On the other hand, rainfall has the maximum effect on the wetland water body. In addition to rainfall, water inflow to the wetland from Halil and Bampour River had an effective role in expanding the water area of the wetland.

Investigation of dynamic monitoring of water surface area in past and its relation with climatological variation has an important role for reclamation and conservation of wetlands. This study investigated the water body of Jazmoriyan seasonal wetland from 1986 to 2017 by Landsat satellite images. Further, the relationship between wetland water area, rainfall, as well as inflow water the wetland in this period was investigated. The results showed in addition to feeding the wetland by Halil and Bempour River, the rainfall around the wetland plays an important role in flood duration of the Jazmurian wetland. Moreover the relation between wetland water body with climatological variables such as evaporation, precipitation and input flow was determined.

Climate change, as a major global challenge, has attracted the attention of many scholars and has found many types of research (Ghahraman and Gharekhani, 2010). Slight changes in the climate can affect other components in varying degrees, as an important and comprehensive component of the ecosystem. Fluctuations and changes occurring in the region over the short and long term in the climate of a region are the sources of much change in the environment. One of the factors that affect the planet is water vapor. The increase in water vapor yields large positive feedback on the surface temperature (Hansen et al., 1984), which causes rainfall in the middle and high latitudes. Therefore, it is obvious that changes in the content of the vapor barley should be investigated. Since Iran is located in a dry and semi-arid region, it lacks large internal and adjacent water resources to provide its own moisture content. As a result, more rainwater sources should be provided with water levels around it. One of the ways in which it is possible to examine the evolution of specific moisture developments in the past and present is to analyze the process of time series at different time scales. Several statistical methods have been presented to analyze the time series process, which can be categorized into two general categories of parametric and nonparametric methods.

The study area includes Hormozgan, Sistan and Baluchistan and Kerman provinces, between 25 to 31 degrees north latitude and 54 to 63 degrees east of the Greenwich Meridian. The variables used in this study include specific data (in grams per kilogram) at 1000, 850, 700, and 600 hpa of ERA-Interim data from a series of predefined data from the European Center for the Meteorological Mean of ECMWF is. Parametric methods are mainly based on the regression relationship between the data series. Nonparametric methods have a relatively larger and more significant application than parametric methods. It is more appropriate to use nonparametric methods for series with a certain statistical distribution that is unsuitable for them and a lot of sloping or elongation. The Kendall test is one of the most common and most widely used nonparametric methods of time series analysis. Using the Man-Kendall method, data changes are identified, its type and time are determined. The non-parametric Man-Kendall test was first developed by me (1945) and then developed by Kendal (1975) based on the data rank in a series of times. This method is commonly used in analyzing the hydrological series process and meteorology. The strengths of this method can be attributed to the suitability of its use for series when it does not follow a specific statistical distribution. The negligible influence of this method is the limit values seen in some time series as well as other advantages of using this method. The zero assumption of this test implies the randomness and absence of a trend in the data series, and acceptance of the hypothesis (zero assumption rejection) indicates the trend in the data series.

Figure 5 shows significant zoning maps of the annual moisture content of stations in the southeast of Iran during the period 2016-1987 based on the index of Man-Kendall. The significant trend at different levels indicates a significant trend at 1000, 850 and 700 hpa in the coastal strip of the Persian Gulf and Oman Sea in the study area and there is no specific trend in the above-mentioned latitudes. Only in two stations of Zahedan (At 1000 hpa) and pomegranate (at 850 and 700 hpa), moisture content have a negative annual trend in the study period.

The significant moisture content at various levels of the atmosphere indicated that the annual humidity trend increased in most southern stations close to the Oman Sea and the Persian Gulf and was decreasing in northern stations. The annual moisture trend of southeast of Iran at different levels of the atmosphere showed a significant trend at 1000, 850 and 700 hpa in the Gulf Coast and Oman Sea in the study area and only at two stations in Zahedan (at 1000 hpa) and Pomegranate (at 850 and 700 hpa) moisture content has a negative annual trend in the studied period. The trend of the average annual moisture content of south-east of Iran at different levels of the atmosphere showed that during the study period, only 1000 hpa positive trend was observed in the average moisture content of southeastern Iran at the significant levels of the test.

Methods and Materials: Dust and dust has become one of the most dangerous natural hazards in Iran in recent years and has affected the lives of the residents of this region in the west and southwest of the country and has caused irregular migration from these areas. Rainstorms also severely affect the power outlets of these areas, resulting in shorter life spans, increased maintenance costs and corrosion of power distribution equipment, which ultimately lead to extinction and widespread social consequences. At the age of fifteen, during the years 96 to 81 The ESDD practices and insulators on power NSDD Khuzestan province. For this purpose, we obtain five parameters of climatic parameters and number of dust days from meteorological organizations, concentration of pollutants from environmental organization, and amount of pollution on insulators from electricity system and by examining the relationship between annual number of days associated with dust storms and climatic parameters and pollution concentration analysis. The sites were zoned using ESDD and NSDD contaminated areas using GIS.

Dust has become one of the most dangerous natural hazards in Iran in recent years and has affected the lives of residents in the western and southwestern parts of the country and has caused undue migration. Rainstorms have also severely affected the power outages of these areas, resulting in shorter life spans and increased maintenance costs. The purpose of this study is to analyze the effects of dust and pollution during the years of 96-81 by ESDD and NSDD methods on electricity consumption in Khuzestan province. Pollution rates on insulators from power plant and zoning of polluted areas using GIS were analyzed by examining the relationship between annual number of days with dust storms and climatic parameters and analysis of pollution concentrations using the above methods.

The results showed that the contamination status of the area was exceeded by international standards and the condition was very heavy and the concentration of contaminants reached from 2010 micrograms / m3 in 2002 to 10000 micrograms / m3 in year 96 and the main concentration of these contaminants in the areas. South of the province is between Ahvaz, Mahshahr, Abadan and Khorramshahr stations. Criterion of contamination due to frequent occurrence of high insoluble matter with high solubility is possible using both ESDD and NSDD methods and due to more power outages, dust deposition and dusts. On insulators and its combination with air humidity and The creation of sticky mud on this factor leads to a short circuit in the network. Amongst the stations, the highest number of stormy days with 64 days is related to Bostan Station due to being located in the western part of the province and close to the eastern and western regions. The Karkheh River, which has the potential to lift particles, as well as the proximity of Arab countries, has most of its foreign-origin storms, with a minimum of 22 days to Dezful Station, located in eastern Tristan, and most of its storms are due to avoiding western dust. It has internal and local origin.

This project is an updated estimate of the climate change trend in Iran. The time series of monthly and annually mean, maximum and minimum temperature (°C), relative humidity (%) at 2 m height, wind speed (m.sat 10 m height, sun hours (h), precipitation (mm), radiation (Mj.m-2day-1) and cloudiness data at 27 weather stations over Iran are collected from IRIMO for the period 1958-2017. Quality control, detection and modification of non-climate heterogeneity of data was performed. Due to missing data, the Sun hours trend was calculated for the period 1992-2017.

Methodology and data:

Among all stations of Iran, 27 stations have 60-yearly data in the period of 1958-2017. In this research the changes in temperature, precipitation, humidity, radiation, cloudiness and wind during the period of 2017-1958 (for sunshine the period was 1992-2017) were investigated. At first, the data were checked for quality controlling. Then their non-climatic heterogeneities were fixed. The slope of the trend was determined using the least squares method and the slope estimator and their significance was assessed using nonparametric Man-Kendal test and regression.

Temperature: The results showed that all stations in the country face a significant increase in the annual minimum temperature. The minimum temperature increasing rate per decade calculated to be between 0.2 and 0.4  degree of Celsius in Bandar-Abbas and Tehran, alternatively. In general, minimum temperatures rise were detected at all stations and in all seasons, especially in autumn and winter. The annual maximum temperature trend is also increasing, but the rate of increase in maximum temperature is less than the minimum temperature. The increasing rate per decade calculated to be between 0.08 and 0.3  degree of Celsius in Zanjan and Ahwaz, alternativelyPrecipitation: The 60, 30, and 10-years averaged annual precipitation of Iran calculated to be 230.8, 222.4 and 199.3 mm, respectively. The results showed that during the 60-year period, the average rainfall of the Iran decreases, with a rate of 0.43 mm per year (4.3 mm per decade), although the precipitation decline is not significant at 95% level.However, during the last 30 years (2017-1988), the average precipitation of the country has dropped by 2.2 millimeter per year (22 mm per decade), which is significant in 95% confidence level; meaning that precipitation reduction in the most recent 30 years is about four times higher than that of past 60 years.In Figures 2 and 3, the all-country time series of rainfall and temperature changes are shown in the 60- and 30-yearly basis.The country's declining precipitation for the most recent 30 years is about four times faster than the decline of most recent 60 years. The rapid decline in country precipitation over the past 30 years, which is significant   in 95% level, is consistent with the intensification of global warming in the most recent 30 years period.Other parameters: The average wind speed in many parts of the country has increasing trend, which is significant in many stations located in the west, center, and northern part of the country. Average relative humidity has decreased in many regions of the country. The most decreasing trend was observed in southwest and west of the country. Of course, in a few cases such as Gorgan and Rasht, there was an increase, which was not statistically significant. Sunny hours trend was calculated in the period of 1992-1992, and interestingly, seasonal and annual trends at most stations indicate an increase in the number of sunny hours. The total number of days with sky overcast in the west of the Caspian Sea, western part of the country, and stations such as Kerman, Sabzevar and Shahrood has decreased significantly..

Although climate seems to be a constant phenomenon, past geological study shows that climate conditions have constantly changed with other internal and external developments of the Earth, with frequent cold, hot, or dry and humid periods often overlapping. Considering the importance of the issue of climate change in the present century, the study of the trend of climate parameters is particular importance. Changes in climate parameters such as temperature and precipitation as the most important climatic parameters can be affect the hydrological processes, agriculture, environment, health, Affecting industry and the economy, and assessing these changes in a region will be of great help to the challenges of water resource managers and planners. Therefore, in this study, the trend of climate change through the trend of temperature and precipitation changes on an annual basis of a 30 year period (1986-2018) at synoptic stations located in Iran and the countries of the region that have been affected by drought crisis in recent decades and was encountered with Dust, done by The trend analysis using statistical methods.

The studied meteorological stations consisted of 66 synoptic stations located in 18 countries of the region, and the methods used to study the trend include non-parametric Mann-Kendall test, Sen Slope estimator and slope of regression line. The Mann-Kendall nonparametric test, completed by me in 1945 and then completed by Kendall in1975, is based on the order of the data in a time series. This test is used to check the randomness of data (no trend) versus trend in hydrological and meteorological time series. The advantage of this test over other trend-setting tests is the use of time series data regardless of the value of the variables. Because of this property, this test can also be used for skewed data, and the data need not be distributed. There will be something special. of Sen slope estimator was presented to identify trends in a time series of data. This method is a nonparametric method that calculates the median slope for the time series of data by analyzing the difference between observations of a time series. The Trend Line helps us to predict the state of the data, in addition to detecting the data change process. The main reason for using nonparametric methods compared to parametric statistical methods is that nonparametric methods work best for data without normal distribution.

 The mean annual temperature distribution of the study area indicates that the southern and southwestern regions of the study area have a higher average temperature than the other regions, and by moving to higher latitudes the average temperature will generally decrease at the regional level. The case study fluctuates between 10 and 28 degrees Celsius. The annual precipitation distribution of the study area also shows that in general the precipitation in the study area is between 38 and 404 mm. As can be seen, the stations located in the north and west and northwest of the region have good rainfall. The lowest rainfall in the study area is also in the east, south and southwest of the region and around the Persian Gulf and most of the countries of Saudi Arabia, Oman, United Arab Emirates, Bahrain, southeastern Iran, and Pakistan and Afghanistan. These areas are among the least rainfall areas in the study area. The results showed that change is significant in some time series and in some stations in the form of short-term fluctuations and in others it is significant. The trend and type of trend in the precipitation series are decreasing and negative, with the highest decrease being in Sanandaj and Van stations with a slope of -5.3. The highest increase in precipitation is also in the station. Slope closet is 34.8. Except for Hyderabad and Lahore stations and to some extent in Shahrekord and Shiraz stations in general, temperature has been increasing in general. Overall, based on the results of the study of annual changes in temperature and precipitation at the regional level, it was found that precipitation in the study area has a decreasing trend and temperature has an increasing trend, with decreasing and increasing trend in some stations and in some series. Has become meaningful. In general, according to the method of Sen slope and the regression slope, the annual precipitation changes are more severe and more pronounced than the regional temperature changes. Therefore, considering the changing climatic parameters in the study area and being aware of the negative effects of climate change, relevant planners especially in the sectors of water resources management, agriculture, environment, health and economic and natural resources sectors. Adopt strategies to mitigate the consequences and adapt to the new climate and, given the current status and trends of climate change, fundamental revisions to environmental planning and the allocation and utilization of resources, especially water resources and it seems necessary.

Teleconnection models are increasingly used to predict the average atmospheric conditions in different time periods. In other words, the connection between atmosphere and its slow changes in oceans provides the possibility of predicting climate conditions in different time scales, such as monthly, seasonal, annual and decennial time scales. Teleconnection has always been defined as the simultaneous connection between climate oscillations of a region with changes in pressure patterns and sea surface temperature in other geographical points. As El Nino South Oscillation (ENSO) is the most obvious teleconnection model in south hemisphere, North Atlantic Oscillation (NAO) is also the most obvious teleconnection model in the north hemisphere. This study aims to consider the relationship between drought and wet years in Iran with NAO in both station and regional scales based on one of the linear models.

Two datasets were used in this study to evaluate the relationship between NAO with droughts and wet years in Iran. The first dataset was monthly precipitation data of 63 synoptic weather stations received from Iran meteorological organization for a 30-year time interval (1986-2016), and in the second dataset, the values related to NAO for the same time interval (1986-2016) were taken from database of National Center for Environmental Prediction/National Center for Atmospheric Research (NECP/NCRA) of US National Oceanic and Atmospheric Administration (NOAA). After collecting the required data from different databases and forming their databank, Standardized Precipitation Index (SPI), which is one of the indices proposed by World Meteorological Organization, was used to quantify the droughts in Iran. In this step, SOI was calculated for all the studied stations in a monthly scale. In the next step, based on the spatial principle, Iran's droughts and wet years were divided in the same monthly scale into three classes of pervasive droughts (wet years), semi-pervasive droughts (wet years) and local droughts (wet years). After determining droughts in both station and regional scales, Pearson moment correlation coefficient was used to evaluate the relationship between Iran's droughts in station and regional scales with NAO in four concurrent, one-month, two-month and three-month delays. 

The analysis of the results related to correlation coefficient between NAO with Iran's drought and wet years in a station scale showed that in autumn, more than 87% of droughts in stations in all delays, except November concurrent delay, have weak correlation with NAO. In November, drought in most stations in the north part of the country, especially Northwest stations, have an average positive significant correlation with NAO. This correlation can indicate the effect of different phases of this index on the rain systems entered from Mediterranean Sea to Northwest of Iran in autumn. More accurately, positive values of this correlation indicates that values higher than zero in NAO are associated with increasing the intensity and frequency of droughts in Iran, especially the given regions.During winter months, spatial pattern of correlation is such that finding a certain spatial arrangement is difficult. It is shown that more than 85% of droughts in stations in different delays have weak correlation with NAO. But negative average correlations were observed for some months in different delays both for southeast and northeast. These negative correlations indicate that the negative phase of NAO is associated with reduction in the intensity and frequency of droughts in these two parts of Iran. The only month with positive correlation of these droughts with NOA is January for concurrent delay.Spring droughts, compared to other seasons’ droughts, have the weakest correlation with NAO. This clearly points out that the oscillations of this index do not play a major role in drought variability of the study stations in this season. In addition, spatial arrangement of few stations that their droughts and wet years have average correlation with NAO, shows no clear pattern. Only in April two-month delay, we observe the density of stations with negative average correlation in the northwest and north of Iran. Perhaps the reason for the weak correlation of NAO with droughts and wet years of Iran' stations in this season is its transitory nature and changing the precipitations from frontal precipitation to other types of precipitation.The results of correlation analysis related to the relationship between NAO with droughts and wet years also indicated that there is no strong relationship between these two variables in Iran and only an average significant relationship is observed in three-month delay of October and concurrent delay of May which was negative for October and positive for May.

Finally, it can be concluded that NAO cannot explain a large part of droughts and wet-years variability in station or regional scales. Therefore, since climatology of Iran's precipitation is very complex and the synoptic patterns which influence Iran in different months and seasons are different, it is necessary to pay attention to nonlinear models for investigation in this regard.

Since thermal comfort in day-to-day housing is generally provided by mechanical equipment, it is necessary to know the degree of thermal comfort in underground and earthsheltered - where no mechanical means are used. Find out. In this paper, the main issue is how the thermal behavior of Earth Sheltered buildings in the village of Meymand Kerman. The ancient village of Meymand is located 38 km northeast of Babak city of Kerman province. The latitude of the village is 30 degrees north and 13 minutes north and 55 degrees 25 minutes. It is also 2240 meters above sea level. The main purpose of this study was to investigate the influence of architectural and climatic variables on the thermal behavior of Meymand Earth Sheltered buildings. The key question is what kind of thermal housing exhibits thermal behavior during the OHP (warm year) period, and which Earth sheltered buildings exhibits better thermal behavior.

 The research method in this article is case study, and the earth sheltered residential buildings - as a case study - in Meymand village are selected to represent the whole statistical population. Therefore, the aforementioned buildings are divided into 4 categories A, B, C and D based on the type of sunlight orientation, altitude position on the slope of the mountain, type of shape and depth of penetration. Finally, out of the approximately 2,500 rooms (8 buildings), 8 Earth Shelterd Buildings representing most of the existing buildings in Meymand are selected as a case study. This study aims to identify the thermal behavior of residential buildings in Meymand village using field measurements. The measurements are performed on the 2th, 3th and 4th of August 2019 as representative of the warm period of the year. Climatic variables of temperature and relative humidity in earth shelterd buildings are measured by a data logger. It is also assisted by Babak City Synoptic Station to obtain climatic information from the village of Meymand. The data obtained by the data logger are analyzed and analyzed using the Givoni thermal comfort index. In the Givoni bioclimatic diagram, the range of thermal comfort is constrained by the variables of temperature and relative humidity.

The results of comparing the thermal behavior of earth sheltered buildings in the Givoni bioclimatic diagram showed that most of the earth sheltered buildings in the OHP period provide thermal comfort to humans. Due to the comfort zone in the Givoni climate chart, most of the points are in the comfort zone. Unlike everyday housing, underground and earth sheltered buildings can provide optimal thermal comfort without the use of mechanical equipment for humans. Also, the bioclimatic diagram of Meymand village showed that there is a large difference in temperature during the day and night which indicates that Meymand village has dry climate. Therefore, one of the things that can be used for human thermal comfort is the utilization of the thermal mass of the earth, which is objectively the case in the rocky village of Meymand. The findings of the Givoni Bioclimatic diagram show that the buildings that are at an average altitude of the mountain have better thermal behavior than the other buildings. Also, buildings with southeast and south orientation have more thermal comfort than other buildings. The west-facing Group A buildings are the coldest in the village. Of course, Group A buildings behave more favorably than other buildings at noon when the local climate (outdoor environment) reaches its maximum. But between sunset and sunrise as the Earth loses heat and cools down, the temperature in Group A buildings is reduced and warm and comfortable clothing should be used to create thermal comfort. The results show that Group B buildings have a more balanced thermal behavior than other buildings throughout the day. It is worth noting that Group B buildings have relatively higher temperatures at noon than other buildings, but fall within the range of thermal comfort. Finally, after studying and analyzing the case samples, it was found that B-2 and C-1 are the best thermal comfort buildings in the daytime.

The results of this study showed that the thermal behavior of Meymand earth sheltered buildings during the warm period is optimal and the maximum thermal comfort is related to the buildings with the most infiltration on Earth depth, two-chamber Formation (two consecutive chambers), south orientation. -East south is exposed to sunlight and be in the middle altitude of the mountain.