برومند صلاحی

The study of climate hazards such as heavy precipitation is very important due to its direct impact on flooding. Due to the climate change that the world has experienced, climate hazards have increased. What is certain is that humans cannot prevent the occurrence of climate hazards, but by being aware of these events in advance, under the influence of climate forecasts, they can reduce the destructive consequences of these hazards. Also, considering the very prominent role of humans in increasing the most important climate forcing, namely greenhouse gases, especially carbon dioxide, by managing fossil fuels and increasing new energy power plants, which are known as clean energies, climate changes that cause extreme events can be reduced. Another issue is the management of heavy precipitation to control large amounts of water for use in agriculture, which seems to be able to benefit from this weather event by taking measures. Another important point regarding heavy precipitation in the northwest region of Iran is to pay attention to the construction of residential areas in places far from rivers, which are vulnerable to flooding caused by heavy precipitation. The most important cause of extreme events such as heavy precipitation is currently climate change. The main factor causing climate change and desertification is greenhouse gases. The most important type of greenhouse gas is carbon dioxide. The main reason for the increase in this gas, which has a long life and is very poorly degradable, is humans. In other words, the main cause of the increase and intensification of extreme events is human misbehavior in dealing with nature. The northwest region of Iran is prone to heavy precipitation due to its mountainous topography and location on the main path of Mediterranean cyclones. This research was conducted with the aim of identifying the moisture sources of heavy precipitation in northwest Iran and also analyzing the instability indicators related to it.

The study area in this study is northwest Iran, including West Azarbaijan, East Azarbaijan, Ardabil, North Kurdistan, and West Zanjan provinces. In this study, daily and hourly (3-hour) precipitation data and hourly (3-hour) wind data (speed and direction) were obtained from the Iranian Meteorological Organization (www.irimo.ir) for 23 synoptic stations located in northwest Iran during the period 1990-2019. The upper atmosphere data of the Tabriz station (the only upper atmosphere station in northwest Iran) were obtained from the University of Wyoming website (http://weather.uwyo.edu/upperair/sounding.html). The upper atmosphere data of this study were obtained from the NCEP/NCAR database (www.cdc.noaa.gov). Trial and error estimates showed that if the percentile is higher than 99 and the area covered by heavy precipitation is more than 30%, synoptic conditions will provide a good justification for heavy precipitation. In this paper, days when at least 7 stations in the study area simultaneously had at least 20 mm of precipitation were selected. In this study, using TTI, CAPE, KI, LI, SI and SWEAT indices, the state of atmospheric instability in northwest Iran was evaluated at a representative station in the region (Tabriz) on days of heavy precipitation (43 days). Based on factor analysis in the SPSS software environment, the main factors were identified from among the 6 indicators, then using cluster analysis, the main clusters were extracted and the Skew-T diagram of the representative days of each cluster was drawn and interpreted in the RAOB software environment. To select representative stations for the northwest region of Iran, 15% (3 synoptic stations) of the stations in the study area were selected based on altitude (meters), climate (number of heavy precipitation and average heavy precipitation during the study period), and large distance from each other (based on kilometers and geographical location). Using cluster analysis in the SPSS software environment, clusters were extracted based on the effective variables (relative humidity, wind vector, precipitable water) of the mid-level atmosphere in the northwest region of Iran. Then, the representative of each cluster was determined and for each representative day of heavy precipitation event (4 days out of 43 heavy precipitation events), in each of the 3 representative stations of the study area (3 stations out of 23 synoptic stations), the path and source of moisture of heavy precipitation were traced using the backward method (72 hours before the days of heavy precipitation in northwest Iran) and using global data analyzed at the  National Centers for Environmental Prediction and the National Center for Atmospheric Research (NCEP/NCAR) with a time step of 6 hours with a spatial resolution of 2.5 × 2.5 longitude and latitude for the levels of 850, 700 and 550 hectopascals, with the HYSPLIT web model. The wind gust diagram was drawn and interpreted using the WRPLOT software for the representative days at the representative stations of northwest Iran. The combined wind and precipitation diagram was drawn and interpreted hourly in the Excel software environment for the representative days at the representative stations of the study area.

According to the criteria for heavy precipitation in this study, 43 extreme precipitation events were identified in the observation period (1990-2019). Using hierarchical cluster analysis using the Ward method with Euclidean distance, 2 main clusters were extracted from the 43 extreme precipitation events. The first cluster shows heavy precipitation events with dynamic ascent in the study area, and the second cluster includes heavy precipitation events with convective ascent in the research area. Of the two clusters, the first cluster has a higher frequency and indicates the dominance of heavy precipitation with dynamic origin over heavy precipitation with thermodynamic nature in the study area during the period under study. By drawing the Skew-T diagram in the RAOB software environment for representative days of each cluster, the instability conditions on representative days indicated the intensification and stability of atmospheric instability at levels above 850 hectopascals for the representative dynamic cluster. In the representative day's Skew-T diagram, the thermodynamic instability cluster was observed up to a maximum level of 850 hectopascals. Calculations showed that, considering the instability indices and the Skew-T thermodynamic diagram, the role of the convection factor in heavy precipitation  in northwest Iran was low and the dynamic factor was the main reason for heavy precipitation. The results of the study based on the windrose diagram indicate that the prevailing winds causing heavy precipitation  events blew from the southwest and had an average speed of 3.5 m/s. The output of the HYSPLIT diagram also confirms the southwest direction of the study area for the moisture input path of extreme precipitation. Also, the results of the combined hourly wind speed and precipitation diagram showed that the maximum wind speed and maximum precipitation on heavy precipitation  days were at 12:00 GMT, equivalent to 15:30 local time, which indicates the strengthening of the effective dynamic system in the region at this hour. In other words, the cyclone located at this hour, with the convergence created, has brought maximum humidity to the region and, with its sharp ascent, has provided the cause of heavy precipitation  in northwest Iran. Based on the calculations, the average atmospheric variability of precipitable water, relative humidity, and wind speed in extreme precipitation events in northwest Iran has been 16 kg/m2, 68 percent, and 20 m/s, respectively.

Based on the research conducted in the northwest region of Iran, in the period 1990-2019 on heavy precipitation, the results showed that, considering the instability indices and the Skew-T thermodynamic diagram, the role of the convection factor in heavy precipitation was very low and the dynamic factor was the main reason for heavy precipitation. The results of the study based on the HYSPLIT model showed that the main path of moisture entry into the study area is the southwest and the main source of moisture supply for heavy precipitation is the Red Sea. The results of the study based on the windrose diagram indicate that the prevailing winds in heavy precipitation events blew from the southwest and their speed was 3.5 m/s on average. The combined hourly wind speed and precipitation diagram showed that the maximum wind speed on heavy precipitation days was at 12:00 GMT, equivalent to 15:30 local time, which indicates the strengthening of the effective dynamic system in the study area at this hour. Humans cannot eliminate weather hazards. Weather hazards are part of nature, and humans can only reduce the frequency and severity of these events. In the northwest of Iran, the best solution to deal with the risks caused by heavy precipitation  is to identify the causes of this event, such as the moisture sources that provide heavy precipitation , and to evaluate instability indicators that indicate the conditions for the formation of heavy precipitation. The next step is to inform the residents of the region, such as farmers, travelers, and others, about the occurrence of this event and warn them of the possibility of flooding. Insuring crops and residential houses, constructing residential houses in susceptible areas on high foundations with a height of 3 or 4 meters, increasing vegetation cover and planting seedlings with the aim of increasing soil permeability, dredging rivers to prevent water levels from rising due to sediment deposition, taking protective measures on river banks with the aim of reducing soil erosion in coastal areas, using mobile concrete dams during precipitation  in agricultural and residential areas with the aim of preventing possible flood damage during heavy precipitation , and avoiding unnecessary transportation due to reduced visibility, slipperiness, and flooding of urban and roadways are considered major solutions to reduce losses caused by heavy precipitation. The results of this study are in good agreement with the results of other researchers in terms of the dominance of dynamic instability in heavy rainfall, the occurrence of heavy rainfall in the spring due to convective causes, the occurrence of extreme rainfall due to the supply of moisture to the Red Sea by the Mediterranean cyclone, and the confirmation of the strengthening of cyclones causing heavy rainfall at 12:00 GMT.

This study investigates the impact of the OLR MJO Index (OMI) and the Real-time Multivariate MJO (RMM) of the Madden-Julian Oscillation on the frequency of dust storms in the stations of Abadan, Ahvaz, Bostan, Bandar Mahshahr, Dezful, Ramhormoz, and Masjed Soleiman, located in Khuzestan province, during the period from April to September 1987-2021. Pearson correlation coefficients were computed to assess the relationship between dust data and the indices, and the findings were depicted through zoning maps. Subsequently, the frequency percentage of each index for both positive and negative phases was quantified. The results indicate a direct and significant correlation between the positive and negative phases of the indices and dust occurrences (with the exception of Dezful station), particularly during the positive phase of the OMI and the negative phase of the RMM. The highest correlation coefficients, ranging from 0.77 to 0.72, were observed for Bandar Mahshahr and Dezful stationsduring the positive phase of the RMM index. Analysis of the relationship between the Madden-Julian Oscillation and dust storms revealed that between 51% and 59% of dust storms in Khuzestan province occurred in the negative phase of the OMI index, while 40% to 49% occurred in the positive phase. In the case of the RMM index, 56% to 63% of dust storms were associated with its negative phase, in contrast to 37% to 50% linked to its positive phase. Notably, the negative phase of the RMM index exhibited a higher percentage of dust storms compared to the negative phase of the OMI index. According to the results of the Monte Carlo test, the displacement of the positive and negative phases of the RMM index significantly contributes to the occurrence of dust storms at most stations in Khuzestan province. Furthermore, tracking the pathways of dust entering Khuzestan province using the HYSPLIT model indicates the movement of particles originating from Iraq, Arabia, and the eastern regions of Syria toward Khuzestan province..

Precipitation is one of the elements of climate that has great variability. These changes occur both in terms of time and space in many regions of Iran. Knowing the characteristics of this atmospheric element is very important for many construction plans, plans and agricultural activities. Any statistically significant change in this element can impose inevitable negative effects on natural resources, especially recoverable water reserves. Knowing the change process of climatic parameters is one of the things that has been the focus of atmospheric and hydrological science researchers in recent years. Most of the studies have been focused on investigating and analyzing the behavior of precipitation, and Mann-Kendall statistical methods have been widely used as the most common non-parametric method for analyzing the trend of precipitation time series. Since the trend finding in terms of the frequency of convective Precipitation has been less noticed by researchers, the purpose of the present study is to investigate the trend of the frequency of convective rainfall in the first half of the year in synoptic stations located in the northwest of Iran. In this research, the study area for the statistical analysis of convective precipitation includes Ardabil, East Azerbaijan, and West Azarbaijan provinces.

To carry out this research, the daily Precipitation data in the first half of the year (April, May, Jun, Jul, Aug, Sep), in synoptic stations located in the northwest of Iran (including Ardabil, Ahar, Urmia, Bonab, Bostan Abad, Tabriz, Pars Abad), Khoy, Sarab, Sardasht, Khalkhal, Marand, Mianeh, Mako, Mahabad, Meshkin Shahr and Maragheh were used during a 17-year period (2000-2016) and daily Precipitation of at least 5 mm was considered as convective Precipitation. Then, the annual frequency of convective precipitation of each station was counted and trending methods based on Mann-Kendall statistical method and Sen’s slope estimator nonparametric method were used to analyze its trend. The Mann-Kendall test does not require a normal frequency distribution or the linearity of the behavior of the data, and it works very strongly compared to the data that deviates from the linear behavior and is used to evaluate the trend. In this test, the null hypothesis (H0) and the opposite hypothesis (H1) are respectively equivalent to no trend and the presence of a trend in the time series of observational data. In Sen's method, the time series is divided into two groups, and the numbers of each time series are arranged in ascending order. Then these two series are plotted against each other on the coordinate axis. It is better to draw the first time series on the horizontal axis and the second series on the vertical axis. In the next step, the 1:1 line is drawn, if the points are above the line, there is an increasing trend, below the line, there is a decreasing trend, and on the line, the data will be without trend.

The changes of precipitation in Tabriz station have been decreasing since 2006 and because these two components outside the critical range (±1.96) have intersected each other in 2013, 2014, and 2015, this trend is not significant. This situation happened at Maragheh station in 2013 and 2014, and since 2015, a decreasing trend in rainy days has been seen, at Midane station, since 2015, there has been a change of direction in the abundance trend from a non-significant increase to 2015. In other stations of East Azarbaijan province, during the studied period, no specific mutation and trend in the frequency of convective precipitation can be seen, and in fact, these stations have no trend. In Sardasht station, the time of the beginning of decreasing and then increasing changes in the frequency of convective precipitation in the years 2000, 2002, and 2016, the two components Ui and Ui' are outside the critical range, which is not significant. A sudden change in the direction of this variable occurred in Mako station in 2001, which is incrementally insignificant. At Urmia station, several sudden changes in the frequency of convective precipitation can be seen during the studied period, which started from 2001 and continued until 2008. The trend of convective precipitation in 2003 was decreasing and after 2007 it was increasing insignificantly. In Mahabad station, the intersection of Ui and Ui components in 2001 reveals an increasing trend in the frequency of convective precipitation. In addition, in 2012, these lines intersected outside the critical range of ±1.96, which shows the increasing trend of the frequency of convective precipitation of a non-significant type. In Khoy station, between the years 2000, 2003, 2006, and 2007, a significant and sudden change began, which is insignificant. For the entire province of West Azerbaijan, a sharp and sudden change in the direction of an insignificant increase can be seen from 2003 to 2013. This sudden change has been revealed in the form of a decrease in 2013, which is not significant. In Parsabad and Meshkinshahr stations, the two components Ui and Ui' have not met each other inside and outside the critical range, and in the years 2008 and 2009, a significant decreasing trend started, which is insignificant, but in Ardabil station, these two components are within the range. In the years 2006 and 2010, they met each other, which indicates the beginning of a change of direction and a decreasing trend, albeit insignificant, in the frequency of convective precipitation at this station. This situation occurred in Khalkhal station in 2001 and 2008, which was accompanied by an increase in 2001 and an insignificant decrease in 2008. In general, the results of the analysis of the frequency of precipitation with the Mann-Kendall test showed that the variable trend, both increasing and decreasing, in the stations is not significant due to the location of the intersection point of Ui and Ui' outside the critical range (±1.96). In this way, the frequency of precipitation in the stations of East Azarbaijan province including Tabriz, Maragheh, Bonab, and Sarab shows a non-significant decreasing trend, and in Marand, Ahar, Bostan Abad, and the middle of a non-significant increasing trend, in the stations of West Azarbaijan province (including Urmia, Mako, Mahabad), Sardasht and Khoy) has had a non-significant increasing trend in the stations of Ardabil province (Parsabad, Ardabil, Meshkin Shahr, and Khalkhal). The results of trend analysis using the age slope estimator method show an increasing trend in Mako, Urmia, and Meshkinshahr stations. Also, the frequency of rainfall in Bostan Abad, Mahabad, Ardabil, and Parsabad stations has no trend and in other stations, it shows a decreasing trend without significance.

In this research, to anticipate the effects of climate change on the precipitation of selected stations in East Azerbaijan province, including Ahar, Maragheh, Mianeh, Jolfa, Sarab, and Tabriz stations, which had 29-year common statistical data from 1985 to 2014, with The use of LARS-WG6 model were studied. In this research, the effectiveness of the LARW-WG model for generating and simulating daily data of temperature, precipitation, and sunshine hours in the study area was investigated using the statistical parameters R2, MAE, and RMSE. The results showed that at the confidence level of 99%. There is no significant percentage difference between the real data and the data obtained from the model, and the model has the necessary efficiency to generate daily data, therefore, after ensuring the model's ability, using HadGEM2 atmospheric general circulation model under RCP 2.6 scenarios, RCP 4.5 and RCP 8.5 in the next two periods from 2021 to 2040 were forecasted for the rainfall changes in the studied area. The results of forecasting the average annual rainfall changes of the studied stations of East Azerbaijan province in the statistical period of 2021-2040 showed that According to RCP 2.6, precipitation will increase in all the studied stations in the next 20 years, but according to RCP4.5 and RCP8.5, we will see an increase in precipitation only in Jolfa station, and in the rest of the studied stations, precipitation will increase by will be significantly reduced.Climate change is one of the most important and complex concerns of the current and future centuries. The dangerous effects of climate change on human life will cover almost all aspects of life. Drought, rising sea levels, violent storms, reduction of freshwater resources, warming of the air, forest fires, and desertification are among these effects (Mohammadi et al., 1400). Climate change is one of the most important ecological problems of the 21st century. According to the fifth report of the Intergovernmental Panel on Climate Change, these global changes are most likely caused by human factors. Various studies show that this phenomenon can have negative effects on various systems of water resources, agriculture, the environment, industry, and the economy. Forecasting the quantity and quality of climatic changes is one of the complex issues that have occupied the minds of climatologists. Now, with the help of obtaining new technologies and possessing multiple series of necessary data of climatic variables and with the help of knowledge to understand the relationships between these variables, basic steps have emerged in understanding and predicting climatic trends, in such a way that now Computer models all respond to climate forecasting issues and factors affecting climate change within their capabilities (Darvand et al., 1400). Currently, the most reliable and developed tool for investigating climate change and its impacts is the 3D Ocean–Atmosphere General Circulation Model (AOGCM). The AOGCM model is based on physical laws. These relationships are resolved in a three-dimensional network around the world. The presented models show climate parameters in the future (until 2099) for different emission periods. By using these methods with different climatic variables, the effects of climate change on the variability and adaptation of plants can be investigated.The research method in this study relies on the LARS WG model. After sorting, processing data, and preparing input files, finally, the model studied in this research was finally implemented for the basic course. In the next step, using the mean absolute error (MAE), coefficient of determination (R2), and the root mean square error (RMSE) to evaluate the data generated from this model and the real data available in the basic period became After checking the results of the confidence stage and evaluating the capability of the LARS-WG model for simulating meteorological data by considering the low values of the error measurement indices and the high values of the calculated coefficient of determination, the new version of LARS WG6 was used in this research. The required data including minimum temperature, maximum temperature, precipitation, and sunshine hours are collected from the synoptic station of East Azarbaijan province and given as input to the model. The data simulated by the CMIP5 atmospheric general circulation model is related to the period 2021-2040, which under the scenarios of RCP 2.6, RCP 4.5, and RCP 8.5 by the LARS-WG model compared to the base period 2014-1985 in the scale of the studied stations. In order to reduce the uncertainty of the average of the calculated profiles for the above model, it has been obtained. In the current research, it has given appropriate and favorable results regarding the study of the effects of climate change scenarios on the precipitation of East Azarbaijan province in the direction of microscale. CMIP5 models are used in LARS-WG6 software, which is one of the most famous and important models for generating random weather data. This model is used to generate the values of precipitation, maximum temperature, minimum temperature, and sunshine hour in a station, regarding the basic climate conditions as well as the future. For this reason, the study has been done on various stations of the studied area. The daily temperature and precipitation data of 6 stations of East Azarbaijan Province, including Jolfa, Ahar, Sarab, Tabriz, Mianeh, and Maragheh stations, were used for exponential micro-scale as well as temperature and precipitation forecasting during recent years. Then, in the present research, to implement the model, first, taking the 29-year period from 1364 to 1393 (1985-2014) as the base period, the necessary data, including precipitation, minimum and maximum temperature, and sunshine hours, were obtained from the National Meteorological Organization.The results of forecasting changes in the average annual precipitation of the studied stations of East Azerbaijan Province in the statistical period of 2021-2040 showed that according to RCP 2.6, the precipitation will increase in all the studied stations during the next 20 years, but according to RCP 4.5 and In RCP8.5, we will see an increase in rainfall only in Jolfa station, and in the rest of the studied stations, rainfall will decrease significantly.

Drought is the most damaging natural phenomenon that occurs in all climates of the world, even in humid regions. Agricultural drought not only damages crops and causes changes in vegetation cover, but also causes social and economic damage. The aim of this study is to monitor agricultural drought and analyze the spatial-temporal distribution of drought severity in the Aras River Basin for the vegetation growth months (April to July) in the statistical period 2012 to 2021. To assess drought conditions, the Standard Precipitation Index (SPI) obtained from precipitation data from 7 synoptic stations was used as the dependent variable, and the Normalized Difference Vegetation Index (NDVI), Vegetation Condition Index (VCI), Temperature Conditions Index (TCI), and Vegetation Health Index (VHI) were used as independent variables. The results of the correlation between meteorological indices and satellite indices showed that the SPI index has the highest correlation with the VCI index. This index also had high accuracy compared to other indices based on validation criteria, indicating its ability to monitor agricultural drought. Examination of classified drought severity maps based on SPI, VCI, VHI, and NDVI indices showed that April and May were the wettest months and July was the driest month. According to the TCI index, April was the driest month and July was the wettest month during the statistical period, and in all indices, 2021 was accompanied by severe drought. In terms of location, stations located in the western half of the Aras River Basin had more severe drought and rainfall deficiency than stations in its eastern half.

The aim of this study is to investigate the long-term fluctuations and trend in horizontal visibility in the northwest of Iran. For this purpose, hourly horizontal visibility data from 7 synoptic stations were used for the period of 1951-2020. The Koschmieder approach was used to calculate the extinction coefficient. Moreover, the Mann-Kendall and Rdit tests were applied to examine the trend of horizontal visibility. Also, the percentages of very good visibility (>19 km) compared with bad (<10 km) visibilities. Based on the results the annual average of horizontal visibility in northwest of Iran is ~13 km. This study showed three different fluctuation periods in the regional average of horizontal visibility: the first period (1951-1985) showed a sharp decrease in the visibility, the second period (1987-2005) was characterized by a low and stable visibility, and the third period showed a recent relative improvement. The regional average of horizontal visibility (extinction coefficient) exhibited a significant decreasing (increasing) trend of -0.167(0.0017) km per year at a confidence level of 0.01. The significant decreasing trend was confirmed in all stations except for the Ardabil station. The most severe decreasing trend was detected in Sanandaj and Zanjan stations with rates of 0.183 and 0.179 km year-1, respectively. The region-average of Rdit statistic in northwest Iran in the early 1950s was ~0.85, but it decreased to around 0.3 in the 1990s. Despite the recent improvement in horizontal visibility, reaching the reference distribution (i.e. Rdit=~0.5), the decreasing trend of horizontal visibility was still confirmed. The percentage of trend analysis of very good and bad visibility showed an increase in bad visibilities (from 5% to 25%) and a decrease in very good visibilities (from 80% to 5%), which confirms the decreasing trend in horizontal visibility. Hazy condition with 38.7% was the most influential weather phenomenon in visibility degradation.

In order to monitor dust storms in Khuzestan province, statistical data were first obtained from the General Meteorological Department, and the days with dust reports with minimum horizontal visibility of less than 10 km were selected as free days. The results of zoning and the average dust concentration map by calling MODIS satellite data in Google Earth Engine revealed that the density of this phenomenon was in the western, southwestern and central regions of the province. For the synoptic investigation of this phenomenon, the most intense dust storms were selected for 9 synoptic stations and these days were analyzed by drawing synoptic maps; And the movement path and origin of this phenomenon and its density were displayed through the HYSPLIT model. The results indicated that in the majority of dust storms in the south of Iran, thermal low-pressure systems were active, and in Europe, high-pressure systems prevailed, and the slope of the pressure gradient between these two systems led to the direction of westerly currents with high wind speed at high altitudes towards the study area. And the prevailing rotating situation in the region has added to the instability of the air. The western winds have also passed through the deserts of North Africa, the north of the Arabian Peninsula, Iraq and Syria, parallel to the Nave canal in the west of Iran, and have led the hot and dry air of these regions to the atmosphere of the region.

Studying and measuring changes in snow levels is very important as one of the important sources of water supply. Due to the harsh physical conditions of mountainous environments, there is no possibility of permanent surface measurement to estimate snow sources and form a database. The use of satellite images and remote sensing due to their low cost, up-to-dateness, and wide coverage is a way forward in this field, and it can be a suitable method for identifying snow catchment areas and evaluating its changes to achieve this goal. The presence of snow in the basins affects the water resources that are stored in the form of frozen water on the surface Therefore, temporal and spatial monitoring of snow cover has been used for hydrological forecasts for years. Remote sensing images are a useful tool for estimating snow cover changes and analyzing the spatial pattern of this important environmental phenomenon, especially in high areas where there are few available ground stations or there are no stations

In this research, data from V006-MOD10A1 of the Terra satellite and V006-MYD10A1 of the Aqua satellite were used for snow coverage from 2003 to 2020, which have a spatial resolution of 500 meters and a daily time resolution. The Modis sensor data in both Terra and Aqua satellites were converted from HDF format to TIF format with a threshold of 0.1-1 to binary and ASCII and with a geographic coordinate system. These data were processed using Python coding language. Cloud effect was reduced by using three algorithms: data combination, spatial, and temporal filtering. For the digital elevation model (DEM), the data of the Japan Space Exploration Agency (JAXA) called ALOS World 3D (AW3D) was used. After preparing the snow cover database, average snow-covered days (SCDs) were calculated on a seasonal scale and maps of the spatial distribution of snow cover in this period were produced. Then, the relationship between the two components of SCAs and SCDs and the relationship between SCDs and altitude in the north-western geographical area were investigated and analyzed.

According to the pattern of winter SCDs, the flat plains around Lake Urmia, the plains around Zanjan City, Moghan Plain, and the lands around Sanandaj City show the lowest rate of SCDs (less than 10 days of snow cover). The maximum of winter SCDs corresponds to Sablan Heights with more than 60 days of snow cover. According to the average spring SCDs in almost the entire northwest area, the drop of SCDs compared to the winter season is evident. This reduction is much more evident in low and flat areas and plains than in high and mountainous areas. Spring SCDs are still high in the main altitudes such as Sahand, Sablan, Qandil, Bozghoush, and Qara Dagh and reach about 54 days in the spring season in Sablan. The decline of SCD started from the south and covered more areas of the northwest region. Summer SCDs are facing a sharp decrease in the northwest region. Except for the heights of Sablan, Sahand, and the heights of the western border including the Qandil range, the rest of the areas have snow cover for less than 2 days in summer. It is noteworthy about the heights of Sablan, which still can maintain snow cover for up to 55 days in the summer season. The average autumn SCDs in the northwest region show an increase again in the autumn season. The heights of Sablan, Bozghoush, Sahand, Qara Dagh, and Qandil all show a significant increase in SCDs in the fall season, and in the high parts of these roughnesses such as Sablan, the fall SCDs increase to over 60 days. In other areas, mainly the main body of the roughness has SCDs above 20 days. Investigating the phenological changes of SCAs and SCDs in the seasonal period showed that the highest SC levels in the winter season have SCDs of about 15 days. This surface is about 18000 square kilometers. In general, the areas and regions that have SCDs between 5 and 25 days have the highest SC levels in this season. In the spring season in the northwest region, the SC levels, which have SCDs of less than about 8 days, reach a maximum of 160,000 square kilometers. In the summer season, lands with SCDs of less than 5 days occupy the highest levels, reaching a maximum of 160,000 square kilometers. In autumn, the regions with SCDs of about 7 days have the highest SC levels with an area of about 50,000 square kilometers.

The findings indicate a decreasing trend of SCDs from winter to summer. Meanwhile, the average autumn SCDs in the northwest area again take on an increasing tone in the autumn season. Analyzing the seasonal changes of SCDs in the studied area showed that this phenological component of snow is strongly influenced by the two factors of altitude and latitude. Sablan mountain range has special conditions to maintain snow cover in the study area even in the summer season. The analysis of seasonal changes of SCAs with SCDs shows that the maximum extents of SCs in winter, spring, summer, and autumn seasons have SCDs of approximately 15, 8, less than 5, and 7 days, respectively. In winter, areas with SCDs of more than 40 days include significant SC levels of about 2000 to 8000 km2. On the contrary, in the autumn season, the areas with SCDs higher than 40 days have almost negligible SC extents, whose values reach below several hundred square kilometers. Analyzing the changes of SCDs with the altitude measurement in the seasonal interval, showed the obvious relationship of this snow phenological component in association with the altitude; In such a way that the highest values of SCDs in winter belong to the altitude band of 2500-3500 meters, in spring to the altitude range of more than 2500 meters, in summer to altitudes above 4000 meters and in autumn to the altitude belt of 2000-3500 meters.

Dust is a significant global challenge. This study aimed to monitor the temporal and spatial changes of suspended atmospheric dust in the western half of Iran, specifically in the provinces of Ilam, Khuzestan, Kermanshah, Kurdistan, and Lorestan. We utilized MODIS sensor products from 2012 to 2021 and Sentinel-5 imagery from 2018 to 2021 within the Google Earth Engine framework. These sensor products are invaluable for managing and addressing dust-related issues. Using Aerosol Optical Depth (AOD) and Absorbing Aerosol Index (AAI) data, we tracked the trends in aerosol concentration over time and across the selected regions. Additionally, we calculated the Enhanced Vegetation Index (EVI) and Bare Soil Index (BSI) from MOD13Q1 images for the same period to assess their average changes. The analysis revealed a downward trend in AOD during January and an upward trend in July. The AAI indicated that January 2021 recorded the lowest aerosol levels, while July and September 2021 experienced the highest concentrations. Classifying aerosol concentrations into three categories highlighted that regions with high aerosol levels, as indicated by MODIS data, were consistently found in Khuzestan and Ilam provinces, while Sentinel data showed sporadic occurrences. Furthermore, the correlation between dust concentration from MODIS imagery and EVI and BSI indices revealed that the highest aerosol concentrations were located in areas with little vegetation and bare soils.

Examining the snow cover and recognizing the patterns of snow formation can inform the environmental planners about the behavior of this phenomenon. In this research, by calling MODIS satellite data in Google Earth Engine environment and using NDSI index, the snow cover condition of Khalkhal city in 2003-2022 was investigated. In order to model snow systems in Khalkhal city, data related to heavy snow report was received from Iran Meteorological Organization. Then, two general patterns for the snow systems of Khalkhal city were determined by the cluster analysis method on the sea level pressure values ​​on the days with snowfall. Then, by selecting a representative day from each model, the situation of sea level pressure, geopotential height, temperature, temperature wind, humidity drift, wind flow and wind speed were analyzed. The results showed that the snow deposits in the central and northern areas of Khalkhal city reach their maximum extent especially in the winter seasons. The results also showed that there are two general patterns for the transfer of cold air mass and snowfall to the study area, each of which has its own mechanisms. The first pattern is influenced by the gradient of pressure and western winds, while the second pattern is influenced by Siberian high pressure systems, air subsidence and northern winds.

Monitoring the changes and fluctuations of precipitation in geographical areas can give a better view of the behavior of this phenomenon in the coming years. The purpose of this research is to investigate the precipitation situation in Ardabil Plain (Ardabil, Bileh-Daragh, and Kolour stations) and forecast it in the coming years based on the output of CMIP6 models by the CMhyd downscaling model. Then, using R2, MAE, MSE, RMSE, and Taylor diagram, the observational data of the base period were compared with the historical data of 5 GCM models from CMIP6, and the best model was selected for each studied station. The output of the top models was corrected for skewness by linear scaling method and based on SSP126, SSP245, and SSP585 scenarios, the precipitation of 2050-2023 for each station, forecast, and its trend were drawn with the Mann-Kendall statistic. The results showed that in the eastern and western areas of Ardabil Plain (leading to the heights of Talesh and Sablan mountains), the rainfall changes were increasing (2.80 mm). In the Ardabil station, the MIROC6 model with a correlation coefficient of 0.94%, and in Bileh-Daragh and Kolour stations, the MPI-ESM1-2-HR model with a correlation coefficient of 0.88% and 0.92%, respectively, have the highest accuracy in simulating the precipitation. Also, the results of the scenarios showed that the precipitation changes in Ardabil station in the future period compared to the base period under the SSP126, SSP245, and SSP585 scenarios will be 0.24, -6.36, and -2%, respectively.

This research has been conducted with the aim of identifying heat waves in Ardabil province and analyzing their synoptic patterns using a circular method. For this purpose, the daily average maximum temperature data of Ardabil, Parsabad and Meshkinshahr synoptic stations were used in the statistical period of 1980-2020. The 95th percentile index was determined as a criterion for identifying a hot day, and its duration of at least 3 days was considered as the basis for defining a heat wave. Examining the trend of SLP, HGT and Tmax of these waves showed that on the hottest days of heat waves, the trend of SLP is downward and the trend of HGT and Tmax is upward. Clustering of heat wave generation patterns based on reanalysis data of sea level pressure and geopotential height of 500 hectopascals using cluster analysis method and Ward's clustering method led to the identification of 5 clusters which were presented in two main groups. In the first pattern, low pressure on the ground surface and high pressure in the middle level of the atmosphere, and in the second pattern, the location of the studied area in the west of the pseudo-Omega blocking of western Russia at the level of 500 hectopascals were identified as the synoptic factors of creating heat waves in Ardabil province. The common feature of both patterns is the high thickness of the atmosphere over the region on the days of this event.

Today, global warming, increasing the Land Surface Temperature, especially in big cities, is one of the environmental problems. The purpose of this article is to estimate the ability of Sebal, improved single-channel and Split window algorithms in estimating Land Surface Temperature on Landsat 8 image data in July 2019 and extracting Land Use maps in 7 classes for Tabriz County using an object-oriented classification method. Matching the temperature maps obtained by using these three mentioned algorithms with the Land Use map and comparing the algorithms with each other in terms of the proximity of their temperature to the Tabriz station is another goal of this article. To estimate the accuracy of the measured temperature, the data of the measured temperature of the Tabriz station at two centimeters above the land (as a representative of Tabriz County) has been used. The results of Sebal, improved single-channel, and Split window algorithms showed the highest temperature for dry pasture use and the lowest temperature for vegetation use, which indicates the importance of vegetation cover in the temperature changes of the studied area. A comparison of the studied algorithms with the temperature measured in the Tabriz station and their adaptation to different uses showed that the improved single-channel algorithm is more consistent with the actual temperature of the actual Land Surface Temperature in Tabriz County. The results of this study can help environmental planners concerned about the increase in air temperature in cities to make more appropriate decisions regarding the control of this phenomenon.

In this research, the spatiotemporal variations of snow-covered days (SCDs) in this region were analyzed using the data of the sixth version of MODIS Terra and MODIS Aqua sensors on a daily basis in the period of 2003-2020. In order to reduce the cloud cover effect, three algorithms were applied to the data. For the digital elevation model, the Digital Surface Model (DSM) of the Japan Space Exploration Agency was used. The relation between two snow-cover phenological components (SCAs and SCDs) and the relation between the SCDs and the altitude were investigated. The findings show an increase in SCDs in the months of November, December, and January. Maximum SCDs are observed in January in Sabalan Mountain and then Sahand. The reduction of SCDs in the spring and summer months is also affected by the two factors of latitude and altitude. The absolute maximum of SCDs in this region is observed at 160 days/ year in the mountain of Sabalan. Examining the changes in SCDs in March and April shows a decrease in SCDs in high-altitude classes. At the same time, it shows the increasing pattern of SCDs in November and December at many altitude levels. Analysis of the relation between SCA and SCDs in different months illustrated that SCAs has decreased in regions with more SCDs (heights) due to the reduction of topographic areas. The relation of SCDs and altitude also showed that the minimum of SCDs occurred in all altitude levels (even altitudes above 3500 m with 4 days) in August and the maximum occurred in December with 22 days at the altitude of 3500 m. SCDs decrease with increasing altitude in mountainous areas of 3500 to 4000 m, due to the increase of land slope and instability of SC in steep areas.

Climate change has a direct impact on hydrological components and water resources and plays an important role in aggravating possible risks such as drought and floods. Therefore, it is necessary to investigate the effects of climate change on water components such as runoff. This study evaluates the effect of climate change on climatic parameters (temperature and precipitation) and the amount of runoff in Kiwi Chai basin from an environmental point of view. The hydrological model of the Soil and Water Assessment Tool (SWAT) was used to analyze the effects of climate change on the water resources of the Kiwi Chai basin, which is one of the Sefidroud sub-basins. Runoff simulation by applying climate change conditions for models (EC-EARTH, HadGEM2-ES, MIROC5, MPI-ESM) under two scenarios (RCP 4.5 and RCP 8.5) in three periods (2040-2021), (2041-2060), ( 2061-2080) and statistical analysis was performed to identify which climate model is more consistent with the changes in the mean and standard deviation of the historical series. An increasing trend in precipitation and a significant increase in average annual temperature were predicted in the early, middle, and late 21st century. The results of simulation of basin runoff with SWAT model also showed a significant decrease in basin runoff in future periods compared to the base period. These findings provide local water management authorities with useful information to aid decision-making in the face of climate change.

Air temperature is very important among the climatic elements. Since Iran is located in the subtropical region, extreme temperature changes are one of its inherent characteristics, and for this reason, investigating temperature changes in Iran is essential. Fluctuations in temperature on a large and regional scale are more or less influenced by teleconnection patterns. This article aim is to investigate the simultaneous occurrence of the North Atlantic Oscillation (NAO) and the Arctic Oscillation (AO) with the Pacific-North American (PNA) and the Pacific Decadal Oscillation (PDO) and their effect on Iran's winter temperature. In this research, the monthly temperature data of 100 selected synoptic stations of Iran from the Islamic Republic of Iran Meteorological Organization (IRIMO) website and the PDO, PNA, NAO, and AO indices from the National Oceanic and Atmospheric Administration (NOAA) website for 1988-2019 were obtained as primary data. To determine Pearson's correlation coefficients, normalized monthly temperature data were entered into STATISTICA software as a dependent variable and teleconnection data as an independent variable. Then, the positive and negative phases of the mentioned teleconnections were identified. Numbers smaller than -0.5 were considered as the negative phase, between -0.5 and 0.5 as the neutral phase, and greater than 0.5 as the positive phase. Then, the monthly temperature anomaly was calculated for the mentioned months. The distribution of temperature anomaly changes in Iran was zoned in the GIS software using the inverse distance weighted (IDW) method. The results of examining the relationship between the studied variables and the winter temperature of Iran showed that there is significant inverse relationship between the AO index and the temperature of December, January, and February in most regions of Iran, especially in the western regions so, correlation coefficients can be seen in the northwestern regions of Iran up to -0.66. Also, in most investigated stations, there is a significant inverse relationship between the NAO index and the February temperature was observed, so the correlation coefficient was observed in the western parts of Iran up to -0.62. By increasing the numerical values of the studied teleconnections from negative to positive phases, the temperature of most regions of Iran decreases throughout the year, especially in February. In some studied stations, a direct and significant correlation was observed between the negative phases of PNA with January temperature and the negative phases of PDO with December temperature. By reducing the numerical values of PNA and PDO teleconnections from neutral to negative phases, the temperature of some regions of Iran decreases. The coincidence of the positive phases of NAO and AO with the negative and neutral phases of PDO and PNA has caused the occurrence of normal and lower-than-normal temperatures in most regions of Iran, especially the regions located in the northern and western half, so, the coldest months of the statistical period happened in this state. The simultaneous occurrence of negative phases of NAO and AO with negative and neutral phases of PDO and neutral and positive phases of PNA has caused normal and higher-than-normal temperatures in the northern and western parts of Iran; in such a way that the hottest months of the statistical period happened in this state. The simultaneous occurrence of neutral phases of NAO and AO with positive and negative phases of PDO and neutral and positive phases of PNA has caused the occurrence of normal and above-normal temperatures in most regions of Iran, especially in the western and northern parts. The coincidence of the neutral phases of NAO and AO teleconnections with the neutral phases of PDO and PNA has caused the absence of severe temperature anomalies (absence of much lower and higher-than-normal temperatures) in Iran. The results showed that the simultaneous occurrence of the positive phases of NAO and AO with the negative and neutral phases of PDO and PNA causes normal and lower-than-normal temperatures in most regions of Iran, especially in the western parts. The simultaneous occurrence of negative phases of NAO and AO with negative and neutral phases of PDO and neutral and positive phases of PNA has also caused higher-than-normal temperatures in the northern parts of Iran. Also, the coincidence of the neutral phases of NAO and AO with different phases of PDO and PNA has caused normal and higher-than-normal temperatures in most regions of Iran, especially in the northern and western parts.

Evapotranspiration is one of the important components of water balance. Estimation of evapotranspiration has been the focus of many researchers in Iran and the world. Accurate estimation of evapotranspiration is very important in hydrological modeling, irrigation design, and water resources management. This variable is one of the most important and effective components in the water balance. Evapotranspiration after rainfall is considered the second largest component of the earth's water cycle on a global scale. The assessment of the future climate and climate change and its effects is done through the output of climate models.

In this research, the average daily minimum and maximum temperature data were scaled according to the CanESM2 model under RCP scenarios using the SDSM for 6 synoptic stations in the southern part of the Aras River basin to draw the perspective of ETp using the Hargreaves-Samani method until the 2050s. For this purpose, observational data of stations and reanalysis data (NCEP) in the daily period (1985-2005) as well as historical data of the CanESM2 model (historical-2005) under RCP scenarios (for the period 2006-2100) has been used.

Estimated ETp values for the Aras basin during the coming period based on the downscaled temperature data of the CanESM2 model under RCP scenarios showed that the value of this variable under the RCP2.6 scenario compared to the base period, slightly decreased and under the RCP4.5 and RCP8 scenarios will have a slight increase. The amount of ETp in this basin will have a decreasing change in the Ardabil, Ahar, and Khoi stations and an increasing change in the Pars-Abad and Jolfa stations. The monthly ETp value of the Aras basin in the future period in January, April to June, and August was estimated to increase with a range between 0.1 to a maximum of 24.3 mm compared to the base period. Comparing the estimated ETp values of the future and the past period showed that the ETp estimated by the Hargreaves-Samani method in the past period compared to the evaporation data of stations except Pars-Abad and Khoi was overestimated by more than 100 mm per year, and it was less in other stations. Hargreaves-Samani ETp values, except for Mako, which is higher from 1985 to 2005 than in 1992-2005, in the other stations in the period of 1992-2005 are greater than the values of the base period.

The estimated ETp values for the Aras basin during the coming period showed that the value of this variable at the annual basin level will increase slightly compared to the ETp of the base period (by the Hargreaves-Samani method), which means that the water requirement of plants will increase in the future in the growing season and this increase means an increase in the water requirement of plants in the future in the growing season, a decrease in infiltration and an increase in evaporation of water resulting from rainfall and snow melting, and a decrease in the feeding of aquifers.

In this study, the spatiotemporal variations of evapotranspiration (ET) were investigated in the southern part of the Aras River catchment. For this purpose, the ET networked data of FLDAS Noah model with horizontal resolution of 0.1 * 0.1 degree were used for a period of 38 years (2019-1982). After validating the data, the average annual ET values ​​for the region were determined first. Then the monthly and seasonal distribution of the parameter were analyzed spatially. Subsequently, ET variations and anomalies were evaluated year to year. Also, the spatial distribution of the occurrence frequency of ET was investigated by considering the absolute thresholds of 50, 80, 100 and 120 mm for the Aras basin. The results show that the annual ET in the east of the basin is higher than the west of the basin. In the seasonal scale, spring and summer have the highest ET values, respectively. In the monthly scale, Mayو June, April and March had the highest ET values, respectively. In contrast, the autumn and winter months have the lowest average ET values. Also, the whole basin during the study period has experienced three distinct periods of ET changes that in the eastern and western parts of the basin, despite the same behavior in the second and third periods, a significant difference was observed in the first period. The results also indicate the existence of positive anomalies after 2002 in the whole basin, the highest values ​​occurred in 2018 in the west of the basin. The study of the frequency of occurrence of absolute ET thresholds on the basin shows the high frequency of ET occurrence at all thresholds in the east of the basin. A study of nearly 4 decades of ET values ​​in the Aras River Basin shows an increase in ET values ​​over the last two decades over the entire basin, which can be attributed to the occurrence of global warming.

Horizontal visibility variations represent changes in air quality. This study is carried out to investigate trend in horizontal visibility in northwest of Iran during 1985-2020. In this regard hourly observational visibility data from 27 meteorological stations was obtained from IRIMO. After screening and selecting the reliable data series, extinction coefficient was calculated by Kashmeider's approach. Ridit analysis and Mann-Kendall statistical tests were used to detect trends in visibility and extinction coefficient. Also, 5 visibility categories were determined and the percentages of each category got calculated. Results showed that the annual regional-average of horizontal visibility (extinction coefficient) was calculated as ~11.4 (0.173) km in the studied period. Ridit output variations showed that visibility improvement / weakening periods was observed in different times according to the environmental conditions of each station. In most stations, the increasing trend of horizontal visibility often formed through the reduction of average visibility percentages; increase in good and very good visibilities and finally stability in bad and very bad visibilities. The highest (lowest) seasonal extinction coefficient obtained for winter (summer) with 0.179 (0.168) km. A perfect inverse correspondence was observed between ridit outputs and extinction coefficient in terms of fluctuation periods and trend direction in all stations. Spatial distribution of parameters showed that the highest (lowest) values of ridit (extinction coefficient) were obtained in several stations located in the north of the studied area. In 18 stations out of 27 studied stations, the increasing (decrease) trend of ridit (extinction coefficient) was detected, which both indicate the increasing trend of the horizontal visibility in the northwest of Iran.

Drought is a natural and repeatable phenomenon that occurs due to a decrease in the amount of rainfall in a certain period. In the current research, the trend of rainfall changes and meteorological drought forecasting of 6 stations located in the Aras watershed have been investigated. To evaluate the severity of the drought, the annual rainfall data of the stations in the statistical period (1987-2021) was checked using the Standardized Precipitation Index (SPI) and simulated using MA, AR, ARMA, and ARIMA time series rainfall models and based on the criteria (AIC) was chosen as the best model for prediction. ARIMA model (0,0,1) was selected for Ardabil, Khoy, and Ahar stations, and the AR model (0, 0, 1) was selected for Parsabad, Maku, and Jolfa stations as the models with the best results. The forecast results for the next 5 years show an increase in rainfall, which makes the results of the fitted models acceptable based on the rainfall statistics available in the respective years. The results of the trend index (SPI) also showed that all 6 stations are close to normal and the highest severity of severe drought is related to Khoy station with 14%. Maku station also observed average wet conditions with 6% frequency during the studied period. Also, the results of precipitation changes using the Sen s slope test show a significant increase in precipitation in Maku station an increase without a trend in Parsabad, Khoy, and Jolfa stations, and a decrease without a trend in Ardabil and Ahar stations.

The purpose of this research is to investigate the relationship between these pollutions in urban areas and heat islands in Ardabil City. In this regard, the land use map of Ardabil City was drawn in the Google Earth Engine system by calling the data of Sentinel 2 and 1 satellites, and its overall accuracy and correctness were checked with the Kappa coefficient. In the next step, to investigate the aerosols of Ardabil City, Sentinel 5 satellite data was used and its daily trend was obtained by regression method. The skewness of aerosols was calculated for 2018-2023 and the pollution density map of Ardabil City was drawn. Then, by calling the MODIS satellite data, the temperature anomaly map and the thermal island of Ardabil City were drawn during 2018-2023, and the night LST trend in the mentioned period and the profile diagram of the night LST thermal island were obtained. By extracting the data of urban aerosol density maps with the thermal islands in ArcGis software, the correlation between temperature and the thermal island of Ardabil City was investigated. The results showed that there is a direct relationship between these two phenomena, such that the amount of daily aerosols in Ardabil City has taken an upward slope since the middle of 2021, and its negative skewness has shown an increase in pollution with high density and occurrence in inner city areas. The pollution density map also showed that the amount of aerosol has increased significantly in the inner city areas and intercity roads of Ardabil City. As a result, the temperature anomaly in urban and residential areas has increased significantly and led to the formation of a thermal island with an average temperature of 8.22 degrees Celsius, which reached its maximum in July.

Teleconnection patterns by affecting the creation and non-creation of wind cause dust particles to rise from the surface of the earth into the air and create dust storms. This research aim is to evaluate the simultaneous effects of El Niño-Southern Oscillation (ENSO), Quasi-Biennial Oscillation (QBO), and Indian Ocean Dipole (IOD) cycles on the fluctuation of dust storms in western Iran. For this purpose, dust storm codes and horizontal visibility of less than 1 km were used in 38 synoptic stations in western Iran during 1987-2022. By applying the statistical method, the outputs were transferred to the Geographic Information System (GIS) environment, and the zoning of the ratio of the averages was done with the kriging interpolation method. The findings showed that there is an inverse relationship between the teleconnections and the frequency of dust storms in most of the studied stations, which account for 95%, 58%, and 87% of the studied area in ENSO, IOD, and QBO indices, respectively. The highest correlation coefficients between -0.94 and -1 were observed in Piranshahr and Khalkhal stations, which is significant in the IOD index at the 99% confidence level. The results of the interpolation of the average dust ratio to the annual long-term average showed that the coincidence of the cold phase of ENSO with the negative phase of QBO had the greatest effect on the increase of annual dust storms in western Iran in the long-term, while the coincidence of the positive phase of IOD with the positive phase of QBO with a lesser effect is associated with increasing the frequency of dust storms.

This study aimed to assess the suitability of land for constructing an outdoor sports stadium in Ardebil Province, focusing on the climate.

Weather variables, such as mean temperature, snowfall, and rainfall, along with environmental factors, were considered. The evaluation utilized a combination of Multiple Attribute Decision Making (MADM), Fuzzy Analytic Network Process (FANP), and Geographic Information System (GIS). A separate layer was created for each variable and after determining their weights, overlaying the layers with fuzzing was used to identify optimal lands for sports facilities in Ardebil Province.

The findings indicated that snowy days and average temperature were the most significant factors influencing the selection of land for sports stadium construction in Ardebil Province with fuzzy degrees of 0.241 and 0.163, respectively. Among the environmental criteria, land slope and elevation were the most important factors for selecting land for sports stadium construction in Ardebil Province with fuzzy degrees of 0.294 and 0.166, respectively. The land suitability results revealed that areas in the northern half of Ardebil Province, particularly Moghan Plain, Garmi, Aslandoz, and Beile-Savar, were highly suitable for constructing a sports stadium.

In deserts and dry areas, sudden changes in temperature cause strong winds and as a result cause the phenomenon of dust. The purpose of this research is to investigate the relationship between the occurrence of dust storms and the North Atlantic Oscillation (NAO) index at selected stations in the west and southwest of Iran (Abadan, Bostan, Ilam, Dehloran, Kermanshah and Sarpol-E-Zahab). For this purpose, we used the codes of days with dust storms, horizontal visibility of less than 1 km, data related to index values (NAO) during the statistical period 1987-2022, and satellite data including dust optical depth index (AOD) obtained from the productions of the gauges of 2000-2017 MISR and 2002-2022 MODIS periods. The results of the correlation coefficients showed that there is a relatively strong inverse relationship between the NAO index in the negative phase and a weak direct relationship with dust in the positive phase, and between 51% and 70% of the dust occurrences were simultaneous with the positive phase. The graph of changes in the time series of the indices in the two negative and positive phases showed that the value of the AOD index increased from west to southwest, which is evident in the Abadan station. According to the index produced by the MISR sensor in the years 2011 and 2015 in the positive phase and in the year 2008 in the negative phase, the peak of dust storm activity in all stations and its lowest value are related to the Sarpol-E-Zahab and Kermanshah stations in the years 2002 and 2004, respectively. According to the Deep Blue index obtained from the MODIS sensor, the positive phase of 2022 and the negative phase of 2009 can be separated as the peak of dust storms. The results of the Wind rose and HYSPLIT models showed that the direction of dust storms entering Abadan, Bostan and Ilam stations was from the northwest and southwest and in Dehlorn, Kermanshah, and Sarpol-E-Zahab from the west, and the storms with external origin in these stations were influenced more by the countries of Iraq, Syria and Saudi Arabia.

Fluctuations in temperature are more or less influenced by teleconnections. The aim of this study is to investigate the simultaneous role of the North Atlantic Oscillation and the Arctic Oscillation with Quasi-Biennial Oscillation on the winter season temperature in Iran. For this purpose, the temperature data of 100 meteorological stations were used in the statistical period of 1988-2019. Correlation coefficients between QBO, NAO, and AO teleconnections data were calculated with monthly temperature. Then the possible states for the simultaneous occurrence of the teleconnection patterns were determined and the temperature anomaly for the determined states was analyzed by drawing a diagram and a map in the GIS environment. The results showed that there is an inverse and significant correlation between the positive QBO phases and February temperature in most of the northern and western regions, so the highest correlation coefficient was calculated at Ilam station as -0.81. An inverse and significant correlation was observed between NAO and especially AO cycles with northern and western half temperatures, especially in January and February. The simultaneous occurrence of the negative (positive) phases of NAO and AO with the negative (positive) phases of QBO causes the occurrence of higher than normal (normal and lower than normal) temperatures in the western (eastern) half so that the warmest winters of the statistical period happened during the simultaneous occurrence of negative NAO, AO, and QBO phases. On the other hand, the simultaneous occurrence of neutral QBO phases with positive NAO and AO phases causes normal and lower-than-normal temperatures in most regions except the southern and southeastern parts of Iran, contrary to this situation, the simultaneity of QBO neutral phases with NAO and AO negative phases has caused the absence of severe temperature anomalies and the possibility of higher than normal temperatures with high intensity is unlikely.