کمال امیدوار

Climate change is a significant environmental concern due to the sensitivity of glacier melting processes and snow density to climate variations. Currently, a range of satellite sensors, including AVHRR, MODIS, GEOS, and MERIS, are employed for snow monitoring and are extensively utilized to analyze fluctuations and changes in global snow cover. The MODIS sensor is particularly favored for its extensive global spatial coverage, suitable spatial accuracy, and frequent temporal coverage across various scales. Consequently, this study utilizes snow products derived from the MODIS sensor. In this investigation, data on snow-related days from the statistical period of 1989 to 2018 were collected for three provinces: Kermanshah, Ilam, and Lorestan. This data was processed using MODIS snow cover information pertinent to the Middle Zagros region, in conjunction with remote sensing techniques. The study provides a detailed examination of snow cover changes within the specified area. The Normalized Difference Snow Index (NDSI) was employed to detect snow cover within the MODIS sensor products. To differentiate pixels and identify various phenomena, the acquired images were processed using Geographic Information Systems (GIS) technology. Analysis of seasonal snow cover changes using MODIS sensor imagery indicates a significant decreasing trend in the majority of the studied area, particularly in the elevated regions. Notably, only the western and southwestern regions of the study area exhibit no discernible decreasing trend. Furthermore, the examination of snow-covered days throughout the study period reveals a decrease in snow cover in the Middle Zagros, with these changes intensifying in recent years, especially in the snow-covered areas of the region. Changes in snow cover during the winter months, particularly in elevated areas, were observed to be more pronounced compared to other seasons and regions within the study area.

Rainfall, as a random variable, is one of those climatic elements that have significant changes in duration and location. Meanwhile, investigating the temporal and spatial distribution of precipitation in a place is of particular importance. In this research, the daily rainfall data of 38 synoptic stations during the 30-year statistical period (1981-2010) have been used to analyze the temporal and spatial changes of Iran's autumn rainfall with the non-parametric Mann-Kendall, ShibSense, and cluster analysis methods. The results of the research show that in the time series of the average autumn rainfall stations in Iran, the increasing trend in October at the Saqez station is at the 95% probability level. In October, the rainfall at Bojnord station decreased by 4.6 mm, and in December at Kermanshah station, it decreased by 16.5 mm. The annual trend of fall season rainfall in Qazvin and Khoi stations has decreased by 24.2 and 21.1 mm in the last decade, respectively. The analysis of autumn rainfall trends using the Mann-Kendall test also showed that Saqez station had a significant increase in rainfall in November, and Bojnord, Kermanshah, Qazvin, and Bojnord stations experienced sudden changes, either increasing or decreasing, during the statistical period. have experienced, but they do not show a significant trend. The results of the cluster analysis also showed that the mentioned region includes low rainfall, high rainfall and medium rainfall, so that the month of December has the highest share of autumn rainfall and the month of October has the lowest share. Autumn rain has taken its place.

The climatic conditions of a region are determined by the frequency and cumulative effects of weather systems passing through that area. The recurrence, change or continuity of weather systems in any given location is of paramount importance in determining and identifying climate. The continuity and change of systems are determined by the process of classifying or determining atmospheric circulation patterns and meteorological types, and for this reason the classification of weather systems is one of the main objectives of synoptic climatology. Ocean-atmosphere interactions are one of the most important discussions in marine meteorology, which has found a wide place in scientific research in the field of meteorology and oceanography. Teleconnection patterns are defined as the simultaneous relationships between fluctuations in climatic elements of a location and changes in atmospheric pressure and sea surface temperature in distant geographical areas. Recent research has focused on explaining climate behavior through the mechanisms of teleconnection patterns. As a global climate feature, teleconnection patterns represent large-scale shifts in atmospheric wave patterns and jet streams, influencing temperature, precipitation, storm tracks, and jet stream positions and intensities across vast regions. Given the significance of teleconnection patterns and their connection to climatic parameters, particularly temperature and precipitation in Iran, this study investigated the monthly temperature and precipitation data from four stations within the AbarkOuh-Sirjan basin, analyzing their relationships with six indices.

In this study, monthly precipitation and average temperature data were used for four stations in the Abarkouh-Sirjan basin (Sirjan, Shahrbabak, Abarkouh and Marvast) during the statistical period of 2003-2022. Data from six teleconnection patterns (IOD, NINO 3+4, NINO1+2, NINO4, NAO and AO) were also used to investigate the effects of teleconnection patterns on changes in the climate data of the study area. The Pearson correlation test was used to evaluate the relationship between the investigated parameters. After determining the correlation of the variables, a multivariate regression was performed to gain a better insight into the effects of the teleconnection patterns on the temperature and precipitation of the stations.

When examining the correlation coefficient between the monthly temperature of the stations and each of the teleconnection patterns, in January the NINO4 pattern, in March the NINO4, NAO and AO patterns, in April the NINO3.4 index in April, NINO4 in May, NINO3.4, NINO1.2 and NINO4 in June, NINO1.2, NINO3.4, NINO4 in July, NAO in October, IOD in November and NINO3.4 in December showed significant correlations. There was no significant correlation in February, August and September. When examining the correlation coefficient between the monthly temperature of the stations and each of the teleconnection patterns with a time lag of three months, the index NINO1.2 in January, NINO4 in March, NINO3.4 and NINO1.2 in April and May, IOD, NINO3.4 and NINO4 in July, NINO4 in August, IOD and NINO1.2 in October showed significant correlations. In February, June, September and November, there was no significant correlation between the index and the temperature. The correlation coefficient between the monthly temperature of the stations and each of the teleconnection patterns with a 6-month time lag showed significant correlations in different months: NINO1.2 in January, NAO in February, NINO1.2 in March and April, NINO3+4 and NINO4 in April, NINO3+4 and NINO4 in May, NINO1.2, NINO3+4, NINO4 in June, IOD in July, NINO1.2 in August, IOD and NINO4 in October and November, and NINO3+4 in December.

When looking at the correlation coefficient between monthly temperatures at different stations and various teleconnection patterns, the NINO3.4 index in the Abarkouh and Marvast stations and the NINO4 index in the Marvast station had the highest correlation at the 99% level. With a 3-month lag between temperature and teleconnection patterns in the Marvast station, the NINO3.4 index in May was significant at the 99% level. With a 6-month lag between temperature and teleconnection patterns, in Sirjan the IOD index, in Shahrbabak and Abarkuh the IOD and NINO1.2 indices had the highest correlation. In the Marvast station, the NAO, IOD, and NINO1.2 indices had the highest correlation with temperature. In the Shahrbabak station, the AO and NINO3.4 indices were significant at the 99% level. In the Abarkouh station, the IOD index had the highest correlation. In the Marvast station, the NAO and NINO4 indices were significant at the 99% level. In examining multivariate regression of temperature with teleconnection patterns, the Marvast station showed the highest correlation in June, with 80% of temperature changes in this station explained by teleconnection patterns. For the regression relationship of precipitation with teleconnection patterns, the highest correlation was identified in the Abarkouh station in July, with 71% of precipitation changes explained by teleconnection patterns. According to the step-by-step regression results, the NINO3.4 pattern in the three stations of Shahrbabak, Abarkouh, and Marvast was identified as the most effective pattern in explaining precipitation changes. In the Sirjan station, none of the indices were identified as an effective pattern for precipitation. In the Sirjan and Shahrbabak stations, the NINO3.4 pattern, and in the Abarkouh and Marvast stations, the NAO pattern were identified as the effective pattern for temperature.

Land surface temperature is one of the key parameters in the study of climate change, which helps in the real assessment of the environmental situation at local to global scales. Investigating the trend of land surface temperature and its long-term changes is necessary to evaluate climate processes in arid and semi-arid regions. The MODIS probe was successfully launched by NASA on December 18, 1999. This sensor is installed on two satellites Terra and Aqua. These two satellites take pictures of the entire surface of the earth twice a day and collect data in 36 spectral bands (wavelengths of 0.4-14.4 micrometers) with a resolution of 250, 500 and 1000 meters. In this research, the daily data of the MYD11A1 product with a spatial resolution of 1000 meters was extracted from the NASA website for 7305 days from 1/1/2003 to 12/30/2023. By choosing this product and the Aqua satellite, due to the convenient time of its passage over the region, a more detailed investigation of the surface temperature of the Daranjir desert basin was carried out. Since Terra's Khurshid Song satellite passes over Iran at 10:30 am and 10:30 pm, it cannot provide an accurate picture of the energy balance, for this reason Aqua satellite monitors Iran at 1:30 pm and 1:30 pm. Slow is selected. The main reason for this choice is the high energy balance in the middle of the day and the middle of the night. Since the statistical period of Terra is longer than that of Aqua, this satellite should have been chosen for Al-Qaeda, but Aqua satellite data is used more for the reasons described (Wan, 2013).Examining the long-term surface temperature trend of the Daranjir desert during the last two decades shows that in a large part of the Daranjir desert (about 72%), the ground surface temperature trend has been positive (increasing). This trend of increasing temperature can be clearly seen especially in the bed of Shore Siriz River and Shore Rafsanjan River. It seems that the increased values of these areas are due to its drying and as a result, a sharp decrease in surface humidity in the long term. Also, Lale-Zar Mountains are among the areas that have shown a positive trend in surface temperature in the last two decades. Since this positive trend happened mostly in the cold seasons of the year (autumn and winter), one of the main reasons can be the reduction of snow cover in the last two decades (Key Khosravi Kiani and Masoudian, 2016). On the other hand, the presence of dams built in the south of Kerman, Rafsanjan and Chale Bafgh and their waterlogging in recent years shows the negative trend (decrease) of the surface temperature in these areas. These areas cover about 25% of the basin area. The rest of the surface of the Daranjir desert, which includes about 3% of the area of the basin, does not show any trend.The results of the long-term seasonal trend showed that in the winter season, a large part of the Daranjir desert (about 95% of the area) has increased the surface temperature trend. It seems that the increase in surface temperature in winter can be due to the decrease in rainfall, snow and vegetation in recent years. In summer, the situation is different. In this season, about 70% of the area of Daranjir desert has a negative (decreasing) surface temperature trend and about 30% of its area has a positive (increasing) trend. It seems that the positive trend in the axis of Kerman-Zarand and Rafsanjan-Anar can be caused by the drying of the Shor Siriz River and the Shor Rafsanjan River during the last two decades. This issue seems normal because with the drying of the river and the decrease in the humidity of the river bed, its surface temperature has increased drastically. In the autumn season, due to the recent droughts, in addition to the above-mentioned axes, the Bafaq hole also shows an increasing trend in surface temperature. The research conducted on the surface temperature of Iran is consistent with this idea (Moradi et al., 2015; Karbalaei et al., 2014; Abad et al., 2015). The results of the long-term annual trend showed that during the last two decades, the temperature of the earth's surface has been increasing in 75% of the surface of the Daranjir desert basin. Due to the drying of the rivers and as a result the decrease of their surface moisture, part of this increasing trend can be seen in the bed of the Shor Siriz and Shor Rafsanjan rivers. Another part of this increasing trend can be seen in the heights of Lale-Zar. One of the main reasons for the increasing trend of surface temperature in this region can be due to the decrease of snow cover during the last two decades (Keikhosravi-Kiani and Masoodian, 2016; Kefayat Mutlagh et al., 2024). Further investigations showed that the presence of dams built in the south of Kerman, Rafsanjan and Bafaq hole and their waterlogging in recent years is the cause of the negative trend (decrease) in the surface temperature in these areas (Azizi et al., 2016; Keihosravi Kayani and Masoodian, 1400).

In recent years, in addition to the common weather and climate disasters in the world, a new phenomenon called dust storms has also been proposed. The prediction of dust storms both in terms of time and space can be effective in reducing its destructive consequences. The purpose of this research is to identify productive synoptic patterns. Dust is in Yazd province and north of Kerman province.

Therefore, the two sets of data include the daily data (code 06) of dust from the synoptic stations of these regions during the statistical years of 1987 to 2021 and the variables of air temperature, geopotential height, sea level pressure, orbital wind, meridional wind, specific humidity and omega as data. The above atmosphere was used. In order to check the synergism of the patterns that cause dust in the years in question, the rough circulation patterns of the geopotential height of 500 hectopascals were drawn in the dusty days of the studied provinces. After determining the days of dust by using cluster analysis and integration by Ward method, two main patterns were extracted and drawn on geopeninsular height unevenness events of 500 hectopascals.

In order to draw better conclusions about the conditions and factors that cause the phenomenon of dust in Yazd province and north of Kerman province, maps of average sea level pressure, specific humidity at 700, 1000 and 850 hectopascals, omega (vertical air velocity in the atmosphere) at 1000 and 400 hectopascal levels, Tavai (convergence, divergence of wind) at the level of 500 hectopascals and the drawing of the jet (wind river) at the level of 300 hectopascals during the rule of each of the patterns were drawn and examined.

The results of this research showed that there is the most significant relationship between the synoptic patterns of the Middle East region and the dust events in the studied provinces by winter and spring circulation patterns at the level of 500 hectopascals.

Air pollution can have serious negative effects on human health, including cardiovascular and respiratory diseases. Monitoring and controlling air pollutants is very important to protect public health and the environment. Like many developing countries, Iran is facing air pollution, especially in its big cities and industrial cities. One of the powerful tools in air pollution monitoring is remote sensing methods. The aim of this study is to use relatively high-resolution satellite data to monitor air quality and air pollution using Sentinel-5 (Sentinel-5P) sensor images. In this study, a comprehensive monitoring based on the values of some of the most important air pollutants (including AI, O3, NO2, SO2, CH4 and CO) has been done using Sentinel-5 satellite images for Iran in 2019-2023. The results of this research showed that the emission of carbon monoxide and sulfur dioxide gases had a decreasing trend (in the months of June as an example of the examined month), but nitrogen dioxide gas, methane gas, ozone gas and aerosols had an increasing trend during the month. from June 2021 to 2023. In general, air pollution is more serious in the northern parts of the country, especially in big cities and several large urban gatherings. In this study, it was investigated how the levels of six air pollutants in Iran vary and differ from June 2019 to 2023. Another important result of this research is that the total amount of air pollution in 2020-2023 has faced an increasing trend compared to 2019. Also, the monitoring by Sentinel-5 satellite images shows that in recent years, Tehran has had the most polluted air in terms of carbon monoxide, nitrogen dioxide, sulfur dioxide and suspended particles (dust). Also, changes in the concentration of pollutants do not follow a specific pattern. It was also found that the GEE system is able to process a large amount of data in a very short time with high accuracy.

Drought monitoring is crucial due to its widespread impacts on the economy, environment, and society. To monitor droughts accurately, comprehensive and integrated meteorological and hydrological data are essential, particularly in areas such as Sistan and Baluchestan Province, which is facing a severe drought. Climatic studies indicate that Sistan and Baluchestan province has evolved into an arid region highly susceptible to prolonged droughts due to a combination of climatic factors such as low precipitation, high evaporation rates, strong winds, and limited vege-tation cover. Based on research conducted between 2000 and 2015, approximately 58% of the province's total area has been directly affected by drought. These findings align with previous studies, which have shown that dry years with below-average rainfall constitute more than 52% of the province's climatic record.

In this study, MODIS and GRACE satellite data were utilized to comprehensively assess drought in Sistan and Baluchestan province. The Vegetation Health Index (VHI) was employed to monitor vegetation changes, while the Terrestrial Water Storage (TSDI) was used to analyze groundwater variations. The Global Land Data Assimilation System (GLDAS) soil moisture data served as a reference for result validation. Given the limitations of GRACE data, a 16-year common period (2002-2017) was selected for the analysis. Additionally, the Standardized Precipitation Evapotranspiration Index (SPEI) was calculated over a 30-year period (1987-2018) to examine long-term drought trends.

Geographically, VHI drought maps revealed that Sistan and Baluchestan province has experienced moderate to severe drought throughout the study period, with increasing drought severity in the years leading up to the study. To monitor hydrological drought, GRACE satellite data was utilized to measure changes in terrestrial water storage. This is a significant step in comprehensively understanding hydrological drought in the study province. The TSDI index, a quantitative measure of drought, was calculated to monitor hydrological drought in all 50 third-level sub-basins. The drought monitoring process began by calculating the deficit, defining drought as four consecutive months with negative TWSA. Results clearly show an increasing drought deficit from south to north in the study province. Almost all basins experienced large deficits from 2011 onwards. By estimating the deficit and the overall decrease in groundwater storage in April, this study demonstrated that all basins were experiencing a water storage deficit in the critical and exceptional range in the years leading up to the end of the study period.

Results from the VHI revealed that the study province has experienced widespread drought in recent years. The southern and central regions of the province have faced more severe drought classes. Analysis of drought index graphs across different severity classes confirmed that all watersheds have experienced drought conditions, particularly in recent years. Data analysis indicates a severe water crisis in the province. Urgent and coordinated actions are required to address this challenge. Shifting to drought-resistant crops, improving irrigation efficiency, and securing water rights are essential steps towards a sustainable future.

Dust is a climatic hazard that occurs more often in arid and semi-arid regions of the world, as well as in areas that are in the direction of the winds of desert regions. The main goal of this research is to identify the effect of teleconnection patterns on the number of days with dust storms in Kerman province during a 30-year statistical period (1988-2017), for this purpose Kerman, Anar, Sirjan, Shahrbabak and Bam stations It had long-term statistics and geographically covered the entire area of the province. The required data of this research is the number of dust days in the selected stations and the data of remote link patterns including the multivariate Enso (MEI) pattern, the Pacific Decadal Oscillation (PDO) pattern, the North Atlantic Oscillation (NAO) pattern, The Arctic pattern (AO), the multi-decadal North Atlantic pattern (AMO) and the Pacific Ocean temperature fluctuation pattern (NINO3.4) were received from the National Meteorological Organization and the National Oceanic Atmospheric Center, respectively. The results showed that the maximum average number of dust days occurred in spring and the minimum was in autumn. In terms of the effect of remote connection patterns on the studied stations, it can be said that Kerman, Anar and Shahrbabak stations are most affected by the AO pattern and the least effect is by the NINO, AMO and PDO patterns and Sirjan and Bam stations respectively. Also, they have had the most influence from the AMO pattern and the least effect from the NINO3.4 and MEI patterns. Also, the correlation results showed that the highest percentage of changes in dust days that can be explained by Teleconnection patterns is 25% in the winter season of Sirjan station and 22% in the spring season of Shahrbabak station. In other stations, the fluctuations of the number of dusty days, less than these values, were related to the link patterns from a distance.

It is challenging to estimate how the climate of a region will change under global warming in the future. Among the essential climate variables, temperature, relative humidity and wind speed are very important, and their changes in relation to each other can have many consequences, such as increasing heat stress and evapotranspiration.    In this research, we investigated future temperature, wind speed, and relative humidity in Iran. Five models, namely GFDL-ESM4, IPSL-CM6A-LR, MPI-ESM1-2-HR, MRI-ESM2-0, and UKESM1-0-LL from the Coupled Model Intercomparison Project Phase 6 (CMIP6) have been evaluated versus observations for 1990-2014. A multi-model ensemble was generated with the Integrated Weighted Mean (IWM) method from selected CMIP6 models, and the performances of individual CMIP6 and CMIP6-MME models in estimating temperature, wind speed, and relative humidity were investigated. The results showed that all five selected models are capable to reproduce the variables; however, the CMIP6-MME significantly improved the results. The CMIP6-MME showed good agreement with observational data both in terms of climatology and spatial distribution of each variable, and its performance is higher compared to individual models.   We have used two Shared Socioeconomic Pathways (SSPs), namely SSP2-4.5 and SSP5-8.5, and three-time periods, namely near-term (2026-2050), midterm (2051-2075), and long-term (2076-2100) relative to the historical period (1990-2014).The results of this study showed that Iran will undergo changes in temperature, wind speed, and relative humidity spatial distribution in the future. The decrease in relative humidity and dryness of the air in the future can have important consequences for agriculture, food security, and water resources management. The findings of this study, in agreement with previous studies, emphasize the significant increase in temperature throughout Iran. Under the SSP2-4.5 (SSP5-8.5) scenario, the average annual temperature of the country increases by 1.40 (1.83), 2.34 (3.58), and 2.99 (5.58) degrees Celsius in the near (2026-2050), middle (2051-2075), and far (2076-2100) future, respectively.    Along with the increasing trend of temperature in Iran, wind speed will decrease in most regions of the country in the middle and end of the 21st century under two SSP scenarios. This decreasing trend can be a result of decreasing atmospheric instability and increasing potential temperature. The northwest of Iran has shown the maximum increasing temperature and the maximum decreasing wind speed. The decrease in relative humidity in Iran has been evident since the 1990s, and the projection results indicate that it will decrease in large parts of Iran in the future. However, the relative humidity in the southeast of Iran shows an increasing rate in the future. Results of this study show that the heat stress will be significantly higher through SSP5-8.5 than SSP2-4.5 in the 21st century due to the decrease in wind speed and increase in temperature throughout Iran. Therefore, Iran should quickly move on to formulate and implement long-term adaptation plans for resilience against climate change.

Fresh water availability and its shortage is one of the most important issues in the world today that some countries are facing. In the last two decades, Kabul province, Afghanistan, has seen a decrease in water resources under the influence of natural and human factors. The purpose of this research is to use satellite data and remote sensing techniques to investigate changes in surface and underground water resources in Kabul province. For this purpose, the satellite data and products available in the Google Earth Engine in the period 2000 to 2022 and the climate data of ground stations in the period 2006 to 2021 have been used, which include: evaporation data- Transpiration, Enhanced Vegetation Index (EVI), Global Product of Surface Water Areas (JRC), GRACE data, OLS Satellite Night Images, Sentinel 2 Satellite Image, Landsat 7 Satellite Image, Temperature, Humidity and Rainfall Data. The change process of the used data was analyzed through the Mann-Kendall test and the significance level of these data was checked. The supervised classification method was used on the two mentioned images to calculate the area of vegetation, water area, residential areas, and barren lands. The obtained results show the reduction of underground water resources (significant reduction trend of GRACE satellite data) and the reduction of the surface water area changes in Kabul province, under the influence of natural and human factors, which among these factors can reduce He pointed out the amount of rainfall, increase in temperature, increase in evaporation-transpiration, increase in the level of vegetation and physical development of Kabul city and increase in the population using water resources.

Rainfall time series analysis is important in that its variability is very high, and should be considered by water resources and agricultural management planners. In this study, the annual rainfall series analysis of three stations in Kerman, Sirjan and Rafsanjan as three major pistachio growing areas in Kerman province during the statistical period of 1986-2086 (35 years) has been investigated. First, the statistical parameters of precipitation data at three stations were examined, followed by the identification of precipitation behavior and norms. To evaluate the homogeneity of mean and variance, mean homogeneity test based on Standard normal homogeneity test and homogeneity of variance based on Van Neumann test were used. Investigation and detection of trends or no trends in the annual precipitation data of the studied stations using parametric methods (autocorrelation test, Pearson correlation coefficient and least squares error test) and non-parametric tests (Mann-Kendall statistical test, test The turning points and the correlation coefficient of Spearman and Kendall Tao) were investigated. The results showed that the precipitation data series in all three stations are heterogeneous in terms of mean and variance. Based on the parametric tests of Pearson correlation coefficient and the least squares error test in Rafsanjan station, there is a decreasing trend of precipitation in the study period. In Kerman and Sirjan stations, the slope of the precipitation line was 0.439 and 0.271, respectively, and the test of the least squares of error in these two stations showed that the slope of the line is not significant. Non-parametric tests of Kendall and turning point test also showed that there is no trend in the annual precipitation data of the studied stations and the null hypothesis test should be accepted.

This research aims to explore the architectural and cultural attractions of the Jame Mosque (Masjid-e-Molk) in Bafrouyeh City. The research findings indicate that this mosque, situated in the center of the lower neighborhood alongside Hosseiniyeh Aazam, encompasses a separate winter and summer prayer area. The spaces within this mosque include the dome chamber, mihrab and wind-catching space, porch and courtyard, winter prayer area, and entrances. In fact, the entire mosque structure is constructed using bricks and clay, with some parts in fired bricks. The mosque has three entrances, including a secondary entrance to the summer prayer area on the northern side, which has undergone recent restoration due to damage from the Qajar era. This entrance door, adorned with exquisite tilework dating back to 866 AH, is sealed with sun-dried brick. The mosque courtyard is rectangular, extending along the Qibla axis, with dimensions of approximately 5.6 by 9 meters. On each side, there are three gateways in the eastern and western facades, with the central gateway being wider and connected to a courtyard-like space resembling a Shabestan (prayer hall) around the courtyard perimeter

To assess thermal comfort of residences, several different factors as meteorological, physiological, and socio-cultural, should be considered. The integrated effect of these variables on thermal stress can be obtained and evaluated using thermal comfort indices. Thermal comfort as a bio-meteorological index is of special importance. The purpose of this research is to analyze the seasonality and trend of heat stress in Iran in the period from 1981 to 2020. In this research, the Universal Thermal Climate Index (UTCI) index as a common thermal index was evaluated for outdoor thermal comfort, using ERA5 dataset. ERA5 is the fifth generation ECMWF reanalysis data for the global climate and weather for the past 4 to 7 decades. The ERA5 provides hourly estimates for a large number of atmospheric, ocean-wave, and land-surface quantities. An uncertainty estimate is evaluated by an underlying 10-member ensemble at three-hour intervals. Such uncertainty estimates are closely related to the information content of the available observing system which has improved considerably over time. They also indicate flow-dependent sensitive areas (Hersbach et al., 2020). Also, root mean square error (RMSE), Percent bias (PBIAS), Nash–Sutcliffe model efficiency coefficient (NSE), and Root Mean Standard Deviation Ratio (RSR) metrics were used to evaluate the quality of ERA5 dataset.The seasonal variability of the UTCI index shows that this index has significant regional heterogeneity in Iran. The increase in UTCI from the north to the south of Iran leads to an increase in thermal stress. The spatial distribution of areas that do not have thermal stress during the hot period of the year are mainly consistent with the altitudes. During the cold seasons of the year, areas with elevations of more than 3,000 meters in Iran have moderate cold stress. The investigation of the trend of thermal stress during the last four decades, which was analyzed with the modified Mann-Kendall test, shows that the UTCI in Iran has a dominant increasing trend. The UTCI index shows an increasing trend in the spring and autumn seasons by 100%, and in the winter and summer seasons at 99.83 and 99.75% of the country, respectively. The maximum significant increasing trend of the index at the level of 0.05 was achieved in the spring. In the same way, the highest value of Sen's slope estimator test of the area-averaged trend is also seen with the value of 0.52 oC in this season in the study period. The results of this study for climatology and the trend of the UTCI index in Iran show that: 1- There is a close relationship between heat and cold stresses in Iran and topography, but this relationship is not a linear; 2- Along with global warming, the UTCI index in Iran during the years 1981-2020 has shown an increasing trend; 3- In general, areas with UTCI cold stress in the country are decreasing and areas with heat stress are increasing; 4- One of the key findings in this study is the significant increase in trend of the UTCI index in the spring season.

Climate change has significantly increased the frequency and intensity of heat stress and has more effects than increasing average temperature. This study has investigated the spatial distribution of the universal thermal climate index (UTCI) during historical and future periods in Iran. The UTCI (°C) refers to “the isothermal air temperature of the reference condition that would elicit the same dynamic response (strain) of the physiological model” (Jendritzky et al., 2012). In this way, the UTCI is an equivalent temperature, similar to PT. The thermal impact of the meteorological conditions is compared to the one of a standardized reference “indoor” environment with RH = 50% (Ta < 29 °C), WS = 0.5 m s−1, pa = 20 hPa (Ta < 29 °C), and Tmrt = Ta (Shin et al. 2022). Three variables of daily temperature, relative humidity, and wind speed from two sets of data, including 124 meteorological stations and five models from the Coupled Model Intercomparison Project phase 6 (CMIP6) model, including GFDL-ESM4, IPSL-CM6A-LR, MPI-ESM1-2-HR, MRI-ESM2-0, and UKESM1-0-LL were investigated with a horizontal resolution of 0.5o. Then, an ensemble model (CMIP6-MME) was generated from these five models using the independent weighted mean (IWM) method. The performances of individual models and the generated ensemble model were examined by Taylor's diagram. The results showed that the multi-model ensemble has higher performance than individual models for all three variables. The results revealed that the spatial distribution of the seasonal averages of the UTCI index has significant variability in Iran, and the variability of this index is affected by the latitude, complex topography, and distance to water resources in Iran. In general, heat stress will increase significantly in Iran by the end of the century. So, we will witness a significant decrease in areas with no heat stress until the end of this century. On the contrary, strong to very strong heat stress events will increase significantly in the country at the end of the century. While the areas with no thermal stress show a spatial displacement to mountainous regions and higher latitudes. These results show that effective adaptation methods should be taken to adapt to global warming and reduce its consequences to avoid the adverse effect of increasing heat stress events in Iran. The results show the overall increasing trend of Iran's heat stress in the near and far future. The highest increase in heat stress anomalies (13.3 degrees Celsius in winter during the far future period under the SSP5-8.5 scenario) can be found in the northwest and west of the country. The increasing intensity of heat stress in the western and northwestern parts of Iran may be related to elevation-dependent warming (EDW).

The excessive emission of greenhouse gases in recent decades and the ongoing changes in the climate have changed the meteorological parameters and, accordingly, the climatic zones. In this study, the future prospect of climatic zones and the risk of desertification in the watershed of Karun basin was investigated using UNEP index and LARS-WG6 microdirection model and HadGEM2-ES model under the RCPs emission scenarios for three periods 2021-2040, 2041-2060, and 2061-2080. The results pertaining to all three future periods and RCPs release scenarios showed that the long-term average annual precipitation will decrease between 1.9 and 14.6% compared to the base period, but the annual average minimum temperature will be between 1.2 and 3.4 °C, maximum temperature between 1.3 and 3.7 °C and the annual average of evaporation and transpiration will increase between 4.7 and 12.3% compared to the observation period. In the upcoming period and based on the emission scenarios, dry climate (the risk of very severe desertification) and semi-arid climate (the danger of severe desertification) increase 3.5% and 4.4%, respectively, and semi-humid (moderate desertification) and humid (no desertification) and very humid (moisture and wet climate) decrease 4% and 4.7%, respectively. However, semi-humid climate zones (low risk of desertification) with 0.8% will be less severe. Under the pessimistic scenario, the semi-arid climate region will reach its maximum level among the publishing scenarios in the near future with 12.4%. Therefore, this displacement in the boundaries of climatic classification will increase the desertification of Karun basin in the upcoming period.

The climate of Kerman province is suitable for pistachio cultivation, so the pistachio cultivars of Kerman province are of high quality. Weather conditions play a decisive role in the planting and growth of fruit trees. Climate change will make the agricultural sector vulnerable to changes in temperature and precipitation parameters. Therefore, it is necessary to investigate the future climatic conditions of fruit tree cultivation areas under the influence of climate change. Therefore, it is necessary to investigate the effects of climate change on the climate indicators of pistachio tree agriculture in Kerman province. For this purpose the indicators of temperature, precipitation, and evaporation in the coming decades have been investigated concerning climate change.

The trend of changes in temperature, precipitation, and evaporation indices during the observation period of each station was determined using Kendall's statistical test. Based on the CanESM6 climate model, under two scenarios ssp2-4.5 and ssp5-8.5, minimum temperature, maximum temperature and precipitation data have been produced in the coming years. One of the general circulation models (GCM) CanESM5 model was used to predict climate parameters. Downscaling of canESM5 model data was done using SDSM software. The error rate of production data was checked with Base data using validation criteria. First, the process of production data changes in different stages of pistachio phenology was calculated.

First, based on the observational data of the period of 1992-2021, the status of the climatic indicators of the pistachio tree and trend of each of them were investigated using the available tests. Based on the temperature of 7.5 degrees Celsius, the five stages of the phenology of the Ohadi cultivar were determined, flowering to full flowering, bone shell hardening, and the beginning of rapid brain growth, the end of brain growth, and the harvest time based on the summary of the day-growth degree. With the downscaling of CanESM5 model output data under SSP5-8.5 and SSP2-4.5 scenarios, daily temperature and precipitation data for Rafsanjan station were simulated for three periods 2075-20100, 2050-2074, and 2025-2049. The results show that in the base period, the temperature indices of the growing season (average daily temperature, average maximum) and average minimum in March and April have a positive significant trend. The flowering (average temperature and average minimum temperature indices) in the future period of 205-2049 under the ssp2-4.5 scenario in the growing season (average daily temperature indices, average maximum, and average minimum temperature) in April have a significant positive trend. The simulation showed that the average daily temperature index in the pistachio growing season under the SSP2-4.5 scenario during 2025-2074 increases compared to the base period (23.5 degrees Celsius) and reaches 24.6 degrees Celsius

the results of the simulation showed that the average daily temperature index in the pistachio growing season in Rafsanjan station under the scenario of SSP2-4.5 were 0.97°C from 2025 to 2074 in the spring season and the months of April and May and the autumn season and the month of October with a positive trend. Under the SSP5-8.5 scenario, the average daily temperature in the two future periods of 2075-2100 and 2050-2074 has a positive increasing trend; and in the final period, the average daily temperature of the growing season will increase by 3.13°C compared to the base period, and the average daily temperature will reach 26.63°C from 23.5°C. The average minimum temperature under the scenario of SSP2-4.5 in the period 2025 - 2049 in April and October has an increasing trend. In addition, the average minimum temperature in March in the period of 2075-2100 has an increasing trend; the average minimum temperature under the above scenario is 0.75°C. The average minimum temperature in March in 2100 - 2075 shows an increase in average temperature under the above scenario. Under the pessimistic scenario SSP5-8.5 during the period 2075 - 2100 and 2050 - 2074, the average minimum temperature has an increasing trend of about 1.76°C. The maximum temperature in the flowering stage under the scenario of SSP2-4.5 in the base period and the future period under 2050 - 2074 and the 2025 - 2049 period will not have a trend. Under the above scenario, the above index has a positive trend in the period of 2075-2100, and on average; it increases by 0.29°C compared to the base period. Under the SSP5-8.5 scenario, the average maximum daily temperature has a positive trend and increases by 1.06°C.The mean daily temperature index in the pistachio-growing season. In Rafsanjan station, the average days with a temperature between 35 and 40 degrees Celsius in the base period are about 39 days. During 2025-2100, under the ssp2-4.5 scenario, the number of days with a temperature of 35 to 40 degrees Celsius shows a decreasing trend. On the other hand, the number of days with temperatures higher than 40 degrees Celsius under the same scenario reveals an increasing trend. From the number of 9 days in the base period, it will reach about 31.5 days between 2025 and 2100. This content also applies to the ssp5-8.5 scenario. And the number of days with temperatures higher than 40 degrees Celsius will reach about 50 days at the end of the future period of 2025-2100, and the trend is significantly positive.

Atmospheric circulation has a variety of patterns in terms of time and space, However, drastic changes in atmospheric factors and elements have caused anomalies in these patterns, which result in changes in temperature, humidity, pressure and precipitation in different parts of the world, one of which can be the snowfall in snow-covered areas. Snow cover has significant effects on climate, such as the effect of radiant energy in the region and atmospheric and thermal circulation, and any changes in climate may have long-term environmental and economic consequences for the time, amount and distribution of snow. Cold, rainy and snowy winters are a feature of the mountainous climate of the Central Zagros region of Iran, which is the gateway for various humid air masses to enter Iran from the Mediterranean and Eastern Europe. With this feature and being mountainous, the region is witnessing low temperatures and snowfall in the mountainous areas of this region. In this research, snowfall in the Middle Zagros, which is the source, the most important catchment area of the country and the source of the most important and largest rivers in Iran to the south and southwest of the country is investigated. This is especially necessary in the mountainous areas of the Zagros, where water from snowmelt plays an important role in runoff, which can be considered a pivotal operation in the field of water resources management and flood control in the Zagros. Therefore, the present study tries to investigate the relationship between changes in snow cover in the Middle Zagros region and changes in atmospheric circulation patterns using synoptic methods. The purpose of this study is to analyze the synoptic characteristics of the Middle Zagros snow cover in relation to atmospheric circulation patterns and to explain the spatial and temporal characteristics of this phenomenon using the statistics of synoptic stations in the study area. The results can help in forecasting and planning surface and groundwater resources as well as drought and prevent damage due to flooding of rivers in this region, most of which is due to snowmelt in the highlands of this region. The data required for the study include hourly and daily meteorological statistics of snowy days at synoptic stations in the three provinces of Ilam, Kermanshah and Lorestan, which were obtained from the Meteorological Organization. Geopotential altitude data of 500 and 850 and sea level were also obtained from the website of the National Center for Environmental Prediction and the National Meteorological Research Center (NCEP / NCAR). These statistics cover the long-term period (2018-1989). After extracting the snow day codes (including codes 70, 71, 72, 73, 74, 75), it was found that among the stations in the region, 14 stations had days with snow to be checked, which in terms of time distribution. And have a statistical period of 30 years and cover the entire Middle Zagros. In this study, after collecting statistics and data on snow-related days during the statistical period (1989-2018) in synonymous stations located in three provinces of Kermanshah, Ilam and Lorestan, as well as data on different atmospheric levels from the site of NCEP/NCAR center in Middle Zagros, they were processed using factor analysis and hierarchical clustering techniques and The results were analyzed and finally different snow-generating companion patterns in the study area were studied in detail. The results of studying circulatory coping patterns after analyzing the data at the levels of 500 and 850 hpa were entered using factor analysis technique and hierarchical clustering method, four models of companionship governing the snowy days of middle Zagros were identified and determined. These results showed that four distinct patterns for snowfall in middle Zagros can be identified. Accordingly, the first and third patterns with a total of 64.5% of the dominant pattern of snowfall in middle Zagros are mainly due to the establishment of high pressure center in the Mediterranean and Black Sea and the study area in the front of the deep landing at a high level Also, in most cases of heavy snowfall, a cold hole is formed at the level of 850 hpe which covers the study area and In most cases, a strong carrier is located in Eastern Europe or stretched diagonally from the west of the Caspian Sea to the south of the Red Sea. The study of patterns shows that the high-elevated center of the western Mediterranean and tibetan plateau is strengthened and by moving to higher latitudes, it helps to create strong graphs with north-south axis in Europe and Asia. The creation of these crypts and the existence of low-lying or cold centers in western Iran strengthens the eastern Mediterranean frigate and stretches to low latitudes and causes cold escape in the study area. On such days, the polar vortex fold extends to low latitudes such as the north of the Caspian and western Iran with the north-south or northeast-southwest axis, causing cold air to fall in the region. In the study of patterns and days of heavy snowfall, it is necessary to penetrate the polar vertex fold into low latitudes for heavy snowfall.

In this study, the future perspective of evaporation from the free water surface of Shahid Abbaspur dam reservoir was investigated using the multiple linear regression analysis of pan evaporation (MLR-Epan), the micro-scale atmospheric circulation model of LARS-WG6, and HadGEM2-ES model output, under RCPs emission scenarios for three time periods of 2021-2040, 2041-2060, and 2061-2080. The results of the Mann-Kendall test from 2021 to 2080 showed that under the RCP2.6 scenario, the trend of annual changes in the minimum temperature, maximum temperature, and evapotranspiration of the Karun catchment basin was insignificant. However, under RCP4.5 and RCP8.5 emission patterns, the annual trend of the mentioned parameters increased significantly (α = 0.01). The accuracy of MLR-Epan in the base period was confirmed by the results of the hypotheses tests, so that 87% of the changes in evaporation from the pan was explained by the three variables of minimum temperature, maximum temperature, and evapotranspiration. The findings showed that in the coming periods and based on all three radiative forcing patterns, the average annual surface evaporation from the Shahid Abbaspur dam reservoir will increase from 7.2 to 20.2 percent compared to the observation period. In the next sixty-year period (2021-2080) under the RCP2.6 scenario, the annual evaporation from the free water surface will be on the raise, but the increasing trend is not significant. Nevertheless, under RCP4.5 and RCP8.5 scenarios, the increasing trend is estimated to be significance (α = 0.01). Based on Sen's Slope estimator, the amount of evaporation from the reservoir surface is predicted to be 1.4, 19.1, and 31.9 mm, respectively, in the next 60-year period and under the investigated scenarios. Consequently, according to the future climatic conditions, it is necessary to reduce evaporation losses in dam reservoirs with proper planning and informed decisions.

Drought monitoring is critical for early warning of drought hazard. This study  is  attempted to develop remote sensing drought monitoring index (VDI), based on Accuracy of different bands of Moderate Resolution Imaging Spectroradiometer data MODIS, VDI focuses about the vegetation water stress. Spectral studies have demonstrated that due to the large absorption by leaf water, shortwave infrared reflectance (SWIR) is negatively related to leaf water content. Being sensitive to leaf water content, SWIR is widly utilized to construct various remote-sensing indices for example VDI for reflecting vegetation water content, . In this study, Vegetation Drought Index (VDI) was evaluated Based on the sensitivity rate to moisture by shortwave infrared reflectance bands SWIR 5 and 6 (VDI5 and VDI6). The data included the MODIS sensor images from Terra satellite in a period of nineteen years from 2000 to 2018 and Precipitation data are obtained from the Global Land Data Assimilation System (GLDAS), in Sistan & Balouchestan Province, Pearson correlation coefficient was used to evaluate the accuracy of the Drought spatial distribution maps calculated based on the two bands.Results indicate high significant correlation rate between VDI6 and Precipitation data . Study also showed that shortwave infrared band 6 (SWIR) is more sensitive to agricultural drought than band 5,in Sistan and Baluchestan province . The study recommends  to use VDI index with and 6 for better early detection and monitoring of agricultural drought in operational drought management programmes.

Rainfall is one of the most complex and accidental natural phenomena. The impact of tangible or intangible factors is so effective in creating an accident Which has led this process from a clear legal system to a complex and chaotic one. In general, understanding how the climate is changing. In particular, the trend of precipitation changes is one of the issues that have been considered by atmospheric and hydrological researchers in recent years. Neural networks are simple computational tools for testing data and creating models of data structures Reduction of rainfall in Kashan station is one of the problems that has caused many agricultural lands to be taken out of the agricultural cycle due to quantitative and quantitative water loss. And it has led to many economic and social problems, such as immigration. Therefore, studying, detecting, and forecasting rainfall in this region can be useful for the present and future of decision-makers to solve the problems of the region.

This research is of applied type and its method is descriptive-analytical and the study area in Kashan city. Required data during the statistical period of the Kashan synoptic station is obtained from the Meteorological Organization to determine the trend of precipitation changes The Man Kendall method is used the Mann-Kendall test is one of the most common and widely used non-parametric methods of time series trend analysis. Using the Man-Kendall method, data changes are identified, and their type and time are determined. In this research, to implement different neural network models and determine the optimal structure, the neural network toolbox in MATLAB software has been used. One of the most widely used neural network models is the multilayer perceptron model, which is based on a post-diffusion algorithm. In this type of network, forward input data is processed and the processing path does not return to the neurons of the previous layer, and the output of each layer will only affect the next layer.

In order to investigate the trend of increasing or decreasing the amount of precipitation in Kashan station, the Sense slope method was used, and to evaluate the significance of the precipitation trend, the Mekendal method was used. The results of the annual analysis showed that in general, no significant trend was observed in the average rainfall time series of Kashan. Therefore, no natural jump has been observed in the average rainfall of the Kashan station. Sense test statistics at 99% and 95% confidence levels confirm the decrease in rainfall at Kashan station. Thus, the rainfall of the studied station during the study time series has decreased by -0.6 mm each year. The greatest impact of reduced rainfall is related to winter. The most important advantage of the neural network over other intelligent systems is the ability of the network to learn from its surroundings. First, the data for entering the network was divided into three categories, 70% of which were for network training, and 30% of the data was allocated for testing and validation. Finally, due to repeated trials and errors to build a suitable network, a network with two hidden layers and 10 neurons had the highest accuracy for prediction. Optimization diagram for artificial neural network Kashan station precipitation data showed that 110 artificial neural networks were created in repetition. In this research, the network was able to predict the amount of precipitation using the introduced variable of correlation rate of 0.47 According to the results obtained from the values predicted by the network in combination with genetic algorithms such as actual precipitation data and predicted data It has a nonlinear process by the network without combination with genetic algorithm. But the difference in results for the simulated precipitation averages at Kashan station is still clear.

n the present study, the precipitation data of the Kashan synoptic station were evaluated seasonally and annually using the Menkendal method and Sense slope. The results of the Menkendal test trend in seasonal and annual average rainfall time series showed that there is no significant trend in 99% and 95% levels. On average, Kashan station has had a decrease of -0.60 mm of precipitation every year. At first glance, it seems that this decrease (0.6 mm) in Kashan's very dry station and low rainfall is a small amount. But for a period of 50 years, this reduction reaches 30 mm, if this trend continues in the long run, it will have very catastrophic consequences for the region's groundwater, agriculture, and drinking water basins. Also, an artificial neural network method was used to predict monthly rainfall. According to the results of using the multilayer perceptron model using the error propagation algorithms and the number of 10 neurons in the middle layers and two hidden layers had less error than other structures. Therefore, the variable introduced in the study with a correlation rate of 0.47 was able to predict the amount of precipitation. By reviewing and adapting the values of real data, the forecast, and the results of the correlation diagram, it was found that the precipitation forecast for Kashan station was to a reasonable extent consistent with the actual results of the station. According to the results, the error obtained from simulating precipitation data of Kashan station using neural network performance was very low, which indicates the proper performance of the network in the testing and validation stage Finally, the ability to estimate and predict precipitation using artificial neural network (ANN) at Kashan station has performed better in the months of low rainfall and the dry period when rainfall is minimized. But in general, the artificial neural network for predicting rainfall in Kashan station has shown high performance

Thunderstorms are one of the most destructive atmospheric phenomena that occur every year in different parts of the country, especially in the southwestern region of the country.The effects of these storms, which are manifested in the form of hail, torrential rains, strong winds, thunderstorms, in addition to providing water resources, lead to irreparable human and financial losses.Therefore, awareness and knowledge of thunderstorms, especially in the southwestern region of the country requires a correct understanding of the distribution of meteorological systems and physical mechanisms responsible for the occurrence of these storms. In this research, first to select the code of thunderstorm phenomenon and perform a cluster analysis is used of synoptic station data, high atmosphere data and to modeling selected days with WRF from FNL data with 1*1 degree separation with A 24-hour period .Using clustering method 3, Thunderstorm events were selected as representative and using WRF model, modeling was performed for selected days with different schemas. The modeling results showed that in the representative days, the status of CAPE, CIN, LCL, LFC indices in the stations where the thunderstorm occurred was appropriate. The model output precipitation maps show that precipitation occurred in the study area, but no thunderstorms occurred at all stations, and only a few thunderstorms were recorded. The modeling results showed that in the representative days, the status of CAPE, CIN, LCL, LFC indices in the stations where the thunderstorm occurred was appropriate. The model output precipitation maps show that precipitation occurred in the study area, but no thunderstorms occurred at all stations, and only a few thunderstorms were recorded. On a global scale, thunderstorms are a phenomenon that is recorded in almost all parts of the world and its occurrence in hot seasons is much higher than cold seasons. The diversity of climatic conditions and geographical features of Iran has caused the phenomenon of thunderstorms to be reported in different parts of it every year. Modeling is one of the methods of studying thunderstorms. The motivation for paying attention to numerical models of the atmosphere is the very poor resolution of planetary climate models in relation to local and regional climatic processes. Numerical atmospheric models have been developed to improve spatial detail and attention to regional and local variations.One of the most important models is the WRF numerical prediction model. The WRF Climate Prediction Model is a mid-scale numerical model that is widely used by researchers today for local-scale weather forecasting, air quality studies, and regional climate research.Statistical calculations and graphs show that the occurrence of this phenomenon in the region depends on the arrival of synoptic and out-of-region systems and convection. In proportion to the seasons, if large-scale and synoptic systems penetrate to low latitudes and affect the atmosphere of the region, the probability of hurricane occurrence also increases. In contrast, with the gradual warming of the air and stabilization of the atmosphere, the occurrence of this phenomenon is reduced. It reaches zero.The results of validation showed that configurations 8, 1 and 2 (Table 1) based on all three statistical tests have the lowest error rate to simulate precipitation, temperature and wind parameters, respectively. In fact, in order to simulate the mentioned parameters, cumulus, boundary layer, surface layer and ground (soil) schemas are the most important schemas that the most probable states of these schemas are considered for model implementation.The output of the WRF model for modeling three-day hurricanes shows that in these days the conditions of the studied indicators in some southwestern regions are quite suitable for the occurrence of thunderstorms and in accordance with rainfall maps and station observations in these parts rain and storm Thunder has occurred which can be a testament to the accuracy of the model results. Finally, it can be concluded that the results of modeling thunderstorms with the WRF model are satisfactory and can be used to model this phenomenon.The output of the WRF model for modeling three-day hurricanes shows that in these days the conditions of the studied indicators in some southwestern regions are quite suitable for the occurrence of thunderstorms and in accordance with rainfall maps and station observations in these parts rain and storm Thunder has occurred which can be a testament to the accuracy of the model results. Finally, it can be concluded that the results of modeling thunderstorms with the WRF model are satisfactory and can be used to model this phenomenon.

One of the most important forms of precipitation in the hydrological cycle of mountainous regions is snow. In this research, after collecting statistics and information related to days with snow during the statistical period (1989-2018) in stations located in the three provinces of Kermanshah, Ilam and Lorestan. Also, the time distribution and frequency of snowy days and their change process were discussed monthly and annually, In order to reveal the trend of annual changes in snowy days, Man-Kendall trend determination tests and Sen's slope estimator were investigated. These tests were carried out for the 30-year time series examined on an annual and monthly scale. The results snow days trend in Middle Zagros showed that the snow days have changed during the statistical period of the study, that the highest and lowest changes correspond to the highest and lowest frequency of snowy days in the studied region, In fact, the southeastern and northeastern regions of the region, which had the highest number of snowy days, have also had the greatest changes. In general, based on the results of the investigation of the trend of snow days in the Middle Zagros, it can be stated that the number of snow days in the study area has a decreasing trend, and this decreasing trend in some stations and some series (months of January and series annually), has become significant at the 95% confidence level. Based on the Sen's slope estimator method, the changes of snow days in the region are noticeable and significant

Droughts, which affect the economy, the environment, migration, groundwater depletion, and many other problems, have posed serious challenges to humans. The aim of this study was to investigate the trend of groundwater changes in piezometric wells and the effect of precipitation on groundwater fluctuations in the Bafgh Basin during a 14-year period (2003-2016). The data were then adjusted and analyzed on a quarterly and annual average. Therefore, using the non-parametric Mann-Kendall statistical method, the significance of precipitation trend and groundwater level was investigated. Also, using Sen's line slope estimator method, the slope of the trend line was tested. The results showed that there is no significant trend in rainfall at Bafgh station. However, the output of Sen's test statistics at 99% and 95% probability levels confirms an increase of 0.8 mm of precipitation per year. It was also found that the water level is declining in most of the wells. This amount of reduction varies in the basin of each well and each year in well number 81, 50 cm decrease, wells number 7 and 39, 40 cm decrease, wells 16 and 11, respectively, have recorded a decrease of 20 cm in the groundwater level trend. It was also found that there was a significant correlation between precipitation fluctuations and groundwater changes (GRI) in piezometric wells No. 7 and 74 on a 3-month scale at a confidence level of 0.05, which increased with a time scale of 6 to 48 months. Confidence level 0/01 becomes significant.

Pistachio, like many subtropical fruit trees, need a cold period in their annual cycle to allow the buds to bloom naturally after the right conditions are in place. There are several models to calculate the chilling needs of pistachio, of which the chilling hours model, Utah and Utah positive are the most important of these models. The studied geographical area is Yazd-Ardakan plain located in Yazd province.

In this study, according to statistical tests based on meteorological variables, the chilling hours model was selected for modeling. To conduct this research, three-hour temperature data of Yazd Synoptic Station during the statistical period of 1367-1396 were used to model and estimate the total monthly chilling hours of pistachio and The daily temperature data of this station during the statistical period of 1961-2005 were used for the SDSM model and the monthly temperature statistics of the years 1385-1397 were used to evaluate the downscaling data of the CanESM2 model under different RCP scenarios and finally modeling for the years 1400 -1429 was done.

Results indicate that there is a significant correlation between monthly cumulative hours of temperatures between zero and 7.2 ° C and monthly temperature parameters such as mean minimum temperature, mean maximum temperature and mean monthly temperature, which in the absence of data hourly temperature can be used to model and determine monthly cumulative hours.

Findings show that the chilling needs of Kalle-Ghuchi, Owhadi and Ahmad- Aghaei species will be met in the coming years and Akbari and Fandoghi species will not be met.

One of the challenges issues of the 21st century is climate change. One of its effects could be a change in climatic parameters that will have a great impact on the planet's climate. Temperature and precipitation are the two most important elements for describing climate that their changes also alter the climatic structure of any region. It is very important to study the temperature and precipitation trends in different time and regions. Therefore, in this study, the effect of climate change on temperature and precipitation in Bandar Abbas was investigated using four General Circulation Models (GCM) in the period 2020-2020. Moreover, LARS-WG6 statistical model was used for exponential microscale and two emission scenarios RCP8.5 and RCP4.5. The results of evaluation models by Correlation Coefficient (R2), Root Mean Square Error (RMSE), Mean Absolute Error (MAE) showed that LARS-WG6 software is a appropriate tool for reproducing precipitation and temperature for the future. The highest correlation coefficient is belonged to the minimum temperature in GFDL-CM3 models (0.92), MIROC5 (0.98) and the maximum temperature of GFDL-CM3 model (0.95). The highest and lowest correlation belonged to the minimum temperature and precipitation, respectively. The forecasting results of temperature and precipitation except for a few cases show that the maximum and minimum of monthly temperatures will increase in all four models and two scenarios. Highest increase in minimum and maximum temperature is determined in June, HadGEM2-ES model, RCP4.5 scenario (3.65 ° C) and January, HadGEM2-ES model, RCP4.5 scenario (2.48 ° C), respectively. This increase is repeated in the annual and seasonal average.  The average of minimum and maximum annual temperatures will increase by 1 and 0.86 ° C, respectively. The results of the models generally predicted an increase in precipitation in most cases. The GFDL-CM3 forecast the biggest increase in the RCP4.5 scenario (25.82%) in March. Forecast data also showed an increase in annual and seasonal rainfall. The RCP4.5 scenario recorded more increase in precipitation than RCP8.5 scenario. According to these results, Bandar Abbas will face an increase in rainfall and temperature in the future. This increase in temperature and rainfall can have devastating consequences in various sectors such as agriculture, tourism, water source, environment and health. As a result of events such as heat stress and devastating floods, damage to plants and animals, the spread of diseases caused by these changes, as well as reduced visits to tourist areas such as Hengam Island are some of the consequences that Bandar Abbas will face in the next twenty years. Therefore, the results of this study can be useful in recognition and solving problems related to climate change. According to the results of this study, managers in different sectors can assume the necessary strategies to adapt and reduce the consequence effects of climate change.