فهرست مطالب احمد مزیدی

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.

The earthquake cloud is an ancient and visual precursor of an earthquake that sometimes is formed several days before the event, above the earthquake fault and often close to the epicenter. The most important characteristic of an earthquake cloud is that it emanates from the ground and remains in the sky for hours. This study aimed to find the major 2017 Azgeleh earthquake cloud by visual interpretation of Meteosat-8 satellite images. The characteristics of the cloud in question were then investigated using the same satellite products. By searching the infrared images, the Azgeleh earthquake cloud was found. The cloud was shown to have had a geological origin, and the formation of such a cloud was unusual in that region. The Azgeleh earthquake occurred near the High Zagros Fault (HZF), whereas its cloud had been formed 12 days earlier and about 700 km away from its epicenter, on other parts of the same fault, along a 160-km-long line. The study revealed that this high cloud consists of two parts: a high-pressure source and a tail diluted by the wind. Moreover, the moderate 2021 Sisakht earthquake cloud was found, which had been formed 6 days earlier and near the epicenter. Visually searching for earthquake clouds from the ground or space is a public scientific hobby, particularly among amateur meteorologists using satellite imagery. The formation of an earthquake cloud above a fault can be a warning of a possible impending earthquake on that fault, which, of course, requires further investigation.

Wetlands are one of the most important ecosystems and living areas in the world, one of the most obvious beauty and masterpieces of creation. As an essential part of the global ecosystem, these wetlands have played an important role in preventing or reducing the intensity of floods, feeding underground aquifers, providing a unique habitat for plants and animals, maintaining water quality, agricultural production, fisheries, saving floods, and controlling soil erosion. The current research aims to investigate the changes in water surface and water area as well as the changes in the land cover of Horul Azim Wetland for the statistical period of 2013 to 2022 using Landsat8 satellite images. In this study, maps of water areas and land cover were made using Landsat8 image integration techniques and by applying the AWEI spectral index and maximum likelihood algorithm in ENVI5.3, ArcGIS software. By checking the accuracy of the results of satellite image processing and classification (2013 Kappa coefficient equal to 95% and overall accuracy 96%, year 2022 Kappa coefficient equal to 90% and overall accuracy 92%) It was found that the supervised image classification, the maximum likelihood algorithm for the studied area is close to ground reality and has acceptable accuracy. Also, the maps related to the monitoring of changes in the water area of Horul Azim wetland (AWEI index) showed that the size of the wetland has been decreasing in the studied years, so in 2013 the area of the water area of the wetland was equal to 336 square kilometers. which has decreased to 147 square kilometers in 2022. The results of the classification of images in the studied years also indicate the decrease of water and plant covers and the increase of barren lands and salt marshes in the studied periods. There have been many influential factors, including environmental and human factors, on this process of changes in the studied periods. 

Wetlands are considered one of the most obvious beauties of nature, the most useful and at the same time the most challenging part of nature's ecosystems. These vital and diverse habitats are among life-giving systems that have absolutely no substitute. Recently, it has been estimated that global wetlands occupy about 6.2 to 6.7 percent of the earth's surface. With more than 251 large and small wetlands, Iran is of special importance in Southwest Asia due to its geographical location. Horul Azim Lagoon has changed its area and water level in the past decades due to various reasons. Considering the ecological importance of this wetland for the country, it is necessary to monitor and evaluate the changes in this wetland and study the consequences of these changes. The study of land cover changes has a very long history and coincides with the beginning of human life. Thus, after the formation of societies, the first humans began to change the cover of unused land to suitable land for agriculture and animal husbandry. Considering the importance and necessity of monitoring the change of Horul Azim wetland. We intend to use satellite images by applying the technique of integrating Landsat 8 satellite images during the period from 2013 to 2022. By applying the maximum likelihood classification method and AWEI spectral index, let's investigate the changing process of land cover and water areas of Horul Azim lagoon.

In this research, Landsat 8 multi-temporal satellite images (OLI meter) have been used. In total, 2 images were obtained from the US Geological Survey website and were used in the analysis of the research steps that will be described below. In total, 2 images were obtained from the US Geological Survey website and were used in the analysis of the research steps that will be described below. In total, 2 images were obtained from the US Geological Survey website and were used in the analysis of the research steps that will be described below. Radiometric (atmospheric) corrections were done for all the used images. In this study, to highlight the images and extract more information from the images from the false color combination, Landsat 8 and (5-4-3 near-infrared bands, red and green). To use different applications, the Gram, Schmidt and Pansharpring methods showed better sharpness of the features of the study area than other methods, so the combination of images was applied with this method. To identify the blue zone of the investigated area from the AWEI spectral index and to investigate land cover changes, the maximum likelihood classification method has been used.

In the present study, using ENVI 5.3 software in band calculations Spectral index, AWEI was selected and calculated for the years 2013 and 2022 to extract the water area of Horul Azim lagoon. the results showed that; the AWEI index has values greater than zero (positive values) indicating the water zone and smaller than zero (negative values) indicating the non-water zone (vegetation and soil). After performing the classification process, it is necessary to calculate the accuracy of this classification. To ensure the accuracy of the classification, the classification accuracy was evaluated, for this purpose, the Kappa coefficient and overall accuracy were calculated using the ground control points for each class separately in ENVI 5.3 software. The result of the accuracy evaluation is shown in the error matrix table. According to the available data, the spatial resolution of the images, and the researcher's knowledge, 4 educational classes, including water cover, vegetation cover, barren land, and saline land, have been selected for each image separately. The results obtained from the maximum likelihood classification method in the ENVI5.3 software environment were changed to vector format and the result was transferred to the GIS software environment in shape file format and land cover classes was determined.

The results of monitoring changes in the water area of Horul Azim lagoon (AWEI index) showed that the size of the wetland has been decreasing in the studied years it was also determined by checking the accuracy of the results of processing and classification of satellite images that the classification of images in a supervised manner, the maximum likelihood algorithm for the studied area is close to the ground realities and has acceptable accuracy. The results of the classification of images in the investigated years also indicate a decrease in water and vegetation cover and an increase in barren and salt marshlands in the investigated periods. There have been many influential factors, including environmental and human factors, on this process of changes in the investigated period, which include these cases: Successive droughts and the occurrence of harmful changes in the climate have caused changes in environmental conditions such as high salinity and inappropriate temperature. This means that significant changes in the ecosystem have reduced the number of aquatic species, animals, and plants, as well as people, which has led to a decrease in diversity indicators and an increase in dominance. The increase in pollution was caused by the entry of industrial, agricultural, and urban effluents into the rivers leading to the wetland. In addition, the construction of roads and embankments, in addition to cutting off the water connection of parts of the wetland, has turned them into salt marshes and dry land. The construction of the Karkheh dam and a result of not respecting the water rights of the lagoon, human encroachments, and occupations, including road construction, Oil activities, and encroachment on wetland lands have caused some parts of the wetland to dry up completely As a result of the fragmentation of the wetland and the increase of dry and salinity spots, the drying of the wetland is spreading. This trend states that the increase in human interventions will lead to an increase in the destruction process.

Subsidence is an environmental phenomenon caused by the gradual subsidence or sudden subsidence of the earthchr('39')s surface. The phenomenon of subsidence in residential, industrial and agricultural areas can cause catastrophic damage. In most parts of Iran, there is a high correlation between land subsidence and the decrease of groundwater level and consequently the density of soil layers. In this study, using two time series of radar images with artificial apertures from Sentinel sensors belonging to 2014 and 2019, the amount of subsidence in Damaneh plain (Frieden city) was calculated. Wells were studied in the period 2014 to 2019, the results of the study of the correlation between land subsidence with changes in groundwater level at the level of 95% was significant. In the continuation of the research, using the logistic regression model, the subsidence trend in the study area was predicted and a subsidence probability map was prepared and created as a dependent variable for the logistic regression model. The independent variables used included altitude, slope, slope direction, geology, distance from the road, distance from the river, land use, distance from the village, groundwater level, piezometric wells. The output of the model is subsidence risk zoning map which was created in five classes. The accuracy and validation of the logistic regression model was evaluated using the system performance characteristic curve and the accuracy (0.89) was obtained. The good accuracy of the logistic regression model in producing the probability map Subsidence is in the study area. In the output of the model, it was found that the area of ​​1980 hectares, equivalent to 7.9%, has a very severe subsidence that has put the situation in a dangerous situation and the need for control and management to reduce this destructive effect.

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.

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.

Investigating and understanding Sand dunes is of great importance in the management of natural resources and the control of risks caused by them. The purpose of this research is to investigate the sand dunes of Darvazeh Quran, in the north of Yazd city, which is of great importance due to its proximity to Yazd city and its use for the development of tourism. In this research, in order to determine the origin of Sand dunes, from the wind data of Yazd synoptic station, during a period of 20 years (2001-2021), topographic maps 1:50000, geological maps 1:100000, TM satellite images of 1993, ETM+ year 2008 and 2017 and field studies have been used for sampling. The research method is also based on the analysis of wind characteristics (direction and speed), the mineralogy of sedimentary grains, and the use of satellite images. The results of the wind survey in the studied area showed that strong winds in the dry seasons along the northwest play an important role in the formation, development, and movement of sand. Mineralogical investigation and XRF analysis indicate that quartz minerals and then carbonates with an average of 45 and 33% are the most abundant among the sediments of the studied area and the classification based on spectral reflection of satellite images of loose sands showed that the band ratios are 4.5, 3.4 and 3.5 in TM images can be used to identify the mentioned minerals. Finally, the results showed that the alluvial and flood plains, along with alluvial deposits located in the northwestern and western part of this reservoir, are the main sources of nutrition for the sandy dunes of Darvazeh Quran.

The surface temperature of the earth is one of the key parameters in the studies of cities.Air pollution is also one of the causes of the creation of heat island in the city, which has been increasing in recent years due to high population density and pollution.The aim was to investigate the temporal changes of urban heat islands and its relationship with air pollution in Kerman in a period of 16 years(2007-2022)And to investigate the trend of temperature changes, the temperature data of Kerman station and the Mann-Kendall test were used, and the data of Kerman air pollution station were used to investigate air pollution.Although the number of polluted days was high, but due to the limited passage of the Badoureh satellite for 16 days from Kerman, the limited number of days with high pollution intensity in Kerman, and the non-compliance of polluted days with the time of satellite passage, finally, seasonal images of five years were selected. The results showed that the temperature of the earth's surface had an upward trend in both seasons, but the heat island occurred only in the summer season.So that in the summer of 2009, the density of very hot temperature was less than 30%, but with the passage of time and in 2022, in the summer season, more than 60% of the urban area has very hot temperature.And parts from the west to the south of the city have formed the center of thermal islands due to the presence of barren lands and also due to the lack of benefit from evaporation and transpiration cooling mechanisms.Also, the correlation between the temperature of the earth's surface and the air pollutants was confirmed at a significant level of 0.01 and 0.05, that in the summer the correlation coefficients are stronger and pollution has also increased.

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.

Sustainability in the field of pavements is one of the subsets of sustainability topics in sustainable development. In the upcoming research, various supervised and object-oriented methods and fusions of satellite and drone images by using the Gram-Schmidt algorithm were used to investigate asphalt damage, including asphalt cracking and wear, in order to provide the best method for investigation. The results showed that the supervised methods of support vector machine with a kappa coefficient of 87% and overall accuracy of 90% provided the best and shortest distance method with the kappa coefficient and overall accuracy of 57% and 67%, respectively, while showing the lowest accuracy in the classification of supervised methods. Also, among the object-oriented methods, the support vector machine algorithm with a kappa coefficient of 86% and an overall accuracy of 91% had a more accurate output compared to the other studied algorithms. The lowest accuracy was related to the nearest neighbor algorithm with a kappa coefficient of 78% and an overall accuracy of 80%. In the UAV fusion output with Sentinel-2, the classification was done by using the most optimal algorithm and the support vector machine in the object-oriented method. The results showed an increase in classification accuracy up to the kappa coefficient of 91% and overall accuracy of 93%. Furthermore, the thresholding method with a Kappa coefficient of 90% showed the best result for detecting asphalt wear.

Replacing natural vegetation cover with impermeable urban surfaces) stone, cement, metal, etc.) has resulted in increased land surface temperature which is considered to be the most important problem of urban areas. Distinct temperature difference between the city and the surrounding areas is called heat island (Melkpour et al., 2018). Increased land surface temperature and resulting heat islands in urban areas built without proper preplanning (Khakpour et al., 2016) especially in developing countries such as Iran experiencing a rapid growth rate have resulted in widespread environmental problems. Heat islands mainly occur due to the presence of man-made surfaces which prevent the reflection of sunlight and result in temperature increase. In general, urban heat islands result in increased air and land surface temperature and thermal inversion (Gartland, 2012).

The present study applies a statistical-analytical research method based upon statistical data received from meteorological stations and extracted from satellite images. Climatic data recorded from 1976 to 2020 in Yazd Meteorological Station were retrieved from the General Meteorological Department of Yazd Province and used to measure temperature changes. Urban climate studies mainly take advantage of long-term patterns and thus, the present study has applied the common Man-Kendall method to measure the trend of temperature changes in warm season (July, August, and September). Also, satellite images collected by Landsat 4-8 in a 33-year period, including four statistical periods with a time interval of 11 years (the average recorded in July, August and September of 1987, 1998, 2009 and 2020), have been used to extract heat islands of Yazd city in warm seasons. These images collected under clear weather conditions were retrieved from the United States Geological Survey website (http://glovis.usgs.gov/) in the WGS-1984 UTM image system. NDVI index was used to investigate the vegetation cover. Main land uses discussed in the present study included barren lands, urban areas, vegetation cover and roads. Sample land uses were collected from Google Earth and visually interpreted in ArcMap. Maximum likelihood algorithm was used for the classification process. Finally, Land Surface Temperature was extracted from satellite images and compared with air temperature trend using the Mann-Kendall test.

Results indicate that due to thicker vegetation cover in summer, there has been a negative relationship between the vegetation cover and land surface temperature. In other words, land surface temperature has increased with decreased vegetation cover and vice versa. Types of land use identified in satellite images collected from Yazd city have showed that the city has experienced a widespread physical expansion during the 33-year statistical period regardless of the season under investigation and thus, built-up urban land use class has expanded significantly. As a result, vegetation cover has experienced a negative trend and decreased. Land surface temperature extracted from thermal images of Yazd city has proved parts of northwest and south of the city to be the core of its heat islands. This is due to the presence of barren lands, lack of evapotranspiration mechanisms, high heat absorption capacity and low conduction capacity. Man-Kendall test has found a significant increasing trend for temperature especially in recent years in which the temperature has increased about 2.3 °C. This is most possibly due to the increasing trend of urban population in recent decades, followed by increased residential structures and resulting heat island phenomenon.

In general, classification of urban land use types in Yazd has shown a significant physical expansion of the city during the statistical period. This physical development has occurred in all directions; beginning from the central and northeast-southeast parts, and moving towards northwest-southwest parts. Maximum NDVI was observed in a strip along the central part of Yazd in which vegetation cover is thicker. Green spaces are also observed in some areas of the city. Color spectrum of the LST map has shown relative changes of the ambient temperature in various parts of the city. High and very high temperature (between 41.5 and 50 °C) show the location of the heat islands on LST maps. Also, areas with a deep red color and a temperature above 50 °C have formed hot clusters formed or strengthened between 2009 and 2020 in the west and southwest parts of the city. Satellite images and related graphs have showed that in 2020, Yazd have witnessed a sharp increase in temperature and a heat island.  Temperature data of Yazd Meteorological Station and Man-Kendall test have shown a significant increasing trend (about 2.3°C), especially in recent years. These are related to the urban population growth in recent decades, followed by increased urban structures (residential-commercial) and heat island phenomenon.

The study of soil temperature at different depths is important for climatology (especially at the micro-scale), agriculture and industry, and affects many soil processes. The method used is descriptive-analytical and its purpose is to determine the linear and non-linear relationships of air temperature and temperature depth of 5, 10, 20, 30, 50 and 100 cm during the statistical period of 1386 to 1390 in Jahrom station... Hourly statistics of air temperature and temperature of different soil depths were obtained from Fars Meteorology and linear and non-linear relationships were extracted for each soil depth using regression method. The results showed that based on the correlation coefficient and determination coefficient, linear regression has better results than non-linear relationships and in linear relationships, the correlation coefficient between air temperature and soil depth temperature for all seasons is significant at the 99% level. The highest correlation coefficient in all seasons was usually at the depth of the soil surface, especially at a depth of 5 cm at 18:30 and the lowest correlation coefficient in all seasons was at a depth of 1 meter at 6:30. As the depth increases, the correlation coefficient and determination coefficient have a decreasing trend. The highest range of temperature fluctuations in the soil occurred at 6:30 and at depths of 5 to 20 cm, and the increase in soil temperature compared to the increase in air temperature in spring and autumn is more than in other seasons.

The purpose of this research is to analyse the drought situation of Kerman city using standardized precipitation drought indices , normal precipitation percentage and Palmer drought intensity and the relationship between these indices with the vegetation of the region. For this purpose, 30yeard of temperature and precipitation data (1988-2017) were obtained from the Iran Meteorological website to determine the value of (SPI) and (PNPI). In order to check the severity of drought, SPDI index was used. In addition, the map and diagram of the vegetation trend were prepared using GIS software and Google Earth Engine. The results showed that the SPI index of Kerman was mostly around the axis of -1 to +1, which represents a mild drought, and the PNPI of Kerman recognized most of the years as normal with 43%, and the Palmer index also showed that the years (2008-2004) 2010) have experienced a very severe drought with an intensity of about 4.8. Finally, the NDVI index showed that the maps obtained from satellite images show a decrease in vegetation of about 15 km, and according to the time series chart of 2008 and 2012, they had the lowest vegetation cover with an area of about 3 to 4 km. Therefore, due to the fact that the drought indicators examined in this study show the increasing trend of the intensity and duration of the drought and the vegetation map of the region has seen a decrease in area and the NDVI trend has been decreasing,

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.

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.

Considering the capability of the artificial neural networks in simulating sophisticated processes, it is being used in estimation and computation of climatic parameters. The goal of this research is to estimate the daily maximum temperature in Kerman province. To this aim, daily climatic parameters as input to the neural networks and daily maximum temperature as the output during a statistical period of 24 years (1989-2013) were used, the findings revealed that the output of the multi-layer perceptron neural network, considering the error amount and correlation among data, is more precise and shows lower error and more correlation in relation to the expected output (daily maximum temperature). Also, among other climatic parameters, minimum temperature and the average of the wet temperature indicated the estimation of the daily maximum temperature with lower error and more correlation in comparison to other climatic parameters.

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.

Snow is a form of precipitation in hydrological cycle of mountain regions that plays an important role to supply drinking and agricultural water as the delay water resource during low-water seasons. Snow distribution is important to hydrological research of watersheds. The temporal and spatial distribution of snow cover has significant influence on snowmelt runoff. Knowing the extent of the snow is valuable information that provides insight as to the amount of water to be expected from snowmelt available for runoff and water supply. In this study there has been an attempt to investigate changes in snow cover of Koohrang watershed. However, the elevation effect on snow cover is also proposed. Koohrang’s catchment is one of sub-basins of northern Karoon with an area estimated over than 2700 km2 which is located in Chaharmahal-va- bakhtiyari Province of Iran. The permanent stream of the rivers; ultra-cold climate and high elevation are good and justifying evidences for solid precipitation in this area. Therefore, awareness of the amount of snow resources in this area is necessary in the storage of the equivalent amount of water, controlling seasonal floodwaters, anticipation of the river’s stream in supplying the required water of downstream land. This study aims to monitor the change of snow cover of the Koohrang watershed during 2010 to 2018 and to determine the association between snow cover and elevation zones.

Two groups of data including remote sensing and Geographic Information System (GSI) have been used in this study, And with respect to the subject, different techniques have been utilized. Moderate Resolution Imaging Spectroradiometer (MODIS) Satellite imagery within the statistical period of 2010-2018 was obtained. Then by calling those images into ENVI software, snow covered areas were differentiated from other areas, using difference index and NDSI ratios band 4 and 6; and also their maps were provided. In order to examine the amount of snow border of basin, with the use of the principle of environmental degradation and regression relationship and linear functions and the map, the daily 0°C (snow border) isothermal lines were defined. Furthermore, we investigate trends and variability of snow cover changes in the Koohrang watershed at different temporal scales (monthly and annual) and at different elevation zones between 1656 to 4074 m.

The results show a decreasing trend of changes in snow cover in the region during the study period. The most decreasing trend among the months was related to February. October and March are on the second and third places. The average snow cover of the region during this period is 440 km2 (equal to 34%). 2010 and 2014 years showed the lowest and highest snow cover, respectively. The highest snow cover among the months of the year belongs to January with an average of 1132.3 km2 (equal to 88.6%). The results also indicate that there is a direct relationship between the elevation and the quantity of snow cover and its persistence. So the highest percentage of snow cover was related to higher elevation zones. The average percentage of snow cover in elevation zones of 1 to 6 were 10, 22, 32, 40, 48 and 58%, respectively.

The results of this study can be very useful and practical for planning water resources in the region, especially in the warm seasons. The trend analysis on the alternation of snow cover during the 9 years period from 2010 to 2018 has shown some important results. If the decreasing trend of the snow cover in February and March as observed in this study continue, this may result in significant changes in the river flows and water resources in the region, particularly in spring. This would have implications for aquatic ecosystems that depend on the seasonal melt water pulse, for irrigation dependent agriculture and, last but not least, for water resources in the densely populated downstream areas. Some of the trends observed in the snow cover changes can be explained by the high correlations observed between the snow cover and the observed temperature. It should be noted here that not only snow covered areas but also the snow depth and snow water equivalent will impact the snow melting, which subsequently influence the river flow regimes and water resources availability. Therefore, future studies should attempt to consider these factors for a more comprehensive assessment of the impact of Koohrang snow on the river flow regimes.

Precipitation as the most important climatic element is always studied from two perspectives of time and place. Emphasis on the time perspective determines temporal changes and emphasis on the location perspective determines the spatial variation of precipitation. Since Iran is located in an arid and semi-arid region, it lacks large internal and adjacent water resources to supply moisture to its precipitation, and as a result, most of its precipitation moisture sources must be supplied from the surrounding water levels. One of the methods that can be used to study the course of rainfall in the past and present is to analyze the trend of time series at different scales. So far, statistical methods have been presented for the analysis of time series routines, which can be divided into two general categories: parametric and non-parametric methods. Southeastern Iran, which includes Sistan-Baluchestan province, Kerman, southern Khorasan and the eastern parts of Hormozgan province, They are one of the arid regions of Iran. Restriction on water resources and annual precipitation in this part of the country have severely limited agricultural and industrial activities. The lives and deaths of the majority of people are also related to the low precipitation that falls throughout the year. Therefore, considering that a comprehensive study on precipitation trends in the region has not been done, the purpose of this study is to investigate the annual and seasonal precipitation trends by stations in southeastern Iran. The results of this study can play an important role in understanding the precipitation trend in the southeast region for planning related to water resources management, management of unexpected events such as floods and also the optimal utilization of those precipitations.

In order to conduct this study, in order to recruit rainy periods, daily precipitation data were obtained from synoptic stations in the southeast of the country, including selected stations in the three provinces of Hormozgan, Sistan and Baluchestan and Kerman during a 30-year period (1987-2016). Two Mann-Kendall tests and the Sen’s Slope Estimate are among the most common non-parametric methods of trend analysis when considering hydrometeorology. Various studies conducted using these two methods indicate the importance of their widespread use in the rapid evolution of time. Identify the data change using the Man-Kendall method, specify its type and time. The non-parametric Kendall test was proposed by Mann and then developed by Kendall (1975) based on ranking data in a time series. This method is widely and widely used in the analysis of the process of hydrological and meteorological series.

The trend of average annual, seasonal precipitation and maximum 24-hour precipitation in southeastern Iran Considering that in the annual, seasonal and maximum 24-hour precipitation trend diagrams, the intersection of the U and U' sequences above and below the significant lines did not occur, which means that acceptance is random (acceptance of H0 hypothesis) and there is no significant trend (H1) variable. Precipitation is in the southeast of Iran and in fact the condition is -1.96 (ti)< U or 1.96 (ti) >U and has a static series.In the Sen's Slope Estimate test, the total reduction of winter precipitation slope was equal to -0.350 mm, which in the case of 95% confidence of the upper and lower slopes of this slope is equal to -0.123 and -0.835, respectively, and in the case of 99% of the slopes. High and low are 1.604 and -2.318, respectively, per year. The Sen’s Slope Estimate test for annual rainfall, summer, autumn, winter and maximum 24-hour precipitation has estimated the negative precipitation slope. Mann Kendall test estimate shows a decrease in precipitation in winter of -1.71 per year.Annual, seasonal and maximum 24-hour precipitation trend by stations in southeastern IranIn the study of changes in annual precipitation using Mann-Kendall test, it is observed that the annual precipitation trend in most stations in the southeast (except Bam and Chabahar) has a decreasing trend. The Sen’s Slope Estimate showed is the highest precipitation at any given time at Bandar-Lengeh station at -4.16 mm per year. The time series of Man-Kendall test in the seasons of the year indicates that in spring, summer and autumn, the precipitation trend in most stations is decreasing, but this trend is not significant in any of the stations in southeastern Iran and only in winter precipitation In Bandar-Lengeh, Jask and Kish stations, it has a decreasing trend at 95% significance level. Sen’s Slope Estimate data showed that the amount of precipitation decreases in the mentioned stations.

The results of by using Methods the Mann-Kendall test showed that the mean annual precipitation trend as well as spring, summer and autumn seasons of southeast Iran was not significant and only winter precipitation had a significant and decreasing trend. Estimates of the slope of sen indicate that the slope of precipitation in southeastern Iran in winter is -0.350 mm in year. The annual precipitation trend of BandarAbbas, BandarLengeh, Abumousa and Kish stations at 95% and Jask and Iranshahr stations at 90% were significant. Test statistics showed that the highest annual precipitation reduction was at the Bandar-e-Lange station at -4.16 mm in year. In spring, summer, and fall, precipitation was not significant in any of the stations in the southeast of Iran and winter precipitation in Bandar-e-Lengh, Jask, and Kish stations had a 95% significant decrease. Assessment of 24-hour rainfall variations showed that the above mentioned precipitation had a decreasing trend at 99.9% at BandarAbbas stations and 95% at Bandarlange, Jask, Abumousa and Kish stations. According to the tests and analyzes performed in this study, what is certain is that in all stations in southeastern Iran, a significant positive trend in annual, seasonal and maximum 24-hour rainfall was not observed at the level of 99% and 95%, and most precipitation trends from It is a kind of decrease and has revealed the fact of decrease in rainfall in the southeastern climate of Iran during the years 1987 to 2016, which decrease in annual rainfall is mostly related to the winter of -1.71 per year.

IntroductionStudying land cover changes has a very long history which coincides with the beginning of human life. Following the formation of societies, primitive humans began to change the cover of wasteland to form suitable lands for agriculture and animal husbandry. More than half of the world's population recently lives in cities, urbanization and urbanism is rapidly increasing, and this trend will continue to reach its peak. Due to their extensive coverage, reproducibility, easy-access, high accuracy and reduction in necessary time and expenses, remote sensing data are generally considered a preferred method used to study land cover, vegetation, and their changes. Many researchers have shown an interest in land cover change in different cities of the world. The history of land cover studies dates back to the early nineteenth century and the studies performed by von Thünen (1826). Von Thünen have determined the economic benefits of different land covers based on their distance from the central city and found an optimal distribution for production and land cover in the form of a series of concentric circles. Land cover changes due to human activities are considered to be an important topic in regional and development planning. Since land cover changes and urban development in the study area have not been previously studied, Landsat time series satellite imagery and a combination of Landsat 7 and 8 panchromatic and multispectral bands were used to identify and detect changes in land cover and urban development in the urban areas of Abarkooh from 2000 to 2020. Materials & MethodsSatellite remote sensing data are used in the present study (Landsat 7 and 8 multi-temporal satellite images collected in 2000, 2010 and 2020). 3 images were retrieved from US Geological Survey website and used in the present study. Raw remote sensing images always contain errors in geometry and the measured pixel values. The former category is called geometric errors and the latter is called radiometric errors. Atmospheric corrections were performed for all images used, and stripping in the imagery collected in 2010 image was also corrected. For image enhancement and extraction of more information from the images, false color composites were used (5-4-3 infrared, red and green bands) for Landsat 8 and Landsat 7 (3-4-3 near infrared, red and green bands) images. Using this technique, vegetation is shown in red. Compared to other methods, Gram-Schmidt based pan sharpening method produced higher spatial resolution images of the study area and thus was used to combine the selected images. Maximum likelihood method is considered to have the highest efficiency among various supervised classification methods. Results & DiscussionThis method assumes the presence of a normal distribution for all training areas. The accuracy of this classification has to be calculated following the classification. To do so, the kappa coefficient and overall accuracy of each class were calculated in ENVI5.3. The results are shown in the error matrix. Overall accuracy is the average of classification accuracy. The kappa coefficient calculates the accuracy of classification as compared to a completely random classification. Based on the available data, spatial resolution of the images and the information researcher has access to, 5 classes of training data (urban constructed space, roads, barren lands, arable lands, and gardens) have been selected for each image. Results obtained from the maximum likelihood classification method in ENVI5.3 environment were changed into the vector format and then used as a shape file in GIS environment. After compiling the land database, land cover maps and its changes were extracted in three periods and the area of each land cover class was determined. Each of the land cover maps, 5 classes with different colors are determined and shown. To ensure the accuracy of the classification, the accuracy of the classification has been evaluated. ConclusionThe resulting kappa coefficient for 2000 and 2020 equaled 86% and overall accuracy equaled 89%, while for 2010 kappa coefficient equaled 90% and overall accuracy equaled 92%. Thus, the error rate is small and acceptable. Finally, post-classification comparison method was used to investigate the nature of changes. 13 square kilometers of land cover were investigated in the present study. To identify the exact type of land cover changes, categorized images collected in these years were compared. Total area of residential land use showed an increasing trend: a total 4.25 square kilometers in 2000 (32.69 percent of the total area under study) has reached 5.58 square kilometers (42.92 percent) in 2020. Overall area of arable land use did not change much in the period of 2000 to 2010. However, a declining trend was observed in 2020 changing a part of this land use into residential and barren lands. Results of satellite image processing and classification indicate that supervised classification and maximum probability algorithm were close to ground realities and had an acceptable accuracy. In general, results indicate that significant amounts of vegetation and agricultural lands have been converted into urban areas and thus, planning for urban growth in these areas should be in favor of preserving gardens and agricultural lands.

Decreasing of precipitation and Increasing of temperature, leads to extreme climate events such as drought which drastically impact on agricultural. Knowledge about the timing, severity and extent of drought can aid planning and decision-making. Drought indices derived from in-situ meteorological data have coarse spatial and temporal resolutions. Thus, obtaining a real-time drought condition over a large area is difficult. Therefore, drought indices which is derived from remote-sensing data, has been widely used for drought monitoring..In this study, Vegetation Drought Index (VDI) was evaluated in Sistan & Balouchestan Province To do this, Terra Moderate Resolution Imaging Spectroradiometer MODIS (MOD02HKM and MOD11A1 ), Standard Precipitation Index (SPI) and monthly precipitation data GLDAS from 2000 to 2018 were utilized to evaluate VDI.Accuracy of the Drought spatial distribution maps based on Pearson correlation coefficient was used data. Results indicate high significant correlation rate in the study area. Thus VDI, has the potential to monitor agricultural drought in the case of study and the drought indices based on remote sensing data could well use in drought early warning systems.