علی محمد خورشید دوست

Buildings presently use 30% of global energy, set to rise due to climate change. This study assesses climate change's impact on energy consumption(heating/cooling loads) in Tabriz over six decades. It also compares the effect of several energy efficient solutions in buildings. Future weather data for 2050 and 2080 was generated by CCWorldWeatherGen based on existing files. Identifying the prevalent housing type in Eram, energy simulations for the current and next 60 years used Design Builder 7.0.0.096. Applying energy reduction strategies, the study evaluates thermal performance, energy consumption, and environmental impact.In all models, heating peaks in 2020, and cooling surges by 28.69% and 63.34% in 2050 and 2080 versus 2020. Model WI shows the lowest cooling energy variation at 66.66%. COM models rank as the most efficient in 2020, 2050, and 2080, cutting energy consumption by 8.24%, 11.95%, and 12.83%.Climate change raises annual energy consumption by 4% in the current building model. Comparing strategies, two models—COM and thermal insulation WI—enhance building performance in 2020, 2050, and 2080. These findings stress the urgency of implementing strategies to mitigate climate-induced escalation in building energy consumption.

Dust storm is a complex process that it was affected by relation between earth-atmophere system and point of veiw climatologist and meteorologist that they assessing atmospheric and climatic change, in general of world veiw, monitoring from dust cover is a need structures. The western region of Iran is the study area. The data used in this study are divided into two categories: ground-based observations in 27 synoptic stations extracted from the Iran’s Meteorological Organization during period (1998-2010) and satellite MODIS images during the first to fourth days of July 2008. Finally, the aim has analyzed using Arc GIS and ENVI softwares and NDDI index. According to results, interpolated map for the number of dusty days during the study period over the western half of Iran showed that extent of case study have not a equal system aspect quantity of occuring from dust phenomenon and how is it trend. The number of dust days increase from north toward south and sites located in northen proprotion of studied area have experienced a lower dust events. While, maximum hotspots are occuring over southwestern sites such as: Ahvaz, Ilam, Boushehr and Shiraz. Therefore, principle offspring of dust input has been out of country boundaries and arrived far way area. On based resultes obtined on satellite images using NDDI index also idicate that maximun of intense cover dust is observed over Fars, Ilam, Boushehr and Ahvaz provinces on the first, second, thrid and forth of July. But, the lowest rate of index situated in extent far such as: Eastern Azarbayjan, Western Azarbayjan provinces. Thus, parts located on the north of the study area experienced less dusty days and the maximum dust core was located in the southwestern (mostly ahvaz). The long-term result was consistent with the use of NDDI index and the daily average of NDDI index in the whole study area indicated the hotspot areas (Ilam, Ahvaz, Omidieh, Bushehr and Shiraz) during the first to fourth days July 2008. However, in the region has reduced the level of dust cover when a wet and cloudy synoptic system pass over the central and northwestern parts of the study area.

One of the major global environmental concerns in recent years is the phenomenon of global warming. Understanding the relationships between the phenomena affecting this phenomenon can be a way to reduce and control the surface temperature and prevent the formation of thermal islands.The aim of this study was to determine the relationship between surface temperature intensity with variables So2, CH4, Co, O3, NO2, HCHO, H2O, PM, LST-Day as well as temporal and spatial changes of thermal islands in Tehran province. In this study, 8-day productions of Aqua satellite and Sentinel-5 data along with coding method in JavaScript language in Google Earth engine system, path analysis and statistical clustering method have been used. According to the research findings, the trend and direction of spatial changes in the thermal islands of Tehran province during the years 2022-2000 from Tehran to the surrounding cities and satellite towns have been moving and relocating.Based on the temperature similarity between the 22 urban areas, six clusters were formed by K-mean clustering method. Based on the results of the final table of path analysis using AMOS software, among the studied variables, the highest coefficient of total impact on the increase in night surface temperature was related to the daily surface temperature, water vapor and carbon monoxide, respectively. PM particles also have the highest overall impact factor in reducing the night temperature.

Climate change refers to a significant and sustained alteration in the average weather data over a specific period of time. This time period is typically ten years or longer and involves changes in the mean climatic conditions or statistically significant changes in the distribution of weather phenomena. One of the climate elements affected by climate change is precipitation. Precipitation is the third factor in the differences of Iran's climate and is the most fluctuating climatic element. Even slight changes in the type (solid and liquid precipitation) and amount of precipitation can disrupt the natural environmental balance. Due to the importance of precipitation, understanding it is crucial and has implications for the management of water resources, agriculture, and other systems. To understand the effects of climate change in the future, research institutions worldwide have simulated the global climate using Global Climate Models (GCMs). GCMs are extensively used to assess the impacts of global warming on weather, climate, and the water cycle. However, the lower resolution of these models hinders the detection of variable features such as precipitation, and using them introduces uncertainties, often showing significant deviations compared to observed data. On the other hand, higher-resolution climate data is needed to accurately evaluate the effects of climate change. Therefore, to address the issue of the large-scale nature of climate model data, statistical correction methods (bias correction) or downscaling techniques are used. Over the past decades, several research groups and international collaborations, including the Intergovernmental Panel on Climate Change (IPCC), have provided sets of predicted data on past and future global climate conditions using global climate models. Simulations from global climate models are archived by the Coupled Model Intercomparison Project (CMIP), which is one of the most important resources for studying 21st-century climate conditions. Recently, a new version of reports on climate models has been released, which represents an advancement in physical processes and higher spatial resolution compared to previous reports. This serves as a scientific basis for evaluating past and future changes in climate conditions. In this study, the daily precipitation outlook of the northwest region of Iran for the period 2021-2100 was investigated based on the sixth phase of the Coupled Model Intercomparison Project (CMIP6) under four scenarios (SSP 1-2.6, SSP 2-4.5, SSP 3-7, SSP 5-8.5). Among the 26 models studied, the MPI-ESM-1-2LR model was selected as the preferred model for predicting the region's daily precipitation. This model is the latest version of the Max Planck Institute's Earth System Model, available in both high and low resolutions, and is the fundamental basis for CMIP6 models and the prediction of climate variables at seasonal and decadal scales. It is generated with a horizontal resolution of 1.9˚*1.9˚ (approximately 250 square kilometers). Due to the large scale of the model data, the data were first bias-corrected using the delta change method, and then the precipitation characteristics of the region were analyzed in terms of percentage change compared to the baseline period, precipitation trends in the future period, and precipitation variations in each month under the four scenarios. The results showed that the precipitation outlook of the region under different scenarios will have both decreasing and increasing values. The highest percentage of change and decreasing trend was observed under the SSP 3-7 scenario (12% decrease compared to the baseline period), which is more prominent in the southwest part of the region. On the contrary, the highest increase in precipitation values was observed under the SSP 1-2.6 scenario, showing an average increase of over 6% in precipitation compared to the baseline period, especially in the central parts of the region. The highest decreasing and increasing trends under all four scenarios were observed in the Sardasht and Tabriz stations, respectively. Another important finding of this study is the increase in heavy precipitation events and their displacement towards the warm months of the year, which will cause uneven distribution of precipitation in limited days. From a seasonal perspective, the predicted precipitation changes in the model compared to the baseline period showed decreasing trends in spring and winter and increasing trends in summer and autumn. The interpolated maps and diagrams showed that although climate change is a global phenomenon, the studied region behaves differently in terms of precipitation changes due to the differences in climate in each part of it.

Assessing and predicting future climate change is of particular importance due to its adverse effects on water resources and the natural environment, as well as its environmental, economic and social effects. Meanwhile, rainfall is also an important climatic element that causes a lot of damage in excess conditions. West Azerbaijan Province is no exception. The aim of this study is to model and predict 30 years of rainfall in West Azerbaijan province. The statistical period studied is 32 years (2019-1987). Selected stations in the province include Urmia, Piranshahr, Takab, Khoy, Sardasht, Mahabad and Mako stations. Average slider time series models, Sarima (seasonal Arima), Health Winters were used for analysis and prediction and also linear regression and Mann-Kendall test were used to determine the data trend. The results show an increasing trend of precipitation in Urmia, Piranshahr, Khoy, Sardasht and Mako stations and a decreasing trend in Takab and Mahabad stations. According to the results of comparing the models used, the Health Winters model with the least error in the absolute mean of deviations, mean squared deviations and the percentage of absolute mean errors was introduced as the best precipitation forecasting model for West Azerbaijan province. province. 

Environmental issues such as the ecological footprint, are the product of intellectual, cultural, and economic factors. Therefore, it is necessary to know the variables effective on the amount of the footprint. The main objective of the present study is to investigate the factors affecting the ecological footprints of the rural settlements in Eslamabad-e Gharb County with a holistic and spatial approach. 

The present study is an applied one regarding the objective and descriptive-correlational regarding the methodology. In terms of the data collection method, it is a field survey. The statistical population includes 25% of the villages in Eslamabad-e Gharb County (40 villages). The sample size was determined as 500 households based on the latent and observable variables. The Structural Equation Modeling (SEM) was used to analyze the data. Also, the Geographically Weighted Regression (GWR) was used to investigate the effects of the locative-spatial factors on the research variables.

The results of the Bootstrap test based on the T values indicated that the variables “ownership”, “environmental awareness”, and “consumerism” had the highest t-value and thus, were most correlated. The variable “ownership” in the economic structure is more correlated with the ecological footprint of the researched villages than other independent variables with a statistic of 26.053. overall, the analysis of the direct and inverse correlations in the SEM indicated that the variables “ownership” and “employment” were the most effective factors on the ecological footprint with coefficients of 0.874 and 0.575, while the “conspicuous consumption” was the least effective variable. Also, the results of spatial regression showed that the villages in the northwest of the county were more effective while moving towards the southeast and getting distant from the center reduces the effectiveness of the research variables on the ecological footprint.Research limitations/implications- The high rate of employment in the agricultural sector, the weakness in environmental issues training, and the high rate of livestock and agricultural ownership among a limited number of people have created obstacles on the road to the ecological sustainability of the region.Practical Solutions: Directing the residents of the researched villages towards non-agricultural employment by providing appropriate facilities and support, promoting an environment-friendly lifestyle, and training the residents to increase their environmental awareness by holding workshops in this field.

The present study is the first to use the SEM and spatial approach to investigate the factors effective on the ecological footprints of rural settlements. The results obtained can aid the planners and decision-makers in the field of rural settlements to advance the goals of sustainable development.

Problems caused by climate change are one of the most important environmental crises and threats of human society, especially in urban environments. In the city of Tabriz and in recent years, due to the growth of the population, a lot of migration from other cities, traffic, the development of industries and production centers have caused an increase in the production and distribution of pollutants. Based on this, in this research, attention has been paid to the evaluation of the quality of dust occurrence in the years 2019 and 2018. The concentration of dust particles in different areas of the studied places varies depending on the geographical location, topographical, climatic conditions and also their origin, both internal and external. Based on the results obtained from the analysis of laboratory results and field studies, in the Tabriz region and during the research period, the concentration of lead metal in dust is moderate for adults and severe for children, and the risk of mercury metal for both the elderly and children. It has been intense. The adverse effects of cadmium metal have been very severe in children and adults. On the other hand, the high air temperature in the city center and the formation of thermal islands in it causes local winds from the suburbs to the city center. With the transfer of pollution from the suburbs to the city center by these winds, the pollution situation in the city center also increases.

Today, long-term forecasting of climate variables has received much attention in order to be aware of the extent of change and, consequently, to take the necessary measures to mitigate the adverse effects of climate change. In this study, minimum temperatures in Kurdistan province were predicted using LARS-WG6 downscaling for the next three 20-year periods (2040-2021, 2060-2041, 2080-2061). For this purpose, the HadGEM2-ES general circulation model and three scenarios of RCP8.5, RCP4.5 and RCP2.6 were used. To generate the time series of future periods, daily data for the statistical period 1989-2019 were used and the trend of its changes was analyzed using Mann-Kendall test. The results showed that LARS-WG6 software simulates the minimum values of the minimum temperature well with low error indicators. Also, based on the results of the HadGM2-ES global model output in the study area, the minimum temperature in the future period will be higher than the base period in all scenarios and periods. The intensity of this increase under the RCP8.5 scenario is related to the last period of the century (2080-2061) and its lesser extent is related to the period (2060-2041) under the RCP4.5 scenario. Examination of seasonal averages also shows that spring has a lower temperature increase and autumn has a higher temperature increase. The trend of changes shows that the trend is positive and negative in both directions, so that in most stations and scenarios in different forecast periods, spring will have the most positive trend and autumn will have the most negative trend. Therefore, it can be concluded that the temperature will increase in future periods and the effect of cold waves will decrease.

Dust storm is a complex process affected by the earth-atmophere system. The interaction between the earth and atmosphere is in the realm of the climatologists and meteorologists, who assess atmospheric and climatic changes, and monitor dust spread. Dust is the main type of aerosols which affects directly and indirectly radiation budget. In addition, altogether they affect the temperature change, cloud formation, convection, and precipitation. The most important studies about dust analysis have considered the use of remote sensing technique and global models for analyzing the behavior and dynamics of dust in recent two decades. To achieve such a goal, this paper has used MODIS and NDDI data to study and identify the behavior of atmospheric dust in half west of Iran.

The western region of Iran is the study area. The data used in this study are divided into two categories: ground-based observations in 27 synoptic stations extracted from the Iran’s Meteorological Organization during the period (1998-2010) and satellite MODIS images during the first to fourth days of July 2008 as atmospheric dust extremes. Data was analyzed by using ArcGIS and ENVI software and NDDI index. 

According to results, interpolated map for the number of dusty days during the study period over the western half of Iran showed that the scope of study area does not involve an equal system aspect quantity of occurrences. The number of dusty days occurrences increase from north toward south and the sites located in northern proportions of the area have experienced lower dust events. In contrast, maximum hotspots are occurring over southwestern sites such as: Ahvaz, Ilam, Boushehr and Shiraz. Therefore, principal offspring of dust input has been out of country boundaries and arrived at distant areas. Also, based on results obtained using satellite remote sensing images and applied NDDI index, maximum of intense dust cover is observed over Fars, Ilam, Boushehr and Ahvaz provinces on the first, second, third and fourth of July. However, the lowest rate of index situated in extent far such as: East and West Azerbaijan provinces. Thus, parts located on the north of the study area experienced less dusty days and the maximum dust cores were located in the southwestern (mostly Khuzestan). The long-term results were consistent with the daily average of NDDI index in the whole study area and indicated the hotspot areas (Ilam, Ahvaz, Omidyeh, Bushehr and Shiraz) during the first to fourth days of July 2008. However, the level of dust cover in the region has reduced when a wet and cloudy synoptic system passes over the central and northwestern parts of the study area.

The climatic interpolated map interpretation indicated that increase of dust concentration based on ground-based stations, which are consistent with dust concentration, is overshadowed by the latitude and proximity of sources of dust source in the Middle East. Also, the long-term climatic results of ground-based observations were consistent with the NDDI index calculated on dust extremes in the whole study area and in the southern areas (Ilam, Ahvaz, Omidyeh, Bushehr and Shiraz) during study days of July, 2008. Therefore, dust occurrence increases from north to south and the maximum hotspots over southwestern confirm the proximity of the south western region of Iran to deserts and sedimentary plains and their direct relationship with dust sources in the Middle East. These regions highlight the volume and expansion of dust outbreaks, which were well detected due to the satellite imagery and spectral characteristics of MODIS for monitoring changes in the dust phenomenon.Overall, the use of satellite remotely sensed data/images not only cover the ground-based observation datasets gap to identify, highlight, and analyse the dust phenomenon, but also takes a much more geographical approach in analysing environmental hazards such as dust. It is also suitable for studies of atmospheric compounds such as atmospheric aerosols.

In the current research, to investigate the future climate of the Urmia Lake catchment, the maximum and minimum temperature, rainfall and drought in the basin were projected for the period of 2015-2099 using NorESM2-MM climate model under the scenarios of SSP. The downscaling output of the model was done using the quantile mapping method and their accuracy was evaluated in the simulation of the base period (1990-2014) using the monthly diagram and RMSE and NRMSE indicators. The evaluation of the results showed that: the minimum and maximum temperature of the basin under the pessimistic scenario (SSP5-8.5) until the end of the century and under the optimistic scenario (SSP1-2.6) until 2075 have an ascending trend and then a descending trend. Average maximum and minimum temperature of the basin in the near future (1.0 to 1.8) and (1.1 to 1.8) ℃ and in the far future (1.5 to 4.8) and (1.3 to 4.3) ℃ will increase. The annual rainfall in the future period does not have a significant trend, but the average rainfall of the basin in the optimistic scenario will increase by 16.5% in the near future and 8.9% in the far future and in the pessimistic scenario will increase by 1.8% in the near future and 7.2% in the far future. According to the SPEI index, in the future period, under the optimistic scenario, moderate drought will have an ascending trend, severe drought will have a descending trend, and under the pessimistic scenario, the drought will have a descending trend.

For this purpose, data on the type, amount, and height of different cloud layers and daily precipitation of 36 synoptic stations located on the southern coast of the Caspian Sea were received from the Meteorological Organization. MODIS images were used to investigate the relationship between precipitation and cloud microphysical parameters (CTT, CTH, COT, CER, CWP). ERA5 and NCEP/NCAR data were also used to identify synoptic patterns leading to cloud formation. Finally, HYSPLIT model and regression method were used to identify the path of moisture flow. The results of observational data showed that Caspian clouds were observed in the form of low Stratus clouds and middle clouds of Altocumulus type in the region. So that among the low clouds, the heights of 750 and 900 meters and among the middle clouds, the heights of 2700 meters had the highest frequency. The results of Caspian clouds rainfall showed that in most areas, 1 to 5 mm of precipitation has occurred. Correlation results showed that precipitation was positively correlated with CTH,COT, CER and CWP, and negatively correlated with CTT. Multivariate regression predicted 17% of precipitation by cloud parameters. The results of the study of synoptic maps showed that with the establishment of a 1012 hPa high pressure core in the north of the Caspian Sea, the north-south wind flow along with the transfer of sea moisture to the south shore of the Caspian Sea, ascending the air mass and the formation of clouds and limited rainfall in the region. Vertical profiles showed maximum specific humidity in the lower levels of the atmosphere (1000 to 900 hPa). The results of HYSPLIT model moisture flow path showed that the main source of regional moisture was the Caspian Sea.

Temperature and precipitation are two important meteorological parameters, especially in arid and semi-arid regions. As a result, it is necessary to know the amount of these parameters, their changes and predict these phenomena, in order to have more accurate planning in the management of agricultural, economic and etc. The aim of this study is numerical simulation and forecasting of Iran's weather changes with emphasis on two climatic parameters of temperature and precipitation. The method used in this research is artificial neural network algorithm for simulating 24-hour temperature and precipitation variables by month during a 31-year period compared to the base period. Factorial variance analysis has been used to compare the average changes in temperature and precipitation during the observed period of 1990-2020 and the simulated period of 2020-2050. The comparison results show that there is no significant difference between temperature changes and precipitation in 24-hour selected meteorological stations in Iran. The computed Partial Eta Squared is 10.6 percent for temperature and 5.7 percent for precipitation. The highest and lowest precipitation observed occurred in the months of March and July. The simulations have predicted the highest and lowest precipitation for the months of March and August. Also, the highest and lowest observed temperatures occurred in the months of July and January, respectively, and the simulations predicted the same values for the same months. The highest and lowest observed precipitation values were recorded in Rasht 109.95 and Yazd 4.36 mm, respectively. The simulations predicted the highest and lowest amount of precipitation for the same stations as 112.46 and 5.63 mm. Also, the highest and lowest observed temperatures were recorded at Bandar Abbas 26.99 and Ardabil 9.36 Celsius degrees, respectively. The simulations have predicted these values 27.10 and 9.45 Celsius degrees, for the same stations, respectively.

Earth surface temperature changes can affect various environmental factors and elements, and by affecting bioclimatic comfort, energy consumption, plant and animal phenology, air pollutant concentration, soil and air moisture density, and other environmental factors can also be affected by these factors.In this research, in order to investigate the role of actual evaporation-transpiration on the temperature of the earth's surface at ten meters, the variables of carbon monoxide, water vapor density, nitrogen dioxide, sulfur dioxide, ozone, water vapor pressure and wind speed in twelve uses With the help of LCZ algorithm, determined for Tehran and Alborz provinces, have been studied and investigated.The studied area has a high potential for the occurrence of thermal islands due to special geographical conditions such as location, variety of man-made structures and special ecological conditions. In this research, according to the impact of the studied variables and the need to examine the relationships between them, hierarchical clustering in a combined manner, path analysis method and remote sensing techniques have been used. According to the obtained results, among the studied variables, actual evaporation-transpiration in none of the uses had a significant effect on increasing or decreasing the temperature of the ground surface at 10 meters. Water vapor pressure in class 14 (areas with grassy vegetation) shows the greatest effect in increasing the temperature of the earth's surface.Based on the obtained results, the type of use is highly dependent on the impact of the studied variables on the surface temperature of the earth.

Climate change is one of the most pressing problems on Earth today, so predicting these changes in the future and their effects on water resources, the natural environment, agriculture, and environmental, economic and social impacts is of particular importance. Therefore, in the present study, the effects of global climate change on different climatic regions of the country were studied in 12 climatic regions. In this study, NCEP data and climatic elements of precipitation, maximum and minimum temperature were used for statistical downscaling with SDSM model. And using the CanEMS2 model output under RCP scenarios for the three statistical periods of 2011-2040, 2041-2070, and 2071-2099 annual climate change data were obtained. Correlation coefficient, determination coefficient and error indexes of RMSE, MSE and MAD were used to evaluate the performance of the model. However, the results showed that the accuracy of the model was different at different stations. In this way, each model performs better than rainfall in simulating minimum and maximum temperatures. The annual long-run results also show that precipitation will decrease in all climates studied in the coming decades, with the largest decrease occurring in semi-warm (35%) and very humid and temperate (32%) desert areas. But minimum and maximum temperature variations will be different in different climatic regions so that under RCP scenarios during all statistical periods at Sabzevar and Tabas stations minimum temperature changes will decrease but in other climatic regions the trend of minimum and maximum temperatures will be incremental. The highest minimum and maximum temperature increases based on RCP scenarios under RCP8.5 scenario during the period 2071-2099 in the cold mountain climatic region will be 3.03, 4.27 ° C, respectively.

 The increase in technology in the exploitation of natural resources and the increase in consumerism have led to environmental crises that threaten the stability of societies, especially rural societies. Consequently, it is important to evaluate the sustainability of these areas and to examine how resources are consumed and exploited. In the interim, one of the best indicators for evaluating sustainable development which emphasizes the amount of consumption with regard to ecological power, is the ecological footprint index. Thus by using the mentioned index, the present study analyzes the spatial pattern of the ecological footprint at the level of the region in Eslamabad-e-Gharb county.

 In terms of purpose, this research is practical and descriptive-analytical in nature.  Documentary and field methods have been applied to collect information. Based on three factors of natural location, distance from the county center and the number of households, 40 villages were selected from eslamabad-gharb county. Grounded on Cochran's formula, the minimal sample size of 360 household was determined. Using the Proportion formula, the share of each village in the number of questionnaire distributions was determined, To analyze the obtained data, the ecological footprint index was used in 6 factors of energy land, pastureland, arable land, forest land, water areas and built land. Moreover, Moran's i statistic was used to identify the spatial distribution pattern of ecological footprint in the study area, and IDW interpolation was used to zone eco-footprints.

The results showed that 72.5 percent of the 40 investigated villages, equivalent to 29 villages, are in unstable conditions and ecological deficit, and 11 villages have an ecological surplus. Meanwhile, the village of Barf Abad Aliya with -8351.23 global hectares has the largest ecological deficit and the village of Mian-Tang Mansouri with +2042 global hectares has the largest ecological surplus. Also, the local Moran's index identified the spatial pattern of footprint distribution at the county level as a cluster type with high value. Based on the interpolation of IDW, it was found that the northern and southern villages of the county, which often have a mountainous and forest location, are located in the middle and high footprint area, and the middle villages with a plain location have a low and very low footprint.

 The natural location of rural settlements influence their ecological footprint, so mountain and forest villages have a larger footprint than plain villages. This can be seen in the less access of these villages to resources as well as the small area of natural resource areas. On the other hand, the amount of energy consumption in the field of fuel is very high due to the environmental conditions in mountainous areas, which is much less in plain areas. Consequently, mountain and forest villages should be given more attention in resource consumption management and sustainability planning.

Urban landscapes are a platform for social interactions and leisure. These spaces are affected by high population, dense vegetation, unprincipled entrances, low visibility, lack of supervision, limited night light sources, and the possibility of hiding and creating a sense of confusion leading to the hidden occurrence of crime. Due to the low per capita green space in some areas of Ardebil, the city's famous parks are witnessing the presence of a large population. One of these parks in Ardebil is the Shurabil tourist entertainment complex, which is visited by many citizens during the holidays and at night, which makes it difficult to control and monitor the park. In this study, effective components (five main components: access, form and morphology, general perception, pleasure and visual quality, physical and urban management systems) in improving the safety of users in urban landscapes using the AHP model (expert opinion in the field of landscape) are obtained. The hierarchical analysis method was used to determine the weights of each of the obtained components (the obtained data were entered into EXPERT CHOICE software). Finally, fuzzy standardized base maps and weighted overlap maps were presented using ArcGIS software. According to the obtained results, improving security weighing 0.274 is the most preferred component of urban management systems, and then the components of form access and morphology, general perception-pleasure, and visual and physical quality with weights of 0.202, 0.198, and 0.197 were obtained respectively. Also, the sub-criterion of security devices weighing 0.122 was determined to be the most preferred case. (The higher the weight of the components,  means the need for increased security in that component, and vice versa, the lower the weight means security is in a good state). Depending on the scale, location, design, and presence of social groups in the park, solutions were proposed to increase the social dimension of the park (meaning people would be free to move around at all times of the day with more freedom) or to reduce crime. The components used in this research can be an important step for landscape designers and urban green spaces in order to increase the level of security in urban parks and also to improve the safety of users in urban landscapes.

Heavy rains often occur in small areas, but they may be the result of large-scale systems and their energy and moisture are provided from distant areas (Mohamadei et al., 2010). Therefore, identification of synoptic systems is of great importance in order to predict precipitation. Although rain has many positive effects on human life, heavy rain can cause one of the most dangerous and damaging natural disasters, namely floods. Every year, floods cause many human and financial losses in different regions of the world. Floods are more effective in vulnerable areas and cause the loss of human lives, damage to property and products, disruption of transportation and services, and other economic losses (Kheradmand et al., 2018). In March 2019, heavy rains occurred in Golestan province, which caused flooding in parts of this province, especially in the cities of Gonbad-Kavus and Aqqala. Most of this heavy rain and flood occurred in the Gorgan River basin. According to meteorological reports, the rain started from the night of 03.17.2019 and continued until 03.21.2019, although the heaviest rainfall occurred from the 03.18.2019. The volume of the flood was so great that the dams on the Gorgan River could not accommodate it. According to the reports of the regional water company of Golestan province, the flood entered the Bostan dam at 1 am on 03/19/2019, and after passing through it, entered the Vashmgir dam at 6 am, and then on 03.21.2019 entered the city of Aqqala. The damage of this flood was estimated at about 4800 billion Tomans, which includes damage to 17800 residential units, damage to farms, transportation infrastructure, 40% reduction in tourism, damage to industrial units, unemployment of about 3000 people, and damage to the nomads of the province. (Islamic Republic News Agency, 04.09.2019). Considering the heavy damage caused by the mentioned heavy rain and flood in Golestan province, it is necessary to identify and analyze the causes of its occurrence in order to plan and take the necessary measures to prepare and deal with such incidents.

The study area is Gorganrood watershed, most of this area is located in Golestan province. Golestan province is one of the northern provinces of the country and is located in the southeast of Caspian sea. In this research, in order to identify and analyze the heavy rain that occurred in Golestan province in March 2019, which led to severe flooding, several types of data were used (data from meteorological stations, NCEP/NCAR reanalyzed data, MODIS satellite images, GPM precipitation products). First, using the rainfall data of the synoptic stations located in the Gorgan River watershed, the time of heavy rainfall was identified, and then using the data of the aforementioned stations and several stations outside the basin, a rainfall zoning map was prepared. MODIS satellite images were also used to check the position of precipitation system and cloudiness of region. Using GPM satellite rainfall products called IMERG, which were extracted on a half-hourly basis, as well as the main synop reports of meteorological stations, which are reported on a six-hourly basis, the intensity of rainfall was investigated. In addition, the physical conditions of the basin were investigated using the topography and slope map of the basin prepared from the DEM layer of the region. In the following, using the reanalyzed data of the NCEP/NCAR database (National Center for Environmental Prediction - National Center for Atmospheric Research of the United States), synoptic maps including maps of land surface pressure, geopotential height of the upper atmosphere, Omega (indicates the speed of vertical movements of the atmosphere), wind direction and speed, moisture flux convergence function, frontal function, specific humidity, atmospheric precipitable water and Hoff-Müller diagram were drawn to identify the synoptic and dynamic factors of the mentioned precipitations.

The results of the present research in the analysis of flood factors can be summarized as follows:Survey of the topography and slope of the Gorganrood basin revealed that the physical conditions of the basin are such that the potential for flooding is high.The amount of rainfall in 24, 6 and a half hour intervals in the study area were investigated and it was shown that the rainfall occurred on March 17, 18 and 19, especially on March 18, in terms of the intensity of rainfall were very intense.Investigation of the state of the middle troposphere showed that the formation of the Rossby wave and the meridional expansion of one of its troughs, along with the creation of a positive vorticity that dominated the studied area on the seventeenth of March, are the main factors in the creation of a baroclinic atmosphere and the dynamic ascent of air.Investigation of the synoptic-dynamic conditions of the lower levels of the troposphere showed that in the lower levels of the low-altitude synoptic system with closed meters, at the same time as the deep trough reigns over the region, it has been formed and strengthened during peak rainfall times and has led to a strong rise of air.Investigating the state of atmospheric humidity in the study area and identifying sources of moisture supply using special humidity maps, moisture flux convergence function and atmospheric flow paths were carried out.Investigating the omega variable in the vertical profile of the atmosphere using the Hoff-Mueller diagram showed that during the times of precipitation events, upward movements prevailed in all levels of the troposphere, especially during the peak of precipitation, the upward movements became more intense in the lower levels.Identifying the type of clouds using MODIS products showed that during heavy rains, especially on March 18, deep convective clouds with a high density of water were formed in the region, which extended up to a height of 300 hectopascals and were very thick.

In this research, the climatic variables of temperature and precipitation of Urmia Lake catchment area were evaluated and projected using CMIP6 models under two scenarios SSP1-2.6 and SSP5-8.5 in the future periods (2031-2055 and 2071-2095). First, the accuracy of the models for the base period (1990-2014) was evaluated using Taylor diagram and RMSE and NRMSE indices after downscaling with different methods of quantile mapping, and among the models, the MRI-ESM2-0 for temperature and INM-CM5-0 for precipitation using the SSPLIN downscaling method were selected to project the future climate, then the future temperature and precipitation data were produced. The results of comparing the temperature and precipitation of the future periods with the base period showed that the average annual temperature of the basin will increase under all scenarios. The average annual temperature increase of the basin in the near future in the optimistic and pessimistic scenarios will be 1.5 and 1.8 degrees Celsius, respectively, and in the far future, 1.4 and 4 degrees Celsius respectively. The average annual precipitation will decrease in all scenarios, in the near future it will decrease by 19.9 and 21.6 percent in the optimistic and pessimistic scenarios, respectively, and by 12 and 28.6 percent in the far future, respectively. Based on the spatial distribution of changes in temperature and precipitation in the future periods, the greatest increase in temperature and the greatest decrease in precipitation will occur in the northern areas of the basin.

Drying up of Urmia Lake has great influence on the surrounding environment. The salinity of the soil on the edge of the lake, the increase of salt particles and the reduction of the usual agriculture in this area are among the effects of this drought. With proper planning and extensive studies, it is possible to turn the created deficiencies into opportunities and take maximum advantage of them. One of the parameters that can be checked using satellite data is soil salinity. Soil salinity is the accumulation of dissolved salts in the form of electrical conductivity, and it can be measured.  In this study, the changes in land use and vegetation from 2000 to 2016 have been investigated using Landsat 5 and 8 images. The analysis of the images has been done using object-oriented methods, and the soil salinity has been calculated using salinity indices and during the studied years. Soil salinity index as well as other indices such as plant indices has been used in the classification and examination of changes. Land use changes were obtained by object-oriented method in Ecognation software and calculations related to the amount of changes is done using Arc map and Idrisi software. The study was carried out on 4 periods of 2000, 2006, 2010 and 2016. The images related to each year are first classified into 9 classes of water, salt marsh, new salt, pastures and weak pastures, city, agriculture, barren land and garden by object-oriented method. After classification, the amount of changes during these years has been obtained.

Most of the study area is located in pass 168 and row 34, and only parts of the lake in the northwestern part of the study area are outside this image, so to get the desired result, we have to mosaic the images of passes 168 and 169 with rows 33 and 34. Processing is divided into three stages: pre-processing, processing and post-processing.Segmentation is the first step in processing images in object-oriented methods. Segmentation is the integration of objects from small to large based on the parameters of color, shape, density, smoothness and scale. In this study for processing have used from ecognation Software. The method used to classify images is the thresholding method. In order to improve the classification method, salinity indices and vegetation indices have used in this study.

The amount of water in the lake has decreased drastically between 2000 and 2016, and it has decreased from 54.39% range to 29.26%. By examining the changes, it can be seen that other land uses have been reduced, and salt marshes have been added.  The comparison of land uses from 2000 to 2016 shows that the amount of salt marshes has increased from 12.37% in 2000 to 13.84% in 2006, 24.7% in 2010 and 33.25% in 2016. Studies show that the intensity of changes in the amount of water and salinity between the years 2006 to 2010 and 2010 to 2016 is much more intense than the period 2000 to 2006. It seems that before 2006, unprincipled exploitation of lake water and underground water was less than after 2006, or the environment had the ability to recover the amount of water harvested. As it can be seen from the map of 2000, the amount of new or wet salt around the lake is very small, which has increased with the decrease of the lake's water. The survey of gardens in the studied area shows that the area occupied by gardens has increased from 2000 to 2010, but the results of changes in use from 2010 to 2016 show that the area of gardens has almost halved (1099 pixels from the image in 2010 And 667 pixels in 2016) that one of the factors of this reduction can be soil salinity.Another thing that can be seen by studying the maps is the distribution of new salt in the areas far away from the lake, which at first was only attached to the water at the edge, but over time, when the amount of this type of salt increased, due to the occurrence of salt storms, it was spread to further areas. If measures are not taken to control this amount of salt and soil salinity that has occurred after that and will increase over time, we will witness more severe problems in the region.

The aim of this study was to predict changes in temperature, precipitation and evaluate the effects of climate change on the status of surface runoff in the Aras catchment. Climatic conditions were simulated in LARS-WG software environment under RCP8.5 scenario. Using the modified Trent White experimental model, the amount of potential evapotranspiration was estimated for both observation and simulation periods. To ensure the validity of the model, the mean error orthography (RMSE), and the determination coefficient and Nash-Sutcliffe efficiency coefficient (ENS) were used. Also, modeling of surface runoff changes in GIS software environment and SWAT plugin was performed. After forming hydrological units (HRU), the baseline model for surface runoff changes was selected to calibrate and validate the model. The results show that by modeling climatic data during the simulation period, the amount of temperature, evapotranspiration and transpiration will increase, and in contrast, the amount of precipitation has occurred and the flow rate will decrease superficially. The results of validation showed that the accuracy of the model in the selected stations was high and for the precipitation parameter due to its discontinuous nature, the correlation between the data is less than the temperature parameter and different. The results of hydrometric modeling of the basins showed that the Nash-Sutcliffe value is close to 1 and the correlation coefficient between the data is 0.99, which indicates the high efficiency of the model for simulating and estimating climate change and its effects on surface runoff.

A landscape consists of two dimensions, natural and man-made, and is one of the most important components of a visual representation of the human-environmental relationship. Natural has undergone many changes. The purpose of this study is to evaluate the aesthetic quality of natural landscapes in the years 1363, 1381, 1398. The nature of research is developmental-applied and descriptive-analytical. Data were collected through library studies, questionnaires, and field surveys. The quality of landscapes was evaluated with the DEMATEL-ANP model. Considering three criteria (uniqueness, naturalness and diversity) and not sub-criteria, in the layers: (tourist places, mines and industries, Arasbaran forest area), for the first criterion, (land cover, Activity centers, ground surface temperature) were prepared for the second criterion and (land cover diversity, land structural diversity, elevation and elevation) for the third criterion. The relationships between influence and governance of the sub-criteria were determined by Demetel method and then analyzed by ANP method in Super Decision software. Finally, using ArcMap software, the beauty quality map of landscapes Was drawn. The results obtained from the final weight of the effective factors by ANP_DEMATEL method show that the highest coefficient belongs to the natural factor of land cover with a weight of 0.385736 and the uniqueness of the land cover with a weight of 0.169653, which indicates a clear display of the cover. The green landscapes of this region are considered by experts as an example of society. And the obtained maps, entitled the quality of the beauty of the landscapes, show the trend of quality reduction from the west to the east of the city. This decrease in quality can be felt in the lands located near the Sungun copper mine and the two populous cities of Varzeqan and Kharvana.

In this research, the performance of CMIP6 general circulation models has been evaluated in the simulation of temperature and precipitation in the catchment area of Lake Urmia. The models used are MPI, CMCC, INM and MRI with a spatial resolution of 100 km. The performance of the models was evaluated before and after the bias correction. To correct the bias of the models, the statistical method of the delta factor change and the data of seven synoptic stations at the basin level have been used. For this purpose, the studied period (1990-2013) was divided into two twelve-year periods for calibration and verification. In this way, the accuracy of the corrected data of the models was evaluated using the delta factor change for the period of 2002-2013 compared to the observational data by using R2, RMSE, NRMSE indicators and Taylor and scatter diagrams. The results showed that for the entire basin, there is a strong linear relationship between the corrected data of models and station observations for temperature and a weak linear relationship for precipitation. The RMSE and NRMSE indices indicate the high accuracy of the models in simulating the monthly temperature and relatively weaker accuracy in simulating the monthly rainfall of the basin, and among the models, CMCC had the highest error. The spatial distribution maps of NRMSE for the total monthly averages showed that all four models have simulated temperature with high accuracy, but the simulation of precipitation in some areas of the basin does not have acceptable accuracy. Therefore, the performance of the studied models was evaluated as good in temperature simulation and poor in precipitation simulation, especially in the CMCC model.

The Balochistan province of Pakistan is mostly affected by severe drought events due to little amount of precipitation. “Several Large Scale Climate Drivers (LSCDs) are known for their effects on precipitation worldwide but studies in the region are missing; a wide variety of LSCDs and lagged associative information”.The current study aimed to identify the significant LSCDs in the Balochistan province of Pakistan and improve the prediction skill of monthly precipitation by employing the Principal Component Analysis, Artificial Neural Network (ANN), Bayesian Regularization Neural Network (BRNN), and Multiple Regression (MR) Analysis using the 12 months lagged LSCDs such as Nino-1+2, Nino-3, Nino-3.4, Nino-4, QBO at 30 and 50hpa (QBOI and QBOII), Sea Surface Temperature (SST), 2m air temperature (T2M), 500hpa and 850hpa geopotential heights (H500 and H850), 500hpa zonal velocity (U500), 500hpa and 850hpa meridional velocity (V500 and V850), Latent and Sensible Heat Fluxes Over Land (LHFOL and SHFOL), and Surface Specific Humidity (SSH). To collect the data, Global Land Data Assimilation System, Tropical Rainfall Measuring Mission, MERRA-2, NOAA, Freie University Berlin, and HadISST datasets were used. The results of the study showed that significant LSCDs with a 99% confidence level were SSH, SST, LHFOL, SHFOL, T2M, U500, Nino-3.4, and Nino-4. During the test period, compared with MR models of 0.15 to 0.49 and principal components of -0.16 to 0.43, the ANN and BRNN models had better predictive skills with correlation coefficients of 0.40 to 0.74 and 0.34 to 0.70, respectively. It can be concluded that the ANN and BRNN models enable us to predict monthly precipitation in the Balochistan region with lagged LSCDs.

What is known as a cloud is actually the accumulation of water vapor particles in the atmosphere around the nuclei of their density and cooling (Ghasemi, 2012). In this study, we will study and identify the clouds that are formed in terms of spatial distribution between the southern coast of the Caspian Sea to the Alborz Mountains and in terms of temporal distribution in all seasons, especially in summer. It seems that these clouds were different in terms of atmospheric formation mechanism and are formed under special environmental conditions of the Caspian coast. Therefore, the main purpose of this study will be to identify and study these clouds. For this purpose, 279 cloud days were selected for study.

This study uses type, amount and height of low, medium clouds, including hourly data (00, 03, 06, 09, 12, 15, 18 and 21 UTC) and daily precipitation of 13 meteorological stations in the study area, for selected samples, were received from the Iran Meteorological Organization (IMO). The characteristics of the physical parameters of the cloud Included CTT, CTH, CER, COT and CWP were obtained from level 2 MODIS (MOD06 TERRA and MYD06 Aqua) with a resolution of 1 km. Upper atmosphere data were obtained from ERA5 at a resolution of 0.25° × 0.25 °. Which includes geopotential height, u- wind, v- wind, specific humidity and omega levels of 1000 to 500 hPa isobaric. Ground surface data (SLP, U-wind and V-wind 10m) were obtained from the NCEP/NCAR database and its circulation patterns were drawn in GRADS. HYSPLIT model and the backward method was used to identify the source of moisture. In this study, Global Data AssimilationSystem (GDAS 1°) meteorological data provided by NOAA HYSPLIT model were used to calculate the backward paths for altitudes of 50, 500 and 1000 m above the ground. First, the frequency percentage of the type and height of different layers of clouds were calculated. The average seasonal and monthly occurrence of Caspian clouds were calculated. The average seasonal and annual rainfall of Caspian clouds were calculated. The relationship between precipitation and cloud parameters was investigated by multivariate regression

 During the 10-year statistical period (2020-2010), 279 cases (days) of the occurrence of Caspian clouds were identified. The research findings showed that the highest average monthly frequency of Caspian clouds occurs in August until its lowest occurrence in November to April. The maximum seasonal frequency of days with Caspian clouds occurs in summer with 16.1 days. These clouds are mainly in the form of low- and middle-level clouds in the region with their most common types being Stratus and Altocumulus. The analysis of rainfall rainfall from Caspian clouds indicates the annual rainfall of Caspian clouds in the region and in most stations more than 80 mm, and its highest amount occurs in summer and autumn chapters, respectively. Spatial distribution the average rainfall derived from Caspian clouds showed that its maximum is on the annual scale and summer and autumn seasons in the southwest and west of the region; but in the spring, it is placed in limited parts of the south. By applying the multivariate regression model, it was found that cloud parameters may predict 57% of the rainfall changes in Caspian clouds. Examination of the synoptic patterns shows that high-pressure settlement in the north of the Caspian Sea provides favorable conditions for wind flow and moisture transfer of the Caspian Sea to its southern coast. So that with the encounter of the humid air mass to the Alborz mountain range, it leads to orographic lift and formation of clouds and rain in the region. The HYSPLIT model indicates that the source of moisture for the formation of Caspian clouds is largely from the Caspian Sea.

The average frequency of the occurrence of Caspian clouds in August to stamp is more than spring and winter months. The average number of summer and autumn, as well as the average rainfall of Caspian clouds in the summer and autumn, is more than other seasons. These clouds are mainly in the form of low- and middle-level clouds in the region with their most common types being Stratus and Altocumulus. By applying the multivariate regression model, it was found that cloud parameters may predict 57% of the rainfall changes in Caspian clouds. Examination of the synoptic patterns shows that high-pressure settlement in the north of the Caspian Sea provides favorable conditions for wind flow and moisture transfer of the Caspian Sea to its southern coast. So that with the encounter of the humid air mass to the Alborz mountain range, it leads to orographic lift and formation of clouds and rain in the region. The HYSPLIT model confirmed the moisture transfer from the Caspian Sea to the study area.

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

The Study Area:

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

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

Analysis of temporal changes in climatic parameters and NDVI index The results show that the distribution of relative humidity in Abadeh and Kerman stations has decreased by 3% and the temperature distribution in these stations has increased by more than one percent. Relative humidity changes in Kashan and Sirjan stations have a weak decreasing trend, while the relative humidity distribution in Isfahan station has decreased by about 2%.The temperature distribution of Shiraz and Yazd stations increased by 3%, Abadeh station increased by 2% and also Isfahan and Kerman stations increased by 1%. The distribution of vegetation in Yazd and Khor Biyabank stations has decreased by one percent, while the growth of vegetation in Isfahan, Abadeh and Sirjan stations is increasing by less than one percent.Distribution of NDVI vegetation index in Central Iran using cluster analysis The stations are located in three distinct areas in terms of distribution of vegetation, each group having the same climatic characteristics in the distribution of similar vegetation. Based on this, three climatic zones in the study area can be identified.

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