aliakbar shamsipour

In the research, the carbon storage capacity of Urmia's urban and suburb green infrastructures were analyzed. Urmia is characterized by a cold climate with a dense building structure with scattered green spaces, especially in the surrounding areas and neighborhoods of the city. The research was carried out using the carbon storage model available in the InVest software package. The results revealed that more than 57% of the study area has a carbon storage capacity of less than 2 tons, and less than 6 percent of the area has more than 30 tons per hectare, which is limited to the gardens and groves along the Shahrchai River. Soil carbon storage has the highest share with 2344.41 tons, and above biomass carbon storage followed closely with 1403.47 tons. The total amount of carbon storage in the studied area is 3900.9 tons per year. The carbon storage was highest in gardens and groves, followed by barren lands with sparse vegetation and rocky outcrops mountain’s without vegetation and soil cover had no carbon storage. Dense plants and trees have the highest storage capacity per unit area, However, the amount of storage depends on the season, so it is necessary to consider the selection and expanding the urban green spaces according to the types of plants and their compatibility with the climatic conditions and urban spaces of Urmia. As a result, it is necessary to create and develop urban green spaces at the same time as physical development, it should be the priority of Urmia's urban development plans.

Ecosystem services (ES) refer to the benefits derived from the structure and function of ecosystems and play an important role in human well-being and welfare (Adelisardou et al., 2021). The two interconnected approaches of carbon sequestration and habitat provisioning are ecosystem services that have the potential to mitigate climate change and biodiversity loss caused by the built environment (Varshney et al., 2022). The rapid urbanization of Tehran in recent decades has led to significant disruptions to both the natural and urban landscape. Sustainable population growth, industrialization, and urbanization processes have led to the reduction or destruction of natural vegetation, the development of urban constructions, various human activities (such as transportation and production centres), and drastic changes in land cover/land use (LULC). These factors have also contributed to an increase in energy consumption, which has resulted in elevated surface temperatures in urban environments. Under these conditions, the current research aims to examine two ecosystem services - regulatory services (carbon storage) and support services (habitat quality) - provided by the InVEST model. The research will focus on the role and impact of these two crucial services in enhancing the quality of the environment and analyzing the climate of the Tehran metropolis.

The current research aims to investigate the effect of land use/land cover on changes in carbon storage (regulating service) and habitat quality (supporting service) at spatial and regional scales using the InVEST software. The carbon storage model integrates land use and land cover (LULC) data with information on carbon storage inventories in four main carbon storage pools: aboveground biomass, belowground biomass, deadwood, and soil organic carbon. This integration enables calculations and spatial distribution of inventories and carbon storage in the area. To conduct habitat quality modelling, several components are necessary, including the (LULC) map, threat sources, impact weight tables, impact distance for each threat, and the sensitivity of each habitat to the threat source. One of the important inputs of the InVEST model is the land use/land cover map. In the present study, the classification of local climate zones (LCZ) was utilized to calculate and generate a land use and land cover (LULC) map for the city and suburbs of Tehran.

The results showed that the highest amount of carbon storage on an annual scale is associated with aboveground biomass and soil organic carbon, while the lowest amounts are found in deadwood and belowground biomass. The aboveground biomass in region 22, specifically the Chitgar Forest Park area and the northeastern areas of the region where trees are densely and sparsely distributed, has the highest carbon storage capacity. It is estimated to be between 218 and 335 tons per hectare per year. After that, the southern and southeastern parts of region 15, as well as the northern and eastern parts of regions 8 and 14, which are characterized by open mid-rise land cover, are ranked next in terms of carbon storage. The results of the Habitat Quality Index also showed that the highest amount of habitat quality, indicated by a 16% coverage (values between 0.7 and 1), was concentrated in the northern parts of the study area. The habitat quality gradually decreased towards the southern and, in particular, the southwestern areas. In 65% of the study area, the habitat quality is extremely low, primarily due to urban development and settlements in the southwest of the city. The central (10, 11, and 12) and western (21 and 22) areas of the municipality have the lowest habitat quality due to the lack of urban green spaces, the destruction of existing ones, and the expansion of settlements and industrial complexes. Finally, to measure the sensitivity of ecosystem services to climate, we analyzed the effect of two primary climate components: temperature and precipitation, on ecosystem services. The results showed that although the correlation coefficients between the studied series were relatively low (temperature and ES=0.221, precipitation and ES=0.234), the regression analysis indicates that both ecosystem services will decrease under conditions of increasing temperature and decreasing precipitation.

In this study, we evaluated the spatial changes of two carbon storage services and habitat quality concerning land use/land cover change in the Tehran metropolis and its surrounding suburbs. The results showed that the land is covered with dense and scattered trees, as well as open mid-rise, sparsely built, open low-rise, and low plants. These areas had the highest absorption values in all four carbon pools. In this sense, Region 22 and the eastern part of the Northern Region had the best conditions for carbon absorption. The output of InVEST habitat quality also indicated that the highest amount of habitat quality, with a coverage of 16% (values between 0.7 and 1), is found in the northern parts of the basin. Gradually, the quality of the habitat decreases towards the southern areas, especially in the southwest. Although the obtained results indicate the favourable ecological potential of some areas in the metropolis of Tehran, climate change and human activities have severely affected this potential. Based on the linear relationship between the normalized series of climatic components (temperature and precipitation) and ES, there was an inverse relationship between temperature and ES and a direct relationship between precipitation and ES. As a result, both regulatory services and ecosystem support will decrease under conditions of increasing temperature and decreasing precipitation.

Precipitation anomalies, especially drought and its changes are among the most important topics in climatology. The current study was carried out to regionalize drought events and evaluate the changes in their extent in annual, seasonal and monthly time scales. For this purpose, EAR5 monthly rainfall data and the RAI drought index were used. Hierarchical cluster analysis based on Ward's correlation-integration distance Mann-Kendall's test and the slope of the regression line were used to extract spatial clusters and their changes. The number of spatial clusters (areas) of co-occurrence of drought in the rainy season and months is wider and more homogeneous than in the less rainy months such as the summer season. Certain geographical-climatic areas such as Southeast, Center, Northwest and West-Southwest are seen most of the time. The annual drought changes in none of the areas show a significant trend, which could be due to intra-seasonal and monthly contrasts in drought changes. So in winter and spring (the main rainfall period of the country), an increasing trend was observed, and on the other hand, in autumn, a decreasing trend of drought was observed. On a monthly scale, the most obvious increase trend has occurred in the two main months of rainfall in the country, namely January and March, and a decrease in November. The results of the research can indicate the temporal shift of rainfall or the change in the country's rainfall regime

Cities have an increasing population and physical growth. Their physical growth by changing land cover/use has many environmental and climatic effects. Neglecting the ecological dimensions of cities has caused a decrease in their environmental quality. Among the most important of them we can mention the increase in thermal loads and air pollution, and reduction of dynamic potential. Two-dimensional urban climate maps are an analytical tool that integrates urban climate factors with urban factors so policymakers and urban planners can easily use them. climate approach is divided into two general categories of weather station data for analyzing the city's climatic conditions and spatial information layers used to integrate and calculate the map of planning recommendations. The results of integrating information layers in the geographic information system, Tehran was classified into five urban climate planning zones (UCPZ). These areas include urban climate planning area one with 25% coverage of protection zones and urban climate planning area two with 35% coverage and strategic measures. The changed areas include urban climate planning of region three with 22% attention to reducing harmful factors, urban climate planning of region four including 16% with strategic action to reduce necessary and recommended measures, and planning of urban climate region five with 3%. With strategic measures for necessary reduction, recommendations are also provided for each class with a detailed urban climate planning approach.

An urban climate map is an analytical tool that integrates urban climate factors with human and urban factors. Then, using obtained the results, it provides instructions and recommendations based on the climate characteristics in the city and creates a two-dimensional spatial map in a format that specialists and urban planners can easily use. In other words, the urban climate map is an interdisciplinary study and practical program that examines urban climatology and urban planning together and focuses on the application of climate knowledge in the field of planning. A proper understanding of climate conditions and planning recommendations can be implemented in actual physical planning processes of cities such as city/municipal master plan, zoning plan and land use plan. As the planning guidelines refer to specific areas, the use of urban climate maps (UCMaps) is also recommended as an information base. The created maps are considered very important physical tools for planning and as a tool with specific information to establish communication with managers, policymakers, decision-makers and those interested in urban issues. 

The data used in this research is of the type of location information that contains the information layers of the city of Tehran; The building blocks are urban land use/cover, topography, urban green spaces, surface hydrographic network and road network, which were prepared from the Information Technology Organization of Tehran Municipality. These layers of information were directly used in the calculation of the urban climate maps of Tehran in the urban climate analysis map section.
The research method is based on the analysis and integration of spatial information layers. First, the urban climate analysis map (UC-AnMap) was prepared. Calculating the climate map of the urban planning guidelines, the information contained in the urban climate analysis map is examined and based on the physical, environmental and activity conditions and realities within the city, this information is classified together. The climate map of urban planning guidelines and recommendations has classes known as urban climate planning zones (UCPZ). Each of these urban climate planning areas has guidelines and strategies with implementation capabilities that provide climatological recommendations to maintain or reduce the thermal conditions of each climate area.

To obtain a map of Tehran's urban planning guidelines, variables and effective components were examined. Based on 8 classes of urban climate analysis maps and according to physical characteristics, natural ventilation potential, thermal load and urban climate classes, Tehran was integrated into 5 Urban Climate Planning Zones (UCPZ). The status of urban development planning zones and their recommendations are as follows: UCPZ1 has covered 24% of the urban area. This part of the city includes areas that, with their natural and suitable vegetation, do not hinder the flow of air and wind. UCPZ2, with 35% of Tehran's urban area, has the highest amount of coverage, and the northern areas of Tehran have this feature of the climate zone. UCPZ3 has covered 22% of the urban area and the central areas of the city have the characteristics of this climate zone. UCPZ 4 has covered 16% of the urban area of ​​Tehran and its distribution includes the central areas of Tehran and areas such as 10, 11, 12, 6 and the south of areas 2 and 5. They cover most of this area. UCPZ5 has covered 3% of the urban surface and the main areas of the climate zone are found in the 21st, 16th, 12th and 11th districts of the municipality. Other southern and central areas of the city have small and limited spots of this climatic zone.
The pattern of population distribution and density determines the amount of man-made environmental thermal load in the city as well as the natural ventilation potential of the air. This study, which is based on urban climate maps (UCM), climate zones and planning recommendations, is based on two components of thermal load and dynamic potential of urban areas. The roads in the central parts of the city are generally narrow and full of traffic, which leads to poor natural ventilation conditions and an increase in thermal load. Weakness in natural air ventilation has a significant effect on the amount of air pollution in urban areas, in addition to intensifying the environmental thermal load. Warehouses, industries, and uses such as gas stations and road traffic are the main sources of air pollution in different areas and neighborhoods. The urban climate planning recommendation map (UC-ReMap) shows five climate zones. This map realistically shows the climatic conditions of the city according to physical characteristics, urban geometry, urban green and open spaces and proximity to natural spaces. Therefore, it provides information on urban climate for urban planners, and based on this, they analyze regional and strategic planning based on the climatic condition of urban areas.

The central areas are characterized by unfavorable conditions of high thermal load and weak natural ventilation with urban heat island phenomenon: therefore, reducing the building load in these areas is necessary; In the vicinity of the central areas of the city, climate zone 4 has been obtained, which is characterized by hot conditions, and in them, it is recommended to reduce the number of structural measures. In districts 21 and 9, the type of roof materials and the continuity of asphalt surfaces and ground covering in parking lots, warehouses and silos of factories are among the reasons for the high environmental thermal load in these areas. Therefore, it is concluded that the areas with the unfavorable urban climate of Tehran are caused by three factors high building density; continuous metal and asphalt pavements; And the road network dense and narrow. On the other hand, the open and green spaces in the inner areas of the urban fabric and the mountainous suburbs in the north and agricultural suburbs in the south of the city provide favorable climatic areas with weak thermal load and high dynamic potential and natural ventilation. In climate zones one to three, it is necessary to protect and maintain relatively favorable climatic conditions, and in this context, it is necessary to avoid extensive land use conversion and restore favorable climatic conditions in the affected areas.

City is a living, dynamic being evolving over time in the context of physical and anthropogenic components and complex relationships between them. It is the reflection of the role and attitude of man-kind influenced by social, economic, political, cultural and geographical factors and conditions. Increased population and density in urban areas have far-reaching consequences, such as increased consumption of natural resources, land-use changes, climate change, and disruptions in the exchange of material and energy. Consequently, cities face many issues and problems, the most important of which are issues related to urban design. These include poor ventilation, high heat load, air pollution caused by the physical characteristics of cities, and insufficient attention to the capabilities, natural characteristics and climate of the region and the city.

The present study seeks to prepare an urban climate analysis map to study and analyze spatial and climatic information collected from Tehran. Urban Climate Map (UCMap) is an information-based and analytical tool that combines factors of urban climate with urban planning factors and some environmental conditions to provide an image of urban climate issues in a two-dimensional environment. Urban climate map consists of an urban climate analysis maps (UCAnMap) and an urban climate recommendation map (UCReMap). Urban climate analysis maps apply various spatial information layers of heat load maps such as building volume, urban topography and green space along with layers of land cover, natural landscape, and proximity to open spaces in dynamic capacity maps. The proposed model is generally based on the evaluation and analysis of variables affecting climatic conditions. Based on six layers of building volume, land cover, topography, proximity to open spaces, green space, and natural landscape, maps were prepared in Arc/GIS10.4.1 environment for Tehran urban area. To eliminate the unit and reach comparability and overlap, the layers were standardized and used to prepare maps of ambient heat load and dynamic capacity.

Three layers of building volume, topography, and green space were weighted and combined to create a heat load map. The other three layers of land cover, natural landscape, and proximity to open spaces were also combined to create a dynamic capacity map. Afterwards, these two maps were combined to create an UCAnMap. The resulting map was close to the on the ground realities. For example, building volume has a negative effect and increases heat load in urban areas. On the other hands, green space reduces heat load and has a positive effect. The central and southwestern parts of the city have a high heat load and core areas of the urban heat island have been calculated and obtained in these areas. The resulting map was classified into 8 categories to create urban climate analysis map of Tehran.

Results indicated that 59% of the urban area in Tehran, mostly located in the northern part of the city, has a good cooling and ventilation condition while 19% of the study area, mainly in the central, southern, and southwestern parts, faces heat stress and lacks an appropriate air ventilation condition. 22% of the study area, scattered all over the city but mostly located in the northern, western and eastern parts, faces an intermediate condition. According to the calculated heat load map, the central, southern, and western parts (in region 21) of the study area face a high and unfavorable ambient heat load. And many parts of the 4th, 1st, 2nd, 5th, and 22nd urban districts are characterized with low ambient heat load and favorable climatic conditions.

Urban spaces have different and more complex environmental conditions than natural environments because they combine human-made elements and natural features. Today, urban climate specialists focus on a combination of urban and natural factors when zoning urban spaces. The Local Climate Classification (LCZs) is a new and systematic classification system for urban spaces proposed by Stuart and Oke (2012). LCZs classify climates according to the physical structure of the city. Each LCZ is characterized by one or more distinctive features, such as land cover, height, and the distance between trees and buildings. Local Climate Zoning classifies the climate of urban spaces by focusing on the city's physical structure and surface coverage. The LCZ classification has 17 different classes, each of which represents a unique set of characteristics. LCZ classes are individually identified by one or more distinctive characteristics, such as land cover or height, the distance between trees and buildings. Classes 1 to 10 focus more on the physical structure created by humans, while classes A to G focus more on the natural aspect of the city.

Theoretical Framework:

The Local Climate Zoning (LCZ) method was extracted and presented by Stewart and Oke (2012) from the Urban Climate Zones (UCZ) method. This method is presented with an emphasis on land cover characteristics and building density for large cities.In this method, 10 climate zones are specified for urban built spaces and 7 climate zones for natural spaces. The most important data required in this method are Landsat satellite images, which are prepared in both winter and summer seasons to accurately identify the land surface cover. Additionally, for each of the 17 climate classes, it is necessary to take samples in Google Earth to use those samples in the image processing process. Therefore, the accuracy and quality of the map of local climate zones depends on the accuracy of sampling.

Three types of data were used in this study: meteorological data, satellite images, and spatial information layers.  Meteorological data included temperature, precipitation, wind speed, and wind direction data from the Doshan Tappeh, Geophysical, and Mehrabad meteorological stations in Tehran for the past 20 years. Satellite images of the city of Tehran were used for two periods: summer and winter.  Spatial information layers included land use data, land cover, and building floors of Tehran.To create a map of the local climate classes in Tehran, the satellite images were converted to a spatial resolution of 100 meters in the SAGA-GIS environment. The measured area was then cut and saved in kml format and added to the Google Earth program. In Google Earth, samples of each climatic class were collected. This stage was the most important and decisive stage of the research, and it was conducted with great accuracy and patience using many samples.

The city of Tehran has a diverse range of local climate classes (LCZs) due to its diverse natural and human environments. Tehran is a heterogeneous metropolis in terms of its form and function, and this heterogeneity is reflected in the distribution of LCZs. The results of this study showed that the most common LCZs in Tehran are:- Dense texture and medium height (LCZ 2): These LCZs are characterized by high ambient heat load and poor ventilation capacity. They are generally concentrated in the central and northeastern parts of Tehran. Dense and short (LCZ 3): These LCZs are also characterized by high ambient heat load and poor ventilation capacity. They are found in other parts of the city, such as the southern and southwestern suburbs. Low-rise and mid-rise (LCZ 4 to LCZ 6): These LCZs are characterized by lower ambient heat load and better ventilation capacity. They are found in the outer parts of the city, such as the northwestern and southeastern suburbs.  Barren land and agricultural land (LCZ 7 to LCZ 9): These LCZs have the lowest ambient heat load and best ventilation capacity. They are found outside the city limits.The distribution of LCZs in Tehran is affected by a number of factors, including:- The density of buildings  The height of buildings  The presence of vegetation  The topography The proximity to water bodiesThe high density of buildings in the central and northeastern parts of Tehran is the main reason for the predominance of LCZs 2 and 3 in these areas. The low density of buildings in the outer parts of the city is the main reason for the predominance of LCZs 4 to 6 in these areas. The presence of vegetation helps to reduce the ambient heat load and improve ventilation, while the proximity to water bodies also helps to cool the air.The distribution of LCZs in Tehran has important implications for the city's climate and environment. The high ambient heat load and poor ventilation capacity of LCZs 2 and 3 can contribute to the formation of the urban heat island effect, while the lower ambient heat load and better ventilation capacity of LCZs 4 to 6 can help to mitigate this effect. The presence of vegetation can also help to improve air quality and reduce noise pollution.Overall, the distribution of LCZs in Tehran is a complex issue that is affected by a number of factors. The understanding of this distribution is important for the development of strategies to mitigate the effects of climate change and improve the city's environment.

The findings of this study have important implications for the planning and management of Tehran. Identifying areas at risk of high urban heat load and flooding can help to prioritize interventions to reduce these risks. For example, the city could plant more trees and vegetation to cool the air and reduce the urban heat island effect. It could also improve the drainage system to reduce the risk of flooding.Overall, this study provides a valuable contribution to the understanding of the urban climate of Tehran. The findings can be used to develop strategies to improve the livability of the city and reduce the risks of heat stress and flooding.

General circulation models (GCMs) provide valuable forecasts of world precipitation and temperature (Schepen et al., 2020). Through improved Seasonal forecasting in recent years several climates centers around the world provide operational climate Such as; the Climate Forecast System version 2 (CFSv2) by National Centers for Environmental Prediction (NCEP) (Saha et al., 2010), the European Centre for Medium-Range Weather Forecasts (ECMWF (Johnson et al., 2019), and the Geophysical Fluid Dynamics Laboratory (GFDL) (Delworth et al., 2020). These GCM outputs generally need to downscale to use in regional-scale relevant applications and more actionable end-user-oriented climate services. One way to transfer world predictions from GCMs to regional or local scales is dynamical downscaling with RCMs such as Weather Research and Forecasting model (WRF). The Initial and lateral boundary conditions from General Circulation Models (GCMs) drive these models. The mesoscale circulations, topography, and land use-land cover are displayed better by RCMs, and these models improve the extremes and regional climate variable compared to the coarse resolution GCMs. The WRF has been coupled with numerous parameterizations to resolve processes occurring within a grid box. Some research has indicated convective and planetary boundary layer (PBL) schemes have a significant influence on precipitation simulation (Li et al 2017; Njuki, S.M., et al 2021). The WRF Model version 4 provides more than 11 convective schemes and 13 planetary boundary layer (PBL) schemes. This study has attempted to assess a suitable combination of physics schemes of the Weather Research and Forecasting (WRF) model for seasonal precipitation simulation over the northeast of Iran. Using the CFSV2 as Initial and lateral boundary conditions data, simulation experiments from winter to spring in seven months (from November to May) have been performed for 2019-2020). Three nested domains have applied with the outer domain at 54 km resolution and two interdomains at 18 and 6 km resolution.

The study area is located in the northeast of Iran, and climatologically, most precipitation occurs from winter to spring (November to May). On average, the western part of this region receives approximately 60% of the annual precipitation, while the rest of the areas in the east receive lower precipitation. The real-time forecast data used in this study is the 6-hourly time series from the 9-month runs operational model for seasonal prediction at the NCEP operational CFSv2. The observed precipitation data is extracted from IRIMO. The new Weather Research and Forecasting model (WRF) is applied to determine how varying physical parameterization of PBL scheme configuration processes simulate seasonal (winter and spring) precipitation. For this purpose, four group configurations have been designed.Group1: convective schemes (KFT, BMJ, GF, KF), Yonsei University PBL (YSU) for the plenary boundary layer, surface layer scheme (Revised MM5), the shortwave radiation scheme (Dudhia), the longwave radiation scheme (RRTM) and land surface models (Noah).Group 2 all four convective schemes, PBL Mellor–Yamada–Janjic (MYJ), RRTMG for long –short radiation, 5-layer thermal diffusion, and Eta for land surface and surface layer. GROUP 3; include second-order Mellor-Yamada-Nakanishi-Niino (MYNN3) as PBL scheme, same shortwave and longwave radiation (New Goddard), the surface layer (MYNN), and land surface (RUC). Finally, group4 set by ACM2 for the plenary boundary layer, The surface layer (Pleim-Xiu), the shortwave and longwave radiation schemes (GFDL), the land surface (PX), four convective schemes have been fixed in all groups. For all WRF simulations, we used the WRF single-moment 6-class microphysics scheme. In this way, a total of 20 simulation sets in 4 groups have run, and one configuration set without any cumulus scheme in domain 3 in each group.The following statistics, the correlation (R), the root mean square error (RMSE), the mean absolute error (MAE), and bias and four verification skills are calculated from the total daily precipitation over the six months out of the seven-month integration time with the first month used as spin-up.

The WRF-CFSv2 model performance was evaluated against precipitation observations from Iran's Meteorological organization. The correlation scores between the observed and predicted 6- month and winter precipitation were moderately acceptable (0.3-0.5) however decreased to 0.36 in spring. In terms of bias, group 1 (PBL1,..) configuration have considerably structures than the group4 (PBL7,..), group2 (PBL2,…), and especially group3 (PBL6,..). All configurations showed a wet bias over the study area (-0.8 mm/d, -3.55mm/d) in the 6-month prediction. It is consistent with previous studies using GCMs in this region. The significant MAE of the 6-month precipitations simulated by group 1 and PBL1-CU2، PBL1-CU0, and PBL1-CU1 scenarios were the lowest among the configuration. Meanwhile, this group of configurations showed a similar RMSE score pattern by MAE, and the lowest RMSE showed in group 1 and group 2. In all configurations, the wet bias has been persistent in the study area.The WRF prediction captured the observed precipitation by groups 2 and 3 with MYJ and MYNN3 planetary boundary layer schemes. However, the false alarm (b) in group 1 and the number of missed events (c) in group 2 of configurations were finer low.

In this study, the WRF model performance was evaluated for various physical parameterizations in predicting precipitation for varying planetary boundary layer (PBL) schemes and Cumulus schemes over northeast Iran.Based on the sensitivity analysis, is concluded that the set that performs best for the region is YSU PBL, MM5 SL, Dudhia shortwave radiation, RRTM longwave radiation, and Noah LSM schemes.And using a cumulus scheme for grid resolutions between 3km and 10km is gray, as respects is not clear whether a cumulus scheme should be used or not. So, recommended testing a configuration set of no cumulus scheme mode to determine if using a cumulus scheme is ideal for your particular run.

Climate is one of the determining factors in the quantity and quality of agricultural products, therefore, in this study, the relationship between precipitation and temperature (as explanatory variables) with rice yield in 40 cities and wheat yield in 30 cities (as dependent variables) was investigated in the Caspian coastal area during 2000-2017. Spatial statistical analyses were performed with using the Moran autocorrelation test and geographically weighted regression. Based on the results (Moran index, z = 0.4342121 for rice and z = 0.719571 for wheat, respectively), it was revealed that the spatial distribution pattern of rice and wheat yield had a cluster pattern. The results of the geographic weighted regression analysis showed that the temperature increase was more desirable than the precipitation increase so the increasing temperature could lead to yield increases. In the eastern parts of the study area, the positive effect of precipitation on rice yield (with 0.020 to 0.540 regression coefficients) was remarkable; the results also revealed a negative relationship between temperature and rice yield in the southeast and eastern parts and a positive effect on rice yield in other areas. Also, the effect of precipitation on wheat yield was negative in the west and central parts of the study area (with -0.481 to -0.871 regression coefficients). According to the results, a negative relationship was dominant between temperature and wheat yield in the east and southeastern parts of the study area and a positive relationship was detected in other areas. Finally, the results indicated that in the western and central parts, due to heavy rainfall and a low number of sunny hours, an increase in temperature is more favourable than an increase in rainfall. In the eastern and southeastern regions of the region, where the amount of precipitation is lower than the threshold required for rice and wheat, an increase in precipitation is more desirable.

Recognition of spatial patterns of drought plays an important role in monitoring, predicting, confronting, reducing vulnerability, and increasing adaptation to this hazard. This study aims to identify the spatial distribution and analyze the spatial patterns of annual, seasonal, and monthly drought intensities in Iran. For this purpose, the European center Medium-Range Weather Forecast (ECMWF) data for the period 1979-2021 and the ZSI index were used to extract the drought intensities. To achieve the research goal and explain the spatial pattern of the frequency of drought intensities (Extreme, severe, moderate, and weak), spatial statistical methods such as global Moran’s I, Anselin local Moran’s Index, and hot spots were used. The results of the global Moran’s I showed that with increasing intensity, the spatial distribution of drought events has become clustered. The spatial distribution of the local Moran’s Index and hot spots also confirms this. Very clear contrast was observed in the local clusters of high (low) occurrence as well as hot (cold) spots of severe (Extreme) yearly droughts in the south, southeast, and east. In autumn, weak to Extreme droughts show a southeast-northwest pattern. But in spring and winter, the spatial pattern of drought is very strong as opposed to severe and moderate drought. Despite the relatively high variability of maximum positive spatial Autocorrelation of severe and Extreme monthly droughts, their spatial pattern is almost similar. The spatial clusters of severe and very severe droughts in the northwest, northeast, and especially on the Caspian coast, are a serious warning for the management of water resources, especially for precipitation-based activities, such as agriculture.

Defining and measuring drought is a difficult concept. Perhaps the most general definition of drought refers to "a specific period of abnormal rainfall in a specific area", in other words, a drought occurs when a significant water shortage is widespread in time and space. But there has long been disagreement over the details. Disputes about the concept of drought and the uncertainty of measuring its characteristics can be an obstacle to research and planning related to drought. This shows that the qualitative and quantitative analysis of drought is a challenging issue. Although several studies have been conducted in connection with the evaluation of drought indices around the world, the somewhat forgotten point in past studies is that drought indices sometimes do not provide the same results in estimating and quantifying the severity of drought. Using the estimates obtained from multiple indicators as input for other similar studies, evaluating and managing the vulnerability and risk caused by this hazard, which is of great interest, and using its results in planning and even allocating budget and insurance coverage, doubles the importance of the issue. Therefore, the present research, focusing on meteorological drought indicators as a basic drought monitoring indicator that requires limited variables (rainfall, and in some cases temperature) but is effective in other types of drought, tries to provide a general presentation (introduction, calculation details, required data) of the main meteorological drought indicators, to address the difference in estimation and quantification of the drought index.

To evaluate the severity of drought for the period of 1979-2021, precipitation and temperature were used as their main variables on a daily and monthly scale according to the input of the indicators. The studied indicators were carried out in the selected year 2016-2017 when different regions of Iran were affected by drought and wet. It should be noted that the beginning of the annual period is considered from September. After developing the database, the drought indicators studied in the research were used to calculate the severity of the drought on an annual scale. Meteorological drought indices were classified into two categories of drought indices. In the last step, the drought hazard index was calculated, which expresses the severity of the drought event and is used as the first step to calculate the vulnerability and drought hazard index.

The 12-month SPI drought intensity values are lower than the results of RAI, DI, and ZSI indices, which is a significant point that is considered one of the disadvantages of this index. The estimation results of the ZSI index are almost equal to the RAI index, and the results of the CZI index are almost the same as the 12-month SPI index. The results of the EDI index showed that although this index can show the severity to some extent, it underestimates the severity of the annual drought events. In addition, this index has not exactly corresponded to the actual conditions caused by the drought. But this index is used to identify the length of the period and the start and end of the drought. which is a great advantage for this index. The spatial distribution of the RDI index estimate shows that half of Iran’s area had normal conditions in the year under review. In terms of space pattern, it is similar to the CZI index. The estimation of the SPEI index, similar to SPI and EDI indices, has not estimated the end of extreme conditions (very severe drought and wets).Among the indicators, the coverage percentage of drought severity in the ZSI, CZI, and RAI indices are almost closer to each other. While the intensity of the EDI index is different from other indices. According to the amount of internal standard deviation, it seems that RAI, DI, and ZSI indices had a more appropriate estimation of drought conditions.In addition to the mentioned cases, the comparison between the intensities of the investigated indicators also showed that the differences in normal conditions, droughts, and wets weak are greater and as the intensity increases, this difference decreases. The results of the drought hazard index resulting from the drought indices have had significant differences from each other.

According to the obtained results, it can be said that although the results of the three indices RAI, DI, and ZSI are closer to each other, the drought indices used (RAI, DI, SPI, PNPI, ZSI, CZI, EDI, RDI, SPEI) in the selected year, the drought intensities have been estimated differently. The difference in drought intensities was more in normal conditions, wet, and weak droughts. The standard deviation of the ranges of negative intensities of the index (drought) is more than that of drought conditions. Calculating the severity of drought hazard (DHI) using the intensity results obtained from the indicators showed a significant and sometimes extreme difference in this estimate. Estimation of drought intensity is used in other important studies such as calculating vulnerability, loss, and risk caused by drought. These differences and as a result the difference in results can affect environmental planning and management and economic considerations such as budget allocation and insurance coverage. Therefore, it is very important to check the accuracy of drought intensities estimation using indicators. Although there have been studies aimed at achieving a suitable index, an index that is approved and accepted in the diverse geographical-climatic area of the Country has not been introduced, which shows the need for a more comprehensive study in this field.

This study identifies the impact of climate change on precipitation in watersheds of Tehran water supply in the horizon 2025-2050 under the scenarios of CMIP6. Therefore, first, the changes in precipitation trends in the base period were calculated using the precipitation data of the study area's 33 synoptic and rain gauge stations for the period 1989-2019. Then changes soon based on four models, CanESM5, CNRM-CM6-1, MIROC6, and MRI-ESM2-0, and under two scenarios, SSP2-4.5 and SSP5-8.5 were projected. Due to the large scale of atmospheric general circulation models, two methods of linear scaling (LS) and distribution mapping (DM) were used for downscaling selected GCMs. Finally, the DM method was chosen to produce climatic scenarios due to its higher accuracy after calculating the validation indices of the models. The trend tests showed that in a significant part of the study basin, an increasing trend (with confidence levels of 0.95 and 0.99) in autumn and a decreasing trend in winter are observed during the observation period. In spring, the eastern and northeastern regions show a decreasing trend, and the northern and northwestern regions show an increasing precipitation trend. According to the output of GCM models, spring precipitation under the SSP2-4.5 scenario increases in all stations and decreases in 17 stations according to SSP5-8.5. Changes in summer precipitation are not significant in the present and future conditions, and winter and autumn are somewhat consistent with the changes in the observation period. Thus, in winter, precipitation, according to both scenarios, is less than in the current situation and is more in autumn under the SSP5-8.5 scenario. The effect of climate change on the amount of water in the watersheds also showed that the maximum water volume in the current conditions is related to the Karaj watershed. Between 2025 and 2050, the water content of this watershed increases by 8.9% in the CNRM-CM6-1 model according to the SSP2-4.5 But in MIROC6, MRI-ESM2-0, and CanESM5 models, it decreases by 5.3, 6.3, and 59.6 percent, respectively.

Identifying homogeneous climatic zones plays an important role in the success of regional development programs. The climate system is composed of various elements, factors, and variables that together form the climatic components of a region. Multi-characteristic and multivariate methods can combine and overlap the types of elements and variables effective in constructing the climate with appropriate weights in the climatic zoning area. In the present study, climatic zoning of the Caspian region was performed using factor analysis and cluster analysis. For this purpose, a 30*30 matrix consisting of 30 meteorological stations and 30 climatic and environmental variables was formed. The results of factor analysis showed that the climate of the region is affected by 5 factors including precipitation-humidity, temperature, wind, sunlight, and environmental factors. These factors explained a total of 92.5% of the variance of the data. Then, cluster analysis was performed by the hierarchical integration method of Ward on the five mentioned factors. The results showed four climatic zones including humid, semi-humid, semi-arid, and arid in the study area.

The wind has always been considered an energy source from two perspectives: pattern and behavior in urban contexts and potential in suburban environments. There are usually two major strategies for this

one based on observational data and the other providing simulation data with the creation of climate models at various numerical scales (Han et al., 2014: 17). Numerical models are used in most studies to evaluate regional winds nowadays (Haman et al., 2010: 954; Shimada et al., 2011: 21). Simulated weather research and forecasting (WRF) has been used to conduct studies on this topic (Liu et al., 582: 2018; Salvaso et al., 276: 2018; Matar et al., 22: 2016; Charabi et al., 1: 2019; Tokhtenhagen et al., 119: 2020). The sensitivity and performance of the WRF model to initial and boundary conditions, as well as its impact on wind simulation, are investigated in this study. A planetary boundary layer scheme is also chosen to simulate the wind field in the city of Tehran.

The Meteorological Organization provided observational data on wind direction and speed for Mehrabad, Chitgar, Geophysical, and North Tehran (Shemiran) synoptic stations from 2018 on a three-hour time scale (Table 2). Data analysis time series from two databases, the National Environmental Forecasting Center (NCEP-FNL) and the European Center for Medium-Term Weather Forecasting (ECMWF-) ERA5), were used as the initial and boundary conditions to achieve the frequency and distribution of wind direction and velocity for January, May, July, and October. The WRF model, version 4.1.2, was used to simulate the components of wind speed and direction using boundary condition data in this investigation. The RRTM longwave radiation model, the Goddard shortwave radiation design, the Noah surface model, the WSM6 microphysical schema, the two-dimensional Cumulus Betts-Miller-Janjic schema, and the three-dimensional Grell-Freitas schema were all employed in the study. The MRF Medium-Range Prediction Model, the Younesi University YSU Scheme, the MYJ Scheme, the second ACM2 Asymmetric Convection Scheme, the QNSE Normal Gaussian Scale, and the second and third MYNN Turbulence Scale are all used to test the performance sensitivity of the planetary boundary layer schemas.

By checking the characteristics of the observation stations according to table 9, all the selected stations have an average height difference of at least 110 meters, and the difference between the lowest (Mehrabad) and the highest (Shimiran) station is 360 meters. According to the results from the selected stations, this feature can be effective in the accuracy of the simulations by the weather prediction research model. It can be stated that the model cannot correctly simulate the topography due to the low horizontal resolution in the inner domain (7 km) and static data (such as DEM and land cover (by default, these data in the model have a horizontal resolution of approximately 1 km)) to do Therefore, it is not possible to establish a meaningful relationship between the height difference of the stations and the output of the model. Still, the lack of proper introduction of the elevations of the land to the model causes the performance of the model to be weak so that it can simulate the surface currents resulting from local factors correctly.

According to the analyzes done with wind and statistics, it seems that the weather research and forecasting model is more weak in estimating the wind direction in the months when the average monthly wind speed is lower, and it can be said that in the months of July and October, the wind is generally controlled by local factors with Low speed is formed, on the other hand, due to static data with low spatial resolution, the morphology and morphology of the model is weak and due to the dependence of surface currents on topography, it causes a large error in the estimation of the wind direction by the model in the mentioned months, but this weakness in The cold months decrease with the passage of dynamic systems and the increase of the monthly average wind speed, but contrary to the wind direction, the wind speed estimation outputs by the model show that the increase of the monthly average wind speed causes a decrease in the accuracy of the model in the estimation of the wind speed variable, that is why in all the statistics, July has the best simulation in wind speed variable. From the results of these studies, the selected configuration for the direction may not necessarily be associated with the desired results for the speed. It may even be possible to achieve the best output in the months of the year with different configurations. According to the selected boundary configurations and data, the results of this study seem to be consistent with the research of Santos et al. (2013), Gholami et al., Ghafarian et al. (2018), and Laighi et al. (2015) are confirmed.

Climatic and regional conditions have provided a good environment for agricultural development, especially in the Caspian Sea provinces. The purpose of this study is to identify the effect of climate variables on spatial analysis of grain yield and Grain cultivation capability in the southern coast of the Caspian Sea. Then, using important functions of spatial statistics including moron index and hot spots analysis of spatial patterns of rice, wheat and barley yields on the southern coast of the Caspian Sea during the statistical period of 2000-2016 were investigated. According to the results of Moran index analysis, it was determined that the yield of rice, wheat and barley were positive and close to one in the study period, indicating the clustering of the spatial distribution of the yield of the products under study. Also, based on the results of Local Indicators of Spatial Association and the analysis of hot spots, high value values or positive spatial correlation of rice yield were mainly found in Mazandaran province and high values or positive spatial correlation of wheat yield in southern parts of Golestan province and limited parts From Mazandaran Province. For yield of barley, the most areas with high positive spontaneous autocorrelation (hot spots) are mainly confined to Babolsar and Joybar cities of Mazandaran province at confidence level of 99%, Babol in 95% confidence level and Aliabad city Located in Golestan province at a confidence level of 90%. Low value of wheat and barley yields were found in the eastern and western parts of Gilan province at 99, 95 and 90 percent confidence levels.

In this study relation between average minimum and maximum of temperature and rainfall as independent variables of 223 synoptic meteorological stations in Iran and wheat yield as dependent variable in 223 regions during the statistical period of 2017-2017 in the monthly time scale of the growing season and the total growth period was investigated. In data analysis, spatial statistics analysis methods in Arc/GIS 10.4.1 software were used, using Moran autocorrelation test, and geographic weighted regression their spatial relationships were tested. The result of Moran index showed that spatial distribution of wheat yield follows cluster pattern. Analysis Geographic weighted regression has showed rainfall has remarkable effect on wheat yield. In addition, analysis showed rainfall has positive effects on wheat yield especially in dry and warm regions of Central, and Southeastern. The results showed the positive effect of temperature increase on wheat yield, which gradually decreased toward the southern parts in proportion to the decrease in altitude and increase in temperature. So that the effect of increasing daily temperatures (maximum) in cold mountainous areas is positive, while in east, central and southern parts of Iran negative effects of temperature over wheat yield were seen due to heat stress. Therefore, in hot and semi-warm regions of the country, increasing the temperature along with increasing rainfall can have a positive effect on wheat yield. In these areas, it is better to cultivate wheat in the highlands or in cold weather and used irrigation systems to reduce drought stress at critical stages of wheat growth.

The aim of the study was to investigate the conditions of atmospheric circulation patterns in the very wet months of Syria. By calculating the standard precipitation index (SPI) and using the precipitation data of 16 Syrian synoptic stations in the statistical period of 2016-2017, very wet months were identified. Then, combined maps of sea level pressure parameters, geopotential height and wind components at 850, 700, 500 and 250 hpa levels, as well as specific humidity at 850 level and 700 hpa level omega from NCEP-NCAR gridded reanalysis data were produced and were used to study the synoptic atmospheric patterns of very wet months. The findings show that formation of a deep trough is the most important model for creating very humid Syrian months, which is centred almost between eastern Turkey and western Egypt, and Syria is located in front of this trough. This trough, especially in the middle and upper atmospheric levels, causes to falling cold air on the eastern Mediterranean. The moving polar trough pattern at 700 and 500 hpa levels towards lower latitudes had caused the aforementioned advections. Formation of negative values of omega and rising air currents, along with southwest winds at sea level and other levels over Syria, have caused above-average rainfalls.

Heat waves are important phenomena in Iran, And its upsurge in recent years seems to have a high upside trend.This climate has a negative impact on agriculture, forest fire and forestry, water resources, energy use and human health.The purpose of the research is to explain the frequency, time distribution, continuity of thermal waves, and the identification of its occurrence in the southern foothills of central Alborz.Therefore, using the statistical methods and maximum daily temperature data of Tehran (Mehrabad), Qazvin and Semnan stations for the statistical period of 30 years (1966-2016), the mentioned characteristics were extracted.In the first step, the nonparametric method of Kendal was used to understand the variability and awareness of the monthly trend of maximum temperatures in the study period.In order to determine the severity, duration and frequency of heat wave events, the percentiles (95.98) and normalized temperature deviation (NTD) were used.The results of the study showed that the frequency of short-wave heat waves was higher.Most frequencies are related to 2-day waves, respectively, and Tehran (Mehrabad), Semnan and Qazvin stations are more frequent.The highest frequency of annual events was detected at stations in Tehran (11 waves in 2010), in Semnan (9 waves in 2015) and Qazvin (7 waves in 2015), respectively.The highest frequency of monthly heat wave events was recorded in June and September.The highest continuation (15 days) was obtained in March 2008 with the percentile method at Mehrabad station.In the normalized deviation method, the temperature was calculated as a warm wave (12 days) in 2008.The highest annual frequency of heat loss occurred in all three stations in 2015.The evolution of the process indicated an increase in the incidence of thermal waves in the cold period of the year But in other chapters, no meaningful changes were made.As it says, the decline in cold winter temperatures is on the southern slopes of Alborz.The results of the two methods showed that in the normalized deviation of the temperature, the number of thermal waves more than the percentile method was recorded, but in the percentile method, the thermal wave was more prominent in the cold period of the year.

The temporal and spatial distribution of precipitation in Iran is affected by the distribution of global and regional atmospheric circulation systems which the slightest change in its pattern leads to severe climatic anomalies. Therefore, it is important to know more precisely the mechanism and operation of effective atmospheric circulation patterns in the occurrence of precipitation, especially action centers. Arabian anticyclone is one of the components of atmospheric circulation affecting Iran's winter precipitation. The anticyclone that is located on the Arabian Sea, transfer humidity to sublatitudes over the Middle East and Iran by clockwise motion. Hence, the spatial behavior of the mentioned atmospheric conditions has been studied in the form of the frequency of its centers spatial distribution with light, medium and heavy precipitation in Iran in the cold period (October to March).

For this study, re-analyzed precipitation data of the ERA Interim of the European Centre for Medium-Range Weather Forecasts (ECMWF) with 1o*1o spatial resolution and atmospheric data of the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) with spatial resolution of 2.5o*2.5° were used. So, in the first step to achieve the research aim, rainy days for the statistical period of 1981-2010 were extracted in three categories; light (1-10 mm), medium (10-30 mm) and heavy (More than 30 mm). Base on the average spatial location of the Arabian anticyclone, for proper division of the range to identify and analyze the temporal and spatial behavior of this anticyclone, the area was considered from 30 to 80 degrees East longitude and 0 to 30 degrees North latitude. The geographical location of anticyclone center was extracted by defining the maximum criterion of geopotential height in all rainy days within its activity range. 20 sub-areas were defined to better represent the position of the anticyclone. Finally, the frequency and dispersion of the geographical location of the Arabian anticyclone at the desired level is plotted and displayed based on the number of closed centers in each sub-areas. Also, for those sub-areas which had more number of anticyclone centers, the daily average geopotential height and daily average precipitation maps were drawn and analyzed.

Rainy days 1-10 mm From 2023 days with precipitation of 1-10 mm, 1622 days (80.2%), Arabian anticyclone has had an independent closed center at the level of 850 hPa. Among these, the highest frequency of anticyclone centers has been observed on the southeast of Arabian Peninsula, the Arabian Sea, the Indian subcontinent and the eastern coasts of the Arabian Sea in the range of 40 to 80 degrees east and 10 to 30 degrees north, respectively. The highest frequency of centers (sub-ranges of expansion) (spatial distribution of precipitation in Iran) in the range of 8 with 675 (southeast of the Arabian Peninsula) (western half, northeastern, eastern and region in central Iran), 9 with 213 (northern part of the Arabian Sea) (all of Iran but concentrated in the western half to the south and northeast), 10 with 197 (Indian subcontinent and the eastern coasts of the Arabian Sea) (almost all of Iran), 3 with 172 (southern Iran and Persian Gulf) (western Iran) and 4 with 99 (Pakistan and southeast) (almost all of Iran and concentrated in the west) have been observed.Rainy days 10-30 mmFrom 167 days of 10-30 mm of precipitation, 67.3% (104 days) of the independent closed center of Arabian anticyclone was observed. In other cases, the extension of a trough from the Siberian and Tibetan anticyclones on the study area and its integration with the Arabian anticyclone has been observed. The frequency of anticyclone centers in this part has been on the southeast of the Arabian Peninsula and the northern part of the Arabian Sea and in the range of 50 to 75 degrees east and 17 to 27 degrees north. The highest frequency of centers (sub-ranges of expansion) (spatial distribution of precipitation in Iran) in the range of 8 with 35 (southeast of the Arabian Peninsula) (west half to south of Iran but concentrated in the west, southwest and Zagros mountains), 9 with 25 (northern part of the Arabian Sea) (almost all of Iran but concentrated in the southwest), 10 with 17 (Indian subcontinent and the eastern coasts of the Arabian Sea) (all of Iran but concentrated in the west, southwest and south) have seen, respectively.Rainy days more than 30 mm Throughout the statistical period, 55 days were accompanied by heavy precipitation, of which 23 days (less than 50%) the Arabian anticyclone had a closed center. 32 days were observed on the study area along with Tibetan and Siberian anticyclones trough. The frequency and spatial distribution of anticyclone centers has been scattered. It covers 50 to 74 degrees east longitude. Its maximum concentration was in the two southeastern regions of the Arabian Peninsula and the Arabian Sea. The highest frequency of centers (sub-ranges of expansion) (spatial distribution of precipitation in Iran) in the range of 8 with 8 (southeast of the Arabian Peninsula and Oman Sea) (western half of Iran but focus on the southwest), 9 with 7 (Arabian Sea) (northwest to south of Iran but concentrated in the west), 10 with 17 (Indian subcontinent and eastern coasts of the Arabian Sea) (all of Iran but concentrated in the west, southwest and south) center is observed, respectively.

The results showed that on light, medium and heavy rainy days 80.2, 67.3 and 41.8%, respectively, the Arabian anticyclone has an independent closed center and on other days, it is combined with a trough of Siberian and Tibetan anticyclones at the level of 850 hPa. There is a high correlation between the location of position centers at level 850 hPa on the sea, and the distribution and amount of precipitation in Iran. As with the occurrence of precipitation in Iran, the frequency of anticyclone centers is concentrated on the southeastern coast of the Arabian Peninsula and the Arabian Sea. But the maximum precipitation (heavy precipitation) in Iran is when anticyclone is located in the Arabian Sea. In general, moving east towards anticyclone and settling on the Arabian and Oman Seas, according to the prevailing atmospheric circulation mechanism, is the most suitable model for transferring moisture to the incoming precipitation systems to Iran. It can be said that the position of this anticyclone and its displacement, especially to the east and northeast, is directly related to the amount and position of the maximum precipitation zone in Iran.

Cities are just as vulnerable to natural phenomena as they are the most influential human population centres on the natural environment. One of the important components of a sustainable city is its resilience to climate change and environmental hazards. This research aims to analyse the resilience of Varamin city by the Analytic Network Process (ANP) method and determine the effective indicators in its climate resilience. Varamin, a satellite city of Tehran in the southern plain of Tehran and adjacent to the desert plain with a hot and dry climate, is one of the major food suppliers of the capital. In this study, firstly, the key components and indicators related to climate resilience in Varamin were extracted using documentary and library research, field surveys and interviews with 44 experts and specialists in the field of urban planning, environmental planning and urban climate using the Delphi method and through a questionnaire. Afterwards, according to the stages of the Analytic Network Process method and the characteristics of Varamin city, the data and basic information were classified and consequently, the ANP conceptual model was developed. With four components and 33 indicators, the ANP model based on climatic resilience of Varamin city was generated using Super Decisions software and the results were then analysed. The outcomes obtained from ANP show that the highest priority in climatic resilience of Varamin city is related to the population living in informal settlements (including Omrabad, Lorabad, Goltapeh, Sakineh-banoo and Deh-Sharifa) with 0.22 normalized weight and 0.213 migration rate from the socio-economic component. Since the city of Varamin has a large share of the marginalized population and the attracted migrants are not able to provide housing in the formal markets, it is very important to increase the empowerment and improve the resilience of these settlements to climate change. Moreover, from the environmental point of view, the city of Varamin is threatened by climate change conditions due to the decrease of groundwater level and salinization of plain water resources. In addition, land subsidence is one of the major threats to the Varamin plain, as a result of vast agricultural lands, uncontrolled harvesting and abundant drainage of groundwater aquifers. Finally, considering the immigration, the physical growth of Varamin and the extensive change and conversion of land cover/land use balance changes the surface energy and water cycle of the region.

In this study, using reanalysis data  (Era-Interim) of European Center for Medium-Scale Atmospheric (ECMWF) ), the meteorological parameters and  structure of patterns  like sea level pressure, geopotential height and temperature  for  standard pressure levels, relative and Specific humidity in low-troposphere, wind field, relative vorticity, wind,  vertical velocity, convergence and cross section of the relevant quantities were studied by temporal and  spatial intervals  3-hour and 0.125 degree resolution (in terms of latitude and longitude during the period of 1987-2019. The results show the existence of independent thermal low-pressure systems in the form of single cells with local dimensions among the holes and plains of the Iran plateau corresponding to warm cores. Thus the theory that the low pressure conditions in the central regions of Iran, especially in the southeast and east, is the result of the spatial development of the Ganges system (Pakistan), was questioned. In other words, within the vast low-pressure belt of South Asia, different topographic, geographical, and surface cover conditions have led to differences in  radiation absorption  and the formation of local low-pressure centers. These independent low-pressure cells include the low-pressure systems of Dasht-e Kavir, Kavirlot, Rigistan Desert (Afghanistan), Balochistan Plateau (Pakistan) and Jazmourian. Rigestan low pressure and Lut desert low pressure have the highest frequency. The maximum height of these low pressures in Lut and Rigistan is up to about 500 hPa. The warming of the Iran plateau that increased their occurrence frequency in the warm season of the year,  more extension and high intensity, and negative pressure anomalies in the middle of the day, indicates the role of short-wave solar radiation of in amplifying their cyclonic rotation. The cross section of the relative vorticity and the vertical velocity indicate their limited extension to the depth of the troposphere and their thermal mechanism, and the positive and negative changes of these parameters confirm their local characters as separate cells. The formation of these low pressures in the vicinity of thermal mountain high-pressure systems has limited the spatial expansion and integration in the form of a large pressure system on the interior plains.

This study aimed to illustrate the applicability of the Humidex index for assessment of outdoor thermal environments in a wide range of weather conditions in different climates in Iran.
Study Design: This is a cross-sectional study.
Both  field measurements (1452 measurements) and the long-term meterological data (between 1965 and 2009) were used in this research. After determining the appropriateness of correlation coefficients between these two types of data,  only meteorological stations data were used to generalize the results to climatic regions. For this purpose, Arc/GIS 10.2 software was used.
The results showed  three levels of comfort including safe, caution and stress regions by graphical maps. The results showed that the center and south of the country, especially at the middle and the end of the shift hours, experienced more thermal stress in summer months (ranging from 39.60±1.07 to 49.29±2.13ºC for central areas and ranging from 47.76±2.59to 57.71±1.65ºC for southern areas. In the northern regions, most of the measurements in different stations and time periods at spring were in caution condition and less than 1% of them experienced stress conditions.
The dependence of this index on the minimum metrological parameters (temperature and humidity), which are easily measured and reported daily in meteorological stations, and its non-dependence on the globe temperature, which is an unusual parameter in the measured metrological parameters, can be used as advantages of the humidex for assessment of the heat stress conditions in outdoor environments in different climates.

The wind is the horizontal displacement of air that is less than one meter per second. The wind is a dynamic phenomenon and has three main characteristics: intensity, direction, and frequency. Therefore, knowledge of wind characteristics in every area of importance is remarkable. The effects of global warming on temperature and precipitation at the global level over the past decades, many studies were considered; However, relatively little attention to climate change is wind speed. Wind speed changes can affect the energy of storms, shipping industries, as well as soil moisture, evaporation, and water resources; and it may even affect the evolution of dry and semi-arid environments. Also, a lot of research on wind and meteorology has shown that the performance of wind turbines is sensitive to climate change. Possible changes to the future wind regime have been widely considered under changing weather conditions; under global warming, the intensity and frequency of wind events are expected to change at the end of this century.

The study area in this study of the eastern strip of Iran includes four provinces of Khorasan Razavi, South Khorasan, Kerman, and Sistan and Baluchestan. The study used wind speed data at a height of 10 meters, 10 synoptic stations with a daily statistical period of 2015-1985, which has 30 years of data; In choosing this station, in addition to proper distribution in the region, an attempt was made to select more stations in the station to be affected by the 120-day winds of Sistan. In this study, 10-meter daily wind speed data of the ERA-Interim version with a resolution of 0.125 × 0.125 degrees period of 1980-2015 were used; for the study area, 3772 pixels with an inter-pixel distance of about 12.5 km have been obtained. To evaluate the performance of simulated data against observational data; There are several indicators used in this study from the Root mean squared error (RMSE), Mean bias error (MBE), mean absolute error (MAE), and the coefficient of determination (R2). The non-parametric Man-Kendall method was used to investigate the trend of wind speed changes in research.

The ECMWF ERA-Interim version has a high and good performance for wind speed. The results showed that the output of the mentioned base in all the studied stations is on average between 0.722 and 0.984. RMSE, MBE and MAE characteristics in Zahedan, Khash and Saravan stations are less than m / s1; In other words, the wind speed of ECMWF base in these three stations has the highest performance of the 11 stations studied. The monthly statistical assessment of wind speed in selected stations in eastern Iran during the statistical period studied (1985-1985) showed that the average wind speed is 3.56 m / s. The relationship between wind speed with negative altitude and positive longitude is significant at the level of 0.05. Also, the relationship between latitude and wind speed showed that this relationship is negative in the cold months of the year and positive in the warm months of the year. The average wind speed fluctuates greatly during the 30-year statistical period. The average wind speed varies between 2.82 and 4.57 m / s. The minimum and maximum wind speeds were calculated in December and July, respectively. The average 30-year wind speed at selected stations in eastern Iran was calculated to be 2 m / s. The maximum wind speed in eastern Iran has many fluctuations; autumn showed the lowest statistical value in terms of maximum wind speed; In December, the maximum wind speed was calculated to be 3.98 m / s. The maximum wind speed is increasing in all the studied months; From a statistical significance level, all the studied months except January, which, despite being increasing, are; But statistically, it is not significant at the level of 0.05 and 0.01; Other studied wind speed studies have a significant incremental trend at α = 0.01. The average wind speed in the study area is negative in 7 months (January, April, May, July, August, October and December) from the negative years and in 5 months (February, March, June, September and November). The maximum wind speed is January 4.42, February 4.86 and March 5.02 m / s. The next area to be obtained in the form of a fertile area in winter; Zabol is also the center of Iran's border with Afghanistan in the border areas of South Khorasan Province. The wind speed trend is positive at the time of onset (June, 0.79), the 120-day wind is positive and negative at the time of termination (October, -0.15).

The average wind speed in the study area (Khorasan Razavi and South Khorasan, Kerman and Sistan and Baluchestan provinces) during the long-term statistical period of 30 years (2015-1985) is 3.56 m / s; The minimum and maximum wind speeds are obtained in July and December, respectively; The reason for the increase in wind speed in July is due to the 120-day wind activity in Sistan, which started in June. The average wind speed in the study area is negative in 7 months (January, April, May, July, August, October, and December) from the negative year and in 5 months (February, March, June, September, and November). Investigation of wind speed process using non-parametric Man-Kendall (M-K) test; It showed that the wind speed trend in eastern Iran in the first month of June (June) 120-day winds showed an increasing trend (Z score of the Man-Kendall test 0.795) and in the last month (October) it decreased (-0.1152). ). Also, in July, when the wind speed is maximum, the average trend in the study area with a score of Z, 0.242 - is decreasing. Pearson correlation test showed that the relationship between wind speed and topography in the study area was statistically significant at 0.05; In contrast, the relationship between longitude and wind speed is significant in all studied moles at the alpha level of 0.05. In contrast, longitude and altitude in the study area did not show a uniform relationship between latitude and wind speed; this relationship is reversed for the cold months of the year and directly for the warm months. In October alone, the relationship between wind speed and latitude is not significant.

Over the last decades, urbanization developmenting and weakness in accurate and comprehensive planning to develop and rapid population growth have caused many challenges for cities. Urban sprawl beginning in the developed countries around 1950 is currently experienced in almost all countries. Many studies on the effects of urban sprawl indicate the emergence of harmful effects of this phenomenon. One of the most important environmental effects is the changes in climate. Most urban settlements are prone to future shocks and tensions due to climate changes, lack of energy and global population growth. Urban managers and planners’ responses to these shocks and what cities should do to adapt to accidents and dangers are now discussed in “resilient cities” topic. Many cities have not yet addressed climate risks due to lack of relevant city policies and action plans, outmoded regulations on urban planning, lack of capacity to respond to climate disasters, and lack of public awareness. The Urban Climate Resilience practice area represents the intersection between WRI Ross Center for Sustainable Cities and World Resources Institute’s Climate Resilience Practice. The urbanization trends in Iran during last decades had been accelerated by high rate of rural-urban migration along with rapid socio-economic and political changes that formed unbalanced urban growth in Iran. Since resilience refers to the ability of a system to return to its natural conditions after an accident, the purpose of the present study is to test and evaluate the level of resilience of Varamin City in the face of climate changes from its citizens’ viewpoint. Materials and

In the present research, two types of data have been used. The first type of data includes the climate components gathering the Varamin weather station (annual average rainfall, temperature, etc.). The second set corresponds to components relevant to urban sprawl, among which, urban area, population density and urban population. These parameters were obtained by questionnaires which have been filled out by varamin citizens. This descriptive analytical study was conducted with the participation of 393 citizens of Varamin City. The research tool used for data collection was a 35-item researcher-made questionnaire based on previous studies containing the appropriate items to test each of the components. To conform to the research population, the questionnaire was investigated and reviewed by professors and experts in multiple steps and its face validity and content validity were confirmed. In order to assess the reliability of the questionnaires, first, 30 copies of the questionnaire were completed by the citizens of Varamin in a preliminary research. The obtained information entered SPSS 21 and each response was assigned a score of 0-5. After the analysis, the reliability of the questionnaire was estimated by Cronbach’s alpha to be 0.89 that was designed based on the environmental, socio-economic, infrastructure, and institutional components. Analysis and prioritization of the descriptive and inferential analytical statistics the resilience of each of the components and their indices were calculated by SPSS 21 using the one sample T-test and then they were prioritized by Friedman test.

Today, the relationships between human societies and their natural environment has been strongly affected by urbanization and urban development. Cities can be considered as ecological units and studied within the framework of a data-retrieval system. That is, to meet various needs of citizens, the city needs inevitable to provide massive data in key inputs, the most important of which are energy, food and water. Results of measuring resilience of Varamin City with an emphasis on climatic aspects showed that from the citizens’ viewpoint, the resilience was 2.15, which was lower than the desirable average level. It indicates that the citizens consider Varamin vulnerable to climate hazards. Results of investigating resilience components of Varamin City showed that the environmental component and its indices were lower than the average level and according to the citizens intensified drought and changes in temperature have the most negative effects on the environmental condition of resilience in Varamin City. Moreover, according to the citizens, Varamin City is vulnerable to the increase in temperature and drought and these two indices need to be taken into consideration to increase the urban resilience. Although, they believed that the socio-economic and infrastructure components had higher resilience levels compared to the environmental and institutional ones. The socioeconomic component and most of its indices were above the average level and according to the citizens, helping the citizens in case of critical situations and kinship are most significant in the socioeconomic resilience of Varamin City when faced with climate changes. Results of investigating the infrastructural component showed that this component and most of its indices were lower than the average level. Therefore, it can be stated that this city is not in a good condition in infrastructural aspect and is vulnerable in this regard. Furthermore, according to the citizens, the index of “access to health centers” was the most significant infrastructural index. Investigation of the resilience level of the institutional component, it was found that all the indices of the institutional component were lower than the average and the scores of Friedman Test showed that from the citizens’ viewpoint, the municipal services in creating green space and satisfaction from the performance of the organizations in charge of informing to face hazards had the highest significance in the institutional resilience of Varamin City. Although apart from the socioeconomic components, other components in the present study were lower than the average level, since the environmental and institutional components were the least resilient components, respectively, strengthening them should be placed in the priorities of the urban development plans of Varamin City.

Since climate change and its effects are increasing more than before in human societies, especially in urban communities, investigation of the indices ad components of resilience and evaluating them when urban communities face future climate crises and preventive measures are very effective and essential. Furthermore, the increase of the general knowledge regarding the climate changes motivates people to investigate the effects of this issue even more. Therefore, the serious cooperation of the government, local entities, educational organizations, municipal and media in increasing the citizens’ awareness will make the citizens respond significantly to reduce and adapt with the consequences of climate changes through citizen participation.

The blizzard incident is one of the climatic hazards that occurs due to the combination of other climatic factors such as temperature (below zero), snow and wind (at 15 m/s). In this research, the conditions of blizzard in Northwest of Iran are carried out using statistical methods. By analyzing all the meteorological codes of the blizzard (36, 37, 38 and 39) during the statistical period from 1987 to 2016 for 11 synoptic stations of the study area, codes with severe blizzard (37, 39) were selected. Then, using the geopotential height, wind and Leveling temperature of 500 and 850 hpa, obtained from the NCEP/NCAR open source database, the synoptic patterns of blizzard incident analyzed. Statistical analysis of the relationship between the effects of geographic factors on severe blizzard has shown that the factor of height has the greatest effect on intensity, increase and incident spatial differences of this phenomenon. The study of the synoptic patterns of the incident of the blizzard phenomenon showed that five main patterns play a role in creating it in the region. The synoptic patterns of development include the formation of a low cut-off center, a long landing passage from Iran, the formation of a relatively deep and drawn Mediterranean East, The rectangular system is a rex-shaped system and is an umbilical bundle system. Among the patterns obtained, the patterns that were bundled were, the most important role in the survival and transfer of flows associated with cold, and other patterns, despite the frequency they had, were periodically of severity and weakness. Keywords: Blizzard; North West; Wind speed; Temperatures below zero; Synoptic patterns Human life is always affected by climatic phenomena, especially the hazards of the two variables of temperature and wind. One of the most important simultaneous phenomena of these two variables is the blizzard, which is caused by heavy snow, stormy winds, and very low temperatures. This climate risk can cause damage to various areas of horticulture, agriculture, urbanization, transportation, and so on. This phenomenon is present in regions such as Canada and North America with a cold weather wave that results from turbulence in the winters and damages the lawns in these areas. There are plenty of local storm in the polar regions and it lasts for a few days. For example, the wind in the Adelie land in the Antarctic is so severe that the area is known as the storm Land. In Iran, the most significant blizzard occurred in mid-February 1350, resulting in the deaths of more than 4,000 people across the country. In this research, considering the characteristics of the blizzard phenomenon at the time of occurrence (severity, continuity, expansion, and time of occurrence), the study has been conducted to determine the statistical synoptic patterns in the northwest region. In this research, the studied area is northwest of Iran, which includes 6 provinces (Ardebil, West Azarbaijan, East Azarbaijan, Zanjan, Kurdistan, Kermanshah, Hamedan). In order to study, the days with the blizzard phenomenon in the form of 3 hours and in codes of this phenomenon (36, 37, 38 and 39), were obtained from the establishment of the stations studied by 2016. In the following, for precise examination, stations with 30 years of statistic from 1987 to 2016 were identified and the statistical (frequency, daily, monthly and annual frequency) codes 39 and 37 were studied. Finally, the relationship between blizzard with the latitude and elevation in the studied stations was determined. To assess the statistical results, the correlation coefficients (R) and coefficient of determination (R2) were used. In the second part, the identification of synoptic patterns was done by Principal Component Analysis in MATLAB software and ocular method. The criterion for identifying synoptic patterns, the days where codes 37 and 39 are more than 1 time (3 hours) within 24 hours or two days behind each other at the stations studied. In order to determine the patterns, at first, the average geopotential data of the 500-hpa level from 1987 to 2016 were obtained from a range of 10-70 degrees north latitude and 0-80 degrees east longitude with a spatial resolution of 2.5 * 2.5 from the NCEP / NCAR data. Statistical analyzes on the relationship between the effect of geographic factors on severe blizzard showed that the factor of height had the greatest effect on the intensity, magnitude and spatial differences of this phenomenon. In sum, the most important factor in the occurrence of this phenomenon is due to atmospheric conditions and synoptic patterns of the region. In this study, the most frequent occurrence of codes 37 and 39 in all stations studied was at Sardasht station and Khalkhal station, respectively. Also, the statistical study of the frequency of the annual and monthly occurrence of each code showed that code 39 in 1990 and code 37 in the years 1989 and 1990, as well as in January, had the highest frequency of each of the two codes. Investigating the patterns of the occurrence of the blizzard phenomenon showed that five main patterns have contributed to its creation in the region, the first pattern due to the formation of a low cut-off center, which, with the cold weather in Central and Eastern Europe, has reduced the temperature in the northwest. The second pattern is due to the high landing passage from Iran, which has crossed the descent from a cold and cold weather zone from Europe to Iran. The third pattern is the location of the studied area in the relatively moderate, dragged, eastern Mediterranean wavelength, causing cold weather to fall to the northwest. The fourth pattern, with the formation of a Rex-type blockade on the Mediterranean, has led to the transfer of cold air from Eastern Europe, Kazakhstan, and high latitudes to Iran. The fifth pattern, with the formation of a blockade, has caused cold weather in northern Europe and Central Asia to enter the country from the north, causing a drop in temperature in the region. Among the known patterns, the patterns that were blocked (pattern 4 and 5) played the most important role in the survival and transfer of cold fluxes and even drawn to lower latitudes. Other patterns, despite frequent periods, provide conditions for the occurrence of this phenomenon and, unlike the blocking patterns, have had severity and weakness.

Drought is one of climate hazards that over time brings a lot of damage on human life and natural ecosystems. Commonly Droughts are divided to four main groups of meteorological, hydrologic, agricultural and socio-economic .All Types of droughts are different from each other significantly. In another sense, the occurrence of an event of drought would be the cause of another draught. Various methods have been used for the analysis and assessment of drought and its impacts on human activities and natural resources. Statistics, Synoptic, Remotely sensed methods, and several types of models such spatial, dynamic and statistics models can be seen in the most studies related to drought. The zoning of drought using spatial-statistics indices and generally the spatial zoning and regional distribution of dry periods is one of important features that makes a better understanding towards the phenomenon of drought and a closer consideration of the effects of it. In the past four decades remote sensing widely provide drought monitoring tools, and many drought monitoring model is presented, which is generally based on vegetation and thermal indices especially Normalized Difference Vegetation Index (NDVI), land surface temperature (LST), moisture and reflection at the visible and infrared areas. Golestan province is located in the North East of Iran in the neighborhood of the Caspian Sea and the northern slopes of the Alborz Mountains, Which following local conditions of the Caspian coast line and high peaks, with increasing altitude, vegetation diversity there are certain bands. From the peaks over 1500 meters to foothills is covered with dense forest broadleaf. Field craps are the dominant vegetation’s From Foothills to the plain. From North Gorganrood to border of Turkmenistan due to rare moisture Resources vegetation is thinner than the southern and central provinces. The research in terms of nature and methods in theoretical basis is parts of the descriptive researches and due to relationship and impact is an applied one. In the present study for following meteorological drought and ecological in Golestan province, two types of data were used. 72 pluviometry stations on monthly rainfall data (period 1971-2010) and remote sensing data in the three periods (July, 1975, 1987 and 2000), derived from Landsat satellite images. Standard Precipitation Index (SPI) is used to identify and to zoning meteorological drought and the Normalized Difference Vegetation Index (NDVI) is used for the detection of the plant tension that were affected by drought. In the following the results of two parameters and their relationships were compared to each other.  Examining maps of drought frequency and severity of droughts indicates that most of frequency droughts of stations occurred in the North and North-East Province. And gradually the intensity and frequency of droughts reduced to the South and South West. The most severe drought that can be seen in parts of the North East belongs to the Hutan station. However the lowest number has occurred in the central and western regions of Golestan province. According to table (3) correlation between both NDVI and SPI are positive in three years. In all the years of study SPI index-correlation with NDVI was fairly good together. Overall the parts of Northern Province and East Sea have most drough in the selected three years. While most of the wet years are related to the southern part of the province (northern slopes of the Alborz mountain range).  According to conducted research either in the country or abroad, it seems that the use of SPI index as representative of meteorological drought and NDVI index as representative of indicators satellite drought are appropriate for monitoring this kind of droughts. According to the study, two SPI drought index and NDVI due to adaptation with each other are proposed for drought monitoring and meteorological satellite in Golestan province.