بهروز سبحانی

Relative humidity is considered to be one of the most important climatic parameters and atmospheric phenomena. The purpose of the present study is to evaluate the regional algorithms for estimating relative humidity using remote sensing data in Hormozgan province. To this end, MOD05 and MOD07 products were employed to estimate total perceptible water, air temperature, and sea-level pressure Additionally, MOD35 was used for cloud verification, , resulting in the identification of 2190 cloudless images with 95% confidence level for analysis. radiosound data of Bandar Abbas ststion and synoptic stations Covering entire Hormozgan Province. were used to evaluate the results. The findings demonstrated high accuracy of the algorithms and experimental model, with acceptable R² and RMSE values between Modis product and ground data. These results align well with ground station measurements. The province's climate was determined to be semi-desert with a long warm season and a short cool period. Further analysis revealed a strong correlation between sea-level pressure and total perceptible water (TPW) with the region's topography. Maximum TPW and sea-level pressure values were recorded in coastal lowlands, while minimum values occurred in the highlands. Based on zoning maps, Hormozgan province can be divided into four regions based on relative humidity: from very dry conditions with less than 20% relative humidity in the highlands to humid areas with over 65% relative humidity along the coast.

Extreme weather events are one of the most important challenges for agricultural producers, and these events are currently increasing. Projection the effects of extreme events on garden crops is one of the most important discussions in food security and agricultural economics. The purpose of this research is to investigate the consequences of climate change on the trend of extreme events and its effect on the phenology of almond trees in East Azerbaijan province. In order to investigate and project Precipitation and minimum and maximum temperature and determine the climate change extreme index that had the greatest impact on almond tree phenology from the Cimate Model Intercomparion Project – Phase 6 (CMIP 6) in the upcoming period (2021 to 2100) was used in Tabriz, Ahar, Jolfa, Maragheh and Midane stations. The results of the investigation of temperature and precipitation indicators for the future periods indicated that the average annual temperature will increase from 0.9 to 4.5 degrees Celsius until the year 2100 and the Precipitation output until the year 2100 indicates that the Precipitation in SSP5-8.5 scenario will decrease and in two scenarios SSP1-2.6 and SSP2-4.5 will increase a bit. These results showed that the length of the almond tree growth season increased from 176 days in the base observed period to 156 days in the SSP1-2.6 scenario, 150 days in the SSP2-4.5 scenario, and 146 days in the SSP5-8.5 scenario.

Land surface temperature (LST) is a key physical parameter of land surface processes, at local and global scales, which is a combination of all results from the land surface and the energy flow between the surface and the atmosphere. Earth surface temperature is an important indicator related to climatic, meteorological, hydrological and environmental phenomena and processes. Currently, data from meteorological stations are the most important decision-making reference in this case (Baidy et al., 2013: 517). What is considered as a basic defect in monitoring the temperature of the earth's surface is the lack of sufficient meteorological stations to know the temperature values in places without stations. Considering the limited information, the need to use remote sensing technology with time conditions, along with the feature of continuity and data collection in wide ranges, can be very efficient (Kake Memi et al., 2019).Soil moisture is the amount of water stored in soil particles and is affected by factors such as precipitation, temperature and other soil properties (Pandi et al., 2020: 1). Estimating soil moisture is of great value for weather forecasting, climate change monitoring, and flood monitoring (Beau et al., 2018). Remote sensing techniques provide tools for mapping soil moisture at large spatial and temporal scales. Remote sensing can be effectively used to estimate soil moisture because soil light reflectance and thermal emission are highly correlated with soil moisture (Acharya et al., 2022: 2).Meteorological and hydrogeological studies of the Sentinel satellite series have been developed by the European Space Agency (ESA) to support the services of the European mission and the demands of the Copernicus program (Zarei et al., 2021: 3980); The first three Sentinel missions contribute to the understanding of the Earth system by detecting, monitoring, and evaluating changes in the ocean, troposphere, and land components (Ruskas et al., 2016: 1).

The studied area is Nair city. This city covers 8% of the total area of Ardabil province with an area of 1495 square kilometers. It is located at 47 degrees 59 minutes east longitude and 38 degrees 2 minutes north latitude. This city of Nir is located 35 kilometers west of Ardabil between Sablan and Bezgosh mountains. In this study, Sentinel 2 satellite images have been prepared for the date of 12/7/2022, and then atmospheric and radiometric corrections were made in ENVI 5.6 software, and a part of the images was cut based on the study area. After the atmospheric and radiometric correction of the image, the land use classification map was prepared in eCognition 9 software using the nearest neighbor method of the object-oriented algorithm; There are two main steps in image base object classification. The first step is image segmentation, which divides the image into separate areas or objects (segments) based on a similar spectral and spatial pattern. After segmentation, the second step is to relate the image segments using spectral and shape statistics, texture parameters and topological information. To perform this method, the results of different scales were analyzed. Finally, considering the scale of 60 and the amount of softness and compression, respectively, 4 0.0 and 0.6 segmentation was done. After segmentation of the study area, they were classified into seven classes: irrigated agriculture, rainfed agriculture, residential area, water areas, snow cover, pasture and communication road; Then, the kappa coefficient and accuracy were estimated for validation. Sentinel 2 image was also used to calculate soil moisture using OPTRAM method. To perform this method, first NDVI and STR index were calculated and then soil moisture value was determined using these two indices.In the continuation of the research, to calculate the temperature of the earth's surface, the daily temperature product of Sentinel 3 images related to the time of 7/11/2022 was prepared for the studied area; And in the SNAP software, the temperature of the earth's surface was estimated. The Sentinel 3 temperature product is an official level 2 product and has a spatial resolution of 1 km. It provides estimates of LST and some related parameters, e.g. LST uncertainty, normalized difference vegetation index (NDVI), vegetation type (biome), atmospheric column water vapor content (CWVC) and parameters related to LST retrieval. has After extracting maps of surface temperature and soil moisture using satellite images, the correlation between the dependent variable of humidity and the independent variable of temperature has been investigated using geographic weighted regression. calculating In this research, in the first step, the classification of land use in seven classes of irrigated agriculture, rainfed agriculture, pasture, residential area, water areas, snow cover and communication road has been done using the object-oriented method in eCognition software. The accuracy of this classification has an overall accuracy of 99% and a kappa coefficient of 98%, which has achieved acceptable results due to the use of Sentinel 2 images. One of the influencing factors on land suitability for different uses is soil surface temperature. In this study, the temperature products of Sentinel 3 images were used to check the soil surface temperature. One of the factors affected by climate and temperature is soil moisture; In this way, Sentinel 2 images and OPTRAM method have been used to estimate soil moisture. Examining the results shows that the temperature of the ground surface and the amount of soil moisture are completely dependent on the nature of the types of use. The highest average temperature related to pasture use is 43 degrees Celsius and the lowest average temperature related to snow and water use is 34 and 37 degrees, respectively. Examining the soil moisture of each land use shows that water areas have the highest average humidity and residential areas have the lowest average. Based on the results, there is a direct and inverse relationship between soil surface moisture, vegetation density and surface temperature. The presence of moisture on the surface of the soil and vegetation leads to a decrease in the temperature of the earth's surface. So that dry lands, or in other words, low humidity, as well as lands with low density vegetation, correspond to the areas that show high temperature in thermal images.

The ongoing climate change will change all aspects of biological systems, from genetics to ecosystems (Scheffers et al. 2016, 724). By 2100, climate change will make extinct one sixth of animal and plant species and will change the abundance and distribution of many remaining species, which will result in new communities (Urban, 2015, 573). Based on this, it will be very important to investigate the annual and decade changes of average temperature and precipitation at the regional level. It is also important to understand how temperature changes and related indicators such as heat stress in different time scales in order to make informed decisions regarding economic development and climate action plans (CAP).
One of the main sources of data for the study of climate change are general circulation models (GCM), which are widely used to monitor and predict past and future climate change (Khan et al. 2020). GCMs have a remarkable ability to simulate temperature and precipitation. However, they also have limitations. Among these limitations are systematic errors in reproducing average temperature and precipitation, especially in areas with complex topography, such as Iran (IPCC, 2013). In this context, IPCC, as the most important reference for researches and forecasts related to climate change, has so far presented several generations of emission surveys and based on the results of different climate change modeling, six climate change assessment reports. has published. In the recent IPCC report, the latest climate change models are called CMIP6 series, which simulate the future climate under ssp emission scenarios (IPCC, 2021). The final version of the CMIP6 model design was confirmed by the two CMIP working groups and the WGCM coupled model work group in October 2014, and the complete results and various models are expected to be published before the end of 2022. The scenarios of the sixth report are a combination of socio-economic trajectories (ssp) (sustainable development sp1, development based on intermediate policies sp2, regional competition sp3, inequality sp4, and fossil fuel development sp5) and greenhouse gas concentration trajectory different levels of coercion) are produced; So that they provide the possibility of feedback analysis between changes and socio-economic factors such as global population growth, economic development and technological progress.

In this report, eight scenarios are presented in two rows. The first row includes the new scenarios SSP1-2.6, SSP2-4.5 and SSP5-8.5, which are respectively the updated scenarios of the forcing levels RCP2.6, RCP4.5 and RCP8.5 of the fifth report. The SSP1-2.6 scenario shows the lowest level of radiative emissions; SSP2-4.5 considers the average forcing level and SSP5-8.5 presents the upper limit of radiative forcing (Stock et al. 2020, Rogelj et al. 2018). In addition to these three scenarios, the non-decreasing forcing scenario (SSP3-7.0) with high emission of suspended particles and land use change has been added in this group. In the second row, two mitigation scenarios have been added to achieve a relatively low forcing output and one scenario considering limiting the average global temperature to below 1.5 degrees Celsius compared to forcing levels over industrialization and a scenario with a very high trajectory.
GCM models are the best tools for investigating the effects of climate change on weather parameters. These models are three-dimensional and are able to model and produce atmospheric and oceanic parameters for a long-term period on a global or continental scale, taking into account the approved IPCC scenarios (Chen et al., 2019). Considering the importance of uncertainty analysis, evaluation and selection of GCMs based on their performance in simulating climate variables is a method that can be used to select the best models and reduce uncertainties (Zamani et al. , 2020). The importance of water in Razavi Khorasan Province led the current research to reveal the trend of spatial and temporal changes of precipitation and temperature in the region in current conditions and also to evaluate the performance of CMIP6 models in reproducing annual and seasonal precipitation under the combined scenarios of common socio-economic paths (SSPS). ) to be concentrated in the future climate. Forecasting climate change plays an essential role in improving the understanding of the climate system and also identifying its social risks in the future. cmip6 activities focused on scenarios were formed in 2013 among climate communities. The Scientific Steering Committee of ScenarioMIP for this project was formed from the 17th meeting of the World Climate Research Working Group (WCRP) in October 2013 in Victoria, Canada. The main activity in Phase 6 of the Coupled Model Cross Project (CMIP6) is the Scenario Cross Project (ScenarioMIP). that the prediction of these climate models is a combination of a new set of release and land use scenarios produced by IAMs models based on the common socio-economic trajectories (SSP) of the future (which includes elements such as population, economic growth, urbanization, age, education and…) and is related to RCPs greenhouse gas concentration scenarios. This new structure provides two important elements in the designed space of scenarios, first: it standardizes all the socio-economic assumptions in each scenario, and secondly, it allows for a more detailed examination of various trajectories that can be It achieves the future climate results. The climate forecast of the CMIP6 project is different from the CMIP5 projects due to the production of updated versions of climate models and the use of SSP-based scenarios based on the updated data in the process of publication. The scenarios of joint socio-economic trajectories (SSPs) are the new group of scenarios of non-climate emissions resulting from coupled models of the sixth phase of climate change (CMIP6) in line with the sixth assessment report of climate change (AR6). These scenarios are presented with the aim of providing forecasts in the common socio-economic path. These scenarios include possible alternative changes in social aspects such as demographic, economic, technological, social, governance and environmental factors based on integrated analyzes of climate impacts, vulnerability, policies related to adaptation and They describe adjustment.
To determine the accuracy of each CMIP6 model, the simulation results of rainfall and temperature of each basin in the historical period were compared with observational statistics. In this step, the Kling-Gupta statistical test (KGE) was used to determine the accuracy of each model (correlations). This measure, while being simple, includes the mean, standard deviation, and correlation coefficient of the series of observation and simulation data obtained from the model, and weighting based on this measure can be of great help in increasing the accuracy of the modeling results. And finally, for micro-scale, Oribi method based on various approaches such as probability distribution mapping, empirical cumulative distribution function mapping, quantile mapping and quadrature density distribution mapping is presented, which is used in many studies to evaluate networked and recorded rainfalls. to be This method works by correcting the mean, standard deviation and quantiles by equalizing the distribution functions of model outputs and observational data. In the bias method, it is assumed that the simulated and observed precipitation follow the same proposed distribution, which in turn may cause bias. Based on this, gamma distribution in the form of α and β scale is often used for the distribution of precipitation events.

The output of general atmospheric circulation models in terms of temporal and spatial resolution is about tens of kilometers on a daily and monthly scale, which are large scale compared to climate processes. In addition, GCM simulations in both temporal and spatial scales have uncertainty in the parameterization of processes, so the output of these models cannot be directly used in climate change studies. Therefore, exponential scaling and skew correction of GCM simulations are necessary to obtain information at a suitable scale. Therefore, the present study investigated and evaluated the accuracy of CMIP6 models, which were recently published by IPCC, for the simulation of temperature and precipitation in Razavi Khorasan Province. For this purpose, the output of GCM models during the period of 1988-2018 was extracted and their accuracy was based on the observational data of seven synoptim stations (Mashhad, Gonabad, Qochan, Sarkhes, Sabzevar, Tarbiat Heydarieh and Torbat Jam) using the index King-Kupta (KGE) was investigated. The results showed that the GESM2 model has the highest accuracy for temperature estimation and the HadGEM3-GC model for the precipitation variable, and also based on this person, it was determined that in CMIP6 models, temperature is more accurate than precipitation. The results of rainfall variable simulation by CMIP6 climate models under three scenarios SSP1-2.6, SSP2-4.5 and SSP5-8.5 during the upcoming period (2020 to 2100) for Razavi Khorasan province showed that in all stations of this province We will witness an increase in rainfall from 0.40% to 6.8%, the most increase in rainfall will occur in the east of the province and the least increase in rainfall will be in the center of the province. The average temperature in all parts of the province under the CMIP6 scenarios will be from 0.29 degrees Celsius to 2.75 degrees Celsius, and the largest increase will be related to Mashhad and Sabzevar stations.

Climate is a complex system that is changing primarily due to the increase in greenhouse gases. To study the effects of climate change on agricultural, hydrological, and environmental systems, general circulation models (GCMs) are used to simulate climate variables. These models, based on approved Intergovernmental Panel on Climate Change (IPCC) scenarios, enable the modeling of climate parameters over extended periods. Globally, various centers and models simulate future climatic conditions using different emission scenarios, physical structures, and computational approaches. The simulations from CMIP6 GCMs form the foundation for many IPCC conclusions regarding future climate changes. These data are utilized directly or after downscaling to evaluate local and regional climate changes (IPCC, 2021). This study analyzes and predicts trends in precipitation and minimum and maximum temperatures in East Azerbaijan Province under climate change conditions from 2021 to 2100.

This study aims to investigate precipitation and minimum and maximum temperatures and their trends from 2021 to 2100 across stations in Tabriz, Ahar, Jolfa, Maragheh, and Miyaneh. Data from 12 CMIP6 models (ACCESS-CM2, BCC-CSM2-MR, CESM2, CNRM-CM6-1, CanESM5, MIROC6, MRI-ESM2-0, IPSL-CM6A-LR, GISS-E2-1-G, HadGEM3-GC31-LL, NESM3, and NorESM2-MM) were used under three Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5, and SSP5-8.5). The Kling-Gupta Efficiency (KGE) method was applied to identify the best models for simulating precipitation and temperature by comparing historical model data (1989–2018) with observed data from selected stations. Bias correction of model outputs was then used to forecast climate variables under the SSP scenarios. Finally, the mean time series of precipitation and minimum and maximum temperatures for the future period were compared with historical data to quantify changes over the 80-year horizon (2021–2100) for East Azerbaijan Province.

The performance of 12 CMIP6 climate models was evaluated for generating past and present climate data (1989–2018). Based on uncertainty analysis, the BCC-CSM2-MR and MIROC6 models were identified as the best for simulating precipitation and temperature. These models were used, with bias correction, to predict precipitation and minimum and maximum temperatures for the future period (2021–2100) under optimistic, moderate, and pessimistic scenarios for East Azerbaijan Province. The results revealed that in all scenarios, annual temperatures are projected to increase while annual precipitation will decrease. Annual maximum temperatures across the selected stations are expected to increase by 0.57–6.41°C, while annual minimum temperatures will rise by 0.46–4.89°C. Precipitation is projected to decrease by 2.3% to 9.18%. The highest temperature increase and precipitation decrease are expected at Jolfa and Tabriz stations, respectively.

This study demonstrates that CMIP6 models effectively simulate future climate parameters and align well with historical climate data for East Azerbaijan Province. The high accuracy of these simulations makes them suitable for forecasting future climatic conditions and facilitating macro-level management strategies. Such strategies can enhance resource productivity, particularly in water resource management, to address the challenges posed by climate change.

The purpose of this study is to identify Paddy Rice field and prepare a land use map based on the phenological characteristics of rice plants using the backscatering of radar data in the Google Earth engine Platform. In order to increase the accuracy of backscatering intensity changes, a 2-year time series was selected. Then, initial identification and land classification were performed. First, the relationship between VV polarization backscatering process and phenological cycle of rice plant was investigated. The results of VV polarization backscatering diagram analysis show that in the first stage of rice plant growth due to moisture, flooding and lack of sufficient vegetation, the backscatering rate was lower. In the second stage of rice plant growth, the amount of backscatering is higher due to increased vegetation and water surface coverage. While in the third stage of rice plant growth, due to the ripening of rice plant seeds and also drying of the paddy fields to harvest, the backscatering rate decreases. Then, by managing time periods and using color combination, the types of uses were identified. After initial identification, in order to achieve better results, a user map of the central part of the effort was prepared using the supervised classification method using the most similar algorithm. After preparing the user map, the accuracy of the map was evaluated using ground samples. The overall accuracy and kappa coefficient of this algorithm are 91.57% and 0.75, respectively. The results showed that the use of phenological data reprocessing time series in accordance with phenology in classifications increases the accuracy of classification. The results also show that the use of Sentinel 1 images along with the Google Earth engine Platform will have a high efficiency in monitoring paddy lands in the northern regions due to the presence of clouds.

The occurrence of heavy rains is related to the synoptic patterns of the earth's surface and the upper levels of the atmosphere. The main goal of this research is to find the synoptic-thermodynamic pattern of heavy rainfall in selected stations of Kermanshah province. For this purpose, daily rainfall data and maps of sea level and height geopotential of 500 hPa were obtained for the statistical period (2010-2019) and were processed using principal component analysis (PCA) to identify the components. CAPE, LI, KI, TTI and SI instability indices were used for thermodynamic analysis. The results of the factor analysis showed that three factors in total explain and justify 80.5% of the variance of productive patterns. Further, four patterns were obtained by clustering using Ward's integration method and Euclidean distance. These patterns include the eastern Mediterranean and Iraq descent, the low-altitude Mediterranean cut bandal, the deep Russian descent and the eastern Mediterranean and Iraq quasi-omega bandal. The results showed that in the days of heavy rain, the dominant pattern of low pressure was located over the eastern Mediterranean Sea and Iraq and the location of the southwestern part of the omega-shaped bandal and the eastern Mediterranean trough in the region, which had the highest frequency of heavy rain with 22 days out of 38 days. The supply of moisture these days from the Mediterranean and Red seas, the presence of relative humidity close to saturated humidity and negative omega has led to an increase in humidity in the air, the rise of moist air, strengthening convection, creating an unstable atmosphere and heavy rains in the region. The results of thermodynamic analysis showed that the instability indices related to the second and third patterns were not very strong, but in the first and fourth patterns, the indices revealed severe instability and high potential.

Environmental changes The environmental environment of any environment can be changed. In case of achieving this goal, first the average daily statistics (2021-2009) were received from the General Meteorological Organization for Nair city. The maximum limit is determined by using 99% of Frein's threshold limit, then for Frein's days, the sea level pressure value was extracted from the Nova site, and on this model, patterning was done with Ward's hierarchical clustering method based on Euclidean distance. As a result, two major patterns were identified and one representative day of each pattern was included in the synoptic analysis. For this, NCEP-NCAR data were used to draw synoptic maps in the software environment. The results showed that the thermal low pressure systems that entered Iran from the south-east and south-west sides covered the entire geographical area of Iran and especially the studied area and brought an unstable atmosphere. At this time, the warm air from the southern latitudes that flowed through the Naveh channel in the west of Iran towards the northwest of Iran was blocked by the blocking barrier located in the western half of Iran. The anomalous state of air temperature showed that on the investigated day, temperature changes increased by 4 degrees Celsius compared to the length of the week. Geodetic maps of temperature and temperature advection at the level of 500 hectopascals also show that the origin of heat movement is from the north-east of Africa and has been drawn towards the study area, and heat convergence has occurred in the study area and has added to the temperature.

Awareness of the environment and better understanding of the climate of each region is very necessary for the future to analyze the current situation of beekeeping in order to advance the goals in this field. In addition to weather factors and climatic variables, the temperature factor plays an essential role in creating a balance between honeybees and the environment. It should be noted that one of the most common problems in the growth of honeybees is unusual changes in environmental conditions, such as a sudden increase or decrease in temperature in mountainous areas, which increases the importance of studying this matter. In this research, due to the high importance of temperature, 20 years of daily temperature statistics of meteorological stations have been used. The dates of occurrence of the first and last temperatures of zero degrees were investigated and used to identify the time of inactive temperatures, to calculate the deviation from the optimal temperature (22 degrees) in the minimum winter consumption of this beneficial insect. The results of station estimates were also zoned using GIS software. The results showed that according to the calendar obtained from the cold months of the year, the thermal conditions in the last four months of the year, based on the occurrence of low temperatures from December to March, are unsuitable for biological activities in the high areas of Sablan, and the colonies should migrate to an altitude of 1000 meters and be monitored. The most optimal months of the year for the activity of bees in the highlands with the results obtained start from May because a huge source of flowering plants grow in these areas. By adapting the temperature thresholds of honeybees to the weather conditions of each region, it is possible to help move the colonies at the right times for the development of beekeeping, and also prevent the significant reduction of the colony population due to the death of bees in cold areas and bring it to the lowest possible level. Migrated to be monitored. The most optimal months of the year for the activity of bees in the highlands with the results obtained start from May because a huge source of flowering plants grow in these areas. By matching the temperature thresholds of honey bees with the weather conditions of each region, it is possible to help move the colonies at the right times for the development of beekeeping and also prevent the significant reduction of the colony population due to the death of bees in cold areas and reduce it to the minimum. as much as possible. Beekeeping has long been one of the important branches of agriculture due to its pollination and valuable products (Allen et al., 1998). The honey bee performs more than 90% of commercial pollination services because it is considered one of the most important pollinators worldwide (Morse and Calderon, 2000). It should be pointed out that Iran's honey bee colonies are a type of common honey bee that are highly dependent on weather conditions . Coordination of beekeeping activities with climatic factors and its temporal and spatial patterns is one of the important issues regarding growth and breeding . Colony health, production efficiency and productivity in beekeeping are all affected by environmental conditions, especially climate. (Haig, 2006). By matching the vital thresholds of this creature with the climatic conditions of different places, it is possible to take advantage of the maximum climatic potential of the regions in the field of bee breeding. The most important aspect of keeping and rearing honey bees is coordinating beekeeping activities with geographical and climatic conditions and its temporal and spatial changes . With the occurrence of frost, many animal activities are affected, and the honey bee, which is a cold-blooded insect, is affected by the surrounding temperature and reacts to this temperature. It should be kept in mind that the development of beekeeping has a close relationship with weather conditions and the time of moving colonies depends on this factor . In the annual climatic calendar of the growth of honey bee colonies, two active and inactive periods have been identified, and in the cold months of the year, the inactive periods start and special care is taken, however, there is a sharp decrease in the population and death of the colony. We are at the end of winter, which is one of the inhibiting factors for using bee pollination services and other products. The loss of the queen due to changes in environmental conditions such as: Temperature, high humidity and prolonged cold in mountainous areas and lack of food in hives are among the most common problems of bee breeding in very low temperatures).Winter deaths and reduction of bee colony population in cold regions, an important part of which is due to environmental and climatic conditions, increase the importance of studying this matter (Pettis et al., 2010). With the climate calendar in hand and the proper location of the apiaries in the cold months of the year by the beekeepers, we will see a significant reduction in losses, business improvement and more productivity. A lot of research has been done in the field of honey bee wintering all over the world, which shows the close relationship between the cold winter weather and the temperature of the investigated environment, and the result shows that the minimum consumption of honey in large colonies is in the temperature range of -10 and 10+ degrees Celsius in temperate regions. ). Researches show that in relation to minimum winter honey consumption of bee colonies, the temperature reaches 4 to 5 degrees Celsius. By examining the effects of climate changes on the population of colonies and their diseases, it was found that considering the climatic conditions plays an essential role in beekeeping management .The research conducted in relation to the effect of climate change on a micro scale and its impact on the migration conditions of colonies in high altitudes were investigated and an advanced monitoring system was proposed ). The effects of different times of the day, temperature, humidity and air pressure on the flight of various types of stingless bees were studied and the results showed that the flight activities of these types of bees are affected by atmospheric conditions and the elements of day length, heat, humidity and pressure.

Millet fodder plant is one of the important plants in Iran and the world, and due to its abundance of nutrients and its resistance to drought, it has a special advantage over other forages.The purpose of this research is to identify the areas prone to millet fodder cultivation in Ardabil province with multi-criteria methods and using the data of climatic elements and factors; Rainfall, average temperature, minimum temperature, maximum temperature, altitude, slope and soil depth.AHP, ANP, DEMATEL, Shannon Antropy and WLC methods were used to determine the weights of criteria and options.By using the weighted linear combination method in the GIS environment, information layers were integrated and the final map of millet fodder cultivation was prepared.The results showed that about 25% are very suitable, 36% are suitable, 30% are slightly suitable and 4.05% are unsuitable for millet fodder cultivation in Ardabil province.The results of AHP, ANP, DEMATE models showed that rainfall and temperature criteria, and in the Shannon Antropy model, height and slope have the most influence during the growth period of millet fodder.The output classification of two WLC and Antropy Shanon models about options showed that millet fodder cultivation areas overlap in both methods and the northern and central regions of the province have suitable conditions for millet fodder cultivation.

Awareness of the environment and better understanding of the climate of each region is very necessary for the future to analyze the current situation of beekeeping in order to advance the goals in this field. In addition to weather factors and climatic variables, the temperature factor plays an essential role in creating a balance between honeybees and the environment. It should be noted that one of the most common problems in the growth of honeybees is unusual changes in environmental conditions, such as a sudden increase or decrease in temperature in mountainous areas, which increases the importance of studying this matter. In this research, due to the high importance of temperature, 20 years of daily temperature statistics of meteorological stations have been used. The dates of occurrence of the first and last temperatures of zero degrees were investigated and used to identify the time of inactive temperatures, to calculate the deviation from the optimal temperature (22 degrees) in the minimum winter consumption of this beneficial insect. The results of station estimates were also zoned using GIS software. The results showed that according to the calendar obtained from the cold months of the year, the thermal conditions in the last four months of the year, based on the occurrence of low temperatures from December to March, are unsuitable for biological activities in the high areas of Sablan, and the colonies should migrate to an altitude of 1000 meters and be monitored. The most optimal months of the year for the activity of bees in the highlands with the results obtained start from May because a huge source of flowering plants grow in these areas. By adapting the temperature thresholds of honeybees to the weather conditions of each region, it is possible to help move the colonies at the right times for the development of beekeeping, and also prevent the significant reduction of the colony population due to the death of bees in cold areas and bring it to the lowest possible level. Migrated to be monitored. The most optimal months of the year for the activity of bees in the highlands with the results obtained start from May because a huge source of flowering plants grow in these areas. By matching the temperature thresholds of honey bees with the weather conditions of each region, it is possible to help move the colonies at the right times for the development of beekeeping and also prevent the significant reduction of the colony population due to the death of bees in cold areas and reduce it to the minimum. as much as possible. Beekeeping has long been one of the important branches of agriculture due to its pollination and valuable products (Allen et al., 1998). The honey bee performs more than 90% of commercial pollination services because it is considered one of the most important pollinators worldwide (Morse and Calderon, 2000). It should be pointed out that Iran's honey bee colonies are a type of common honey bee that are highly dependent on weather conditions . Coordination of beekeeping activities with climatic factors and its temporal and spatial patterns is one of the important issues regarding growth and breeding . Colony health, production efficiency and productivity in beekeeping are all affected by environmental conditions, especially climate. (Haig, 2006). By matching the vital thresholds of this creature with the climatic conditions of different places, it is possible to take advantage of the maximum climatic potential of the regions in the field of bee breeding. The most important aspect of keeping and rearing honey bees is coordinating beekeeping activities with geographical and climatic conditions and its temporal and spatial changes . With the occurrence of frost, many animal activities are affected, and the honey bee, which is a cold-blooded insect, is affected by the surrounding temperature and reacts to this temperature. It should be kept in mind that the development of beekeeping has a close relationship with weather conditions and the time of moving colonies depends on this factor . In the annual climatic calendar of the growth of honey bee colonies, two active and inactive periods have been identified, and in the cold months of the year, the inactive periods start and special care is taken, however, there is a sharp decrease in the population and death of the colony. We are at the end of winter, which is one of the inhibiting factors for using bee pollination services and other products. The loss of the queen due to changes in environmental conditions such as: Temperature, high humidity and prolonged cold in mountainous areas and lack of food in hives are among the most common problems of bee breeding in very low temperatures).Winter deaths and reduction of bee colony population in cold regions, an important part of which is due to environmental and climatic conditions, increase the importance of studying this matter (Pettis et al., 2010). With the climate calendar in hand and the proper location of the apiaries in the cold months of the year by the beekeepers, we will see a significant reduction in losses, business improvement and more productivity. A lot of research has been done in the field of honey bee wintering all over the world, which shows the close relationship between the cold winter weather and the temperature of the investigated environment, and the result shows that the minimum consumption of honey in large colonies is in the temperature range of -10 and 10+ degrees Celsius in temperate regions. ). Researches show that in relation to minimum winter honey consumption of bee colonies, the temperature reaches 4 to 5 degrees Celsius. By examining the effects of climate changes on the population of colonies and their diseases, it was found that considering the climatic conditions plays an essential role in beekeeping management .The research conducted in relation to the effect of climate change on a micro scale and its impact on the migration conditions of colonies in high altitudes were investigated and an advanced monitoring system was proposed ). The effects of different times of the day, temperature, humidity and air pressure on the flight of various types of stingless bees were studied and the results showed that the flight activities of these types of bees are affected by atmospheric conditions and the elements of day length, heat, humidity and pressure.

Awareness of the environment and better understanding of the climate of each region is very necessary for the future to analyze the current situation of beekeeping in order to advance the goals in this field. In addition to weather factors and climatic variables, the temperature factor plays an essential role in creating a balance between honeybees and the environment. It should be noted that one of the most common problems in the growth of honeybees is unusual changes in environmental conditions, such as a sudden increase or decrease in temperature in mountainous areas, which increases the importance of studying this matter. In this research, due to the high importance of temperature, 20 years of daily temperature statistics of meteorological stations have been used. The dates of occurrence of the first and last temperatures of zero degrees were investigated and used to identify the time of inactive temperatures, to calculate the deviation from the optimal temperature (22 degrees) in the minimum winter consumption of this beneficial insect. The results of station estimates were also zoned using GIS software. The results showed that according to the calendar obtained from the cold months of the year, the thermal conditions in the last four months of the year, based on the occurrence of low temperatures from December to March, are unsuitable for biological activities in the high areas of Sablan, and the colonies should migrate to an altitude of 1000 meters and be monitored. The most optimal months of the year for the activity of bees in the highlands with the results obtained start from May because a huge source of flowering plants grow in these areas. By adapting the temperature thresholds of honeybees to the weather conditions of each region, it is possible to help move the colonies at the right times for the development of beekeeping, and also prevent the significant reduction of the colony population due to the death of bees in cold areas and bring it to the lowest possible level. Migrated to be monitored. The most optimal months of the year for the activity of bees in the highlands with the results obtained start from May because a huge source of flowering plants grow in these areas. By matching the temperature thresholds of honey bees with the weather conditions of each region, it is possible to help move the colonies at the right times for the development of beekeeping and also prevent the significant reduction of the colony population due to the death of bees in cold areas and reduce it to the minimum. as much as possible. Beekeeping has long been one of the important branches of agriculture due to its pollination and valuable products (Allen et al., 1998). The honey bee performs more than 90% of commercial pollination services because it is considered one of the most important pollinators worldwide (Morse and Calderon, 2000). It should be pointed out that Iran's honey bee colonies are a type of common honey bee that are highly dependent on weather conditions . Coordination of beekeeping activities with climatic factors and its temporal and spatial patterns is one of the important issues regarding growth and breeding . Colony health, production efficiency and productivity in beekeeping are all affected by environmental conditions, especially climate. (Haig, 2006). By matching the vital thresholds of this creature with the climatic conditions of different places, it is possible to take advantage of the maximum climatic potential of the regions in the field of bee breeding. The most important aspect of keeping and rearing honey bees is coordinating beekeeping activities with geographical and climatic conditions and its temporal and spatial changes . With the occurrence of frost, many animal activities are affected, and the honey bee, which is a cold-blooded insect, is affected by the surrounding temperature and reacts to this temperature. It should be kept in mind that the development of beekeeping has a close relationship with weather conditions and the time of moving colonies depends on this factor . In the annual climatic calendar of the growth of honey bee colonies, two active and inactive periods have been identified, and in the cold months of the year, the inactive periods start and special care is taken, however, there is a sharp decrease in the population and death of the colony. We are at the end of winter, which is one of the inhibiting factors for using bee pollination services and other products. The loss of the queen due to changes in environmental conditions such as: Temperature, high humidity and prolonged cold in mountainous areas and lack of food in hives are among the most common problems of bee breeding in very low temperatures).Winter deaths and reduction of bee colony population in cold regions, an important part of which is due to environmental and climatic conditions, increase the importance of studying this matter (Pettis et al., 2010). With the climate calendar in hand and the proper location of the apiaries in the cold months of the year by the beekeepers, we will see a significant reduction in losses, business improvement and more productivity. A lot of research has been done in the field of honey bee wintering all over the world, which shows the close relationship between the cold winter weather and the temperature of the investigated environment, and the result shows that the minimum consumption of honey in large colonies is in the temperature range of -10 and 10+ degrees Celsius in temperate regions. ). Researches show that in relation to minimum winter honey consumption of bee colonies, the temperature reaches 4 to 5 degrees Celsius. By examining the effects of climate changes on the population of colonies and their diseases, it was found that considering the climatic conditions plays an essential role in beekeeping management .The research conducted in relation to the effect of climate change on a micro scale and its impact on the migration conditions of colonies in high altitudes were investigated and an advanced monitoring system was proposed ). The effects of different times of the day, temperature, humidity and air pressure on the flight of various types of stingless bees were studied and the results showed that the flight activities of these types of bees are affected by atmospheric conditions and the elements of day length, heat, humidity and pressure.

The aim of this study is simulation and forecasting of maximum temperature and heat waves in Urmia city from 2020 to 2050 in order to predict and reduce the negative effects of a sudden increase in temperature, which is simulated using SDSM software and CanESM2 model And is predicted using the most appropriate RCP scenario for the next 31 years. For this purpose, the maximum daily temperature data of Urmia from 1961 to 2005 were obtained from the Meteorological Organization and forecasted. The innovation of this study is using the least error RCP scenario for more accurate prediction. According to the results, RCP 8.5 scenario was selected as the least error scenario for forecasting. According to the results the average maximum temperature in Urmia will decrease from late winter to late spring compared to the base period and in mid-summer there will be a slight increase. In general, during the years 2020-2050, the maximum temperature trend of Urmia will be increasing. According to the results of the Baldi index, The heat waves will be short and maximum four days. One-day heat waves will have the highest frequency and will have a slight upward trend, Two, three and four day heat waves will have a decreasing trend. In general, short-term heat waves are more likely to occur than long-term heat waves. Also, since the detected heat waves often showed the highest frequency in autumn and winter, so the probability of this hazard occurring in cold seasons is higher than warm seasons.

Today, fine dust and dust storms have become one of the important issues and problems of Iran and other regions of the world. This study is conducted to identify synoptic patterns of the dust storms in Ardabil Province (Iran) and monitor the origin and pathway of its places. The studied period is 34 years (1979-2013). The data used in this work are from synoptic stations of Ardabil province and the NASA’s upper atmosphere database. To achieve the goals of the study, the Environment to Circulation, Hierarchical Clustering Methods, and HYSPLIT model were used. According to the results, three patterns of geopotential height at 500 hPa levels were identified. Pattern No. 1 and 2 are almost similar to each other and represent the occurrence of a deep trough that formed from Central Europe and extended to the middle of the Red Sea. Based on these patterns, the study area is located in the East of this trough. The third pattern represents the occurrence of cut-of-low pressure blocking over the Black Sea and adjacent areas and placement of Ardabil province in the East trough of the formed blocking. The origin of dust in patterns 1 and 2 is the Eastern regions of Iraq and West of Iran while in pattern 3 is at 500 hPa level of the central areas of Iraq and at pressure levels of 850 and 1000 hPa of the central regions of Syria.

In this study, the prediction of precipitation and temperature values using the general atmospheric circulation models during 1964-2005 is investigated. Climatic data including daily values of total precipitation and mean temperature were obtained from the Iranian Meteorological Organization. Considering the climate change scenarios RCP4.5, RCP2.6 and RCP8.5 for the coming period 2010-2100 were evaluated under the canESM2 climate change model of Aharchai Basin. Due to the low accuracy of the general circulation models, the SDSM4.2 miniaturization model was used and the changes in precipitation and mean temperature parameters were simulated for future time periods. In this study, to calibrate the SDSM model, among the 26 large scale climate parameters (NCEP), on average, 3 parameters have the highest correlation with the mean temperature and 5 parameters have the highest correlation with the average precipitation in the Aharchai Basin. The results of climatic parameters prediction showed that simulation of climatic parameters mean temperature was performed with higher accuracy than mean precipitation values. The reason is that the precipitation data are not normal and unconditional. The results show that the basin temperature averaged more during the period 2070-2099 under the scenario RCP8.5 than the observations period of 1964-2005 and the basin precipitation during the period 2070-2099 under the scenario RCP2.6 showed the highest increase in the entire study period.

Assessment of agricultural crop potential areas based on potential climatic and natural talents plays an effective role in land use design and planning. Therefore, the present study aimed to evaluate the elements and factors affecting grape crop cultivation by multi-criteria decision making methods. Using data such as temperature (mean, minimum and maximum), rainfall, percentage of slope, elevation above sea level and soil depth data were used to evaluate and zonation of grape crops. In this research, hierarchical process (AHP), ARAS method and weighted linear combination method (WLC) were used to evaluate the effects of elements and climatic factors for cultivating grapes in Meshgin region. The results of the study showed that eastern and southern areas of Meshginshahr, Doostbeigloo and Moshiran, which are located in Gharehsoo River and downstream of other rivers, are suitable for growing grapes. The evaluation carried out by AHP method, land use, altitude, sunshine hours, rainfall and temperature among the studied criteria had the greatest effect on the cultivation of grapes in the region, respectively. According to the maps extracted from WLC method in GIS medium, about 34% of the area of Meshginshahr area is very suitable, 28% is suitable, 22% is slightly suitable and 16% is unsuitable for grape crop cultivation.

The expansion of sustainable agriculture and the proper use of the country's water and soil resources require the selection of suitable plant species that are compatible with the climatic conditions of the country. Therefore, the optimal use of natural resources through the planting of plants compatible with the conditions of the region can guarantee human health, the sustainability and progress of sustainable agriculture, in fact, for the optimal use of land, the issues of water, soil and air cannot be analyzed separately For such researches, a framework such as zoning is proposed to plan, operate and properly use homogeneous pieces of land. Rosa damascene has a low water requirement and is highly adaptable to dry conditions. Considering Iran's climatic and geographical characteristics, the expansion of the cultivation of the Rosa damascene plant has gained double value.

Ardabil province in the north-west of Iran, with an area of 17,953 square kilometers, covers 1.09% of the total area of the country. Its geographical coordinates are between 37 degrees 25 minutes to 39 degrees 42 minutes north latitude and 47 degrees 3 minutes to 48 degrees 55 minutes east longitude. In the current research, using climatic criteria (Growth Degrree Days, annual precipitation, precipitation of the growing season, average temperature of the growing season, sunny hours of the growing season, and altitude) The growth of each plant starts from a certain thermal threshold, and the growth threshold of the rose is 2.5 degrees Celsius and the fuzzy TOPSIS method and the ANP network analysis process, the stations of Ardabil, Khalkhal, Parsabad and Meshginshahr have been selected for the cultivation Rosa damascene. The growth of each plant starts from a certain thermal threshold, and the growth threshold of the rose is 2.5 degrees Celsius. The steps of the TOPSIS technique include forming a decision matrix, forming a scale-free matrix, determining the Euclidean distance and choosing the best option, and the steps of the ANP method include creating a network structure between criteria, options and the goal, pairwise comparison of matrices and Formation of super matrix and selection It is the best option. In this model, each cluster and elements have a mutual relationship and the relationship of each criterion and clusters is checked with each other and they get a score between 1 and 9. The zoning and final drawing of the maps were done in the Surfer software environment and interpolation was done with the Radial Basis Functions method.

After weighting the effective parameters for the cultivation of Rosa damascene based on TOPSIS models and the ANP network analysis process and performing modeling and analysis of climate data, the final interpolation map for Rosa damascene cultivation was prepared based on the climatic potential and abilities. The results showed that in prioritizing the options for growing roses using the TOPSIS and ANP methods, the priority of the options is related to Meshgin Shahr, Khalkhal, Ardabil and Parsabad stations. In fact, the unrestricted lands include Meshgin Shahr station, which is the best place for cultivation due to its good climatic capabilities, and Ardabil and Khalkhal stations have low and medium restrictions for cultivation. The lands of this station have relatively weaker conditions than is the MeshginS hahr station , but good performance is expected from it. Parsabad station has very limited lands, which according to the assessment of climatic capability, this station lacks suitable potential for growing Rosa damascene.

In evaluating the ability of the habitat to plant a crop, not all criteria are equal. Some criteria play a key role. For this reason, factors are weighted in order to obtain a ranking of the value of decision criteria regarding suitable places for agriculture. The current research was conducted using TOPSIS and ANP methods and 6 effective atmospheric elements in the cultivation of Rosa damascene, standardization and formation of a ranking decision matrix based on these scores to determine and rank the Rosa damascene cultivation. These methods are more capable than other common methods. are doing the ranking more accurately. Finally, by interpolation (RBF) method, a map of the areas suitable for the cultivation of Rosa damascene was prepared. In the zoning map, there are very suitable areas for Mashgin Shahr station and unsuitable areas for cultivation at Parsabad station.

IntroductionHeat waves are one of the most dangerous climate threats associated with global warming, affecting society, economy and environment (Kogenhof et al., 2015: 930). We can say that the first step to control or at least reduce the damage caused by climatic hazards, including heat waves, is to define, detect and identify their characteristics using scientific methods (Abbasnia et al., 2016: 25). Therefore the purpose of this study is Simulation and prediction of maximum temperature and heat waves Southwest of Iran (Case study: Ahvaz and Ilam cities)MethodologyIn the present study to identify heat waves, Maximum daily temperature data of each studied stations, from 1961 to 2005, were obtained from the Meteorological Organization and SDSM software and CanESM2 model and three RCP scenarios (2.6, 4.5 and 8.5) were used. Then using error measurement indicators the lowest risk scenario was selected for each city and the maximum temperature was predicted based on that scenario and using the Fumiaki index and through programming in MATLAB software, Days that had temperatures above +2 standard deviation or above average (NTD) and these conditions lasted for at least two days, were identified and selected as the day with heat waves. The Fumiaki index is obtained by relation 1:(1 (T(i,j) ) ̅=∑_(n=2020)^2050▒〖T(i,j,n)÷N〗 Where T (i, j, n) temperature of day i th from month j th in year n th, (T (i,j)) ̅ the average temperature of day i from month j. To eliminate the noise in the mean, a 9-day moving average filter was performed on these data three times and calculated by the following equation (Fujibi et al., 2007; Ismail Nejad et al. 2014).(2 ∆T=(i,j,n)=T(i,j,n)-T(I,J)Where ∆T= (i, j, n) absolute deviation of temperature from the average on day j th of the month i th, in year n th compared to the average temperature of the same day. In order to the values of temperature deviation of different times and places to be comparable at a certain time and place, it is necessary to standardize these absolute values of temperature deviation by means of temperature diffraction. Like day-to-day changes, diffuse T∆ at 31 days for each day is calculated by the following equation:(3 (σ^2(i,j) ) ̅=∑_(n=2021)^2050▒〖 ∑_(j-31)^(j+31)▒〖[∆T(i,j^',n)-(∆T(i,j^',n) ) ̅ ]^2÷31N 〗〗 The value (T (i,j)) ̅ is the average temperature deviation in 31 days that is calculated by the following equation:(∆T*(i,j)) ̅=∑_(n=2020)^2050▒∑_(j=j-31)^(j+31)▒〖T(i,j^',n)÷31N 〗 (4Finally, (NTD) is calculated by the following equation:(5 (i,j,n)=∆T(i,j,n)÷(σ(i,j)) ̅ x Where .Then days with temperatures +2 above average (NTD) and lasting at least two days, were selected as the day with the HW. (Ismail Nejad et al, 2014). Equation 6:(6 2 ≥ NTD (i+p) NTD (i+p-1) ≥ 2, NTD (i-1), NTD (i+1)… NTD (i), To evaluate the ability of CanESM2 model scenarios to predict the average maximum temperature for the next 31 years, the outputs of each scenario are averaged by the absolute error values (MAD), mean square error (MSE), root mean square error (RMSE) were compared and the most accurate and least error scenario was selected. Equation 7-9.(7 MAD= (∑_(t=1)^n▒〖〖|A〗_t-F_t |〗)/n (8 MSE= (∑_(t=1)^n▒〖〖(A_t-F_t)〗^2 |〗)/n(9 RMSE=√((∑_(t=1)^n▒〖(A_t-F_t)〗^2 )/n) Results and discussionAccording to the results, during the next 31 years (2020-2020) there will be an increasing trend in the average maximum temperature in both Ahvaz and Ilam stations, which will be the most increasing trend in Ahvaz. Results also showed during the years 2050-2020, the heat waves of these two cities were identified and divided into two categories: short -term and Long-term waves. (Heat waves lasting between 2 to 5 days as short-term heat waves and waves lasting 6 days or more, long-term heat waves). According to the results, in both cities, 2-day heat waves will have the highest frequency, which is predicted to be higher in Ahvaz than in Ilam. But three-day waves in Ilam more than Ahvaz and 4-day heat wave in Ahvaz more and 5-day heat wave in both Ahvaz and Ilam only one case is predicted. The highest frequency of 2-day heat waves in Ahvaz cities in the months related to spring was predicted in Ilam in summer. The 3-day heat waves in both cities will be in the fall. Ahvaz four-day heat waves in autumn, in Ilam in summer. Five-day heat waves, one of which was forecast in the fall at both stations. The highest frequency of this hazard in both stations was related to 2-day warm waves, which were predicted more in Ahvaz than in Ilam, and in terms of time of occurrence in Ahvaz in late winter and early spring, and in Ilam in late summer and early autumn. Most heat waves will be experienced. In general, the occurrence of heat waves in the predicted period in both cities will often occur in the cold seasons of the year.ConclusionThe purpose of this study was to use the CanESM2 model in simulating and predicting the maximum temperature and heat waves of Ahvaz and Ilam cities as selected cities in southwest of Iran. Based on the results of simulation of maximum temperature and forecast of heat waves during the years 2020-2050. The maximum temperature in both stations will have an increasing trend, which will be the highest increasing trend in Ahvaz. The maximum continuity of heat waves in both stations will be 5 days and therefore short-term, and the 2-day heat wave will have the highest frequency. The trend of two-day heat waves in Ahvaz will be increasing and decreasing in Ilam, but the 3-day, 4, and 5-day waves did not show special trend in any of the stations. Over the next 31 years, heat waves will be less continuous and often occur in the cold months of the year.

The sorghum forage plant with the scientific name Sorghum bicolor Moench belongs to the cereal family. This fodder is one of the most likely crop plants to drought stress and it has been called the camel of the crop plants of the world. Sorghum is considered a short-day plant and its suitable day length is 10 to 12 hours. Sorghum is resistant to dehydration and consumes 35-40% less water than corn during its growth period. You can grow it in the tropics all year round very sensitive about the spring cold. Length of forage growth period of sorghum based on climatic influence, environmental, agronomic and genotype factors has suggested at least 90 to more than 150 days in three main phases: vegetative, inflorescence development and seed growth. Also, it has specified the stages of development of sorghum as follows: stage zero; greening, observation of stalk pods on the soil surface. Step 1; View the third leaf collar. Step 2; View the fifth leaf collar. Step 3: Transition from vegetative to reproductive. Step 4 – Exit the flag leaf vertices. Step 5, Cluster Cover. Stage 6; 50 percent pollination. Step 7; Soft paste. Step 8; Stiff paste. Stage 9: Physiological Proceedings. The results showed that sorghum is cultivated at latitude between 45° of the northern hemisphere to 45° in the southern hemisphere, and in Philippines the height of sorghum is 1500 meters and in East Africa between 90 and 1500 m in East Africa, in America 25 to 850 m and in Africa from 400 m to 2500 m and generally cultivated from zero to 1500 m above sea level. Considering the importance of protein in the human diet, it is necessary to increase the production of fodder plants that are the source of animal protein production. In this research, an attempt is made to prepare suitable areas for the cultivation of sorghum fodder plant, which is very important for livestock feeding in Ardabil province, using the methods and criteria of the study of agricultural climate zoning map of sorghum fodder cultivation in Ardabil province.

Ardabil province is located in north-west of Iran and its location is at latitude of 37° and 45 minutes to 39° 42 minutes in the northern hemisphere and in geographical length of 47° and 3 minutes to 48° and 55 minutes of eastern longitude. Its area is about 17953 square kilometers (about 1.09% of the country's area). In this study, the optimum climatic requirement of sorghum forage cultivation from available sources was determined. Then, according to the favorable climatic requirement, precipitation, temperature, 12 synoptic stations during the statistical period (1400-2000) were obtained from Ardabil Meteorological Department and soil elevation, slope and depth maps were prepared from the relevant organizations. Also, multi-criteria decision making models, Shannon entropy, ANP and WLC weighted combined method and ARC GIS software, Export Choice and Super Decision software have been used for research. Shannon entropy model: It was introduced in 1972 by Shannon and Weaver. Shannon's entropy method works by saying that the greater the dispersion in a given index, the more important the index is (Shannon, 1988).Analytic Network Process (ANP):Network analysis is one of the multi-criteria decision making techniques and the developed form of analytical hierarchy process. This method is used to solve problems in which criteria and options are not independent of each other. In this process, measurement of relative values and significance such as analytic hierarchy process is done by paired comparison.Weighted Linear Composition (WLC):Weighted linear combination method is the most common technique in multi-criteria evaluation analysis. The purpose of multi-criteria assessment is to select the best option (alternative) based on their ranking through the evaluation of the main multi-criteria. This method is based on the concept of weighted average. Direct analysis and decision making based on the relative importance of the study gives weights to the criteria. The weighted combination method can be implemented using the geographic information system and the overlapping capabilities of this system. It is practical to use this method in both raster and vector GIS formats. After the final value of each option is determined, the options with the highest value will be the most suitable option for the intended purpose.

Shannon Entropy: In Shannon's entropy model, quantitative data collection is used by using questionnaires. In this model, five criteria: precipitation, temperature, elevation, slope and depth of soil were analyzed in seven stages. Data matrix formation was selected based on i option and j index. Then, the normalization of the data matrix numbers was done in order to perform other phases of Shannon's entropy model. The final ranking of the studied options for sorghum cultivation in Ardabil province was based on the studied criteria and after determining the importance factor of the indicators (WJ), the final prioritization of the options was formed by multiplication of the components of the group normalized decision matrix in the importance factor of indicators (WJ) Formation of priority coefficient matrix. Suitable options for sorghum cultivation compared to each index was formed. Finally, from the total row of priority coefficient of each option, the appropriate areas were formed for the Sorghum cultivation was estimated. The results showed that Bilehsavar and Parsabad regions with the degree of importance of 0.105 and 0.095 in terms of the studied criteria are in the appropriate condition for sorghum cultivation in Ardabil province. The results of data analysis based on the method WLC showed that the areas are suitable and very suitable for sorghum fodder cultivation in Ardabil province based on rainfall 49%, temperature 65%, altitude 52%, slope 44% and soil resistance 36%. The results of agricultural climate zoning of sorghum fodder cultivation in Ardabil province with WLC method in GIS environment showed that about 26% are very suitable, 34% are suitable, 35% are slightly suitable and 5% are unsuitable for sorghum fodder cultivation. Also, agricultural climate zoning with Shannon's entropy method showed that about 10% are very suitable, 19% are suitable, 59% are slightly suitable and 12% are unsuitable for sorghum cultivation in Ardabil province. Therefore, based on the methods studied at the level of Ardabil province, in case of water demand, tropical areas such as; Parsabad, Bileh Sowar, Dasht Givi and Ardabil are known to be suitable for sorghum cultivation.

Horizontal visibility is one of the most important optical characteristics of the atmosphere. Predicting horizontal visibility is of great importance in various aspects such as air pollution, air traffic, flight safety, road traffic, and maritime travel safety. The aim of the present study is to analyze the spatiotemporal variation of horizontal visibility in the southern coasts of the Caspian Sea. For this purpose, hourly data of horizontal visibility, present weather phenomenon, and relative humidity were used for a 70-year statistical period (1951-2020) in the study area. In this study, the extinction coefficient was calculated using the Koshmider formula. Finally, using the non-parametric Mann-Kendall test, the trend analysis of the frequency reduction of horizontal visibility phenomenon was conducted in the region. The results showed that among the studied stations, Babolsar, Ramsar, and Astara stations experienced an increasing trend, Nowshahr station experienced a decreasing trend, and Gonbad-e Kavus and Maraveh Tappeh stations had no trend in the extinction coefficient. Based on the Mann-Kendall output among the seasons, significant decreasing (increasing) trends were observed in the autumn and winter seasons, and no trend was observed in other time scales. The results of the analysis of the impact of weather parameters showed that among the factors affecting horizontal visibility, precipitation (39.75%) and dust (0.83%) had the highest and lowest percentage of impact, respectively, compared to other effective factors in the region. In general, it can be said that due to the forested nature and suitable vegetation cover in the region, the possibility of the occurrence of local dust storms is very low.

Today, climate issues threaten the security of the world, security which is considered necessary and vital in all fields and for all people. The phenomenon of climate change can affect the water requirement of crops by changing the amount of evaporation and transpiration of plants and the duration, intensity and time of rainfall. The studies related to climate change that have been conducted in Iran in recent years have focused more on climatic indicators and the effects of these changes on agricultural production have been given less attention. Therefore, assessing the effects of climate change on agriculture is an essential need. Due to the fact that Ardabil province is one of the poles of agriculture and animal husbandry and any change in the climate will endanger the lives of most of the residents of this region and will cause a change in the use of farms, pastures and the loss of agricultural production.kkkT Therefore, assessing the effects of climate

The study area is located on the slopes of Sabalan Mountain in Ardabil and East Azerbaijan provinces. Its geographical location is located at latitudes of 37 degrees and 44 minutes to 38 degrees 25 minutes and longitude 46 degrees and 22 minutes to 48 degrees 41 minutes. The minimum height of the area is 371 meters and its maximum is 4811 meters above sea level (Fig, 1). This study was conducted to influence climate change in wheat cultivation on slopes of Sabalan mountain. The important part of this research is based on statistics and information about meshkinshahr and sarab synoptic stations. In order to investigate the climate change conditions, a basic statistical period and a period as climate change should be determined. Therefore, statistical periods in this study from 1995 to 2015 and climate change period from 2016 to 2045 and 2046 were selected. Statistics about the studied stations were obtained from the Statistics and Information Bank of the National Meteorological Organization. The data taken from these statistics include: maximum and minimum temperature,daily rainfall and sunshine. to work with the LARS-WG model; First, the studied data should be sorted as Julius days. After collecting data in the Excel environment, it should be noted that there is no missing data, in case of missing, it must be encoded with -99. Aggregated data must be stored in a folder in nodpad with st extension.The address of folders stored in the N environment should be provided to the model from the option series, and the output data address should be given to the model. How to choose a scenario. First, in Excel environment, statistical data is placed in a column and in the next step, the generated data for each scenario is placed in the columns in front of each column of observational statistics, after preparing this step, the data is taken to the spss environment and correlation is taken between observational and production data. Production scenarios that are highly correlated with observational statistics are accepted as the studied scenario. CROPWAT software was used to estimate water requirement and effective precipitation. By entering minimum temperature, maximum temperature, relative humidity, wind speed and number of sunshine hours related to the plant, as well as the environment and region and its cultivation time, you can calculate the water requirement of the plant at different growth stages. Figure 2 shows the steps of data analysis in CROPWAT software.

In this study, first, the power of LARS-WG model for the basic statistical period of the years (1995-2015) was measured. The purpose of this assessment is whether the model has the ability to simulate for future periods. To do this, the tst file containing the results of comparing the statistical characteristics of the observed data with the simulated data was plotted as a diagram (Shapes, 2 and 3). The results showed that in Meshkinshahr station, the model has better efficiency for simulating maximum temperature and minimum temperature. The observed temperature and simulated temperature for 1995-2015 are similar and the diagrams are overlapping. Also, the deviation of production criteria is in the range of number one. This model is not effective in simulating the sunshine hour because it simulates the amount of sunshine hours in the first half of the year less than the actual size. In the case of rainfall, the model is better than April and May in other months. In Sarab station, the data were performed based on LARS-WG model by comparing the statistical period data and the produced data. To ensure the ability of computational data model, they were compared by model and observational data in the studied stations. Comparative results show the data of minimum temperature, maximum temperature, precipitation and sunshine in mirage synoptic stations for the base period. The capability of LARS-WG model in modeling minimum temperature, maximum temperature and radiation in these stations is completely in accordance with the observed data. The standard deviation rate is between 0.5 and 1. The results of this study show that in Meshkinshahr station, precipitation and temperature in the period 2016 to 2045 are -3.9 and +1.73, respectively, and these two variables in the period 2046 to 2065 are -6.67 and +1.80, respectively. In Sarab station, precipitation and temperature in the period 2016 to 2045 were +6.17 and -0.69, respectively, and in the statistical period of 2046 to 2065 were -12.91 and +0.37, respectively. As a result, rainfall in the coming periods is associated with a decrease in Sarab and Meshkinshahr stations, respectively. Temperatures are also rising at about 2 °C in Meshkinshahr station and the temperature increase is low in Sarab station. In comparison, Sarab plain has a wonderful state because by the period 2046 to 2065 the rainfall in this plain will decrease about 13% compared to the base period, in the same time period, wheat evapotranspiration will reach from 530 mm in the base period to 887 mm in the period 2046 to 2065. Wheat water requirement also increases from 422 mm in the base period to 810 mm, i.e. about 92%. Also, modeling shows that the average minimum temperature of this region decreases from -3 in the base period to -9.67 °C in January and from -4.3 to -8.23 in February. According to the modeling and with the decrease in rainfall in this region, wheat cultivation in this plain will face limitations in the future. However, the results of the models indicate that meshkinshahr plain is better in future periods than Sarab plain.

The purpose of this research is to estimate and evaluate the future climate change of Iran using climatic elements (minimum temperature, maximum temperature and precipitation) until the year 2100 in Iran. For this purpose, in the current research, the innovative method and tools of the algorithm and coding and NASA data to evaluate and predict the aforementioned climate elements based on two intermediate scenario (RCP 4.5) and worst-case scenario (RCP 8.5) of the Canadian CanESM2 model from the system Google Earth Engine was used under the web. In order to better analyze, investigate and compare Iran's future climate changes, the studied period of 80 years, was divided into the first 40-year period (2021-2060) and the second 40-year period (2061-2100). The results of the current research based on the intermediate scenario (RCP 4.5) indicate that the minimum and maximum; minimum temperature of the second 40-year period compared to the first 40-year period is 2.69 and 0.62 degrees Celsius, respectively, and the minimum and maximum; maximum temperature The second 40-year period compared to the first 40-year period was 3.37 and 0.91 degrees Celsius respectively; Also, based on the worst-case scenario (RCP 8.5), the minimum and maximum; minimum temperature of the second 40-year period compared to the first 40-year period is 0.54 and 3.32 degrees Celsius, respectively, and the minimum and maximum; maximum temperature of the second 40-year period compared to the period An increasing trend was predicted for the first 40 years of 2.47 and 3.46 degrees Celsius respectively. Based on the results obtained from the present research, in the studied area, the frequency of rainfall decreases in the first 40-year period and increases in the second 40-year period.

Urbanization changes natural landscapes to human-made spaces and uses. With the expansion of cities, many of these spaces give way to roads, buildings and urban facilities and cause changes in different levels of the city, and these changes have very important effects on weather conditions (Shamsipour et al. 2013: 59). )The development of urbanization is one of the effective factors in increasing the air temperature in urban areas, which causes the creation of thermal islands in these places compared to the surrounding environment. This factor can have a negative effect on air quality and endanger the general health of society. (Mousavi Baighi et al., 2010. 190). What is considered as a fundamental defect in monitoring the temperature of the earth's surface is the lack of sufficient meteorological stations to know the temperature values. Today, this shortcoming has been solved by remote sensing and it can cover a large area of the earth's surface.

The study area is Amol city. The city of Amol is located in the Mazandaran province and the sides of the Heraz River with a height of 76 meters above sea level at 52 degrees and 21 minutes east longitude and 36 degrees and 25 minutes north latitude and at a distance of 70 kilometers west of Sari, the capital of the province, 18 kilometers south of the Caspian Sea and 6 It is located one kilometer north of Alborz mountain and 180 kilometers northeast of Tehran.In this research, Landsat 8 satellite images and Landsat 5 satellite images were used for 1990 in order to extract the land use map and surface temperature of 2020. In order to remove the effect of cloud cover from the images as well as the high intensity of sunlight, the desired images were taken from the summer season. Google Earth software was used for better accuracy of images, ENVI 5.3 software was used for atmospheric and radiometric corrections, and finally ARC GIS 10.8 software was used to prepare relevant maps.Using the atmospheric correction model (FLAASH), the data were qualitatively controlled and the radiometric error of the satellite images was corrected. In order to obtain a statistical set that represents the spectral pattern of land cover, training data must be selected before supervised classification of images. At this stage, information from the uses and topographical maps of the region were prepared using the visual interpretation of the images for all five floors, to prepare educational data for use in supervised classification operations. Maximum likelihood classification method was used for land use classification. This method is considered a part of the supervised methods for classification and for this purpose it uses a set of training data. In this method, after evaluating the probabilities in each class, the pixels are assigned to the classes that have the most similarity, and if the probability values are lower than the introduced threshold, they are considered as unclassified pixels.After that, the brightness temperature of the sensor is done by converting the digital values of band 6 in Landsat 4 and 5 and also band 10 in Landsat 8 to spectral radiance and converting the spectral radiance to the brightness temperature of the sensor in terms of Kelvin.Then, red and near-infrared bands were used to calculate NDVI to obtain the normalized vegetation difference index. After calculating NDVI we need to get Emissivity. Emissivity is the amount of reflection of a phenomenon relative to the black body. Then the land surface temperature (LST) is calculated. By using LST, it is possible to calculate the temperatures near the surface of the earth. In order to know and evaluate the correctness and accuracy of the classification, the user's accuracy, overall accuracy and Kappa coefficient were calculated in 1990 and 2020.

In this research, in the first step, the classification and the resulting changes were done in a specific time frame in Amol city and its surroundings. The classification results indicate that the classification in both periods, especially in 2020, was highly accurate, and its kappa coefficient and overall accuracy were at their highest coefficient, i.e. 100.After classification, the changes obtained in the area were examined for a period of 30 years and the changes were extracted for each land use in terms of hectares. The change of use from agriculture to the city and also from the city to roads and streets have the most changes. These changes indicate that the increase in urban use has caused a decrease in agricultural use and the size of urban areas has increased.Using Landsat satellite images, the temperature of the earth's surface has been studied in relation to land use and the results showed that the temperature is different in different uses. The highest temperature recorded for the years 1990 and 2020 in Amol city is related to urban use, the recorded temperature of which is 32.6 and 40.5, respectively, which shows the concentration of heat in urban areas. Urban use has the highest temperature due to the presence of man-made factors and heat absorbers such as asphalt, concrete and the presence of machinery. Also, the presence of tall buildings acts as a barrier to the heat escaping to the surroundings and in some way traps the heat inside the cityWith the development of urbanization in Amel city, a significant part of the area of natural and forest areas has been replaced by industrial areas, buildings and other infrastructures. The lowest temperature recorded in Amol city is related to forest use with 23.8 and 28.4 degrees Celsius. In forest areas, due to high albedo, high humidity and more open space, the temperature is lower and heat absorption is low there.The relevant researchers and experts in the region can use the results of this research to obtain information about the temperature of the earth's surface, land use, and also the changes that have occurred in the region, In order to predict the future situation of the region, they will take appropriate and correct policies.

Sainfoin fodder plant is one of the fodder legumes, which is considered among fodder plants in terms of producing good, high-quality and acceptable fodder and competing with alfalfa fodder. Cultivation of this plant is not only compatible with cold regions, it is also established and cultivated well in tropical regions. A study of the morphological stages and evolution of Sainfoin fodder yield in spring season in (first China) showed that Sainfoin fodder has many species in 9 growth periods and is considered one of the most important pasture and crop fodder plants, and the place of its cultivation is the Iranian-Turanian region. In recent years, the development of Sainfoin fodder cultivation has been promoted with the combination of alfalfa fodder, which was effective in increasing the weight of livestock. Also, in Saskatchewan, Canada, in order to provide safe food for livestock, evaluation of Sainfoin and alfalfa fodder cultivation was carried out using rainfall and temperature, and the results showed that temperature and rainfall play an effective role during the growth period from germination to harvest of these fodder plants. The main goal of this research is the zoning of Sainfoin fodder cultivation in Ardabil province using effective criteria during the Sainfoin growth period. The difference between the present research and the studies conducted in this field is that more criteria and new data were used, as well as the efficiency of four methods during the growth period of Sainfoin fodder.

Ardabil province is located in the northwest of Iran, between latitude 37 degrees 45 minutes to 39 degrees 42 minutes and longitude 47 degrees 3 minutes to 48 degrees 55 minutes (Figure 1). This province with an area equal to 17953 square kilometers, the minimum and maximum altitude of the province is 32 meters in Parsabad and 4811 meters in Sablan, respectively. In this research, the data of precipitation, average temperature, minimum temperature and maximum temperature during the statistical period (1990 to 2020) and altitude, land slope and soil depth were used, as well as ARC GIS and Export Choice software for data analysis and analysis process methods. Hierarchy (AHP), TOPSIS model, Shannon entropy and weighted linear combination (WLC) method were used for weighting and zoning. Hierarchical analysis process method The first step in the hierarchical analysis process is to determine the importance coefficient of the criteria, sub-criteria and options in Sainfoin fodder cultivation. Export Choice software was used to analyze the criteria in the hierarchical process. After determining the relative weight of the criteria, the inconsistency coefficient is calculated. If this coefficient is less than or equal to 0.1, the consistency in the judgments is acceptable. Topsis method The TOPSIS multi-criteria analysis process is as follows. Formation of data matrix: It is calculated based on m criteria and n options. Shannon entropy method The main idea of this method is that the greater the dispersion in the values of an index, the more important that index is. The steps of this method are as follows. Forming the data matrix: First, a two-dimensional matrix of options and indicators is formed, and the value of each indicator in each option is estimated using official statistics and field studies. And the closer the value of dj is to zero, it indicates of Weighted Linear Combination (WLC) method This method is the most common technique in multi-criteria analysis based on the concept of weighted average, where the final value of each specific option and the options that have the highest weight will be the target. Equation (12) is used to evaluate each option or Ai: where Xij represents the i-th option in relation to the j-th criterion and Wj is the standardized weight of the j-th criterion, so that the sum of Wj is equal to one. The relative importance weight of each criterion is displayed. By determining the maximum value of Ai, the most priority option is selected. In addition to combining all parameters or layers, the WLC method also considers the importance of each parameter based on the weight given to that parameter. Table (2) shows the optimal climatic requirement of spruce fodder cultivation.

In this research, to locate Sainfoin fodder cultivation from the studied methods and also from the criteria; Precipitation, average temperature, minimum temperature, maximum temperature, altitude, slope and soil depth were used. The results showed that in all methods; Precipitation has the most important effect during the growth period of Sainfoin; Therefore, precipitation is the most limiting criterion in the cultivation of Sainfoin fodder in Ardabil province. The results of the weight criteria with AHP, TOPSIS, and Shannon's Antropy methods showed that precipitation, average temperature and minimum temperature with the highest weight are respectively the most important influencing parameters in Sainfoin fodder cultivation. Also, the results of combining the criteria with the WLC method in the GIS environment showed that the lands of Ardabil province have very suitable (29%), suitable (40%), slightly suitable (26%) and unsuitable (5%) for growing Sainfoin fodder(Fig,5). The results of the weighted value by AHP method showed that precipitation with a weighted criterion of 0.143, maximum temperature of 0.126 and average temperature of 0.089 among the studied parameters have the greatest effect on the growth stages of Sainfoin Finally, with the use of TOPSIS methods, about 5% and Shannon's entropy about 12% of the area of the province is very suitable for cultivating Sainfoin fodder; Therefore, compared to the studied methods in this research, the results showed that the weighted linear combination (WLC) method is the most suitable method for the zoning of Sainfoin in Ardabil province compared to the current situation. Therefore, the suitable areas for growing spruce fodder are located in the north and center of Ardabil province, which is due to the suitable climatic and topographical conditions. Therefore, the suitable areas

The alfalfa plant with the scientific name Medicago sativa L is considered the most important fodder plant in the world and it is a very high quality fodder suitable for all kinds of livestock (Kirimi, 2002). Alfalfa fodder plant is known as the queen of fodder plants in terms of nutritional value and palatability due to the variety of species compared to other fodder plants (Toran et al., 2017). In Iran, the area under alfalfa cultivation is 340,767 hectares and its production amount is 3,551,850 tons, and in Ardabil province, the area under alfalfa cultivation is 9,065 hectares and the annual production amount of alfalfa is 63,105 tons (Ministry of Jihad Agriculture, 2020). Examining weather data and their effect on plants is one of the most important factors in increasing productivity. Therefore, each area has potential and limitations regarding crop cultivation that is compatible with a specific climate, which studies the feasibility of suitable areas for cultivation. The main goal of this research is to locate alfalfa fodder cultivation in Ardabil province using water and meteorological criteria during alfalfa growth period. The difference between the present research and other studies in this field is that it studies the efficiency of four methods during the alfalfa fodder growth period.

Ardabil province is located in the northwest of Iran, and its location is in the latitude of 37 degrees and 45 minutes to 39 degrees and 42 minutes of north latitude and the geographical longitude of 47 degrees and 3 minutes to 48 degrees and 55 minutes of east longitude. In this research, from the data of annual precipitation, average annual temperature, minimum annual temperature and maximum annual temperature, six synoptic stations during the statistical period (1990 to 2020) and the height and slope of the land, as well as from the software; ARC GIS, Export Choice and Super Decision have been used. In order to weight and locate alfalfa crops, the methods of Analytical Hierarchy Process (AHP), Analytical Network Model (ANP), Fuzzy Hierarchical Process (FAHP) and Weighted Method (WLC) have been used.

In this research, alfalfa fodder cultivation location in Ardabil province was evaluated and investigated using AHP, FAHP, ANP and WLC methods. In the AHP method, the results with ExportChoice software analysis showed that annual precipitation with a weight of 0.377, average temperature with a weight of 0.258, and maximum temperature with a weight of 0.112 were respectively recognized as the most important criteria during the alfalfa growth period. The results of the FAHP method study showed that precipitation with a weight of 0.367, average temperature with a weight of 0.259, and minimum temperature with a weight of 0.105 are respectively effective in the stages of alfalfa fodder cultivation. In the ANP method with the analysis of Super Dicision software, the results showed that temperature criteria (average, minimum and maximum) with a weight of 0.404, annual precipitation with a weight of 0.289 and topography (height and slope) with a weight of 0.056 are the most important effective parameters, respectively. During the growth period, fodder is alfalfa. By combining the studied criteria during the growth period of alfalfa fodder with the WLC method in the GIS environment, the location of alfalfa cultivation in Ardabil province was done.

The final results of the data analysis with the studied methods showed that rainfall, temperature and topography criteria respectively play an important role during the alfalfa growth period and also by combining the data; A location map of alfalfa fodder cultivation in Ardabil province was prepared. About 22% is very suitable, 21% is suitable, 23% is slightly suitable and 24% is unsuitable for alfalfa cultivation and The results of network analysis (ANP) showed that; Pars Abad region with a score of 238/., Sablan and Meshkin Shahr area with a score of 226/. domain with a score of 228/. compared to other studied stations, they have priority for alfalfa cultivation. Therefore, it is suggested; A- Studies that are carried out with multi-criteria methods on agricultural products, their weighting should be done based on the study of the optimal climatic needs of that crop by the researcher, not through a questionnaire. B- Ardabil province is suitable for the cultivation of alfalfa fodder in terms of climatic conditions and place. It is recommended to grow alfalfa fodder instead of non-strategic crops that require a lot of water.