alireza movaghari

In this research, in order to reveal possible climate changes, the time series of annual average temperature of Urmia synoptic station during the statistical period of 1951-2017 was studied. Box-Jenkins prediction model (integrated auto-regressive and moving average model) was used to find the change model and predict the behavior of the time series. After fitting different models, R2, Akaike information criterion (AIC), regression standard error (SEE) and the Schwartz-Bayesin criterion (BIC) were measured to select the optimal model. The results showed that the time series model ARIMA (2,2) (1,0) has better results compared to other models and the trend of time series changes of this model has less error. After choosing the best model, the average annual temperature values for the next 13 years were predicted using it. The temperature prediction was done using this model with a good fit and the correlation between the actual and fitted values was around 82%. After ensuring the correctness of the forecast, the time series until 2030 was estimated. The forecast of the model indicates a mild temperature increase in the coming years with an upward linear trend. The study of the general circulation of the atmosphere of the region by drawing and analyzing the difference maps for the parameters of geopotential height of 500 hpa, relative rotation of 500 hpa, vertical velocity of the atmosphere (Omega) and air temperature on the surface of the earth indicates the strengthening of the stability of the atmosphere as well as the increase in temperature. The air of Urmia city is 0.4 degrees Celsius compared to the past, which confirms the results of the Box-Jenkins model.

Extreme precipitation has a significant impact on the frequency, severity, and duration of natural hazards, such as floods, droughts, and landslides. This has a significant impact on human life, the economy, natural ecosystems, and agriculture (Song et al, 2015: 34). Between 1880 and 2012, there was a 0.85 °C increase in the average global temperature, with a general increase in precipitation in the mid-latitudes of the Northern Hemisphere (IPCC, 2013: 2; Lio et al, 2017: 822). In addition, there is a possibility of a rise in extreme precipitation in the future (Klein Tank et al, 2006: 1), and so far, the reason for these changes and their relationship with the general circulation of the atmosphere have not been considered. The aim of this study is to analyze the trend of changes in extreme precipitation indices in northwestern Iran and its association with the general circulation of the atmosphere.

In order to analyze the changes in extreme precipitation events in northwestern Iran, daily precipitation data was collected from 20 synoptic stations in the region between 1986 and 2010. The region that is being studied encompasses West Azerbaijan, East Azerbaijan, Ardabil, Zanjan, and Kurdistan. In assessing limit events, high quality and reliable long-term climate data with daily (or higher) resolution is required (Clintanak et al., 2009: 9). The first step was to examine the quality control and homogeneity of data. The RClimDex software package, introduced as a standard tool by ETCCDI, was used to perform quality control and evaluate data homogeneity in this research. The Expert Team on Climate Change Detection, Monitoring and Indices (ETCCDMI) introduced 11 indexes to examine changes in precipitation level indices in northwest Iran. RClimDex software calculates these indicators with a significance level of 0.05. This process seeks to establish a standard set of indicators to examine and compare the characteristics of different regions. The software was used to calculate precipitation indices and display the trend and rate of change on a map.

The extreme precipitation indices were calculated to determine the regional trend and percentage of stations with positive and negative trends for the studied stations in northwestern Iran. Afterward, a map was created showing the spatial distribution of the slope for each of the indices. All precipitation indexes, except for the maximum growth period index (CDD), are declining according to the results. The probability of precipitation has decreased due to the more stable winter atmosphere in the region from the point of view of general atmospheric circulation. The region's spring atmosphere, similar to that of winter, shows an increase in stability, which will result in less rainfall. In summer, except for the coastal provinces of the Caspian Sea and the coasts of the Oman Sea, the rest of the country has recorded a decrease in rainfall of 1 mm per day. Most parts of the country experienced an increase in atmosphere thickness to 6 meters in autumn in the study area. Autumn in the region is typically stable and barotropic, but the study area is experiencing less rainfall. This study examines the trend of changes in extreme precipitation indices in northwestern Iran and its relation to a large-scale general circulation of the atmosphere. According to the results, 75% of stations in the region are experiencing a decrease in the maximum daily rainfall (RX1day) and 80% are experiencing a decrease in the maximum five-day rainfall (RX5day). While both the very wet (R95P) and ultra-wet (R99P) day indices are experiencing a downward trend, the R95P index is experiencing a more pronounced downward trend. All three indices R10, R20, and R25 have been declining for the past 25 years, but the R10 index has fallen more rapidly than the other two indices. Sarab station has a positive CWD trend alone, while other stations have a negative and decreasing trend of this index. In most stations throughout the region, the CDD index is increasing. In 85% of stations in the region, the PRCPTOT index is decreasing and there is a noticeable increase in rainfall. The SDII index is experiencing a decrease in 60% of the stations in northwestern Iran, while an increase is being observed in 40%. All precipitation indices, except for the CDD index, have a decreasing trend in general. Drawing and analyzing combined difference maps for geopotential height parameters of 500 hPa, relative rotation of 500 hPa, vertical velocity (omega), rainwater and precipitation rate to study the general atmospheric circulation of the region indicates an increase in altitude has led to a 500 hPa increase in climate stability in the study area (northwest of Iran). The study of omega and relative rotation shows that the region is experiencing a decrease in upward currents and positive rotation. The lack of atmospheric moisture load and rainfall in all seasons can be seen in rainfall water difference maps and rates. Precipitation indices and the general circulation model of the region's atmosphere are compared, indicating that the moisture load of the region's atmosphere has decreased, resulting in drought.

In order to minimize the undesirable effects of unsteady growth of Urmia and to apply the smart-growth model for this city, recognizing the characteristics of different areas and their inequality in planning, is the basis of the work. Therefore, proper planning should be done to eliminate these inequalities and transform the optimal situation. Areas need to be categorized in terms of "development" in order to plan for whether or not they are developed. In measuring smart growth indices, there are different types of statistical methods and techniques. Using quantitative criteria and methods to classify the neighbourhoods and urban areas of Urmia in terms of smart growth indices not only recognizes the differences between areas, but also these criteria for determining the types. The services needed and the adjustment of inequality between areas of the city. The present study tries to study the spatial distribution of urban smart growth components in the five urban areas of Urmia and based on the obtained scores, the rate of urban smart growth indices in three levels of smart, semi-intelligent and less intelligent. Therefore, the following objectives were considered for the study: Identification of Smart Areas of Urmia, Prioritizing Urban Areas of Urmia for Future Planning in line with Urban Smart Growth Pattern and Identifying Homogeneous Neighborhoods of Urmia in terms of Urban Smart Growth Indicators.

The approach of the research method in this study is of applied type and it is descriptive-analytical and correlational. Spatial statistics tests were used to model the spatial pattern of smart neighbourhood distribution. To identify the spatial pattern of intelligent living spaces and finally to identify the desired zones. First, library studies were used to identify smart city indices from different sources and databases. Accordingly, six main indices (intelligent dynamics, intelligent people, intelligent living, intelligent environments, intelligent governance, and smart economics) with 91 items were used. The field was determined. Regarding the research subject, random sampling was used in order to obtain the maximum accuracy coefficient in obtaining samples with a high degree of characteristics of the statistical population and the results of which can be generalized to the whole population. According to Cochran's formula, 384 people were selected as the sample population. Simple random stratified sampling and distribution of samples for 30 neighbourhoods were done based on proportional allocation. Questionnaires were collected based on field method through direct interviews with residents of five districts. In this study, Cronbach's alpha coefficient was calculated using SPSS software. The reliability of the research is significant since the reliability of the questionnaire was assigned to each of the answers 1 to 5 with a Cronbach's alpha of 0.785. In order to complete the questionnaire, a questionnaire was distributed in each of the five urban areas. According to the objectives of the study, Shannon entropy (as a multi-criteria decision-making method) was used to evaluate and rank Urmia metropolitan areas in terms of smart growth indices. Regression analysis (Pearson function and linear regression) were used in SPSS software.

To achieve definitive ranking in terms of smart growth indices, all 91 variables were computed using Shannon entropy model and results were slightly different. In terms of integrated indices of Region 3, with an entropy value of 1 was ranked first. The region was also ranked first in smart living indicators. The last rank came in the 4th place with an entropy value of 0 which was in the last place in relation to the smart governance index. Overall, Region 3 was ranked as one of the most prosperous regions in terms of smart growth indices with economic and social structure, good accessibility, favourable environment, dynamic economy, proper urban infrastructure, proportional distribution of land uses and construction density. The mean of the integrated indices is 0.39 and the standard deviation is 0.39. Area one has the highest score above average and other areas are below average. Using the inequality coefficient, the coefficient of equilibrium in urban smart growth indices between urban areas for these indicators was calculated and a value of 1.01 was obtained, indicating heterogeneity and divergence between urban areas in terms of intelligence indicators. This inequality is affected by the inadequate distribution of facilities and services throughout the city. According to the calculated entropy and inequality coefficient, there are differences and inequality between the neighbourhoods of Urmia in terms of smart growth indices. In other words, this paper investigates and ranks the neighbourhoods of the five urban areas of Urmia for urban smart growth index using Shannon entropy model. The results of the ranking show that the neighbourhoods of Urmia city achieved different scores and scores in each of the indicators of a smart economy, smart people, smart governance, smart dynamics, and smart environment. This indicates significant inequality and differences in some indicators. The highest inequality between the indicators of smart governance and the lowest inequality between the indicators of intelligent life. All six indices (91 items) were combined and then tested for composite rank. Then the entropy of each index was calculated and classified using three clusters using cluster analysis. According to the consolidated results, smart growth index in neighbourhood 8 Shahrivar with entropy value 0.799 located in region 3 is in the first rank among smart neighbourhoods. The neighbourhoods of the school, Isarah, Imamate and Ayatollah Dastgheib are in second to fifth place, respectively. The last rank of this ranking is for the Kohnavard neighbourhood with an entropy value of 0.16 located in District 2 of Urmia.

Combined regression fitting shows that smart living variables have the greatest impact on predicting and developing the spatial structure of smart growth in urban neighbourhoods so that one unit change in the deviation of smart living indices will cause 0.680 units to change in integrated growth indices. The results emphasize the need for attention and prioritization of the Kohnavard neighbourhood in Zone 2 in future development and planning.

Ultraviolet radiation in the short wavelength range has a lot of energy and can cause skin and eye disorders, genetic DNA damage, immune system dysfunction and premature aging in humans. In this study, in order to calculate the amount of ultraviolet radiation, 94 points selected that represent the best distribution of the range. The ultraviolet index calculated in the period 2011-2000 for all months. Monthly and quarterly index of spatial distribution of the UV was studied using spatial analysis the IDW interpolation method. The results showed that the warm period of the year, higher altitudes and lower latitudes, especially in the provinces of Sistan and Baluchestan, Kerman, Hormozgan and Fars higher levels of ultraviolet radiation and the risk of eye damage and skin diseases is high. The provinces of Gilan, Mazandaran, Golestan, Ardebil, East Azerbaijan and West Azerbaijan receive less light. Different geostatistical methods evaluated to determine the best way to map the annual average for UV index. The results showed that four-dimensional spherical model technique with ordinary kriging is the most appropriate model parameters to fit the average annual ultraviolet light across Iran. Final map showed that the annual average of 4 percent of the total area of the country has an ultraviolet index consists of Caspian provinces. Nationally, 54 percent of the northern half of the country, including high ultraviolet index receive 42 percent of the area. Generally encompasses the southern provinces of the country, the index is very high.

This study aimed to identify the cause of intense rainfall occurred on April 30, 1994 in Kermanshah, The maps of surface pressure in the upper atmosphere Sudan and low pressure systems result in precipitation patterns were identified The role of this system as one of the major systems of the West was to climax. Maps and atmospheric pressure at the upper and lower levels, it was determined that Starting with the establishment of precipitation over the Mediterranean trough Turkey and Iraq and the location of the study area at the front of the upper levels, Sudan is associated with low pressure at the surface occurs. There is a high pressure system over the Arabian Peninsula and North-West Indian Ocean This system leads to amplification. Omega plans and volubility of the story of instabilities weather is rainy day. The moisture content of the maps, Most moisture levels 850 hPa and the Mediterranean Sea to be supplied the region is imported from the West. But the 700 hPa level is the main source of water that flows from the Red Sea to the south west of the study area appears.