@2024 Afarand., IRAN

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ISSN: 2538-4384    Geographical Researches 2020;35(4):333-341.

Background
Understanding the impact of physical development on urban unstable weather is a challenging issue.
Previous Studies
The rapid expansion of urbanization has led to the expansion of urban sprawling [Sudhira et al., 2004]. This type of urban expansion makes the authorities put more pressure on natural resources [Rafiee et al., 2009]. Land-use changes and dividing a large piece of land into some scattered small pieces have deteriorated the environment [Arsanjani et al., 2013], and have caused regional climate change [Emadodin et al., 2016]. Accordingly, the scientists showed that urbanization has caused weather chaos in recent decades [Bazkar et al., 2015]. The impact of urban physical expansion on thermal and dynamic changes of the global climate, thermal islands, greenhouse gases emission, and its impact on future climate is mentioned in various articles such as Fan & Sailor (2005), Alpert et al. (2011), Makar et al. (2006), Dodman (2009), Dulal et al. (2011), Daneshvar & Abadi (2017). According to the statistical analysis, the correlation between precipitation, relative humidity, and cloudiness degree and sprawling indicator varies from 0.63 to 0.93 [Rabbani et al., 2020].
Aim(s)
This study aimed to analyze the daily changes of SWEAT indicators in four urban areas in Iran and Turkey to assess the impact of urban sprawling on making severe storms in urban areas.
Research type
This is an empirical study.
Research society, place and time
The current research is carried out using data from the upper-air level in the radiosonde stations of Tehran, Mashhad, Ankara, and Istanbul in 365 days of 2018 and their prediction for 2030.
Sampling method and number
There is no sampling method and number reported.
Used devices&materials
Used devices and materials are not reported.
Findings by Text
Urban sprawling model analysis in 2018 and 2030 Urban development structures in the studied areas in 2018 in Tehran, Mashhad, Ankara, and Istanbul show that they are sprawled in zones with the areas of 1000, 500, 1400, and1300 〖km〗^2, respectively (Table 2). The studied areas' population is also of importance and Istanbul, for example, is much more populated than three other cities. It is predicted that the sprawling rate in 2030 in Mashhad and Tehran will be twice as much as their predicted population. It stresses the importance of the controlling physical expansion and striking a space balance in these cities (Table 3). Environmental changes indicator (∆Y) is an indicator based on urban sprawling level (∆X) in 2018 and its prediction in 2030. HBASE database based on LULC remote sensing data was used for this purpose. Assessments and space analysis of the studied database in GIS were used for the estimation of the indicator of (∆Y) (Table 4). The evaluation of environmental changes indicator as a result of sprawling According to the results of the current situation (2018), ∆Y is between 0.41 and 0.56 in the studied cities. These results will be practical when they are compared with the corresponding values in 2030. ∆Y is predicted to be between 0.61 and 1.98 in 2030. In other words, environmental changes caused by urban sprawling will increase. Tehran is predicted to have the most severe environmental changes. Other cities, on the other hand, are predicted to have ∆Y less than 1 despite their increase in the sprawling. ∆X turbulence analysis in 2018 and its prediction for 2030 shows that the highest level of turbulence caused by sprawling is predicted to occur in Mashhad and Tehran rising from 1.43 and 1.67 to 2.7 and 3.14. Increased ∆X in 2030 shows that all studied cities especially Tehran need to revise their urban sprawling control and prevention plans. Otherwise, urban land expansion demolishes the open space and green space in the future. It is suggested to make plans for an urban green belt in suburbs. Urban agriculture plan in the farms surronding the cities is another suggestion to determine urban sprawling borders. Severe weather threat index evaluation in 2018 SWEAT index is calculated for all four urban areas in 2018. Then, mentioned daily data were categorized into three classes of low, high, and severe instability (Table 5). According to the results, Istanbul is the only city in the severe weather instability class (SWEAT>300) in 2018. Annual SWEAT mean in Tehran, Mashhad, Ankara, and Istanbul is 83.7, 62.5, 110.3, and 128.5, respectively showing that stormy weather is more frequent in Istanbul and Ankara. Then these cities are in more severe weather instability in comparison to Mashhad and Tehran.
Main comparison to the similar studies
According to the results of the current research, there is a significant correlation between urban sprawling and regional climate changes that is approved by Rabbani et al. (2020) and Daneshvar et al. (2019).
Suggestions
There is no suggestion reported.
Limitations
The most important limitation of the current research is due to the problems in collecting daily data from radiosonde stations that limit the feasibility of researching for consecutive years.
Conclusions
Urban growth has had a severe impact on regional weather in recent decades [Bazrkar et al., 2015]. SWEAT index is a forecasting index showing the stormy weather in urban areas. SWEAT index, in the current research, is studied in 4 urban areas in Turkey and Iran including Istanbul, Ankara, Tehran, and Mashhad and evaluated the relationship between urban sprawling, population growth, and environmental changes index caused by sprawling. According to the results, increased urban sprawling may cause an increase in urban air turbulence that may raise fuel and energy consumption for physical constructions and transportation. The correlation between the SWEAT index and the environmental changes index shows that urban sprawling has a significant impact on increasing the SWEAT index in all studied cities. The current article tends to bring future possible changes into consideration.
Acknowledgments
The author thanks the judges for evaluating her research.
Conflict of interest
None.
Ethical Permissions
None.
Funding Sources
This study is carried out at the author's personal expense.

TABLES and CHARTS

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