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Volume 35, Issue 4 (2020)
GeoRes 2020, 35(4): 333-341 |
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Received: 2020/08/9 | Accepted: 2020/09/2 | Published: 2020/09/8
Received: 2020/08/9 | Accepted: 2020/09/2 | Published: 2020/09/8
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Rabbani G. Impact Analysis of the Environmental Changes Index of Urban Sprawl on the Severe Weather Storm;Case Studies of Tehran, Mashhad, Ankara, and Istanbul. GeoRes 2020; 35 (4) :333-341
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Authors
Ghazaleh Rabbani *
Department of Geography and Urban Planning, Research Center of Geography, Research Institute of Shakhes Pajouh, Isfahan, Iran
Abstract (2128 Views)
Aims: Understanding the effect of urban physical development on urban air instability is a challenging issue. The aim of the present study was to analyze the environmental change index of urban sprawls and its relationship with the severe weather threat index (SWEAT) within four major cities of Iran and Turkey in the current situation (2018) and future probabilities (2030).
Methodology: In this study, severe weather storm index data were extracted from upper-air level data in the radiosonde stations of Tehran, Mashhad, Ankara, and Istanbul. Also, urban sprawl data were obtained from the global data of the extent of human settlements.
Findings: Analyzing the urban sprawl data revealed that the cities of Ankara and Istanbul have the highest physical development, respectively. According to the findings of the urban sprawl development model, the most severe rate of urban sprawl development is predicted for the city of Tehran. Based on the calculation of the environmental change index (ΔY) caused by sprawl dispersion, it was determined that the values of this index will be estimated between 0.61 to 1.98 for the four urban case studies in the future, where the most severe index (1.98) can be calculated for Tehran.
Conclusion: Calculation of SWEAT in 2018 shows that the urban sprawl development and its resulting environmental changes had a significant effect on increasing occurrences of severe weather storms in the all urban areas with a correlation rate of 0.913. The forecast of this index also shows that the average severe weather storm index will increase between %19 to %118 by 2030 in the all urban areas, in which the most value is predictable for the city of Tehran.
Methodology: In this study, severe weather storm index data were extracted from upper-air level data in the radiosonde stations of Tehran, Mashhad, Ankara, and Istanbul. Also, urban sprawl data were obtained from the global data of the extent of human settlements.
Findings: Analyzing the urban sprawl data revealed that the cities of Ankara and Istanbul have the highest physical development, respectively. According to the findings of the urban sprawl development model, the most severe rate of urban sprawl development is predicted for the city of Tehran. Based on the calculation of the environmental change index (ΔY) caused by sprawl dispersion, it was determined that the values of this index will be estimated between 0.61 to 1.98 for the four urban case studies in the future, where the most severe index (1.98) can be calculated for Tehran.
Conclusion: Calculation of SWEAT in 2018 shows that the urban sprawl development and its resulting environmental changes had a significant effect on increasing occurrences of severe weather storms in the all urban areas with a correlation rate of 0.913. The forecast of this index also shows that the average severe weather storm index will increase between %19 to %118 by 2030 in the all urban areas, in which the most value is predictable for the city of Tehran.
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References
1. Abshaev MT, Abshaev AM, Mikhailovskiy YP, Sinkevich AA, Popov VB, Adzhiev AK (2019). Characteristics of the supercell Cb thunderstorm and electrical discharges on 19 August 2015, North Caucasus: A case study. Preprints. [DOI:10.20944/preprints201912.0033.v1]
2. Alpert P, Kishcha P, Kaufman YJ, Schwarzbard R (2005). Global dimming or local dimming?: effect of urbanization on sunlight availability. Geophysical Research Letters. 32(17):802. [DOI:10.1029/2005GL023320]
3. Arsanjani J, Helbich M, Vaz ED (2013). Spatiotemporal simulation of urban growth patterns using agent-based modeling: The case Tehran. Cities. 32:33-42. [DOI:10.1016/j.cities.2013.01.005]
4. Baharvand S, Daneshvar MRM (2019). Impact assessment of treating wastewater on the physiochemical variables of environment: A case of Kermanshah wastewater treatment plant in Iran. Environmental Systems Research. 8:18. [DOI:10.1186/s40068-019-0146-0]
5. Baltaci H, Akkoyunlu BO, Tayanc M (2018). An extreme hailstorm on 27 July 2017 in Istanbul, Turkey: Synoptic scale circulation and thermodynamic evaluation. Pure and Applied Geophysics. 175:3727-3740. [DOI:10.1007/s00024-018-1841-x]
6. Bauman WH, Wheeler MM, Short DA (2005). Severe weather forecast decision aid. NASA Technical Reports Server. Washington, D.C: NASA Publictions.
7. Bazrkar MH, Zamani N, Eslamian S, Eslamian A, Dehghan D (2015). Urbanization and climate change. In: Filho WL, editors. Handbook of Climate Change Adaptation. Berlin: Springer; pp. 619-655. [DOI:10.1007/978-3-642-38670-1_90]
8. Beck HE, Zimmermann NE, McVicar TR, Vergopolan N, Berg A, Wood EF (2018). Present and future 374 Köppen-Geiger climate classification maps at 1-km resolution. Scientific Data. 5:(1). [DOI:10.1038/sdata.2018.214] [PMID] [PMCID]
9. Bhat PA, Shafiq M, Mir AA, Ahmed P (2017). Urban sprawl and its impact on landuse/land cover dynamicsof Dehradun City, India. International Journal of Sustainable Built Environment. 6(2):513-521. [DOI:10.1016/j.ijsbe.2017.10.003]
10. Bhatta B (2009). Analysis of urban growth pattern using remote sensing and GIS: A case study of Kolkata, India. International Journal of Remote Sensing. 30(18):4733-4746. [DOI:10.1080/01431160802651967]
11. Burdon FJ, Reyes M, Alder AC, Joss A, Ort C, Räsänen K, et al (2016). Environmental context and magnitude of disturbance influence trait-mediated community responses to wastewater in streams. Ecology and Evolution. 6(12):3923-3939. [DOI:10.1002/ece3.2165] [PMID] [PMCID]
12. Das S (2017). Severe thunderstorm observation and modeling-a review. Vayu Mandal. 43(2):1-29.
13. Daneshvar MRM, Abadi NH (2017). Spatial and temporal variation of nitrogen dioxide measurement in the Middle East within 2005-2014. Modeling Earth Systems and Environment. 3(1). [DOI:10.1007/s40808-017-0293-0]
14. Daneshvar MRM, Rabbani G, Shirvani S (2019). Assessment of urban sprawl effects on regional climate change using a hybrid model of factor analysis and analytical network process in the Mashhad city, Iran. Environmental Systems Research. 8:23. [DOI:10.1186/s40068-019-0152-2]
15. Derubertis D (2006). Recent trends in four common stability indices derived from U.S. radiosonde observations. Journal of Climate. 19(3):309-323. [DOI:10.1175/JCLI3626.1]
16. Dewan AM, Yamaguchi Y (2009). Land use and land cover change in greater Dhaka, Bangladesh: using remote sensing to promote sustainable urbanization. Applied Geography. 29(3):390-401. [DOI:10.1016/j.apgeog.2008.12.005]
17. Dodman D (2009). Blaming cities for climate change? An analysis of urban greenhouse gas emissions inventories. Environment and Urbanization. 21(1):185-201. [DOI:10.1177/0956247809103016]
18. Dulal HB, Brodnig G, Onoriose CG (2011). Climate change mitigation in thetransport sector through urban planning: A review. Habitat International. 35(3):494-500. [DOI:10.1016/j.habitatint.2011.02.001]
19. Emadodin I, Taravat AR, Rajaei M (2016). Effects of urban sprawl on local climate: A case study, north central Iran. Urban Climate. 17:230-247. [DOI:10.1016/j.uclim.2016.08.008]
20. Epstein J, Payne K, Kramer E (2002). Techniques for mapping suburban sprawl. Photogrammetric Engineering and Remote Sensing. 63(9):913-918.
21. Fan H, Sailor DJ (2005). Modeling the impacts of anthropogenic heating on the urban climate of Philadelphia: A comparison of implementations in two PBL schemes. Atmospheric Environment. 39(1):73-84. [DOI:10.1016/j.atmosenv.2004.09.031]
22. GDEM (2018). Global digital elevation model archived by national aeronautics and space administration. United States: NASA.
23. Hijmans RJ, Cameron SE, Parra JL, Jones PG, Jarvis A (2005). Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology. 25(15):1965-1978. [DOI:10.1002/joc.1276]
24. Jat MK, Garg PK, Khare D (2008). Monitoring and modelling of urban sprawl using remote sensing and GIS techniques. International Journal of Applied Earth Observation and Geoinformation. 10(1):26-43. [DOI:10.1016/j.jag.2007.04.002]
25. Kardani-Yazd N, Kardani-Yazd N, Daneshvar MRM (2019). Strategic spatial analysis of urban greenbelt plans in Mashhad city, Iran. Environmental Systems Research. 8:30. [DOI:10.1186/s40068-019-0158-9]
26. Kaltenböck R, Diendorfer G, Dotzek N (2009). Evaluation of thunderstorm indices from ECMWF analyses, lightning data and severe storm reports. Atmospheric Research. 93(1): 381-396. [DOI:10.1016/j.atmosres.2008.11.005]
27. Liu Y, Chen J, Cheng W, Sun C, Zhao S, Pu Y(2014). Spatiotemporal dynamics of the urban sprawl in a typical urban agglomeration: A case study on Southern Jiangsu,China (1983-2007). Frontiers of Earth Science. 8:490-504.
28. Makar PA, Gravel S, Chirkov V, Strawbridge KB, Froude F, Arnold J, Brook J (2006). Heat flux, urban properties, and regional weather. Atmospheric Environment. 40(15): 2750-2766. [DOI:10.1016/j.atmosenv.2005.11.061]
29. Miller PW, Mote TL (2018). Characterizing severe weather potential in synoptically weakly forced thunderstorm environments. Natural Hazards and Earth System Science. 18(4):1261-1277. [DOI:10.5194/nhess-18-1261-2018]
30. Mundia CN, Aniya M (2006). Dynamics of landuse/cover changes and degradation of Nairobi City Kenya. Land Degradation and Development. 17(1):97-108. [DOI:10.1002/ldr.702]
31. Pham HM, Yamaguchi Y, Bui TQ (2011). A case study on the relation between city planning and urban growth using remote sensing and spatial metrics. Landscape and Urban Planning. 100(3): 223-230. [DOI:10.1016/j.landurbplan.2010.12.009]
32. Rabbani G, Kardani-Yazd N, Daneshvar MRM (2020). Factors affecting severe weather threat index in urban areas of Turkey and Iran. Environmental Systems Research. 9:9. [DOI:10.1186/s40068-020-00173-6]
33. Rabbani G, Shafaqi S, Rahnama MR (2017). Urban sprawl modeling using statistical approach in Mashhad, northeastern Iran. Modeling Earth Systems and Environment. 4:141-149. [DOI:10.1007/s40808-017-0404-y]
34. Rafiee R, Mahiny AS, Khorasani N, Darvishsefat AA, Danekar A (2009). Simulating urban growth in Mashad City, Iran through the SLEUTH model (UGM). Cities. 26(1):19-26. [DOI:10.1016/j.cities.2008.11.005]
35. Rhodes CL, Senkbeil JC (2014). Factors contributing to tornadogenesis in landfalling Gulf of Mexico tropical cyclones. Meteorological Applications. 21:940-947. [DOI:10.1002/met.1437]
36. Siedlecki M (2009). Selected instability indices in Europe. Theoretical and Applied Climatology. 96:85-94. [DOI:10.1007/s00704-008-0034-4]
37. Sioutas MV, Flocas HA (2003). Hailstorms in Northern Greece: Synoptic patterns and thermodynamic environment. Theoretical and Applied Climatology. 75:189-202. [DOI:10.1007/s00704-003-0734-8]
38. Sudhira HS, Ramachandra TV, Jagdish KS (2004). Urban sprawl: Metrics, dynamics and modelling using GIS. International Journal of Applied Earth Observation and Geoinformation. 5(1) :29-39. [DOI:10.1016/j.jag.2003.08.002]
39. Tewolde MG, Cabral P (2011). Urban sprawl analysis and modeling in Asmara, Eritrea. Remote Sensing. 3(10): 2148-2165. [DOI:10.3390/rs3102148]
40. Tyrrell J (2007). Winter tornadoes in Ireland: The case of the Athlone tornado of 12 January 2004. Atmospheric Research. 83(2-4):242-253. [DOI:10.1016/j.atmosres.2005.09.017]
41. Wang J, Sheng Z, Zhou B, Zhou S (2014). Lightning potential forecast over Nanjing with denoised sounding-derived indices based on SSA and CS-B Pneural network. Atmospheric Research. 137:245-256. [DOI:10.1016/j.atmosres.2013.10.014]
42. World Urbanization Prospects [Internet]. Population division of the department of economic and social affairs of the United Nations. [Cited 2019, 01 Jun Published 2019, 30 May]. Madrid: UN Publications.
43. Wyoming Weather Web [Internet]. Weather Information for Wyoming. [Cited 2019, 01 Jun Published 2018, 25 February]. United States: University of Wyoming Publications.
44. Yuan F, Sawaya KE, Loeffelholz BC, Bauer ME (2005). Land cover classification and change analysis of the Twin Cities (Minnesota) metropolitan area by multitemporal landsat remote sensing. Remote Sensing of Environment. 98(2-3):317-328. [DOI:10.1016/j.rse.2005.08.006]
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