Investigating the Relationship between Telecommunication Patterns and Climate of Tourism Comfort in Guilan Province by UTCI Method

Subject Areas :

Amir  Gandomkar ¹ (Department of Geography, Najafabad Branch, Islamic Azad University, Najafabad, Iran)
Saeideh  Moradmand ² (Department of Geography, Najafabad Branch, Islamic Azad University, Najafabad, Iran)
Ahmad  Khademolhosseini ³ (Department of Geography, Najafabad Branch, Islamic Azad University, Najafabad, Iran)
Alireza  Abbasi ⁴ (Department of Geography, Najafabad Branch, Islamic Azad University, Najafabad, Iran)

Received: 2020-08-14 Accepted : 2020-09-16 Published : 2020-11-21

Keywords: Gilan province, Climate Comfort, Teleconnection, UTCI Model, Man-kendal Test,

Abstract :

One of the most important climatic elements that is influenced by Teleconnection patterns is temperature and precipitation, both of which are the most influential factors in the study of tourism comfort climate. The purpose of this study is to investigate the climate of tourism comfort in Gilan province in the interval (1980-2016) using the UTCI model and also to investigate the relationship between tourism comfort climate of Gilan province with Teleconnection patterns. The climate comfort of Gilan province is one of the atmospheric signals. Investigating this relationship can help the necessary planning for tourism development in this province and proper location. Using the Mann-Kendall trend test, the results show that in the last 37 years, the value of the UTCI index has decreased in July and the extreme heat stress in this month has decreased; But in other months of the year, no specific trend was seen for this index. Also, the correlation between the indicators of tourism comfort climate in the province with Teleconnection patterns through Pearson correlation coefficient showed that out of 22 models studied, 9 models including TNA, NTA, SOLAR FLUX, NCP, SOI, NAO, MOON SOON, AMM, AMO are directly or inversely related to UTCI in different months of the year and at 95% and 99% significance levels.

References:

1. جوان، خ. ( 1396)، مقایسۀ شاخص اقلیم گردشگری (TCI) و شاخص اقلیمی تعطیلات (HCI) (مطالعۀ موردی: ارومیه)، پژوهشهای جغرافیای طبیعی، دوره 49، شماره 3، صص 439- 423 .


2. سلمانی مقدم، م. جعفری، م. (1394)، ارزیابی اقلیم آسایش گردشگری استان زنجان با استفاده از شاخص (TCI) و تکنیک GIS، فصلنامه فضای گردشگری، شماره 17، صص 151- 133.


3. عبدالهی، ع. ( 1393 )، بررسی رابطه نوسانات بارش در حوضه آبی دریای خزر با شاخص‌های پیوند از دور، رساله دکتری، گروه جغرافیا، دانشگاه آزاداسلامی واحد نجف آباد.


4. محمدی، ح، (1385)، "آب و هواشناسی کاربردی"، انتشارات دانشگاه تهران


5. محمودی، ح. پازوکی نژاد، ز. ( 1392 )، تغییرات آب و هوا و سیاست‌های مقابله در صنعت گردشگری، برنامه ریزی و توسعه گردشگری، شماره 6، صص 108- 93.


6. میرحسینی (1395)، ارزیابی و پهنه‌بندی اقلیم آسایش گردشگری طبیعی در استان یزد با استفاده از GIS و شاخص اقلیم گردشگری (TCI)، فصلنامه علمی- پژوهشی جغرافیا ( برنامه ریزی منطقه ای)، شماره 1، صص 115-101.


7. نبی بیدهندی، غ. (1387)، مفاهیم و پیامدهای تغییر آب و هوا، انتشارات دانشگاه تهران، ص 3.


8. Dube, K., Nhamo, Godwell. (2018) Climate variability, change and potential impacts on tourism: Evidence from the Zambian side of the Victoria Falls, Environmental Science & Policy, Volume 84, Pages 113-123.


9. Frasati, F Mozafar, F Nsrollahi, F Mollaei N (2018), Environmental analysis of the internal spaces of indigenous peoples of mountainous regions of Gilan with an emphasis on thermal comfort (Case study: Dosaldeh village, Roodbar), Journal of Planning Human Resource Studies, No. 1, p. 1- 17.


10. Grillais, M., outroulis, A., Seiradais, . (2016) Implications of 2 °C global warming in European summer tourism. Climate Services, Volume 1, Pages 30-38.


11. Barimalala, A. Bracco, F. Kucharski, J. P. McCreary, A. Crise, Arabian Sea ecosystem responses to the South Tropical Atlantic teleconnection, Journal of Marine Systems, Volumes 117–118, May 2013, Pages 14–30


12. Scott, D., Rutty, M., Amelung, B., & Tang M. (2016) an Inter-Comparison of the Holiday Climate Index (HCI) and the Tourism Climate Index (TCI) in Europe. Atmosphere j, 7, 80: 2-17.


13. Grillakis, M., Koutroulis, A., Seiradakis, K. (2016) Implications of 2 °C global warming in European summer tourism. Climate Services, Volume 1, Pages 30-38.


14. Michailidou, A., Vlachokostas, C., Moussiopoulos, N. (2016) Interactions between climate change and the tourism sector: Multiple-criteria decision analysis to assess mitigation and adaptation options in tourism areas. Tourism Management, Volume 55, Pages 1-12.


15. Wang, L., Fang, B., Law, R. (2018) Effect of air quality in the place of origin on outbound tourism demand: Disposable income as a moderator. Tourism Management, Volume 68, Pages 152-161.


16. Farajzadeh, Hasan and Matzerakis, Andreas (2009), Quantification of climate for tourism in the northwest of Iran, Journal of Meteorological Applications, Volume 16 Issue 4


17. Daniel Scott, C. Michael Hall, Stefan Gössling, (2019), Global tourism vulnerability to climate change, Annals of Tourism Research,Volume 77, Pages 49-61


18. Roberta Atzori, Alan Fyall, Graham Miller, ( 2018 ), Tourist responses to climate change: Potential impacts and adaptation in Florida's coastal destinations, Tourism Management,Volume 69, Pages 12-22.