Investigation of climate change impacts on tourism climate comfort in Iran

Abstract

In this study, Mieczkowski's Tourism Climate Index (TCI) was used for Iran to investigate the climate change impacts on outdoor human comfort. The long-term data covering a network of 153 stations were used to compute TCI under baseline conditions and a climate change scenario (HadCM3-A1B) for 2016-2045. In this study LARS-WG was used for downscaling of large spatial resolution GCM outputs to a finer spatial resolution. User-friendly and multi-platform software which is called ITCIC (Iran Tourism Climate Index Calculator) was designed to calculate TCI. The spatial distribution of TCI for baseline and climate change conditions was investigated and the covered area by each TCI class was calculated by using ArcGIS 10. The annual distributions of TCI were investigated based on Scott and McBoyle (2001) Models. Also, a suite of multiple linear and non-linear regression models was used to determine the relationship between TCI, latitudes, longitudes and elevations of regions. Root mean square error (RMSE), mean error (ME), mean absolute relative error (MARE) and coefficient of determination (R 2 ) were used to evaluate the modeling accuracy. The best time and regions for outdoor activities in the base and future periods were determined. Comparison of the covered area by each TCI class in the base and future periods showed that the climate change occurrence was led to improving climate comfort. The results of error evaluation criteria showed that non-linear regression was appropriate for all month except January and October.

Estimation of the spatial climate comfort distribution using tourism climate index (TCI) and inverse distance weighting (IDW) (case study: Fars Province, Iran)

“…4), the southern cities and stations such as Lamerd, Larestan, Khonj, Zarrin dasht, and Ghirkarzine are more favorable for tourism in the cold season, and the northern cities such as Eglid and Abadeh offer more favorable conditions in the hot summer season. These results are consistent with the general findings of Bakhtiari et al (2018) andFarajzadeh andAhmadabadi (2010), which present the TCI distribution throughout the country of Iran. This pattern is also consistent with the result of Arvin et al (2013).…”

Spatial distribution and influencing factors of high-quality tourist attractions in Shandong Province, China

“…In this study, we obtained data on temperature, humidity, sunshine hours and wind speed from the National Meteorological Science Data Center (data. cma.cn) to evaluate the climate comfort of different cities in Shandong Province [39]. The temperature and humidity index mainly reflects the human body's perception of temperature and humidity (Eq 6), while the wind efficiency index accounts for the influence of temperature, wind speed and sunlight on the human body's temperature perception (Eq 7); these indices are calculated as follows [40]:…”

Spatio–temporal evolution and factors of climate comfort for urban human settlements in the Guangdong–Hong Kong–Macau Greater Bay Area

“…A previous study used the Grand Shangri-La region as a case study and investigated the change in tourism climate from 1980 to 2016 under intensifying global warming (Cheng and Zhong., 2019). As a result, the spatial distribution of the TCI was analyzed and quantified, thereby covering the areas with different TCI classes in Iran (Bakhtiari et al, 2018). Another study applied the Mediterranean outdoor comfort index and the predicted the mean vote for international tourism to examine the climatic conditions and outdoor thermal comfort in Venice, Rome, and Palermo, located in the north, center, and south of Italy, respectively (Salata et al, 2017).…”