عنوان مقاله [English]
Today, tourism as a dynamic and vast industry has become one of the largest economic sectors in the world. In a way that includes all the basic elements of the world community and accounts for a significant share of national and local economies (Scott et al., 2016; Masoudi, 2021). Empirical evidence suggests that climate resources directly affect destination choice, season length and, quality of tourism, as well as destination costs (Li et al. 2017; Wilkins et al. 2018; Yu et al. 2021 ). Climate change and extreme events such as heat and cold waves, droughts, storms and heavy rainfall can also affect tourism demand. Therefore, it is important to study the climatic conditions of an area in terms of climate comfort and determine suitable periods for all elements of tourism, such as planning, implementation, accommodation, etc. Climatic conditions and weather are essential components of the holiday experience and are among the main motivations for travel (Goh, 2012). Providing threshold values and indices can give an idea of the comfort level of the climatic conditions of the environment. For this purpose, tourism climate indices have been prepared (Oztürk and Göral, 2018; Scott et al. 2016). tourism Climate indices, which are formed from indices designed for use in health and agriculture, are tools that use raw meteorological data to describe the suitability of a particular climate for tourism activities. Such indices have been widely used to compare climate resources and their impact on tourism for more than 35 years (Rutty et al., 2020).
Materials and methods
In this study, in order to evaluate the tourism climate of Yazd province, the Tourism Climate Index (TCI) and the Holiday Climate Index (HCI) have been used. To calculate these indices from the data of maximum air temperature, mean air temperature, minimum relative humidity, mean relative humidity, precipitation, cloud cover, sunny hours and wind speed of synoptic stations of Yazd province and neighboring provinces between 2004 and 2021 used.
Climate Tourism Index (TCI)
The first composite index designed to assess tourism resources was the Tourism Climate Index (TCI) proposed by Mieczkowiski (1985) and it consists of five sub-indices including daytime comfort index (CID), daily comfort index (CIA), precipitation (P), sunshine (S) and wind (W) (Equation 1).
TCI=2*(4 CID +CIA+2 P+2 S+W) (1)
Holiday Climate Index (HCI)
Despite the widespread use of TCI, this index has been significantly criticized. The four main shortcomings of the TCI index are: subjective rating and weighting system of climate variables, ignoring the possibility of the impact of physical climate variables, low temporal resolution of climate data and not paying attention to the different climatic needs of the main tourism sectors. (Scott et al., 2016). In response to these inherent weaknesses in TCI, the Holiday Climate Index (HCI) was proposed (Equation 2).
Which TC ic a thermal comfort index, A index is cloud coverage, P and W are precipitation and wind respectively.
Results and discussion
Using the IDW interpolation method and GIS software, Point data were converted to surface data and finally TCI and HCI index maps for Yazd province were drawn in different months of the year. The spacial distribution of TCI and HCI indices showes in April and May, according to the TCI index, most of the province's area is in the excellent and ideal class. In June, according to the TCI index, due to the gradual dominance of summer weather in the province, with the exception of mountainous areas, other areas are in good and very good class. According to the HCI index, in April, the eastern half of the province is in the excellent category and the western half is in the very good category. In May, all areas of the province are classified into excellent and ideal classes. But according to the HCI index, almost the entire area of the province in the months of June to September is in the ideal class for tourism and holidays. In October, according to the TCI index, most parts of the province are in ideal and excellent classes. During the autumn season, with the gradual decrease of temperature, the desirability of climatic conditions for tourism also decreases over time. The results of the HCI index are almost similar to the TCI index. The results of HCI and HCI indices show that in January, almost the entire area of the province is in good condition, and with the passage of time towards the end of winter and the improvement of temperature conditions, in March, the conditions for tourism in the province will improve. The temporal distribution of TCI and HCI indices also shows that in most stations in the second half of the year, October to March, as well as April and May, both indices are in good agreement with each other. This issue is more evident in the cold months of the year, December, January and February. Also, changes and fluctuations of both indices in these months are very limited.
The results of the study of these two indices showed that in general the quantitative values of the HCI index are larger than the values of the TCI index. The reason for this difference is due to the difference in the weight of the components, especially the thermal comfort component and the rating system of the variables. According to HCI index estimates, the warmest months of the year, June to September, are the most suitable months for tourism and leisure. During these months, all areas of the province have ideal conditions for tourism. But the results of the TCI index show that fewer months have ideal climatic conditions. Most stations have these conditions for a maximum of two or three months of the year, which includes the months of April, May and, October. In the cold season, in most stations, the results of the two indices are in complete agreement with each other, but in the warm season, the difference between the two indices is very large.
Yu, D. D., Rutty, M., Scott, D., & Li, S. (2021). A comparison of the holiday climate index: beach and the tourism climate index across coastal destinations in China. International Journal of Biometeorology, 65(5), 741-748.