Identification and Selection of the Best Tourism Comfort Climate Index in Mangrove Forest Habitats Case Study: Mangrove Protected Area

ABSTRACT
Using comfort climate indices for tourism development in protected areas is essential for proper planning. The present study ranked climate indicators to identify and select the best tourism climate index to determine a good time for tourism activities in the Hara protected area. The results of the TCI index revealed that the best time for tourists is December, December, February, March, and April. In the HCI index, December, January, February, and March are the best months of the year. In Becker's results, the environmental conditions are suitable for recreational activities in December, January, February, March, and April. The SET index identified December, January, February, and March as the most suitable months. According to the results of the CI index, the best time for tourism is October and March. Also, in the results of the Tarjong index, January, February, and March were selected as the best months. Finally, the results of Mahoney's index indicate that December, January, and February were more favourable. In general, the ranking and analysis of the effects of tourism comfort climate indices in this study displayed that TCI is more comprehensive compared to other indices. According to the weather conditions of the area, it has more realistic results. Among the indicators, the highest rank was assigned to Tarjong, HCI, Mahoney, SET, Becker, and CI indices. The results of this study will help tourism planners determine the appropriate time for tourism in this area
Extended Abstract
Introduction
Currently, the tourism industry is considered one of the important sources of production, income, employment, and creating infrastructure to achieve sustainable development. Knowing the climatic limitations and threats as well as climatic attractions and potentials is very important for planning and developing tourism activities. In this regard, accurate knowledge of the climatic characteristics of the tourist destination can provide a platform for accurate planning and management decisions in the area. Measuring comfort climate is one of the ways to properly manage human activities, which should be considered for natural ecosystems, especially the developable zones of protected areas. In general, the climate indices indicate which months of the year have the best climate and comfort for tourism in the studied area.
Unfortunately, in recent years, this issue has not been paid attention to in tourism destinations, especially sensitive natural ecosystems and protected areas, which results in the destruction of the habitat in terms of the occurrence of climatic events, tourism development without proper time planning, and also the lack of tourists’ satisfaction of the area. Therefore, the present study investigated the identification and selection of the best climate indices of tourism comfort to determine the suitable time for tourism activities development and management planning in mangrove habitats.
This area to many tourists and visitors is very important due to its high biological diversity and many tourist attractions; therefore, it is essential to determine the proper time for tourism activities and also to select a single index to measure the tourism climate in this area. Accordingly, the most important questions of the current research are:
 1) Based on the comfortable climate, which are the most suitable and desirable months for nature tourism in this area?
2) Which of the indicators has a higher rank, more accurate results, and is closer to the real climate conditions of the area?
 
Methodology
To measure tourism climate, the weather data needed for 25 years (1996 to 2021) was exerted from the Qeshm synoptic station as the nearest station to the area. Likewise, indices examined in this study include Tourism Climate Index (TCI), Holiday Climate Index (HCI), Becker Index, Standard Effective Temperature Index (SET), Nervous Stress Index/Comfort Index (CI), Tarjong, and Mahoney. In the TCI survey, seven climate variables were used, which include the monthly average maximum daily temperature, average daily temperature, minimum daily relative humidity, average daily relative humidity, long-term average rainfall of each month, number of sunny hours, and average wind speed. In the estimation of HCI, five climatic variables were used based on three main aspects of tourism including thermal, aesthetic, and physical comfort. Climatic variables studied in this index are maximum temperature, average relative humidity, cloudiness degree, precipitation, and wind speed. Becker's index is one of the most widely used indices in determining the conditions of human bioclimate concerning the environment. Climate variables of temperature and wind speed were used to calculate this index. The SET index cannot be a fixed number and depends on environmental conditions such as wind speed and dry temperature, physiological conditions, the number of clothes worn and the level of personal activities. The basis for calculating this index is based on air temperature and humidity. The CI index specifies the type of physiological pressure exerted on the establishment of comfort between absorption and heat dissipation, which aims to explain comfort degrees using the elements of temperature, humidity, and wind. The Tarjong index is one of the bioclimatic methods for evaluating human comfort, which can be used to determine the most suitable area and the most favourable months of the year for tourism and accommodation. In calculating this index, temperature and relative humidity were applied. The Mahoney index determines each month's day and night comfort zone according to the average temperature of the studied location and the average relative humidity in each month.
 
Results and discussion
According to the TCI index results, the best time for tourists to visit this area is five months of the year, including December, January, February, March, and April. In the HCI index, the best time for tourism activities is between 4 and 5 months, including December, January, February, and March. The results of Becker's index indicate that in December, January, February, March, and April, the environmental conditions for recreational activities in mild and pleasant days with cool nights and tolerable hot days and mild nights, and the climate is suitable. According to the SET index, December, January, February, and March are the most suitable months for tourism in the area. The CI index revealed that the most favourable time for tourists to visit this area is in October and March, and also, according to the results of the Tarjong index, the best time for tourism climate is during the day in January, February, and March, and also during the night, it is dedicated to April and November. Finally, the results of Mahoney's index indicate that December, January, and February are more favourable for performing tourism activities. In general, the ranking and analysis of the tourism comfort climate indices in this study indicate that the TCI index is more comprehensive than other studied climate indices, and also, considering the area climate conditions, it has more realistic results. After the TCI index, according to the common favourable months, the highest rank is assigned to the Tarjong index, the HCI index, the Mahoney index, the SET index, the Becker index, and the CI index.
 
 
Conclusion
Accordingly, the results of the present study, according to the climatic conditions of the mangrove-protected area, the advantages and limitations of climatic indicators, as well as access to the data of synoptic or climatology stations in the area, help tourism planners to determine the optimal time of tourism by selecting the appropriate climatic index. Also, the present study improves our understanding of the correlation of the tourism climate index with the time of presence and the number of visitors during other months of the year in the area, and managers can make more informed decisions and sustainable development of tourism in the mangrove protected area.
 
Funding
This research received with the cooperation and financial support of Iran National Science Foundation (INSF) project number 4005972.‌
 
Authors’ Contribution
All of the authors approved thecontent of the manuscript and agreed on all aspects of the work.
Conflict of Interest
Authors declared no conflict of interest.
 
Acknowledgments
We are grateful to all the scientific consultants of this paper.