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The Impacts of Global Warming on Tourism Climate in Iran
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Katayoon Mazloom1   , Hasan Zolfaghari * 1, Ruhollah Oji2 , Andreas Matzarakis3 |
1- Razi University
2- Guilan University
3- University of Freiburg |
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Abstract: (962 Views) |
Introduction
The impact of climate change on tourism has been the subject of many research studies. It is for this reason that tourism researchers have continued to explore the relationship between tourism and climate change and further explored response strategies among tourism stakeholders. The aim of this research is the evaluation of climate change and its impacts on thermal comfort and tourism climate of Iran. For this purpose daily maximum and minimum temperature, wind speed and relative humidity data of 91 synoptic stations during a period of 30 years (1987-2017) were used. In this research the (CDFt)[1] model is used to downscaling the GCMs data. ERA5 data was used to evaluate the performance of the downscaling model. The CanESM2 and GFDL_ESM2G daily data were used to draw future landscape changes of thermal comfort (based on the 2.6, 4.5, 8.5 RCPs). Then the thermal comfort for tourists was calculated using PET[2]. The thermal comfort results compared between the present and future. Thermal comfort of Iran was predicted for 2 periods (2021-2040) & (2041-2060). The results are displayed in ten-day classes. The number of stations that have optimal thermal comfort conditions in observation period indicates 0.83% increase compared to the futures perspective. The best conditions of climate comfort is predicted in the 24s and 25s decades and the most undesirable conditions are in the first to fifth and 34 to 36 decades. The most PET decrease is predicted in the northeast of Iran and the least increase of PET are occurred in the northwest. The results indicate an increasing on PET (between 0.06 to 1.56%) at future due to the increase in average temperature.
Materials and methods
The daily variables of maximum and minimum temperature, wind speed and relative humidity of Synoptic stations as well as the ERA5 database in a 30-year period (1987-2017) and GCMs data were used to simulate the PET from 2021 to 2060. The CDFt model was validated using the Pearson correlation coefficient and the Kolmogorov-Smirnov tests along with a climatic RMSE. There are often abrupt changes in the climate time series. The homogeneity of observed data evaluated using the RhtestV4 software package based on the maximal penalized T and F (MPF, MPT) tests. Data imputation was performed using Sequential K-Nearest Neighbor Method.
Then, the research process was done as follows:
- The variables of maximum and minimum temperature, relative humidity and wind speed was simulated by CDFt statistical downscaling. The CDFt can be considered as an approach to the Quintile Mapping (QM) method by providing cumulative distribution functions (CDFs). Assuming that the ERA5 data are converted to the cumulative distribution function of the local climate variable as predictand at the desired station. In this study the CDFt software package has been used in R software. Validation of the simulated data was performed by Pearson correlation coefficient, Kolmogorov-Smirnov test and tidal error of the mean square squares.
- The RayMan model was used to calculate the thermal comfort based on PET index using both the observed and the GCMs downscaled data (CanESM2 & GFDL_ESM2G). This model is able to calculate the effect of short and long wave radiation flux on the human body, which is required in the human energy balance model.
Results and Discussion
After making sure that the CDFt, in general, showed a good performance in downscaling of the variable applied to calculation of climate comfort in the study area. Therefore, it is reliable to project the future thermal comfort of the region under the climate change conditions.Then the historical period of CanESM2 & GFDL_ESM2G Models were used to show a comparison between PET observations and PET simulations. The results of the thermal comfort verification calculated based on the historical period simulated values in comparison to the observed values are as follows:
There is a high correlation between the time series of the PET observed and PET simulated the historical period of GCM Models. However, a linear and positive relationship was observed for all-time series. According to the Kolmogorov-Smirnov test, the simulated values of all PET time series showed good fit with the observed data at the 0.01 significance level. The calculated value of RMSE test for PET indicates the high performance of the downscaling method in simulation of historical period of GCM Models. The results showed that the CanESM2 model with minor differences performed better than the GFDL_ESM2G model. Incremental changes of physiological equivalent temperature have occurred in higher latitudes and in the high places like Alborz and Zagros. Also the PET decreasing changes are in low latitudes and central parts of Iran.
Therefore, it is expected that more area of Iran will be at the desired threshold of thermal comfort at the future.
The results showed that in the rest of the year, the largest utilitys are in the 24 and 25 th decades. Regardless of the model type towards the Rcp4.5 and Rcp8.5 and the 2041 to 2060 period increasing the values of the Physiological Equivalent Temperature will proceed. Also, the length of comfort decades is increasing in future periods.
Conclusion
This study was performed in order to provide infrastructure for tourism in future periods from 2021 to 2060. For this purpose, the climate comfort of Iran was predicted by GCMs data. Currently, most parts of the Iran are in the range of mild to severe cold stress. It is predicted that wider parts of study area will have optimal comfort conditions in future. Also, the number of optimal comfort decades is increasing compared to the observation period. The results of the climate comfort study showed that mild to severe cold stress conditions are predicted in More than half of this country at different decades of the year. So it will create opportunities and constraints in the future which requires long-term planning and strategies in this area.
[1] . Cumulative Distribution Function- transform
[2] . Physiological Equivalent Temperature
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| Keywords: Tourism Climate, Global warming, PET, CDFt, GCMs, Iran. |
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Full-Text [PDF 2323 kb]
(223 Downloads)
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Type of Study: Research |
Subject:
Special
Received: 2021/08/8 | Accepted: 2022/01/26 | Published: 2024/04/30
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