Investigation of climate change impacts on tourism climate comfort in IranView Crossref metadata(active tab) <div id="edit-xml" class="form-item form-type-item"> <?xml version="1.0"?> <doi_batch version="4.4.1" xmlns="http://www.crossref.org/schema/4.4.1" xmlns:jats="http://www.ncbi.nlm.nih.gov/JATS1" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.crossref.org/schema/4.4.1 http://www.crossref.org/schemas/crossref4.4.1.xsd"><head><doi_batch_id>000696</doi_batch_id><timestamp>1714162146</timestamp><depositor><depositor_name>University of the Aegean</depositor_name><email_address>uoa@university.com</email_address></depositor><registrant>Global NEST Journal</registrant></head><body><journal><journal_metadata><full_title>Global NEST Journal</full_title><full_title>Global NEST: the international Journal</full_title><issn media_type="print">1790-7632</issn><issn media_type="electronic">2241-777X</issn></journal_metadata><journal_issue><titles><title>Issue 2</title></titles><publication_date><month>07</month><year>2024</year></publication_date><journal_volume><volume>20</volume></journal_volume><issue>2</issue></journal_issue><journal_article><titles><title>Investigation of climate change impacts on tourism climate comfort in Iran</title></titles><abstract xmlns="http://www.ncbi.nlm.nih.gov/JATS1"><p>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 (1981–2015) 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 (R2) 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.</p></abstract><publication_date><month>05</month><day>23</day><year>2018</year></publication_date><pages><first_page>291</first_page><last_page>303</last_page></pages><publisher_item><identifier id_type="pii">gnest_02435</identifier></publisher_item><doi_data><doi>10.30955/gnj.002435</doi><timestamp>1714162146</timestamp><resource>https://journal.gnest.org/publication/gnest_02435</resource></doi_data></journal_article></journal></body></doi_batch> </div>
