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      Insights into climate change dynamics: A tourism climate index-based evaluation of Gilgit-Baltistan, Pakistan

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          Abstract

          The tourism industry is a significant contribution to the economy of many countries, including Pakistan. However, its activities often have a negative impact on the environment, particularly related to climate change. Notably, Pakistan ranks fifth among countries most affected by climate change, which requires a targeted analysis of the tourism sector to determine its potential impacts. Despite the critical nature of this issue, there is currently a lack of research that examines how climate change specifically impacts Pakistan's tourism industry. This study aims to address this gap by using the Tourism Climate Index (TCI) to assess the impact of climate change on the suitability of different tourism locations across the country. Our results show that popular tourist destinations such as Gilgit-Baltistan experience their peak season in the warmer third quarter (summer), which corresponds to the highest observed TCI values. This suggests that warmer temperatures could boost tourism activity and spending in these regions. Furthermore, our analysis shows that air temperature plays a crucial role in determining tourist comfort levels and significantly influences tourists' TCI values and sensory experiences. The results of this study show that the TCI methodology can be an effective tool for further research into the geographical impacts of climate change on tourism. By identifying necessary changes due to climatic changes, future studies could provide valuable insights into how the tourism industry can adapt to and reduce its environmental footprint.

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          Welcome to the Tidyverse

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            The ERA5 Global Reanalysis

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              Climatologies at high resolution for the earth’s land surface areas

              High-resolution information on climatic conditions is essential to many applications in environmental and ecological sciences. Here we present the CHELSA (Climatologies at high resolution for the earth’s land surface areas) data of downscaled model output temperature and precipitation estimates of the ERA-Interim climatic reanalysis to a high resolution of 30 arc sec. The temperature algorithm is based on statistical downscaling of atmospheric temperatures. The precipitation algorithm incorporates orographic predictors including wind fields, valley exposition, and boundary layer height, with a subsequent bias correction. The resulting data consist of a monthly temperature and precipitation climatology for the years 1979–2013. We compare the data derived from the CHELSA algorithm with other standard gridded products and station data from the Global Historical Climate Network. We compare the performance of the new climatologies in species distribution modelling and show that we can increase the accuracy of species range predictions. We further show that CHELSA climatological data has a similar accuracy as other products for temperature, but that its predictions of precipitation patterns are better.

                Author and article information

                Contributors
                Journal
                Heliyon
                Heliyon
                Heliyon
                Elsevier
                2405-8440
                30 July 2024
                15 August 2024
                30 July 2024
                : 10
                : 15
                : e35315
                Affiliations
                [a ]School of Architecture, Tianjin University, Tianjin, 300072, China
                [b ]HUASHI Branch Company of CCCC (China Communications Construction Company Ltd.) Second Highway Engineering Co., Ltd., China
                [c ]Guangdong Key Laboratory of Animal Conservation and Resource Utilization, Guangdong Public Laboratory of Wild Animal Conservation and Utilization, Institute of Zoology, Guangdong Academy of Sciences, Guangzhou, 510260, China
                [d ]School of Civil Engineering, Tianjin University, Tianjin, China
                [e ]School of Environmental Science and Engineering, Tianjin University, Tianjin, 300072, China
                [f ]Zhejiang Provincial Key Laboratory of Germplasm Innovation and Utilization for Garden Plants, School of Landscape Architecture, Zhejiang Agriculture and Forestry University, Hangzhou, 311300, China
                [g ]Key Laboratory of National Forestry and Grassland Administration on Germplasm Innovation and Utilization for Southern Garden Plants, Hangzhou, 311300, China
                Author notes
                [* ]Corresponding author. huyike11@ 123456tju.edu.cn
                [∗∗ ]Corresponding Author zhangyazhuo@ 123456tju.edu.cn
                Article
                S2405-8440(24)11346-1 e35315
                10.1016/j.heliyon.2024.e35315
                11336619
                39170213
                623b5f91-7e6b-49ba-9787-9c7e6de8df57
                © 2024 Published by Elsevier Ltd.

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

                History
                : 26 January 2024
                : 20 July 2024
                : 26 July 2024
                Categories
                Research Article

                climate change impacts,tourism comfort,climate index,bioclimatology,gilgit-baltistan-baltistan,economic contribution

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