An Assessment of Bioclimatic Conditions for Tourists—A Case Study of Mashhad, Iran

Esmaili, Reza; Ghalhari, Gholamabbas Fallah

doi:10.4236/acs.2014.41016

Cited by 6 publications

(7 citation statements)

References 20 publications

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“…Besides, Firdausi's mausoleum (the greatest Persian epic poet who lived in tenth century) attracts millions of tourists annually. According to the records of Cultural Heritage and Tourism Organization of Khorasan Razavi, 25 to 30 million pilgrims and tourists visit Mashhad annually (Esmaili and Fallah Ghalhari 2014b). For Mashhad warm bioclimatic condition with a focus on summer, especially on July, is the most important inhibiting factor of thermal comfort.…”

Section: Discussionmentioning

confidence: 99%

“…For Mashhad warm bioclimatic condition with a focus on summer, especially on July, is the most important inhibiting factor of thermal comfort. Esmaili and Fallah Ghalhari (2014b) showed for Mashhad that summer days are not ideal for tourism due to the heat stress. According to their findings, the daily course of PET shows a Gaussian curve, i.e., a peak in July due to heat stress.…”

Section: Discussionmentioning

confidence: 99%

“…These factors induce highly-diverse bioclimatic conditions in terms of temporal and spatial scale in different parts of Iran. The reality of these various conditions are reflected in numerous studies conducted in Iran (Ramezani Gourab and Foroughe, 2010, Delavar et al 2012, Ramazanipoor and Behzadmoghaddam 2013, Safaeipoor et al 2013, Esmaili and Fallah Ghalhari 2014b, Roshan et al 2015, Mokhtari and Anvari 2015. Despite the fact that several studies have focused on the different bioclimate features of Iran so far, their reliance on monthly data and shortterm time series is among their weaknesses.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A comprehensive analysis of physiologically equivalent temperature changes of Iranian selected stations for the last half century

Roshan

Yousefi

Kovács

et al. 2016

Theor Appl Climatol

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As a preliminary and major step for land use planning of the coming years, the study of variability of the past decades' climatic conditions with comprehensive indicators is of high importance. Given the fact that one of the affected areas by climatic change includes variability of thermal comfort, this study uses the physiologically equivalent temperature (PET) to identify and evaluate bioclimatic conditions of 40 meteorological stations in Iran. In this study, PET changes for the period of 1960 to 2010 are analyzed, with the use of Mann-Kendall non-parametric test and Pearson parametric method. The study focuses particularly on the diversity in spatio-temporal distribution of Iran's bioclimatic conditions. The findings show that the mean frequency percentage of days with comfort is 12.9 % according to the total number of selected stations. The maximum and minimum frequency percentage with values of 17.4 and 10.3 belong to Kerman and Chabahar stations, respectively. The findings of long-term trend analysis for the period of 1960-2010 show that 55 % of the stations have significant increasing trend in terms of thermal comfort class based on the Pearson method, while it is 40 % based on Mann-Kendall test. The results indicate that the highest frequency of days with thermal comfort in the southern coasts of Iran relates to the end of autumn and winter, nevertheless, such ideal conditions for the coastal cities of Caspian Sea and even central stations of Iran relate to midspring and mid-autumn. Late summer and early autumn along with late spring can be identified as the most ideal times in the west and northwest part of Iran. In addition, the most important inhibiting factors of thermal comfort prove to be different across the regions of Iran. For instance, in the southern coasts, warm to very hot bioclimatic events and in the west and northwest regions, cold to very cold conditions turn out to be the most important inhibiting factors. When considering the variations across the studied period, an increase in the frequency of thermal comfort condition is observed in almost half of the stations. Moreover, based on Pearson and Mann-Kendall methods, the trend of changes in monthly averages of PET has decreased in most stations and months, which can lead to different consequences in each month and station. Thus, it is expected that due to PET changes in recent decades and to the intensified global warming conditions, Iran's bioclimatic conditions change in a way that transfers the days with comfort to early spring and late autumn.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A comprehensive analysis of physiologically equivalent temperature changes of Iranian selected stations for the last half century

Roshan

Yousefi

Kovács

et al. 2016

Theor Appl Climatol

View full text Add to dashboard Cite

show abstract

“…The output of the indices is presented in values easily comprehensible by different individuals [11]. PET has been applied for studying the bioclimatic conditions in different parts of the world: Hungary [12], Nigeria [13], Iran [14,15], Tanzania [16], Greece [17] and Serbia [18]. A number of studies have also applied UTCI globally such as over Saudi Arabia [19], Crete Island [8].…”

Section: Introductionmentioning

confidence: 99%

Assessment of the bioclimatic conditions over some selected stations in Nigeria

2019

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This study seeks to assess the bioclimatic conditions of some selected stations in Nigeria from 1981 to 2010 using meteorological data input from the Nigerian Meteorological Agency (NIMET). The specific objectives were to analyze the spatiotemporal variability of the key weather parameters critical to bioclimatic conditions, evaluate the monthly and seasonal variation of the bioclimatic conditions and analyze the spatiotemporal variation of the human bioclimatic conditions over the area. The study applied the use of two bioclimatic indices, namely physiological equivalent temperature (PET) and Universal Thermal Climate Index (UTCI). PET was computed using the RayMan model, while UTCI was computed using the Bioklima model. The bioclimatic conditions were observed to vary seasonally as thermal stress levels particularly increased in the months of March to May and these months correspond to the pre-wet period of the year. The conditions, however, improved during the wet and dry periods. The early hours of the day (0600 Local Standard Time) were characterized mostly by no thermal stress conditions; however, the northern regions were cooler than the other parts of the country. The frequency of thermal stress categories for each station also revealed the threshold of the thermal stress condition over each station. The result from this study provides useful bioclimatic information applicable in the development of adaptive strategies and efficient planning with respect to the thermal environment.

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“…Nasirihendkhaleh et al, (2014) evaluated and separated Fars province (south-central Iran) touristic climate using TCI and GIS framework. Esmaili and Fallah Ghalhari, (2014) evaluated the bio-climatic conditions of the tourists in Mashhad (northeast of Iran), through the use of PET index on an hourly scale. Morris et al (2017) considered the impact of urbanization level on the interactions of the urban area, the urban climate, and human thermal comfort.…”

Section: Introductionmentioning

confidence: 99%

Investigation of climate change impacts on tourism climate comfort in Iran

2018

Global NEST Journal

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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 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 (R 2 ) 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.

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An Assessment of Bioclimatic Conditions for Tourists—A Case Study of Mashhad, Iran

Cited by 6 publications

References 20 publications

A comprehensive analysis of physiologically equivalent temperature changes of Iranian selected stations for the last half century

A comprehensive analysis of physiologically equivalent temperature changes of Iranian selected stations for the last half century

Assessment of the bioclimatic conditions over some selected stations in Nigeria

Investigation of climate change impacts on tourism climate comfort in Iran

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