1887
Volume 2014, Issue 1
  • EISSN: 2223-506X

Abstract

Heat waves are considered to be the major cause of environmental and weather-related fatalities. Heat waves also have a severe impact on people with chronic cardiac and respiratory diseases, such as asthma. With climate change and global warming processes taking place, general global climatic models predict that heat wave events will increase in frequency, duration, and intensity. Therefore, heat wave modelling has attracted considerable attention from scientists and decision-makers alike. Yet it remains challenging, complex, and an imperative problem. This complexity is introduced mainly by land surface and atmospheric spatial variability, such as land use and air pollution concentration.

This study addresses this spatial complexity by using remotely sensed thermal data in the form of Land Surface Temperature (LST) images, along with meteorological data to model heat waves in Qatar. Multi-criteria/multi-parameters/multi-layer analysis is carried out using Geographic Information System (GIS) by combining many complex parameters that influence or determine heat waves in the region. Gumble statistical frequency analysis is carried out on time series data to predict heat wave events.

Results from the model show that a high portion of the population's vulnerable age groups are likely to be severely affected by future heat wave events in Qatar- based on a five year return period. The analysis revealed that at least 87% of children aged 4 or under would be exposed to a very high intensity level of heat wave events, while more than 86% of elderly people, over 65 years of age, would be exposed to the same intensity level of hazard.

The study proves that thermal satellite imaging improves heat wave hazard modelling, as it addresses the complex spatial variability of land surface. The developed model is applicable at a local, as well as regional, scale, making an original contribution to heat wave modelling.

Loading

Article metrics loading...

/content/journals/10.5339/connect.2014.9
2014-06-01
2024-11-07
Loading full text...

Full text loading...

/deliver/fulltext/connect/2014/1/connect.2014.9.html?itemId=/content/journals/10.5339/connect.2014.9&mimeType=html&fmt=ahah

References

  1. Reid C, O'Neill M, Gronlund C, Brines S, Brown D, Diez-Roux A, Schwartz J. Mapping community determinants of heat vulnerability. Environmental Health Perspectives. 2009; 117:11:17301736.
    [Google Scholar]
  2. Baccini M, Biggeri A, Accetta G, Kosatsky T, Katsouyanni K, Analitis A, Anderson H, Bisanti L, D'Ippoliti D, Danova J, Forsberg B, Medina S, Páldy A, Rabczenko D, Schindler C, Michelozzi P. Heat effects on mortality in 15 European cities. Epidemiology. 2008; 9:5:711719.
    [Google Scholar]
  3. WHO: World Health Organization: The health impacts of 2003 summer heat waves. Briefing note for the delegations of the fifty-third session of the WHO Regional Committee for Europe. 2003;12. http://www.new-pdf.com/ebook.php?id = 2673073 Accessed 06/03/2014.
  4. Robinson P. On the definition of a heat wave. Journal of Applied Meteorology. 2001; 40:4:762775.
    [Google Scholar]
  5. NOAA. National Oceanic and Atmospheric Administration Magazine. Heat- The number one non-severe weather related killer in the United States. 2006. http://archive.is/MQLR Accessed 6/03/2014.
  6. Robine J, Cheung S, Le Roy S. Death toll exceeded 70 000 in Europe during the summer of 2003. Comptes Rendus Biologies. 2008; 331:2:171178.
    [Google Scholar]
  7. Schiermeier Q. Mediterranean most at risk from European heatwaves: Increased heat and humidity predicted to have biggest health impact in valleys and coastal cities. Nature. 2010;, http://www.nature.com/news/2010/100517/full/news.2010.238.html. Accessed 06/03/2014.
    [Google Scholar]
  8. Janahi IA, Bener A, Bush A. Prevalence of asthma among Qatari schoolchildren: International Study of Asthma and Allergies in Childhood, Qatar. Pediatratric Pulmonology. 2006; 41:1:8086.
    [Google Scholar]
  9. Baldi M, Pasqui M, Cesarone F, De Chiara G. Heat waves in the Mediterranean region: analysis and model results. 16th Conference on Climate Variability and Chang, San Diego, California: 2005.
  10. WHO: World Health Organization: Improving public health responses to extreme weather/heat-waves: EuroHEAT. Technical summary. World Health Organization Regional Office for Europe. 2009. http://www.euro.who.int/__data/assets/pdf_file/0010/95914/E92474.pdf Accessed 06/03/2014.
  11. Gosling S, Lowe J, McGregor G, Pelling M, Malamud B. Associations between elevated atmospheric temperature and human mortality: a critical review of the literature. Climatic Change. 2008; 92:3-4:299341.
    [Google Scholar]
  12. IPCC. Summary for policymakers. Climate Change: The physical science basis. Contribution of working group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge and New York. https://www.ipcc.ch/publications_and_data/ar4/wg1/en/spm.html. Accessed 6/03/2014.
  13. Meehl G, Tebaldi C. More intense, more frequent, and longer lasting heat waves in the 21st century. Science. 2004; 305:5686:994997.
    [Google Scholar]
  14. Tamrazian A, LaDochy S, Willis J, Patzert W. Heat waves in southern California: are they becoming more frequent and longer lasting? Association of Pacific Coast Geographers. 2008; 70::5969.
    [Google Scholar]
  15. Oke T. Boundary Layer Climates. 2nd Edition. New York, NY, USA: Methuen 1987.
    [Google Scholar]
  16. Shahmohamadi P, Che-Ani AI, Maulud KNA, Tawil NM, Abdullah NAG. The impact of anthropogenic heat on formation of urban heat island and energy consumption balance. Urban Studies Research. 2011; 2011::19.
    [Google Scholar]
  17. Oke T. The heat island of the urban boundary layer: characteristics, causes and effects. In: Cermak JEDavenport AGPlate EJViegas DX, eds. Wind climate in cities. Vol. 277. Netherlands: Springer 1995;:81107.
    [Google Scholar]
  18. Xu W, Wooster W, Grimmond C. Modelling of urban sensible heat flux at multiple spatial scales: A demonstration using airborne hyperspectral imagery of Shanghai and a temperature-emissivity separation approach. Remote Sensing of Environment. 2008; 112:9:34933510.
    [Google Scholar]
  19. WHO: World Health Organization: The WHO e-Atlas of disaster risk for Eastern Mediterranean region. Exposure to natural hazards. 2011;1. http://www.who-eatlas.org/eastern-mediterranean Accessed 06/03/2014.
  20. Souch C, Grimmond S. Applied climatology: urban climate. Progress in Physical Geography. 2006; 30:2:270279.
    [Google Scholar]
  21. Hajat S, Armstrong B, Baccini M, Biggeri A, Bisanti L, Russo A, Páldy A, Menne B, Kosatsky T. Impact of high temperatures on mortality: Is there an added heat effect? Epidemiology. 2006; 17:6:632638.
    [Google Scholar]
  22. Ebi K, Meehl G. Heatwaves and global climate change. The heat is on: Climate change and heatwaves in the Midwest. Regional impacts of climate change: four case studies in the United States. Arlington, Virginia: Pew Center on Global Climate Change 2007;:821.
    [Google Scholar]
  23. Steadman R. The assessment of sultriness. A temperature-humidity index based on human physiology and clothing science. Journal of Applied Meteorology. 1979; 18:1:861873.
    [Google Scholar]
  24. Steadman R. A universal scale of apparent temperature. Journal of Climate and Applied Meteorology. 1984; 23::16741687.
    [Google Scholar]
  25. CAA: Civil Aviation Authority, Climate Section, Department of Meteorology, Qatar 2011.
  26. El Morjani Z. Methodology document for the WHO e-atlas of disaster risk. Volume 1. Exposure to natural hazards Version 2.0: Heat wave hazard modelling. Taroudant poly-disciplinary faculty of the Ibn Zohr University of Agadir, Morocco. 2011, ISBN:978-9954-0-5396-6.
  27. Gumbel E. Bivariate exponential distribution. Journal of the American Statistical Association. 1960; 55:292:698707.
    [Google Scholar]
  28. Hamby D. A review of techniques for parameter sensitivity analysis of environmental models. Environmental Monitoring and Assessment. 1994; 32:2:135154.
    [Google Scholar]
  29. In: Saltelli AChan KScott M, eds. Handbook of Mathematical and Statistical Methods for Sensitivity Analysis. Probability and Statistics Series, Wiley 2000.
    [Google Scholar]
/content/journals/10.5339/connect.2014.9
Loading
/content/journals/10.5339/connect.2014.9
Loading

Data & Media loading...

  • Article Type: Research Article
Keyword(s): GISheat wave hazard modellingland surface temperature and thermal remote sensing
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error