1887
Volume 2014, Issue 1
  • E-ISSN: 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.

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2014-06-01
2019-10-17
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  • Article Type: Research Article
Keyword(s): GIS , heat wave hazard modelling , land surface temperature and thermal remote sensing
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