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Abstract

Background & Objectives: Heat wave hazard modelling is attracting a lot of attention, especially with the onset of climate change and global warming currently taking place. General global climatic models and trends predict that heat waves will increase in frequency, duration, and intensity. Yet, heat wave hazard modelling remains a challenging and imperative problem because of the complexity introduced by natural and human elements such as land-use, air temperature variability, topography, soils characteristics, and air pollution. In this study, heat wave hazard in Qatar is mapped, modelled and predicted for two and five years. Methods: Geographic information system (GIS) and remote sensing (RS) techniques are used to carry out multilayer analysis by combining different parameters that influence and determine heat wave in the region. Land surface temperature (LST) derived from remotely sensed data (Landsat ETM thermal infra-red band) is also used in the analysis. The LST image proved to be extremely useful as the variation of the thermal phenomenon is highly related to and reflects the land surface variability in the study area. Heat wave index (HWI) is calculated using in situ and Gumbel frequency analysis is used for head wave (HW) prediction. Step-wise regression analysis is used to identify the predictive variables/parameters of HWI and to determine the model. Results: The magnitude and spatial distribution of heat wave in Qatar are mapped. These results can be used address environmental, health, and urban planning issues. Population-census data is used to estimate the proportion of the vulnerable age groups that will be affected by HW in Qatar. More than 87% of children aged 4 and less are found to be at very high risk to HW, while more than 86% of people above 65 years are at the same level of risk. Conclusions: GIS and RS techniques are valuable research tools for environmental studies. The model developed here can be used by decision makers and planners to make better informed decisions on planning of hospitals and schools in low heat wave risk areas. Furthermore, the model gives a good indication for planning future electric energy consumption by air-conditioning and cooling of buildings.

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/content/papers/10.5339/qfarf.2012.EEP45
2012-10-01
2019-12-09
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http://instance.metastore.ingenta.com/content/papers/10.5339/qfarf.2012.EEP45
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