Authors / CoAuthors
Sanabria, A. | Cechet, R.
Abstract
A model to study Fire Weather Potential has been developed at Geoscience Australia (GA). Fire Weather Potential refers to the set of climatic conditions which can lead to bushfires if the other two elements of the bushfire development process are present: fuel load and ignition. Detecting high fire weather danger would allow emergency authorities warn the population concerned and ban the lit of fires in the open. They can also be better prepared to mitigate the consequences of a bushfire if such an event occurs. FWP is assessed by calculating Return Periods (RP) of the McArthur Forest Fire Danger Index (FFDI). The RP is an indicator of the frequency and intensity of extreme values and hence it is calculated by fitting an Extreme Value Distribution to the tail of the FFDI data distribution. The FFDI depends on four variables: Drought Factor, Relative Humidity, Air Temperature and Wind Speed. We are particularly interested in studying the impact of climate change on FWP and hence our model calculates FFDI based on climate model simulations. Comparison of climate model based results and observations show that the model tends to underestimate the FFDI in the regions of high FFDI. In the regions of medium FFDI the reverse can be observed: the simulations overestimate the FFDI. Our results show that it is necessary to develop a bias correction procedure for RP calculation if we want to have confidence in our simulation based model to study Fire Weather Potential in Australia. This paper discusses the characteristic of the problem and proposes an algorithm suitable for bias correction of extreme values, the values of interest in the calculation of RP. To illustrate the algorithm an example based on the calculation of FFDI in south-eastern Australia is discussed.
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nonGeographicDataset
eCat Id
76030
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Cnr Jerrabomberra Ave and Hindmarsh Dr GPO Box 378
Canberra
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- External PublicationAbstract
- Australian and New Zealand Standard Research Classification (ANZSRC)
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- Earth Sciences
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- Published_Internal
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2013-01-01T00:00:00
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