Use of the Extreme Value Analysis in Determining Annual Probability of Exceedance for Bushfire Protection Design

Douglas, Grahame; He, Yaping; Xiang, Yang; Morris, E C

doi:10.3801/iafss.fss.11-1379

Cited by 3 publications

(2 citation statements)

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“…Some work has been done recently in relation to fire weather or fire behavior in Australia at the extreme by Douglas et al (2014). In their study, the prevailing extreme values were determined by comparing different statistical approaches at the 1:50 year recurrence with existing policy values based on previously limited data.…”

Section: Generalized Extreme Value Methods For Selection Of Design Busmentioning

confidence: 99%

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Design Bushfire Selection for Bushfire Protection in Adaptation to Global Warming

Douglas

2019

Front. Mech. Eng.

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In this article, a risk based approach to design for bushfire protection in view of adaptation to global warming is discussed. The concept of design bushfire is explained in an analogy to design flood or design earthquake in terms of event of prescribed return period. In lieu of using the Global Climate Model, the current study is based on the analysis of historical fire weather data from multiple locations in a state wide region. The generalized extreme value (GEV) analysis method is employed to establish the recurrence models for predicting the fire weather index of given return period and the associated fire intensity. To examine the impacts of the climate change, a moving GEV method is utilized to the weather data records over the period of 44 years. The result demonstrated a heterogeneity in the impact of climate change in terms of a given recurrence fire danger index and the potential bushfire severity over the region studied. The implication of this outcome is that the traditional prescriptive approach to design for bushfire protection may not be suited for adaptation to climate change.

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Section: Generalized Extreme Value Methods For Selection Of Design Busmentioning

confidence: 99%

“…The method is based on the work of Makkonen (2006). A detailed description of the application of GEV method to historical weather data can be found in Douglas et al (2014). From a sufficient length of data, a log-linear model of the form…”

Section: Methods Of Analysismentioning

confidence: 99%