Generalised Extreme Value (GEV) distribution can be used effectually to model extreme climatic events like bushfire. The major predicament of using GEV distribution is accurate determination of three parameters (location, scale, and shape); nevertheless, there are no specific guidelines to identify the most apposite parameter estimation technique of the GEV distribution for bushfire studies. In this study, influence of different GEV parameters estimation techniques were investigated in Victoria, Australia for extreme bushfire event modelling, using annual maximum forest fire danger index (FFDI), which is a combination of manifold climatic and fuel variables to indicate the potential for bushfires to propagate, and withstand suppression. Four GEV parameters estimation methods namely: Maximum Likelihood Estimation (MLE), Generalised Maximum Likelihood Estimation (GMLE), Bayesian and L-moments were used for two different timescale data (full data set and last 10 years of full dataset) to estimate the GEV distribution parameters. The return levels of FFDI for different Average Recurrence Interval (ARI) were estimated using the above mentioned four methods and two timescales. The study demonstrates that Fréchet (type II) extreme value distribution is pertinent for modelling the annual maximum FFDI for most of the selected stations; nonetheless, GEV distribution parameters can vary considerably due to variation in the length of the data series. Several applied statistical parameters namely: Mean square error (MSE) and Mean absolute error (MAE) were used to identify the most pertinent parameter estimation technique of the GEV distribution. The study reveals that L-moments can be used, even in the presence of the smaller data set. In addition, L-moments is the most appropriate parameters estimation technique of GEV distribution because of the presence of the lowest MSE and MAE values for most of the stations. The outcomes of this research are pivotal for Victorian public and private stakeholders to forecast the severity and intensity of imminent bushfire events due to recent bushfire events in fire prone areas.

通用极值（GEV）分布可有效地用于对极端气候事件（例如丛林大火）进行建模。使用GEV分布的主要困难是要准确确定三个参数（位置，比例和形状）。但是，目前尚无用于确定林火研究中GEV分布中最合适的参数估计技术的特定指南。在这项研究中，使用年度最大森林火灾危险指数（FFDI）（包括多种气候变量和燃料变量来表明森林大火的可能性），在澳大利亚维多利亚州调查了不同GEV参数估计技术对极端森林大火事件建模的影响。传播，并承受压制。四种GEV参数估计方法，即：最大似然估计（MLE），广义最大似然估计（GMLE），贝叶斯和L矩用于两个不同的时标数据（完整数据集和完整数据集的最近10年），以估计GEV分布参数。使用上述四种方法和两个时间尺度来估计不同平均复发间隔（ARI）的FFDI的回报水平。研究表明，弗雷谢特（II型）极值分布与大多数选定电台的年度最大FFDI建模有关。但是，由于数据序列长度的变化，GEV分布参数可能会有很大变化。应用了几个应用的统计参数，即：均方误差（MSE）和均值绝对误差（MAE）来确定GEV分布中最相关的参数估计技术。研究表明，即使存在较小的数据集，也可以使用L矩。另外，由于大多数电台的最低MSE和MAE值的存在，L矩是GEV分布的最合适的参数估计技术。这项研究的结果对于维多利亚州的公共和私人利益相关者预测由于近期在火灾多发地区发生的丛林大火事件即将到来的丛林大火事件的严重性和强度至关重要。