Statistical assessment of Australian bushfire conditions : long-term changes and variability
- Authors: Biswas, Soubhik
- Date: 2023
- Type: Text , Thesis , PhD
- Full Text:
- Description: In the wake of increasing bushfire impacts in recent decades across the Australian landscape, questions arise regarding the role played by weather conditions, climate variability and long-term climate change. This thesis seeks to quantify the following components that can influence fire risk: (1) the effects of weather and mean climate conditions, (2) large-scale drivers of natural climate variability, (3) the influence of extreme weather events and (4) the contribution of long-term anthropogenic climate change. Bushfire risks associated with weather and climate factors in Australia are generally assessed using indices such as the Forest Fire Danger Index (FFDI). The FFDI is used in this study, calculated from daily values of rainfall, relative humidity, temperature and wind speed, providing a generalised approach for combining those four weather factors known to influence fire behaviour. This study also aims to fill several knowledge gaps in the literature. For example, a comprehensive study of climatology, variability and trends in Australia's fire weather conditions was never attempted before using a high-resolution and a very long-term fire weather dataset. The fire weather conditions were analysed using a long-term FFDI dataset constructed from 20th Century reanalysis climatic data with bias correction applied because reconstructed weather datasets like 20th Century reanalysis products often show systemic biases. Various statistical bias correction approaches based on quantile-quantile matching were compared, and a spline-based method was selected due to its higher precision in correcting a distribution for the purposes of this study. The relationship of this calibrated FFDI dataset with the climate drivers of El Niño-Southern Oscillation (ENSO), Indian Ocean Dipole (IOD), Southern Annular Mode (SAM) and Interdecadal Pacific Oscillation (IPO) was analysed. Results are mapped to show the regional and seasonal fluctuations in the severe fire weather across Australia during different combinations of ENSO, IOD, and SAM phases. During the austral spring and summer seasons, the highest frequency of severe fire weather conditions occurred for the combination of positive ENSO (i.e., El Nino), positive IOD and negative SAM. The calibrated FFDI dataset derived from bias-corrected Twentieth Century Reanalysis data was further used to study the long-term climate change trends in Australian fire weather conditions. A general positive trend in the number of extreme FFDI days was reported across Australia, except for New South Wales in Spring where a statistically non-significant negative trend was observed. Temperature and relative humidity were found to be the most critical climatic variables influencing fire weather trends across the country, noting that relative humidity is partly based on temperature. The applications of this work range from being useful for various stakeholders in framing new climate change adaptation policies to being used for seasonal outlooks and planning by fire management teams.
- Description: Doctor of Philosophy
Statistical calibration of long-term reanalysis data for australian fire weather conditions
- Authors: Biswas, Soubhik , Chand, Savin , Dowdy, Andrew , Wright, Wendy , Foale, Cameron , Zhao, Xiaohui , Deo, A
- Date: 2022
- Type: Text , Journal article
- Relation: Journal of Applied Meteorology and Climatology Vol. 61, no. 6 (2022), p. 729-758
- Full Text: false
- Reviewed:
- Description: Reconstructed weather datasets, such as reanalyses based on model output with data assimilation, often show systematic biases in magnitude when compared with observations. Postprocessing approaches can help adjust the distribution so that the reconstructed data resemble the observed data as closely as possible. In this study, we have compared various statistical bias-correction approaches based on quantile–quantile matching to correct the data from the Twentieth Century Reanalysis, version 2c (20CRv2c), with observation-based data. Methods included in the comparison utilize a suite of different approaches: a linear model, a median-based approach, a nonparametric linear method, a spline-based method, and approaches that are based on the lognormal and Weibull distributions. These methods were applied to daily data in the Australian region for rainfall, maximum temperature, relative humidity, and wind speed. Note that these are the variables required to compute the forest fire danger index (FFDI), widely used in Australia to examine dangerous fire weather conditions. We have compared the relative errors and performances of each method across various locations in Australia and applied the approach with the lowest mean-absolute error across multiple variables to produce a reliable long-term biascorrected FFDI dataset across Australia. The spline-based data correction was found to have some benefits relative to the other methods in better representing the mean FFDI values and the extremes from the observed records for many of the cases examined here. It is intended that this statistical bias-correction approach applied to long-term reanalysis data will help enable new insight on climatological variations in hazardous phenomena, including dangerous wildfires in Australia extending over the past century. © 2022 American Meteorological Society.