Spatial interpolation of McArthur's Forest Fire Danger Index across Australia: Observational study

  • Authors:

  • L. A. Sanabria;X. Qin;J. Li;R. P. Cechet;C. Lucas

  • Affiliations:

  • Environmental Geoscience Division, Geoscience Australia, Canberra, Australia and Bushfire Cooperative Research Centre, Melbourne, Australia;Environmental Geoscience Division, Geoscience Australia, Canberra, Australia;Environmental Geoscience Division, Geoscience Australia, Canberra, Australia;Environmental Geoscience Division, Geoscience Australia, Canberra, Australia and Bushfire Cooperative Research Centre, Melbourne, Australia;Centre for Australian Weather and Climate Research, Melbourne, Australia

  • Venue:
  • Environmental Modelling & Software
  • Year:
  • 2013

Quantified Score

Hi-index 0.00

Visualization

Abstract

Fire danger indices are used by fire management agencies to assess fire weather conditions and issue public warnings. The most widely used fire danger indices in Australia are the McArthur Fire Forest Danger Index and the Grassland Fire Danger Index. These indices are calculated at weather stations using measurements of weather variables and fuel information. For a vast country like Australia when assessing the risk of severe fire weather events, it is also important to calculate the spatial distribution of these indices considering the extreme tail of the distribution. The spatial distribution of one of the fire weather danger indices regularly used in Australia is presented in this paper. In particular, we present the spatial distribution of the long-term tendency of extreme values of the McArthur Forest Fire Danger Index (FFDI). This indicator of fire weather conditions was assessed by calculating the return period of its extreme values by fitting extreme value distributions to data sets of FFDI at 78 recording stations around Australia. The spatial distribution of these return periods was obtained by using spatial interpolation algorithms with the recording stations measurements. Two conventional and two new algorithms based on machine-learning techniques were tested. This study shows that the best interpolation results for the FFDI can be obtained by using a combination of random forest and inverse distance weighting interpolation algorithms. The spatial distribution of the seasonal FFDI return period shows that the highest FFDI over large parts of southern Australia occurs during the summer months whilst in northern Australia it occurs in spring. The results also show that the FFDI in eastern Australia, the most populated region of the country, is higher inland than in the coastal areas particularly during spring and summer.