Abstract | Following an increase in the frequency of extreme fire weather in recent years and the
subsequent loss of life and damage to property, fire agencies in Australia are under pressure to increase fuel
reduction burning. However, at the same time, there is an increasing pressure to reduce the risk of population
exposure to smoke. Reconciling these competing demands is a major challenge.
Currently, wind speed and direction are the main guides used by agencies for estimating regions at risk from
smoke plume strikes. However, plume dispersion models and computing resources are now sufficiently
developed so that the risk to towns and cities from planned fuel reduction and agricultural fires can also be
assessed using these tools. This is of particular relevance in north central Victoria, Gippsland and the
Riverina where there is a need to identify those areas of forest and farmland most likely to pose a smoke
hazard to nearby towns during the autumn and spring burning seasons.
In this paper, we introduce a numerical modelling system which can be used to undertake an assessment of
the risk to communities resulting from smoke generated by fuel reduction burning. The paper demonstrates
how the system can be used in an inverse modelling mode to investigate the relationship between sensitive
receptor- and upwind source regions. Using the Ovens Valley in north-eastern Victoria as an example, we
apply the CSIRO dispersion model, The Air Pollution Model ( TAPM) coupled to the CSIRO Chemical
Transport Model (CTM) to simulate the dispersion of PM2.5 emitted daily between 11:00 and 16:00 during
April 2009 from each 3 km x 3 km grid cell in a 50 x 50 cell domain centred on Harrietville. From these data
we can assess the relative impact of each source cell on any receptor cell within the domain. Taking the
towns of Myrtleford, Harrietville and Mt Beauty as test cases, we find that the greatest likelihood of smoke
impact is from fires close to the receptor cell, however more distant sources are also significant, with the
strongest located on the valley slopes. Vegetated source areas in the bottom of the valleys and on ridges have
least impact. Harrietville and Mt Beauty, which lie in different valleys, nevertheless have similar source risk
distributions, in contrast to Myrtleford, which lies downstream of Harrietville on the Ovens River, and has a
totally different source risk profile. Significantly, there is no indication of a prevailing flow for any of the
three receptor cells. We discuss how the system can be used to provide a self-consistent framework for
testing smoke transport screening approaches for use by fire managers for planning prescribed burning
schedules. |