Smoke impacts from prescribed burning in Victoria; developing a risk climatology

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 receptorand 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.