Large area estimates of fuel loads for bushfire emissions estimates and fire risk assessment

Large area estimates of fuel loads for bushfire emissions estimates and fire risk assessment

Savannas cover about 25% of the Australian continent. They form a highly fire prone environment with fire return intervals of 1-5 years (Maier and Russell-Smith 2012). In 2010 non-CO2 emissions alone from these fires contributed 10.8% of Australia’s greenhouse gas emissions from Agriculture or 1.6% of Australia’s total greenhouse gas emissions. Therefore these estimates are vital to our understanding of the carbon budget and of the role savannas play in relation to climate change.

However estimates of greenhouse gas emissions from savanna fires are associated with large uncertainties; estimated at ±25% and -45/+93%, depending on the method applied. In order to reduce these uncertainties it is necessary to ensure low uncertainties in the input variables: burnt area, burning efficiency, fuel load and emission factors. Significant improvements in estimating burnt area (Maier and Russell-Smith 2012), burning efficiency and emission factors (Meyer et al. 2012) have been achieved in recent years, leaving fuel load as the major contributor to uncertainties in emission estimates.

Currently fuel loads in savannas are mostly estimated from field observations. Due to the vast expanse and remoteness of Australian savannas these observation provide very limited spatial and temporal coverage. This has led to the adoption of state based constant values, i.e. one value for prescribed fires and one value for wildfires for each state (!). For fine fuels (grass and litter) Russell-Smith et al. (2009) have improved on this by estimating fuel loads from the “number of years since last fire”. However savannas vary widely in tree density and water availability and therefore annual fuel accumulation rates. Furthermore due to the pronounced seasonality of rainfall, savannas are highly dynamic systems that show strong seasonality in fuel accumulation which cannot be captured with a simple “number of years since last fire” approach.

To capture the spatio-temporal dynamics of fuel loads in savannas this project will develop a remote sensing methodology to estimate fuel loads in Australian savannas.

Lead supervisor: Stefan Maier

RIEL Headlines

Pages

Jump to NRBL themeJump to CMEM themeJump to FEM themeJump to SMWC themeJump to TRF themeJump to RIEL home

Innovative Research University

© 2011-2013 Charles Darwin University
Research Institute for the Environment and Livelihoods
Privacy Policy
CRICOS Provider No. 00300K | RTO Provider No. 0373

Phone (+61) 8 8946 6413
Email riel@cdu.edu.au