Fuel moisture forecast system for prescribed burning

Forestry Fire Management

Matthews, Stuart

CSIRO

DOI  :  10.4225/08/584af3fa87cf3
RP-ID  :  EP128507

学科分类：地球科学(综合)

澳大利亚|英语

来源: CSIRO Research Publications Repository

【 摘 要 】

- This report describes a system for predicting the moisture content and availability to burn of dead fine forest fuels in NSW. This system was developed as part of the NSW Rural Fire Service's Prescribed Burning Forecast Tool project. Outputs from the fuel moisturemodel are to be used as inputs to the fuel availability and flame height components of the Prescribed Burning Forecast Tool. The outputs of this system are to be used to identify conditions that may be suitable for conducting prescribed burns. In situ measurements offuel moisture must be made prior to commencement of any burn.- The system is based on an existing fuel moisture model (Matthews 2006) adapted to work with Bureau of Meteorology 6 km weather forecast grids. Predictions of fuel moisture content and fuel availability are calculated hourly for forested areas of the state for 4 or 7 days in advance. For non-forest areas the McArthur grass fire danger meter is used to estimate fuel moisture (McArthur 1966). Forecasts are output as 3 dimensional grids (latitude, longitude, time) which may be visualised as maps or time series.- A single set of predictions are made for each grid cell, based on the assumption of a forest on flat ground with the mean fuel load within a grid cell. Fuel loads are derived from Rural Fire Service vegetation shapefiles. Field observations have shown that sheltered, downslope locations may be much wetter than flat areas, which needs to be taken into account when interpreting predictions.- Model output was tested against an existing data set (Matthews et al. 2007) and measurements made in the Blue Mountains. The model was able to replicate these observations with some limitations and was able to represent differences in fuel wetting and drying asdetermined by fuel load and forest structure.- Future work to improve the fuel moisture prediction system could include further testing against field measurements, refinement of forest structure parameters, inclusion of sub-grid scale variability, particularly slope and aspect effects, and inclusion of forecast uncertainty.