@conference {bnh-6411, title = {Fire weather and prototype fire danger ratings for the Gell River fire, Tasmania}, booktitle = {Bushfire and Natural Hazards CRC Research Day AFAC19}, year = {2019}, month = {12/2019}, address = {Melbourne}, abstract = {

We provide a preliminary analysis of the meteorology of key aspects of the Gell River fire in Tasmania during late December 2018 and early January 2019, including the lightning storm that ignited the fire, and conditions on 4 January 2019, when the fire increased substantially in size. We also briefly assess the performance of the Australian National Fire Danger Rating System (AFDRS) Research Prototype available on 4 January against observations of fire spread and routine McArthur Forest Fire Danger forecasts.

The Gell River fire occurred within a context of declining October {\textendash} April rainfall in western Tasmania over the last two decades, in comparison to the average rainfall for the period since 1900 (Fig. 1, Bureau of Meteorology and CSIRO (2018)). It was one of several large fires that competed for fire management resources over an extended period during the 2018-2019 Tasmanian fire season. The fire impacted natural values including those of the Tasmanian Wilderness World Heritage Area (TWWHA) and risked spreading into parts of the iconic Mt Field National Park, as well as threatening a number of communities in the Derwent Valley, particularly Maydena. The Gell River fire also threatened major electrical transmission infrastructure connecting the large Gordon-Pedder power generation facility in the west of Tasmania to population centres in the east, and burnt approximately 500 ha of a 5,000 ha pine plantation. On 4 January, thick smoke from the fire crossed over the Greater Hobart area, sparking concern and raising awareness within the wider community about the fire activity (Fig. 2).

}, keywords = {Fire behaviour, Fire weather, modelling}, url = {https://knowledge.aidr.org.au/resources/australian-journal-of-emergency-management-monograph-series/}, author = {Paul Fox-Hughes and Stuart Matthews and Chris Collins and Saskia Grootemaat and Jennifer J Hollis and Alexander Holmes and Belinda Kenny and John Runcie and Samuel Sauvage} } @article {bnh-5264, title = {Determining the minimum sampling frequency for ground measurements of burn severity}, journal = {International Journal of Wildland Fire}, year = {2018}, month = {06/2018}, abstract = {

Understanding burn severity is essential to provide an overview of the precursory conditions leading to fires as well as understanding the constraints placed on fire management services when mitigating their effects. Determining the minimum sampling frequency for ground measurements is not only essential for accurately assessing burn severity, but also for fire managers to better allocate resources and reduce the time and costs associated with sampling. In this study, field sampling methods for assessing burn severity are analysed statistically for 10 burn sites across Victoria, Australia, with varying spatial extents, topography and vegetation. Random and transect sampling methods are compared against each other using a Monte Carlo simulation to determine the minimum sample size needed for a difference of 0.02 (2\%) in the severity classes proportions relative to the population proportions. We show that, on average, transect sampling requires a sampling rate of 3.16\% compared with 0.59\% for random sampling. We also find that sites smaller than 400 ha require a sampling rate of between 1.4 and 2.8 times that of sites larger than 400 ha to achieve the same error. The information obtained from this study will assist fire managers to better allocate resources for assessing burn severity.

}, doi = {10.1071/WF17055}, url = {http://www.publish.csiro.au/WF/WF17055}, author = {Alexander Holmes and Sarah Harris and Nigel Tapper and Christoph R{\"u}diger} } @mastersthesis {bnh-6619, title = {Investigating the effects of soil moisture, temperature and preciptiation extremes on fire risk and intensity in Australia}, year = {2018}, month = {09/2018}, school = {Monash University}, address = {Melbourne}, abstract = {

The likelihood of extreme events such as forest fires is expected to increase due to climate change. However, the mechanisms promoting the conditions leading to fires, their risk, and likely intensity are not well studied. This thesis identifies the relationship between soil moisture, fire danger, extreme temperature and fire intensity in Australia.

}, keywords = {fire intensity, Fire risk, precipitation, Soil moisture}, url = {https://monash.figshare.com/articles/Investigating_the_effect_of_Soil_Moisture_Temperature_and_Precipitation_Extremes_on_Fire_Risk_and_Intensity_in_Australia/7133438}, author = {Alexander Holmes} } @article {bnh-4201, title = {Mitigating the effects of severe fires, floods and heatwaves through the improvements of land dryness measures and forecasts: annual project report 2016-17}, number = {317}, year = {2017}, month = {09/2017}, institution = {Bushfire and Natural Hazards CRC}, address = {Melbourne}, abstract = {

The Bushfire and Natural Hazards CRC project titled {\textquotedblleft}Mitigating the Effects of Severe Fires, Floods and Heatwaves through the Improvements of Land Dryness Measures and Forecasts{\textquotedblright} examines the use of detailed land surface models, satellite measurements and ground based observations for the monitoring and prediction of landscape dryness. This project will address a fundamental limitation in our ability to prepare for fires, floods and heatwaves and is directly linked to pre-event planning as well as forecasting of events.\ 

Currently landscape dryness is estimated in Australia using simple empirical models developed in the 1960{\textquoteright}s. The most prominent of those used in Australia are the Keetch-Byram Drought Index (KBDI; Keetch \& Byram 1968) and the Soil Dryness Index (SDI; Mount 1972). An initial study performed as part of this project suggest that analyses of soil moisture can be improved by using physically based land surface models, remote sensing measurements and data assimilation. The project has developed a stand alone prototype land surface modelling system to produce daily soil moisture analyses at 5km resolution and at 4 soil layers. Verification against ground based soil moisture observations show that this prototype system is significantly more skilful than both KBDI and SDI.

The present report documents the activities undertaken in 2016-2017. The main focus of the year has been on the calibration of soil moisture from a new high resolution land surface modelling system allowing for easier utilisation within existing operational fire prediction systems. The calibrated outputs will be evaluated against numerous case studies that include past bush fire occurrences and fuel reduction burns conducted by fire agencies. This work is in progress and eight case studies have been identified so far. These case studies were selected with the help of end users. All case studies will be documented and could be used as training documentation by fire agencies.

}, issn = {317}, author = {Vinod Kumar and Imtiaz Dharssi and Alexander Holmes} }