@article {bnh-7417, title = {Climatology of wind changes and elevated fire danger over Victoria, Australia}, journal = {Journal of Southern Hemisphere Earth System Science}, year = {2020}, month = {10/2020}, abstract = {

Wind changes are a critical factor in fire management, particularly on days of elevated fire danger, and have been shown to be a factor in many firefighter entrapments in Australia and the USA. While there have been numerous studies of frontal wind changes over southeastern Australia since the 1950s, a spatial climatology of wind change strength and frequency over Victoria has hitherto been limited by the relatively low number of observation sites that have both high temporal resolution observations and sufficient length of record. This study used a recently developed high spatial (4-km grid) and temporal (1 hour) resolution, 46-year, homogeneous gridded fire weather climatology data set to generate a climatology of wind change strength by season at each gridpoint across Victoria. The metric used to define a wind change is the vector difference between the wind speed and direction over each 1-hour interval, with the highest value occuring on each day being selected for spatial analysis of strength and frequency. The highest values of wind change strength are found along the crest of the Great Dividing Range (the Great Divide), with a peak in spring. Elsewhere, the highest values occur in summer, with the areas south of the Great Divide, west of Melbourne and in central Gippsland showing higher values than the remainder of the state. The strength of wind changes generally decreases north of the Great Divide, although it is stronger in the northwest of the state in spring rather than in autumn. Lowest summertime (and other seasons) values occur in the northeast of the state and in far-east Gippsland. Exploring the frequencies of days when the highest daily Forest Fire Danger Index and the highest daily wind change strength jointly exceed defined thresholds shows that the northwest of the state has the highest springtime frequencies, whereas the highest autumn frequencies occur west of Melbourne and south of the Great Divide. The highest numbers of joint events in summer (when the greatest frequencies also occur) extend from central Victoria west to the South Australian border, with a secondary maximum in central Gippsland. These analyses offer important information for fire weather forecasters and for fire practitioners when preparing for a fire season or managing a fire campaign (for example, for allocating resources or understanding risks).

}, keywords = {Australia, bushfires, climatology, extreme weather, fire danger, fire management, firefighter entrapment, Victoria, wind changes}, doi = {https://doi.org/10.1071/ES19043}, url = {https://www.publish.csiro.au/es/ES19043}, author = {Graham A. Mills and Sarah Harris and Brown, T and Alex Chen} } @mastersthesis {bnh-8091, title = {How do wet forests burn? : Fuels and fire danger in the world{\textquoteright}s tallest flowering forest}, volume = {Doctor of Philosophy}, year = {2020}, month = {02/2020}, pages = {222}, school = {University of Tasmania}, address = {Hobart}, abstract = {

Wildfire is possibly the most widespread natural disturbance globally, as its spatial and temporal patterns shape vegetation assemblages throughout the world. This is especially true in Australia, where the dominant trees, Eucalyptus spp., are among the most well-adapted to fire on the planet. To protect communities in such a fire-prone landscape, we need to be able to predict fire behaviour and manage fuels in a fashion that reduces risk. But to do this effectively, we must understand the natural fire regimes that occur in Australian ecosystems. One Australian ecosystem for which fire regimes are poorly understood is the tall wet Eucalyptus forest (TWEF). These globally unique forests support a mix of flammable and fire-sensitive vegetation and are among the world{\textquoteright}s most carbon-dense forests, so understanding their flammability is critical.

This thesis attempts to describe the fire regime TWEF. It does so in a fashion that can inform fuels management and fire behaviour prediction, with a particular focus on the island of Tasmania. First, I used fire behaviour model simulations to analyse the effectiveness of fuel treatment on fire behaviour in different Tasmanian vegetation, which allowed me to contextualise fire risk across the island{\textquoteright}s diverse vegetation types. I then described the fire regime, namely the expected frequency and severity of wildfires, of TWEF. I did this using a combination of fuels, microclimate, and weather data, along with fire behaviour modelling, to estimate potential fire severities in these forests. I first investigated what drives fire severity across a continental-scale macroecological gradient of forests in an {\textquoteleft}early-mature{\textquoteright} successional stage. However, flammability of older forests can be different from that of younger forests, so I investigated how the flammability of TWEF changes as a stand develops from regrowth to old-growth forest. For the latter exercise I focused, on Tasmanian TWEF.

Fire behaviour model simulations indicated that an impractical extent of landscape-scale prescribed burning would be needed for the treatment to be effective, and that this is especially true for TWEF. The results also suggested that Tasmanian TWEF are capable of sustaining among the most intense fires on the planet. I suggest that prescribed burning needs to be conducted at local, targeted scales, and that alternatives to prescribed burning need to be investigated for Tasmanian TWEF. Analysis of the continent-wide dataset of fuels and microclimate in early-mature TWEF indicated both low-and high-severity fires play an important role, but that high-severity fire is much more likely in TWEF in the hotter and drier regions of the continent. Further, similar analysis of a chronosequence of TWEF stands in Tasmania indicated that high-severity fires become more likely in younger TWEF. Importantly, the results highlighted that the TWEF understorey (or elevated fuel layer) is the most important fuel layer from a fire danger perspective. Hence, I suggest that management needs to mimic mixed-severity fires to maintain the structural heterogeneity and microclimate that makes these forests fire resistant.

Results from fire behaviour model simulations and from measuring fire severity in eight burnt field sites also indicated that current operational models over-predict flame height and fireline intensity in TWEF, likely due to an over-simplistic representation of forest structure. As a result, I performed a detailed review of the three major current operational fire behaviour models, along with a next-generation physics-based model, and documented their shortcomings. I then investigated how applicable these different models were in TWEF. Despite its importance, the live understorey is composed of many fire-sensitive species whose flammability is poorly understood. Hence, I propose that small-scale burning studies combined with physical simulation of wildfire are needed to quantify flammability and fire behaviour in forests composed of such species.

}, keywords = {burn, fire danger, flowering forest, fuels, wet forests}, url = {https://eprints.utas.edu.au/34888/}, author = {Furlaud, James M.} } @article {bnh-6974, title = {A Live Fuel Moisture Content Product from Landsat TM Satellite Time Series for Implementation in Fire Behavior Models}, journal = {Remote Sensing}, volume = {12}, year = {2020}, month = {05/2020}, abstract = {

Live Fuel Moisture Content (LFMC) contributes to fire danger and behavior, as it affects fire ignition and propagation. This paper presents a two layered Landsat LFMC product based on topographically corrected relative Spectral Indices (SI) over a 2000{\textendash}2011 time series, which can be integrated into fire behavior simulation models. Nine chaparral sampling sites across three Landsat -5 Thematic Mapper (TM) scenes were used to validate the product over the Western USA. The relations between field-measured LFMC and Landsat-derived SIs were strong for each individual site but worsened when pooled together. The Enhanced Vegetation Index (EVI) presented the strongest correlations (r) and the least Root Mean Square Error (RMSE), followed by the Normalized Difference Infrared Index (NDII), Normalized Difference Vegetation Index (NDVI) and Visible Atmospherically Resistant Index (VARI). The relations between LFMC and the SIs for all sites improved after using their relative values and relative LFMC, increasing r from 0.44 up to 0.69 for relative EVI (relEVI), the best predictive variable. This relEVI served to estimate the herbaceous and woody LFMC based on minimum and maximum seasonal LFMC values. The understory herbaceous LFMC on the woody pixels was extrapolated from the surrounding pixels where the herbaceous vegetation is the top layer. Running simulations on the Wildfire Analyst (WFA) fire behavior model demonstrated that this LFMC product alone impacts significantly the fire spatial distribution in terms of burned probability, with average burned area differences over 21\% after 8 h burning since ignition, compared to commonly carried out simulations based on constant values for each fuel model. The method could be applied to Landsat-7 and -8 and Sentinel-2A and -2B after proper sensor inter-calibration and topographic correction.

}, keywords = {data normalization, fire behavior simulator, fire danger, fire propagation, Landsat-5 TM, live fuel moisture content}, doi = {https://doi.org/10.3390/rs12111714}, url = {https://www.mdpi.com/2072-4292/12/11/1714}, author = {Mariano Garcia and David Ria{\~n}o and Marta Yebra and Javier Salas and Adrian Cardil and Santiago Monedero and Joaquin Ramirez and Pillar Martin and Lara Vilar and John Gajardo and Susan Ustin} }