@article {bnh-7475, title = {Exploring the key drivers of forest flammability in wet eucalypt forests using expert-derived conceptual models}, journal = {Landscape Ecology}, volume = {35}, year = {2020}, month = {06/2020}, pages = {1775{\textendash}1798}, abstract = {

Context

Fire behaviour research has largely focused on dry ecosystems that burn frequently, with far less attention on wetter forests. Yet, the impacts of fire in wet forests can be high and therefore understanding the drivers of fire in these\ systems\ is vital.

Objectives

We sought to identify and rank by importance the factors plausibly driving flammability in wet eucalypt forests, and describe relationships between them. In doing so, we formulated a set of research priorities.

Methods

Conceptual models of forest flammability in wet eucalypt forests were elicited from 21 fire experts using a combination of elicitation techniques. Forest flammability was defined using fire occurrence and fireline intensity as measures of ignitability and heat release rate, respectively.

Results

There were shared and divergent opinions about the drivers of flammability in wet eucalypt forests. Widely agreed factors were drought, dead fine fuel moisture content, weather and topography. These factors all influence the availability of biomass to burn, albeit their effects and interactions on various dimensions of flammability are poorly understood. Differences between the models related to lesser understood factors (e.g. live and coarse fuel moisture, plant traits, heatwaves) and the links between factors.

Conclusions

By documenting alternative conceptual models, we made shared and divergent opinions explicit about flammability in wet forests. We identified four priority research areas: (1) quantifying drought and fuel moisture thresholds for fire occurrence and intensity, (2) modelling microclimate in dense vegetation and rugged terrain, (3) determining the attributes of live vegetation that influence forest flammability, (4) evaluating fire management strategies.

}, keywords = {Cognitive mapping, Conceptual models, Expert elicitation, Fire behaviour, fire intensity, flammability, Structured decision-making, Structured expert judgement, Wet forest, Wildfire}, doi = {https://doi.org/10.1007/s10980-020-01055-z}, url = {https://link.springer.com/article/10.1007/s10980-020-01055-z}, author = {Jane Cawson and Victoria Hemming and Ackland, A and Wendy R. Anderson and David Bowman and Ross Bradstock and Brown, T and Jamie Burton and Geoffrey J. Cary and Thomas Duff and Alex Filkov and Furlaud, James M. and Tim Gazzard and Kilinc, Musa and Petter Nyman and Ross Peacock and Mike Ryan and Jason J. Sharples and Gary J. Sheridan and Tolhurst, K.G. and Tim Wells and Phil Zylstra and Trent Penman} } @article {bnh-6321, title = {Geographic Patterns of Fire Severity Following an Extreme Eucalyptus Forest Fire in Southern Australia: 2013 Forcett-Dunalley Fire }, journal = {Fire}, volume = {1}, year = {2018}, month = {10/2018}, abstract = {

Fire severity is an important characteristic of fire regimes; however, global assessments of fire regimes typically focus more on fire frequency and burnt area. Our objective in this case study is to use multiple lines of evidence to understand fire severity and intensity patterns and their environmental correlates in the extreme 2013 Forcett-Dunalley fire in southeast Tasmania, Australia. We use maximum likelihood classification of aerial photography, and fire behavior equations, to report on fire severity and intensity patterns, and compare the performance of multiple thresholds of the normalised burn ratio (dNBR) and normalized difference vegetation index (dNDVI) (from pre- and post-fire Landsat 7 images) against classified aerial photography. We investigate how vegetation, topography, and fire weather, and therefore intensity, influenced fire severity patterns. According to the aerial photographic classification, the fire burnt 25,950 ha of which 5\% burnt at low severities, 17\% at medium severity, 32\% at high severity, 23\% at very high severities, while 22\% contained unburnt patches. Generalized linear modelling revealed that fire severity was strongly influenced by slope angle, aspect, and interactions between vegetation type and fire weather (FFDI) ranging from moderate (12) to catastrophic (\>100). Extreme fire weather, which occurred in 2\% of the total fire duration of the fire (16 days), caused the fire to burn nearly half (46\%) of the total area of the fireground and resulted in modelled extreme fireline intensities among all vegetation types, including an inferred peak of 68,000 kW{\textperiodcentered}m-1 in dry forest. The best satellite-based severity map was the site-specific dNBR (45\% congruence with aerial photography) showing dNBR potential in Eucalyptus forests, but the reliability of this approach must be assessed using aerial photography, and/or ground assessment.

}, keywords = {aerial photography, Eucalyptus forest, fire intensity, fire severity mapping, generalized linear modelling, geospatial validation, normalized burn ratio, Tasmania}, doi = {https://doi.org/10.3390/fire1030040}, url = {https://www.mdpi.com/2571-6255/1/3/40}, author = {Mercy Ndalila and Grant Williamson and David Bowman} } @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} }