@article {bnh-8377, title = {Implementation of spatially-varying wind adjustment factor for wildfire simulations}, journal = {Environmental Modelling \& Software}, volume = {163}, year = {2023}, month = {05/2023}, pages = {105660}, abstract = {

The behaviour and rate of spread of a wildfire is strongly affected by local wind conditions depending on topography and surrounding vegetation. The wind speed within dense vegetation can be substantially lower than the open wind speed above the vegetation. This is commonly accounted for by applying a local correction factor to the local wind speed which can be called the wind adjustment factor (WAF). WAFs are often difficult to calculate and are usually based estimates for a particular vegetation type. Variation in the vegetation may result in spatially varying WAFs. We implement a model for spatially-varying WAFs based on leaf area index (LAI) and vegetation height data, which can be derived from remote sensing data sources. The model is implemented within Spark an operational wildfire prediction framework. Simulations of historical fires using the spatially-varying WAF model generally provided improved predictions compared to recorded fire extents than simulations without a spatially-varying WAF.

}, keywords = {Bushfire, modeling, spread, WAF, Wildfire, wind adjustment factor}, doi = {https://doi.org/10.1016/j.envsoft.2023.105660}, url = {https://www.sciencedirect.com/science/article/abs/pii/S1364815223000464?dgcid=coauthor}, author = {Duncan Sutherland and Mahmood Rashid and James Hilton and Khalid Moinuddin} } @article {bnh-7996, title = {Fire spread across different fuel types: research and utilisation {\textendash} final project report}, number = {668}, year = {2021}, month = {05/2021}, institution = {Bushfire and Natural Hazards CRC}, address = {Melbourne}, abstract = {

It is crucial for emergency and disaster management organisations to predict of the rate of spread and intensity of bushfires for operational planning, community warnings and the deployment of their resources. Currently, this is achieved by simulation using simplified operational models that have the useful attribute of providing results on time scales commensurate with those required by emergency managers. However, when Cruz \& Alexander [1] reviewed the performance of the operational fire models used by fire and emergency service analysts on seven vegetation types found in Australia, they found that on an average most of the fire models have an error of 20{\textendash}80\% in estimating the rate of fire spread. These differences in prediction are due to the assumptions and limitations of these models. Therefore, it is essential that these simplified operational tools be refined so that they can better predict fire behaviour. Additionally, a more physically based firebrand model needs to be included in operational models to predict firebrand distribution and subsequent spotting, which lead to an increased rate of fire spread (ROS). Currently, no such model exists. With an increased population in the rural{\textendash}urban interface (or wildland{\textendash}urban interface, WUI), it is also important to understand the vulnerability of houses from radiant heat and firebrand flux in order to minimise such vulnerability.

In this project, we tested two established reliable physics-based models: Fire Dynamics Simulator (FDS) and FIRESTAR3D to simulate bushfire scenarios in three broad areas:

(1)\  sub-canopy wind flow,

(2)\  firebrand transport, and

(3)\  propagation of grass and forest fires.

We have made significant inroads into providing usable outputs as well understanding various aspects of bushfire behaviour. The following are particular highlights:

This project was also established to create a capability and capacity in Australia to conduct research and understand physical-based wildfire modelling approaches. There are several international groups developing these models, and it is imperative that Australia can interact and work alongside these researchers to translate the findings to the Australian context.

Overall, we have achieved our goal of obtaining greater insight into bushfire physics and we are now utilising those insights to parameterise various phenomena for operational models.

This report explains these issues in more detail.

[1] Meaning WRF will vary for the geographical location and driving wind velocity and direction

}, keywords = {Fire, fuel, research, spread, types, utilisation}, issn = {668}, author = {Nazmul Khan and Amila Wickramasinghe and Mahmood Rashid and Khalid Moinuddin} }