@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} } @article {bnh-8284, title = { Studying leaf area density based wind adjustment factor in Spark}, number = {713}, year = {2021}, month = {11/2021}, institution = {Bushfire and Natural Hazards CRC}, address = {Melbourne}, abstract = {

In some operational fire prediction models such as the McArthur model [1] or Vesta model [2], a static (constant) wind reduction factor (WRF) is used to account for subcanopy wind profile. However, the WRF is far more complicated than what can be described by a constant value. A dynamic WRF depends on wind velocity well above the forest canopy, canopy height and canopy density which may vary from location to location. In our earlier work, we developed a simpler model considering uniform vertical canopy density, known as the Harman-Finnigan model [3].\  However, Moon et al. [4] and Sutherland et al. [5] demonstrated that vertically heterogonous leaf area density (LAD) can significantly change the WRF. Massman et al [6] presented a set of equations accounting for vertically heterogonous LAD,\  plant area index (PAI) and above-canopy wind profiles towards calculating subcanopy wind adjustment factor (WAF). WAF is basically the inverse of WRF. In this study, we implement this LAD based WRF (by inversing Massman{\textquoteright}s WAF) model in CSIRO{\textquoteright}s Spark platform within its Vesta fire propagation model [2].

This research was initially tested using a synthetic dataset to apply canopy parameters that represent Australian vegetation properties. The preliminary Spark simulation results show some variation in fire rate of spread based on various vegetation types. Further, we assess the implementation using three case studies based on three fire incidents, Mount Cooke Fire 2003 of Western Australia, Kilmore Fire 2009 of Victoria, and Lithgow Fire (State Mine) 2013 of New South Wales. Taking PAI as half of leaf area index (LAI), testing four different LAD configurations, it is found that for Tall Open Eucalyptus LAD, best results are obtained in relation to actual final fire perimeters

}, keywords = {adjustment, area density, factor, leaf, spark, wind}, issn = {713}, author = {Mahmood Rashid and Khalid Moinuddin} } @article {bnh-6900, title = {Fire spread prediction across fuel types: annual report 2018-19}, number = {564}, year = {2020}, month = {05/2020}, institution = {Bushfire and Natural Hazards CRC}, address = {Melbourne}, abstract = {

The prediction of the rate of spread and intensity of bushfires is crucial for emergency and disaster management organisations for operational planning and the deployment of resources.\  Currently, simplified operational models are used since the prediction can be obtained on time scales commensurate with those required by emergency managers. Our aim is to refine these non-physics-based operational tools so that they can predict fire behaviour under a wide range of localised topographic and weather conditions, and also that they are able to account for a range of inhomogeneity, slope, and thermal instability within vegetation and over the terrain. Furthermore, a more physically-motivated firebrand model needs to be included in operational models to predict firebrand landing and the increased rate of fire spread (RoS).

In the last few years, we have numerically tested and established a reliable physics-based model that is based on basic fire dynamics theory and corresponding differential equations to simulate bushfire scenarios. We now embark upon utilization of our research as well as extending parametric study using the physics-based model. The following aspects have been the highlights of our endeavour:

Overall we have achieved our goal to obtain greater insight into bushfire physics and we are now utilising these insights to parameterise various phenomenon for operational models.

}, keywords = {fire spread, fuel types, prediction}, issn = {564}, author = {Mahmood Rashid and Duncan Sutherland and Khalid Moinuddin} } @article {bnh-6642, title = {A report on WRF software development (preliminary)}, number = {539}, year = {2020}, month = {01/2020}, institution = {Bushfire \& Natural Hazards CRC}, address = {Melbourne}, abstract = {

Bushfire is one of the major natural hazards Australia encounters every year because of its dry weather and widespread bushlands. During the dry season, occurrence of simultaneous bushfires at different locations is quite common. The fire management authorities face challenges on deployment of resources (human and other logistics) properly to mitigate multiple bushfires. To prioritize, they depend on the modelling of real-time behaviours of the fire under consideration. Fire rate of spread (RoS) is one of the most important parameters that the authority wants to determine before proceeding to resource allocation. RoS during forest fire can be slowed by reduced sub-canopy wind. In operational models, it is accounted by a parameter, wind reduction factor (WRF). Current values used as WRF are not based on science. In this work, we are presenting a software (preliminary version) that can calculate WRF scientifically. We apply a dynamic WRF obtained from a mathematical model in an operation model, Spark. This will lead to better prediction of RoS, once fully implemented.

}, keywords = {Bushfire, fire management, model, Rate of spread, software development}, issn = {539}, author = {Mahmood Rashid and James Hilton and Nazmul Khan and Duncan Sutherland and Khalid Moinuddin} }