@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-7359, title = {Physics-Based Simulations of Flow and Fire Development Downstream of a Canopy}, journal = {Atmosphere}, volume = {11}, year = {2020}, month = {06/2020}, abstract = {

The behavior of a grassland fire propagating downstream of a forest canopy has been simulated numerically using the fully physics-based wildfire model FIRESTAR3D. This configuration reproduces quite accurately the situation encountered when a wildfire spreads from a forest to an open grassland, as can be the case in a fuel break or a clearing, or during a prescribed burning operation. One of the objectives of this study was to evaluate the impact of the presence of a canopy upstream of a grassfire, especially the modifications of the local wind conditions before and inside a clearing or a fuel break. The knowledge of this kind of information constitutes a major element in improving the safety conditions of forest managers and firefighters in charge of firefighting or prescribed burning operations in such configurations. Another objective was to study the behavior of the fire under realistic turbulent flow conditions, i.e., flow resulting from the interaction between an atmospheric boundary layer (ABL) with a surrounding canopy. Therefore, the study was divided into two phases. The first phase consisted of generating an ABL/canopy turbulent flow above a pine forest (10 m high, 200 m long) using periodic boundary conditions along the streamwise direction. Large Eddy Simulations (LES) were carried out for a sufficiently long time to achieve a quasi-fully developed turbulence. The second phase consisted of simulating the propagation of a surface fire through a grassland, bordered upstream by a forest section (having the same characteristics used for the first step), while imposing the turbulent flow obtained from the first step as a dynamic inlet condition to the domain. The simulations were carried out for a wind speed that ranged between 1 and 12 m/s; these values have allowed the simulations to cover the two regimes of propagation of surfaces fires, namely plume-dominated and wind-driven fires

}, keywords = {canopy, fire spread, grassland fire, Large Eddy Simulation, physics-based model}, doi = {https://doi.org/10.3390/atmos11070683}, url = {https://www.mdpi.com/2073-4433/11/7/683}, author = {Gilbert Accary and Duncan Sutherland and Nicolas Frangieh and Khalid Moinuddin and Ibrahim Shamseddine and Sofiane Merdji and Dominique Morvan} } @article {bnh-5684, title = {Use of remote sensing measurements and data assimilation techniques to improve estimates of landscape dryness}, number = {482}, year = {2019}, month = {07/2019}, institution = {Bushfire and Natural Hazards CRC}, address = {Melbourne}, abstract = {

Fire intensity, spread rate and ignition are very sensitive to the fuel dryness which in turn is strongly linked to soil moisture deficit. Though the value of soil moisture deficit in predicting fire danger has been long established, very few fire danger rating systems employ a comprehensive methodology to estimate it. Most fire danger rating systems use very simple empirical water balance models which are found to have errors. Hence they are poor drivers of the sophisticated fire models used operationally to manage and warn for dangerous fire conditions and spread. With advances in the science of measurement, in the form of satellite remote sensing, and in prediction, in the form of physically based land surface models, soil moisture can now be better analysed and predicted. Neither observations nor models give a complete picture of the soil moisture state in isolation, however. Data assimilation combines observational and model information optimally, yielding increasingly consistent and complete estimates of soil moisture. In this paper, we touch on the various operational satellite observations available. We also discuss land surface
data assimilation methods used widely in soil moisture research and operations. This report is prepared for those with very limited technical and scientific background in satellite remote sensing or data assimilation. Hence complex mathematical and physical formulations are carefully omitted. However, the problems discussed here are highly non-trivial and inter-desciplinary, with much progress made in recent decades. Hence some technicalities are unavoidable. Also, the discussion is not intended to be complete. Our intention is to highlight, especially to the emergency management community, soil moisture estimation methods that may not be well known outside the scientific community.

}, keywords = {dryness, fire rate, fire spread, Landscape ecology, Soil moisture deficit}, issn = {482}, author = {Vinod Kumar and Imtiaz Dharssi} } @article {BF-4267, title = {Uncertainty associated with model predictions of surface and crown fire rates of spread}, journal = {Environmental Modelling \& Software}, volume = {47}, year = {2013}, month = {2013}, pages = {16-28}, chapter = {16}, abstract = {The degree of accuracy in model predictions of rate of spread in wildland fires is dependent on the model{\textquoteright}s applicability to a given situation, the validity of the model{\textquoteright}s relationships, and the reliability of the model input data. On the basis of a compilation of 49 fire spread model evaluation datasets involving 1278 observations in seven different fuel type groups, the limits on the predictability of current operational models are examined. Only 3\% of the predictions (i.e. 35 out of 1278) were considered to be exact predictions according to the criteria used in this study. Mean percent error varied between 20 and 310\% and was homogeneous across fuel type groups. Slightly more than half of the evaluation datasets had mean errors between 51 and 75\%. Under-prediction bias was prevalent in 75\% of the 49 datasets analysed. A case is made for suggesting that a 35\% error interval (i.e. approximately one standard deviation) would constitute a reasonable standard for model performance in predicting a wildland fire{\textquoteright}s forward or heading rate of spread. We also found that empirical-based fire behaviour models developed from a solid foundation of field observations and well accepted functional forms adequately predicted rates of fire spread far outside of the bounds of the original dataset used in their development.}, keywords = {bushfire behaviour, bushfire spread, Fire behaviour, fire spread}, doi = {http://dx.doi.org/10.1016/j.envsoft.2013.04.004}, author = {Cruz, Miguel G. and Alexander, ME} }