@article {bnh-1843, title = {Erratum to {\textquotedblleft}Using discrete event simulation cellular automata models to determine multi-mode travel times and routes of terrestrial suppression resources to wildland fires"}, journal = {European Journal of Operational Research}, volume = {245}, year = {2015}, month = {08/2015}, pages = {339-340}, chapter = {339}, abstract = {

Forest fires can impose substantial social, environmental and economic burdens on the communities on which they impact. Well managed and timely fire suppression can demonstrably reduce the area burnt and minimise consequent losses. In order to effectively coordinate emergency vehicles for fire suppression, it is important to have an understanding of the time that elapses between vehicle dispatch and arrival at a fire. Forest fires can occur in remote locations that are not necessarily directly accessible by road. Consequently estimations of vehicular travel time may need to consider both on and off road travel. We introduce and demonstrate a novel framework for estimating travel times and determining optimal travel routes for vehicles travelling from bases to forest fires where both on and off road travel may be necessary. A grid based, cost-distance approach was utilised, where a travel time surface was computed indicating travel time from the reported fire location. Times were calculated using a discrete event simulation cellular automata (CA) model, with the CA progressing outwards from the fire location. Optimal fastest travel paths were computed by recognising chains of parent{\textendash}child relationships. Our results achieved comparable results to traditional network analysis techniques when considering travel along roads; however the method was also demonstrated to be effective in estimating travel times and optimal routes in complex terrain.

}, doi = {doi:10.1016/j.ejor.2015.03.025}, url = {http://www.sciencedirect.com/science/article/pii/S0377221715002325}, author = {Thomas Duff and Chong, Derek and Tolhurst, K.G.} } @article {bnh-2241, title = {National Fire Danger Rating System Probabalistic Framework: year three report}, number = {116}, year = {2015}, month = {09/2015}, institution = {Bushfire and Natural Hazards CRC}, address = {Melbourne}, abstract = {

The objective of the Probabilistic Framework Project is to develop a new consequence-based fire danger rating system able to integrate a wide range of variables and link their complex interactions to the probability of property loss. The project aims at delivering a spatially-explicit framework capable of generating daily maps representing the distribution of the probability of property loss at 5km spatial resolution.

In year two, a {\textquotedblleft}consequence-based{\textquotedblright} system (developed in Year 1) was refined and applied in two case study regions: the Sydney Basin, and the Victorian East Central Risk Landscape. The BN framework was successfully integrated with GIS facilities to generate spatially explicit predictions of the probability of a fire spreading to and reaching the urban interface and then burning there at high intensity (\>4000kWm-1, hereafter unsuppressible fires). Overall, the model indicated that the highest risk areas may potentially be identified by accounting for not only fire weather, but also fuels, the distribution of property, plus features inherent in the landscape that influenced fire spread.

The objective for the third stage of the project was to develop and test an operational application of the daily fire danger rating Bayesian Network model for two case study areas - Sydney Basin and the East Central Risk Landscape in Victoria. In this report, we present the results of the operational application of the model, quantify sensitivities of the model and finally we make recommendations for the future of the modelling approach.
We developed a prototype Fire Danger Rating (FDR) website for updating fire risk in real-time (30-60 seconds per case study). Maps are generated through use of landscape data and additional daily 3pm weather data downloaded from the Bureau of Meteorology. Across the 2014/15 season spatial variation was seen between the predictions from the FDR and that of FFDI. It is important to note that these variations exist despite the fact FFDI is an input into the model influencing ignition probability, fire spread and fire intensity. One of the main reasons for these differences is that FFDI only accounts for weather and does not consider topography, fuels, spatial arrangement of assets or the directionality of the wind.

The sensitivity analysis suggested that the model is performing well relative to expectations. Logical relationships and coarse scale patterns are holding true. The results indicate strong reliance on the empirical analysis of ignition probabilities in the landscape. FFDI was found to be the input node that required the greatest accuracy.

A number of recommendations were made by state agencies during an end of study review of the project. These included expanding the FDR website to new landscapes, with particular interest in assessing the model for grassland environments. In addition, the group thought the model had considerable capacity for longer term planning of fuel treatments, accounting for changing human patterns and future climates.

Overall, the project has succeeded in delivering a spatially-explicit framework capable of generating daily maps representing the distribution of the probability of property loss at 5km spatial resolution.

}, issn = {116}, author = {Trent Penman and Parkins, K.A. and Mascaro, S. and Chong, Derek and Ross Bradstock} } @article {bnh-1841, title = {Using discrete event simulation cellular automata models to determine multi-mode travel times and routes of terrestrial suppression resources to wildland fires}, journal = {European Journal of Operational Research}, volume = {241}, year = {2015}, month = {03/2015}, pages = {763-770}, chapter = {763}, abstract = {

Forest fires can impose substantial social, environmental and economic burdens on the communities on which they impact. Well managed and timely fire suppression can demonstrably reduce the area burnt and minimise consequent losses. In order to effectively coordinate emergency vehicles for fire suppression, it is important to have an understanding of the time that elapses between vehicle dispatch and arrival at a fire. Forest fires can occur in remote locations that are not necessarily directly accessible by road. Consequently estimations of vehicular travel time may need to consider both on and off road travel. We introduce and demonstrate a novel framework for estimating travel times and determining optimal travel routes for vehicles travelling from bases to forest fires where both on and off road travel may be necessary. A grid based, cost-distance approach was utilised, where a travel time surface was computed indicating travel time from the reported fire location. Times were calculated using a discrete event simulation cellular automata (CA) model, with the CA progressing outwards from the fire location. Optimal fastest travel paths were computed by recognising chains of parent{\textendash}child relationships. Our results achieved comparable results to traditional network analysis techniques when considering travel along roads; however the method was also demonstrated to be effective in estimating travel times and optimal routes in complex terrain.

}, keywords = {Transport; Network; environment and climate change; Routing; Simulation}, doi = {doi:10.1016/j.ejor.2014.09.019}, url = {http://www.sciencedirect.com/science/article/pii/S0377221714007401}, author = {Thomas Duff and Chong, Derek and Tolhurst, K.G.} } @article {BF-4281, title = {Quantifying spatio-temporal differences between fire shapes: Estimating fire travel paths for the improvement of dynamic spread models}, journal = {Environmental Modelling \& Software}, volume = {46}, year = {2013}, month = {08/2013}, pages = {33-43}, chapter = {33}, abstract = {Dynamic fire spread models are a recent development in landscape management that provide for the simulation of the spread of fires through time under complex weather conditions. These allow risks to be assessed and resources to be strategically managed. The need for reliable and accurate fire models is of particular importance in the face of recent catastrophic wildfires in Australia, Europe and the United States. However, while fire spread models are developed using physical knowledge and empirical observations, there are few techniques which can be used to objectively assess the {\textquoteleft}goodness of fit{\textquoteright} of spatial predictions of fire spread. We propose a new method to allow the comparison of fire perimeters, providing for the discrimination of sources of simulation error and assisting in the collection of empirical spread data from observed fires. Differences between fire perimeters are quantified using linear vectors aligned with the direction of spread of the perimeter being sampled. These can provide an indication of difference in terms of the fire spread distance on the ground. The location, direction and length of these vectors can be used to assess spread rates to assist with model calibration. We demonstrated the utility of this method using a case study which assessed differences between the observed and simulated progression of an Australian wildfire. The new indices were found to be effective descriptors of differences in fire shape and hold potential for the spatial evaluation of fire spread models. The indices can be used to compare similar fire shapes; however they are unsuited for cases where there are large differences between perimeters.}, keywords = {Bushfire, Morphometrics, Perimeter, Shape analysis, Spatial validation, Wildfire}, doi = {http://dx.doi.org/10.1016/j.envsoft.2013.02.005}, url = {http://www.sciencedirect.com/science/article/pii/S1364815213000455}, author = {Thomas Duff and Chong, Derek and Tolhurst, K.G.} } @article {BF-4295, title = {Sensitivity Analysis of PHOENIX RapidFire}, year = {2013}, month = {05/2013}, abstract = {An analysis of the sensitivity of the outputs of PHOENIX Rapidfire (PHOENIX) to a range of inputs and simulation parameters was undertaken. This was done using two separate methods; assessment of model response in an artificially generated idealised landscape and assessment using case-studies of real fires. The ideal landscape was used to evaluate model sensitivity in response to temperature, relative humidity, wind speed, fuel type and wind direction relative to slope. The model was evaluated under two sets of weather conditions, mild (representing moderate fire spread potential) and extreme (representing high fire spread potential). Each scenario was evaluated for each of two fuel types, forest and grass. Sensitivity was evaluated in terms of the gross area burnt when the input of interest was systematically changed while all other inputs were held constant. For all evaluations except relative wind direction, model sensitivities were compared to an equivalent area burnt using the corresponding McArthur Forest or grassland fire danger meter (assuming an elliptical fire shape). The combination of wind direction and slope resulted in simulated fires that were not elliptical, so comparisons with shapes generated with the fire danger meters were not valid. PHOENIX predictions differed from those generated using point estimates for some circumstances; however without further investigation it is unclear on what is causing these differences. Differences in predictive performance are not necessarily representative of model error, as there are a number of differing assumptions between the systems used. However, specific situations have been flagged for follow up work. Two case study areas were used for PHOENIX model sensitivity evaluation; Wangary and Kilmore. Case study fires were simulated using observations from the day that the fires occurred with one input systematically varied. Three inputs were evaluated using the case studies; simulation resolution, start time (simulating fire ignition to occur earlier and later than observed) and start location (varying the ignition location in space). Sensitivity was evaluated by considering the change in the Area Difference Index (ADI, an index of the ratio between incorrectly predicted burnt area and the correctly predicted burnt area) from the baseline scenario (simulation resolution of 180m, ignition location and time as observed. Predictive performance varied wide with changing inputs. In general as the difference in input value to the {\textquoteleft}best estimate{\textquoteright} increased, predictive performance degraded.}, author = {Chong, Derek and Tolhurst, K.G. and Thomas Duff and Brett Cirulis} } @article {BF-4292, title = {Evaluation of weather data at different spatial and temporal scales on fire behaviour prediction using PHOENIX RapidFire 4.0 - Kilmore Case Study}, year = {2012}, abstract = {High resolution models might be expected to produce more accurate predictions, but in the case of weather forecasting data used to predict the spread of the 2009 Kilmore fire, this was not found to be true. Current weather forecasts are available on a 3600 m grid at hourly intervals. In time, computing power will enable finer spatial and temporal forecast weather to be produced operationally. This study was undertaken to understand what benefit finer scaled data might be to fire spread prediction. PHOENIX RapidFire was used to model the 2009 Kilmore fire with different spatial and temporal weather inputs. Because the fire of this size interacts with the local weather, it was found that courser level weather inputs performed better than very fine resolution data. Overall, weather forecasts at 30 minutes intervals and 1200 m spacing provided the best inputs for matching the progression of the fire. Once the fire had reached about 100,000 ha, 60 minute, 3600 m data gave the best predictions. Modelling weather at 400 m resolutions and at 5 minute intervals provides good insights into the dynamics of the weather which assists a weather forecaster, but that additional detail is not of the same benefit to fire spread predictions because large fires "smooth" the weather, terrain and fuel in the landscape. More case-studies need to be undertaken, including smaller fires burning under milder conditions to better understand the relationship between weather data scale and fire spread prediction.}, author = {Chong, Derek and Thomas Duff and Tolhurst, K.G.} } @article {BF-4291, title = {Incorporating Vertical Winds into PHOENIX RapidFire{\textquoteright}s Ember Dispersal Model}, year = {2012}, month = {12/2012}, abstract = {PHOENIX RapidFire is a fire simulation model developed in Australia as part of the Bushfire CRC. One unique aspect of the modelling process is the way spotfires are modelled and incorporated into fire spread. Part of the spotting process requires modelling of ember release, transport and spotfire ignition. In the first iteration of spotfire modelling, surface wind (10 m in the open) was used to estimate the distance and spread of embers falling ahead of a fire. However, it is acknowledged that upper-winds usually differ in speed and direction from surface winds. It was therefore thought that that fire modelling could be improved by using the wind speed and direction data produced by the numerical weather prediction models. PHOENIX was modified to incorporate multi-level wind data, however, the forecast data had an uncorrected bias that results in incorrect ember transport modelling results. The lack of systematic, high resolution and comprehensive upper-wind observations makes bias correction and fire model testing impossible at the current time. It was therefore not possible to develop an improved ember transport model for PHOENIX. This report describes the process and results obtained from this research. In the end, a version of PHOENIX that can use a single layer of upper-wind data as input was developed. This will allow some theoretical testing of PHOENIX, but cannot be used for operational fire predictions with the currently available data.}, author = {Chong, Derek and Tolhurst, K.G. and Thomas Duff} } @article {BF-4294, title = {PHOENIX RapidFire 4.0 Convection and Ember Dispersal Model}, year = {2012}, month = {12/2012}, abstract = { PHOENIX RapidFire is unique in the way it models and incorporates the spotting process in bushfires. There are three main components to the spotting process modelled: lofting, transport, and spotfire ignition. The method of modelling these three processes is described. Lofting is related to the convective strength of the fire and the amount of available ember material, transport is related to wind speed and direction, and spotfire ignition is related to the available fine fuel on the ground and its moisture content. An absence of reliable ember transport and spotfire data has necessitated an empirical approach using the Black Saturday fires in Victoria for model development. As a result, there may be some aspects of the spotting process not well captured in PHOENIX, but to date, the results on new fires have been encouraging. The effective rate of spread of bushfires can be a factor of two or three times greater if the spotting process is well developed compared with a fire, burning under generally similar conditions, but without spotting contributing to the effective rate of spread. Being able to effectively model the spotting process has dramatically increased our ability to model bushfires in eucalypt forests. Modelling the conditions associated with the spotting process has also improved our ability to model potential house loss.}, author = {Chong, Derek and Tolhurst, K.G. and Thomas Duff} } @article {BF-4293, title = {PHOENIX RapidFire 4.0{\textquoteright}s Convective Plume Model}, year = {2012}, month = {12/2012}, abstract = {Bushfires are a 3-dimensional phenomenon with significant interaction between the surface and the atmosphere. Complex coupled fire-atmosphere models have been produced and give amazingly realistic results, however the computational complexity means that they take many times real-time to run, even on super computers, and are therefore restricted to small areas and short periods of time. PHOENIX RapidFire is primarily a 2-dimensional fire model and only takes a few minutes to run fires in excess of 100,000 ha. This report describes how PHOENIX RapidFire has been developed to include elements of plume development and ember transport resulting in spot fires. This was done to try and capture some of the important 3-dimensional aspects of bushfires without large computational overheads. Development of the plume rise and spotting components of PHOENIX has been done with the knowledge of some of the key thermodynamic processes, but a number of assumptions have been made. Validation of the plume rise and spotting model is difficult because there are no detailed observations recorded for the plume, embers, spotfires and upper-level winds. Ground-based weather radar data was found to be a useful validation dataset for the plume model. The plume model in PHOENIX was calibrated against weather radar data recorded on Black Saturday, 2009. Early indications are that there has been a significant improvement in the simulation of the Black Saturday fires with the incorporation of the plume and spotting models. Further testing will be required to fully understand the limitations of the model.}, author = {Chong, Derek and Tolhurst, K.G. and Thomas Duff} } @article {BF-3174, title = {Procrustes based metrics for spatial validation and calibration of two-dimensional perimeter spread models: A case study considering fire}, journal = {Agricultural and Forest Meteorology}, volume = {160}, year = {2012}, month = {7/2012}, pages = {110 - 117}, abstract = {A number of phenomena in natural systems exhibit spread from a point source facilitated by a transport vector. Such occurrences are an important focus of landscape management, and include fires, wind driven disease and pollutant spills. Two-dimensional dynamic spread models are used to simulate the impacts of such events, determine risks and optimise responses. These models produce spatially coherent outputs that are not easily verified through traditional regression approaches. Validation of predictions is an essential part of model development and is necessary for the improvement of predictive performance. Current methods of evaluation are rarely systematic and are typically undertaken through subjective comparison of simulation outputs with observed features. There are few methods suitable for the objective analysis of freeform spread patterns, and it is proposed that a pseudo-landmark approach be adopted to allow the use of landmark based analysis methods. Vector driven spread patterns exhibit a degree of spatial structure, with distinct origin points and elongate shapes resulting from the predominant vector trajectory. These can be used as references to generate analogous landmarks for perimeter comparison. To describe differences, three indices derived from Procrustes analysis are proposed. These provide metrics to evaluate differences in perimeter orientation, size and shape. A case study simulating wildfire spread was used to demonstrate the proposed methodology. It was found to be effective for the description of perimeter differences and has potential for the validation and calibration of spread models. A number of assumptions were recognised and limitations in assigning pseudo-landmarks considered.}, issn = {01681923}, doi = {10.1016/j.agrformet.2012.03.002}, author = {Thomas Duff and Chong, Derek and Peter Taylor and Tolhurst, K.G.} } @proceedings {BF-3042, title = {Assessing Potential House Losses Using PHOENIX RapidFire}, year = {2011}, month = {2011}, publisher = {Bushfire CRC}, address = {Sydney, Australia}, issn = {978-0-9806759-9-3}, url = {http://www.bushfirecrc.com/resources/pages-74-86-assessing-potential-house-losses}, author = {Tolhurst, K.G. and Chong, Derek} }