april 2016

by Rachael Quill

Uncertainty is all around is. We account for it in all aspects of everyday life; “It takes ten minutes to get there but I’ll allow fifteen, just in case.” So why is it that when it comes to the “wicked problem” of wildfire, we have put uncertainty to one side for so long?

Traditionally operational fire prediction has been based on deterministic methods – for each set of input conditions there is a single output, with no allowance for uncertainty in the process. After almost 30 years of discussion in the literature, probabilistic approaches to fire spread and behavior modeling are now beginning to emerge. These probabilistic approaches account for uncertainty in fire spread by allowing for random fluctuations in the input variables and predicting a range of fire propagation scenarios. When considered together, these multiple predictions are overlaid to form what is known as an ensemble. Ensemble-based predictions allow fire spread across the landscape to be defined in terms of probabilities, such as likelihood of burning and risk to assets.

However, within these developing frameworks we still rely upon deterministic models and simplified probabilistic inputs. That is to say, we are trying to understand the variability of fire spread without capturing the true variability of the driving factors. With Cruz and Alexander (2013) showing that input errors are one of the major sources of prediction error in fire modeling, it is imperative that we seek to acknowledge the uncertainties of these inputs.

Wind, in particular, is known to account for much of the variability displayed in the spread of wildfires (Cruz and Alexander, 2013). Due to the constraints of operational requirements (i.e. real-time or near real-time prediction), the current physics-based deterministic wind models for fire prediction do not well capture the variability of wind flow in key areas, particularly across complex terrain. In the worst case, on leeward slopes, errors in wind direction of up to 180° have been seen (Fig 1). Even in the new ensemble-based approaches, wind direction is only characterized as random whereas analysis of data collected across complex terrain has shown that wind direction in fact takes a highly structured form. To better capture the uncertainty of fire spread across the landscape, we must characterize the structured nature of wind direction within fire prediction frameworks.

Observed wind direction rose from a valley in complex terrain against the predicted wind direction given by a state-of-the-art operational wind model. Illustration: Rachael Quill.

Without capturing the true variability of wind flow across complex terrain, the curse of error accumulation through the modeling process leaves us to question the uncertainty of fire spread predictions in these key regions. Simpson et al. (2013) have already suggested that traditional fire modeling techniques are failing to capture dynamic processes such as Vorticity-driven Lateral Spread (dynamic spread of the fire front on a leeward slope in a direction perpendicular to that of the wind) in areas where flow separation, and lee-slope eddies are not accounted for by the wind models used today.

The first step in handling the “wicked problem” of uncertainty in wind modeling is to understand the impacts of the physical environment, such as vegetation or topography, on the statistical representation of wind fields. A statistical representation, rather than the traditional physics-based approach, allows discussion of probability – leading to analysis of scenarios with quantified likelihoods.

Statistical analyses of the impacts of topographical aspect on wind direction clearly indicate thresholds for dynamic behavior. This can of course be understood using detailed physical analysis and has indeed been studied using sophisticated mathematical models (Simpson et al., 2013). However, from the fire fighter perspective, we must look to understand the uncertainty around this behavior and capture it within our operational models under the constraints of real-time prediction.

When considering the impacts of vegetation on wind direction across complex terrain, the story becomes less clear – and the role of uncertainty becomes yet more important. Changing vegetation structures have distinct impacts on wind direction in some parts of the terrain – and the behaviors are consistent with the current predictions. However, in other areas of the terrain, the impact of vegetation on wind direction is far less obvious – and observed behaviors vary from those currently captured by state-of-the-art models.

Better statistical understanding of the variations in wind fields across the landscape, will improve on current physics-based methods by better capturing wind dynamics in complex terrain. Development of hybrid models, combining probabilistic information with deterministic approaches to wind modeling will provide better understanding of uncertainty within the fire modeling process while maintaining operational real-time (or near real-time) prediction. The result of such a hybrid model would ultimately provide more information to the fire managers and decisions makers dealing with the “wicked problem” on the ground.


Cruz, M. G. and Alexander, M. E. (2013) Uncertainty associated with model prediction of surface and crown fire rates of spread. Environmental Modelling & Software, 47, pp16-28.

Simpson, C. C., Sharples, J. J., Evans, J. P. and McCabe, M. F. (2013) Large eddy simulation of atypical wildland fire spread on leeward slopes. International Journal of Wildland Fire, 22 (5) 599-614.


20140117adfa3443372_0003 Ms Rachael Griffiths

Bio: Rachael Quill holds an MSci in Mathematics and Statistics from Lancaster University, UK, and is currently studying for a PhD at the University of New South Wales (UNSW) Canberra, Australia. She is researching the statistical characterization of wind fields over complex terrain for bushfire modeling applications, with particular interest in the impacts of surface roughness on wind fields as well as the development of probabilistic approaches within fire modeling. She receives funding support from UNSW Canberra and the Bushfire and Natural Hazards Cooperative Research Centre, Australia.