Abstract
With emerging research into the dynamics of extreme fire behaviour, it is increasingly important for wind models used in operational fire prediction to accurately capture areas of complex flow across rugged terrain. Additionally, the emergence of ensemble and stochastic modelling frameworks has led to the discussion of uncertainty in fire prediction. To capture the uncertainty of modelled fire outputs, it is necessary to recast uncertain inputs in probabilistic terms. WindNinja is the diagnostic wind model currently applied within a number of operational fire prediction frameworks across the world. For computational efficiency, allowing for real-time or faster than real-time prediction, the physical equations governing wind flow across complex terrain are often simplified. The model has a number of well documented limitations, for instance, it is known to perform least well on leeward slopes. This study first aims to understand these limitations in a probabilistic context by comparing individual deterministic predictions to observed distributions of wind direction. Secondly, a novel application of the deterministic WindNinja model is presented and shown to enable prediction of wind direction distributions that capture some of the variability of complex wind flow. Recasting wind fields in terms of probability distributions enables better understanding of variability across the landscape, and provides the probabilistic information required to capture uncertainty through ensemble or stochastic fire modelling. The comparisons detailed in this study indicate the potential for WindNinja to predict multimodal wind direction distributions that represent complex wind behaviours, including recirculation regions on leeward slopes. However, the limitations of using deterministic models within probabilistic frameworks are also highlighted. To enhance fire prediction and better understand uncertainty, it is recommended that statistical approaches also be developed to complement existing physics-based deterministic wind models.
Highlights
The accurate prediction of wind fields across all types of terrain is fundamental to capturing the range of possible spreading patterns a fire can exhibit
Traditional deterministic models must be complemented with probabilistic information informed by empirical data
This study shows a stark comparison between the application of a diagnostic wind model using the traditional approach and a novel ensemble-style application
Summary
The accurate prediction of wind fields across all types of terrain is fundamental to capturing the range of possible spreading patterns a fire can exhibit. Wind fields and fire spread are driven by a range of processes which can experience variations at multiple scales, some of which cannot be fully captured within operationally constrained models This gives rise to uncertainty in predicted fire behaviors. It was demonstrated that the ensemble-based decision support tool, FireDST, was able to produce a probabilistic prediction that adequately covered the actual extent of an observed fire, as such implying that it is plausible to view the actual fire as a realization of the ensemble distribution This approach allowed for the provision of detailed probabilistic information about exposure and potential losses (French et al, 2013). Such information could be scrutinized by emergency service managers to analyze the variety of potential outcomes and impacts from a single event
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
