@article {bnh-5376, title = {Modelling wind direction distributions using a diagnostic model in the context of probabilistic fire spread prediction}, journal = {Frontiers in Mechanical Engineering: Thermal and Mass Transport}, year = {2019}, month = {02/2019}, 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.

}, keywords = {complex terrain, deterministic, ensemble modelling, Probability distributions, uncertainty, von Mises, wind modelling, WindNinja}, doi = {10.3389/fmech.2019.00005}, url = {https://www.frontiersin.org/articles/10.3389/fmech.2019.00005/abstract}, author = {Rachael Quill and Jason J. Sharples and Natalie Wagenbrenner and Leesa Sidhu and Jason Forthofer} } @article {bnh-5771, title = {A Statistical Approach to Understanding Canopy Winds over Complex Terrain}, journal = {Environmental Modeling \& Assessment}, year = {2019}, month = {08/2019}, pages = {1-20}, abstract = {

Winds that flow within a forest canopy in complex terrain exhibit considerable variability that can have significant consequences for environmental processes such as the spread of bushfires, seed dispersal and transport of pollutants. This variability is still poorly understood, due in part to limited observations of canopy winds over complex terrain, and as such is often unaccounted for in deterministic modelling approaches. Probabilistic representation of wind fields can better characterise variability and build understanding of uncertainty in applications such as bushfire prediction. This study introduces two new, publicly available, datasets that support analyses of canopy winds over complex terrain using novel statistical approaches. Wind fields are characterised using bivariate distributions, representing the response of synoptic winds to changing landscapes, including post-bushfire forest regrowth and varying topography across uniform vegetation. For statistical comparison, non-parametric Kolmogorov-Smirnov style tests are considered. A new test is proposed for the comparison of bivariate circular distributions arising from analyses of wind direction. The study broadly reaffirms established theory while highlighting the variability of canopy winds over complex terrain and the importance of developing statistical techniques that result in a better quantitative understanding of these wind fields and the processes driving them.

}, keywords = {Circular statistics, Flow separation, forest canopy, uncertainty, Wind field observations, wind modelling}, doi = {https://doi.org/10.1007/s10666-019-09674-w}, url = {https://link.springer.com/article/10.1007/s10666-019-09674-w}, author = {Rachael Quill and Jason J. Sharples and Leesa Sidhu} } @conference {bnh-2932, title = {Wind speed reduction induced by post-fire vegetation regrowth}, booktitle = {AFAC16}, year = {2016}, month = {08/2016}, publisher = {Bushfire and Natural Hazards CRC}, organization = {Bushfire and Natural Hazards CRC}, address = {Brisbane}, abstract = {

In the current suite of operational fire spread models, wind speeds measured in the open environment (above the vegetation layer) are modified to represent wind speeds at {\textquoteleft}mid-flame{\textquoteright} height using adjustment factors. In general, these adjustment factors assume constant vertical wind speed profiles throughout the vegetation layer. However, empirical studies have shown that wind speeds beneath canopies can vary significantly with height above ground as well as with forest type and prevailing wind speed. Empirical wind reduction profiles have been developed for a number of different forest types in flat terrain using data collected across Victoria, Australia.


The present research aims to extend these empirical studies to better understand the impacts of topography and post-fire vegetation regrowth on the reduction of wind speeds beneath the canopy. Wind data collected over fire affected regions of rugged terrain in South Eastern Australia are used to analyse wind speed reduction induced by post-fire regrowth and complex topography. A secondary study is used to analyse wind speed reduction caused by Radiata pine plantation in undulating terrain.


Results of this study suggest that empirical wind reduction profiles perform well at the broader landscape-scale, i.e. ridge tops and valley floors. However, more complex topographical features appear to have a compounding affect on wind speed reduction within rugged terrain. Through better understanding of wind speed reduction beneath the canopy across landscapes from mountainous ranges through to flat plains, wind speed reduction models for bushfire spread prediction can be adapted to incorporate the variation observed in vertical wind speed profiles within the vegetation layer.

}, author = {Rachael Quill and Kangmin Moon and Jason J. Sharples and Leesa Sidhu and Thomas Duff and Tolhurst, K.G.} }