@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} } @article {bnh-4619, title = {Statistical characterisation of wind fields over complex terrain with applications in bushfire modelling}, journal = {Research Gate}, year = {2017}, month = {12/2017}, abstract = {

The propagation of bushfire across the landscape is dependent on a variety of environmental factors, but the wind, in particular, has a major effect on both the speed and direction of fire propagation. As such, bushfire spread models, which underpin successful bushfire management, require accurate knowledge of the pattern of winds across the landscape. This can be problematic over complex terrain where winds exhibit considerable spatial variability due to wind-terrain interactions, and where detailed measurements of wind characteristics are comparatively rare. This thesis contributes two new wind datasets to address the previous lack of data available to develop and validate wind models over complex terrain. It also details analyses that focus on the statistical characterisation of wind as joint wind direction distributions, which represent the directional wind response to changing topography and surface roughness. A novel method for toroidal surface fitting is introduced and implemented to estimate the true continuous response from discrete observed data. This new method, which relies on a conceptually simple adaptation of planar techniques, is compared to the limited range of available toroidal surface estimation techniques and is shown to perform as well as, if not better than, these more sophisticated methods. Monte Carlo simulations are employed to highlight the sensitivity of statistical comparison tests to alternative distribution structures, and to validate bivariate and circular extensions of the Kolmogorov-Smirnov test. These tests are applied to directional wind response pairs, showing that vegetation regrowth has a significant but varying impact across complex terrain. Finally, this thesis demonstrates how statistical approaches can be used to complement current physics-based wind modelling methods. The resulting probabilistic representations provide more accurate predictions of wind direction variability, and are better suited to emerging ensemble-based bushfire prediction frameworks. As such, they provide a superior characterisation of uncertainty across the fire modelling process; ultimately enabling fire managers to make more informed decisions.

}, keywords = {Bushfire, bushfire., Emergency management, Fire, propagation, wind modelling}, url = {https://www.researchgate.net/publication/322212511_Statistical_characterisation_of_wind_fields_over_complex_terrain_with_applications_in_bushfire_modelling}, author = {Rachael Quill} } @mastersthesis {bnh-4426, title = {Statistical Characterisation Of Wind Fields Over Complex Terrain With Applications In Bushfire Modelling}, year = {2017}, month = {11/2017}, pages = {274}, school = {University of New South Wales}, address = {Canberra}, abstract = {

The propagation of bushfire across the landscape is dependent on a variety of environmental factors, but the wind, in particular, has a major effect on both the speed and direction of fire propagation. As such, bushfire spread models, which underpin successful bushfire management, require accurate knowledge of the pattern of winds across the landscape. This can be problematic over complex terrain where winds exhibit considerable spatial variability due to wind-terrain interactions, and where detailed measurements of wind characteristics are comparatively rare.

This thesis contributes two new wind datasets to address the previous lack of data available to develop and validate wind models over complex terrain. It also details analyses that focus on the statistical characterisation of wind as joint wind direction distributions, which represent the directional wind response to changing topography and surface roughness. A novel method for toroidal surface fitting is introduced and implemented to estimate the true continuous response from discrete observed data. This new method, which relies on a conceptually simple adaptation of planar techniques, is compared to the limited range of available toroidal surface estimation techniques and is shown to perform as well as, if not better than, these more sophisticated methods.

Monte Carlo simulations are employed to highlight the sensitivity of statistical comparison tests to alternative distribution structures, and to validate bivariate and circular extensions of the Kolmogorov-Smirnov test. These tests are applied to directional wind response pairs, showing that vegetation regrowth has a significant but varying impact across complex terrain.

Finally, this thesis demonstrates how statistical approaches can be used to complement current physics-based wind modelling methods. The resulting probabilistic representations provide more accurate predictions of wind direction variability, and are better suited to emerging ensemble-based bushfire prediction frameworks. As such, they provide a superior characterisation of uncertainty across the fire modelling process; ultimately enabling fire managers to make more informed decisions.

}, keywords = {Bushfire, bushfire management, propagation, spatial variability, toroidal surface, wind, wind-terrain}, url = {https://www.researchgate.net/profile/Rachael_Quill/publication/322212511_Statistical_characterisation_of_wind_fields_over_complex_terrain_with_applications_in_bushfire_modelling/links/5a4c20cf458515a6bc6bf931/Statistical-characterisation-of-wind-fields-ov}, author = {Rachael Quill} } @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.} }