@article {bnh-7106, title = {The Australian Natural Disaster Resilience Index: Volume II {\textendash} Chapter 6: Uncertainty and sensitivity analysis}, number = {493}, year = {2020}, month = {07/2020}, institution = {Bushfire and Natural Hazards CRC}, address = {Melbourne}, abstract = {

In this chapter

Section 6.1 Explains the role of uncertainty and sensitivity analysis in composite index construction.

Section 6.2 Describes the uncertainty analysis applied to the Australian Natural Disaster Resilience Index.

Section 6.2 Describes the sensitivity analysis applied to the Australian Natural Disaster Resilience Index.

}, keywords = {analysis, ANDRI, computation, disaster resilience, index design, sensitivity, uncertainty}, isbn = { 978-0-6482756-2-6}, issn = {493}, author = {Melissa Parsons and Ian Reeve and James McGregor and Graham Marshall and Richard Stayner and Judith McNeill and Peter Hastings and Sonya Glavac and Phil Morley} } @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-5623, title = {Describing adaptation tipping points in coastal flood risk management}, journal = {Computers, Environment and Urban Systems}, volume = {69}, year = {2018}, month = {05/2018}, abstract = {

Assessing changing coastal flood risk becomes increasingly uncertain across multi-decadal timeframes. This uncertainty is a fundamental complexity faced in\ vulnerability\ assessments and adaptation planning. Robust decision making (RDM) and dynamic adaptive policy pathways (DAPP) are two\ state-of-the-artdecision support\ methods that are useful in such situations. In this study we use RDM to identify a small set of conditions that cause unacceptable impacts from\ coastal flooding, signifying that an adaptation tipping point is reached. Flexible adaptation pathways can then be designed using the DAPP framework. The methodology is illustrated using a case study in Australia and underpinned by a\ geographic information system\ model. The results suggest that conditions identified in scenario discovery direct the attention of decision-makers towards a small number of uncertainties most influential on the vulnerability of a community to changing flood patterns. This can facilitate targeted data collection and coastal\ monitoring activities\ when resources are scarce. Importantly, it can also be employed to illustrate more broadly how uncontrolled\ societal development, land use and historic building regulations might exacerbate flood impacts in low-lying urban areas. Notwithstanding the challenges that remain around\ simulation modelling and\ detection of\ environmental change, the results from our study suggest that RDM can be embedded within a DAPP framework to better plan for changing coastal flood risks.

}, keywords = {adaptation, Climate change, Inundation, Tipping point, uncertainty, Vulnerability}, doi = {https://doi.org/10.1016/j.compenvurbsys.2018.01.002}, url = {https://www.sciencedirect.com/science/article/pii/S0198971517304520}, author = {Timothy Ramm and Christopher Stephen Watson and Christopher J. White} } @mastersthesis {bnh-5391, title = {Improving adaptation planning for future sea level rise and coastal flooding}, year = {2018}, school = {University of Tasmania}, address = {Hobart}, abstract = {

Sea level rise has the potential to exacerbate coastal flooding around the world, causing more frequent extreme sea levels, nuisance flooding and permanent inundation. Projecting diverse physical, environmental and socio-economic impacts to coastal communities across multi-decadal timeframes is vital for informed adaptation planning. However, this is increasingly uncertain and challenged by conflicting stakeholder priorities.

This thesis develops an interdisciplinary approach to advance the planning of long-term adaptation pathways in the context of coastal flood risk management. Utilising three case studies in south-east Australia, it combines the strengths of robust decision making (RDM) and dynamic adaptive policy pathways (DAPP) {\textendash} both prominent tools to support decision-making under conditions of uncertainty {\textendash} together with solicited values-based information to make three novel advances towards flexible adaptation pathways planning.

First, this thesis combines the strengths of RDM and DAPP for planning adaptation pathways by using scenario discovery to provide a multi-dimensional description of adaptation tipping points. The scenario discovery process uses an existing cluster finding algorithm to identify future conditions where adaptation policies no longer keep flood risk at tolerable levels. Combining RDM and DAPP is a novel approach in coastal flood risk management and scenario discovery provides greater visibility on the physical factors driving adaptation tipping points. Second, values-based information is integrated into the adaptation planning process in an attempt to consider subjective social and cultural impacts from coastal flooding. In this regard, the novel combination and explanatory value of lived and landscape values is investigated. Consideration of these more intangible effects are often omitted in coastal adaptation planning but can contribute to more informed adaptation decisions. Third, the combined RDM and DAPP method developed uses both geographic information system (GIS) software and open source programming tools to analyse flood impacts. This improves the usability and relevance of a combined RDM and DAPP approach, enabling the efficient redeployment and customisation of the method to suit different location-specific needs. Such an approach also provides a lower cost entry point for local government agencies to begin planning coastal adaptation pathways.

Each of these advances combine to provide a framework that improves the way coastal adaptation pathways are planned and developed. The findings elucidated from the case studies show how a combined RDM and DAPP approach can account for spatial and temporal interactions between hazard, exposure and vulnerability flood risk factors, which improves the way robustness is measured for adaptation policy options. Values-based approaches can shape the selection of adaptation objectives, improve the design of options, support the evaluations of adaptation pathways and inform monitoring systems. The resultant adaptation pathways framework can support local government in planning sustainable strategies to manage long-term flood impacts. This has global applications for coastal flood risk management that will become increasingly important throughout the coming century.

}, keywords = {adaptation, Climate change, coastal flooding, decision-making, Planning, risk, sea level rise, uncertainty}, url = {https://eprints.utas.edu.au/29624/}, author = {Timothy Ramm} }