@article {bnh-5068, title = {Improved treatment of non-stationary conditions and uncertainties in probabilistic models of storm wave climate}, journal = {Coastal Engineering}, volume = {127}, year = {2017}, month = {09-2017}, pages = {1-19}, chapter = {1}, abstract = {

A framework is presented for the probabilistic modelling of non-stationary coastal storm event sequences. Such modelling is required to integrate seasonal, climatic and long-term non-stationarities into\ coastal erosion\ hazard assessments. The framework is applied to a study site on the East Australian Coast where storm waves are found to exhibit non-stationarities related to\ El Ni{\~n}o-Southern Oscillation\ (ENSO) and seasonality. The impact of\ ENSO\ is most prominent for storm wave direction, long term mean sea level (MSL) and the rate of storms, while seasonal non-stationarity is more ubiquitous, affecting the latter variables as well as storm wave height, duration, period and surge. The probabilistic framework herein separates the modelling of ENSO and seasonal non-stationarity in the storm\ wave properties\ from the modelling of their marginal distributions, using copulas. The advantage of this separation is that non-stationarities can be straightforwardly modelled in all storm wave variables, irrespective of whether parametric or non-parametric techniques are used to model their marginal distributions. Storm wave direction and steepness are modelled with non-parametric distributions whereas storm wave height, duration and surge are modelled parametrically using extreme value mixture distributions. The advantage of the extreme value mixture distributions, compared with the standard extreme value distribution for peaks-over-threshold data (Generalized Pareto), is that the statistical threshold becomes a model parameter instead of being fixed, and so uncertainties in the threshold can be straightforwardly integrated into the analysis. Robust quantification of uncertainties in the model predictions is crucial to support hazard applications, and herein uncertainties are quantified using a novel mixture of parametric percentile bootstrap and Bayesian techniques. Percentile bootstrap confidence intervals are shown to non-conservatively underestimate uncertainties in the extremes (e.g. 1\% annual exceedance probability wave heights), both in an idealized setting and in our application. The Bayesian approach is applied to the extreme value models to remedy this shortcoming. The modelling framework is applicable to any site where multivariate storm wave properties and timings are affected by seasonal, climatic and long-term non-stationarities, and can be used to account for such non-stationarities in coastal hazard assessments.

}, doi = {https://doi.org/10.1016/j.coastaleng.2017.06.005}, url = {https://www.sciencedirect.com/science/article/pii/S0378383916302861}, author = {Davies, G and David Callaghan and Uriah Gravois and Jiang, W and David Hanslow and Scott Nichol and Tom Baldock} } @conference {bnh-3891, title = {Improving resilience to storm surge hazards}, booktitle = {AFAC17}, year = {2017}, month = {09/2017}, publisher = {Bushfire and Natural Hazards CRC}, organization = {Bushfire and Natural Hazards CRC}, address = {Sydney}, abstract = {

Winds, waves and tides associated with storms are capable of causing severe damage to coastal property and infrastructure. Locations that are prone to erosion and inundation first require an accurate assessment of risk before deciding the most cost effective mitigation option. This research aims to produce probabilistic assessments of the coastal erosion and inundation risks associated with storms, particularly for coincident or clustered events, thereby helping to strengthen the resilience of coastal communities.

Coastal erosion and inundation hazard is modelled in this study by simulations of realistic storm condition forcing (waves and tides) through a morphodynamic model to calculate return periods for maximum extent of shoreline retreat. This approach of estimating erosion return periods is superior to the assumption that the most energetic storm causes maximum erosion. The methodology is demonstrated at Old Bar, NSW, which us currently an erosion hotspot. The model will also be applied for the metropolitan Adelaide beaches. These sites were selected to test the methodology for a span of geographic conditions in terms of storm climate and deep-water wave exposure, working towards developing this method into a transportable framework applicable to other coastal areas. \ 

Desktop and field assessments of each site were conducted to document geomorphic and sediment characteristics to inform shoreline modelling. Having established the historical framework at each location, multivariate statistical analysis of wave (buoy or hindcast models) and tides for peak storm events has allowed for the synthesis of realistic future conditions. This complex sequencing of cycling between accretion and erosion incorporating cross-shore and alongshore sediment transport has been estimated using a probabilistic shoreline translation model. Here, model outputs for Old Bar are illustrated, which indicate a complex response over decadal time frames. Further work will then assess risk to infrastructure based on the most probable envelope of shoreline position. This information can then be used to inform coastal management strategies.\ 

}, author = {Uriah Gravois and Tom Baldock and David Callaghan and Davies, G and Jiang, W and Moore D and Scott Nichol} } @article {bnh-2700, title = {Review of beach profile and shoreline models applicable to the statistical modelling of beach erosion and the impacts of storm clustering}, number = {163}, year = {2016}, month = {05/2016}, institution = {Bushfire and Natural Hazards CRC}, address = {Melbourne}, abstract = {

The Bushfire and Natural Hazards CRC project, Resilience to Clustered Disaster Events on the Coast {\textendash}\ Storm Surge proposes to develop a beach profile or shoreline evolution model appropriate for assessing the\ behaviour of selected beaches to clustered storm events.

This paper outlines the characteristics required of\ an appropriate model and reviews literature and model results to determine if existing approaches include\ the required physics and can be embedded within a suitable statistical framework for a stochastic analysis. It\ is anticipated a priori that a hybrid model approach will be required, utilizing existing process models in a\ new model suite. A discussion of modelling approaches is provided, together with a justification of the model\ proposed for the project.


The chosen model, with appropriate refinements and modifications, will be applied at two case study sites;\ Old Bar (NSW) and Adelaide Beaches (SA). This review focuses on models that can be applied at these field\ sites within the constraints imposed by the available data. These data are summarized in a previous report -\ Identified data on study sites: Old Bar and Adelaide Beaches Geoscience Australia (2015), IDSS henceforth.


Coincident with the model development, will be the development of a model framework for its\ implementation. These potential approaches for model and modelling frameworks are discussed in this\ paper. This modular separation of the model and model framework allows for an iterative development
approach where refinements can be made as they are required.


While the overarching aim is to develop a model and model framework that accurately quantifies erosional\ risk at these study sites, the approach should be transportable to other coastal sites of interest. Even in the\ development stage we may include third site at Narrabeen beach due to its rich data history and relative\ close proximity to the Old Bar site. Furthermore the authors have an active study site at Wooli beach where\ we also expect to test the model.\ 

This report is structured as follows. Section 2 discusses the requirements of the model with regard to the\ coastal processes characteristic of the field sites and outlines the basic model types for coastal\ morphodynamic modelling.\ Section 3 reviews general coastal morphodynamic modelling and lists specific\ models considered to potentially suit the project aims and discusses the merits of each with respect to the\ dominant coastal sediment processes at the two field sites. Section 4 provides details on common statistical\ modelling frameworks and those considered for this study. A concluding discussion and preliminary\ recommendation for the model and model framework are presented in section 5.

}, issn = {163}, author = {Uriah Gravois and David Callaghan and Tom Baldock and Katrina Smith and Bronte Martin} }