A model framework for assessing risk and adaptation to climate change on Australian coasts

Adaptation Research Grants Program
Researcher/s: 
Prof Colin D. Woodroffe
Institution/s: 
University of Wollongong
State: 
New South Wales

Executive summary from final report

AIM: This study develops and validates a modelling framework that integrates geomorphological, engineering and economic approaches for assessing risk to climate change along the Australian coast. Coastal land use planning and management need an indication of the degree of risk. This approach is based on a probabilistic assessment that incorporates a range of possible factors to consider potential setback lines that would provide planners with greater flexibility and a way to incorporate a range of uncertainties. 

INTRODUCTION: Coastal systems are particularly vulnerable to the effects of climate change, and with a large proportion of the Australian population living along the coast, coastal settlements and infrastructure are disproportionately at risk as a consequence of sea-level rise and storm impacts. Coastal planning and management needs to be based on risk-informed decisions in relation to climate change. This project addresses this need by providing a framework for coastal risk assessment that can be readily applied throughout Australia, while directly addressing priority research questions 3.2, 3.3 and 3.4 in the National Climate Change Adaption Research Plan (NARP). This combined research is aimed at improving ‘methods currently used to determine the physical risk on a regional basis of extreme inundation and coastal erosion’, and will help better understand the ‘relationship between climate and coastal processes’ and provide information towards detecting key thresholds, such as a change from ‘accreting to erosion’ on our coasts. 

FRAMEWORK: The framework for coastal risk assessment was developed based on an overview of existing approaches, and synthesises these. Coastal landforms are the outcome of geomorphological processes that operate over long time scales (decades to millennia), but they are also responsive to short-term events, such as storms. These two perspectives are incorporated to address the probability of shoreline retreat due to coastal erosion risks as a result of storm events and incorporating sea-level rise. The strength of this conceptual framework is integration of best practice engineering approaches with geologically-informed assessments of past coastal behaviour to enable managers and policy-makers to incorporate estimated risk into considerations of adaptation options with greater confidence that the underlying risk assessment is transparently evidence-based. No one model is advocated in this framework and a brief literature review of process (engineering), behaviour (geomorphology) and vulnerability models that can be applied to assess the risks that coastlines will recede as a consequence of climate change is provided.

METHODS: The methods used to test this conceptual framework occurred within a geomorphologically based modelling framework, and included application of a geomorphological Coastal Tract (CT) approach integrated with probabilistic engineering-based models of wave characteristics, such as the Joint Probability Method (JPM); and combined with a model of risk of coastal recession, known as the Probabilistic Coastline Recession (PCR) model. 

GEOMORPHIC: The Coastal Tract (CT) approach provides boundary conditions for the JPM/PCR models in the form of long-term coastal evolution trends, which are often not taken into account by contemporary hazard modelling approaches. The CT provides quantitative estimates of exchanges between significant sediment stores to forecast future sediment transport and delivery, and can indicate uncertainty through Monte Carlo modelling using the range of likely values for key parameters, including the rate of sea-level rise. This involves interpreting geological archives of sediment accumulation to reconstruct sediment budgets for key sections of coast, and identifying the principal sediment sources and sediment sinks. Long-term trends (millennial to century scale) in sediment movement may be inferred from existing coring and stratigraphic data, together with geochronology (dating) of key sites. The CT framework uses computer simulations that combine analysis of geomorphic change with detailed representation of sources and sinks and the range of plausible climate change effects. 

ENGENEERING: The Joint Probability Method (JPM) and Probabilistic Coastline Recession (PCR) model applications are described in detail. The JPM procedure involves Monte Carlo simulation of a 110-year time series of storms derived from joint probability distributions of storm characteristics which enable the effects of the occurrence of more than one storm to be incorporated into estimates of beach erosion. The PCR adopts this methodology to then generate estimates of recession, using a process-based dune impact model, and provides an approach to incorporating the sealevel rise, which is based on IPCC projections. This provides probabilistic estimates of risk exceedance that can be used as a basis for economic modelling. Integration of this storm-related probabilistic modelling with geomorphological modelling over longer timescales, such as the Shoreface Translation Model (STM) offers the prospect of model outcomes that integrate considerations over a wider range of timescales.

ECONOMIC: Economic modelling is an integral and innovative component of this project in order to translate the outcomes of the probabilistic beach-erosion modelling approach into risk information that can help policymakers decide on alternative adaptation strategies. Application of the combined geomorphological and engineering process components into a conceptual framework informs the economic approach to minimise risk by using buffer zones or setback lines. The innovative economic risk model developed herein was integrated with the physical hazard estimation models to result in a unified model framework (CT, JPM, PCR) for assessment of at-a-site recession risk and setback options, an approach called the Probabilistic Coastal Setback Line (PCSL) model.

NARRABEEN: The geomorphic, engineering and economic techniques were employed to examine a case study, Narrabeen Beach in northern Sydney, chosen because beach morphology has been surveyed at monthly intervals for over 30 years at this site. Selected models were applied to this data-rich coast. The integration of these probabilistic approaches has been shown to be possible, and the Narrabeen Beach data set provided the necessary data to validate the approach and derive probabilistic estimates of erosion hazard, and hence guidelines for adaptation such as setback lines, for this site. 

EXTENSION: The potential to extend this framework to a range of other, less intensively researched, coastal settings around Australia is examined. The application of the framework beyond Narrabeen Beach is discussed for various coastlines around Australia. Extension of the CT framework and proof of concept involved selecting study sites in each of the other sectors of the Australian coast, where we have reliable geological histories derived from past research studies and ongoing collaborations.

CONCLUSIONS: The framework proposed enshrines many of the practices that are already adopted by consultants tasked with providing forecasts of future coastal erosion and recession around Australia. A number of gaps and limitations are identified. Even at the data-rich Narrabeen Beach, there are components of the sediment budget that are inadequately known, for example it is clear that sand is sequestered into the inlet at the northern end of the beach, indicating the important role that estuaries play in the sediment budget of adjacent shorelines. Future modelling will need to more adequately address the issues associated with longshore transport of sediment. This beach is also subject to a number of interventions which mean that its behaviour is not entirely natural. Further consideration of these aspects will be an important element of future research that can be directly incorporated into adaptation measures to ameliorate the risks from climate change related drivers.

View final report here

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