A review of shoreline response models to changes in sea level Journal Paper
- Author
- Tom Shand, Roger Shand, Richard Reinen-Hamill, James Carley and Ron Cox
- Year
- 2013
- Publisher / Organisation
- Tonkin & Taylor
- Pages
- 6
- Keywords
- Coastal hazards, sea level rise, climate change, shoreline erosion, recession
- Summary
- parts of Australia. Methods for assessment of erosion hazard are well established, and uncertainty in the present hazard can be reasonably well estimated. However, uncertainty in defining future climate-change associated erosion/recession hazard increases due to both the assumptions surrounding sea-level rise (SLR) as well as limitations of the models used to evaluate the associated shoreline response. The most widely used methods for defining the coastal erosion hazard extent utilise a modular approach whereby various independent components are quantified and summed to provide a final value (e.g. see [14]). The SLR response component is based on the well-accepted concept that an elevation in sea level will result in recession of the coastline. This component is often the largest contributor to erosion hazard zones, so understandably this term is often the subject of intense debate, media scrutiny and a focus in litigation. With the trends of increasing populations on the coast this controversy is only likely to escalate. A range of models for estimating coastal response to changes in sea level have been developed over the past 50 years. These methods range from the application of basic geometric principles to more complex process-based assessment. While some methods are used more widely than others, none have been proven to be categorically correct or adopted universally. While most attention has focussed on the response of open coast beaches to SLR, other shoreline types including gravel beaches and low energy coastlines such as lagoons and estuaries are also affected. This paper briefly reviews existing shoreline response models including the process assumptions, limitations, development and application history. While most models are based on similar underlying process assumptions, variation in the definition of model parameters (e.g. closure depth) can produce significant differences in predicted recession values. As such, robust and informed selection of model parameters are required to derive defensible conclusions.