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EPSRC Reference: EP/C005392/1
Title: A Risk-based Framework for Predicting Long-term Beach Evolution
Principal Investigator: Reeve, Professor D
Other Investigators:
Chadwick, Professor AJ Spivack, Dr M Davidson, Dr M
Simmonds, Dr D Dong, Professor P
Researcher Co-Investigators:
Project Partners:
CH2M Hill (Halcrow) New Forest District Council
Department: Sch of Marine Science & Engineering
Organisation: University of Plymouth
Scheme: Standard Research (Pre-FEC)
Starts: 01 October 2005 Ends: 28 February 2009 Value (£): 423,123
EPSRC Research Topic Classifications:
Coastal & Waterway Engineering
EPSRC Industrial Sector Classifications:
Environment
Related Grants:
EP/C00602X/1
Panel History:  
Summary on Grant Application Form
Effective management of the coastline is crucial to the protection and preservation of all coastal communities. In the UK, authorities responsible for engineering coastal defences against erosion and providing protection against coastal flooding are now required by government to produce plans that will look 70 years into the future. The aim of this project is to protect existing and future coastal development and land use against coastal erosion, the effects of rising sea levels and increased storminess aggravated by geological subsidence and global warming. Currently, there are no reliable methods for predicting either how the natural coastline will evolve over this timeframe or the likelihood of a piece of coastline being damaged beyond recovery such that it fails to protect from flooding. The evolution of the coastline over time is determined by its exposure to processes that add to or erode from it. Beaches are the most dynamic of coastal features and evolve in relation to the supply of sediment from adjacent cliffs, sandbanks or other beaches and the wind, waves and currents that move the sediment around. The research proposed here aims to develop a means of making predictions of long term beach evolution and to assess the probability that a given piece of coast will fail to protect from flooding. The predictions will be made using computer models that will be calibrated against a field experiment and tested against historical surveys of the coastline. The models will be developed of this behaviour using two techniques: System Reliability Theory and Probabilistic Process Modelling. System Reliability Theory treats beaches, cliffs and other coastal features as individual features that make up a coastal system. By studying the manner in which each element might fail through, for example, erosion during storms, the risk of an entire region of the coast being irreparably damaged can be assessed. Probabilistic Process Modelling looks at the range of possible shapes that a length of coastline might adopt in the future, based upon assumptions about the occurrence and magnitudes of the different processes that shape the coast.These theoretical predictions will be checked to see if they can successfully predict changes of the shoreline evident in historical data before being used to make predictions into the future. We will use a very high quality and well documented data set of survey measurements collected by New Forest District Council at a selected beach on the south coast. We will also make our own measurements of how the beach behaves when the alongshore movement of the sand and gravel material is restricted by a temporary groyne (wall). This will be built from large sand bags filled with material from the field site. The change of beach shape will be monitored using a new technique based on data collected by a video camera from a tower above the field site and surveying. This will allow us to work out how much sand is moving for the wave and current conditions that we measure.In this way we will not only develop a framework of guidelines and techniques that will demonstrate how local authorities might make predictions into the future about their own particular coastline, but we will also demonstrate its use at a chosen field site. Our framework will be outlined in a project handbook and shall comprise software tools and a methodology for calibrating and using those tools, together with recommendations concerning suitable levels of data collection.
Key Findings
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Project URL:  
Further Information:  
Organisation Website: http://www.plym.ac.uk