In 2005, severe flooding in the aftermath of Hurricane Katrina focussed the world's attention on the importance of accurate knowledge of the topography of the coastal zone in natural disaster management and prediction. The topography of the sea floor, generally known as the bathymetry, evolves over time as sediment is eroded, transported and deposited by water action. The change in bathymetry itself changes the motion of the water, which is also influenced by tides and weather patterns, such as storm surges. An accurate, up-to-date knowledge of coastal bathymetry would allow improved flood forecasting. Improved prediction of future bathymetry, and knowledge of the uncertainty in that prediction, would allow construction of better sea defences, better management of coastal habitats, and better understanding of the effects of changes in land use near the coast. It may also provide better understanding of the effects of climate change (e.g. sea level rise, and increased numbers of extreme storm events) on the longer-term evolution of an estuary.

Coastal sediment transport models are becoming increasingly sophisticated. However, observed bathymetric samples typically only provide partial coverage of the domain of such a model. Hence, initialisation of such models using only a set of recent observations is not feasible. The effective and efficient use of limited data, such as these, requires state-of-the-art mathematical, statistical and computational methods, known as data assimilation techniques. Data assimilation combines empirical observations with model predictions to give more accurate and well-calibrated forecasts and enables the uncertainties in the forecasts to be calculated. Whilst data assimilation has been in use in the context of atmospheric and oceanic prediction for some years, its use in the context of coastal sediment modelling is novel.

This project will use data assimilation techniques with a coastal sediment transport model to maintain up-to-date near-shore bathymetry, predict future bathymetry, answer statistical questions regarding uncertainty and predictability, gain insight into physical processes taking place during intense storm events and to design an optimal observation strategy for coastal monitoring. Three coastal sites have been identified for numerical experiments: River Dee, Morecambe Bay and East Lincolnshire coast, UK.

Methodologies will be developed and tested using data from the first site and validated using independent data from the other sites, demonstrating the wider applicability of ideas. The novel use of data assimilation will allow improved estimates of the current bathymetry, and improved predictions of future bathymetry via better initialisation, error estimates for the improved bathymetry, and a means to estimate model parameters from indirect observations. The direct involvement of the Environment Agency in the project will ensure that the resulting benefits are transferred into operational practice.

Project Duration: January 2007 - October 2010.

This project is funded by NERC - Grant Ref. NE/E002048/1 - through the Flood Risk for Extreme Events (FREE) NERC directed mode programme.

This project will use incorporate data assimilation techniques into a coastal sediment transport model to model near-shore bathymetry and predict future bathymetry. It will also address model uncertainty and predictability, give insight into physical processes taking place during intense storms and design an optimal observation strategy for coastal monitoring. Three models will be developed - one for each study site of the River Dee, Morecambe Bay and East Lincolnshire coast.

A range of datasets will be used by collated for use by this project including airborne LiDAR, swathe bathymetry, and beach transect data (from EA); satellite data- waterlines (instantaneous land-sea boundaries) from ENVISAT ASAR, ERS-2 SAR and MDA RADARSAT; X-band radar data (from POL); tide gauge data (from BODC); wind speed and atmospheric pressure (from BADC). Where these data are not already in a NERC data centre and where the source permits it, this data will be stored at BADC or BODC as appropriate to the data type.

The output from the project will be improved models and understanding rather than any particular data set. However the data from model runs used in any publications will be archived. Such model output would consist of bathymetry on a 2D grid covering the model domain with associated parameters including tidal velocities and elevations.

The data from this project will be stored at the BODC.

BODC - http://www.bodc.ac.uk

FREE Programme documentation:

• List of projects funded under the FREE programme.
• FREE Science Plan
• FREE Implementation Plan (January 2008)
• FREE Progress Report (May 2008)
• FREE Progress Report (Oct 2008)
• Data Inputs to FREE projects

This FREE project is headed by Dr Sarah Dance of the University of Reading, with co-investigators also at the University of Reading. The project data contact is Dr Tania Scott, University of Reading.

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