University of Edinburgh logo British Antarctic Survey logo National Oceanographic Centre logo University of Leeds logo

Projects

‘Tipping the Balance’ – Understanding coastal change using novel multi-platform ground and space-based remote sensing methods

Background & Motivation: What tips the balance between a stable coastline and one that erodes or accretes? What causes an estuarine channel to favour one position or another? Changes seen at the shore and monitored with traditional beach profiling and occasional LiDAR surveys may only represent the tip of the iceberg in terms of seabed changes over a much wider scale. Continually evolving systems of sand banks and channels can form an overall balanced sedimentary system to which storms merely contribute to natural cycles of sedimentary features. Alternatively, or sometimes simultaneously, storms can cause devastating local erosion that demands intervention to protect life and property. Understanding how episodic events and recovery fit into the wider context of a coastline is key to enabling coastal managers, users and residents to make informed decisions about future coastal planning.

Methodology: New developments for mapping intertidal regions using a temporal waterline method with both ground-based and satellite observations together with wave inversion methods for mapping subtidal bathymetry, waves and currents are currently in the trials phase leading on from research at the National Oceanography Centre in Liverpool. The spatial & temporal coverage and resolution made possible with these methods opens up new and previously unachievable approaches to understanding the rates and impact of coastal change. Some of these techniques are already being adapted and put into operational use to assist in cost effective management of beach levels in front of the sea defences on the Lancashire coast.  This novel capability to observe volumetric changes at the beach and in the nearshore morphology over a range of timescales, including pre- and post-storm, as well as map the driving forces of the waves and currents, presents a host of new possibilities in oceanography and coastal management.

Aim: To investigate the wider context of sub-tidal and inter-tidal sediment redistribution, comparing storm-induced changes and recovery with long-term environmental variability.

Objective 1: Integrate the outputs of novel multi-platform ground-based remote sensing with state-of-the-art satellite methods to provide a multi-year, wide spatial view of natural coastline morphodynamics.

Objective 2: Use the results to investigate qualitatively and quantitatively how and where sediment is redistributed during individual storm events and post-storm recovery periods, placed in the context of wider natural cycles.

A multidisciplinary supervisory team from the National Oceanography Centre, The Universities of Leeds and Edinburgh and CASE-Partner Marlan Maritime Technologies Ltd are looking for a highly motivated, curiosity driven and outcome-oriented student with skills/interests in geospatial data and computer science/modelling to explore what different combinations of the remote sensing data can tell us about the patterns and drivers of coastal change.  The student will be embedded in the teams at Marlan and the NOC in Liverpool who will use these data to liaise with coastal managers to inform coastal planning. This provides a rare opportunity for the student to be involved in the process, and see how their work can be rapidly applied to inform decision-making.

The focus will be the NW coast of England due to: the concentration of novel ground-based radar systems operated by Marlan; a network of tide gauges and other sensors; excellent coverage provided by satellite systems including the European Space Agency’s Sentinel series and the soon to be launched SWOT system. The student will have the opportunity to review the datasets and develop a network of suitable study sites for detailed investigation during the PhD. Strong aptitudes for signal analysis, programming and handling large datasets are required and some experience of Matlab and/or Python is advised. Machine learning algorithms, such as convolutional neural networks, will be used to link ground-based and satellite remote sensing products, as well as derive relationships between environmental forcing and coastal change.

References:

Bell P.S., Bird C.O. & Plater A.J. 2016, A temporal waterline approach to mapping intertidal areas using X-band marine radar. Coastal Engineering, https://doi.org/10.1016/j.coastaleng.2015.09.009

Bell P.S. & Osler J.C., 2012, Mapping bathymetry using X-band marine radar data recorded from a moving vessel. Ocean Dynamics, https://doi.org/10.1007/s10236-011-0478-4

McCann D.L. & Bell P.S., 2014, Marine radar derived current vector mapping at a planned commercial tidal stream turbine array in the Pentland Firth, U.K.. Oceans 2014, https://doi.org/10.1109/OCEANS.2014.7003186

Bell P.S., 1999, Shallow water bathymetry derived from an analysis of X-band marine radar images of waves. Coastal Engineering, https://doi.org/10.1016/S0378-3839(99)00041-1 

Daley C.J. et al, 2020, The New Era of Regional Coastal Bathymetry from Space: A Showcase for West Africa using Sentinel-2 Imagery, https://doi.org/10.31223/osf.io/f37rv