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C326: Automating foreshore roughness quantification for coastal flood and erosion risk modelling (Lead Supervisor: Iris Möller, Geography)

Supervisors: Iris Möller (Geography) and Tom Spencer (Geography)

Importance of the area of research:

Coastal foreshores (the regions between low and high water on tidal coasts) are both (i) critically important in protecting landward lying people and assets from storm surge impacts and (ii) highly resilient to such high energy events (Spencer et al., 2015). To accurately predict the flood and erosion risk faced by communities residing landwards of such foreshores, the ‘roughness’ of the foreshore surface must be adequately quantified and built into coastal storm surge and wave models.

Project summary:

The ability to accurately capture coastal foreshore surface roughness is a key challenge when it comes to modelling the impact of storm surges and wave on coastal sea defences and the populations that live behind them.  

This challenge is particularly great due to the various scales of surface roughness present on intertidal foreshores: topographic roughness can be high where creeks and cliffs dissect tidal mudflats and vegetated wetlands, and bed roughness is enhanced through the presence of vegetation that may show a range of different structural properties and densities, often species and location specific. This project will develop novel approaches towards the capture, mapping, and parameterisation of such surface roughness as well as test the performance of different measures of roughness in existing numerical models that are used to predict water depths and wave heights at the seaward side of coastal protection structures (e.g. sea walls).

What the student will do:

The student will develop a novel method for capturing and parameterising different types and scales of surface roughness within hydrodynamic models such as SWAN and XBEACH. Based on existing point-based and remote sensing based methods for roughness quantification and the outputs of a recent PhD project within the CCRU, the student will explore methods for roughness parameterisation using Unmanned Aerial Vehicles (UAVs) and both spectral and spatial algorithms for the processing of elevational (e.g. stereoscopic aerial photography) and spectral (e.g. infrared and visible light) imagery. Use of emerging time-series of high spatial/spectral resolution satellite products such as the Copernicus Sentinel suite of platforms will also be investigated.

Alongside the quantification of bed roughness, various potential routes for its parameterisation into numerical models (such as SWAN or XBEACH or other) will be investigated and validated against existing storm surge height data from the 2013 storm surge on the UK east coast for which detailed surveys are available (Spencer et al. 2015).

Please contact the lead supervisor directly for further information relating to what the successful applicant will be expected to do, training to be provided, and any specific educational background requirements.

References:

Loder, N.M. Irish, J.L. Cialone, M. a. Wamsley, T.V. 2009. Sensitivity of hurricane surge to morphological parameters of coastal wetlands, Estuar. Coast. Shelf Sci., vol. 84, pp. 625–636. doi:10.1016/j.ecss.2009.07.036.

Smith, J.M. Bryant, M.A. Wamsley, T. V. 2016. Wetland buffers: Numerical modeling of wave dissipation by vegetation, Earth Surf. Process. Landforms, vol. 41, pp. 847–854. doi:10.1002/esp.3904.

Spencer, T., Brooks, S.M., Evans, B.R., Tempest, J.A. and Möller, I. 2015 Southern North Sea storm surge event of 5 December 2013; Water levels, waves and coastal impacts. Earth-Science Reviews, vol. 146, pp. 120-145. doi: 10.1016/j.earscirev.2015.04.002.

 Follow this link to find out about applying for this project

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