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Title: Uncertainties within future flood risk storm surge inundation modelling
Author: Lewis, Matthew
Awarding Body: University of Bristol
Current Institution: University of Bristol
Date of Award: 2012
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Key uncertainties within inundation modelling of storm tide overflow were investigated for two regions. A northern Bay of Bengal LISFLOOD-FP inundation model was developed from freely available data sources, and forced with a storm surge model (IIDT) hind-cast of the 2007 cyclone Sidr flood event because no quality water-level records exist. Validation showed inundation prediction accuracy, with a Root Mean Squared Error (RMSE) on predicted water-level of...., 2 rn, which was similar in magnitude to the forcing water-level uncertainty. Indeed, when observed natural variability within five key cyclone parameters was propagated through the IID-T storm surge model, extreme water-level uncertainty was found to be very high in the Bay of Bengal, and should be considered in future work (and flood risk managers). Future flood hazard mapping uncertainty is much less in the data rich UK; however, when some key uncertainties were propagated through a North Somerset LISFLOOD•FP inundation model of the 1981 historic flood, storm tide spatial variability was found to significantly affect flood risk estimates, second only to sea level rise. A new method for prescribing the still peak water-level along a coastline was developed (Method C), which characteristics the spatial variability using a relatively short record of modelled extreme water-level events, relative to a tide gauge. Good agreement (RMSE 36 cm) was found between Method C predicted water-levels and tide gauge observations for two historic flood events in East Anglia (1953 and 2007). Furthermore, remotely sensed storm tide observations along the North Somerset coast indicated the accuracy of Method C between tide gauge observations; however, fine-scale wave and bathymetry effects need to be resolved for accurate coastal flood risk estimates in the UK. Indeed, the quantification of uncertainty, and the characterisation of natural variability, is necessary for a robust flood risk prediction.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available