Uncertainties within future flood risk storm surge inundation modelling
PublisherUniversity of Bristol
MetadataShow full item record
Key uncertainties within inundation modelling of storm tide overflow were investigatedfor two regions. A northern Bay of Bengal LISFLOOD-FP inundation model wasdeveloped 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 recordsexist. Validation showed inundation prediction accuracy, with a Root Mean SquaredError (RMSE) on predicted water-level of...., 2 rn, which was similar in magnitude to theforcing water-level uncertainty. Indeed, when observed natural variability within fivekey cyclone parameters was propagated through the IID-T storm surge model, extremewater-level uncertainty was found to be very high in the Bay of Bengal, and should beconsidered in future work (and flood risk managers). Future flood hazard mappinguncertainty is much less in the data rich UK; however, when some key uncertaintieswere propagated through a North Somerset LISFLOOD•FP inundation model of the1981 historic flood, storm tide spatial variability was found to significantly affect floodrisk estimates, second only to sea level rise. A new method for prescribing the still peakwater-level along a coastline was developed (Method C), which characteristics thespatial 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 Cpredicted water-levels and tide gauge observations for two historic flood events in EastAnglia (1953 and 2007). Furthermore, remotely sensed storm tide observations alongthe North Somerset coast indicated the accuracy of Method C between tide gaugeobservations; however, fine-scale wave and bathymetry effects need to be resolved foraccurate coastal flood risk estimates in the UK. Indeed, the quantification of uncertainty,and the characterisation of natural variability, is necessary for a robust flood riskprediction.