Simulating tidal and storm surge hydraulics with a simple 2D inertia based model, in the Humber Estuary, U.K

• CAESAR-Lisflood shown to accurately reproduce tidal elevations.
• Storm surge peak tides and flood extents both modelled close to observed.
• Model used data available publically in real/near time.

The hydraulic modelling of tidal estuarine environments has been largely limited to complex 3D models that are computationally expensive. This makes them unsuitable for applications which make use of live data to make real/near time forecasts, such as the modelling of storm surge propagation and associated flood inundation risks. To address this requirement for a computationally efficient method a reduced complexity, depth-integrated 2D storage cell model (Lisflood-FP) has been applied to the Humber Estuary, UK. The capability of Lisflood-FP to reproduce the tidal heights of the Humber Estuary has been shown by comparing modelled and observed tidal stage heights over a period of a week. The feasibility of using the Lisflood-FP model to forecast flood inundation risk from a storm surge is demonstrated by reproducing the major storm surge that struck the UK East Coast and Humber Estuary on 5 December 2013. Results show that even for this 2013 extreme event the model is capable of reproducing the hydraulics and tidal levels of the estuary. Using present day flood defences and observed flooding extents, the modelled flood inundation areas produced by the model were compared, showing agreement in most areas and illustrating the model's potential as a now-casting early warning system when driven by publically available data, and in near real-time. The Lisflood-FP model used was incorporated into the CAESAR-Lisflood GUI, with the calibration and verification of the estuarine hydraulics reported herein being a key step in creating an estuary evolution model, capable of operating in the decadal to century timescales that are presently underrepresented in estuarine predictive capability, and ultimately developing a model to predict the evolution of flood risk over the longer term.