IMPROVING THE RESILIENCE OF MAJOR PORTS AND CRITICAL SUPPLY CHAINS TO EXTREME COASTAL FLOODING: A COMBINED ARTIFICIAL NEURAL NETWORK AND HYDRODYNAMIC SIMULATION APPROACH TO PREDICTING TIDAL SURGE INUNDATION OF PORT INFRASTRUCTURE AND IMPACT ON OPERATIONS.

Abstract:

Ports are vital to the global economy, but assessments of global exposure to flood risk have generally focused on major concentrations of population or asset values. Few studies have examined the impact of extreme inundation events on port operation and critical supply chains. Extreme water levels and recurrence intervals have conventionally been estimated via analysis of historic water level maxima, and these vary widely depending on the statistical assumptions made. This information is supplemented by near-term forecasts from operational surge-tide models, which give continuous water levels but at considerable computational cost.

As part of a NERC Infrastructure and Risk project, we have investigated the impact of North Sea tidal surges on the Port of Immingham, eastern, UK. This handles the largest volume of bulk cargo in the UK and flows of coal and biomass that are critically important for national energy security. The port was partly flooded during a major tidal surge in 2013. This event highlighted the need for improved local forecasts of surge timing in relation to high water, with a better indication of flood depth and duration.

We address this problem using a combination of data-driven and numerical hydrodynamic models. An Artificial Neural Network (ANN) is first used to predict the surge component of water level from meteorological data. The input vector comprises time-series of local wind (easterly and northerly wind stress) and pressure, as well as regional pressure and pressure gradients from stations between the Shetland Islands and the Humber estuary. The ANN achieves rms errors of around 0.1 m and can generate short-range (~ 3 to 12 hour) forecasts given real-time input data feeds. It can also synthesize water level events for a wider range of tidal and meteorological forcing combinations than contained in the observational records. These are used to force Telemac2D numerical floodplain simulations using a LiDAR digital elevation model of the port. Functional relationships between peak water level and surge 'shape' allow estimation of flood depths and durations for any location. Supplementing existing surge warning systems, our approach predicts the location and duration of flooding in detail, and allows port managers to take steps to minimize its impact on the most critical aspects of port operation.