Using hydrodynamic flood modelling to support impact-based forecasting: a case study for Super-Typhoon Haiyan in the Philippines

The Philippines is one of the countries most at risk to natural disasters. Amongst these disasters, typhoons and its associated landslides, storm surges and floods have caused the largest impact. Due to increased typhoon intensity, the country’s high population density in coastal areas and rising mean sea levels, the coastal flood risk in the Philippines is only expected to increase. The 510 initiative of the Netherlands Red Cross uses an Impact Based Forecasting (IBF) model based on machine learning to anticipate the impact of an incoming typhoon to set early action into motion. The IBF model underperformed in regions that are susceptible to storm surges. Most notably, it showed a poor performance for Super-Typhoon Haiyan (2013), which caused storm surges to reach up to over five meters high. The goal of this research is to evaluate how the IBF model can be improved by applying a fast hydrodynamic modelling approach that can forecast storm surges and coastal flooding associated with typhoons. First, the accuracy of the Global Tide and Surge Model (GTSM) in simulating Haiyan’s coastal water levels was examined. GTSM was forced with two different meteorological datasets: a gridded climate reanalysis dataset, ERA5, and observed track data combined with Holland’s parametric windfield model. Second, GTSM’s water levels were used as input for a hydrodynamic inundation model to simulate the flood depth and extent in San Pedro Bay, which was subjected to a widespread coastal flood during Haiyan. This was explored both with and without the inclusion of wave setup. Our results show that Haiyan’s flood cannot adequately be indicated using the ERA5 reanalysis dataset as meteorological forcing, as it underestimated Haiyan’s extreme wind speeds with ~60 m/s. By applying the Holland parametric wind field model, more accurate flood predictions and storm surge simulations can be made. Additionally, GTSM’s temporal resolution influences the models performance substantially. By increasing the 1 hour resolution to a 30 minute resolution the prediction of the overall flood extent improved by 16%. In future research we recommend examining the applicability of the Global Tide and Surge Model when using a higher spatial resolution to help better represent local processes. Additionally, exploring the accuracy for other typhoons that struck the Philippines and the applicability in operational setting using forecasted track data can contribute to further improving forecast-based early action systems in anticipating coastal flood occurrences.