Short term spatially dense prediction of storm surge along the new zealand coastline

Storm surge is the rise in water level generated by wind and atmospheric pressure changes associated with tropical or mid-latitude storms. In conjunction with tides, it is one major driver of coastal flooding associated with storms events. Because local inundation is strongly modulated by the local shape of the coastline and the bathymetric slope, accurate storm surge prediction by the mean of traditional numerical models requires the use of very fine grids and is hence very resource intensive. This means that the performance of a live prediction system based on such methods will likely be subject to a trade-off between prediction accuracy, prediction speed and cost (Wang et al., 2009). Several publications have demonstrated the potential of machine learning approaches for the prediction of storm surge (e.g. (Tiggeloven et al., 2021), (Cagigal et al, 2020)). However, the developed methods often focus on local predictors and aim at predicting storm surge at a single location at a time. In this study, we explore the use of several data driven methods as an alternative to numerical methods to predict storm surge along the coast of New Zealand.