Rapid response data-driven reconstructions for storm surge around New Zealand

In conjunction with tides, storm surge is one major driver of coastal flooding associated with storm events. Because local inundation is strongly modulated by the local shape of the coastline and the bathymetric slope, accurate storm surge predictions using traditional numerical models require the use of very fine grids and are hence resource intensive. Therefore, 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. This study explores the use of data driven methods as an alternative to numerical models to reconstruct the daily storm surge maximum levels along the entire coast of New Zealand. Firstly, several atmospheric predictors are utilized that incorporate different variables, time lags and spatial domains, using 3 statistical models, in a selected number of locations in New Zealand, to find the combination that optimizes the reconstruction. Finally, the storm surge daily maxima are reconstructed with the different statistical models along the entire coast, using the best performing predictor. Results show very good performance for the best atmospheric predictor and statistical model, providing average values of 0.88 for the Pearson correlation coefficient and 4.3 cm for the root mean squared error metric (RMSE) (the average value for the RMSE in the 99% percentile is 8.2 cm). For the Kling-Gupta Efficiency (KGE; incorporating 3 sub-metrics: correlation, bias term and variability term), which is the metric used to rank the models, the average value is 0.82.Our results highlight the suitability of data driven models to simulate storm surge maximum levels, and prove the methodology is appropriate for finding a well performing atmospheric predictor that is able for reconstruct these values. Moreover, this methodology can be also applied to new variables, regions and problems, as there are no physical restrictions on the used predictors nor predictands.