Spatial and temporal analysis of extreme sea level and skew surge events around the coastline of New Zealand

Abstract. Coastal flooding is a major global hazard, yet few studies have examined the spatial and temporal characteristics of extreme sea level and associated coastal flooding. Here we analyse sea-level records around the coast of New Zealand (NZ) to quantify extreme sea level and skew-surge frequency and magnitude. We identify the relative magnitude of sea level components contributing to 85 extreme sea level and 135 extreme skew-surge events recorded in NZ since 1900. We then examine the spatial and temporal clustering of these extreme sea-level and skew-surge events and identify typical storm-tracks and weather types associated with the spatial clusters of extreme events. We find that most extreme sea levels were driven by moderate skew-surges combined with high perigean-spring tides. The spring–neap tidal cycle, coupled with a moderate surge climatology, prevents successive extreme sea-level events from happening within 4–10 days of each other and generally there are at least 10 days between extreme sea-level events. This is similar to findings from the UK (Haigh et al., 2016), despite NZ having smaller tides. Extreme events more commonly impacted the east coast of the North Island of NZ during Blocking weather types, and the South Island and west coast of the North Island during Trough weather types. The seasonal distribution of both extreme sea-level and skew-surge events closely follows the seasonal pattern of mean sea-level anomaly (MSLA) – MSLA was positive in 92 % of all extreme sea-level and in 88 % of all extreme skew-surge events. The strong influence of low-amplitude (−0.06 to 0.28 m) MSLA on the timing of extreme events shows that mean sea-level rise (SLR) of similarly small height will drive rapid increases in the frequency of presently rare extreme sea levels. These findings have important implications for flood management, emergency response and the insurance sector, because impacts and losses may be correlated in space and time.