Development of a National-Scale Rip Current Forecast for Aotearoa New Zealand

Abstract. Rip currents are dangerous flows in the surfzone of wave-exposed coasts and can take bathers from the shallows into deeper water. They cause hundreds of drownings globally each year and are the leading cause of all beach lifeguard rescues. In New Zealand, with a population of approx. 5 million people, rip currents typically cause 500–1000 lifeguard rescues each year and are attributed to 53 % of all Surf Life Saving New Zealand rescues. This study aims to identify environmental conditions associated with rip current incidents and develop a simple algorithm for forecasting rip current risk and hazard. A dataset of ~9,000 recorded rip current rescues along with water user head counts made at 58 beaches by lifeguards around the coast of New Zealand between 2001 and 2022 was used to assess rip current risk (parameterised from the total number of incidents) and rip current hazard (parameterised as the likelihood of an individual being in a rip incident) under different wave, tide, and wind conditions. In concurrence with previous findings, most rip incidents in New Zealand were recorded at beaches with intermediate ‘bar-rip’ beach morphology and occurred disproportionately during wave conditions at or above average breaker height with tide level at or below average low tide. Although rip incidents were also recorded at dissipative and reflective beaches lacking in bar-rip morphology, water users were 4 and 24 times more likely, respectively, to be in a rip-related incident at intermediate beaches with bar-rip morphology. A simple, threshold-based algorithm was developed using only breaker height, relative tide level, and a binary bar-rip morphology variable as predictors for use as a national-scale rip forecast across New Zealand. The algorithm achieves a high incident hit rate, capturing 98 % of historic rip incidents across New Zealand, and captures exponentially increasing hazard at each of its five Rip Index levels, with a water user 6 times more likely to be in a rip incident at the highest Rip Index (~1-in-200) compared to the lowest (~1-in-1200). It also conservatively replicates a lifeguard’s perception of rip hazard, with an overall agreement rate of approximately 81 %, indicating it could provide useful forewarnings to the public especially at non-lifeguarded beaches or outside lifeguard patrol hours. To our knowledge, this represents the longest running rip incident data set analysed, and most widely validated rip forecast in the literature to date.