| 08 Jul 2026
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.
The authors analyse an outstanding 22-year dataset of approximately 9,000 rip current rescues and beach user counts from 58 beaches across New Zealand to identify the environmental controls on rip current incidents and develop a simple, operational forecasting system. The manuscript is well written, the methodology is sound, while also acknowledging some dataset limitations, and the resulting forecasting tool are both robust and practical. I agree with the authors that this is likely the longest-running rip incident dataset analysed and the most extensively validated rip current forecast presented to date, which alone makes this a valuable contribution. I therefore recommend minor revision. My comments below are primarily suggestions for clarification and minor improvements that I believe will further strengthen an already solid contribution. Some of my comments are simply suggestions for the authors' consideration, and I will not be offended if they decide that not all of them should be incorporated into the revised manuscript.
L33–36: The authors jump directly to channel rip currents here, whereas at this stage I think the text should remain more general. Rip currents can arise from a range of mechanisms and do not necessarily require alongshore gradients in wave setup (e.g. some headland rip currents). I would simply state that rip currents are generated by the action of breaking waves and move the discussion of this specific mechanism to the third paragraph.
L49: I would not cite this equation here.
L83 (and elsewhere): Castelle et al. (2024, the discussion paper) should be updated to 2025 (the published paper).
L99 (and elsewhere): "Sect. 0" appears in several places. Please fix the section cross-referencing.
L113–115: Since you explain the dominant wave regime mechanism for the southwest-facing coast, it would be useful to do the same for this part of the coastline. I assume it is primarily exposed to trade-wind waves, with occasional tropical cyclone swells explaining the reported 8 m extremes.
Figure 1: I would use different symbols and/or colours to distinguish dissipative, intermediate, and reflective beaches. This would immediately show the distribution of beach states, information that is only touched upon much later in the manuscript. For example, it would be interesting to see the relative proportion of each beach state, whether reflective and dissipative beaches are predominantly located on east- and west-facing coasts, respectively, and how well-known sites such as Muriwai are classified. Section 2.1 could then be expanded slightly to describe these patterns.
L50: Wow, this is the kind of dataset one can only dream about. However, I may have missed something, but this sentence suggests hourly estimates, whereas later (e.g. L405, "4.3 assessments per day on average") it seems different, i.e. with estimates not necessarily collected throughout the supervising hours. Please clarify.
L177: "Only forcing combinations associated with at least 20 hours of lifeguard observations were used to estimate Prip." Given that Prip is computed using 2-hour bins, I am not sure I understand this sentence. Could you clarify exactly what is meant here?
L178–179: "1 rip-related incident per 5 people in the water" as a "high hazard" event seems extremely high to me. It made me wonder how beach lifeguarding and supervised bathing operate in New Zealand. For example, are rescues primarily associated with swim-between-the-flags areas? Do incidents occur both inside and outside the flagged bathing zones, and if so, in what approximate proportions? Is the red flag (no swimming) sometimes displayed, and if so, are those days excluded from the analysis to avoid biasing the model? Were some (all?) of the Prip>0.2 related to mass rescue events ? Including this type of information in Section 2 would help readers interpret the results.
L254: Lifeguard perception on a 1–5 scale. I am wondering whether this scale reflects a hazard assessment normalised to each individual beach. For example, at a very sheltered beach where 0.5 m waves represent the most energetic conditions, would that be rated 5/5, whereas the same surf conditions at Muriwai would be rated 1/5? Or is this intended as a more general, transferable assessment of hazard? From the later discussion, it seems to be the latter, but it would be useful at this stage of the paper to explain how lifeguards were instructed to use this scale.
Sections 3.2 and 3.3 : Tables and figure are great and insightful by provide average information and metrics, but I would love to see example of time series, ideally for three representatives beaches of the three different beach states, including long time series and maybe zooms on specific events. I understand that this may sound a bit useless, but as a reader I would really like to see how the rescue data is distributed and how the algorithm behaves to have a better understanding
Discussion : Nice Fig. 9 !