Sand budget failure underlies coastal erosion on the west coast of South Africa

Sand budget failure underlies coastal erosion on the west coast of South Africa | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Sand budget failure underlies coastal erosion on the west coast of South Africa Stephan Woodborne, Mary Evans, Jennifer Murray, Elhadi Adam, Duncan Miller, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4647471/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Current trends in coastal erosion combined with projected climate change impacts place half of the global sandy beaches at risk of extinction. Approximately 190 million people are likely to experience coastal environmental risks by 2100, and mitigation of the risk related to coastal erosion requires insight into the underlying causes. Here we use a multi-proxy approach to determine the cause of coastal erosion on the west coast of South Africa. Erosion trends were determined using satellite analysis for a storm event in June 2017, and foredune morphology changes were measured using unmanned aerial vehicle photogrammetry following a storm in September 2023. Significant erosion was driven by intense wave energy over multiple spring tide cycles, resulting in northward sand movement. Climate change is rejected as a cause of the erosion based on optically stimulated luminescence dates that show some of the eroded sediments to be approximately 8 900 years old. Instead, the cause is thought to be the interruption of long-term sand supply. Long-term sand dynamics are seldom considered in coastal erosion vulnerability assessments, and sediment age may be a novel factor in understanding this dynamic. Earth and environmental sciences/Environmental sciences Earth and environmental sciences/Natural hazards Earth and environmental sciences/Ocean sciences Coastal erosion coastal vulnerability sand supply South Africa west coast Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction An estimated 2.15 billion people live in the near-coastal areas worldwide, 42% of whom live in the low-lying coastal zones [ 1 ]. Of these, nearly 190 million are expected to be prone to coastal environmental risks by 2100 CE [ 2 ]. While the most catastrophic environmental threat to coastal zones is tsunamis [ 3 ], there is also a globally ongoing process of coastal erosion and accretion that is most prominent where sandy shore sediments are mobilized by waves, tidal and littoral currents, and deflation [ 3 , 4 ]. It is possible that nearly half of the global sandy beaches may go extinct under the current trajectory of erosion and the anticipated acceleration of climate change [ 6 ]. Understanding shoreline evolution is becoming increasingly important to the society and economy of coastal regions, as erosion typically destroys infrastructure, negatively affects land values, and can strongly influence ecological processes (e.g., where coastal wetland systems become inundated with salt water), but the mechanisms underlying erosion events are often complex and difficult to predict. Coastal erosion occurs when the landward portion of the swash zone mobilizes the sandy substrate [ 7 ]. The wave setup is the way in which energy interacts with the sandy substrate as waves propagate into shallower water and cause elevated sea levels after the wave breaking point. The physics that conserves the radiation stress (basically the energy mass balance) of the water drives the formation of rip currents, which are an important part of sand redistribution. The mechanism is coupled with the dynamics of the swash zone and the concept of wave runup [ 8 ]. In a simple 2-dimensional conceptual framework, beach gradient, wave height and period, tidal effects and the local sea level rise associated with atmospheric low-pressure systems during storms will determine where sand will be eroded and where it will be deposited. This is captured in Bruun’s rule [ 9 ] which describes the physics of beach gradient responses to wave regimes. Most beach systems have seasonal (storm season vs non-storm season) and lunar period (spring vs neap tide) variations in the gradient of the beach. These typically operate around a long-term median, but some interactions exacerbate the erosive nature of waves. As wave runup is the most tangible predictor of coastal erosion, models of coastal erosion vulnerability use this as the key parameter. Several coastal erosion vulnerability indices have been developed [ 10 ] and almost all are parameterized using the same input variables [ 11 ]. They typically use coastal geomorphology and slope, relative sea level change, shoreline erosion/accretion rates, tidal range, and wave height, which all are essentially wave runup variables, but some also consider the role of the foredune barrier system [ 11 , 12 ] and sea level rise through climate change [ 12 ]. Although input variables from climate and wave models, tidal models, and remotely sensed shoreline characteristics can be obtained over large spatial scales, the models are typically only locally applicable [ 11 ]. The models can become computationally challenging with the addition of other variables, but most achieve satisfactory approximations without further parameterization. When wave runup extends beyond the swash regime to coastal foredune barriers, the potential exists for a collision regime in which sand from the base of the foredune is eroded and deposited elsewhere, leading to coastal erosion [ 7 ]. Incorporating foredune barrier parameters into coastal vulnerability models is important because while wave runup models indicate coastal erosion risk, coastal vulnerability is related to the consequences of the erosion and flooding. Boruff et al. [ 14 ] developed a coastal social vulnerability index that considers the socio-economic impacts of sea level rise, and the IPCC Common Methodology for Vulnerability Assessment [ 15 ] uses sea level increase forecast to extrapolate wetland loss, climate change risk and adaptation, community vulnerability, and coastal vulnerability. Coastal foredune barriers are the final mitigation before erosion leads to impacts. Shoreline position is a representative feature of beach dynamics and is useful in describing coastal erosion and accretion [ 16 ]. Remote sensing-based approaches and automatic computation in GIS-based platforms have become a preferred choice to map and monitor shoreline changes [ 17 ] due to their synoptic-scale coverage, consistent acquisition of sensitive spectral bands for mapping surface features, and their cost-effectiveness compared to spatially limited field-based techniques [ 18 ]. Recent remote sensing techniques enable the determination of shoreline position from open-access mid-resolution satellite products using advanced subpixel extraction algorithms [ 19 , 20 ]. Sunny et al. [ 21 ] compared the performance of Sentinel-2, Landsat, and MODIS for quantifying shoreline changes at the regional scale and their results showed that Sentinel-2 is more effective due to its finer resolution. The Sentinel-2 coverage and the algorithms that calculate changes in shoreline position are sensitive to changes in beach gradient, but they are not designed to estimate the changes that extreme wave runup has on foredune barriers. Using unmanned aerial vehicle (UAV) photogrammetry surveys, local erosion hotspots can be mapped in detail [ 22 ], and sand redistribution during foredune barrier erosion can be determined empirically. The accuracy of low-cost UAV systems and photogrammetry enables rapid reconstruction of complex environments and is particularly suitable for reconstructing dune profiles, as heterogenous sandy textures are ideal for the photogrammetric process [ 23 , 24 ]. Sixteen Mile Beach on the west coast of South Africa is a long sandy beach stretching from Yzerfontein town in the south to the Langebaan Peninsula in the north (Fig. 1 ), and it has been the subject of long-term sand dynamics research [ 25 – 27 ] as well as historical analysis of beach sand dynamics [ 28 ], which provided empirical evidence that this coastline is vulnerable to coastal erosion. In June 2017, a severe storm caused extensive damage to a section of the dune cordon and destroyed properties and businesses along the eroded dune [ 27 ]. Another storm in September 2023 led to further erosion of the foredunes. Understanding the erosion caused by such events can inform future shoreline changes and vulnerabilities but detailed analysis also reveals erosion dynamics that are of broader relevance. Here we analyse shoreline and foredune morphology changes in response to the 2017 and 2023 storms at Sixteen Mile Beach. Our objective is to determine whether coastal erosion is spatially uniform, and, if not, to determine the underlying cause of the spatial patterning. Assessing the age of eroded sediments, we consider the novelty of these coastal erosion event relative to the short-term context of monthly and seasonal stochastic variability of coastal morphology changes. The study provides new insights into the sand supply mechanisms underlying coastal erosion. Results Ocean and atmosphere synoptic data The June 2017 storm was typical of austral winter cold fronts that make landfall over the southern part of South Africa and generate high wave conditions [ 29 , 30 ]. The low-pressure system had a minimum barometric pressure of 984 hPa, and the extent of the front spanned approximately 2 500 km to its northernmost extent where the pressure was approximately 1018 hPa (Fig. 2 a). The pressure gradient and fetch during the buildup of this storm led to significant wave heights (H s ) in excess of 8 m and maximum wave heights of 12.5 m (Fig. 3 a, b). The September 2023 storm was of similar magnitude, although the minimum barometric pressure was 980 hPa, and it passed further south of the continent (Fig. 2 b). The maximum wave height was 7.7 m (Fig. 3 c, d). In both storms the wave height was high over several tide phases. Satellite imagery and shoreline extraction An example of the wet/dry interface on Yzerfontein main beach for the period 2015–2020 is shown in Fig. 4 . This data is expressed as the cross-shore changes, and the results for six transects are shown in Fig. 5 . All six sites exhibit high variability in shoreline position over time, with an average variation of 77 m. The erosion hotspot (c) has the greatest range of variation of 91 m, followed by Rooipan (d) with a range of variation of 86 m. Pearl Bay (f) has the smallest range of variation of 50 m. Pearl Bay has the most cyclical trends in the dataset, with cross-coastal changes of about + 20 m during the summer months while dropping to − 20 m from the mean position in the middle of each of the five years (winter months). Orthophoto analysis and foredune profile extraction Changes in the foredune and beach profiles before and after the September 2023 storm are shown in Fig. 6 . The northern transects show a small degree of sand wedge accretion at the interface between the beach and foredune profiles, with evidence of some slumping of the erosion face established in the June 2017 storm. In the southern transects, the elevation of the beach profile was reduced in the September 2023 storm, and the erosion face from the June 2017 storm was further eroded by approximately 1.5 m with the apex of the dune reduced by approximately 1 m in elevation. Optically Stimulated Luminescence (OSL) dating The two OSL samples from the base of the erosion face of the foredune represent sediments that were actively eroded in the June 2017 storm. The central age model estimates for YZF1 and YZF3 are 8.89 ± 0.16 ka and 8.91 ± 0.48 ka respectively (Table 1 ). The overdispersion values of 24% and 20% are consistent with well-bleached grains indicating there is little scatter in the signal population of the grains (see Galbraith et al. 1999 [ 31 ]). This suggests there was minimal post-depositional reworking of the dune sediment, and the ages constrain the timing of deposition of these dune sediments. Table 1 Results of OSL analysis of foredune deposits exposed by erosion during the September 2023 storm event. Sample Th (ppm) U (ppm) K (%) WC (%) Depth (m) Dr(Gy.ka-1) Cosmic Dr (Gy.ka-1) OD (%) CAM De (Gy) CAM Age (ka) YZF1 1.50 ± 0.15 1.49 ± 0.03 0.15 ± 0.02 6.62 0.56 0.82 ± 0.03 0.235 ± 0.012 24 7.38 ± 0.43 8.89 ± 0.16 YZF3 0.75 ± 0.15 0.99 ± 0.03 0.14 ± 0.02 7.84 4.44 0.58 ± 0.02 0.165 ± 0.008 20 5.14 ± 0.18 8.91 ± 0.48 Discussion Storm wave conditions in southern Africa are linked to the passing of cold fronts in the westerly wind belt, and these frontal systems displace northward in the Austral winter leading to conditions for coastal erosion [ 29 , 30 ]. The winter storm tracks are indexed by the Southern Annular Mode (SAM) [ 32 ]. It might be anticipated that negative SAM excursions (expansion of the circumpolar westerly vortex) would result in greater storm impact, but the role of SAM on wave regimes in southern Africa is most prominent in summer when positive SAM conditions lead to decreased summer wave heights [ 33 ]. A more profound effect on the northward/southward position of the winter storm tracks is the Rossby wave form followed by the westerlies [ 34 ]. The frontal system that produced the June 2017 storm surge was displaced northward in the Rossby wave (Fig. 2 A), and it took place at spring tide (Fig. 3 a). In Cape Town (80 km south) the storm surge (the effect of wind, wave setup , and the low atmospheric pressure) resulted in a 20 cm increase in sea level [ 30 ], although the maximum wave height and the maximum storm surge did not coincide. This is included in the tide measure in Fig. 3 , an empirical measure of the sea level. The storm wave conditions during the September 2023 erosion event were driven by a more intense storm that passed further south than the June 2017 storm. It also occurred during spring tides, and the modest increase in wave height also spanned several tidal cycles (Fig. 3 c). The comparison between the ocean and atmosphere synoptics between these two events suggests that wave height alone is insufficient to explain the erosion, and wave direction and duration may be as important as it was in the erosion event on the South African east coast in 2011 [ 35 ]. A common feature between these storms is that elevated wave conditions took place over several spring tide high/low cycles when wave runup combined with rip currents could maximise sand transport potential. To the extent that high tides exacerbate the erosive effect of wave runup , it is not surprising that the coincidence between both storms and spring tide contributed to their erosive impact, but the spring tides were not of particularly high amplitude. In both storms the spring high reached just over 2 m, whereas the maximum tide height is close to 2.4 m in the dataset. The cross-shore change analysis (Fig. 5 ) reflects the temporal changes in the wet/dry interface on the beach and is not a direct reflection of the erosion that takes place in the foredune system. Each of the transects in the analysis contains an annual cycle with negative excursions (shoreward migration) during winters and positive excursions (seaward migration) during summers. Except for the impact of the June 2017 storm event, the year-to-year seasonal variation at each site reflects the lower wave energy during summers, and the cyclicity indicates decadal-scale equilibrium with sand migrating inshore in summer, and offshore in winter. Higher resolution analysis of the dataset also shows the spring/neap tide cyclicity. The analysis is, therefore, sensitive to the dynamics of the beach profile, and the changes that took place during the June 2017 storm are noteworthy. At three of the sites (Tsaarbank, Pearl Bay and Abrahamskraal) there is no discernable change in the beach profile. On the Main Beach, there appears to be sand accretion, while at Rooipan and the Erosion Hotspot, there appears to be a shift towards negative values that do not recover in the following summer. This indicates quasi-permanent sand loss at these locations. Previous erosion events along the South African coastline took two years to recover their original equilibrium profiles [ 36 ], but at Rooipan and the Erosion Hotspot there is no indication of beach profile restoration in three years, and the impact of the storm on the beach profile here appears to be permanent. While the cross-shore analysis is a good indicator of beach profile dynamics, the pre- and post-photogrammetric analysis of the September 2023 storm indicates changes in both the beach profile and the foredune structure. The three northern transects show little effect by the storm, apart from some slumping and the formation of a sand wedge at the base of the scarp face in the foredune. The two middle transects show significant deflation of the beach by about 1.5 m but no impact on the foredune. The two southern transects show similar sand deflation from the beach profile but also scarp face retreat of the foredune by about 1 m. The indication is that sand was redistributed northward with erosion taking place where the deflated beach profile afforded less protection against wave runup . These locations at the southernmost end of Sixteen Mile Beach are also where permanent beach profile changes took place in the June 2017 storm. The efficacy of a foredune as a barrier to beach erosion is captured in the “540 rule” [ 37 ] which suggests the cross-sectional area of sand seaward of the dune apex and above the 100-year still water flood level should exceed 500 square feet (44.5 m 2 ) to be an effective barrier. That erosion has surpassed the dune apex at the southern end of Sixteen Mile Beach, and the modified dune and beach profiles appear to be permanent, suggest severe vulnerability to future erosion events. Was the coastal erosion at Yzerfontein the result of extraordinary events, or sea level change, and is there likely to be a return to previous equilibrium conditions given sufficient time for sand dynamics to be restored? Guastella & Rossouw [ 29 ] suggest that storm intensity in the region increased from 1994 CE to 2008 CE, but Theron et al. [ 38 ] used the same data and identified no trend. There is evidence from freshwater ingress into Verlorenvlei (110 km NE of Yzerfontein) that storminess has increased in the last 2 000 years [ 39 ], but sediment that is being eroded from the southern end of Sixteen Mile Beach is more than 8 000 years old, which suggests that these storms would have been the most intense in 8 000 years. This seems unlikely as historic storms generating waves of a similar magnitude have been recorded [ 29 ] without causing erosion. Late Holocene storm beach deposits preserved at elevations up to 10 m above present mean sea level on the exposed rocky shore just south of Yzerfontein Point [ 27 ] are evidence of much more extreme storms in the past 8 000 years. Sea level changes in response to global warming may be affecting the erosion dynamics of storms [ 13 ], but local sea level on the west coast of South Africa has varied very little in the past 5 500 years, with possible minor positive stands between 4 000 and 5 000 years ago [ 40 ]. Secular sea level change is unlikely to be a significant factor behind the erosion events as the tide data presented in Fig. 3 shows far greater variation than anthropogenically forced sea level rise, and the effects of sea level rise would manifest broadly rather than as locally as noted in this analysis. The two OSL dates are from a buried soil profile that formed approximately 9 000 years ago in aeolian sand on the margins of ancestral Rooipan; a landscape feature that extended seaward into the current intertidal zone until at least 5 500 years ago [ 27 ]. All these deposits were exposed by erosion, along with overlying younger late Holocene dune sand in the 2017 and 2023 erosion events. Re-occurring erosion may ultimately lead to the extinction of the current foredune barrier and the inundation of Rooipan. A marine shoreline discovered 450 m inland at a similar elevation to the current beach on the eastern shore of Rooipan, that is dated to > 42 200 years ago [ 27 ], indicates that this is not without precedent. The transience of the Rooipan foredune over long time periods must be considered against persistent northward longshore sand transport. Periods of foredune formation around 9 000 years ago associated with a prograded beach indicate sand supply in excess of longshore transport, while the loss of the foredune barrier indicates a sand supply in deficit of longshore transport. Longshore transport is invariable, and so the recent erosion of the southern end of Sixteen Mile Beach is likely the result of interrupted sand supply. The conditions that led to the Sixteen Mile Beach erosion events include large storms with high waves that persist over several spring tide cycles. These conditions are consistent with wave setup and wave runup models for coastal erosion, but erosive mechanisms must be combined with sand redistribution mechanisms to appreciate the erosion risk fully. The wave runup that eroded the southern end of Sixteen Mile Beach in September 2023 did not involve a vigorous collision zone , but the longshore transport was pervasive (Supplementary Videos S1-S7). Where longshore sand transport is the dominant process in coastal erosion, sand supply controlled by long-term dynamics must be considered. Our dating analysis provides insights additional to those obtained from remotely sensed cross-shore analyses and photogrammetry. Dating sediment is not a technique that can be applied across a wide geographic region, and the principle of sand supply rather than the dating per se emerges from this case study. This mechanism is not captured in even the most sophisticated coastal risk models and is not readily modeled because it is governed by local coastal re-engineering, as well as palaeo-processes that need specialized analysis to quantify. It is suggested that indices for coastal vulnerability should incorporate sediment age where it is possible to do so and should probably be a requirement for assessing the potential impact of coastal erosion for new coastal developments because there are very few engineering solutions (often the most expensive and least successful possible responses) that would mitigate erosion where the sand supply is the underlying cause. Methods This study was conducted on the southwestern coast of South Africa (33°20'50"S; 18°08'60"E) (Fig. 1 ). This study area includes three distinct sandy beaches, which are the main study areas: Sixteen Mile Beach, Yzerfontein Main Beach, and Pearl Bay. Wave data was collected by the Transnet National Port Authority (TNPA) from the Saldanha Bay mooring and was supplied by the Council for Scientific and Industrial Research (CSIR). Tide data for Simonstown (80 km to the south of Yzerfontein) was provided by the South African Navy Hydrographic Office and accessed via the University of Hawaii Sea Level Centre [ 41 ]. The tide data for the September 2023 storm event are incomplete, and the Hydrographic Office acknowledges that data gaps occur and are not responsible for the impact that this may have on the interpretation. The synoptic charts showing the systems that caused the storm surges were replicated from the online resources made available by the South African Weather Services ( https://www.weathersa.co.za/home/historicalsynoptic , accessed 17 January 2023). For shoreline mapping we used the Sentinel-2 constellation that has a resolution of 10–30 m and delivers imagery at an interval of 3–5 days. A set of 304 cloud-free images was used for shoreline position detection between 2015 and 2020. The shoreline positions were obtained using CoastSat ; a novel Python toolkit that automatically extracts shoreline positions from public satellite imagery. A reference shoreline position and maximum search distance from this reference were defined to avoid spurious results from Yzerfontein Salt Pan, Rooipan, and Langebaan Lagoon. All imagery was automatically classified into water, sand, white water (breaking waves), and "other" based on existing training data models. Pixels were converted to MNDWI values that distinguish between water and non-water, and then Otsu's thresholding algorithm was applied to determine the maximum variance between the sand and water classes (for more details see [ 20 ]). We adopted a case study-based approach to examine six cross-shore change locations, namely at Tsaarbank; Abrahamskraal; Rooipan; Yzerfontein Main Beach; Pearl Bay; and at the "Erosion Hotspot" identified in the historic analysis [ 28 ]. A Mavic 2 Pro UAV was used to survey an erosion-prone stretch of beach on two occasions: 20 October 2020 and 2 October 2023. All UAV flights were pre-programmed and autonomously executed using the DroneDeploy application for iOS ( https://dronedeploy.com/ ). Flights were programmed with 85% frontal and side photographic overlap at an altitude of 60 m above the takeoff location and we used the same flight plan for both flight dates. All photographs were processed using Agisoft Metashape Professional [ 42 ] into dense point clouds and meshed models from which orthophotos and digital elevation models (DEMs) were derived (Supplementary Video 8). Model alignment accuracy was set to “High”, depth map quality to “High”, with filtering mode to “Mild”. Each digital elevation model (DEM) was derived from the dense point cloud with interpolation enabled and each orthophoto was created using the “Mosaic” blending mode with the DEM as the surface and hole filling selected as “Yes”. We did not use Ground Control Points (GCPs) during our flights, resulting in altitude offsets (bowing) along the length of our models, which we rectified by using the relative “height above a ground plane” as the elevation models from which dune profiles were derived. We used the SOR (statistical outlier filter) noise filtering algorithm in CloudCompare (v2.13.alpha) using 6 points for mean distance estimation and a standard deviation multiplier threshold of 1 to remove outlier points that could potentially skew reference layer derivations. Elevation models were developed using Cloud Compare by establishing a ground reference through minimum elevation sampling within each point cloud, across a 50 x 50 m area. This ground layer facilitated height calculations for all points, effectively standardising the minimum elevation to zero and ensuring z-axis alignment. For x and y axis alignment, we identified and matched key features between models from consecutive flights. The accuracy of alignment was verified through fixed feature comparison. Models were rasterized at 5 cm resolution for detailed analysis in QGIS, where we compared dune elevation profiles from identical locations across models. OSL samples YZF1 and YZF3 were collected by hammering an opaque tube into the dune face and sealing the ends to ensure the quartz grains were not exposed to light. Each sample was divided In the laboratory under safe light conditions for equivalent dose (D e ) and dosimetry (D r ) measurements. Quartz grains were extracted for OSL measurements following standard sample preparation techniques (see [ 43 , 44 ]). Organic matter and carbonates were removed using H 2 O 2 and HCl, respectively, before the 180–212 µm quartz fraction was isolated by dry sieving and density separation using a solution of sodium polytungstate at 2.70 g.cm − 3 and 2.63 g.cm − 3 . The quartz grains were etched in HF for 40 minutes to remove the alpha contribution to the dosimetry, along with any remaining feldspars, followed by an HCl wash to remove any acid-soluble fluorides. The equivalent dose for each sample was measured using Risø TL/OSL DA-15 and DA-20 readers, in which optical stimulation was provided by blue LEDs (478 nm) of ~ 40 mW cm-2 constrained by a long-pass filter (GG-420). The OSL signal was detected with an EMI 9235QB photomultiplier tube via a single 7.5 mm Hoya U340 detection filter. The D e values were derived by calibrating the optical signal acquired naturally during burial to the regenerated optical signals obtained through controlled laboratory doses, using the single aliquot regenerative (SAR) protocol of Murray & Wintle [ 45 ]. The suitability of the SAR protocol for determining D e was evaluated using a combined preheat and dose recovery test, and the recycling ratio. The temperature at which a known dose was recovered within unity was selected for further measurements. The preheat also assessed the consistency of D e as temperature varied [ 45 ]. In addition, any aliquots where the recycling ratio was not within unity (between 0.9–1.1) were rejected. The dosimetry (D r ) was determined by measuring the radionuclide abundances for U and Th by ICP-MS and K, using XRF. Adjustments were made to account for water content, and cosmic dose contributions were determined following the method outlined by Prescott & Hutton [ 46 ]. Dose rate conversion followed the approach by Guérin et al. [ 47 ], and the age was calculated by dividing D e by D r , following procedures described in Aitken [ 43 ] (see also [ 44 ]). All error terms provided are computed at one sigma level. Declarations Acknowledgments Authors would like to thank Elli Berman, the owner of Strandkombuis, for videos of the September 2023 storm and permission to cross her property for sampling. Authors also acknowledge the South African National Parks (SANParks) for beach/dune sampling permit (Permit number: CRC/2021-2022/006--2018/V1) and Ursula von Lorne von Saint Ange from the Council for Scientific and Industrial Research (CSIR) for the wave data. Wave data are used with permission but remain the property of Transnet National Port Authority. Tide data are used with their permission (tide release permit: FOF/HYD/R/320/13/1). Author contributions SW, DM and HC conceptualized the research; JM, EA, SX performed the remote sensing analyses; AM and SD performed the photogrammetry analyses, and ME performed the dating analyses. All authors reviewed the manuscript. Data availability statement Data is provided within the manuscript or supplementary information files. Competing interests The authors declare no competing interests. Supplementary Information Supplementary Videos 1-7: Amateur video of the collision regime during the September 2023 storm. 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Pagán, J.I., Bañón, L., López, I., Bañón, C. and Aragonés, L., 2019. Monitoring the dune-beach system of Guardamar del Segura (Spain) using UAV, SfM and GIS techniques. Science of the total environment, 687, pp.1034–1045. Myburgh, A., Botha, H., Downs, C.T. and Woodborne, S., 2021. The application and limitations of a low-cost UAV platform and open-source software combination for ecological mapping and monitoring. African Journal of Wildlife Research, 51(1), pp.166–177. Laporte-Fauret, Q., Marieu, V., Castelle, B., Michalet, R., Bujan, S. and Rosebery, D., 2019. Low-cost UAV for high-resolution and large-scale coastal dune change monitoring using photogrammetry. Journal of Marine Science and Engineering, 7(3), p.63. Franceschini, G. and Compton, J.S., 2006. Holocene evolution of the sixteen mile beach complex, Western Cape, South Africa. Journal of Coastal Research, 22(5), pp.1158–1166. Compton, J.S. and Franceschini, G., 2005. Holocene geoarchaeology of the sixteen mile beach barrier dunes in the Western Cape, South Africa. Quaternary Research, 63(1), pp.99–107. Woodborne, S., Miller, D., Evans, M., Cawthra, H.C. and Winkler, S., 2023. Radiocarbon-dated evidence for Late Pleistocene and Holocene coastal change at Yzerfontein, Western Cape, South Africa. South African Journal of Science, 119(11/12). Murray, J., Adam, E., Woodborne, S., Miller, D., Xulu, S., Evans, M. 2023 “Monitoring shoreline changes along the southwestern coast of South Africa from 1937 to 2020 using varied remote sensing data and approaches”. Remote Sensing. 15, 317. Guastella, L.A. and Rossouw, M., 2012. Coastal vulnerability: What will be the impact of increasing frequency and intensity of coastal storms along the South African coast. Reef Journal, 2, pp.129–139. Rautenbach, C., Daniels, T., de Vos, M. and Barnes, M.A., 2020. A coupled wave, tide and storm surge operational forecasting system for South Africa: validation and physical description. Natural Hazards, 103, pp.1407–1439. Galbraith, R.F., Roberts, R.G., Laslett, G.M., Yoshida, H., Olley, J.M., 1999. Optical dating of single and multiple grains of quartz from Jinmium rock shelter, northern Australia: Part I, experimental design and statistical models. Archaeom. 41, 339–364. https://doi.org/10.1111/j.1475-4754.1999.tb00987.x . King, J., Anchukaitis, K.J., Allen, K., Vance, T. and Hessl, A., 2023. Trends and variability in the Southern Annular Mode over the Common Era. Nature Communications, 14(1), p.2324. Veitch, J., Rautenbach, C., Hermes, J. and Reason, C. 2019. The Cape Point wave record, extreme events and the role of large-scale modes of climate variability. Journal of Marine Systems, 198, p.103185. Jury, M.R., MacArthur, C.I. and Brundrit, G.B., 1990. Pulsing of the Benguela upwelling region: large-scale atmospheric controls. South African Journal of Marine Science, 9(1), pp.27–41. Smith, A., Guastella, L.A., Mather, A.A., Bundy, S.C. and Haigh, I.D., 2013. KwaZulu-Natal coastal erosion events of 2006/2007 and 2011: A predictive tool?. South African Journal of Science, 109(3), pp.1–4. Corbella, S., Stretch, D.D., 2012. Shoreline recovery from storms on the east coast of Southern Africa. Natural Hazards and Earth System Science 12, 11–22. MacArthur, B., 2005: Event-Based Erosion. FEMA Coastal Flood Hazard Analysis and Mapping Guidelines Focused Study Report, 84 pp. Theron, A.K.; Rossouw, M.; Barwell, L.; Maherry, A.; Diedericks, G.; De Wet, P. 2010. Quantification of Risks to Coastal Areas and Development: Wave Run-Up and Erosion; In Proceedings of the CSIR 3rd Biennial Conference 2010. Science Real and Relevant, CSIR International Convention Centre, Pretoria, South Africa, 30 August–1 September; Available online: http://hdl.handle.net/10204/4261 (accessed on 10 February 2024) Kirsten, K.L., Kasper, T., Cawthra, H.C., Strobel, P., Quick, L.J., Meadows, M.E. and Haberzettl, T., 2020. Holocene variability in climate and oceanic conditions in the winter rainfall zone of South Africa—inferred from a high-resolution diatom record from Verlorenvlei. Journal of Quaternary Science, 35(4), pp.572–581. Cooper, J.A.G., Green, A.N. and Compton, J.S., 2018. Sea-level change in southern Africa since the Last Glacial Maximum. Quaternary Science Reviews, 201, pp.303–318. Caldwell, P. C., M. A. Merrifield, P. R. Thompson (2015), Sea level measured by tide gauges from global oceans — the Joint Archive for Sea Level holdings (NCEI Accession 0019568), Version 5.5, NOAA National Centers for Environmental Information , Dataset, doi: 10.7289/V5V40S7W . Over, J.R., Ritchie, A.C., Kranenburg, C.J., Brown, J.A., Buscombe, D., Noble, T., Sherwood, C.R., Warrick, J.A., and Wernette, P.A., 2021, Processing coastal imagery with Agisoft Metashape Professional Edition, version 1.6—Structure from motion workflow documentation: U.S. Geological Survey Open-File Report 2021–1039, 46 p., https://doi.org/10.3133/ofr20211039 Aitken, M.J., 1997. Luminescence dating. In Chronometric dating in archaeology (pp. 183–216). Boston, MA: Springer US. Mahan, S.A., Rittenour, T.M., Nelson, M.S., Ataee, N., Brown, N., DeWitt, R., Durcan, J., Evans, M., Feathers, J., Frouin, M. and Guérin, G. 2023. Guide for interpreting and reporting luminescence dating results. Bulletin, 135(5–6), pp.1480–1502. Murray, A.S. and Wintle, A.G., 2000. Application of the single-aliquot regenerative-dose protocol to the 375 C quartz TL signal. Radiation Measurements, 32(5–6), pp.579–583. Prescott, J.R. and Hutton, J.T., 1994. Cosmic ray contributions to dose rates for luminescence and ESR dating: large depths and long-term time variations. Radiation measurements, 23(2–3), pp.497–500. Guérin, G., Mercier, N. and Adamiec, G., 2011. Dose-rate conversion factors: update. Ancient Tl, 29(1), pp.5–8. Additional Declarations No competing interests reported. Supplementary Files SupplementaryVideo1.mov SupplementaryVideo2.mov SupplementaryVideo3.mov SupplementaryVideo4.mov SupplementaryVideo5.mov SupplementaryVideo6.mov SupplementaryVideo7.mov SupplementaryVideo8.mp4 Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4647471","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":327388554,"identity":"2af738f2-650a-4cfc-9b2e-ee4c37c027b6","order_by":0,"name":"Stephan Woodborne","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA50lEQVRIiWNgGAWjYBAC+QYgwQsiGJgPMDxgOEBYi8EBuBa2xIYEorQwwLXwGBKpRSL54IO3O2zyzdl7vj9IqLljz8B++AHjjxrcWuRnpCUbzj2TZrmz5+zGhoRjzxIbeNIMGCSO4bHmdo6ZNG/bYQODG7lALWyHExgYcoCWsxGj5f6bhw0J/w7bM/C/YWBI+EeULTyMDYlthxkbJIC2HGzD4/37z8B+MTA4k2Y4I7HvWWKbxDODg419eLzfcxgcYgYGxw8/+PDh2x17fv7khw9/fMPjMAwA8vgBUjSMglEwCkbBKMAEAEPYWkTxvTl7AAAAAElFTkSuQmCC","orcid":"","institution":"iThemba Laboratory","correspondingAuthor":true,"prefix":"","firstName":"Stephan","middleName":"","lastName":"Woodborne","suffix":""},{"id":327388555,"identity":"8d65bd92-07e9-4b9a-b48c-4dcc02db5757","order_by":1,"name":"Mary Evans","email":"","orcid":"","institution":"University of the Witwatersrand","correspondingAuthor":false,"prefix":"","firstName":"Mary","middleName":"","lastName":"Evans","suffix":""},{"id":327388556,"identity":"2716f54a-0402-4350-a299-a5cab9d46137","order_by":2,"name":"Jennifer Murray","email":"","orcid":"","institution":"University of the Witwatersrand","correspondingAuthor":false,"prefix":"","firstName":"Jennifer","middleName":"","lastName":"Murray","suffix":""},{"id":327388557,"identity":"e167ed2f-2a46-417c-b3a5-c7e4fcfb4fd0","order_by":3,"name":"Elhadi Adam","email":"","orcid":"","institution":"University of the Witwatersrand","correspondingAuthor":false,"prefix":"","firstName":"Elhadi","middleName":"","lastName":"Adam","suffix":""},{"id":327388558,"identity":"d7c9a01f-5dae-48df-bfff-46c1c524a284","order_by":4,"name":"Duncan Miller","email":"","orcid":"","institution":"University of the Free State","correspondingAuthor":false,"prefix":"","firstName":"Duncan","middleName":"","lastName":"Miller","suffix":""},{"id":327388559,"identity":"64b91d86-c497-462a-b7a2-367b52ce7cee","order_by":5,"name":"Albert Myburgh","email":"","orcid":"","institution":"University of Pretoria","correspondingAuthor":false,"prefix":"","firstName":"Albert","middleName":"","lastName":"Myburgh","suffix":""},{"id":327388560,"identity":"94cf6455-e60d-473b-9b6c-1c5bd29547be","order_by":6,"name":"Stephen Davey","email":"","orcid":"","institution":"Klipberg Consulting (Pty) Ltd","correspondingAuthor":false,"prefix":"","firstName":"Stephen","middleName":"","lastName":"Davey","suffix":""},{"id":327388561,"identity":"fc6fc8f8-2584-4f17-ae93-d9ae5534efda","order_by":7,"name":"Halyey Cawthra","email":"","orcid":"","institution":"Council for Geoscience Western Cape regional office","correspondingAuthor":false,"prefix":"","firstName":"Halyey","middleName":"","lastName":"Cawthra","suffix":""},{"id":327388562,"identity":"0259674d-ceb1-48bc-97a5-7136b9057ad3","order_by":8,"name":"Sifiso Xulu","email":"","orcid":"","institution":"University of KwaZulu-Natal","correspondingAuthor":false,"prefix":"","firstName":"Sifiso","middleName":"","lastName":"Xulu","suffix":""}],"badges":[],"createdAt":"2024-06-27 09:40:49","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4647471/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4647471/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":60825706,"identity":"705215c1-2ddf-4222-a03e-7f3d44f31e1b","added_by":"auto","created_at":"2024-07-22 14:04:19","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":291065,"visible":true,"origin":"","legend":"\u003cp\u003ea) Location of the study area in southern Africa. b) Location of cross-shore transect analyses: a - Tsaarbank, b – Abrahamskraal, c – Erosion Hotspot, d – Rooipan, e – Main Beach, and e – Pearl Bay. The white rectangle is exaggerated in c) showing the location of the OSL dating samples and the UAV photogrammetry survey coverage (white box in c)).\u003c/p\u003e","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4647471/v1/985cdae2232e4cff09d6f6f7.png"},{"id":60825707,"identity":"23e8107e-bd9d-40c1-85d8-79ccd46ca6d8","added_by":"auto","created_at":"2024-07-22 14:04:19","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":175170,"visible":true,"origin":"","legend":"\u003cp\u003eSynoptic charts for the storms of a) the June 2017 and b) September 2023 that led to coastal erosion at Yzerfontein (red dot).\u003c/p\u003e","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4647471/v1/4e9891ceaaacd7e4e5036a35.png"},{"id":60825708,"identity":"0ac48db5-d98b-41c1-b2ff-151abafa9b59","added_by":"auto","created_at":"2024-07-22 14:04:19","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":17650,"visible":true,"origin":"","legend":"\u003cp\u003eTide (blue), significant wave height (H\u003csub\u003es\u003c/sub\u003e) (gray), and maximum wave height (red) for a) the storm of June 2017 and c) the storm of September 2023. The 10-week windows in a) and c) show the occurrence of the storms during spring tides, while the 6-day windows in b) and d) emphasise the distribution of the wave energy over several tide cycles.\u003c/p\u003e","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4647471/v1/fbdb9e012936218ef4494b8e.png"},{"id":60826387,"identity":"655a0a2c-4304-4555-b63c-bc513076dd0c","added_by":"auto","created_at":"2024-07-22 14:12:19","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":181233,"visible":true,"origin":"","legend":"\u003cp\u003eTransect of the shoreline positions derived using \u003cem\u003eCoastSat\u003c/em\u003e for Yzerfontein Main Beach in the period 2015–2020.\u003c/p\u003e","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-4647471/v1/a4fee9e57fd48c06b80f190b.png"},{"id":60825714,"identity":"cb448c03-9f16-42fe-a30a-d39ce35a6f34","added_by":"auto","created_at":"2024-07-22 14:04:20","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":25151,"visible":true,"origin":"","legend":"\u003cp\u003eCross-shore change analysis for six locations along the study site between August 2015 and December 2020 based on Sentinel-2 data. The vertical red line represents the June 2017 storm.\u003c/p\u003e","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-4647471/v1/2c90274314b883a3fd463dc5.png"},{"id":60825716,"identity":"ae6bf74f-ebec-4ce7-a9ac-73fc8de4948a","added_by":"auto","created_at":"2024-07-22 14:04:20","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":94715,"visible":true,"origin":"","legend":"\u003cp\u003eForedune and beach profiles derived from UAV photogrammetry. The profiles plotted in blue represent the situation before the September 2023 storm and are mostly the legacy of the June 2017 storm. Profiles plotted in amber are from the survey after the September 2023 storm.\u003c/p\u003e","description":"","filename":"Onlinefloatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-4647471/v1/f2bc78e6e179e893043a25dd.png"},{"id":79773900,"identity":"a1dc41df-6edf-4556-a29a-578c49d3e371","added_by":"auto","created_at":"2025-04-02 13:47:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1646743,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4647471/v1/3b0a4290-3a61-4994-b32a-aabb7d107ef4.pdf"},{"id":60825712,"identity":"3a8676c5-2bf8-4c9b-b22c-69e7bd9329e8","added_by":"auto","created_at":"2024-07-22 14:04:20","extension":"mov","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":16970963,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryVideo1.mov","url":"https://assets-eu.researchsquare.com/files/rs-4647471/v1/1e165da5f3888e965fae24fa.mov"},{"id":60825710,"identity":"d849e9cd-2ffb-48ef-81b5-a64d9962c5f1","added_by":"auto","created_at":"2024-07-22 14:04:20","extension":"mov","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":23611146,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryVideo2.mov","url":"https://assets-eu.researchsquare.com/files/rs-4647471/v1/b8ee321e2ecdc286c2c07f4f.mov"},{"id":60825718,"identity":"826ed998-b92f-401d-9793-bc96adb84071","added_by":"auto","created_at":"2024-07-22 14:04:21","extension":"mov","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":21578846,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryVideo3.mov","url":"https://assets-eu.researchsquare.com/files/rs-4647471/v1/72c642ada985a66979b0bb96.mov"},{"id":60825717,"identity":"16be5e4f-9f41-4c72-87a6-c4a25e200593","added_by":"auto","created_at":"2024-07-22 14:04:21","extension":"mov","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":54901448,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryVideo4.mov","url":"https://assets-eu.researchsquare.com/files/rs-4647471/v1/dbb7b2df567c42adb6de77c2.mov"},{"id":60825711,"identity":"3132fe41-d77c-4ee2-a10e-b87503e0bd09","added_by":"auto","created_at":"2024-07-22 14:04:20","extension":"mov","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":29385017,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryVideo5.mov","url":"https://assets-eu.researchsquare.com/files/rs-4647471/v1/924ef81016ab02cfb00eeba2.mov"},{"id":60825713,"identity":"50137e59-df7c-4119-8852-a2709710e17f","added_by":"auto","created_at":"2024-07-22 14:04:20","extension":"mov","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":16914625,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryVideo6.mov","url":"https://assets-eu.researchsquare.com/files/rs-4647471/v1/39c1184c45771ad95cf503f8.mov"},{"id":60825720,"identity":"c120793b-3d20-44fd-9c3b-6fbb75df6955","added_by":"auto","created_at":"2024-07-22 14:04:21","extension":"mov","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":13171044,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryVideo7.mov","url":"https://assets-eu.researchsquare.com/files/rs-4647471/v1/4e7d9d2d240e727e104d1ee8.mov"},{"id":60825719,"identity":"6e2cc59b-a4d1-4c71-9935-404439d2ee8a","added_by":"auto","created_at":"2024-07-22 14:04:21","extension":"mp4","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":74351787,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryVideo8.mp4","url":"https://assets-eu.researchsquare.com/files/rs-4647471/v1/99a26170f572345ca477ce7b.mp4"}],"financialInterests":"No competing interests reported.","formattedTitle":"Sand budget failure underlies coastal erosion on the west coast of South Africa","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAn estimated 2.15\u0026nbsp;billion people live in the near-coastal areas worldwide, 42% of whom live in the low-lying coastal zones [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Of these, nearly 190\u0026nbsp;million are expected to be prone to coastal environmental risks by 2100 CE [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. While the most catastrophic environmental threat to coastal zones is tsunamis [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], there is also a globally ongoing process of coastal erosion and accretion that is most prominent where sandy shore sediments are mobilized by waves, tidal and littoral currents, and deflation [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. It is possible that nearly half of the global sandy beaches may go extinct under the current trajectory of erosion and the anticipated acceleration of climate change [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Understanding shoreline evolution is becoming increasingly important to the society and economy of coastal regions, as erosion typically destroys infrastructure, negatively affects land values, and can strongly influence ecological processes (e.g., where coastal wetland systems become inundated with salt water), but the mechanisms underlying erosion events are often complex and difficult to predict.\u003c/p\u003e \u003cp\u003eCoastal erosion occurs when the landward portion of the \u003cem\u003eswash zone\u003c/em\u003e mobilizes the sandy substrate [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. The \u003cem\u003ewave setup\u003c/em\u003e is the way in which energy interacts with the sandy substrate as waves propagate into shallower water and cause elevated sea levels after the wave breaking point. The physics that conserves the radiation stress (basically the energy mass balance) of the water drives the formation of rip currents, which are an important part of sand redistribution. The mechanism is coupled with the dynamics of the \u003cem\u003eswash zone\u003c/em\u003e and the concept of \u003cem\u003ewave runup\u003c/em\u003e [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. In a simple 2-dimensional conceptual framework, beach gradient, wave height and period, tidal effects and the local sea level rise associated with atmospheric low-pressure systems during storms will determine where sand will be eroded and where it will be deposited. This is captured in Bruun\u0026rsquo;s rule [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] which describes the physics of beach gradient responses to wave regimes. Most beach systems have seasonal (storm season vs non-storm season) and lunar period (spring vs neap tide) variations in the gradient of the beach. These typically operate around a long-term median, but some interactions exacerbate the erosive nature of waves.\u003c/p\u003e \u003cp\u003eAs \u003cem\u003ewave runup\u003c/em\u003e is the most tangible predictor of coastal erosion, models of coastal erosion vulnerability use this as the key parameter. Several coastal erosion vulnerability indices have been developed [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] and almost all are parameterized using the same input variables [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. They typically use coastal geomorphology and slope, relative sea level change, shoreline erosion/accretion rates, tidal range, and wave height, which all are essentially \u003cem\u003ewave runup\u003c/em\u003e variables, but some also consider the role of the foredune barrier system [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] and sea level rise through climate change [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Although input variables from climate and wave models, tidal models, and remotely sensed shoreline characteristics can be obtained over large spatial scales, the models are typically only locally applicable [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The models can become computationally challenging with the addition of other variables, but most achieve satisfactory approximations without further parameterization.\u003c/p\u003e \u003cp\u003eWhen \u003cem\u003ewave runup\u003c/em\u003e extends beyond the \u003cem\u003eswash regime\u003c/em\u003e to coastal foredune barriers, the potential exists for a \u003cem\u003ecollision regime\u003c/em\u003e in which sand from the base of the foredune is eroded and deposited elsewhere, leading to coastal erosion [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Incorporating foredune barrier parameters into coastal vulnerability models is important because while \u003cem\u003ewave runup\u003c/em\u003e models indicate coastal erosion risk, coastal vulnerability is related to the consequences of the erosion and flooding. Boruff et al. [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] developed a coastal social vulnerability index that considers the socio-economic impacts of sea level rise, and the IPCC \u003cem\u003eCommon Methodology for Vulnerability Assessment\u003c/em\u003e [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] uses sea level increase forecast to extrapolate wetland loss, climate change risk and adaptation, community vulnerability, and coastal vulnerability. Coastal foredune barriers are the final mitigation before erosion leads to impacts.\u003c/p\u003e \u003cp\u003eShoreline position is a representative feature of beach dynamics and is useful in describing coastal erosion and accretion [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Remote sensing-based approaches and automatic computation in GIS-based platforms have become a preferred choice to map and monitor shoreline changes [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] due to their synoptic-scale coverage, consistent acquisition of sensitive spectral bands for mapping surface features, and their cost-effectiveness compared to spatially limited field-based techniques [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Recent remote sensing techniques enable the determination of shoreline position from open-access mid-resolution satellite products using advanced subpixel extraction algorithms [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSunny et al. [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] compared the performance of Sentinel-2, Landsat, and MODIS for quantifying shoreline changes at the regional scale and their results showed that Sentinel-2 is more effective due to its finer resolution. The Sentinel-2 coverage and the algorithms that calculate changes in shoreline position are sensitive to changes in beach gradient, but they are not designed to estimate the changes that extreme wave runup has on foredune barriers.\u003c/p\u003e \u003cp\u003eUsing unmanned aerial vehicle (UAV) photogrammetry surveys, local erosion hotspots can be mapped in detail [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], and sand redistribution during foredune barrier erosion can be determined empirically. The accuracy of low-cost UAV systems and photogrammetry enables rapid reconstruction of complex environments and is particularly suitable for reconstructing dune profiles, as heterogenous sandy textures are ideal for the photogrammetric process [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSixteen Mile Beach on the west coast of South Africa is a long sandy beach stretching from Yzerfontein town in the south to the Langebaan Peninsula in the north (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), and it has been the subject of long-term sand dynamics research [\u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] as well as historical analysis of beach sand dynamics [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], which provided empirical evidence that this coastline is vulnerable to coastal erosion. In June 2017, a severe storm caused extensive damage to a section of the dune cordon and destroyed properties and businesses along the eroded dune [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Another storm in September 2023 led to further erosion of the foredunes. Understanding the erosion caused by such events can inform future shoreline changes and vulnerabilities but detailed analysis also reveals erosion dynamics that are of broader relevance.\u003c/p\u003e \u003cp\u003eHere we analyse shoreline and foredune morphology changes in response to the 2017 and 2023 storms at Sixteen Mile Beach. Our objective is to determine whether coastal erosion is spatially uniform, and, if not, to determine the underlying cause of the spatial patterning. Assessing the age of eroded sediments, we consider the novelty of these coastal erosion event relative to the short-term context of monthly and seasonal stochastic variability of coastal morphology changes. The study provides new insights into the sand supply mechanisms underlying coastal erosion.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eOcean and atmosphere synoptic data\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe June 2017 storm was typical of austral winter cold fronts that make landfall over the southern part of South Africa and generate high wave conditions [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. The low-pressure system had a minimum barometric pressure of 984 hPa, and the extent of the front spanned approximately 2 500 km to its northernmost extent where the pressure was approximately 1018 hPa (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). The pressure gradient and fetch during the buildup of this storm led to significant wave heights (H\u003csub\u003es\u003c/sub\u003e) in excess of 8 m and maximum wave heights of 12.5 m (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea, b). The September 2023 storm was of similar magnitude, although the minimum barometric pressure was 980 hPa, and it passed further south of the continent (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). The maximum wave height was 7.7 m (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec, d). In both storms the wave height was high over several tide phases.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e\u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eSatellite imagery and shoreline extraction\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eAn example of the wet/dry interface on Yzerfontein main beach for the period 2015\u0026ndash;2020 is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. This data is expressed as the cross-shore changes, and the results for six transects are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. All six sites exhibit high variability in shoreline position over time, with an average variation of 77 m. The erosion hotspot (c) has the greatest range of variation of 91 m, followed by Rooipan (d) with a range of variation of 86 m. Pearl Bay (f) has the smallest range of variation of 50 m. Pearl Bay has the most cyclical trends in the dataset, with cross-coastal changes of about\u0026thinsp;+\u0026thinsp;20 m during the summer months while dropping to \u0026minus;\u0026thinsp;20 m from the mean position in the middle of each of the five years (winter months).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eOrthophoto analysis and foredune profile extraction\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eChanges in the foredune and beach profiles before and after the September 2023 storm are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. The northern transects show a small degree of sand wedge accretion at the interface between the beach and foredune profiles, with evidence of some slumping of the erosion face established in the June 2017 storm. In the southern transects, the elevation of the beach profile was reduced in the September 2023 storm, and the erosion face from the June 2017 storm was further eroded by approximately 1.5 m with the apex of the dune reduced by approximately 1 m in elevation.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e\u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eOptically Stimulated Luminescence (OSL) dating\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe two OSL samples from the base of the erosion face of the foredune represent sediments that were actively eroded in the June 2017 storm. The central age model estimates for YZF1 and YZF3 are 8.89\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16 ka and 8.91\u0026thinsp;\u0026plusmn;\u0026thinsp;0.48 ka respectively (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The overdispersion values of 24% and 20% are consistent with well-bleached grains indicating there is little scatter in the signal population of the grains (see Galbraith et al. 1999 [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]). This suggests there was minimal post-depositional reworking of the dune sediment, and the ages constrain the timing of deposition of these dune sediments.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of OSL analysis of foredune deposits exposed by erosion during the September 2023 storm event.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSample\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTh (ppm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eU (ppm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eK (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWC (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDepth (m)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eDr(Gy.ka-1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eCosmic Dr (Gy.ka-1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eOD (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eCAM De (Gy)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eCAM Age (ka)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYZF1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1.50\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.49\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e0.15\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e0.82\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e0.235\u0026thinsp;\u0026plusmn;\u0026thinsp;0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c10\"\u003e \u003cp\u003e7.38\u0026thinsp;\u0026plusmn;\u0026thinsp;0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c11\"\u003e \u003cp\u003e8.89\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYZF3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.75\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.99\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e0.14\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c7\"\u003e \u003cp\u003e0.58\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c8\"\u003e \u003cp\u003e0.165\u0026thinsp;\u0026plusmn;\u0026thinsp;0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c10\"\u003e \u003cp\u003e5.14\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c11\"\u003e \u003cp\u003e8.91\u0026thinsp;\u0026plusmn;\u0026thinsp;0.48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eStorm wave conditions in southern Africa are linked to the passing of cold fronts in the westerly wind belt, and these frontal systems displace northward in the Austral winter leading to conditions for coastal erosion [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. The winter storm tracks are indexed by the Southern Annular Mode (SAM) [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. It might be anticipated that negative SAM excursions (expansion of the circumpolar westerly vortex) would result in greater storm impact, but the role of SAM on wave regimes in southern Africa is most prominent in summer when positive SAM conditions lead to decreased summer wave heights [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. A more profound effect on the northward/southward position of the winter storm tracks is the Rossby wave form followed by the westerlies [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. The frontal system that produced the June 2017 storm surge was displaced northward in the Rossby wave (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA), and it took place at spring tide (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). In Cape Town (80 km south) the storm surge (the effect of wind, \u003cem\u003ewave setup\u003c/em\u003e, and the low atmospheric pressure) resulted in a 20 cm increase in sea level [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], although the maximum wave height and the maximum storm surge did not coincide. This is included in the tide measure in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, an empirical measure of the sea level.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe storm wave conditions during the September 2023 erosion event were driven by a more intense storm that passed further south than the June 2017 storm. It also occurred during spring tides, and the modest increase in wave height also spanned several tidal cycles (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec). The comparison between the ocean and atmosphere synoptics between these two events suggests that wave height alone is insufficient to explain the erosion, and wave direction and duration may be as important as it was in the erosion event on the South African east coast in 2011 [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA common feature between these storms is that elevated wave conditions took place over several spring tide high/low cycles when \u003cem\u003ewave runup\u003c/em\u003e combined with rip currents could maximise sand transport potential. To the extent that high tides exacerbate the erosive effect of \u003cem\u003ewave runup\u003c/em\u003e, it is not surprising that the coincidence between both storms and spring tide contributed to their erosive impact, but the spring tides were not of particularly high amplitude. In both storms the spring high reached just over 2 m, whereas the maximum tide height is close to 2.4 m in the dataset.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThe cross-shore change analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) reflects the temporal changes in the wet/dry interface on the beach and is not a direct reflection of the erosion that takes place in the foredune system. Each of the transects in the analysis contains an annual cycle with negative excursions (shoreward migration) during winters and positive excursions (seaward migration) during summers. Except for the impact of the June 2017 storm event, the year-to-year seasonal variation at each site reflects the lower wave energy during summers, and the cyclicity indicates decadal-scale equilibrium with sand migrating inshore in summer, and offshore in winter. Higher resolution analysis of the dataset also shows the spring/neap tide cyclicity. The analysis is, therefore, sensitive to the dynamics of the beach profile, and the changes that took place during the June 2017 storm are noteworthy. At three of the sites (Tsaarbank, Pearl Bay and Abrahamskraal) there is no discernable change in the beach profile. On the Main Beach, there appears to be sand accretion, while at Rooipan and the Erosion Hotspot, there appears to be a shift towards negative values that do not recover in the following summer. This indicates quasi-permanent sand loss at these locations. Previous erosion events along the South African coastline took two years to recover their original equilibrium profiles [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], but at Rooipan and the Erosion Hotspot there is no indication of beach profile restoration in three years, and the impact of the storm on the beach profile here appears to be permanent.\u003c/p\u003e\u003cp\u003eWhile the cross-shore analysis is a good indicator of beach profile dynamics, the pre- and post-photogrammetric analysis of the September 2023 storm indicates changes in both the beach profile and the foredune structure. The three northern transects show little effect by the storm, apart from some slumping and the formation of a sand wedge at the base of the scarp face in the foredune. The two middle transects show significant deflation of the beach by about 1.5 m but no impact on the foredune. The two southern transects show similar sand deflation from the beach profile but also scarp face retreat of the foredune by about 1 m. The indication is that sand was redistributed northward with erosion taking place where the deflated beach profile afforded less protection against \u003cem\u003ewave runup\u003c/em\u003e. These locations at the southernmost end of Sixteen Mile Beach are also where permanent beach profile changes took place in the June 2017 storm. The efficacy of a foredune as a barrier to beach erosion is captured in the \u0026ldquo;540 rule\u0026rdquo; [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] which suggests the cross-sectional area of sand seaward of the dune apex and above the \u003cem\u003e100-year still water flood level\u003c/em\u003e should exceed 500 square feet (44.5 m\u003csup\u003e2\u003c/sup\u003e) to be an effective barrier. That erosion has surpassed the dune apex at the southern end of Sixteen Mile Beach, and the modified dune and beach profiles appear to be permanent, suggest severe vulnerability to future erosion events.\u003c/p\u003e\u003cp\u003eWas the coastal erosion at Yzerfontein the result of extraordinary events, or sea level change, and is there likely to be a return to previous equilibrium conditions given sufficient time for sand dynamics to be restored? Guastella \u0026amp; Rossouw [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] suggest that storm intensity in the region increased from 1994 CE to 2008 CE, but Theron et al. [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] used the same data and identified no trend. There is evidence from freshwater ingress into Verlorenvlei (110 km NE of Yzerfontein) that storminess has increased in the last 2 000 years [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], but sediment that is being eroded from the southern end of Sixteen Mile Beach is more than 8 000 years old, which suggests that these storms would have been the most intense in 8 000 years. This seems unlikely as historic storms generating waves of a similar magnitude have been recorded [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] without causing erosion. Late Holocene storm beach deposits preserved at elevations up to 10 m above present mean sea level on the exposed rocky shore just south of Yzerfontein Point [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] are evidence of much more extreme storms in the past 8 000 years.\u003c/p\u003e\u003cp\u003eSea level changes in response to global warming may be affecting the erosion dynamics of storms [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], but local sea level on the west coast of South Africa has varied very little in the past 5 500 years, with possible minor positive stands between 4 000 and 5 000 years ago [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Secular sea level change is unlikely to be a significant factor behind the erosion events as the tide data presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows far greater variation than anthropogenically forced sea level rise, and the effects of sea level rise would manifest broadly rather than as locally as noted in this analysis.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThe two OSL dates are from a buried soil profile that formed approximately 9 000 years ago in aeolian sand on the margins of ancestral Rooipan; a landscape feature that extended seaward into the current intertidal zone until at least 5 500 years ago [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. All these deposits were exposed by erosion, along with overlying younger late Holocene dune sand in the 2017 and 2023 erosion events. Re-occurring erosion may ultimately lead to the extinction of the current foredune barrier and the inundation of Rooipan. A marine shoreline discovered 450 m inland at a similar elevation to the current beach on the eastern shore of Rooipan, that is dated to \u0026gt;\u0026thinsp;42 200 years ago [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], indicates that this is not without precedent. The transience of the Rooipan foredune over long time periods must be considered against persistent northward longshore sand transport. Periods of foredune formation around 9 000 years ago associated with a prograded beach indicate sand supply in excess of longshore transport, while the loss of the foredune barrier indicates a sand supply in deficit of longshore transport. Longshore transport is invariable, and so the recent erosion of the southern end of Sixteen Mile Beach is likely the result of interrupted sand supply.\u003c/p\u003e \u003cp\u003eThe conditions that led to the Sixteen Mile Beach erosion events include large storms with high waves that persist over several spring tide cycles. These conditions are consistent with \u003cem\u003ewave setup\u003c/em\u003e and \u003cem\u003ewave runup\u003c/em\u003e models for coastal erosion, but erosive mechanisms must be combined with sand redistribution mechanisms to appreciate the erosion risk fully. The \u003cem\u003ewave runup\u003c/em\u003e that eroded the southern end of Sixteen Mile Beach in September 2023 did not involve a vigorous \u003cem\u003ecollision zone\u003c/em\u003e, but the longshore transport was pervasive (Supplementary Videos S1-S7). Where longshore sand transport is the dominant process in coastal erosion, sand supply controlled by long-term dynamics must be considered. Our dating analysis provides insights additional to those obtained from remotely sensed cross-shore analyses and photogrammetry. Dating sediment is not a technique that can be applied across a wide geographic region, and the principle of sand supply rather than the dating \u003cem\u003eper se\u003c/em\u003e emerges from this case study. This mechanism is not captured in even the most sophisticated coastal risk models and is not readily modeled because it is governed by local coastal re-engineering, as well as palaeo-processes that need specialized analysis to quantify. It is suggested that indices for coastal vulnerability should incorporate sediment age where it is possible to do so and should probably be a requirement for assessing the potential impact of coastal erosion for new coastal developments because there are very few engineering solutions (often the most expensive and least successful possible responses) that would mitigate erosion where the sand supply is the underlying cause.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThis study was conducted on the southwestern coast of South Africa (33\u0026deg;20'50\"S; 18\u0026deg;08'60\"E) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This study area includes three distinct sandy beaches, which are the main study areas: Sixteen Mile Beach, Yzerfontein Main Beach, and Pearl Bay.\u003c/p\u003e \u003cp\u003eWave data was collected by the Transnet National Port Authority (TNPA) from the Saldanha Bay mooring and was supplied by the Council for Scientific and Industrial Research (CSIR). Tide data for Simonstown (80 km to the south of Yzerfontein) was provided by the South African Navy Hydrographic Office and accessed via the University of Hawaii Sea Level Centre [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. The tide data for the September 2023 storm event are incomplete, and the Hydrographic Office acknowledges that data gaps occur and are not responsible for the impact that this may have on the interpretation. The synoptic charts showing the systems that caused the storm surges were replicated from the online resources made available by the South African Weather Services (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.weathersa.co.za/home/historicalsynoptic\u003c/span\u003e\u003cspan address=\"https://www.weathersa.co.za/home/historicalsynoptic\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, accessed 17 January 2023).\u003c/p\u003e \u003cp\u003eFor shoreline mapping we used the Sentinel-2 constellation that has a resolution of 10\u0026ndash;30 m and delivers imagery at an interval of 3\u0026ndash;5 days. A set of 304 cloud-free images was used for shoreline position detection between 2015 and 2020. The shoreline positions were obtained using \u003cem\u003eCoastSat\u003c/em\u003e; a novel Python toolkit that automatically extracts shoreline positions from public satellite imagery. A reference shoreline position and maximum search distance from this reference were defined to avoid spurious results from Yzerfontein Salt Pan, Rooipan, and Langebaan Lagoon. All imagery was automatically classified into water, sand, white water (breaking waves), and \"other\" based on existing training data models. Pixels were converted to MNDWI values that distinguish between water and non-water, and then Otsu's thresholding algorithm was applied to determine the maximum variance between the sand and water classes (for more details see [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]). We adopted a case study-based approach to examine six cross-shore change locations, namely at Tsaarbank; Abrahamskraal; Rooipan; Yzerfontein Main Beach; Pearl Bay; and at the \"Erosion Hotspot\" identified in the historic analysis [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA Mavic 2 Pro UAV was used to survey an erosion-prone stretch of beach on two occasions: 20 October 2020 and 2 October 2023. All UAV flights were pre-programmed and autonomously executed using the \u003cem\u003eDroneDeploy\u003c/em\u003e application for iOS (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://dronedeploy.com/\u003c/span\u003e\u003cspan address=\"https://dronedeploy.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Flights were programmed with 85% frontal and side photographic overlap at an altitude of 60 m above the takeoff location and we used the same flight plan for both flight dates. All photographs were processed using \u003cem\u003eAgisoft Metashape Professional\u003c/em\u003e [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e] into dense point clouds and meshed models from which orthophotos and digital elevation models (DEMs) were derived (Supplementary Video 8). Model alignment accuracy was set to \u0026ldquo;High\u0026rdquo;, depth map quality to \u0026ldquo;High\u0026rdquo;, with filtering mode to \u0026ldquo;Mild\u0026rdquo;. Each digital elevation model (DEM) was derived from the dense point cloud with interpolation enabled and each orthophoto was created using the \u0026ldquo;Mosaic\u0026rdquo; blending mode with the DEM as the surface and hole filling selected as \u0026ldquo;Yes\u0026rdquo;. We did not use Ground Control Points (GCPs) during our flights, resulting in altitude offsets (bowing) along the length of our models, which we rectified by using the relative \u0026ldquo;height above a ground plane\u0026rdquo; as the elevation models from which dune profiles were derived. We used the SOR (statistical outlier filter) noise filtering algorithm in \u003cem\u003eCloudCompare\u003c/em\u003e (v2.13.alpha) using 6 points for mean distance estimation and a standard deviation multiplier threshold of 1 to remove outlier points that could potentially skew reference layer derivations.\u003c/p\u003e \u003cp\u003eElevation models were developed using \u003cem\u003eCloud Compare\u003c/em\u003e by establishing a ground reference through minimum elevation sampling within each point cloud, across a 50 x 50 m area. This ground layer facilitated height calculations for all points, effectively standardising the minimum elevation to zero and ensuring z-axis alignment. For x and y axis alignment, we identified and matched key features between models from consecutive flights. The accuracy of alignment was verified through fixed feature comparison. Models were rasterized at 5 cm resolution for detailed analysis in QGIS, where we compared dune elevation profiles from identical locations across models.\u003c/p\u003e \u003cp\u003eOSL samples YZF1 and YZF3 were collected by hammering an opaque tube into the dune face and sealing the ends to ensure the quartz grains were not exposed to light. Each sample was divided In the laboratory under safe light conditions for equivalent dose (D\u003csub\u003ee\u003c/sub\u003e) and dosimetry (D\u003csub\u003er\u003c/sub\u003e) measurements. Quartz grains were extracted for OSL measurements following standard sample preparation techniques (see [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]). Organic matter and carbonates were removed using H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e and HCl, respectively, before the 180\u0026ndash;212 \u0026micro;m quartz fraction was isolated by dry sieving and density separation using a solution of sodium polytungstate at 2.70 g.cm\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e and 2.63 g.cm\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e. The quartz grains were etched in HF for 40 minutes to remove the alpha contribution to the dosimetry, along with any remaining feldspars, followed by an HCl wash to remove any acid-soluble fluorides.\u003c/p\u003e \u003cp\u003eThe equivalent dose for each sample was measured using Ris\u0026oslash; TL/OSL DA-15 and DA-20 readers, in which optical stimulation was provided by blue LEDs (478 nm) of ~\u0026thinsp;40 mW cm-2 constrained by a long-pass filter (GG-420). The OSL signal was detected with an EMI 9235QB photomultiplier tube via a single 7.5 mm Hoya U340 detection filter. The D\u003csub\u003ee\u003c/sub\u003e values were derived by calibrating the optical signal acquired naturally during burial to the regenerated optical signals obtained through controlled laboratory doses, using the single aliquot regenerative (SAR) protocol of Murray \u0026amp; Wintle [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. The suitability of the SAR protocol for determining D\u003csub\u003ee\u003c/sub\u003e was evaluated using a combined preheat and dose recovery test, and the recycling ratio. The temperature at which a known dose was recovered within unity was selected for further measurements. The preheat also assessed the consistency of D\u003csub\u003ee\u003c/sub\u003e as temperature varied [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. In addition, any aliquots where the recycling ratio was not within unity (between 0.9\u0026ndash;1.1) were rejected.\u003c/p\u003e \u003cp\u003eThe dosimetry (D\u003csub\u003er\u003c/sub\u003e) was determined by measuring the radionuclide abundances for U and Th by ICP-MS and K, using XRF. Adjustments were made to account for water content, and cosmic dose contributions were determined following the method outlined by Prescott \u0026amp; Hutton [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Dose rate conversion followed the approach by Gu\u0026eacute;rin et al. [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e], and the age was calculated by dividing D\u003csub\u003ee\u003c/sub\u003e by D\u003csub\u003er\u003c/sub\u003e, following procedures described in Aitken [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e] (see also [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]). All error terms provided are computed at one sigma level.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthors would like to thank Elli Berman, the owner of Strandkombuis, for videos of the September 2023 storm and permission to cross her property for sampling. Authors also acknowledge the South African National Parks (SANParks) for beach/dune sampling permit (Permit number: CRC/2021-2022/006--2018/V1) and Ursula von Lorne von Saint Ange from the Council for Scientific and Industrial Research (CSIR) for the wave data. Wave data are used with permission but remain the property of Transnet National Port Authority. Tide data are used with their permission (tide release permit: FOF/HYD/R/320/13/1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSW, DM and HC conceptualized the research; JM, EA, SX performed the remote sensing analyses; AM and SD performed the photogrammetry analyses, and ME performed the dating analyses. All authors reviewed the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData is provided within the manuscript or supplementary information files.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSupplementary Videos 1-7: Amateur video of the \u003cem\u003ecollision regime\u003c/em\u003e during the September 2023 storm.\u003c/p\u003e\n\u003cp\u003eSupplementary Video 8: 3-dimensional photogrammetry model of the eroded foredune barrier.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eReimann, L., Vafeidis, A.T. and Honsel, L.E., 2023. Population development as a driver of coastal risk: current trends and future pathways. 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Ancient Tl, 29(1), pp.5\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Coastal erosion, coastal vulnerability, sand supply, South Africa west coast","lastPublishedDoi":"10.21203/rs.3.rs-4647471/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4647471/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCurrent trends in coastal erosion combined with projected climate change impacts place half of the global sandy beaches at risk of extinction. Approximately 190\u0026nbsp;million people are likely to experience coastal environmental risks by 2100, and mitigation of the risk related to coastal erosion requires insight into the underlying causes. Here we use a multi-proxy approach to determine the cause of coastal erosion on the west coast of South Africa. Erosion trends were determined using satellite analysis for a storm event in June 2017, and foredune morphology changes were measured using unmanned aerial vehicle photogrammetry following a storm in September 2023. Significant erosion was driven by intense wave energy over multiple spring tide cycles, resulting in northward sand movement. Climate change is rejected as a cause of the erosion based on optically stimulated luminescence dates that show some of the eroded sediments to be approximately 8 900 years old. Instead, the cause is thought to be the interruption of long-term sand supply. Long-term sand dynamics are seldom considered in coastal erosion vulnerability assessments, and sediment age may be a novel factor in understanding this dynamic.\u003c/p\u003e","manuscriptTitle":"Sand budget failure underlies coastal erosion on the west coast of South Africa","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-22 14:04:14","doi":"10.21203/rs.3.rs-4647471/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b70c3501-1a7c-4748-849c-d74b72348574","owner":[],"postedDate":"July 22nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":34647363,"name":"Earth and environmental sciences/Environmental sciences"},{"id":34647364,"name":"Earth and environmental sciences/Natural hazards"},{"id":34647365,"name":"Earth and environmental sciences/Ocean sciences"}],"tags":[],"updatedAt":"2025-04-02T13:38:51+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-22 14:04:14","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4647471","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4647471","identity":"rs-4647471","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}