The core of the challenge lies in a trade-off that has long frustrated coastal forecasters. Process-based models such as XBeach, an open-source model developed to simulate the hydrodynamic and morphodynamic response of beaches to storms, can capture the intricate physics of wave runup, overtopping and flooding with impressive fidelity. But a single high-resolution simulation of a storm striking a beach can take hours of computation on a cluster, far too slow for an operational early warning system that must issue alerts while the storm is still offshore. Simpler empirical formulas run instantly but gloss over the site-specific geometry of a beach, the very factor that often determines whether a seawall is overtopped or left untouched.

The researchers’ solution is to let the two approaches do what each does best. They ran XBeach thousands of times, generating a vast library of simulated storm scenarios spanning the range of offshore oceanic conditions that could plausibly strike the study area. Each simulation produced a detailed picture of the resulting hazards and impacts, from wave overtopping discharges to damage levels. These thousands of physically consistent scenario-impact pairs then became the training data for a Bayesian Network, a probabilistic graphical model that learns the statistical relationships between offshore forcing conditions and onshore impacts. Once trained, the Bayesian Network acts as a computationally cheap surrogate of the numerically intensive model, producing probabilistic estimates of storm-induced impacts in a fraction of the time a direct XBeach run would demand.

Bayesian Networks are particularly well suited to this role because they do not merely spit out a single deterministic answer. Instead, they quantify uncertainty explicitly, returning probability distributions over possible outcomes given the observed offshore conditions. For an early warning system, this matters enormously: emergency managers need to know not just the most likely impact but the chance that impacts will cross critical thresholds for pedestrians, vehicles and property. The approach also remains interpretable, a quality emphasized in the machine learning literature on Bayesian networks, which allows coastal scientists to trace which combinations of wave height, water level and beach state drive the most severe predicted responses.

The framework was tested at two contrasting beach profiles along an embayed beach in Santa Catarina, southern Brazil: one strongly urbanized and one semi-natural. This deliberate pairing allowed the team to assess explicitly how storm response varies over short distances along a single embayment, a spatial variability that coarser regional forecasts routinely miss. Embayed beaches, hemmed in by headlands, are notorious for focusing wave energy unevenly, so the same offshore storm can produce dramatically different overtopping at opposite ends of the bay. The extensive training dataset generated for the study provides new insight into this variability and helps identify the critical storm conditions that trigger extreme responses at each profile.

Perhaps the most innovative aspect of the work is how the team confronted the data scarcity that plagues coastal regions worldwide. Accurate XBeach simulations of wave overtopping require quantitative information on the underwater bathymetry, the shape of the seafloor, and on overtopping discharges for calibration, neither of which was readily available at the Brazilian study site. Rather than abandon the effort, the researchers reconstructed beach profiles using an empirical equilibrium profile formulation, drawing on the classic Dean equilibrium profile concept that relates the shape of a sandy beach to its sediment characteristics and wave climate. This allowed physically plausible cross-shore geometry to be generated where survey data were absent.

The second data gap, the lack of measured overtopping discharges, was bridged in an equally pragmatic way: the team used non-professional imagery, photographs and videos typically taken by members of the public during storm events, to estimate observed damage levels and infer the overtopping discharges associated with them. This strategy taps into a growing body of research showing that social media and crowdsourced imagery can meaningfully improve coastal flood forecasts and hazard alerts, turning an everyday byproduct of the smartphone era into a calibration resource for communities that cannot afford dedicated field campaigns.

The significance of the work extends well beyond one beach in Brazil. The United Nations has championed global early warning initiatives as a cornerstone of climate adaptation, recognizing that every dollar spent on early warning saves many more in avoided losses. Yet the regions that stand to benefit most, low-income and rapidly urbanizing coastlines, are precisely those with the least monitoring infrastructure. By demonstrating that a transferable, computationally efficient methodology can explicitly address data limitations, the study offers a template that coastal managers in data-limited settings can adapt without waiting for expensive bathymetric surveys or instrumented seawalls. The authors argue that coupling physically based modeling with data-driven probabilistic inference is what makes the framework both robust and practical for operational deployment.

Technically, the hybrid design also sheds light on the broader debate over surrogate modeling in coastal hazard prediction. Previous studies have benchmarked numerical models for wave overtopping, weighing accuracy against speed, and have explored neural networks and other machine learning tools for overtopping prediction at coastal structures. The Bayesian Network approach distinguishes itself by combining probabilistic output with modest training requirements and by being trained not on sparse field observations but on thousands of physically consistent numerical simulations. This means the surrogate inherits the physics of XBeach while shedding its computational burden, and it can be retrained as new bathymetry, imagery or storm observations become available, progressively sharpening its predictions over time.

For the residents and businesses lining vulnerable shores from Santa Catarina to the Basque coast, the practical promise is straightforward: earlier, more localized and more honest warnings that state not only what a storm is likely to do but how confident the forecast is. As sea levels rise and extreme meteo-oceanographic events intensify, the exposure of low coastal areas to storm surges and energetic waves will only grow. Frameworks like the one developed by Garzon, Husemann and Bonetti suggest that the barrier to protecting these communities is no longer the physics of flooding, which models already capture well, but the clever engineering of prediction systems that can run fast, learn from whatever data exist, and quantify what remains unknown. In that sense, the study marks a meaningful step toward making sophisticated coastal flood early warning systems a realistic option for the places that need them most.

Subject of Research: Hybrid numerical and Bayesian network modeling for rapid coastal storm flooding prediction and early warning in data-scarce coastal environments

Article Title: A hybrid approach for rapid coastal flooding impact prediction in data-scarce environments: an early warning system perspective

Article References: Garzon, J. L., Husemann, P., & Bonetti, J. (2026). A hybrid approach for rapid coastal flooding impact prediction in data-scarce environments: an early warning system perspective. Natural Hazards, 122(20), Article 650. https://doi.org/10.1007/s11069-026-08366-5

Image Credits: AI Generated

DOI: 10.1007/s11069-026-08366-5

Keywords: coastal flooding, early warning systems, XBeach, Bayesian networks, wave overtopping, storm surge, Santa Catarina, data-scarce environments, bathymetry reconstruction, probabilistic modeling, coastal hazard prediction, machine learning

Overwash occurs when the combined elevation of storm surge, wave runup and astronomical tide exceeds the crest of a barrier’s frontal dunes, sending pulses of water and sand surging over the top. The sand carried by these flows is deposited on the landward side as lobate features called washover fans, which can merge into terraces or spread out as sheetwash where dunes are absent. Geologists regard this landward transfer of sediment as the engine of barrier ‘rollover’, the process by which a retreating barrier climbs across its own back-barrier environment instead of simply eroding away. It is one of nature’s strangest survival strategies: the barrier keeps its shape only by abandoning its old footprint and rolling inland.

The research team, led by Edvan Casagrande dos Santos of the Federal University of Rio Grande do Sul, took advantage of the PlanetScope constellation of small imaging satellites to watch this process unfold almost in real time. Over eighty-four months, the team visually inspected 1,240 true-color images at spatial resolutions of three to five metres, tracking the appearance of fresh washover deposits by their shape and by the telltale tonal contrast between wet and dry sand. They subdivided the sand spit barrier into five sectors based on dune heights and the recurrence of overwash, then cross-checked each candidate event against tide gauge measurements from Balneário Rincón, synoptic weather charts from the Brazilian Navy, and hourly ocean and atmosphere data from the ERA5 reanalysis.

The screening protocol was deliberately conservative. Any observed tide of 1.2 metres or more above local mean sea level flagged a potential event, yielding 129 candidates. Each one was then validated against the storm impact scale developed by Sallenger and later refined by Stockdon and colleagues, which compares the maximum water level to the elevation of the dune base and crest. Only when the hydrodynamic threshold, the modeled overwash regime and a visually confirmed washover fan all converged within a window of a few days was the event counted. The result: ninety-three confirmed overwashes, concentrated overwhelmingly between April and July, the southern hemisphere’s autumn and winter.

The meteorological fingerprint of these events is remarkably consistent. Virtually every overwash coincided with the passage of low-pressure systems, typically extratropical cyclones spawned near Patagonia, the Río de la Plata or the coasts of Uruguay and Rio Grande do Sul, often trailing cold fronts across southern Brazil. Winds swung from the northeast ahead of the front to the south and southwest behind it, piling coastal water against the shore through wind shear and Ekman transport. Storm surge emerged as the dominant driver of extreme water levels, contributing an average of forty-one percent of the observed tide during events, and in the record April 2020 storm a staggering seventy-seven percent of the elevation above mean sea level. On that day, a surge of 1.314 metres combined with six-metre significant waves and maximum individual waves of 11.2 metres to drown the barrier entirely.

Not every stretch of the spit suffered equally, and the differences map directly onto dune geometry. Sector 2, protected by foredunes averaging more than four metres high and reaching six to seven metres at their peaks, remained essentially untouched throughout the study, with washover fans appearing only once, in 2023, and even then only after human activity had disturbed adjacent Sector 1. Sector 3, where segmented dunes average just 2.2 metres, responded to nearly every moderate storm, hosting continuous washover fans, terraces and narrow overwash channels since 2017. Sectors 4 and 5, near the migrating river mouth, experienced repeated complete overtopping and inundation. The message is blunt: dune height and continuity are the barrier’s first line of defense, and even modest waves can breach it where the dunes are low or fragmented.

Human engineering turns out to be a co-conspirator in the barrier’s retreat. Since the late nineteenth century, the Araranguá River mouth has been artificially opened at least five times, and machinery was dispatched again in May 2022 and October 2023 to cut drainage channels through the spit during river floods. Each artificial breach lowers and segments the foredune field, creating depressions that invite subsequent overwash. The study draws parallels with the Ria Formosa barrier system in Portugal, where human interventions accounted for roughly twelve percent of washover occurrences, and with Masonboro Island in North Carolina, where hurricane-eroded dunes left the barrier prone to years of follow-on overwash. In Araranguá, the engineered inlets transformed Sector 1 into a persistent vulnerability that later amplified the ocean’s reach into the river channel.

Perhaps the most consequential finding lies in the eighty-three-year historical record. By orthorectifying aerial photographs from 1938, 1957 and 1978 using Structure-from-Motion photogrammetry, and combining them with later orthophotos and satellite scenes, the team computed four morphometric indicators along transects spaced every hundred metres: shoreline position, the outer and inner margins of the river channel, and channel width. Three evolutionary phases emerged. From 1938 to 1978 the system prograded seaward by an average of 207 metres, a textbook regressive barrier fed by ample sediment supply. From 1978 to 2005 a milder transgression set in, with the shoreline retreating an average of 24 metres between key transects. Then, after 2005, the retreat accelerated sharply, averaging 65 metres in just sixteen years, with erosion extending deep into the barrier interior, including a 94-metre landward migration of the inner channel margin.

The synchrony is hard to ignore: the sectors retreating fastest on the decadal scale are precisely those where washover fans recur most often on the monthly scale. The authors conclude that the Araranguá barrier has entered a rollover regime driven by the sheer frequency of overwash, reversing the regressive behavior that dominated the Holocene evolution of the broader coastal system. They also note a hydrodynamic compensation mechanism: no single parameter triggers overwash on its own, but when a surge nears a threshold, even modest increases in wave height or period push the total water level over the dune crest. This explains why overwash can occur with significant wave heights as low as two metres in the low sectors, while tall dunes shrug off far larger storms elsewhere along the spit.

The implications stretch well beyond one Brazilian estuary. Coastal barriers line up to fifteen percent of the world’s shorelines and ten percent of open-ocean coasts, sheltering lagoons, wetlands and communities from the sea. As sea level rises and storm climates intensify, the balance between sediment supply and overwash frequency will determine which barriers roll landward and survive, and which narrow into breaching and collapse. Washover deposits in Araranguá proved ephemeral, buried by wind-blown sand within days and reworked by the river, a caution for anyone hunting these features in the geological record. But the real-time satellite record offers something the rock archive cannot: a front-row seat to a barrier deciding, one storm at a time, to move inland.

Subject of Research: Overwash processes and the interdecadal evolution of the Araranguá sand spit barrier in southern Santa Catarina, Brazil

Article Title: Overwash processes: spatio-temporal scales and dynamics of the coastal system in southern Santa Catarina, Brazil

Article References: dos Santos, E. C., Medina, V. V., da Costa Cristiano, S., & da Camara Rosa, M. L. C. (2026). Overwash processes: spatio-temporal scales and dynamics of the coastal system in southern Santa Catarina, Brazil. Natural Hazards, 122(19), Article 630. https://doi.org/10.1007/s11069-026-08399-w

Image Credits: AI Generated

DOI: 10.1007/s11069-026-08399-w

Keywords: overwash, washover fans, coastal barrier, sand spit, storm surge, extratropical cyclones, shoreline retreat, barrier rollover, remote sensing, Santa Catarina, Brazil, coastal geomorphology