The index was built using the Coastal Vulnerability Model within the open-source InVEST software suite, developed by the Natural Capital Project at Stanford. Seven variables feed into the calculation: shoreline type, topographic relief, the presence of protective coastal habitats, wind exposure, wave energy, storm surge and the rate of sea-level rise. Each variable is ranked on a one-to-five scale, and the final exposure value is computed as the geometric mean of the seven ranks. The researchers chose this multiplicative approach deliberately, because an additive index could allow a high score in one variable to mask low scores elsewhere, potentially hiding genuine danger. The geometric mean ensures that a coastline must score poorly across multiple dimensions before it can be labelled highly exposed.

The underlying data are among the most detailed ever assembled for the Irish coast. Wind and wave conditions were drawn from thirty years of records spanning 1995 to 2024, using the Copernicus CERRA reanalysis at 5.5-kilometre resolution and the NWSHELF wave physics reanalysis at 1.9 kilometres — both considerably finer than the global datasets often used in such studies. Storm surge and sea-level rise rates came from CMIP6-based Copernicus climate projections, while relief was calculated from a 10-metre national digital terrain model derived from LiDAR and camera sensors. Notably, the team found that the default storm surge proxy built into the InVEST model, which estimates surge from the distance to the edge of the continental shelf, correlated negatively with properly modelled surge levels around Ireland. They replaced it with hydrodynamically modelled surge data, a methodological correction that materially changed the results.

The headline finding is that exposure is not a simple story of a wild, storm-battered west versus a sheltered east. The west coast does record the highest absolute exposure values and the largest total extent of Very High Exposure coastline — Mayo alone contains 414.5 kilometres of it, with Galway County and Donegal contributing another 36 percent of the national total between them. But when exposure is measured as a proportion of each authority’s own shoreline, the picture inverts dramatically. Leitrim, with just 5.5 kilometres of coast, sees 68 percent of it classified as Very High Exposure. Meath records 57 percent of its 11 kilometres in the High Exposure band. Eastern local authorities including Dublin City, Dún Laoghaire–Rathdown, Fingal, Louth, Wexford and Wicklow hold some of the highest proportional exposure in the country, a consequence of soft, erodible Quaternary sediments and low-lying terrain rather than Atlantic storm tracks.

One of the study’s most consequential results concerns the protective role of coastal habitats. The researchers mapped four habitat types — dunes, machair, saltmarsh and seagrass — and found that 38 percent of Ireland’s coastline, some 3,299 kilometres, sits within the potential protective distance of at least one of them. Saltmarshes exert the widest influence, with over 5,000 kilometres of coast lying within 500 metres of a saltmarsh of some kind. To quantify what these ecosystems actually do, the team ran a counterfactual no-habitat scenario in which every coastal point was assigned the worst possible habitat rank. The difference was stark: in Dublin City the share of Very High Exposure coastline jumped by 15 percentage points to 29.5 percent, in Meath it rose to 89 percent, and in Galway City to 64 percent. In Fingal, the removal of habitats pushed more than 7,000 additional residents into the highest exposure zone.

The population analysis relied on a dasymetric mapping technique rather than coarse census blocks, distributing 2022 census data according to the locations of individual residential buildings at 100-metre resolution. Voronoi polygons were then used to attribute populations to their nearest coastal exposure category at buffer distances of 125, 250 and 500 metres. The results show that roughly 308,000 people live within 500 metres of the Irish coastline, and that in Dublin City and Galway City almost 10,000 residents each live within 500 metres of a Very High Exposure shoreline. In Galway City, that figure represents about 11.4 percent of the entire urban population. The vulnerability of the city was underlined in January 2025, when Storm Éowyn drove a 2.6-metre surge into Galway Bay that narrowly missed causing widespread flooding only because it coincided with a mid-tide.

Cultural heritage emerges as a second, quieter casualty. Of the 8,871 registered national monuments within 500 metres of the coast, 1,797 — one in five — fall within Very High Exposure zones, concentrated along the west and northwest. Sligo alone accounts for 383 of these sites, including the uninhabited island of Inishmurray, which holds a medieval monastic settlement and 132 registered monuments, from inscribed cross stones to leachts, the stone sepulchral monuments of early Christian Ireland. In the no-habitat scenario, the number of exposed heritage sites rises to 2,118, with the steepest proportional increases in Wexford and Fingal. Middens — ancient refuse heaps that preserve evidence of millennia of coastal life — are the most common monument type in the danger zones, a reminder that the eroding edge of Ireland is also its longest-running archaeological archive.

The team did not simply publish the index; they stress-tested it. A leave-one-out sensitivity analysis showed that removing shoreline type produced the largest shift in results, confirming that substrate erodibility is the dominant control on exposure, particularly along the east coast. Removing habitats produced the smallest overall change, which the authors attribute to the heavily modified nature of much of the Irish shoreline. They also compared four statistical methods for ranking continuous variables — quintiles, equal intervals, Jenks natural breaks and geometric intervals — and found that the choice matters enormously. Equal-interval classification collapsed the national Very High Exposure category to just 97 points, none of them on the east coast, effectively erasing the risk profile of counties that are known to be eroding fastest. The quintile approach, the model’s default, was vindicated for skewed variables such as wind, waves and relief, though the authors caution it may be less suitable for spatially uniform quantities like sea-level rise.

The findings arrive at a moment when Irish coastal policy is in flux. The Office of Public Works has completed national flood mapping and erosion projections, but no prior assessment had combined hazard characterisation with the exposure of people, ecosystems and heritage in a single framework. The new index aligns closely with independent evidence: 81 percent of points within OPW-identified wave overtopping zones rank as High or Very High Exposure, and 83 percent of segments experiencing high erosion rates in the Geological Survey Ireland shoreline change study fall into the same categories. The study also documents the cost of habitat degradation. At Rossbeigh beach in Kerry, vegetated dune cover fell by 43.2 percent between 2001 and 2019, while the Maharees Peninsula — 92 percent of which the index classifies as High or Very High Exposure — has recorded average beach retreat of 0.63 metres per year over more than a century.

The authors are careful about what the index can and cannot do. It measures relative exposure, not vulnerability; it does not account for existing engineered defences, sediment dynamics, river discharge or socioeconomic adaptive capacity, and national habitat condition assessments rate many Irish dune and saltmarsh habitats as unfavourable, meaning the baseline model may overstate the protection currently on offer. Yet as a screening tool it offers something Ireland has never had: a consistent, evidence-based map of where nature already shields the coast, where it could be restored to do so, and where hard choices about holding the line, realigning or retreating will soon be unavoidable. With global mean sea levels projected to rise by as much as 1.1 metres above 1900 levels by 2100 under high-emission scenarios, and an estimated 1.1 billion euros of Irish coastal assets at stake from a single metre of rise, that map may prove one of the most consequential documents in the country’s climate adaptation arsenal.

Subject of Research: National-scale assessment of coastal hazard exposure and the protective role of coastal habitats in Ireland

Article Title: A coastal exposure index for Ireland: relative hazard exposure and the protective role of coastal habitats

Article References: Walsh, K., de Oliveira, L. M. C., Keenahan, J., & Lim, A. (2026). A coastal exposure index for Ireland: relative hazard exposure and the protective role of coastal habitats. Natural Hazards, 122(18), Article 627. https://doi.org/10.1007/s11069-026-08361-w

Image Credits: AI Generated

DOI: 10.1007/s11069-026-08361-w

Keywords: coastal exposure, Ireland, sea-level rise, coastal habitats, nature-based solutions, InVEST, storm surge, saltmarsh, sand dunes, cultural heritage, coastal erosion, climate adaptation

The research, led by L. Carpi of the University of Genova together with N. Oneto and M. Ferrari, tackles a deceptively simple question: which parts of a coastal town will waves actually reach during the worst storms? Answering it required reconstructing four decades of wave history. The team drew on a 43-year hindcast database maintained by the Department of Civil, Chemical and Environmental Engineering at Genoa, which contains numerical simulations of offshore wave conditions produced with the WaveWatchIII spectral wave model and the Weather Research and Forecasting atmospheric model. From the period between January 1979 and December 2022, the researchers selected the twenty largest storms arriving from the southeast and the twenty largest from the southwest, the two dominant and most destructive wave directions in the Ligurian Sea.

Simulating what those forty storms did when they reached the coast demanded an equally detailed picture of the seafloor and the land. Bathymetric data out to depths of twenty metres were collected with a multibeam echosounder offering centimetre-level resolution, while the beach and the seafront urban fabric were mapped with a LiDAR system mounted on an unmanned aerial vehicle. Offshore bathymetry came from regional government datasets. All of these sources were merged and interpolated into a high-resolution digital elevation model that captured not only the natural morphology but also the buildings, seawalls and narrow alleys that shape how floodwater moves through the town.

The heart of the modelling chain is XBeach, an open-source model that solves the time-dependent short wave action balance, roller energy equations and nonlinear shallow water equations, allowing it to reproduce wave groups, run-up and morphological change during storms. The researchers ran XBeach in surfbeat mode on a computational grid covering six square kilometres, with cell sizes shrinking to 2.5 metres in the coastal strip, fine enough to resolve the small streets and gaps between buildings through which water can surge. For storms from the southwest, which strike Camogli directly, XBeach alone proved sufficient. For storms from the southeast, however, the Portofino Promontory partially shelters the coast, and waves had to be translated to the offshore boundary of the model domain using D-Waves, the SWAN-based wave module of the Delft3D suite, applied over a grid of 115 square kilometres encompassing the promontory.

Validating the model posed a familiar problem: no field measurements exist for the historical storms of interest. The team therefore compared simulated significant wave heights and directions with measurements from an oceanographic buoy located inside the model domain during two events in May and August 2025. The agreement was strong. For the August event, the model achieved a coefficient of determination of 0.96, a correlation coefficient of 0.98 and a scatter index of just 0.16 for wave height, while wave direction errors, expressed as circular root-mean-square error, ranged from 12.0 to 19.6 degrees across the two events, figures comparable to those reported in other coastal wave-modelling applications. A second, more dramatic validation came from simulating the storm of 3 to 5 November 2023, which was not part of the exposure dataset. That southwest storm destroyed a beach restaurant built on stilts, and the model’s simulated maximum run-up of 2.6 metres reached precisely the location of the collapsed structure.

With the model chain validated, the researchers computed two run-up indicators for every storm along six coastal transects: the run-up exceeded by two percent of the waves, and the maximum run-up. The contrast between wave directions was stark. For the twenty most intense southeast storms, run-up values almost never exceeded 1.8 metres and typically stayed below 1.3 metres, never crossing the two-metre threshold that marks the elevation above which tourism infrastructure begins to be threatened. Camogli is, in effect, only marginally exposed to storms from that quadrant, a consequence of the sheltering geometry of the Portofino Promontory.

Southwest storms told a very different story. Maximum run-up exceeded two metres in nearly every simulated event, and in five storms the two-percent run-up reached between 2.4 and 2.6 metres. The outlier was the storm of 29 October 2018, widely known as the Vaia Storm, which produced run-up values between 3.89 and 5.28 metres for the two-percent parameter and between 4.94 and 5.72 metres for maximum run-up. It was the only event in the entire 43-year record that pushed waves above the three-metre threshold, corresponding roughly to the maximum beach elevation before the urban settlement begins, where flooding of the town itself becomes a concrete possibility. That finding is consistent with the documented damage from the event and underscores a central message of the study: only long-term wave datasets can capture the rare but catastrophic storms that dominate coastal risk.

The spatial pattern of exposure revealed an uncomfortable structural problem. In earlier work on the nearby bay of Bonassola, a medium exposure class had acted as a buffer zone between low and high exposure, allowing authorities to issue graduated warnings and phase in mitigation measures. Along more than half of the Camogli coastline, that buffer is absent. Exposure jumps directly from low to high, because the beach is only ten to fifteen metres wide, has the steep profile typical of gravel and pebble shores, and is backed immediately by seawalls and building walls that leave no room for a wider dissipative beach. Small variations in wave run-up therefore produce abrupt transitions in exposure level, a dynamic that complicates emergency management and increases the likelihood of frequent high-level alerts.

That abruptness carries a human cost that the authors address head-on. Warning systems that cry wolf too often breed alert fatigue, eroding public trust and responsiveness, while overly precautionary closures of commercial activities impose economic costs and damage institutional credibility. The study found that choosing the more conservative maximum run-up indicator over the two-percent parameter would have generated only one additional flood warning across the entire historical record, limiting the fatigue risk in this particular setting, though the authors stress that the choice of indicator must be evaluated case by case. Because risk perception is strongly shaped by direct personal experience, and because the missing buffer zone is rooted in geomorphology and urban form rather than in any fixable technical shortcoming, the researchers argue that targeted risk communication and community preparedness must complement the modelling work.

The implications stretch well beyond one Ligurian town. As sea levels rise, coastlines like Camogli, with narrow beaches, rocky margins, intense urbanisation up to the waterline and no capacity to migrate landward, are likely to face growing storm exposure, making accurate, site-specific hazard assessment increasingly urgent. The study also flags its own limits: reliable results depend on accurate, up-to-date topographic and bathymetric data, which human development can rapidly invalidate, and on long-term offshore wave datasets, which remain scarce where buoy networks are sparse. Hindcast databases fill that gap, but uncertainties in them can propagate through the modelling chain. Even so, the message is clear and broadly applicable: in crowded coastal towns where a single metre of elevation separates safety from flooding, high-resolution modelling of decades of historical storms is not a luxury but a prerequisite for evidence-based warning thresholds, sensible mitigation planning and, ultimately, the protection of lives and livelihoods.

Subject of Research: Storm-induced coastal exposure modelling using hindcast wave data in a densely urbanised coastal area

Article Title: Modelling storm-induced coastal exposure using hindcast wave data in a densely urbanised coastal area

Article References: Carpi, L., Oneto, N., & Ferrari, M. (2026). Modelling storm-induced coastal exposure using hindcast wave data in a densely urbanised coastal area. Natural Hazards, 122(20), Article 649. https://doi.org/10.1007/s11069-026-08420-2

Image Credits: AI Generated

DOI: 10.1007/s11069-026-08420-2

Keywords: coastal exposure, wave run-up, storm hindcast, XBeach, coastal flooding, Ligurian Riviera, wave modelling, risk assessment, alert fatigue, sea-level rise, digital elevation model, Natural Hazards