The technical approach behind the study is as important as its findings. The researchers combined satellite altimetry-based sea level anomalies from the Copernicus Marine Environment Monitoring Service with atmospheric and wave parameters from the ECMWF’s ERA-Interim reanalysis, storm surge outputs from the MOG2D-G barotropic model, and hourly astronomical tides from the FES30 global tide model. Wave run-up was modelled as a function of deep-water significant wave height, wavelength, and the subaerial coastal slope. Because these datasets arrived at different temporal resolutions, ranging from daily sea level fields to six-hourly surge and hourly tides, the team interpolated everything to a common hourly time step. That harmonisation matters: extreme coastal water levels are born from compound interactions that evolve on hourly scales, and a daily average would wash out precisely the peaks that flood homes and erode shorelines.

Once reconstructed, the ECWL record revealed a clear hierarchy of drivers. Tidal forces and wave run-up dominate the regional differences in extreme water levels, directly shaping coastal erosion and flooding, with Guinea-Bissau standing out as a hotspot because its wide, low-lying coastal plains amplify every centimetre of elevated water. Sea level anomalies, though small in mean terms at roughly 0.03 metres across countries, act as a slowly shifting baseline that reflects the cumulative state of the ocean and climate system, exacerbating the impacts of tides and waves when they coincide. The dynamic atmospheric contribution, essentially the storm surge signal driven by surface winds and pressure, remained comparatively minor, averaging around 0.02 metres across most of the coast.

The trend analysis added a sobering dimension. Sea level anomalies rose at an average of 3.08 millimetres per year along the coast, with local maxima reaching 4.23 millimetres per year, while total ECWL climbed at an average of 2.65 millimetres per year. Wave run-up, by contrast, declined slightly on average, at roughly 0.47 millimetres per year, though with substantial local variability. The dynamic atmospheric contribution was essentially flat. In other words, the steady rise in West Africa’s flood baseline is being driven primarily by the ocean itself, not by changing waves or winds, and that baseline keeps lifting the floor beneath every high tide and every swell event.

The spatial contrasts are striking. Mauritania and Senegal both show mean ECWL values above 2 metres with a consistent 98th percentile of 2.9 metres, indicating a stable but high extreme threshold. Guinea-Bissau records the highest 98th percentile in the region, far above its mean of 2.42 metres, signalling occasional very high extremes. Ghana presents perhaps the most dramatic gap: a mean of just 1.59 metres against a 98th percentile of 4.01 metres, meaning that when extremes arrive there, they arrive as sharp, exceptional spikes. Liberia shows the lowest exposure overall, while Nigeria sits in the middle with a mean near 1.57 metres and a 98th percentile of 2.6 metres.

Duration matters as much as height, and here the study quantified how long water stays dangerously high. Defining an extreme ECWL day as one exceeding the 98th percentile of a country’s distribution, the team found that Guinea-Bissau and Guinea logged the most extreme days, at 23 and 22 respectively in a single year, while Sierra Leone, Liberia, Togo, Benin, and Cameroon each recorded 15 to 17 days. Senegal, The Gambia, Ivory Coast, Ghana, and Nigeria hovered around 13 to 14 days. For communities in the Guinea-Bissau and Guinea cluster, flooding is not a brief shock but a prolonged condition, demanding continuous monitoring and rapid-response mechanisms rather than one-off defences.

Seasonality emerged as a second organising principle. Countries along the northwestern coast, including Mauritania, Senegal, The Gambia, Guinea-Bissau, and Guinea, see sea level anomalies peak around November at 0.10 to 0.14 metres, with tides peaking in September, while the lowest values arrive in May and June. Further south and east, the pattern shifts: Sierra Leone, Liberia, Ivory Coast, and Ghana peak earlier, with wave run-up reaching up to 1.33 metres in Ivory Coast in August, and Ghana recording run-up values as high as 1.44 metres. These rhythms track the migration of the Inter-Tropical Convergence Zone and the seasonal swell climate, in which long-period northwest swells and southwest trade-wind waves between 1 and 3 metres shape the coast from June to October.

To translate these patterns into policy, the researchers applied a k-means clustering algorithm to annual tide and wave run-up data, sorting the region’s countries into three hydrodynamic families. Cluster 1, Guinea-Bissau and Guinea, combines high tides of 0.88 and 0.79 metres with lower run-up, pointing to tidal barriers as the most logical investment. Cluster 2, spanning Liberia, Ivory Coast, Ghana, Togo, and Benin, features low tides between 0.26 and 0.34 metres but the region’s highest run-up values, from 1.05 to 1.25 metres, arguing for shoreline defences engineered against wave attack. Cluster 3, comprising Mauritania, Senegal, The Gambia, Sierra Leone, Nigeria, and Cameroon, shows a balanced interplay of moderate tides and run-up, calling for hybrid strategies. The clustering framework allows scarce adaptation resources to be matched to the actual physics of each coastline rather than applied uniformly.

Perhaps the most consequential finding concerns climate teleconnections. When the team correlated ECWL anomalies with major climate indices, the Atlantic Niño emerged as the clear regional heavyweight, with a moderate positive correlation of 0.5 and spatially coherent influence along the central and eastern coastline. The mechanism is physically intuitive: warm sea surface temperature anomalies in the tropical Atlantic alter the land-sea thermal gradient, strengthening the West African Monsoon and shifting the Intertropical Convergence Zone northward, which boosts rainfall, river discharge, and ultimately coastal water levels. By contrast, ENSO showed only a weak correlation of 0.15, the North Atlantic Oscillation a weak negative one of −0.17, and the Southern Annular Mode a negligible −0.04. Spatial correlation maps confirmed the pattern, with the Atlantic Niño reaching correlations of 0.49 in its warm phase and −0.47 in its cool phase, while ENSO, SAM, and NAO produced inconsistent, patchy signals across the coast.

The authors are careful to note that their zero-lag Pearson correlations indicate relative climatic influence rather than predictive or causal relationships, and that non-linear or lagged effects remain unexplored. Even so, the practical message is hard to ignore: flood prediction systems for West Africa should be tuned to the Atlantic Niño, a regional index, rather than leaning on global modes like ENSO that dominate headlines elsewhere. With urbanisation rates in countries such as Ghana, Nigeria, and Côte d’Ivoire projected to approach 70 percent by 2050, and sea level rise steadily raising the baseline beneath every tide, the study offers something the region has lacked, a physics-based, country-by-country blueprint for where the water comes from, when it rises, and what kind of defence actually fits each shore.

Subject of Research: Drivers of extreme coastal water levels and coastal flooding in West Africa

Article Title: Understanding the drivers of Extreme Coastal Water Levels in West Africa from oceanic and climatic processes

Article References: Brempong, E. K., Almar, R., Angnuureng, D. B., Abessolo, G. O., Dada, O. A., & Cissé, C. O. T. (2026). Understanding the drivers of Extreme Coastal Water Levels in West Africa from oceanic and climatic processes. Discover Oceans, 3(1), Article 23. https://doi.org/10.1007/s44289-026-00136-2

Image Credits: AI Generated

DOI: 10.1007/s44289-026-00136-2

Keywords: West Africa, extreme coastal water levels, Atlantic Niño, sea level rise, coastal flooding, wave run-up, tides, storm surge, ENSO, Guinea-Bissau, coastal erosion, flood management

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