The research, conducted by ASM Alauddin Al Azad and Reza Marsooli of Stevens Institute of Technology and published in the journal Ocean Dynamics, tackles a persistent blind spot in coastal oceanography. Satellite radar altimeters measure offshore sea surface height and wave height, but not the nearshore water-level rise caused by breaking waves. Tide gauges, some with records stretching back centuries, are typically housed in sheltered harbors where wave influence is minimal. Field campaigns with buried pressure sensors can capture wave setup directly, but only for days to weeks before currents, sediment transport, and wave energy destroy the instruments or interrupt the data. As a result, most existing estimates of long-term wave setup have relied on empirical formulas that depend on beach slope, deep-water wave energy flux, and limited field measurements — assumptions that can introduce substantial error when applied across diverse coastlines.

To move beyond these constraints, the team turned to dynamical modeling using a fully coupled hydrodynamic-wave system. The hydrodynamic component, ADCIRC, solves the depth-averaged barotropic shallow water equations to simulate tides and storm surges, while the spectral wave model SWAN solves the depth-integrated wave-action balance, incorporating wind input, whitecapping, bottom friction, nonlinear wave-wave interactions, and depth-limited breaking. On a shared unstructured mesh, the two models exchange information at every time step: ADCIRC passes water levels and currents to SWAN, which uses them to account for wave-current interaction and wave transformation through refraction, shoaling, and dissipation. SWAN then computes wave radiation stresses — the momentum flux transferred from breaking waves to the water column — and feeds the gradients of those stresses back into ADCIRC’s momentum equations. This two-way coupling allows wave-induced forcing on coastal water levels to emerge explicitly from the physics rather than from a formula.

The computational demands were considerable. The model domain covers the western North Atlantic between 6°N and 46°N and 98°W to 53°W, discretized into a mesh of more than 1.7 million nodes and 3.4 million triangular elements, with coastal resolution of 500 meters to 1 kilometer in waters shallower than 300 meters. SWAN’s spectral domain contained 36 directional bins and 31 frequencies spanning 0.04 to 0.667 hertz. Both models were forced with hourly surface pressure and 10-meter wind fields from the ERA5 reanalysis, along with open-ocean boundary water levels and direction-frequency wave spectra that account for swells generated far outside the domain. The team’s earlier validation work showed that the ST6 source-term package for wave physics gave the best agreement with National Data Buoy Center observations along both coasts. A single 31-day coupled simulation required roughly 26 hours on two compute nodes of Purdue University’s Anvil system, each carrying 128 AMD EPYC cores.

The core analytical trick was elegant in its simplicity: the researchers ran two parallel sets of decade-long simulations from 2006 to 2015, one with the full coupled system and one with stand-alone ADCIRC that excluded wave effects. At every coastal site and time step, wave setup was computed as the difference in simulated water level between the two runs, isolating the wave contribution under identical tidal and meteorological conditions. Thirty-two representative sites were selected where nearshore bathymetry is gently sloping, ensuring that the surf zone is wide enough to be resolved by at least two mesh nodes and that radiation stress gradients decrease smoothly toward shore. Steep, heterogeneous regions such as the Gulf of Maine — with its bedrock-framed, glaciated shelf — were deliberately excluded, because accurately capturing wave setup there would require ultra-high-resolution models that are computationally prohibitive at regional scale.

The results paint a clear picture of asymmetry between the two coasts. Wave setup along the U.S. East Coast is consistently larger than along the Gulf of Mexico, reflecting the Atlantic’s exposure to open-ocean fetch, frequent intense storms, and long-period swells. Across the Northeast and Mid-Atlantic sites, mean wave setup ranged from 0.8 to 1.47 centimeters, with extremes — defined as the 99th percentile — between 5.0 and 8.2 centimeters. The single largest extreme value, 8.19 centimeters, occurred near Virginia Beach, Virginia, a region exposed to some of the most energetic wave events on the eastern seaboard. The largest mean value, 1.7 centimeters, appeared in South Carolina. By contrast, Gulf Coast sites showed mean setups of just 0.2 to 1.0 centimeter and extremes of 1.5 to 4.7 centimeters. Averaged across all sites, Gulf Coast mean wave setup was only 44 percent of the East Coast average, and extreme wave setup just 48 percent — a gap rooted in the Gulf’s semi-enclosed geography and limited fetch, where large waves are almost entirely the product of hurricanes and winter cold fronts known as nortes.

Seasonality emerged as a dominant signal. Winter months, defined as October through March, produced substantially higher mean and extreme wave setup than summer months at every region analyzed. Along the Northeast Atlantic coast, winter mean wave setup averaged 1.4 centimeters against a summer average of 0.9 centimeters, a difference the authors attribute to the frequent passage of slow-moving extratropical cyclones — nor’easters — that batter the coast with northeast winds for days at a time. Month-by-month analysis showed that Atlantic sites peak in November, when late-season tropical cyclones overlap with the onset of the winter storm season, while Gulf sites peak slightly later, in December, consistent with the dominance of winter frontal systems. July registered the lowest values everywhere, reflecting mid-summer quiescence. Interestingly, along the Southeast Atlantic coast the seasonal gap in extreme values narrows considerably, because powerful swells generated by distant Atlantic hurricanes propagate toward the coast even in summer and elevate water levels far from any local storm.

Year-to-year variability told a similar story of Atlantic dominance. The interannual variability of annual mean wave setup was about 57 percent larger along the East Coast than the Gulf, and that of extreme values about 28 percent larger. Hotspots of variability aligned with physical geography: central Florida sites fronted by narrow continental shelves showed the highest fluctuations, because narrow shelves allow waves to retain energy until breaking close to shore, so small changes in incident wave energy translate into comparable changes in setup. Conversely, the broad, shallow shelves off Georgia and South Carolina dissipate incoming swells and shelter the coast behind a concave shoreline, damping variability to the lowest values recorded. The authors link the Atlantic’s interannual swings to large-scale climate drivers — the El Niño–Southern Oscillation, which modulates both Atlantic hurricane activity and mid-latitude storm tracks, and the Pacific North American pattern, which covaries with winter wave power along the western North Atlantic boundary. Along the Gulf, variability is governed mainly by hurricane landfalls, winter fronts, and coastally trapped Kelvin waves.

The spatial statistics added a further layer of insight. Monthly wave-setup anomalies were strongly correlated between nearby sites on both coasts — mean Pearson correlations of 0.69 and 0.76 within 75 kilometers along the East and Gulf coasts, respectively — but coherence decayed far more slowly along the Atlantic. A fitted spherical variogram yielded a decorrelation range of 642 kilometers for the East Coast against just 292 kilometers for the Gulf, indicating that the Atlantic coastline responds coherently to basin-scale storm systems and swells over vast stretches, while Gulf Coast behavior transitions quickly to locally differentiated patterns shaped by variable shelf width, bathymetry, and coastal orientation.

Trend analysis over the decade revealed a mixed and geographically patchy picture. East Coast sites showed both positive and negative trends, often with adjacent sites displaying opposite signs and no consistent north–south gradient; the largest positive trend, +0.30 millimeters per year, occurred at Virginia Beach, while a site in New York recorded −0.35 millimeters per year. The Southeast Atlantic trended predominantly upward, averaging +0.1 millimeters per year. The Gulf Coast, by contrast, was dominated by negative trends, with the steepest decline of −0.324 millimeters per year in the Florida Panhandle. Nearly all trends were statistically significant at the 95 percent confidence level, though the authors caution that a ten-year window is short, and the detected patterns may partly reflect internal climate oscillations rather than persistent, climate-driven change. The patterns do, however, mirror observed multidecadal trends in significant wave height at nearby buoys.

The practical implications extend beyond academic climatology. Wave setup is a spatially variable addition to coastal water levels that current sea-level assessments largely ignore, and even modest wave-induced increases can push high tides above flooding thresholds, sharply raising the frequency of minor high-tide flooding. Previous research has shown that wave setup contributed up to 17 percent of peak storm tides from historical tropical cyclones along these very coasts, and up to half of the 100-year surge on narrow-shelf segments. By identifying hotspot segments and quantifying the natural variability against which future changes must be judged, this study provides a dynamic baseline that could improve flood forecasting, sharpen sea-level rise projections, and ultimately prevent the systematic underestimation of coastal water levels in one of the world’s most densely developed shoreline regions.

Storm surge is the abnormal rise in sea level caused primarily by a tropical cyclone’s winds pushing ocean water toward the coast, with atmospheric pressure and coastal geometry adding to the effect. As the surge moves across shallow continental shelves, through bays, and around headlands, it can intensify, spread, or persist long after the storm’s strongest winds have passed. These processes mean that two hurricanes with similar wind speeds can create completely different flooding timelines. One may produce a sharp, short-lived rise, while another may generate a slower buildup followed by prolonged inundation.

In a study published in Coastal Engineering, Virginia Tech researchers analyzed the evolution of storm surge rather than focusing only on its maximum height. Doctoral researcher Atefeh Alipour led the work with Jennifer Irish, professor of civil and environmental engineering; Robert Weiss, professor of geosciences; and David Muñoz, assistant professor of civil and environmental engineering. Their analysis used two decades of high-resolution hurricane simulations representing 62 named storms that affected the United States coastline between 2003 and 2022. In total, the researchers examined more than 1,000 individual storm surge events.

To identify recurring behavior, the team applied k-means clustering, a machine-learning technique that groups data according to shared characteristics. Instead of treating every surge curve as a unique event, the researchers compared how water levels changed from the beginning of a storm through the peak and subsequent recession. The method revealed eight characteristic patterns of surge evolution. Some events featured a rapid rise followed by a gradual decline, while others developed more slowly, remained near their maximum for an extended period, or receded in ways that prolonged exposure to flooding.

The distinction is important because coastal damage is not controlled by water depth alone. A rapidly rising surge can overwhelm warning systems and leave little time for evacuation, even if the eventual peak is moderate. A surge that remains elevated can produce sustained wave attack against dunes, barrier islands, seawalls, roads, and foundations. Prolonged flooding can also saturate soils, damage electrical and transportation networks, contaminate freshwater supplies, and prevent residents from returning safely. When the water finally retreats, a slow recession may continue to block evacuation routes and delay rescue, inspection, and recovery operations.

The researchers found that the variety of surge behavior differs across the United States. The Gulf Coast displayed the greatest diversity of patterns, a result linked to its shallow continental shelf, complex shoreline, broad bays, and frequent hurricane landfalls. Shallow water allows wind-driven water to accumulate over a large area, while inlets, wetlands, estuaries, and coastal embayments can reshape the timing and magnitude of the surge. The Atlantic Coast showed fewer overall patterns, but the distribution of those patterns varied substantially along the shoreline, indicating that neighboring communities may experience different flooding timelines during the same storm.

The analysis also showed why hurricane category is an incomplete guide to coastal flooding. The Saffir-Simpson Hurricane Wind Scale classifies storms by maximum sustained wind speed, but storm surge depends on a much wider set of interacting variables. Storm size determines how broadly wind stress is applied to the ocean. Forward speed affects how long water is pushed toward the shore and how quickly the storm’s forcing changes. The direction of travel influences which side of the circulation drives water onshore, while the wind field, central pressure, angle of approach, tides, and the underwater shape of the continental shelf all modify the result.

This complexity can make surge forecasting especially difficult near irregular coastlines. A storm’s winds may generate a regional response that is then amplified locally by bays, estuaries, channels, and low-lying land. In some locations, water can arrive before the eye or strongest winds, while in others the highest levels may occur after the storm has moved inland. The eight patterns identified in the study provide a way to describe these differences systematically. Rather than communicating only a single expected peak, forecasting systems could eventually provide information about the likely rise time, duration of dangerous water levels, and recession period.

The findings could influence the design of coastal infrastructure and emergency plans. Roads, bridges, power systems, drainage networks, and flood barriers may need to withstand not only a specified water depth but also the duration and timing of exposure. Emergency managers could use surge-evolution patterns to determine when evacuation routes are most likely to become unusable and when they may reopen. Engineers could incorporate different flooding timelines into reliability assessments, while coastal planners could identify communities that face unusually long periods of inundation even when their peak surge is not the highest in a region.

As sea levels rise, the same storm-driven surge will begin from a higher baseline, increasing the likelihood that moderate events cross damaging flood thresholds. Future changes in tropical cyclone intensity, size, rainfall, and movement could further complicate coastal risk, although the precise regional effects remain an active area of research. By shifting attention from a single maximum value to the full life cycle of storm surge, the Virginia Tech study offers a more detailed framework for understanding how hurricanes flood the coast. The researchers say that recognizing these repeatable patterns could strengthen prediction, improve decision-making, and help communities prepare not simply for how high the water will rise, but for how the flood will unfold.

Subject of Research: Storm surge evolution during tropical cyclones and its implications for coastal flooding, infrastructure, emergency response, and resilience.

Article Title: Characterization of tropical cyclone surge evolution

Web References: https://www.sciencedirect.com/science/article/pii/S0378383926001407?dgcid=coauthor ; https://cee.vt.edu/ ; https://geos.vt.edu/index.html

References: Alipour, A., Irish, J., Weiss, R., and Muñoz, D., “Characterization of tropical cyclone surge evolution,” Coastal Engineering, DOI: 10.1016/j.coastaleng.2026.105086

Keywords: Storm surge, hurricanes, tropical cyclones, coastal flooding, machine learning, k-means clustering, coastal engineering, emergency planning, climate change, coastal resilience

The primary aim of this research is to provide a comprehensive assessment of the risks posed by compound floods, which occur when two or more sources of flooding converge—such as storm surges and heavy rainfall. Unlike traditional flooding models that focus on singular threats, this innovative approach combines multiple variables to project amplified risk levels. The authors employ advanced data analytics techniques to ensure that the findings are not only robust but also highly relevant to policymakers and communities on the front lines of climate change.

Utilizing high-resolution geospatial data, specifically at 0.1-degree resolution, the study synthesizes information related to climate models, socio-economic factors, and geographical features. By marrying these different data sets, Zhang and Convertino created risk maps that encapsulate varying scenarios, helping to visualize potential flooding events within specific geographical contexts. The fine-grained nature of this analysis presents a stark contrast to previous studies that often provided broad estimates at larger scales, which can overlook critical localized vulnerabilities.

One of the study’s most significant contributions is its incorporation of real-time data regarding rising sea levels, which, as per the authors, is pivotal in accurately assessing compound flood risk. Sea levels have been rising due to the melting of polar ice caps and thermal expansion induced by global warming—a phenomenon that is well-documented in climate science. The researchers effectively illustrate how even minor changes in sea level can exponentially increase the risk of flooding when combined with storm surges, particularly in low-lying coastal regions.

Moreover, the research underscores the role of urbanization in amplifying flood risk. Coastal cities, known for their dense populations and critical infrastructure, are often ill-prepared for the compounded effects of climate change. Through their mapping, Zhang and Convertino demonstrate how urban development has increasingly encroached upon vulnerable shorelines without adequate flood defenses, creating a recipe for disaster as global temperatures rise.

A critical aspect of the study revolves around the practical applications of these findings. By providing detailed geographic risk assessments, local governments can make informed decisions regarding urban planning, infrastructure investments, and emergency preparedness strategies. Resilience planning becomes essential in light of these findings, which push for the integration of climate risk into local governance frameworks. The authors advocate for proactive measures to buffer against flooding, including the restoration of natural barriers such as wetlands and mangroves, which serve as effective buffers against storm surges.

However, while the research illuminates the challenges posed by compound floods, it also emphasizes the importance of equitable solutions. Vulnerable communities—often those with limited resources—face the brunt of climate impacts. The authors stress that any adaptive strategies or interventions must be inclusive, considering the needs of all community members to ensure fairness and resilience in a changing environment.

Another compelling element of the study is its visualization tools. The researchers employed cutting-edge data visualization techniques to make their findings accessible to non-specialists. By converting complex data into intuitive visual formats, they hope to enhance public understanding of flood risks. Such tools empower communities by fostering awareness and encouraging civic engagement in climate adaptation efforts.

The ramifications of this research extend beyond mere academic interest. As coastal populations continue to grow in the face of climate change, the implications of compound flooding become critical to food security, infrastructure stability, and overall public health. Failure to address these risks not only endangers coastal livelihoods but can also trigger wider socio-economic consequences, making this research of utmost importance to international audiences concerned about global stability.

Furthermore, it raises an essential point of discourse regarding global climate policy. The findings could serve as a catalyst for more rigorous international cooperation in addressing climate-related flooding risks. Countries must work together to establish standardized risk assessments and data-sharing agreements to effectively respond to the uneven threats posed by climate change across various regions.

Zhang and Convertino’s work ultimately challenges policymakers, urban planners, and community activists to rethink how coastal flood risks are perceived and managed. It spurs a call to action to implement forward-thinking solutions that can withstand the compounded threats posed by our changing climate. As we stand on the precipice of catastrophic climate events, the need for detailed, predictive models becomes increasingly evident—one that not only strives to identify risks but also seeks to promote resilience against them.

In conclusion, the sophisticated analysis presented in this study boldly outlines the pressing realities of coastal compound flood risks as climate change accelerates. The authors have provided vital insights that will empower communities and governments to take meaningful action, ultimately aiming to protect vulnerable populations from the devastating impacts of climate-driven flooding.

The road ahead is fraught with challenges, but armed with this knowledge, society stands a better chance of navigating the uncertain tides of climate adversity.

Subject of Research: Coastal Compound Flood Risk and its Global Mapping

Article Title: Global mapping of potential coastal compound flood risk at 0.1° resolution by Zhang, J., Convertino, M.

Article References:

Zhang, J., Convertino, M. Global mapping of potential coastal compound flood risk at 0.1^∘ resolution.
Commun Earth Environ (2026). https://doi.org/10.1038/s43247-025-03155-7

Image Credits: AI Generated

DOI: 10.1038/s43247-025-03155-7

Keywords: Coastal flooding, climate change, risk mapping, natural disasters, sea level rise, urbanization, resilience planning, socio-economic impacts.