Search Results (151)

This study examines urban expansion, coastal erosion, and extreme wave events in Licantén, Maule Region, following the 2010 earthquake and tsunami. Using multi-source data—Landsat and Sentinel-2 imagery, ERA5 reanalysis, high-resolution Maxar images, UAV surveys, and the CoastSat algorithm—we detected significant urban growth in tsunami-prone areas: Iloca (36.88%), La Pesca (33.34%), and Pichibudi (20.78%). A 39-year shoreline reconstruction (1985–2024) revealed notable changes in erosion rates and shoreline dynamics using DSAS v6.0, influenced by tides, storm surges, and wave action modeled in R to quantify storm surge events over time. Results underscore the lack of urban planning in hazard-exposed areas and the urgent need for resilient coastal management under climate change. Full article

As climate change intensifies the frequency and magnitude of extreme weather events, such as storm surges, understanding how extreme weather events alter mangrove carbon dynamics is critical for predicting the resilience of blue carbon ecosystems under climate change. Mangrove forests are generally recognized for their resilience to natural disturbances, a characteristic largely attributed to the evolutionary development of species-specific functional traits. However, limited research has explored the impacts of storm surges on carbon flux dynamics in both natural and restored mangrove ecosystems. In this study, we analyzed short-term responses of storm surges on carbon dioxide flux and methane flux in natural and restored mangroves. The results revealed that following the storm surge, CO₂ uptake decreased by 51% in natural mangrove forests and increased by 20% in restored mangroves, while CH₄ emissions increased by 14% in natural mangroves and decreased by 22% in restored mangroves. GPP is mainly driven by PPFD and negatively affected by VPD and RH, while Reco and CH₄ flux respond to a combination of temperature, humidity, and hydrological factors. NEE is primarily controlled by GPP and Reco, with environmental variables acting indirectly. These findings highlight the complex, site-specific pathways through which extreme events regulate carbon fluxes, underscoring the importance of incorporating ecological feedbacks into coastal carbon assessments under climate change. Full article

Sea level rise (SLR) and increased urbanisation of coastal areas have exacerbated coastal flood threats, making them even more severe in important cultural sites. In this context, the role of hard coastal defences such as promenades and embankments needs to be carefully assessed. Here, a thorough investigation is conducted in Grado, one of the most significant coastal and historical towns in the Friuli Venezia Giulia region of Italy. Grado is located on a barrier island of the homonymous lagoon, the northernmost of the Adriatic Sea, and is prone to flooding from both the sea and the back lagoon. The mean and maximum sea levels from the historical dataset of Venice (1950–2023) were analysed using the Gumbel-type distribution, allowing for the identification of annual extremes based on their respective return periods (RPs). Grado and Trieste sea level datasets (1991–2023) were used to calibrate the statistics of the extremes and to calculate the local component (subsidence) of relative SLR. The research examined the occurrence of annual exceedance of the minimum threshold water level of 110 cm, indicating Grado’s initial notable marine ingression. The study includes a detailed analysis of flood impacts on the urban fabric, categorised into sectors based on the promenade elevation on the lagoon side, the most vulnerable to flooding. Inundated areas were obtained using a high-resolution digital terrain model through a GIS-based technique, assessing both the magnitude and exposure of the urban environment to flood risk due to storm surges, also considering relative SLR projections for 2050 and 2100. Currently, approximately 42% of Grado’s inhabited area is inundated with a sea level threshold value of 151 cm, which occurs during surge episodes with a 30-year RP. By 2100, with an optimistic forecast (SSP1-2.6) of local SLR of around +53 cm, the same threshold will be met with a surge of ca. 100 cm, which occurs once a year. Thus, extreme levels linked with more catastrophic events with current secular RPs will be achieved with a multi-year frequency, inundating more than 60% of the urbanized area. Grado, like Venice, exemplifies trends that may impact other coastal regions and historically significant towns of national importance. As a result, the generated simulations, as well as detailed analyses of urban sectors where coastal flooding may occur, are critical for medium- to long-term urban planning aimed at adopting proper adaptation measures. Full article

Coastal cities are increasingly exposed to the risks posed by climate change, including rising sea levels, intensified storms, and coastal erosion. In this context, risk communication plays a crucial role, as it can shape public perception, promote preparedness, and influence both emergency responses and long-term mitigation strategies. This study investigated how disaster-related risks are framed in the media, focusing on the case of Naples, Italy, following a severe coastal storm surge that struck the city’s waterfront on December 2020. Using Dynamic Latent Dirichlet Allocation (DLDA), the research analyzed 297 newspaper articles published between 2020 and 2024 to examine the evolution of media narratives over time. The findings reveal four dominant patterns: (1) a prevailing economic discourse centered on financial damages and compensations, with limited references to resilience planning; (2) a temporal framing that presents the storm as a sudden, exceptional event, disconnected from historical precedents or future climate projections; (3) a lack of emphasis on the social experiences and vulnerabilities of local residents; and (4) minimal discussion of tourists’ exposure to risk, despite their presence in high-impact areas. These results highlight key limitations of media-driven risk communication and underscore the need for more inclusive, forward-looking narratives to support urban resilience and climate adaptation in coastal cities. This research offers valuable insights for urban planners, policymakers, journalists, and disaster risk reduction professionals, helping them to better align communication strategies with long-term adaptation goals and the needs of diverse urban populations. Full article

The primary contribution of this study lies in analyzing the dynamic drivers during two anomalous sea level rise events in the Bohai Sea through coupled numeric modeling using the Weather Research and Forecasting (WRF) model and the Finite-Volume Community Ocean Model (FVCOM) integrated with the Simulating Waves Nearshore (SWAN) module (hereafter referred to as FVCOM-SWAVE). WRF-derived wind speeds (0.05° grid resolution) were validated against Haiyang-2 (HY-2) scatterometer observations, yielding a root mean square error (RMSE) of 1.88 m/s and a correlation coefficient (Cor) of 0.85. Similarly, comparisons of significant wave height (SWH) simulated by FVCOM-SWAVE (0.05° triangular mesh) with HY-2 altimeter data showed an RMSE of 0.67 m and a Cor of 0.84. Four FVCOM sensitivity experiments were conducted to assess drivers of sea level rise, validated against tide gauge observations. The results identified tides as the primary driver of sea level rise, with wind stress and elevation forcing (e.g., storm surge) amplifying variability, while currents exhibited negligible influence. During the two events, i.e., 20–21 October and 25–26 August 2024, elevation forcing contributed to localized sea level rises of 0.6 m in the northern and southern Bohai Sea and 1.1 m in the southern Bohai Sea. A 1 m surge in the northern region correlated with intense Yellow Sea winds (20 m/s) and waves (5 m SWH), which drove water masses into the Bohai Sea. Stokes transport (wave-driven circulation) significantly amplified water levels during the 21 October and 26 August peak, underscoring critical wave–tide interactions. This study highlights the necessity of incorporating tides, wind, elevation forcing, and wave effects into coastal hydrodynamic models to improve predictions of extreme sea level rise events. In contrast, the role of imposed boundary current can be marginalized in such scenarios. Full article

Zhejiang Province in China is one of the most typhoon-prone regions globally, making typhoon and storm surge hazard analysis critically important for disaster mitigation. This study integrates the Tropical Cyclone Risk Model (TCRM) with a machine learning-based storm surge forecasting model to analyze typhoon hazards and storm surge risks at four representative coastal sites in Zhejiang Province: Haimen, Ruian, Wenzhou, and Zhapu. Firstly, the input database of the TCRM has been updated and subsequently used to generate a 1000-year synthetic typhoon event catalog for the Northwest Pacific region. Secondly, four machine learning models—Long Short-Term Memory (LSTM), Back Propagation (BP), Support Vector Regression (SVR), and Random Forest (RF)—were developed to forecast storm surge component at the four sites, with sensitivity analysis conducted on the input parameters. Among the four models, RF consistently outperformed the others across all four sites. Thirdly, by integrating the storm surge forecasting model with the Yan Meng (YM) typhoon wind field model, extreme wind speed sequences and extreme surge component sequences were derived for the four coastal sites. Finally, four extreme value distribution models—empirical distribution, Weibull, Gumbel, and Generalized Pareto Distribution (GPD)—were applied to fit the extreme wind and surge sequences. Goodness-of-fit tests indicated that the GPD best captured extreme wind speeds at all four sites and extreme surge levels at Haimen, Ruian, and Wenzhou. Using the optimal distributions, return periods (10-, 50-, 100-, and 200-year) for extreme wind speeds and surge components were calculated, providing actionable references for disaster risk management authorities. Full article

This study investigates the potential increase in flood hazard in the city of Elbląg, Poland, due to the climate-induced intensification of wind surges in the Vistula Lagoon. Using the HEC-RAS 2D (version 6.6) model—typically applied to riverine systems but here adapted for wind-driven lagoon dynamics—we simulate both historical and hypothetical storm events to evaluate water level changes under varying wind speeds. Model validation was performed using the January 2019 surge event, demonstrating strong agreement with observed water levels (NSE > 0.93). Subsequent simulations using synthetic wind scenarios show that extreme NE winds of 35 m·s⁻¹ could raise water levels above 3.5 m asl, significantly surpassing warning and alarm thresholds. The results reveal a non-linear response between wind speed and water accumulation, underscoring the elevated hazard for low-lying areas such as Żuławy Elbląskie. The novelty of this study lies in the innovative application of HEC-RAS to a wind-driven lagoon environment and in the generation of synthetic surge scenarios for climate resilience planning. These findings provide critical insight for improving flood risk assessment and infrastructure adaptation in the face of ongoing climate change. Full article

This study analyzes the 96 h flow velocity time series data from the Zhoushan Sea during Typhoon In−fa to investigate the conditions for extreme flow velocities. Through force analysis of the unit fluid and statistical analysis of topographic features, we identified the critical water depth, slope, and sea surface width for extreme flow velocities under ideal conditions as 15 m, 4.5°, and 2000 m, respectively. The Submarine Topographic Characteristic Index (STCI) is introduced for the first time in this study, revealing its significant impact on extreme flow velocities. Three types of “extreme velocity points”—associated with constant storm surge, astronomical tide, and typhoon storm surge—were defined, occurring over 85% of the time during typhoon events. These extreme velocity points were analyzed in relation to their topographic characteristics, including water depth, slope, and sea surface width. Simulations of Typhoon In−fa in the Zhoushan Sea area were used to construct the STCI model, resulting in the following weightings: water depth = 0.96, slope = 0.39, and sea surface width = 0.49. Typhoon In−fa occurred in 2021, exhibited a maximum wind speed of approximately 35 m/s, and played a key role in the hydrodynamic processes investigated in this study. Validation with Typhoons Muifa (2021) and Bebinca (2413) confirmed the model’s high consistency. The STCI model provides insight into the occurrence of extreme velocities, categorizing them according to tidal phase and typhoon influence. Preliminary findings indicate the model’s applicability under varying typhoon intensities, offering a robust tool for predicting extreme seabed flow velocities during typhoon events. Full article

This study advances coastal flood risk assessment by incorporating El Niño–Southern Oscillation (ENSO) phase information into the Joint Probability of Maxima Method (ENSO-JPMM) for extreme water level prediction in Annapolis, MD. Using data from GLOSS/Extended Sea 135 Level Analysis Version 3 (GESLA-3) dataset and water level records from 1950–2021, we demonstrate that ENSO phases significantly affects flood risk probabilities through their influence on mean sea level, astronomical tides, and skew surge components. We introduce an enhanced JPMM framework that employs phase-specific scaling factors and vertical offsets derived from historical observations, with El Niño conditions associated with higher mean water levels (0.433 m) compared to La Niña (0.403 m) and Neutral phases (0.409 m). The ENSO-JPMM demonstrates improved predictive accuracy across all phases, with root mean square error reductions of up to 5.96% during Neutral conditions and 3.56% during El Niño phases. By implementing a detailed methodology for mean sea level estimation and skew surge analysis, our approach provides a more detailed framework for separating tidal and non-tidal components while accounting for climate variability. The results indicate that traditional extreme value analyses may underestimate flood risks by failing to account for ENSO-driven variability, which can modulate mean water levels by up to 3.0 cm in Annapolis. This research provides insight for coastal infrastructure planning and flood risk management, particularly as climate change potentially alters ENSO characteristics and their influence on extreme water levels. The methodology presented here, while specific to Annapolis MD, can be adapted for other coastal regions to improve flood risk assessments and enhance community resilience planning. Full article

Extreme flood occurrences are becoming increasingly common due to global climate change, with coastal cities being more vulnerable to compound flood disasters. In addition to excessive precipitation and upstream river discharge, storm surge can complicate the flood disaster process and increase the hazard of urban flooding. This study proposed an integrated trivariate-dimensional statistical and hydrodynamic modeling approach for assessing the compound flood hazard associated with extreme storm surges, precipitation events, and upstream river discharge. An innovative trivariate copula joint modeling and the frequency amplification method were used to simulate designed values under different return periods (RPs), considering the correlation of the three factors. The results show remarkable differences between the inundated areas of flood events with trivariate drivers and a single driver under the same RPs, indicating that univariate frequency values are insufficient to manage flood threats in compound flood events. The proposed method provides guidelines for comprehending the compound flood process and designing flood control projects in coastal cities. Full article

The historical City of Venice, with its lagoon, has been severely exposed to repeated marine flooding since historical times due to the combined effects of sea level rise (SLR) and land subsidence (LS) by natural and anthropogenic causes. Although the sea level change in this area has been studied for several years, no detailed flooding scenarios have yet been realized to predict the effects of the expected SLR in the coming decades on the coasts and islands of the lagoon due to global warming. From the analysis of geodetic data and climatic projections for the Shared Socioeconomic Pathways (SSP1-2.6; SSP3-7.0 and SSP5-8.5) released in the Sixth Assessment Report (AR6) of the Intergovernmental Panel on Climate Change (IPCC), we estimated the rates of LS, the projected local relative sea level rise (RSLR), and the expected extent of flooded surfaces for 11 selected areas of the Venice Lagoon for the years 2050, 2100, and 2150 AD. Vertical Land Movements (VLM) were obtained from the integrated analysis of Global Navigation Satellite System (GNSS) and Interferometry Synthetic Aperture Radar (InSAR) data in the time spans of 1996–2023 and 2017–2023, respectively. The spatial distribution of VLM at 1–3 mm/yr, with maximum values up to 7 mm/yr, is driving the observed variable trend in the RSLR across the lagoon, as also shown by the analysis of the tide gauge data. This is leading to different expected flooding scenarios in the emerging sectors of the investigated area. Scenarios were projected on accurate high-resolution Digital Surface Models (DSMs) derived from LiDAR data. By 2150, over 112 km² is at risk of flooding for the SSP1-2.6 low-emission scenario, with critical values of 139 km² for the SSP5-8.5 high-emission scenario. In the case of extreme events of high water levels caused by the joint effects of astronomical tides, seiches, and atmospheric forcing, the RSLR in 2150 may temporarily increase up to 3.47 m above the reference level of the Punta della Salute tide gauge station. This results in up to 65% of land flooding. This extreme scenario poses the question of the future durability and effectiveness of the MoSE (Modulo Sperimentale Elettromeccanico), an artificial barrier that protects the lagoon from high tides, SLR, flooding, and storm surges up to 3 m, which could be submerged by the sea around 2100 AD as a consequence of global warming. Finally, the expected scenarios highlight the need for the local communities to improve the flood resiliency plans to mitigate the consequences of the expected RSLR by 2150 in the UNESCO site of Venice and the unique environmental area of its lagoon. Full article

Among the frequent natural disasters, there is a growing concern that storm surges may cause enhanced damage to coastal regions due to the increase in climate extremes. It is widely believed that storm surge risk assessment is of great significance for effective disaster prevention; however, traditional risk assessment often relies on the land use data from the government or manual interpretation, which requires a great amount of material resources, labor and time. To improve efficiency, this study proposes a framework for conducting fast risk assessment in a chosen area based on social sensing data and a deep learning method. The coupled Finite Volume Coastal Ocean Model (FVCOM) and Simulating Waves Nearshore (SWAN) model are applied for simulating inundation of five storm surge scenarios. Social sensing data are generated by fusing POI kernel density and night light data through wavelet transform. Subsequently, the Swin Transformer model receives two sets of inputs: one includes social sensing data, Normalized Difference Water Index (MNDWI) and Normalized Difference Chlorophyll Index (NDCI), and the other is Red, Green, Blue bands. The ensembled model can be used for fast land use identification for vulnerability assessment, and the accuracy is improved by 3.3% compared to the traditional RGB input. In contrast to traditional risk assessment approaches, the proposed method can conduct emergency risk assessments within a few hours. In the coast area of Huizhou city, the area considered to be at risk is 135 km², 89 km², 82 km², 72 km² and 64 km², respectively, when the central pressure of the typhoon is 880, 910, 920, 930 and 940 hpa. The Daya Bay Petrochemical Zone and central Huangpu waterfront are two areas at high risk. The conducted risk maps can help decision-makers better manage storm surge risks to identify areas at potential risk, prepare for disaster prevention and mitigation. Full article

Tropical cyclones (TCs) frequently encompass multiple hazards, including extreme winds, intense rainfall, storm surges, flooding, lightning, and tornadoes. Accurate methods for forecasting TC tracks are essential to mitigate the loss of life and property associated with these hazards. Despite significant advancements, accurately forecasting the paths of TCs remains a challenge, particularly when they interact with complex land features, weaken into remnants after landfall, or are influenced by abnormal satellite observations. To address these challenges, we propose a generative adversarial network (GAN) model with a multi-scale architecture that processes input data at four distinct resolution levels. The model is designed to handle diverse inputs, including satellite cloud imagery, vorticity, wind speed, and geopotential height, and it features an advanced center detection algorithm to ensure precise TC center identification. Our model demonstrates robustness during testing, accurately predicting TC paths over both ocean and land while also identifying weak TC remnants. Compared to other deep learning approaches, our method achieves superior detection accuracy with an average error of 41.0 km for all landfalling TCs in Australia from 2015 to 2020. Notably, for five TCs with abnormal satellite observations, our model maintains high accuracy with a prediction error of 35.2 km, which is a scenario often overlooked by other approaches. Full article

Storm surge barriers are crucial for the flood protection of the Netherlands and other deltas. In the Netherlands, the reliability of flood defenses is typically assessed based on extreme water levels and wave height statistics. Yet, in the case of operated flood defenses, such as storm surge barriers, the temporal clustering of successive events may be just as important. This study investigates the evolution and associated flood risk of clusters of successive storm tide peaks at the Maeslant Storm Surge Barrier in the Netherlands. Two mechanisms are considered. Multi-peak storm surge events, as a consequence of tidal movement on top of the surge, are studied by means of stochastic storm tide events. Clusters of storm tides resulting from different, but related storms are investigated by means of time series analysis of a long sea-level record. We conclude that the tendency of extreme storm tide peaks to cluster is especially related to the seasonality in storm activity. In the current situation, the occurrence of clusters of storm tide peaks have only a minor influence of the flood risk in the area behind the Maeslant Storm Surge Barrier. We envision, however, that this influence is likely to increase with sea-level rise. The numbers are, however, uncertain due to the strong sensitivity to assumptions, model choices and the applied data set. More insight into the statistics of the time evolution of extreme sea water levels is needed to better understand and ultimately to reduce these uncertainties. Full article

Comprehensive understanding and prediction of storm surge are vital for coastal hazard mitigation and prevention. To estimate extreme sea levels in the Ayeyarwady Sea of Myanmar, where long-term tidal data are unavailable, a hydrodynamic model capable of simulating storm surge, along with the Monte Carlo method for generating synthetic cyclones, was utilized. The effectiveness of this modeling approach in the Ayeyarwady seas was confirmed through validation against tidal levels and storm surges. After analyzing 17 selected historical cyclones, a synthetic cyclone history comprising 354 events was developed. Simulations driven by the generated cyclones were subsequently conducted. Based on the simulation results, the 50-year, 100-year, 200-year, and 1000-year sea levels at the research station were estimated to be 4.43 m, 4.83 m, 6.06 m, and 7.24 m, respectively. With a 10% intensification of cyclones and a sea level rise of 310 mm, these four vital parameters were predicted to be 5.03 m, 5.48 m, 6.95 m, and 8.43 m. The results of this study confirmed the significant effects of cyclone intensification and sea level rise. Moreover, the results provide valuable scientific insights for flood management and engineering design in the Ayeyarwady Sea of Myanmar. Full article