Storm Surge Research Articles

A machine learning approach to evaluate coastal risks related to extreme weather events in the Veneto region (Italy)

A significant and unprecedented increase of temperature has been recorded worldwide during the last decades, leading to the occurrence of numerous extreme events. Coastal areas, with their high population density, interconnected economic activities, fragile ecosystems, are particularly vulnerable to climate change impacts. These impacts can be intensified by the interactions of multiple hazards which operate at different spatio-temporal scales and affect exposure and vulnerability patterns. An integrated approach is here proposed to assess the relationship between risk factors and to evaluate the multiplicity of impacts that may affect the coastline. A new path to tackle these multi-risk events is offered by ML algorithms to effectively handle vast amounts of heterogenous data, and model complex non-linear relationships between multiple factors and feedback mechanisms. To assess impacts caused by extreme events (storm surges, extreme precipitation, wind events) in the Veneto coastal municipalities, a ML approach was developed to understand connections between atmospheric and marine hazards and impacts recorded by the Veneto region emergency archive during the 2009–2019 timeframe, identifying the most influencing factors triggering multiple risks. Additionally, the coastal municipalities were clustered considering the intrinsic relationships between impact occurrences, exposure and vulnerability features. Several algorithms were compared to estimate daily risk of impacts to occur providing hazards, exposure and vulnerability information. The MLP algorithm showed satisfactory performances (weighted-F1-score of 0.94) to estimate the relative importance of input features. The proposed algorithm was designed as support tool to increase the understanding of impacts’ occurrence in coastal areas, thus helping the adaptation planning process.

Four Storm Surge Cases on the Coast of São Paulo, Brazil: Weather Analyses and High-Resolution Forecasts

The coast of São Paulo, Brazil, is exposed to storm surges that can cause damage and floods. These storm surges are produced by slowly traveling cyclone–anticyclone systems. The motivation behind this work was the need to evaluate high-resolution forecasts of the mean sea-level pressure and 10 m winds, which are the major drivers of the wave model. This work is part of the activity in devising an early warning system for São Paulo coastal storm surges. For the evaluation, four case studies that had a major impact on the coast of São Paulo in 2020 were selected. Because storm surges that reach the coast may cause coastal flooding, precipitation forecasts were also evaluated. The mesoscale Eta model produces forecasts with a 5 km resolution for up to an 84 h lead time. The model was set up in a region that covers part of southeast and south Brazil. The ERA5 reanalysis was used to describe the large-scale synoptic conditions and to evaluate the weather forecasts. The cases showed a region in common between 35° S, 40° S and 35° W, 45° W where the low-pressure center deepened rapidly on the day before the highest waves reached the coast of São Paulo, with a mostly eastward, rather than northeastward, displacement of the associated surface cyclone and minimal or no tilt with height. The winds on the coast were the strongest on the day before the surge reached the coast of São Paulo, and then the winds weakened on the day of the maximum wave height. The pattern of the mean sea-level pressure and 10 m wind in the 36 h, 60 h, and 84 h forecasts agreed with the ERA5 reanalysis, but the pressure was slightly underestimated. In contrast, the winds along the coast were slightly overestimated. The 24 h accumulated precipitation pattern was also captured by the forecast, but was overestimated, especially at high precipitation rates. The 36 h forecasts showed the smallest error, but the growth in the error for longer lead times was small, which made the 84 h forecasts useful for driving wave models and other local applications, such as an early warning system.

Coastal Flood Induced Salinity Intrusion Risk Assessment Using a Spatial Multi-criteria Approach in the South-Western Bangladesh

AbstractBangladesh is extremely vulnerable to sea-level rise and other climate-induced extreme events, such as flooding, storm surge, and salinity intrusion. The south-western coastal region of Bangladesh is particularly vulnerable to salinity intrusion caused by cyclone induced storm surges and coastal floods. Salinity intrusion endanger land productivity by increasing both soil and surface water salinity. Detailed risk assessment using spatial mapping approach can contribute to mitigating the effects of salinity intrusion on natural capital and the environment. In this study, we established and evaluated a spatial multi-criteria approach for mapping the risk levels of areas to salinity intrusion impacts using field data and geospatial techniques at the local scale. We evaluated the viability of the proposed approach using Khulna District, a major coastal city and saline prone area in the south-western Bangladesh. We considered three risk components (i.e. vulnerability, exposure and hazard) with 16 relevant criteria for the study. For each criterion, an Analytical Hierarchy Process (AHP) was used to build and weight spatial raster map layers. Individual maps for each risk component were generated using a weighted sum technique, and lastly, a risk map was created by combining those. Our generated maps correctly identified relevant spatial dimensions as well as risk levels (i.e. very-high to very-low). The outcomes of our study suggest that the southern (east and west) parts of the study area are mostly susceptible to salinity intrusion due to higher storm surge impacts, lower elevation, and land use patterns than other parts. We validate our findings using a qualitative and quantitative approach. We believe that this novel approach would be useful to create risk maps that policymakers and relevant stakeholders could potentially use to evaluate risks posed by flood induced salinity intrusion in coastal regions of Bangladesh and elsewhere with similar geo-climatic context.

Forecasting Super Typhoon Saola and its effects on Hong Kong

AbstractHong Kong was severely impacted by Super Typhoon Saola (2309), necessitating the issuance of the highest tropical cyclone warning signal. Saola skirted past the south‐southwest of Hong Kong, bringing hurricane force winds and significant storm surge. Saola had its closest approach to Hong Kong on 1 September 2023, posing a unique challenge in forecasting and early warning for the start of the new school term, where higher impact to traffic and public safety was anticipated. This paper covers the challenges on the forecasting aspect of the super typhoon.

Compound Flooding Potential from the Joint Occurrence of Precipitation and Storm Surge in the Qiantang Estuary, China

Abstract In coastal regions, compound flooding, driven by multiple flood hazard sources, can cause greater damage than when the flood drivers occur in isolation. This study focuses on compound flooding from extreme precipitation and storm surge in China’s Qiantang Estuary. We quantify the potential of compound flooding by measuring bivariate joint statistical dependence and joint return period (JRP). We find a significant positive dependence between the two flood drivers considered, as indicated by Kendall’s rank correlation coefficients. Compound events occur frequently, with an average of 2.65 events per year from 1979 to 2018, highlighting the significant concern of compound flooding for this estuary. Using a copula model, we demonstrate that considering the dependence between the two flood drivers shortens the JRP of compound flooding compared to the JRP assuming total independence. For a 1-in-10-yr precipitation event and 1-in-10-yr storm surge event, the JRP is 1 in 100 years when assuming total independence. However, it decreases to 1 in 32.44 years when considering their dependence. Ignoring the dependence between flood drivers can lead to an increase in the JRP of compound events, resulting in an underestimation of the overall flood risk. Our analysis reveals a strong link between the weather patterns creating compound events and extreme storm surge only events with tropical cyclone activity. Additionally, the extreme precipitation only events were found to be connected with the frontal system of the East Asian summer monsoon. This study highlights the importance of considering the dependence between multiple flood drivers associated with certain types of the same weather systems when assessing the flood risk in coastal regions.

A Major Improvement of Atmospheric Wave Boundary Layer Model for Storm Surge Modeling by Including Effect of Wave Breaking on Air–Sea Momentum Exchange

Abstract Accurate estimation of the wind stress under extreme conditions is crucial for modeling storm surges and storm waves, which is important to the development of a warning system for coastal disaster prevention. The problem, however, is highly challenging owing to the presence of complex ocean surface processes under the action of unusually strong wind. In this study, the existing atmospheric wave boundary layer model is significantly enhanced by including various effects of wave breaking. Both the effect of wave breaking on the dissipation of energy and its effect on the transfer of momentum within the atmospheric boundary layer are carefully formulated. The wind stress coefficients obtained with the enhanced model are shown to be in good agreement with the measurements in not only deep but also shallow waters. The enhanced atmospheric wave boundary layer model is coupled with ocean wave as well as circulation models to simulate typhoon-induced storm surges and storm waves in the Pearl River delta region. The computational results show that the coupled model with improved evaluation of the wind stress is substantially advantageous when compared with existing approaches.

Advanced Sea Ice Modeling for Short-Term Forecasting for Alaska’s Coasts

Abstract In Alaska’s coastal environment, accurate information of sea ice conditions is desired by operational forecasters, emergency managers, and responders. Complicated interactions among atmosphere, waves, ocean circulation, and sea ice collectively impact the ice conditions, intensity of storm surges, and flooding, making accurate predictions challenging. A collaborative work to build the Alaska Coastal Ocean Forecast System established an integrated storm surge, wave, and sea ice model system for the coasts of Alaska, where the verified model components are linked using the Earth System Modeling Framework and the National Unified Operational Prediction Capability. We present the verification of the sea ice model component based on the Los Alamos Sea Ice Model, version 6. The regional, high-resolution (3 km) configuration of the model was forced by operational atmospheric and ocean model outputs. Extensive numerical experiments were conducted from December 2018 to August 2020 to verify the model’s capability to represent detailed nearshore and offshore sea ice behavior, including landfast ice, ice thickness, and evolution of air–ice drag coefficient. Comparisons of the hindcast simulations with the observations of ice extent presented the model’s comparable performance with the Global Ocean Forecast System 3.1 (GOFS3.1). The model’s skill in reproducing landfast ice area significantly outperformed GOFS3.1. Comparison of the modeled sea ice freeboard with the Ice, Cloud, and Land Elevation Satellite-2 product showed a mean bias of −4.6 cm. Daily 5-day forecast simulations for October 2020–August 2021 presented the model’s promising performance for future implementation in the coupled model system. Significance Statement Accurate sea ice information along Alaska’s coasts is desired by the communities for preparedness of hazardous events, such as storm surges and flooding. However, such information, in particular predicted conditions, remains to be a gap. This study presents the verification of the state-of-art sea ice model for Alaska’s coasts for future use in the more comprehensive coupled model system where ocean circulation, wave, and sea ice models are integrated. The model demonstrates comparable performance with the existing operational ocean–ice coupled model product in reproducing overall sea ice extent and significantly outperformed it in reproducing landfast ice cover. Comparison with the novel satellite product presented the model’s ability to capture sea ice freeboard in the stable ice season.

Perception and Reality: How the Depths of the High Waters in Venice Apparently Change with the Reference System

Over the centuries, the depths of the most severe storm surges that have flooded Venice have been measured using different reference frames, i.e., related to the algae belt (CM), mean sea level (MSL), local land (ZMPS), large-scale leveling (IGM), and satellite altimetry (SA). Some reference frames, i.e., IGM and SA, are absolute, while the others are relative and represent two different physical points of view, i.e., CM and MSL refer to the sea that is rising and ZMPS refers to the land that is subsiding. The perceptions derived from the different systems are contradictory. This paper discusses and compares surges from 1821 to 2021 measured with these frames, also including the commemorative plaques that report the flood depths on walls in Venice. The paper explains the consequences of a change in frame and zero reference, and it transforms the flooding depths from the original systems to make them homogeneous. The severity of flooding changes in terms of rating with the choice of frame. In the 19th century, five storm surges exceeded the famous level of 1966 and, if they were to recur today or in the future, the sea level rise and the local land subsidence that have occurred in the meantime would greatly exacerbate the situation.

Experimental study on the stability of dike reinforced by steel sheet pile under storm surge

Over recent decades, upgrading dikes that defend against storm surges increasing in both intensity and frequency resulting from global warming, face challenges such as environmental impact and susceptibility to brittle failure. To address this issue, the steel sheet pile can be employed to improve the dike stability due to its resilience in withstanding substantial deformation. To investigate the reinforcement mechanism of the steel sheet pile, this study conducted three wave flume tests including the dike without configuration, the dike with single sheet pile, and the dike with double sheet pile. The dike stability and pile bending moment were analyzed based on the monitoring data. The experimental results showed that the single pile significantly improved the dike stability by reducing the seepage. The maximum bending moment for the single sheet pile occurred at approximately 1/3 of the pile depth, primarily due to its proximity to the wave impact area on the dike. The double pile further enhanced the dike stability primarily through the reduction of wave scour. The maximum bending moment for the double pile occurred at a lower position (2/3 of the pile depth) and was smaller due to the external force distribution and tie rod limitation.

FloodNet: Low‐Cost Ultrasonic Sensors for Real‐Time Measurement of Hyperlocal, Street‐Level Floods in New York City

AbstractFlooding is one of the most dangerous and costly natural hazards, and has a large impact on infrastructure, mobility, public health, and safety. Despite the disruptive impacts of flooding and predictions of increased flooding due to climate change, municipalities have little quantitative data available on the occurrence, frequency, or extent of urban floods. To address this, we have been designing, building, and deploying low‐cost, ultrasonic sensors to systematically collect data on the presence, depth, and duration of street‐level floods in New York City (NYC), through a project called FloodNet. FloodNet is a partnership between academic researchers and NYC municipal agencies, working in consultation with residents and community organizations. FloodNet sensors are designed to be compact, rugged, low‐cost, and deployed in a manner that is independent of existing power and network infrastructure. These requirements were implemented to allow deployment of a hyperlocal, city‐wide sensor network, given that urban floods often occur in a distributed manner due to local variations in land development, population density, sewer design, and topology. Thus far, 87 FloodNet sensors have been installed across the five boroughs of NYC. These sensors have recorded flood events caused by high tides, stormwater runoff, storm surge, and extreme precipitation events, illustrating the feasibility of collecting data that can be used by multiple stakeholders for flood resiliency planning and emergency response.

Projecting Changes in the Drivers of Compound Flooding in Europe Using CMIP6 Models

AbstractWhen different flooding drivers co‐occur, they can cause compound floods. Despite the potential impact of compound flooding, few studies have projected how the joint probability of flooding drivers may change. Furthermore, existing projections may not be very robust, as they are based on only 5 to 6 climate model simulations. Here, we use a large ensemble of simulations from the Coupled Model Intercomparison Project 6 (CMIP6) to project changes in the joint probability of extreme storm surges and precipitation at European tide gauges under a medium and high emissions scenario, enabled by data‐proximate cloud computing and statistical storm surge modeling. We find that the joint probability will increase in the northwest and decrease in most of the southwest of Europe. Averaged over Europe, the absolute magnitude of these changes is 36%–49% by 2080, depending on the scenario. The large‐scale changes in the joint probability of extreme storm surges and precipitation are similar to those in the joint probability of extreme wind speeds and precipitation, but locally, differences can exceed the changes themselves. Due to internal climate variability and inter‐model differences, projections based on simulations of only 5 to 6 randomly chosen CMIP6 models have a probability of higher than 10% to differ qualitatively from projections based on all CMIP6 simulations in multiple regions, especially under the medium emissions scenario and earlier in the twenty‐first century. Therefore, our results provide a more robust and less uncertain representation of changes in the potential for compound flooding in Europe than previous projections.

Monitoring Change and Recovery of an Embayed Beach in Response to Typhoon Storms Using UAV LiDAR

The monitoring of beach topographical changes and recovery processes under typhoon storm influence has primarily relied on traditional techniques that lack high spatial resolution. Therefore, we used an unmanned aerial vehicle light detection and ranging (UAV LiDAR) system to obtain the four time periods of topographic data from Tantou Beach, a sandy beach in Xiangshan County, Zhejiang Province, China, to explore beach topography and geomorphology in response to typhoon events. The UAV LiDAR data in four survey periods showed an overall vertical accuracy of approximately 5 cm. Based on the evaluated four time periods of the UAV LiDAR data, we created four corresponding DEMs for the beach. We calculated the DEM of difference (Dod), which showed that the erosion and siltation on Tantou Beach over different temporal scales had a significant alongshore zonal feature with a broad change range. The tidal level significantly impacted beach erosion and siltation changes. However, the storm surge did not affect the beach area above the spring high-tide level. After storms, siltation occurred above the spring high-tide zone. This study reveals the advantage of UAV LiDAR in monitoring beach changes and provides novel insights into the impacts of typhoon storms on coastal topographic and geomorphological change and recovery processes.

Prediction of storm surge in the Pearl River Estuary based on data-driven model

Storm surges, a significant coastal hazard, cause substantial damage to both property and lives. Precise and efficient storm surge models are crucial for long-term risk assessment and guiding emergency management decisions. While high-fidelity dynamic models offer accurate predictions, their computational costs are substantial. Hence, recent efforts focus on developing data-driven storm surge surrogate models. This study focuses on the Pearl River Estuary in Guangdong Province. Initially, the dynamic ADvanced CIRCulation (ADCIRC) model was utilized to construct storm surge data for 16 historical typhoons, serving as training, validation, and testing data for data-driven models. Subsequently, Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), and Informer deep learning (DL) models were employed for forecasting of storm surge over the next 1h, 3h, 6h, 12h, and 18h. Finally, Shapley Additive exPlanations (SHAP) values were used for interpretability analysis of the input factors across different models. Results indicated that the proposed DL storm surge prediction model can effectively replicate the dynamic model’s simulation results in short-term forecasts, significantly reducing computational costs. This model offers valuable scientific assistance for future coastal storm surge forecasts in the Greater Bay Area.

Prediction of Storm Surges in the East Coast of Peninsular Malaysia in Response to Climate Change

This study employed the MIKE 21HD modeling framework, integrating data from the future climate database, "Policy Decision Making for Future Climate Change (d4PDF)" on selected critical ensembles to investigate the impact of climate change on storm surge height (SSH) along the East Coast of Peninsular Malaysia during the Northeast Monsoon season. Three scenarios, including past-historical data, future climate with a 2°C increment (+2K), and future climate with a 4°C increment (+4K), were analyzed. The study had validated model accuracy through root mean square error (RMSE), demonstrating alignment with observed data. Results indicated a correlation between temperature increase and storm surge height (SSH), with higher temperature scenarios leading to more severe surge outcomes. The analysis identified the Future +4K scenarios as yielding the most critical SSH across all stations, with recorded SSH at Kuala Pahang (SSHmax = 1.379 m), Chendering (SSHmax = 1.344 m), Geting (SSHmax = 1.251 m), and Tanjung Sedili (SSHmax = 1.251 m) stations. Considering the findings, it was recommended to reassess the baseline provision for coastal engineering projects, suggesting a storm surge resilience design exceeding the observed maximum. The utilization of d4PDF data in this study laid the groundwork for targeted strategies to mitigate the impacts of climate-induced storm surges in vulnerable coastal regions.

TYPHOON KARDING (NORU) STORM SURGE ANALYSIS USING THE COAWST MODELING SYSTEM

Abstract. The Philippines, frequently affected by typhoons, faces the hazard of storm surges. This study examined the Coupled-Ocean-Atmosphere-Wave-Sediment Transport Modeling System (COAWST) to simulate Typhoon Karding's September 2022 storm surge. COAWST integrates the Regional Ocean Modeling System (ROMS), Simulating WAves Nearshore (SWAN), and Weather Research and Forecasting (WRF) coupled using the Model Coupling Toolkit (MCT). Four setups were analyzed: i. ROMS only, ii. ROMS-SWAN, iii. ROMS-WRF, and iv. ROMS-SWAN-WRF, focusing on four variables: a. Surface air pressure, b. Wind speed, c. Free-surface elevation, and d. Significant wave height. Results show that the ROMS-WRF and ROMS-SWAN-WRF setups accurately simulated Typhoon Karding's track with minimal positional error and wind speed. However, the models overestimated the typhoon's minimum air pressure with p-biases of 7.74% (i and ii), 4.1% (iii), and 3.9% (iv), and RMSE values of 68.529 hPa (i and ii), 36.744 hPa (iii), and 36.789 hPa (iv). Additionally, water levels were underestimated, with RMSE ranging from 0.31 to 0.35 meters and p-biases from −72.56% to −154.89% at Baler, Aurora validation point. At the Real, Quezon validation point, RMSE and p-bias ranged from 0.30 to 0.34 meters and −84.80% to −166.44%, respectively. Nonetheless, the models were able to simulate the storm surge and significant wave height at Baler and Real points similar to recorded data, with setup iv performing best in storm surge simulation. In summary, COAWST may be employed for typhoon simulations, with coupling being able to increase accuracy.

A Perfect Tidal Storm: HD 104067 Planetary Architecture Creating an Incandescent World

The discovery of planetary systems beyond the solar system has revealed a diversity of architectures, most of which differ significantly from our system. The initial detection of an exoplanet is often followed by subsequent discoveries within the same system as observations continue, measurement precision is improved, or additional techniques are employed. The HD 104067 system is known to consist of a bright K-dwarf host star and a giant planet in a ∼55 days period eccentric orbit. Here we report the discovery of an additional planet within the HD 104067 system, detected through the combined analysis of radial velocity (RV) data from the High Resolution Echelle Spectrometer and High Accuracy Radial velocity Planet Searcher instruments. The new planet has a mass similar to Uranus and is in an eccentric ∼14 days orbit. Our injection-recovery analysis of the RV data exclude Saturn-mass and Jupiter-mass planets out to 3 au and 8 au, respectively. We further present Transiting Exoplanet Survey Satellite observations that reveal a terrestrial planet candidate (R p = 1.30 ± 0.12 R ⊕) in a ∼2.2 days period orbit. Our dynamical analysis of the three planet model shows that the two outer planets produce significant eccentricity excitation of the inner planet, resulting in tidally induced surface temperatures as high as ∼2600 K for an emissivity of unity. The terrestrial planet candidate may therefore be caught in a tidal storm, potentially resulting in its surface radiating at optical wavelengths.

Coinciding storm surge and wave setup: A regional assessment of sea level rise impact

Coastal locations around the world face the multifaceted challenge of rising sea levels, posing threats to coastal communities, particularly during storm surges and high wave conditions. We have examined the dynamics of sea-level fluctuations along Denmark’s coastline, with focus on wave-induced phenomena, such as wave setup. We analysed wave data from DMI’s operational model spanning from 2006 to 2022, as well as climate projections that extend until the middle of the 21st century. We conducted an analysis of the 2% exceedance of the significant wave height maxima during specific time frames (denoted “storm days”) and “storm surge conditions” (the maximum sea level within a 30-year period). We found that the impact of climate change on extreme wave events was insignificant when focusing solely on the wave component, but heightened wave setup during “storm surge conditions” along the north-western coasts of Jutland. In this area, there is a notable increase in wave setup due to longer swells, ranging from 5 to 10 cm, by the mid-century, which is double the current wave setup in the region. Interestingly, other parts of the country did not show significant changes in wave setup. This regional variation highlights the nuanced impact of wave-induced processes on sea level dynamics and underscores the need for tailored adaptation strategies targeting specific vulnerable zones.

The Impacts of Multiple Tropical Cyclone Events and Associated Precipitation on Household Income and Expenditures

Tropical cyclones (TCs) are one of the most destructive natural hazards. Damages arise from strong winds, compounded by associated flood-inducing hazards such as heavy rainfall and storm surge. Recent papers have shown that the modelled TC damage estimates fall short of the observed estimates due to the use of wind speed as a sole damage proxy. Damage estimates may be further confounded by inaccurate representations of vulnerability of people and economic sectors, for example, calling for adjusted damage thresholds in less developed regions. This paper evaluates the impacts of compounded TC hazards on household income and expenditure in the Philippines, with adjustments in vulnerability representation drawn from local information. Our results show that the omission of TC-associated precipitation leads to an underestimation of impacts, as well as the number of areas and economic sectors affected by TCs. We find that households cope through a reallocation of budgets and reliance on alternative income sources. Despite extensive public and private disaster risk reduction and management strategies, we still find significant losses in income and expenditures at any number of TC exposure.

A dataset of storm surge reconstructions in the Western North Pacific using CNN

The relatively short duration of available tide gauge records poses challenges for conducting comprehensive statistical analyses of storm surges in the Western North Pacific. To address this issue, we employ a convolutional neural network model to reconstruct the maximum daily storm surge at 160 tide gauges from 1900 to 2010 in the Western North Pacific. The reconstructed dataset serves multiple purposes. Firstly, it facilitates the identification of regions where notable changes in the storm surges have occurred in the past. Additionally, the dataset enables long-term analyses of the storm surge climate, offering insights into historical patterns and variations. Furthermore, it provides a solid foundation for conducting robust extreme value analyses. To ensure accessibility, the data are publicly available through a repository, allowing for easy access and utilization by the broader scientific community and the general public. Overall, our research contributes to the field of oceanography by providing a dataset that aids in understanding the historical storm surge dynamics in the Western North Pacific region.

Effect of wave-dependent mechanisms on storm surge and current simulation during three extreme weather systems

Abstract The wave effect is crucial to coastal ocean dynamics, but the roles of the associated wave-dependent mechanisms, such as the wave-enhanced surface stress, wave-enhanced bottom stress, and three-dimensional wave force, are not yet fully understood. In addition, the parameterizations of each mechanism vary and need to be assessed. In this study, a coupled wave-current model based on the Coupled Ocean-Atmosphere-Wave-Sediment Transport (COAWST) model system was established to identify the effect of the wave-dependent mechanism on storm surges and currents during three typical extreme weather systems, i.e., cold wave, extratropical cyclone, and typhoon systems, in a semi-enclosed sea. The effects of the three coupled mechanisms on the surface or bottom stress, in terms of both the magnitude and direction, were investigated and quantified separately based on numerical sensitive analysis. A total of seven parameterizations is used to evaluate these mechanisms, resulting in significant variations in the storm surge and current vectors. The similarities and differences of the wave-induced surge and wave-induced current among the various mechanisms were summarized. The change in the surface stress and bottom stress and the excessive momentum flux due to waves were found to mainly occur in shallow nearshore regions. Optimal choice of the combination of parameterization schemes was obtained through comparison with measured data. The wave-induced current in the open waters with a deep-water depth and complex terrain could generate cyclonic or anticyclonic current vorticities, the number and intensity of which always increased with the enhanced strength and rotation of the wind field increased.