Storm Surge Levels Research Articles

Estimating the combined risks of sea level rise and storm surges using a numerical model: Application to Macao

The storm surges caused by tropical cyclones may lead to overtopping floods in low-lying coastal communities, which will be further exacerbated by sea level rise. In this study, a numerical model is developed that can simulate the two-dimensional surface flow and one-dimensional drainpipe flow simultaneously. Then this model is validated and applied to Macao Peninsula. The compound flood scenarios of storm surge and sea level rise are therefore forecasted, with storm surge return periods of 5-, 10-, 20-, 50-, 100-, and 200-years, and sea level rises by 0 m, 0.5 m and 1.0 m. The overtopping floods caused by storm surges are mainly distributed in the west and northwest of Macao Peninsula. When the sea level rises by 1.0 m, the backflow occurs 2 h earlier than that does under the current sea level. Adding pumping station can reduce backflow and improve drainage efficiency significantly. The effectiveness of the drainage system during floods can be quantified using backflow coefficient and drainage efficiency coefficient, which both decrease as the storm tide level increases. In the worst-case scenario of 200-year-return-period storm surge and 1-m sea level rise, the flooding area accounts for 41.1% of the land area, and the population at risk reaches 68.4% of the total population. The storm-surge-prone area overlaps with densely populated area in western Macao Peninsula, where the effects of storm surge and sea level rise are intensified.

Storm surge variability and prediction from ENSO and tropical cyclones

Storm surges are among the deadliest natural hazards, but understanding and prediction of year-to-year variability of storm surges is challenging. Here, we demonstrate that the interannual variability of observed storm surge levels can be explained and further predicted, through a process-based study in Hong Kong. We find that El Niño-Southern Oscillation (ENSO) exerts a compound impact on storm surge levels through modulating tropical cyclones (TCs) and other forcing factors. The occurrence frequencies of local and remote TCs are responsible for the remaining variability in storm surge levels after removing the ENSO effect. Finally, we show that a statistical prediction model formed by ENSO and TC indices has good skill for prediction of extreme storm surge levels. The analysis approach can be applied to other coastal regions where tropical storms and the climate variability are main contributors to storm surges. Our study gives new insight into identifying ‘windows of opportunity’ for successful prediction of storm surges on long-range timescales.

Storm surge level prediction based on improved NARX neural network

The northern Gulf of Mexico coast is affected by the North Atlantic hurricane season, which causes storm surge disasters every year and brings serious economic losses to the southern USA; therefore, it is necessary to make an accurate advance prediction of storm surge level. In this paper, a model with simple structure, fast computation speed, and accurate prediction results has been constructed based on nonlinear auto-regressive exogenous (NARX) neural network. Five types of data collected from observation stations are selected as the input factors of the model. To improve the model's computational efficiency, a neuron pruning strategy based on sensitivity analysis is introduced. By analyzing the output weights of the neurons in the hidden layer on the sensitivity of the model prediction output, the model structure can be adjusted accordingly. Moreover, a modular prediction method is introduced based on the tide harmonic analysis data so as to make the model prediction results more accurate. At last, a complete storm surge level prediction model, pruned modular (PM)-NARX, is constructed. In this paper, the model is trained by using historical data and used for storm surge level prediction along the northern Gulf of Mexico coast in 2020. The simulation test results show that the correlation between the predicted data and the observed data is stable above 0.99 at 12 h in advance and the model is able to produce the results within one minute. The prediction speed, accuracy, and stability are higher than those of conventional models. In addition, two sets of follow-up tests show that the prediction accuracy of the model can still maintain a high level. The above can prove that the pruned modular (PM)-NARX model can effectively provide early warning before the storm surge to avoid property damage and human casualties.

Foredune growth and storm surge protection potential at the Eiderstedt Peninsula, Germany

In the context of climate change and associated sea level rise, coastal dunes can provide an essential contribution to coastal protection against wave attack and flooding. Since dunes are highly dynamic systems, their potential safety levels are related to their long-term development, varying in time and space, however pertinent research that ties those aspects together are generally scarce. The objective of this study is to analyze the long-term development of a young coastal foredune at the Eiderstedt peninsula, Germany and assess its coastal protection potential. This research presents (i) a novel semi-automated Dune Toe Tracking (DTT) method to systematically extract dune toes from cross-shore elevation profiles; (ii) established tools to derive the extraction of characteristic dune parameters and (iii) a newly defined Critical Storm Surge Level (CSSL) to relate spatio-temporal dune growth with coastal storm surge protection. Based on geospatial survey data, initial dune formation was identified in the 1980s. By 2015, the foredune had developed over a 6.5 km coastal stretch with a mean annual growth of 7.4m³/m. During the course of dune evolution, the seaward dune toe shifted seaward by an average of 2.3m/yr, while simultaneously increasing in height by an average of 1.1 cm/yr. Overall, the foredune formation established a new line of defense in front of an existing dike/dune line that provides spatially varying protection against a mean CSSL of 3.4m + NHN and can serve as an additional buffer against wave attack during severe storm events.

Field Survey of 2021 Typhoon Rai –Odette- in the Philippines

Typhoon Rai struck the Philippines on the 16th December 2021, damaging and inundating many coastal areas along the Visayas region of the country due to the high winds, storm surges and wind driven waves it generated. In order to understand the various damage mechanisms, the authors conducted a field survey to measure the storm surge heights at several locations in the provinces of Cebu and Bohol. As part of the survey, local residents were interviewed to understand the phenomena and survey the height reached by the storm surge. The maximum storm surge level measured were 2.54 m, 4.24 m and 4.06 m along the provinces of Cebu and Bohol. Finally, some interesting characteristics of the storm surge are summarised, and the lessons learnt in terms of disaster risk management are discussed

Numerical Study on Storm Surge Level Including Astronomical Tide Effect Using Data Assimilation Method

In the storm surge model, the wind drag coefficient Cd is a critical parameter that has a great influence on the forecast of the storm surge level. In the present study, the effect of various wind drag coefficient parameterizations on the storm surge level is investigated in the Bohai Sea, Yellow Sea and East China Sea for Typhoons 7203 and 7303. Firstly, the impacts of initial values of a and b in the linear expression Cd = (a + b × U10) × 10−3 on the pure storm surge model are evaluated based on the data assimilation method. Results indicate that when a and b (i.e., the wind drag coefficients given by Smith, Wu, Geernaert et al. and Mel et al.) are non-zeros, the performance of the model has little difference, and the result from Wu is slightly better. However, they are superior to the performance of the model adopting zero initial values. Then, we discuss the influences of diverse ways of calculating wind drag coefficients, which are inverted by data assimilation method (including both linear and constant Cd) and given in the form of linear formulas, on simulating pure storm surge level. They show that the data assimilation-based coefficients greatly exceed those of the ordinary coefficient formulas. Moreover, the wind drag coefficient in the linear form is a little better than that in constant form when the data assimilation method is used. Finally, the assessment of the impact of astronomical tides on the storm surge level is conducted, and the simulation demonstrates that the storm surge model, which has the combination of four constituents (M2, S2, K1, O1) and wind drag coefficient inverted by the data assimilation method with the linear Cd, exhibits the best performance.

Using Neural Networks to Predict Hurricane Storm Surge and to Assess the Sensitivity of Surge to Storm Characteristics

AbstractHurricane storm surge represents a significant threat to coastal communities around the world. Here, we use artificial neural network (ANN) models to predict storm surge levels using hurricane characteristics along the US Gulf and East Coasts. The ANN models are trained with storm surge levels from a hydrodynamic model and physical characteristics of synthetic hurricanes which are downscaled from National Centers for Environmental Prediction (NCEP) reanalysis using a statistical‐deterministic hurricane model. The ANN models are able to accurately predict storm surge levels with root‐mean‐square errors (RMSE) below 0.2 m and correlation coefficients > 0.85. The ANN models trained with the NCEP data also show satisfactory accuracy (RMSE below 0.7 m; correlation > 0.7) in predicting storm surge levels for hurricanes downscaled from future climate projections. Once trained, we use the ANN models to assess the sensitivity of storm surge levels to variations in hurricane characteristics and local geophysical features. Progressively stronger maximum wind speeds and larger outer radius sizes independently increase storm surge levels at all locations along the US East and Gulf Coasts. The response of storm surge levels to changes in hurricane translation speed, however, is found to be sensitive to coastal configuration, with increases in hurricane translation speed amplifying (reducing) storm surge levels in open ocean (semi‐enclosed) regions.

Changes in storm surges based on a bias-adjusted reconstruction dataset from 1900 to 2010

To increase our understanding of multidecadal and longer-term variations in storm surges around the world, we require long-term and trustworthy data. The quantile delta mapping method was used to rectify the statistical biases of storm surge levels using data-driven methodologies to produce high-quality data on long-term storm surge levels. The results showed that the bias-adjusted surge levels were better than those from the raw ERA-20C reconstruction. Bias-adjusted surge levels indicated that most tide gauges globally showed an overall positive trend from 1900 to 2010, and more extreme storm surges occurred after the 1960s. The El Niño-Southern Oscillation (ENSO) has been identified as one of the key climate patterns influencing storm surges globally, especially at tide gauges around the Pacific Ocean coast in North America, the northwestern Atlantic Ocean, and the Mediterranean Sea. At regions of the Mediterranean Sea, northwestern Atlantic, and Pacific in North America, the North Atlantic Oscillation (NAO) and Arctic Oscillation (AO) were significantly negatively correlated with storm surge. In contrast, the storm surges along the coast of northern Europe were considerably positively correlated with the AO. Storm surges were positively correlated with the Atlantic Multidecadal Oscillation (AMO) at various tide gauges along the Mediterranean coasts, the northwestern Atlantic and Pacific coasts of North America.

The temporal clustering of storm surge, wave height, and high sea level exceedances around the UK coastline

The temporal clustering of storms presents consecutive storm surge and wave hazards that can lead to amplified flood and erosional damages; thus, clustering is important for coastal stakeholders to consider. We analyse the prevalence of storm clustering around the UK coastline by examining the temporal and spatial characteristics of storm surge, wave height, and high still sea level exceedances at the 1 in 1- and 5-year return levels. First, at the interannual timescale, we show that there are periods of high/low exceedance counts on national and regional scales. Elevated annual counts of exceedances with smaller magnitudes can occur without a respective signal of higher-magnitude exceedances. Secondly, at the intra-annual timescale, we show that high proportions of exceedances are clustering over short timescales. Storm surge, wave height and still sea level exceedances occurring < 50 days after the prior exceedance at a given site account for between ~ 35–44% and ~ 15–22% of all exceedances at the 1 in 1- and 5-year return levels, respectively. Still sea levels have the highest proportion of exceedances clustered in quick succession, with ~ 25% of 1 in 1-year exceedances occurring < 2 days after the previous at the same site. Spatially, for UK storm surges and still sea levels, the North Sea has the lowest proportion of clustering, whereas the North Atlantic and Bristol Channel have the highest. For English wave records, the highest proportions of clustering are found in the North Sea for exceedances of a lower magnitude and the English Channel for exceedances of a higher magnitude. These findings illuminate the prevalence of the clustering of coastal hazards around the UK—helping coastal stakeholders evaluate the threat of surges, waves, and sea levels clustering over short periods.

A Flood Risk Management Model to Identify Optimal Defence Policies in Coastal Areas Considering Uncertainties in Climate Projections

Coastal areas are particularly vulnerable to flooding from heavy rainfall, sea storm surge, or a combination of the two. Recent studies project higher intensity and frequency of heavy rains, and progressive sea level rise continuing over the next decades. Pre-emptive and optimal flood defense policies that adaptively address climate change are needed. However, future climate projections have significant uncertainty due to multiple factors: (a) future CO2 emission scenarios; (b) uncertainties in climate modelling; (c) discount factor changes due to market fluctuations; (d) uncertain migration and population growth dynamics. Here, a methodology is proposed to identify the optimal design and timing of flood defense structures in which uncertainties in 21st century climate projections are explicitly considered probabilistically. A multi-objective optimization model is developed to minimize both the cost of the flood defence infrastructure system and the flooding hydraulic risk expressed by Expected Annual Damage (EAD). The decision variables of the multi-objective optimization problem are the size of defence system and the timing of implementation. The model accounts for the joint probability density functions of extreme rainfall, storm surge and sea level rise, as well as the damages, which are determined dynamically by the defence system state considering the probability and consequences of system failure, using a water depth–damage curve related to the land use (Corine Land Cover); water depth due to flooding are calculated by hydraulic model. A new dominant sorting genetic algorithm (NSGAII) is used to solve the multi-objective problem optimization. A case study is presented for the Pontina Plain (Lazio Italy), a coastal region, originally a swamp reclaimed about a hundred years ago, that is rich in urban centers and farms. A set of optimal adaptation policies, quantifying size and timing of flood defence constructions for different climate scenarios and belonging to the Pareto curve obtained by the NSGAII are identified for such a case study to mitigate the risk of flooding and to aid decision makers.

Estimating Compounding Storm Surge and Sea Level Rise Effects and Bias Correction Impact when Projecting Future Impact on Volcanic Islands in Oceania. Case Study of Viti Levu, Fiji

Oceania comprises many Small Island Developing States (SIDS), the majority of which are founded on volcanic islands. Small islands are generally vulnerable to the effects of climate change. However, a high number of islands and different coastal morphology make it challenging to accurately estimate climate change impact on this region. Nevertheless, quantifying hazards and thus assessing vulnerability is crucial for policymaking and adaptation efforts regarding SIDS. Meanwhile, Viti Levu is the principal island of Fiji. Therefore, climate change projection in Viti Levu helps estimate how volcanic islands in Oceania will be affected under future climate. This study projects the compound impact of storm surge by tropical cyclone (TC) and SLR on Viti Levu under current and future climate conditions. The primary goal of this study is to estimate the impact of extreme 50- and 100-years return storms on coastal areas and populations. This study also assesses the impact of the bias correction of TC intensity for impact assessment. Even though limited to one island, the results could facilitate the application on other volcanic islands, primarily in Melanesia. Even though Viti Levu is a high island, tropical cyclones can sustain extensive economic damage and result in high numbers of the temporarily displaced population in some low-lying coastal locations. The results show that bias can be significant when comparing observed and estimated datasets, particularly for less intense and future extreme events.

Probability forecast of storm surge levels in the Changjiang Estuary induced by tropical cyclones based on the Error-Estimation Ensemble method

This paper proposed a probability forecast methodology for the storm surges induced by tropical cyclones, including a probability forecast method of tropical cyclone tracks and a probability forecast scheme of storm surge levels. The probability forecast method of tropical cyclone tracks is developed based on the Error-Estimation Ensemble (EEE) method. Empirical distributions of the positions of the tropical cyclone centers around the EEE-forecasted ones are gained and used to make probability forecast of tropical cyclone tracks. It performs well in hindcasting the tropical cyclones that moved in the 24-h warning line of China in 2018. The probability forecast of storm surge levels is based on the probability forecast of tropical cyclone tracks. Under a given target fall-in probability, five possible tracks are used to drive the storm surge model and yields five forecasts of storm surge levels, the envelope of which is taken as the possible variation range of water level under the target fall-in probability. Using the probability forecast method, six launches of the probability forecast of three typical storm surges that affects the Changjiang Estuary are conduced, and the performance of the probability forecast method is assessed via a comparison between the target and realistic fall-in probabilities.

Shallow Water Equations in Hydraulics: Modeling, Numerics and Applications

This Special Issue aimed to provide a forum for the latest advances in hydraulic modeling based on the use of non-linear shallow water equations (NSWEs) and closely related models, as well for their novel applications in practical engineering. NSWEs play a critical role in the modeling and simulation of free surface flows in rivers and coastal areas and can predict tides, storm surge levels and coastline changes from hurricanes and ocean currents. NSWEs also arise in atmospheric flows, debris flows, internal flows and certain hydraulic structures such as open channels and reservoirs. Due to the important scientific value of NSWEs, research on effective and accurate numerical methods for their solutions has attracted great attention in the past two decades. Therefore, in this Special issue, original contributions in the following areas, though not exclusively, have been considered: new conceptual models and applications; flood inundation and routing; open channel flows; irrigation and drainage modeling; numerical simulation in hydraulics; novel numerical methods for shallow water equations and extended models; case studies; and high-performance computing.

Impact Assessment of Storm Surge and Climate Change-Enhanced Sea Level Rise on Atoll Nations: A Case Study of the Tarawa Atoll, Kiribati

The Pacific region consists of numerous Small Island Developing States (SIDS), one of the most vulnerable to flooding caused by compound effects of sea level rise (SLR) and storms. Nevertheless, individual studies regarding the impact assessment for SIDS, such as the low-lying Kiribati, remain scarce. This study assessed the impact of climate change-induced storm surge and SLR compounding effects on Tarawa, the most populous atoll of Kiribati, the largest coral atoll nation. It projected the impact using a combined dynamic surge and SLR model based on the IPCC AR5 RCP scenarios and 1/100 and 1/50 years return period storm events. This approach allows estimating the inundation scope and the consecutive exposed population by the end of the 21st century. The results of this study show that the pace of SLR is pivotal for Tarawa, as the sea level rise alone can claim more than 50% of the territory and pose a threat to over 60% of the population under the most intense greenhouse gas emissions scenario. Furthermore, most coasts on the lagoon side are particularly vulnerable. In contrast, the contribution of extreme events is generally minimal due to low wind speeds and the absence of tropical cyclones (TC). Despite this, it is clear the compound effects are critical and may inescapably bring drastic changes to the atoll nations by the end of this century. The impact assessment in this study draws attention to the social impact of climate change on SIDS, most notably atoll islands, and evaluates their adaptation potential.

Multi-step ahead short-term predictions of storm surge level using CNN and LSTM network

Multi-step ahead short-term predictions of storm surge level using CNN and LSTM network

Assimilation Research of Wind Stress Drag Coefficient Based on the Linear Expression

The wind stress drag coefficient plays an important role in storm surge models. This study reveals the influences of wind stress drag coefficients, which are given in form of formulas and inverted by the data assimilation method, on the storm surge levels in the Bohai Sea, Yellow Sea, and East China Sea during Typhoon 7008. In the process of data assimilation, the drag coefficient is based on the linear expression Cd = (a + b × U10) × 10−3 (generally speaking, a and b are empirical parameters determined by observed data). The results showed that the performance of the data assimilation method was far superior to those of drag coefficient formulas. Additionally, the simulated storm surge levels obviously changed in the neighborhood of typhoon eye. Furthermore, the effect of initial values of a and b in the Cd expression on the storm surge levels was also investigated when employing the data assimilation method. The results indicated that the simulation of storm surge level was the closest to the observation when a and b were simultaneously equal to zero, whereas the simulations had slight differences when the initial values of a and b were separately equal to the drag coefficients from the work of Smith, Wu, and Geernaert et al.. Therefore, we should choose appropriate initial values for a and b by using the data assimilation method. As a whole, the data assimilation method is much better than drag coefficient parameterization formulas in the simulation of storm surges.

Compound Hydrometeorological Extremes: Drivers, Mechanisms and Methods

Compound extremes pose immense challenges and hazards to communities, and this is particularly true for compound hydrometeorological extremes associated with deadly floods, surges, droughts, and heat waves. To mitigate and better adapt to compound hydrometeorological extremes, we need to better understand the state of knowledge of such extremes. Here we review the current advances in understanding compound hydrometeorological extremes: compound heat wave and drought (hot-dry), compound heat stress and extreme precipitation (hot-wet), cold-wet, cold-dry and compound flooding. We focus on the drivers of these extremes and methods used to investigate and quantify their associated risk. Overall, hot-dry compound extremes are tied to subtropical highs, blocking highs, atmospheric stagnation events, and planetary wave patterns, which are modulated by atmosphere-land feedbacks. Compared with hot-dry compound extremes, hot-wet events are less examined in the literature with most works focusing on case studies. The cold-wet compound events are commonly associated with snowfall and cold frontal systems. Although cold-dry events have been found to decrease, their underlying mechanisms require further investigation. Compound flooding encompasses storm surge and high rainfall, storm surge and sea level rise, storm surge and riverine flooding, and coastal and riverine flooding. Overall, there is a growing risk of compound flooding in the future due to changes in sea level rise, storm intensity, storm precipitation, and land-use-land-cover change. To understand processes and interactions underlying compound extremes, numerical models have been used to complement statistical modeling of the dependence between the components of compound extremes. While global climate models can simulate certain types of compound extremes, high-resolution regional models coupled with land and hydrological models are required to simulate the variability of compound extremes and to project changes in the risk of such extremes. In terms of statistical modeling of compound extremes, previous studies have used empirical approach, event coincidence analysis, multivariate distribution, the indicator approach, quantile regression and the Markov Chain method to understand the dependence, greatly advancing the state of science of compound extremes. Overall, the selection of methods depends on the type of compound extremes of interests and relevant variables.

Towards Sustainable, Resilient and Adaptive Urban Underground Space (UUS) Exploration, Land Subsidence and Economic Impact Spatial Model (USEM) in Shanghai, P.R. China: Systematic Reviews, Model Framework, Initial Results and Pre-Determined Challenges

As a coastal megacity, Shanghai despite having one of the world’s leading UUS exploration for utilities, transportation, metro system, commercial and residential spaces is continuously vulnerable to serious geo-environmental hazards risks and climate change impact: land subsidence, flooding, storm surge, and seawater level rise. Hence, it is imperative to study the continuous impact of rapid UUS development, land subsidence mechanisms, related geo-environmental hazards and its socio-economic impacts towards establishing a resilient, adaptive and sustainable UUS development via spatial planning and development model to lessen future adverse consequences. The aim of this paper is to present current progressive findings of the entitled research work. Methods conducted include systematic reviews of existing online journals available at open-sourced databases such as Google Scholar and Research Gate, determined USEM’s concrete steps consists of cause-effect, spatiotemporal, scenarios modelling and comparative analysis. The data gathered are mostly secondary. The four major findings are: (1) systematic reviews summarised outcomes; (2) determined USEM model framework; (3) Initial results produced from the USEM’s first step of cause-effect analysis in Shanghai and (4) Pre-determined challenges for the model. The model’s methods are expected to be used to study Shanghai condition in comparison with developing coastal megacity like Jakarta, Indonesia. It can also be referred by related interested experts especially towards assisting deeper understanding on geo-dynamics of land subsidence, UUS and economic impact via spatial modelling and formulating policies of adaptation and resilience in developed and developing coastal megacities in the world.

The relative influence of dune aspect ratio and beach width on dune erosion as a function of storm duration and surge level

Abstract. Dune height is an important predictor of impact during a storm event given that taller dunes have a lower likelihood of being overtopped than shorter dunes. However, the temporal dominance of the wave collision regime, wherein volume loss (erosion) from the dune occurs through dune retreat without overtopping, suggests that dune width must also be considered when evaluating the vulnerability of dunes to erosion. We use XBeach, a numerical model that simulates hydrodynamic processes, sediment transport, and morphologic change, to analyze storm-induced dune erosion as a function of dune aspect ratio (i.e., dune height versus dune width) for storms of varying intensity and duration. We find that low aspect ratio (low and wide) dunes lose less volume than high aspect ratio (tall and narrow) dunes during longer and more intense storms when the beach width is controlled for. In managed dune scenarios, where sand fences are used to construct a “fenced” dune seaward of the existing “natural” dune, we find that fenced dunes effectively prevent the natural dune behind them from experiencing any volume loss until the fenced dune is sufficiently eroded, reducing the magnitude of erosion of the natural dune by up to 50 %. We then control for dune morphology to assess volume loss as a function of beach width and confirm that beach width exerts a significant influence on dune erosion; a wide beach offers the greatest protection from erosion in all circumstances while the width of the dune determines how long the dune will last under persistent scarping. These findings suggest that efforts to maintain a wide beach may be effective at protecting coastal communities from dune loss. However, a trade-off may exist in maintaining wide beaches and dunes in that the protection offered in the short-term must be considered in concert with potentially long-term detrimental effects of limiting overwash, a process which is critical to maintaining island elevation as sea level rises.

Comprehensive Flood Risk Assessment for Wastewater Treatment Plants under Extreme Storm Events: A Case Study for New York City, United States

Wastewater treatment plants (WWTPs) in the City of New York, United States, are particularly vulnerable to frequent extreme weather events, including storm surges, high-intensity rainfall, and sea level rise, and are also affected by the cascade of these events. The complex structural configuration of WWTPs requires very fine-scale flood risk assessment, which current research has not pursued. We propose a robust technique to quantify the risk of inundations for the fourteen WWPTs through an automated sub-basin creation tool; 889 sub-basins were generated and merged with high-resolution building footprint data to create a comprehensive database for flood inundation analysis. The inundation depths and extents for the WWTPs and flood-prone regions were identified from hydrodynamic modeling of storm surge and sea level rise. The economic damage due to flooding for the WWTPs was also quantified using the HAZUS-MH model. Results indicated that the storm surges from various categories of hurricanes have the dominant impacts on flood depths around WWTPs, followed by high-intensity rainfall. Sea level rise was shown to have a relatively minor impact on flood depths. Results from economic damage analysis showed that the WWTPs are subjected to damage ranging from USD 60,000 to 720,000, depending on the size of the WWTP and the extremity of storm surge. The method of analyzing the inundation status of the research object through the sub-basin enables more accurate data to be obtained when calculating the runoff. It allows for a clearer view of the inundation status of the WWTPs when combined with the actual buildings. Using this database, predicting flood conditions of any extreme event or a cascade of extreme events can be conducted quickly and accurately.