Coastal Flooding Research Articles

A climate-impact-related transport infrastructure risk assessment for the City of Cape Town

Climate-related severe weather events pose a threat to transport infrastructure. Damage to transport infrastructure, in turn, threatens access to socio-economic opportunities (such as employment, healthcare and education). The aim of this paper is to quantify the volume of transport infrastructure at risk of weather-related hazards in the City of Cape Town, and to estimate the direct economic value thereof. Geographical Information Systems are used to overlay areas that are highly exposed to climate-related threats on the city's transport network and infrastructure, enabling the identification and quantification of the volume of infrastructure at risk. Cost estimates for the rehabilitation or reconstruction of this infrastructure obtained from various experts in the South African transport arena are applied to calculate the economic value of the infrastructure at risk. In total, the transport infrastructure at high risk in the case city is valued at R20 billion. Fires and coastal flooding pose the greatest threat. The analysis presented can inform planners and decision-makers on how to improve the resilience and where to reduce the vulnerability of transport infrastructure in the city.

Wave contributions to sea levels along the coast of northern South China Sea over 2002–2022

To better understand the wave contribution to coastal sea level variation in the northern South China Sea (SCS), the total sea level time series, which is estimated as the sum of waves, tides, atmospheric surges and altimetric sea levels, over the period of 2002–2022 are used in this study. It is found that tides and waves are two dominate contributors to coastal sea level changes at seasonal (45.7 % and 40.8 %) and interannual (58.6 % and 28.4 %) time scales. The sea level trend is dominated by the altimetric sea levels, but waves can also influence the trends in the Taiwan Strait and Beibu Gulf. The amount of sea level rise that doubles the frequency of the former 50-year return level is analyzed by using the Generalized Extreme Value Distribution (GEVD) model. The results show that the amount of sea level rise is closely related to the amplitude and variance of extreme sea levels. Because waves can significantly increase probability of occurrence of exceedingly large extreme sea levels, the amount of sea level rise (∼1 m) that doubles the coastal flooding frequency is much greater than that (∼10 cm) without the wave contribution. This result demonstrates the important role of waves in changing the coastal flooding frequency.

Numerical modelling and quantification of coastal urban compound flooding

Low-lying coastal cities are vulnerable to compound floods caused by many factors including river flows, tides and local rainfall. Many previous studies focus on the impacts of rainfall and tidal levels (two driving factors) on estuaries or regions near the main single river, while research about the three influencing factors on the floods for complex urban river networks remain relatively scarce. This paper proposes a framework to simulate the coastal compound flooding process, with a series of scenarios designed to explore the main flooding drivers and their worst possible combination. The approach is used to quantify the induced flooding consequences, in which a two-dimensional model is employed to conduct a case study for a city with complex river networks. The Qianshan River basin in Zhuhai City, located in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) of China is used as the case study. Application results show that: (i) the occurrence and properties of flooding in this coastal area are jointly affected by upstream river flows, downstream tidal levels and local rainfall intensity with different contribution levels, (ii) compared to tidal levels, local rainfall intensity has a larger impact on the flooding levels of the study region, and (iii) the worst scenario where a 50-year return period upstream river flow co-occurring with high tide and a 100-year return period rainfall can produce a flooding area up to 50 km2 for the study region, with an average of 1.3 m flooded depth. The proposed framework with its findings offers an in-depth understanding and quantification of coastal urban flood characteristics within the GBA of China.

Coastal Compound Flood Simulation through Coupled Multidimensional Modeling Framework

Coastal watersheds are prone to compound flooding caused by heavy rainfall, storm surge, and high tide. Accurate prediction and modeling of coastal compound floods (CCF) require precise physical representation in numerical models with appropriate numerical approximation associated with multi-driver physical processes for simulating the flood dynamics. To address this, an integrated and tightly coupled modeling approach is implemented to capture the nonlinear and complex processes involved in CCF and interconnectivity among Multidimensional Hydraulics (pipe and channel), Hydrodynamics (2D overland flow), and distributed Hydrologic (MH3) models are developed. The coupled modeling framework focuses on the development of interconnected meshes of the node-link-basin using the Interconnected Channel and Pond Routing (ICPR) model. This framework effectively incorporates the complex drainage network system of tidal creeks, tidal channels, underground sewer networks, and detention ponds of the Charleston Peninsula of South Carolina (SC). The dynamics of the urbanized floodplain in the peninsula are represented using high-resolution LiDAR-derived Digital Elevation Model (DEM) and Digital Surface Model (DSM). The overland flow is simulated using energy balance, momentum balance, and diffusive wave methods. The performance of the CCF model is tested for the 2015 SC historical flood and a high tide flood event. The momentum balance-based CCF model shows 98.35% accuracy in identifying street-level flooding locations. Furthermore, the model shows that incorporating DSM processing in the current study improves the accuracy of urban flood simulation by approximately 15% to 33% compared to LiDAR DEM. It is revealed that the momentum balance method outperforms to simulate the flood dynamics. Consequently, this study contributes to the modeling of CCF by developing an integrated MH3 system that enhances the physical representation of coastal hydrology at a fine scale.

A multi-risk approach for projecting climate change-associated coastal flood, applied to India

A multi-risk approach for projecting climate change-associated coastal flood, applied to India

The Study of Climate Change in Guam and The Effects of It

This research aims to investigate the effects that climate change has on the people of Guam. The earth's atmosphere will change over the next few decades, which will affect many areas of Guam life. Sea levels are rising, the air and ocean are getting warmer, and the ocean's acidity is increasing. Guam's coral reef ecosystems are predicted to be destroyed or severely damaged by these changes, which will also increase the flooding and typhoons causing damage, decrease the amount of freshwater available during the dry season, and increase the frequency of uncomfortable high air temperatures. This project explores questions such as: How is Guam being impacted by climate change? What damages did Typhoon Mawar of 2023 cause to Guam in aspects of people’s life? We investigated more on the detailed information on : Climate Change and Coral Loss, Tropical Storms, Rising Sea Level and Coastal Flooding, Rainfall and Water Supplies, Ocean Pollution Levels, Inland Plants and Animals, and Human Health of Guam.

Dynamic Modeling of Coastal Compound Flooding Hazards Due to Tides, Extratropical Storms, Waves, and Sea-Level Rise: A Case Study in the Salish Sea, Washington (USA)

The Puget Sound Coastal Storm Modeling System (PS-CoSMoS) is a tool designed to dynamically downscale future climate scenarios (i.e., projected changes in wind and pressure fields and temperature) to compute regional water levels, waves, and compound flooding over large geographic areas (100 s of kilometers) at high spatial resolutions (1 m) pertinent to coastal hazard assessments and planning. This research focuses on advancing robust and computationally efficient approaches to resolving the coastal compound flooding components for complex, estuary environments and their application to the Puget Sound region of Washington State (USA) and the greater Salish Sea. The modeling system provides coastal planners with projections of storm hazards and flood exposure for recurring flood events, spanning the annual to 1-percent annual chance of flooding, necessary to manage public safety and the prioritization and cost-efficient protection of critical infrastructure and valued ecosystems. The tool is applied and validated for Whatcom County, Washington, and includes a cross-shore profile model (XBeach) and overland flooding model (SFINCS) and is nested in a regional tide–surge model and wave model. Despite uncertainties in boundary conditions, hindcast simulations performed with the coupled model system accurately identified areas that were flooded during a recent storm in 2018. Flood hazards and risks are expected to increase exponentially as the sea level rises in the study area of 210 km of shoreline. With 1 m of sea-level rise, annual flood extents are projected to increase from 13 to 33 km2 (5 and 13% of low-lying Whatcom County) and flood risk (defined in USD) is projected to increase fifteenfold (from 14 to USD 206 million). PS-CoSMoS, like its prior iteration in California (CoSMoS), provides valuable coastal hazard projections to help communities plan for the impacts of sea-level rise and storms.

Stochastic simulation of storm surge extremes along the contiguous United States coastlines using the max-stable process

Extreme sea levels impact coastal society, property, and the environment. Various mitigation measures are engineered to reduce these impacts, which require extreme event probabilities typically estimated site-by-site. The site-by-site estimates usually have high uncertainty, are conditionally independent, and do not provide estimates for ungauged locations. In contrast, the max-stable process explicitly incorporates the spatial dependence structure and produces more realistic event probabilities and spatial surfaces. We leverage the max-stable process to compute extreme event probabilities at gridded locations (gauged and ungauged) and derive their spatial surfaces along the contiguous United States coastlines by pooling annual maximum (AM) surges from selected long-record tide gauges. We also generate synthetic AM surges at the grid locations using the predicted distribution parameters and reordering them in the rank space to integrate the spatiotemporal variability. The results will support coastal planners, engineers, and stakeholders to make the most precise and confident decisions for coastal flood risk reduction.

Mapping Compound Flooding Risks for Urban Resilience in Coastal Zones: A Comprehensive Methodological Review

Coastal regions, increasingly threatened by floods due to climate-change-driven extreme weather, lack a comprehensive study that integrates coastal and riverine flood dynamics. In response to this research gap, we conducted a comprehensive bibliometric analysis and thorough visualization and mapping of studies of compound flooding risk in coastal cities over the period 2014–2022, using VOSviewer and CiteSpace to analyze 407 publications in the Web of Science Core Collection database. The analytical results reveal two persistent research topics: the way to explore the return periods or joint probabilities of flood drivers using statistical modeling, and the quantification of flood risk with different return periods through numerical simulation. This article examines critical causes of compound coastal flooding, outlines the principal methodologies, details each method’s features, and compares their strengths, limitations, and uncertainties. This paper advocates for an integrated approach encompassing climate change, ocean–land systems, topography, human activity, land use, and hazard chains to enhance our understanding of flood risk mechanisms. This includes adopting an Earth system modeling framework with holistic coupling of Earth system components, merging process-based and data-driven models, enhancing model grid resolution, refining dynamical frameworks, comparing complex physical models with more straightforward methods, and exploring advanced data assimilation, machine learning, and quasi-real-time forecasting for researchers and emergency responders.

The Dunes Contribution to the Ecosystem Service of Coastal Protection on the Northern Coast of RS

Coastal dunes have a crucial role in offering the ecosystem service of natural coastal protection. This paper investigates the importance of dunes in providing the ecosystem service of natural coastal protection on the North Coast of the State of Rio Grande do Sul (RS) in southern Brazil. The Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST®) Coastal Vulnerability Model was used to quantify the relative susceptibility to coastal erosion and flooding within the study area. First, the model was run to assess the susceptibility to coastal hazards considering the presence of dunes. Then, the model was run again without the presence of dunes. By comparing the results of the two model simulations, it was possible to identify where dunes contribute to reducing coastal exposure, which indirectly reflects their role in providing the ecosystem service of natural coastal protection. There are two main dune fields in the study area, one in the northern coastal sector and one in the southern sector, respectively in the municipalities of Torres and Cidreira. The index of exposure values produced by the two model simulations were very similar for locations along the Torres coastline. In contrast, along the coast of Cidreira, results from the model simulation without dunes produced higher exposure values than the simulation with the presence of dunes, suggesting that the dunes have an important role in offering natural coastal protection. Dunes are a natural barrier against coastal erosion and flooding, but they are not the only factor influencing the ecosystem service of coastal protection. In Torres, for instance, the local topography and the wind patterns contribute to reducing local exposure. The greatest threat in this sector is the urban sprawl.

Compound Impact of Storm Surge and Flood Characteristics in Coastal Area Based on Copula

In low-lying coastal areas, the interplay of various factors including precipitation, river flow, and storm surge can lead to greater influence on floods when they occur simultaneously. The copula method was used in this study to investigate the bivariate flood risk of compounding storm surge and river discharge events in the Pearl River Delta (PRD). Our results indicate that while the correlation between storm surge and flood peak (S-Q) was weak, there was a strong dependence between the pairs of storm surge–flood volume (S-V) and storm surge–flood duration (S-D). For these three pairs, the Clayton copula was the optimal function for S-Q, while the Frank copula was the optimal function for S-V and S-D, respectively. When the flood volume exceeds 2.0 × 104 m3/s and the flood duration is more than 10 days, the bivariate hydrologic risk for S-V and S-D is observed to decrease rapidly. Furthermore, the failure probability (FP) would be underestimated when the combined impact of river flow and storm surge is ignored in coastal flood risk assessment. Such bivariate hydrologic risk analysis implies that when determining design values in coastal flood risk assessment, the combined impact of river flow and storm surge should be taken into account.

Simulation of Flood-Induced Human Migration at the Municipal Scale: A Stochastic Agent-Based Model of Relocation Response to Coastal Flooding

Human migration triggered by flooding will create sociodemographic, economic, and cultural challenges in coastal communities, and adaptation to these challenges will primarily occur at the municipal level. However, existing migration models at larger spatial scales do not necessarily capture relevant social responses to flooding at the local and municipal levels. Furthermore, projecting migration dynamics into the future becomes difficult due to uncertainties in human–environment interactions, particularly when historic observations are used for model calibration. This study proposes a stochastic agent-based model (ABM) designed for the long-term projection of municipal-scale migration due to repeated flood events. A baseline model is demonstrated initially, capable of using stochastic bottom-up decision rules to replicate county-level population. This approach is then combined with physical flood-exposure data to simulate how population projections diverge under different flooding assumptions. The methodology is applied to a study area comprising 16 counties in coastal Virginia and Maryland, U.S., and include rural areas which are often overlooked in adaptation research. The results show that incorporating flood impacts results in divergent population growth patterns in both urban and rural locations, demonstrating potential municipal-level migration response to coastal flooding.

Stochastic properties of coastal flooding events – Part 2: Probabilistic analysis

Abstract. Low-intensity but high-frequency coastal flooding, also known as nuisance flooding, can negatively affect low-lying coastal communities with potentially large socioeconomic effects. Partially driven by wave runup, this type of flooding is difficult to predict due to the complexity of the processes involved. Here, we present the results of a probabilistic analysis of flooding events measured on an eroded beach at the Texas coast. A high-resolution time series of the flooded area was obtained from pictures using convolutional neural network (CNN)-based semantic segmentation methods, as described in the first part of this contribution. After defining flooding events using a peak-over-threshold method, we found that their size follows an exponential distribution. Furthermore, consecutive flooding events were uncorrelated at daily timescales but correlated at hourly timescales, as expected from tidal and day–night cycles. Our measurements confirm the broader findings of a recent multi-site investigation of the probabilistic structure of high-water events that used a semi-empirical formulation for wave runup. Indeed, we found a relatively good statistical agreement between our CNN-based empirical flooding data and predictions using total-water-level estimations. As a consequence, our work supports the validity of a relatively simple probabilistic model of high-frequency coastal flooding driven by wave runup that can be used in coastal risk management and landscape evolution models.

Assessing Coastal Vulnerability and Evaluating the Effectiveness of Natural Habitats in Enhancing Coastal Resilience: A Case Study in Shanghai, China

This research evaluates the coastal vulnerability of Shanghai, evaluates the effectiveness of existing natural habitats in reducing that vulnerability, and, finally, provides recommendations to improve the resilience of the coastal areas. Shanghai is an important economic center that is home to a large population. However, the combination of ground subsidence, rising sea levels, and more frequent coastal flooding due to tropical cyclones poses escalating climate risks for Shanghai, demanding urgent mitigation measures. The InVEST Coastal Vulnerability Model was used in this study to assess Shanghai’s coastal vulnerability under the current situation and various scenarios that simulated the absence of natural habitats. The assessment results were analyzed through a comparison between different scenarios and spatial aggregation analysis. This study pinpointed highly vulnerable areas, primarily located on the east coast of Chongming Island, the east and northeast coasts of Hengsha Island, and the east coast of the mainland of Shanghai. These areas need to be prioritized for intervention. Also, it demonstrated the effectiveness of existing natural habitats in reducing coastal vulnerability, with large green spaces and salt marshes playing a greater role compared to small green spaces. This is the first study applying the InVEST Coastal Vulnerability Model to Shanghai, demonstrating the model’s potential in providing valuable information regarding coastal protection against the impacts of climate change in Shanghai. Insights from the findings of this study are useful in crafting sustainable land-use policies and plans for Shanghai.

Compound flood impacts from Hurricane Sandy on New York City in climate-driven storylines

Abstract. High impact events like Hurricane Sandy (2012) significantly affect society and decision-making around weather/climate adaptation. Our understanding of the potential effects of such events is limited to their rare historical occurrences. Climate change might alter these events to an extent that current adaptation responses become insufficient. Furthermore, internal climate variability in the current climate might also lead to slightly different events with possible larger societal impacts. Therefore, exploring high impact events under different conditions becomes important for (future) impact assessment. In this study, we create storylines of Sandy to assess compound coastal flooding on critical infrastructure in New York City under different scenarios, including climate change effects (on the storm and through sea level rise) and internal variability (variations in the storm's intensity and location). We find that 1 m of sea level rise increases average flood volumes by 4.2 times, while maximised precipitation scenarios (internal variability) lead to a 2.5-fold increase in flood volumes. The maximised precipitation scenarios impact inland critical infrastructure assets with low water levels, while sea level rise impacts fewer coastal assets though with high water levels. The diversity in hazards and impacts demonstrates the importance of building a set of relevant scenarios, including those representing the effects of climate change and internal variability. The integration of a modelling framework connecting meteorological conditions to local hazards and impacts provides relevant and accessible information that can directly be integrated into high impact event assessments.

Sea level rise estimation and projection from long-term multi-mission satellite altimetry and tidal data in the Southeast Asia region

ABSTRACT The Southeast Asian (SEA) region represents a complex coastal geographical location. Most countries in SEA are encircled by the ocean, with lowlands coastal areas. The advantages of long-term satellite altimetry and tide gauge measurements have enabled climate-related research, particularly sea level rise, to be examined extensively in spatial and temporal scales. In this study, sea level anomaly (SLA) derived from 26 years of multi-mission satellite altimeter data plus approximately 28 years of tidal data have been utilized. Then, the projection for every 10 years starting from 2030 until 2100 has been analysed using machine learning regression. The projections are then evaluated based on the global model of the Intergovernmental Panel on Climate Change (IPCC). The findings presented that sea level rise trends around SEA with overall means of 4.70 ± 0.37 mm yr−1 for the satellite altimeter and 3.41 ± 0.24 mm yr−1 for the coastal tide gauge. Sea level projection from coastal tide gauge and satellite altimeter are expected to rise up to 34.87 and 33.71 cm, respectively, in 2100. These outcomes prove that the altimetry data from Radar Altimeter Database System (RADS) and the redundant tidal data are capable in measuring the sea level in SEA. These results are also expected to be valuable for multidisciplinary environmental studies in SEA, such as coastal flooding, coastal erosion, and other effect of global warming. Thus, it would make a meaningful contribution towards Sustainable Development Goal (SDG) 13, climate action, by providing the sea level rate and prediction information.

Spatial Design Thinking in Coastal Defence Systems: Overtopping Dikes in Southend-On-Sea

Coastal dikes have been built for millennia to protect inhabited lands from exceptional high tides and storm events. Currently, many European countries are developing specific programs to integrate the construction of new dikes (or the raising of existing ones) into the built environment to face sea level rising. Technical difficulties in succeeding in this operation are questioning the paradigm of protection for the long term, pointing out the need for alternative strategies of adaptation that are not yet fully explored. This paper elaborates on innovative models to deal with coastal flooding, presenting the results of an interdisciplinary research and design process for the case-study of Southend-on-Sea (UK). Detailed numerical simulations are used to develop a spatial strategy to accommodate water during extreme events, introducing different prototypes of dike designs that include seawalls, enhanced roughness through rock and stepped revetments, as well as vegetation. The overall goal is to push forward the traditional approach of planning water protection infrastructure within the solely field of civil engineering. It elaborates on the integration of the disciplines of spatial design and engineering and presents novel advances in terms of spatial design for the revetment of overtopping dikes.

An analysis of coastal sand dune management in Oregon (United States) from the 19th to the 21st century

The Drivers-Pressures-State-Impact-Response (DPSIR) framework was employed to understand the land use policies developed to manage coastal sand dunes and their consequences in Oregon, United States of America, during two contrasting periods: from the 19th to the late 20th century and from there to the early 21st century. A combination of historical data and scientific literature was used for this study. Dune destabilization became a socio-economic issue as Euro-Americans settled in Oregon in the 19th century. Ammophila arenaria and Ammophila breviligulata were widely used for stabilization. This led to a paradigm shift regarding dunes, at a time when their management was becoming more complex due to socio-natural factors. As non-native beachgrasses turned invasive causing the loss of biodiversity and habitats, their removal became the focus to restore the active dunes to support the natural processes of the ecosystem. However, the removal of these beachgrasses, particularly, Ammophila arenaria, results in low dune heights, increasing the risk of coastal flooding by reducing their effectiveness as a natural defense against sea-level rise and extreme storm surges. The reason for the contrasting dune management policies in Oregon since the 1930 s is that the management response to environmental impacts due to human drivers creates new drivers, pressures, and corresponding impacts, as shown in the DPSIR analysis. Thus, land use policies for managing coastal dunes in Oregon and other places must balance efforts to restore the native biodiversity while minimizing coastal flooding in a context of accelerating and continuous sea-level rise in the 21st century.

Stochastic properties of coastal flooding events – Part 1: convolutional-neural-network-based semantic segmentation for water detection

Abstract. The frequency and intensity of coastal flooding is expected to accelerate in low-elevation coastal areas due to sea level rise. Coastal flooding due to wave overtopping affects coastal communities and infrastructure; however, it can be difficult to monitor in remote and vulnerable areas. Here we use a camera-based system to measure beach and back-beach flooding as part of the after-storm recovery of an eroded beach on the Texas coast. We analyze high-temporal resolution images of the beach using convolutional neural network (CNN)-based semantic segmentation to study the stochastic properties of flooding events. In the first part of this work, we focus on the application of semantic segmentation to identify water and overtopping events. We train and validate a CNN with over 500 manually classified images and introduce a post-processing method to reduce false positives. We find that the accuracy of CNN predictions of water pixels is around 90 % and strongly depends on the number and diversity of images used for training.

To rebuild or not to rebuild in coastal flood zones: The effects of framing and partisan cues on public support

AbstractCoastal storms are likely to increase in frequency and become even more damaging. An unresolved policy issue is whether current residents should receive federal subsidies through such programs as the Federal Flood Insurance Program or the FEMA disaster relief program that encourage thembe encouraged to rebuild in the same locations that have suffered damage through federal subsidies such as subsidized flood insurance or the FEMA disaster relief fund. This research investigated the impact that messages framed to either support or oppose such government assistance for rebuilding had on respondent opinion. We looked at opinion on not only the rebuilding itself but also other related subjects such as the economic and environmental impacts of rebuilding in situ. The research further looked at the ways in which attributing these messages to partisan sources affected response. We found that message frames either highlighting the benefits or the costs of rebuilding had a marked impact on public opinion and that partisan source cues played a smaller role. This issue of responsibility for rebuilding is likely to continue to become even more important as climate change puts even more coastal residents at risk.