Coastal Flood Risk Research Articles

EXPERIMENTAL EVALUATION OF THE PERFORMANCE OF A HYBRID ARTIFICIAL CORAL REEF WITH BRAIN AND STAGHORN CORALS

Considering that more than 40 percent of the world's population resides within 100 km of coastal areas [WRI, 2007], the protection of coastal communities is critical. Coral reefs provide a large variety of ecosystem services from fishing, recreation and tourism to coastal flood risk reduction to nearby populations. They act as a green, self-building and self-repairing breakwaters dissipating wave energy through wave breaking and bed friction [Beck, 2018]. However, coral reefs are not typically accounted for as a coastal infrastructure as their effects are not easy to quantify [Ferrario, 2014] as their interaction with waves depends on multiple parameters including the variable morphologies of the organisms that form the reef. In this study, the wave energy dissipation of a scaled reef model with two coral species (staghorn and brain corals) of variable cover was evaluated through a series of laboratory experiments at the University of Miami SUrge STructure Atmosphere INteraction (SUSTAIN) Facility, a wind/wave tank with hurricane capabilities. A scaled coral reef model was tested under the direct impact of swell and hurricane generated waves considering Froude similarity with a prototype reef in South Florida.

Evaluating the Effectiveness of Land-Use Policies in Preventing the Risk of Coastal Flooding: Coastal Regions of Helsinki and Espoo

Effectively resolving environmental problems often involves not only technical solutions but also examining and potentially reforming the political and institutional frameworks that govern how societies relate to the environment. Coastal flooding due to rising sea levels, for example, poses a significant threat to waterfront areas. Land-use regulations represent an effective means of mitigating this risk. Despite the multitude of strategies developed for tackling similar problems, relatively few have concentrated on assessing the effectiveness of land-use policies in managing such issues. This study examines a framework for evaluating the effectiveness of land-use policies in preventing sea flood risks in the capital regions of Finland. While the focus of this research is on the coastal regions of the cities of Helsinki and Espoo, its implications extend internationally. By integrating Geographical Information System (GIS) and simulation tools, we simulate future land-use scenarios based on values that reflect the effects of land-use policies. This framework can be applied to other coastal regions worldwide facing similar challenges. Using land cover data and GeoSOS-FLUS software, land-use simulations of the target areas were generated. Land-use planning performance in the target areas exhibited positive changes, as fewer vulnerable land-use types were located within the sea flood risk zones in 2018 compared to 2000. This simulation also shows a strong similarity to actual land-use in 2018, confirming the framework’s reliability. This paper presents a novel framework for evaluating the effectiveness of land-use policies in mitigating coastal flooding risks, focusing on the coastal regions of Helsinki and Espoo, Finland. By integrating GIS and simulation tools, the research demonstrates the utility of these tools in tracking land-use changes and analyzing policy impacts, enabling a nuanced assessment of policy effectiveness. Furthermore, the study highlights the importance of local knowledge and a localized approach to policy development, contributing to a deeper understanding of complex issues in urban planning and land-use management.

A Framework for Creating Virtual Reality Models for More Effective Coastal Flood Risk Communication

A Framework for Creating Virtual Reality Models for More Effective Coastal Flood Risk Communication

Determining Undersampled Coastal Tidal Harmonics Using Regularized Least Squares

AbstractSatellite altimetry, which measures water level with global coverage and high resolution, provides an unprecedented opportunity for a wide and refined understanding of the changing tides in the coastal area. But its sampling frequency is too low to satisfy the Nyquist frequency requirement and too few data points per year are available to recognize a sufficient number of tidal constituents to capture the trend of tidal changes on a yearly basis. To address these issues, a novel regularized least‐square approach is developed to relax the sampling interval limit to the range of 9–11 days, reaching the revisit time of the existing satellites. In this method, the prior information of the regional tidal amplitudes is used to support a least square analysis to obtain the amplitudes of the tidal constituents for water elevation time series of different lengths and time intervals. Synthetic data experiments performed in Delaware Bay and Galveston Bay showed that the proposed method can determine the tidal amplitudes with high accuracy and the sampling interval can be extended to the application level of major altimetry satellites. The proposed algorithm was further validated using the data of the altimetry mission, Jason‐3, to show its applicability to irregular and noisy data. The new method could help identify the changing tides with sea‐level rise and anthropogenic activities in coastal areas, informing coastal flooding risk assessment and ecosystem health analysis.

A spatially-dependent synthetic global dataset of extreme sea level events

Current coastal flood risk assessments fail to capture flood spatial dependence at large scales. In this paper, we develop the first global synthetic dataset of spatially-dependent extreme sea level events, by applying an existing conditional multivariate statistical model to 40-year global reanalysis sea levels. The resulting dataset contains 10,000 years of extreme events with realistic spatial dependence under current climate conditions. The benchmarking against reanalysis data demonstrates a high agreement, with a coefficient of determination (R2) of 0.96 for the mean event footprint sizes and a mean bias of −0.04 m for 1 in 50-year water levels. By comparing well-known historic events, we show that our approach can produce events with similar spatial characteristics. Our dataset enables the future development of spatially-dependent flood hazard maps for deriving accurate large-scale risk profiles, which can help yield new insights into the spatial patterns of coastal flooding and support coastal communities in devising effective management plans.

Urban Planning of Coastal Adaptation under Sea-Level Rise: An Agent-Based Model in the VIABLE Framework

Coastal flood risk and sea-level rise require decisions on investment in coastal protection and, in some cases, the relocation of urban areas. Models that formalize the relations between flooding costs, protective investments, and relocation can improve the analysis of the processes and issues involved and help to support decision-making better. In this paper, an agent-based model of a coastal city is represented in NetLogo. This model is based on the VIABLE modeling framework and describes adaptive dynamic agent behavior in a changing system. The hypothetical city faces damage caused by gradually rising sea levels and subsequent extreme sea-level events. To mitigate these risks, an “urban planner” agent has two adaptation measures at their disposal: developing coastal defenses or, as a more extreme measure, relocating vulnerable areas inland. As the simulation progresses and the decisions change with rising sea levels, the agent alters investments in these two measures to increase its value function, resulting in dynamic reactive behavior. Additionally, gradual sea-level rise is implemented in various modes, along with extreme sea-level events that cause severe short-term damage. The results of simulations under these modes and with multiple scenarios of agent action are presented. On average, agent behavior is quite reactive under limited foresight. Individual simulations yield a ‘priming’ effect when comparing different timings of extreme sea-level events, wherein an earlier extreme event primes the agent to adapt and thus be better prepared for subsequent events. Agent success with adaptation is also found to be sensitive to the costs involved, and these varying degrees of adaptation success are quantified using three parameters of adaptation success.

Linking human perception and scientific coastal flood risk assessment (Anlo Beach Community, Ghana)

Policymakers concerned with coastal management issues have recently focused their efforts on how people perceive flood risk. Understanding the public's perception of risk has become an essential component of contemporary flood risk management, as it provides a basis for designing practical and effective flood mitigation strategies. This study was conducted in Anlo Beach, one of the most vulnerable coastal communities in Ghana. It investigated household perceptions of flood risk and examined them against independent physical measures that assess exposure. In parallel, multivariate regression analysis was conducted to identify and establish the key factors influencing household perceptions of flood risk in the study area. The results showed that two variables, previous flood experience (data collected through the social survey) and factual exposure (assessed through GIS measurements), played an important role in determining the level of flood risk reported by households. In particular, the relationship between previous flooding experience and perceived level of coastal flood risk was both positive and statistically significant. Socio-demographic factors did not have a significant influence on risk reporting. We conclude that perception variables collected through social surveys can be used as proxy indicators of environmental risks when physical measures are not available. Biases based on the socio-economic status of respondents may exist, but they do not outweigh information derived from people's factual relationships with the environment. Further studies on the above factors would support flood risk reduction measures in the study area and West Africa, particularly in light of climate change.

Coevolution of Extreme Sea Levels and Sea‐Level Rise Under Global Warming

AbstractDesign of coastal defense structures like seawalls and breakwaters can no longer be based on stationarity assumption. In many parts of the world, an anticipated sea‐level rise (SLR) due to climate change will constitute present‐day extreme sea levels inappropriate for future coastal flood risk assessments since it will significantly increase their probability of occurrence. Here, we first show that global annual maxima sea levels (AMSLs) have been increasing in magnitude over the last decades, primarily due to a positive shift in mean sea level (MSL). Then, we apply non‐stationary extreme value theory to model the extremal behavior of sea levels with MSL as a covariate and quantify the evolution of AMSLs in the following decades using revised probabilistic sea‐level rise projections. Our analysis reveals that non‐stationary distributions exhibit distinct differences compared to simply considering stationary conditions with a change in location parameter equal to the amount of MSL rise. With the use of non‐stationary distributions, we show that by the year 2050 many locations will experience their present‐day 100‐yr return level as an event with return period less than 15 and 9 years under the moderate (RCP4.5) and high (RCP8.5) representative concentration pathways. Also, we find that by the end of this century almost all locations examined will encounter their current 100‐yr return level on an annual basis, even if CO2 concentration is kept at moderate levels (RCP4.5). Our assessment accounts for large uncertainty by incorporating ambiguities in both SLR projections and non‐stationary extreme value distribution parameters via a Monte Carlo simulation.

Adding sea ice effects to a global operational model (NEMO v3.6) for forecasting total water level: approach and impact

Abstract. In operational flood forecast systems, the effect of sea ice is typically neglected or parameterized solely in terms of ice concentration. In this study, an efficient way of adding ice effects to the global total water level prediction systems, via the ice–ocean stress, is described and evaluated. The approach features a novel, consistent representation of the tidal relative ice–ocean velocities, based on a transfer function derived from ice and ocean tidal ellipses given by an external ice–ocean model. The approach and its impact are demonstrated over four ice seasons in the Northern Hemisphere, using in situ observations and model predictions. We show that adding ice effects helps the model reproduce most of the observed seasonal modulations in tides (up to 40 % in amplitude and 50∘ in phase for M2) in the Arctic and Hudson Bay. The dominant driving mechanism for the seasonal modulations is shown to be the under-ice friction, acting in areas of shallow water (less than 100 m) and its accompanied large shifts in the amphidromes (up to 125 km). Important contributions from baroclinicity and tide–surge interaction due to ice–ocean stress are also found in the Arctic. Both mechanisms generally reinforce the seasonal modulations induced by the under-ice friction. In forecast systems that neglect or rely on simple ice concentration parameterizations, storm surges tend to be overestimated. With the inclusion of ice–ocean stress, surfaces stresses are significantly reduced (up to 100 % in landfast ice areas). Over the four ice seasons covered by this study, corrections up to 1.0 m to the overestimation of surges are achieved. Remaining limitations regarding the overestimated amphidrome shifts and insufficient ice break-up during large storms are discussed. Finally, the anticipated trend of increasing risk of coastal flooding in the Arctic, associated with decreasing ice and its profound impact on tides and storm surges, is briefly discussed.

Demonstrating the value of beaches for adaptation to future coastal flood risk

Cost-effective coastal flood adaptation requires a realistic valuation of losses, costs and benefits considering the uncertainty of future flood projections and limited resources for adaptation. Here we present an approach to quantify the flood protection benefits of beaches accounting for the dynamic interaction of storm erosion, long-term shoreline evolution and flooding. We apply the method in Narrabeen-Collaroy (Australia) considering uncertainty in different shared socioeconomic pathways, sea-level rise projections, and beach conditions. By 2100, results show that failing to consider erosion can underestimate flood damage by a factor of 2 and maintaining present-day beach width can avoid 785 million AUD worth assets from flood damage. By 2050, the flood protection and recreational benefits of holding the current mean shoreline could be more than 150 times the cost of nourishment. Our results give insight on the benefits of beaches for adaptation and can help accelerate financial instruments for restoration.

The Coast-RiskBySea model for climate proof decision-making support

Climate scenarios require the implementation of climate proof actions. The effectiveness of such projects directly depends on the knowledge of local contexts and on the built environment’s characteristics in relation to climate risk conditions. Innovative methods of knowledge can be implemented through the use of enabling technologies. Based on these considerations, the paper proposes the Coast-RiskBySea model as support for climate proof decision-making in relation to coastal flood risk, with the aim of developing new synergies between environmental design and enabling technologies. The proposed model was tested on an application case in the city of Naples by simulating a climate proof measure to assess the effectiveness of this solution in terms of damage reduction, and to define the potential and criticality of the approach.

Review of modelling approaches for flood risk assessment in Chinese coastal cities

Floods are thought of to be the foremost frequent natural disaster, and the flood risk assessment is the evaluation of inundation and the damage suffered when subjected to floods of different frequencies, based on conditions such as the natural environment, flood characteristics and the socio-economic situation of the study area. Climate change, sea level rise, ground subsidence and rapid urbanization are expected to increase the risk of coastal urban flooding. Inundation scenarios based on hydrological and hydrodynamic models of future extreme storm floods are now becoming an effective method for studying future trends in flooding. This paper reviews several models for simulating floods and their advantages and disadvantages, focusing on coastal areas of China, to provide a basis for scientific understanding and effective adaptation to future flood risks. Flood hazard elimination is predicated on hydrological and hydraulic modelling, ground information assortment and remote sensing. This article analyses the capabilities and limitations of the current tools used for flood hazard assessment and simultaneously demonstrates that the scientific understanding and effective methods can be used to eliminate the risk of future floods.

Modified foredune eco-morphology in southeast Australia

Human activities are playing increasingly significant roles in the dynamics and evolution of coastal dunes, one of the most intensively used environments on Earth. Not only do beaches and adjacent foredunes provide tourism and recreational uses but they also form an important buffer between coastal settlements and the ocean. Modified or “urbanised” foredunes often differ from their natural counterparts in terms of characteristics such as location, dimension, orientation, topographic variability, sediment features, mobility, and the degree to which they can initiate and/or develop further through natural processes (e.g. progradation). Much of this research has been focused on dunes found in Europe and the USA, with limited work done elsewhere. The availability of LiDAR-derived topographic data for the entire coast enables a state-wide assessment of foredune morphology in New South Wales (NSW), and a basis for determining the effects of human modification. This study examines the current morphological characteristics of foredunes in NSW, by comparing modified with natural settings, to understand how human activities and uses have impacted foredune morphology. Results show highly modified incipient and established foredunes were significantly narrower and reached lower elevations, consequently with smaller volumes of sand than their more natural counterparts. On average, both highly modified established and incipient dunes were half the size of the unmodified systems, and on average modified dunes had volumes of ∼350 m3 m−1, whereas unmodified dunes contained sand volumes of ∼740 m3 m−1. By lowering dune elevation and sand volumes, human modifications have created environments that are more exposed and potentially vulnerable to erosion, placing coastal properties, infrastructure and communities (especially those found behind modified dune systems) at greater risk of erosion, coastal flooding and inundation, particularly during storm events.

Ensemble Hindcasting of Coastal Wave Heights

Long records of wave parameters are central to the estimation of coastal flooding risk and the causes of coastal erosion. This paper leverages the predictive power of wave height history and correlations with wind speed and direction to build statistical models for time series of wave heights to develop a method to fill data-gaps and extend the record length of coastal wave observations. A threshold regression model is built where the threshold parameter, based on lagged wind speed, explains the nonlinear associations, and the lagged predictors in the model are based on a well-established empirical wind-wave relationship. The predictive model is completed by addressing the residual conditional heteroscedasticity using a GARCH model. This comprehensive model is trained on time series data from 2005 to 2013, using wave height and wind data both observed from a buoy in Long Island Sound. Subsequently, replacing wind data with observations from a nearby coastal station provides a similar level of predictive accuracy. This approach can be used to hindcast wave heights for past decades given only wind information at a coastal station. These hindcasts are used as a representative of the unobserved past to carry out extreme value analysis by fitting Generalized Pareto (GP) distribution in a peaks over threshold (POT) framework. By analyzing longer periods of data, we can obtain reliable return value estimates to help design better coastal protection structures.

Estimating coastal flood damage costs to transit infrastructure under future sea level rise

Future sea level rise (SLR) and associated increases in the frequency and intensity of coastal flooding poses significant threats to coastal communities and transit systems. Yet current literature and practice lack methods for estimating flood damage costs to transit systems. Here, we construct an event-specific flood damage cost estimation framework for transit systems, simulating separately flood ingress into underground spaces. We apply this framework to the MBTA rail transit system in Boston, estimating damages under several coastal flood events with SLR and project expected annualized losses (EAL) through 2100 with uncertain SLR. We estimate EALs to the MBTA system have doubled since 2008 to $24.4 M/year and are expected to reach $58 M/year by 2030 under all SLR scenarios. Our results suggest that absent adaptation schemes, particularly at tunnel ingress locations, coastal flood risk will continue to accelerate, potentially resulting in permanent inundation of underground and low-lying sections of the transit system.

Economic optimization of coastal flood defence systems including storm surge barrier closure reliability

AbstractStorm surge barriers are large movable hydraulic structures which close during a storm surge to prevent coastal floods. In the regions they protect, a failure to close the barrier is often the most likely cause for a catastrophic flood. Nevertheless, flood risk assessments usually only focus on raising flood defences behind the barrier. Despite its importance, there is no general method to assess the costs and benefits of improving the closure reliability. This paper presents a model that optimises investments considering both closure reliability improvements and raising flood defences behind the barrier, using the region protected by the Maeslant barrier as a case. We substantiate that constructing the Maeslant barrier was an optimal economic decision. Moreover, we demonstrate large investments such as a redundant barrier already being economically sound with a few decimetres of sea level rise. Based on our experience with this case study, we expect the model is useful in finding strategies to adapt to rising sea levels and other developments that cause coastal flood risk to rise worldwide.

Determination of Current and Future Extreme Sea Levels at the Local Scale in Port-Bouët Bay (Côte d’Ivoire)

The Port-Bouët Bay shoreline is threatened by extreme sea level (ESL) events, which result from the combination of storm tide, wave run-up, and sea level rise (SLR). This study provides comprehensive scenarios of current and future ESLs at the local scale along the bay to understand the evolution of the phenomenon and promote local adaptation. The methodological steps involve first reconstructing historical storm tide and wave run-up data using a hydrodynamic model (D-flow FM) and the empirical model of Stockdon et al. Second, the Generalized Pareto Distribution (GPD) model fitted to the Peaks-Over-Thresholds (POT) method is applied to the data to calculate extreme return levels. Third, we combine the extreme storm tide and wave run-up using the joint probability method to obtain the current ESLs. Finally, the current ESLs are integrated with recent SLR projections to provide future ESL estimates. The results show that the current ESLs are relatively high, with 100-year return levels of 4.37 m ± 0.51, 4.97 m ± 0.57, and 4.48 m ± 0.5 at Vridi, Petit-Bassam, and Sogefiha respectively. By end-century, under the SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios, the future SLR is expected to increase the current ESLs by 0.49 m, 0.62 m, and 0.84 m, respectively. This could lead to a more frequent occurrence of the current 100-year return period, happening once every 2 years by 2100, especially under SSP5-8.5. The developed SLR scenarios can be used to assess the potential coastal flood risk in the study area for sustainable and effective coastal management and planning.

Modelling economic risk to sea‐level rise and storms at the coastal margin

AbstractWe develop a methodological approach through integrated assessment using System Dynamics modelling and Scenario Planning to investigate the economic vulnerability of coastal communities to the compounding impacts of sea‐level rise (SLR) and storm flooding and inundation associated with climate change. The approach uses a coastal flood risk assessment that quantifies physical drivers alongside socio‐economic well‐being for coastal communities to provide a methodology for managing uncertain futures through causal relationships in System Dynamics. A New Zealand case study is used to illustrate the long‐term economic impacts of inaction under different SLR projections and recognise critical tolerance thresholds to help exposed property owners plan their future. Modelling scenarios using this integrated approach identified two stand‐out drivers that influence a behavioural response of communities to coastal inundation at the local scale: first, the ongoing likelihood of risk transfer to the insurance industry, and second, the decisions of households and firms to accept risk for the added value of coastal living. Model outputs suggest that the threat posed by coastal hazards drives a behavioural, socio‐economic response that exceeds the initial economic exposure of capital assets. In the economic short term (1–10 years) and medium term (10–20 years), vulnerable communities accept the risk of capital loss and loss of insurability, favouring the amenity of coastal living. However, in the long term (+20 years), economic losses from repeat flooding increase risk‐based insurance premiums, promote insurance withdrawal and drive negative corrections in property valuations. Unanticipated insights were obtained from the modelling, including the likely timing of tolerance thresholds, particularly the insurance withdrawal point, which is critical to insurer/consumer decision‐making and community planning.

A coupled agent-based model for France for simulating adaptation and migration decisions under future coastal flood risk

In this study, we couple an integrated flood damage and agent-based model (ABM) with a gravity model of internal migration and a flood risk module (DYNAMO-M) to project household adaptation and migration decisions under increasing coastal flood risk in France. We ground the agent decision rules in a framework of subjective expected utility theory. This method addresses agent’s bounded rationality related to risk perception and risk aversion and simulates the impact of push, pull, and mooring factors on migration and adaptation decisions. The agents are parameterized using subnational statistics, and the model is calibrated using a household survey on adaptation uptake. Subsequently, the model simulates household adaptation and migration based on increasing coastal flood damage from 2015 until 2080. A medium population growth scenario is used to simulate future population development, and sea level rise (SLR) is assessed for different climate scenarios. The results indicate that SLR can drive migration exceeding 8000 and 10,000 coastal inhabitants for 2080 under the Representative Concentration Pathways 4.5 and 8.5, respectively. Although household adaptation to flood risk strongly impacts projected annual flood damage, its impact on migration decisions is small and falls within the 90% confidence interval of model runs. Projections of coastal migration under SLR are most sensitive to migration costs and coastal flood protection standards, highlighting the need for better characterization of both in modeling exercises. The modeling framework demonstrated in this study can be upscaled to the global scale and function as a platform for a more integrated assessment of SLR-induced migration.

A climate-conditioned catastrophe risk model for UK flooding

Abstract. We present a transparent and validated climate-conditioned catastrophe flood model for the UK, that simulates pluvial, fluvial and coastal flood risks at 1 arcsec spatial resolution (∼ 20–25 m). Hazard layers for 10 different return periods are produced over the whole UK for historic, 2020, 2030, 2050 and 2070 conditions using the UK Climate Projections 2018 (UKCP18) climate simulations. From these, monetary losses are computed for five specific global warming levels above pre-industrial values (0.6, 1.1, 1.8, 2.5 and 3.3 ∘C). The analysis contains a greater level of detail and nuance compared to previous work, and represents our current best understanding of the UK's changing flood risk landscape. Validation against historical national return period flood maps yielded critical success index values of 0.65 and 0.76 for England and Wales, respectively, and maximum water levels for the Carlisle 2005 flood were replicated to a root mean square error (RMSE) of 0.41 m without calibration. This level of skill is similar to local modelling with site-specific data. Expected annual damage in 2020 was GBP 730 million, which compares favourably to the observed value of GBP 714 million reported by the Association of British Insurers. Previous UK flood loss estimates based on government data are ∼ 3× higher, and lie well outside our modelled loss distribution, which is plausibly centred on the observations. We estimate that UK 1 % annual probability flood losses were ∼ 6 % greater for the average climate conditions of 2020 (∼ 1.1 ∘C of warming) compared to those of 1990 (∼ 0.6 ∘C of warming), and this increase can be kept to around ∼ 8 % if all countries' COP26 2030 carbon emission reduction pledges and “net zero” commitments are implemented in full. Implementing only the COP26 pledges increases UK 1 % annual probability flood losses by 23 % above average 1990 values, and potentially 37 % in a “worst case” scenario where carbon reduction targets are missed and climate sensitivity is high.