Coastal Flood Risk Assessment Research Articles

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.

Coastal Flood risk assessment using ensemble multi-criteria decision-making with machine learning approaches

Coastal areas are at a higher risk of flooding, and novel changes in the climate are induced to raise the sea level. Flood acceleration and frequency have increased recently because of unplanned infrastructural conveniences and anthropogenic activities. Therefore, the assessment of flood susceptibility mapping is considered the most significant flood management model. In this paper, flood susceptibility identification is performed by applying the innovative Multi-criteria decision-making model (MCDM) called Analytical Hierarchy Process (AHP) by ensembles with Support vector machine (AHP-SVM) and Decision Tree (AHP-DT). This model combines two Representation concentration pathway (RCP) scenarios such as RCP 2.6 & RCP 8.5. The factors influencing the coastal flooding in Bandar Abbas, Iran, identified through Flood susceptibility mapping. Multi-criteria decision-making (MCDM) has been applied to evaluate the Coastal flood conditioning factors, and ensemble machine learning (ML) approaches are employed for Coastal risk factor (CRF) prediction and classification. The statistical variances are measured through Friedman and Wilcoxon signed rank tests and statistical metrics such as Accuracy, sensitivity, and specificity. Among the models, AHP-DT obtained an improved AUC value of ROC as 0.95. After applying the ML models, the northern and western park of Raidak Basin River recognises very low and low flood susceptibility because of their topographic characteristics. The eastern part of the middle section fell very high and high CFSM. Observed from this result analysis, the people living nearer to the coastline are distributed by the low to medium exposure in the region of the west and middle of the considered study area. The results of this study can help decision-makers take necessary risk reduction approaches in the high-risk flooding zones of the coastal system.

Coastal Flood Risk Assessment: An Approach to Accurately Map Flooding through National Registry-Reported Events

The escalating frequency and severity of climate-related hazards in the Mediterranean, particularly in the historic town of Piran, Slovenia, underscore the critical need for enhanced coastal flood prediction and efficient early warning systems. This study delves into the impediments of available coastal flood hazard maps and the existing early warning system, which rely on distant sensors, neglecting the town’s unique microclimate. The current study leverages the public registry maintained by the Administration of the Republic of Slovenia for Civil Protection and Disaster Relief (URSZR), an underutilized resource for generating comprehensive and accurate flooding maps for Piran. Here, we show that in the historic town of Piran, floodings reported through the national registry can be used to map coastal flooding by means of verification and validation of the georeferenced reports therein, with subsequent correlation analysis (hotspot, cluster, and elevation polygons) that show temporal and spatial patterns. The innovative approach adopted in this study aims to bolster the accuracy and reliability of flooding data, offering a more nuanced understanding of flood patterns (in Piran, but generally applicable where national or regional registries are available). The findings of this research illuminate the pressing need for localized field-report and sensor systems to enhance the precision of flood predictions. The study underscores the pivotal role of accurate, localized data in fortifying coastal towns against the escalating impacts of climate change, safeguarding both the inhabitants and the invaluable architectural heritage of historic areas.

Coastal Flood Mapping with Two Approaches Based on Observations at Furadouro, Northern Portugal

This study assesses coastal flooding extension mapping based on two innovative approaches. The first is based on the coupling of two robust numerical models (SWASH and LISFLOOD); in this case, discharges were extracted from the wave overtopping results from SWASH 1D and set as boundary conditions for LISFLOOD on the crest of an existing seawall where overtopping typically occurs. The second, hereby called the ‘Tilted Bathtub Approach’ (TBTA), is based on wave run-up levels and buffering the affected area of a prior flooding event, adjusting it for expected sea states according to different return periods. The proposed approaches are applied to a case study on the Northern Portuguese coast, at Furadouro beach, in the municipality of Ovar, which has been facing multiple flooding episodes throughout recent years, including a dramatic storm in February 2014. This event was used as validation for the proposed methods. A 30-year-long hourly local wave climate time series was used both to perform an extreme value analysis in order to obtain expected sea states according to different return periods and also for performing a sensitivity test for established empirical formulas to estimate wave run-up in this particular case. Results indicate both approaches are valuable: they yield coherent flood extension predictions that align well with the real inundated area from the 2014 storm. The convergence of these findings underscores the potential for these methods in future coastal flood risk assessment, planning, and understanding of coastal responses under extreme weather conditions.

A saturated stochastic simulator: synthetic US Gulf coast tropical cyclone precipitation fields

The space–time fields of rainfall during a hurricane and tropical storm (TC) landfall are critical for coastal flood risk preparedness, assessment, and mitigation. We present an approach for the stochastic simulation of rainfall fields that leverages observed, high-resolution spatial fields of historical landfalling TCs rainfall that is derived from multiple instrumental and remote sensing sources, and key variables recorded for historical TCs. Spatial realizations of rainfall at each time step are simulated conditional on the variables representing the ambient conditions. We use 6 hourly precipitation fields of tropical cyclones from 1983 to 2019 that made landfall on the Gulf coast of the US, starting from 24 h before landfall until the end of the track. A conditional K-nearest neighbor method is used to generate the simulations. The TC attributes used for conditioning are the preseason large-scale climate indices, the storm maximum wind speed, minimum central pressure, the latitude and speed of movement of the storm center, and the proportion of storm area over land or ocean. Simulation of rainfall for three hurricanes that are kept out of the sample: Katrina [2005], Rita [2005], and Harvey [2017] are used to evaluate the method. The utility of coupling the approach to a hurricane track simulator applied for a full season is demonstrated by an out-of-sample simulation of the 2020 season.

BEACH GROUNDWATER IMPACTS ON WAVE OVERTOPPING FLOODING

Coastal flooding is a growing socioeconomic and humanitarian hazard (e.g., Nicholls, 2010). Increased beach groundwater levels may inundate low-lying areas and simultaneously propagate swash impacts onto higher beach and backshore elevations. Generally, coastal flood modeling and risk assessment characterize only surface flows, neglecting beach groundwater and swash zone processes such as infiltration and porous media flow. Numerous studies have considered the effects of swash on groundwater (e.g., Gourlay, 1992). Infiltration leads to reduced wave runup (Pintado-Patino et al., 2015) and is promoted by low beach groundwater levels (Bakhtyar et al., 2011), suggesting that beach groundwater plays a critical role in infiltration/exfiltration processes. Notably, the impacts of beach groundwater on swash flows and subsequent consequences on coastal flooding have not been explored. Coastal flooding from wave overtopping is expected to occur around the maximum tide. However, recent field observations suggest maximum overtopping lags behind high tide and is in phase with maximum groundwater levels. In this study, a turbulence and depth resolving numerical model is developed to examine the interaction between beach groundwater and wave overtopping processes.

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.

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.

The complexity of the coastal zone: Definition of typologies in Portugal as a contribution to coastal disaster risk reduction and management

Mainland Portugal presents an extensive, diversified and complex coastal zone. This diversity gives rise to diverse geomorphologic features which support different densities of human occupancy and a range of land uses and activities. In this sense, the coastal zone becomes an area of great national strategic value, but has also into a multi-hazard zone. To understand this complexity, two statistical techniques, namely Cluster Analysis and Principal Component Analysis, were used to identify and differentiate, between the different coastal typologies, based on a set of variables that express the territorial complexity and the occurrences and impacts of coastal flooding. The chosen methodology enabled a diverse set of coastal typologies to be identified, which contributes towards differentiating local specific characteristics. The holistic and differentiating nature of the applied methodology and the results obtained allows it to contribute to the definition of mitigation strategies and the implementation of adaptation measures. The results enable us to define a set of typologies associated with Coastal Disaster Risk Reduction and Management which are an important contribution to comprehensive coastal flood risk assessment and management.

Coastal Flood Risk and Smart Resilience Evaluation under a Changing Climate

Coastal areas are highly vulnerable to flood risks, which are exacerbated by the changing climate. This paper provides a comprehensive review of the literature on coastal flood risk assessment and resilience evaluation and proposes a smart-resilient city framework based on pre-disaster, mid-disaster, and post-disaster evaluations. First, this paper systematically reviews the origin of the resilience concept and the development of flood resilience. Next, it introduces the social-acceptable risk criteria and the flood resilience level for different flood phases. Then, a coastal flood resilience management system for smart cities is proposed, covering 3 phases of flood disasters (before, during, and after). Risk assessment is essential in pre-disaster scenarios because it provides a comprehensive understanding of the potential hazards and vulnerabilities of an area or system. Big data monitoring during disasters is an essential component of effective emergency management and response that can allow for more informed decisions and thus quicker, more effective responses to disasters, ultimately saving lives and minimizing damage. Data-informed loss assessments are crucial in providing a rapid, accurate understanding of post-disaster impact. This understanding, in turn, is instrumental in expediting recovery and reconstruction efforts by aiding decision-making processes and resource allocation. Finally, the impacts of climate change on the 3 phases of flood disasters are summarized. The development of more resilient coastal communities that are better equipped to withstand the impacts of climate change and adapt to changing environmental conditions is crucial. To address coastal compound floods, researchers should focus on understanding trigging factor interactions, assessing economic and social risks, improving monitoring and emergency response systems, and promoting interdisciplinary research with data openness. These strategies will enable comprehensive and holistic management of coastal flood risks in the context of climate change.

Enhancing a Coastal Territorial Vulnerability Index: Anticipating the Impacts of Coastal Flooding with a Local Scale Approach

The coastal zone of mainland Portugal is characterized by its morpho-sedimentary diversity such as estuaries, lagoons, barrier islands, beaches, dunes and cliffs. The high population density and the multiplicity of land use, occupation and activities, makes it an area of great national strategic value. This transforms the coastal zone into a multi-hazard zone, where the occurrences related to coastal flooding and overtopping stand out. In the present work, a multidimensional methodology called Coastal Territorial Vulnerability Index (CTVI) was developed and applied in three selected areas with a historical record of coastal impacts, to analyze, evaluate and interpret the local vulnerability. The methodology considers four components of coastal territorial vulnerability: morphology, land value, buildings and public areas characteristics. These four components are combined to calculate the CTVI. The results highlight the differences for the analyzed areas, allowing the differentiation of natural and artificial areas. In the natural areas a moderate CTVI predominates, while in the latter, a high and very high CTVI stands out. The results contribute to the development of a comprehensive coastal flood risk assessment and forecasting the impacts.

EMPOWERING GEO-BASED AI ALGORITHM TO AID COASTAL FLOOD RISK ANALYSIS: A REVIEW AND FRAMEWORK DEVELOPMENT

Abstract. Climate change and current susceptibilities exacerbated the coastal flood loss and damage resulting in livelihoods and property damage. Urban areas in the Low to Lower-Middle Income Countries are expected to be disproportionately impacted by the disaster, given a higher share of citizens living in the Low Elevation Coastal Zone, limited financial resources, and poorly constructed disaster protection. Documentation of historical coastal floods, population, and property affected, could advance the assessment by considering those parameters in risk analysis. Besides, incorporating such geographic features e.g., mangroves as the ecological solution for alternative coastal flood protection in the prediction is also essential. Mangrove is considered fit for the LLMIC primarily situated in the tropical zone. The prediction utilizing spatial Machine Learning (ML) could aid climate-related disaster risk analysis and contribute to risk reduction and policy suggestions to improve disaster resilience. The research aims to archive recent studies on the application of geospatial science empowering Artificial Intelligence, notably ML in coastal flood risk assessment, so-called GIS-based AI. Another aim is to document population, property, and mangrove distribution across the LLMIC. Artificial Neural Networks were mostly utilized for disaster risk assessment in past research. The number of 58 historical coastal flood events and 908 expected coastal flood hotspots for 2006 to 2021 has been documented. Over 1,2 million Km2 falls under vulnerable areas toward coastal flood in LLMIC under different settlement types where Large City (urban areas) dominates it. Mangrove distribution is mainly distributed across tropical regions mostly distributed along the Southeast Asia coast.

Storm surge contributions to flood hazards on Canada's Atlantic Coast

AbstractA numerical hydrodynamic model was used to simulate the generation and evolution of storm surges in Atlantic Canada in response to synoptic‐scale surface wind and atmospheric pressure fields. The modelling was conducted as part of a broader initiative to support community‐scale inundation modelling and coastal flood risk assessment for communities located in the Acadian Peninsula region of New Brunswick. The 44 largest storm surge events on record at a tide gauge proximate to the region of interest were simulated using the numerical model. Initially, a comparison between simulated storm surges and peak non‐tidal residuals from tide gauge records showed relatively poor agreement, producing an R2 value of 0.403. Model skill was improved by incorporating the influence of sea ice cover on air‐sea momentum transfer in the hydrodynamic model, and improved correlation with measured residuals was obtained by adding estimates of wave set‐up to the predicted storm surges, ultimately resulting in an R2 value of 0.803. The results of the simulations provided a basis for identifying distinct regional factors affecting storm surges and water level residuals and demonstrated conditions where wave set‐up and sea ice cover play an important role in contributing to extreme high water levels.

Compounding Effects of Fluvial Flooding and Storm Tides on Coastal Flooding Risk in the Coastal-Estuarine Region of Southeastern China

In coastal areas of southeastern China, multiple flood drivers such as river flow, precipitation and coastal water level can lead to compound flooding which is often much greater than flooding simulated by one flood driver in isolation. Bivariate probability distributions accounting for compound flooding from river discharge and sea level were constructed based on MvCAT (Multivariate Copula Analysis Toolbox) combined with goodness of fit tests in 15 coastal-estuarine regions of Southeastern China. Flood typing-based bivariate probability distributions considering multiple flood-generating mechanisms were also built. Our results indicated that the performance of flood typing-based bivariate distribution was not significantly better than the bivariate probability distribution in coastal-estuarine regions based on the Mann–Whitney U test; the compounding effects of river discharge and sea level had limited impact on bivariate return periods, but had greater impact on coastal flooding risk in terms of design values. Ignoring compounding effects of river discharge and sea level leads to significant underestimation of design values. The results suggest that the compounding effect of river discharge and sea level should be considered when calculating design values in coastal flood risk assessment.

Multi-dimensional parametric coastal flood risk assessment at a regional scale using GIS

Multi-dimensional parametric coastal flood risk assessment at a regional scale using GIS

New ICESat-2 Satellite LiDAR Data Allow First Global Lowland DTM Suitable for Accurate Coastal Flood Risk Assessment

No accurate global lowland digital terrain model (DTM) exists to date that allows reliable quantification of coastal lowland flood risk, currently and with sea-level rise. We created the first global coastal lowland DTM that is derived from satellite LiDAR data. The global LiDAR lowland DTM (GLL_DTM_v1) at 0.05-degree resolution (~5 × 5 km) is created from ICESat-2 data collected between 14 October 2018 and 13 May 2020. It is accurate within 0.5 m for 83.4% of land area below 10 m above mean sea level (+MSL), with a root-mean-square error (RMSE) value of 0.54 m, compared to three local area DTMs for three major lowland areas: the Everglades, the Netherlands, and the Mekong Delta. This accuracy is far higher than that of four existing global digital elevation models (GDEMs), which are derived from satellite radar data, namely, SRTM90, MERIT, CoastalDEM, and TanDEM-X, that we find to be accurate within 0.5 m for 21.1%, 12.9%, 18.3%, and 37.9% of land below 10 m +MSL, respectively, with corresponding RMSE values of 2.49 m, 1.88 m, 1.54 m, and 1.59 m. Globally, we find 3.23, 2.12, and 1.05 million km2 of land below 10, 5, and 2 m +MSL. The 0.93 million km2 of land below 2 m +MSL identified between 60N and 56S is three times the area indicated by SRTM90 that is currently the GDEM most used in flood risk assessments, confirming that studies to date are likely to have underestimated areas at risk of flooding. Moreover, the new dataset reveals extensive forested land areas below 2 m +MSL in Papua and the Amazon Delta that are largely undetected by existing GDEMs. We conclude that the recent availability of satellite LiDAR data presents a major and much-needed step forward for studies and policies requiring accurate elevation models. GLL_DTM_v1 is available in the public domain, and the resolution will be increased in later versions as more satellite LiDAR data become available.

Scenario-based flood risk assessment for urbanizing deltas using future land-use simulation (FLUS): Guangzhou Metropolitan Area as a case study

Preparing cities for sea-level rise is one of the critical challenges of the twenty-first century. Extreme weather events, natural hazards, and the failure of climate mitigation and adaptation are substantial risks. These risks are especially significant in fast-urbanizing deltas, such as the Pearl River Delta in China, because the conflict between urbanization and flooding caused by climate change will be more significant in the future. This paper elaborates on an approach that employs a future land-use simulation (FLUS) model for scenario-based 100-year coastal flood risk assessment. Storylines of future scenarios from the Intergovernmental Panel on Climate Change (IPCC), called the representative concentration pathways (RCPs) 2.6 and 8.5, are utilized in the present study. The Guangzhou Metropolitan Area (GMA) is used as a case study to explore the probable implications of future land-use changes due to the ongoing urbanization process in the region in relation to projected environmental changes (sea-level rise, storm surge, and land subsidence). The results indicate that there will be a significant increase in flooded urban areas in the future. The simulations show that, as compared to 2015, the built-up area in the GMA will increase by 246.57 km2 in 2030 and 513.03 km2 in 2050. As compared to 2015, the flooding of built-up areas in 2030 and 2050 will respectively increase by about 31.32 km2 and 48.49 km2 under the RCP 8.5 scenario. It is also found that, as the main driving factor, urbanization will increase the flooding of built-up areas in Guangzhou in 2030 and 2050 by about 1.9 km2 and 5.9 km2, respectively, under the RCP 2.6 scenario as compared to 2015. Additionally, due to environmental changes, the flooding of built-up areas in Guangzhou will increase by about 24.2 km2 and 26.8 km2, respectively, under the RCP 8.5 scenario by 2030 and 2050 as compared to 2015. This increasing flood risk information determined by the simulation provides insight into the spatial distribution of future flood-prone urban areas to facilitate the development and prioritization of flood mitigation measures at the most critical locations in the region.

A methodology for urban micro-scale coastal flood vulnerability and risk assessment and mapping

One of the most dangerous challenges to settlements in the UK comes from flooding. Currently, there is extensive map coverage of flood hazards zones in the UK; however, it is increasingly recognised that risk associated with natural hazards cannot be reduced solely by focussing on the hazard. There is also an urgent need for methods of evaluating and mapping flood vulnerability and risk in detail. Despite its significance, conventional flood risk assessment methodologies often underestimate likely levels of vulnerability in areas prone to hazards, yet it is the degree of vulnerability within a community that determines the consequences of any given hazard. The research presented proposes a general methodology to assess and map Coastal Flood Vulnerability and Risk at a detailed, micro-scale level. This captures aspects that are considered crucial and representative of reality (socio-economic, physical and resilient features). The methodology is then applied to a UK case study (city of Portsmouth). Environment Agency flood hazard data, National Census socio-economic data and Ordnance Survey topographic map data have been used to evaluate and map coastal flood vulnerability, examining neighbourhoods within census wards. This led to a subsequent analysis of Coastal Flood Risk, via the combination of a Coastal Flood Vulnerability Index and a Coastal Flood Hazard Index, for the Portsmouth ward Hilsea. This, consequently, identifies potential weaknesses that could lead to more effective targeting of interventions to improve resilience and reduce vulnerability in the long term and provides a basis for hazard and environmental managers/planners to generate comprehensive national/international vulnerability and risk assessments.

Understanding epistemic uncertainty in large-scale coastal flood risk assessment for present and future climates

Abstract. An upscaling of flood risk assessment frameworks beyond regional and national scales has taken place during recent years, with a number of large-scale models emerging as tools for hotspot identification, support for international policymaking, and harmonization of climate change adaptation strategies. There is, however, limited insight into the scaling effects and structural limitations of flood risk models and, therefore, the underlying uncertainty. In light of this, we examine key sources of epistemic uncertainty in the coastal flood risk (CFR) modelling chain: (i) the inclusion and interaction of different hydraulic components leading to extreme sea level (ESL), (ii) the underlying uncertainty in the digital elevation model (DEM), (iii) flood defence information, (iv) the assumptions behind the use of depth–damage functions that express vulnerability, and (v) different climate change projections. The impact of these uncertainties on estimated expected annual damage (EAD) for present and future climates is evaluated in a dual case study in Faro, Portugal, and on the Iberian Peninsula. The ranking of the uncertainty factors varies among the different case studies, baseline CFR estimates, and their absolute and relative changes. We find that uncertainty from ESL contributions, and in particular the way waves are treated, can be higher than the uncertainty of the two greenhouse gas emission projections and six climate models that are used. Of comparable importance is the quality of information on coastal protection levels and DEM information. In the absence of large datasets with sufficient resolution and accuracy, the latter two factors are the main bottlenecks in terms of large-scale CFR assessment quality.

Big Data Approaches for coastal flood risk assessment and emergency response

Big Data Approaches (BDAs) refers to the combined use of historic datasets, incoming data streams, and the array of related technologies designed to shed new light on societal and environmental complexities through novel organizational, storage, and analytical capabilities. Despite widespread recognition of the commercial benefits of BDAs, application in the environmental domain is less well articulated. This represents a missed opportunity given that the dimensions used to characterize BDAs (volume, variety, velocity, and veracity) appear apt in describing the intractable challenges posed by global climate change. This paper employs coastal flood risk management as an illustrative case study to explore the potential applications in the environmental domain. Trends in global change including accelerating sea level rise, concentration of people and assets in low‐lying areas and deterioration of protective coastal ecosystems are expected to manifest locally as increased future flood risk. Two branches of coastal flood risk management are considered. First, coastal flood risk assessment, focusing on better characterization of hazard sources, facilitative pathways, and vulnerable receptors. Second, flood emergency response procedures, focusing on forecasting of flooding events, dissemination of warnings, and response monitoring. Critical commentary regarding technical, contextual, institutional, and behavioral barriers to the implementation of BDAs is offered throughout including a discussion of two fundamental difficulties associated with applying BDAs to coastal flood risk management: the role of BDAs in the broader flood system and the skill requirements for a generation of data scientists capable of implementing Big Data Approaches.This article is categorized under: Social Status of Climate Change Knowledge > Knowledge and Practice Climate, History, Society, Culture > Technological Aspects and Ideas