Compounding effects of sea level rise and fluvial flooding

Nature-based solutions as buffers against coastal compound flooding: Exploring potential framework for process-based modeling of hazard mitigation

As coastal regions face escalating risks from flooding in a changing climate, Nature-based Solutions (NbS) have garnered attention as promising adaptation measures to mitigate the destructive impacts of coastal flooding. However, the challenge of compound flooding, which involves the combined effects of multiple flood drivers, demands a deeper understanding of the efficacy of NbS against this complex phenomenon. This manuscript reviews the literature on process-based modeling of NbS for mitigating compound coastal flooding and identifies knowledge gaps to enhance future research efforts. We used an automated search strategy within the SCOPUS database, followed by a screening process that ultimately resulted in 141 publications assessing the functionality of NbS against coastal flooding. Our review identified a dearth of research (9 %) investigating the performance of NbS against compound flooding scenarios. We examined the challenges and complexities involved in modeling such scenarios, including hydrologic, hydrodynamic, and ecological feedback processes by exploring the studies that used a process-based modeling framework. Key research gaps were identified, such as navigating the complex environment, managing computational costs, and addressing the shortages of experts and data. We outlined potential modeling pathways to improve NbS characterization in the compound flooding framework. Additionally, uncertainties associated with numerical modeling and steps to bridge the research-to-operation gaps were briefly discussed, highlighting the bottlenecks in operational implementation.

Complex adaptive systems-based framework for modeling the health impacts of climate change

Introduction

Climate change is a global phenomenon with far-reaching consequences, and its impact on human health is a growing concern. The intricate interplay of various factors makes it challenging to accurately predict and understand the implications of climate change on human well-being. Conventional methodologies have limitations in comprehensively addressing the complexity and nonlinearity inherent in the relationships between climate change and health outcomes.

Objectives

The primary objective of this paper is to develop a robust theoretical framework that can effectively analyze and interpret the intricate web of variables influencing the human health impacts of climate change. By doing so, we aim to overcome the limitations of conventional approaches and provide a more nuanced understanding of the complex relationships involved. Furthermore, we seek to explore practical applications of this theoretical framework to enhance our ability to predict, mitigate, and adapt to the diverse health challenges posed by a changing climate.

Methods

Addressing the challenges outlined in the objectives, this study introduces the Complex Adaptive Systems (CAS) framework, acknowledging its significance in capturing the nuanced dynamics of health effects linked to climate change. The research utilizes a blend of field observations, expert interviews, key informant interviews, and an extensive literature review to shape the development of the CAS framework.

Results and discussion

The proposed CAS framework categorizes findings into six key sub-systems: ecological services, extreme weather, infectious diseases, food security, disaster risk management, and clinical public health. The study employs agent-based modeling, using causal loop diagrams (CLDs) tailored for each CAS sub-system. A set of identified variables is incorporated into predictive modeling to enhance the understanding of health outcomes within the CAS framework. Through a combination of theoretical development and practical application, this paper aspires to contribute valuable insights to the interdisciplinary field of climate change and health. Integrating agent-based modeling and CLDs enhances the predictive capabilities required for effective health outcome analysis in the context of climate change.

Conclusion

This paper serves as a valuable resource for policymakers, researchers, and public health professionals by employing a CAS framework to understand and assess the complex network of health impacts associated with climate change. It offers insights into effective strategies for safeguarding human health amidst current and future climate challenges.

Spatio-temporal compounding of connected extreme events: Projection and hotspot identification

In general, the impact of two different connected extreme events is noticed on the same duration and spatial area. However, the connected extreme events can have footprint over different temporal and spatial scales. Thus, this article analyses the connected extreme events over India using the spatio-temporal compounding technique to understand the impact at different temporal and spatial scales. This approach is applied to analyse the historical and future connected extreme events. In the present study, coincident heat waves and droughts (Event C1), coincident heat waves and extreme precipitation (Event C2) are considered as connected extreme events. The future events are investigated using the suitable global climate models (GCMs) projections under three climate change scenarios (Shared Socioeconomic Pathways (SSP) 2-4.5, SSP3-7.0, and SSP5-8.5). The suitable GCMs are identified with the help of compromise programming. Subsequently, the hotspot regions are identified applying the Regional Climate Change Index (RCCI) method. The outcomes from the study suggest that with increasing temporal compounding, the mean duration of extreme events also increases. Highest increase in mean duration is observed for Event C1 over PI (Peninsular India), WCI (West Central India), and some parts of CNI (Central Northeast India) regions. The regions with high magnitude of duration have low magnitude of occurrence. The duration of Event C1 is likely to increase with respect to climate change scenarios and temporal compounding, especially in the PI region and some parts of WCI. However, there is insignificant change in the duration of Event C2. The PI region identified as the most vulnerable region followed by WCI and HR regions. The highest percentage of area under the emerging hotspot category is noticed under SSP5-8.5 climate change scenario.

A new method to compile global multi-hazard event sets

This study presents a new method, the MYRIAD-Hazard Event Sets Algorithm (MYRIAD-HESA), that compiles historically-based multi-hazard event sets. MYRIAD-HESA is a fully open-access method that can create multi-hazard event sets from any hazard events that occur on varying time, space, and intensity scales. In the past, multi-hazards have predominately been studied on a local or continental scale, or have been limited to specific hazard combinations, such as the combination between droughts and heatwaves. Therefore, we exemplify our approach by compiling a global multi-hazard event set database, spanning from 2004 to 2017, which includes eleven hazards from varying hazard classes (e.g. meteorological, geophysical, hydrological and climatological). This global database provides new scientific insights on the frequency of different multi-hazard events and their hotspots. Additionally, we explicitly incorporate a temporal dimension in MYRIAD-HESA, the time-lag. The time-lag, or time between the occurrence of hazards, is used to determine potentially impactful events that occurred in close succession. Varying time-lags have been tested in MYRIAD-HESA, and are analysed using North America as a case study. Alongside the MYRIAD-HESA, the multi-hazard event sets, MYRIAD-HES, is openly available to further increase the understanding of multi-hazard events in the disaster risk community. The open-source nature of MYRIAD-HESA provides flexibility to conduct multi-risk assessments by, for example, incorporating higher resolution data for an area of interest.

Influence of El Niño on the variability of global shoreline position

Coastal zones are fragile and complex dynamical systems that are increasingly under threat from the combined effects of anthropogenic pressure and climate change. Using global satellite derived shoreline positions from 1993 to 2019 and a variety of reanalysis products, here we show that shorelines are under the influence of three main drivers: sea-level, ocean waves and river discharge. While sea level directly affects coastal mobility, waves affect both erosion/accretion and total water levels, and rivers affect coastal sediment budgets and salinity-induced water levels. By deriving a conceptual global model that accounts for the influence of dominant modes of climate variability on these drivers, we show that interannual shoreline changes are largely driven by different ENSO regimes and their complex inter-basin teleconnections. Our results provide a new framework for understanding and predicting climate-induced coastal hazards.

The increase in intensity and frequency of surface air temperature extremes throughout the western South Atlantic coast

The climate is changing. At this stage, it is important to specify an 'extreme' climate and identify patterns that indicate its potential harm worldwide, including the coastal zones. Herein, we considered extremes based on the "Peaks Over Threshold" method from the "Extreme Value Theory". We looked after geographical patterns of surface air temperature (SAT) extremes (e.g., Tmax, Tmin, daily temperature range (DTR), and inter-daily temperature range) over the last 40 years throughout the Brazilian coast. Overall, we found a trend increase in intensity and frequency, but the duration was barely affected. The latitudinal pattern of extremes and the temperatures considered extremes followed the settled perception that areas in higher latitudes will be more affected by the extent of warming. Additionally, the seasonal pattern of DTR demonstrated to be a good approach to make inferences about air mass changes, but joint analyses on extremes with other atmospheric variables are desirable. Given the potential effects of extreme climates on society and natural systems over the world, our study highlights the urge for action to mitigate the effects of the increase in SAT in coastal zones.

Toward Collaborative Adaptation: Assessing Impacts of Coastal Flooding at the Watershed Scale

The U.S. Mid-Atlantic coastal region is experiencing higher rates of SLR than the global average, especially in Hampton Roads, Virginia, where this acceleration is primarily driven by land subsidence. The adaptation plans for coastal flooding are generally developed at the municipal level, ignoring the broader spatial implications of flooding outside the individual administrative boundaries. Flood impact assessments at the watershed scale would provide a more holistic perspective on what is needed to synchronize the adaptation efforts between the neighboring administrative units. This paper evaluates flooding impacts from sea level rise (SLR) and storm surge among watersheds in Hampton Roads to identify those most at risk of coastal flooding over different time horizons. It also explores the implications of flooding on the municipalities, the land uses, and land covers throughout this region within the case study watershed. The 2% Annual Exceedance Probability (AEP) storm surge flood hazard data and NOAA's intermediate SLR projections were used to develop flooding scenarios for 2030, 2060, and 2090 and delineate land areas at risk of combined flooding. Findings show that five out of 98 watersheds will substantially increase in inundation, with two intersecting multiple municipalities. They also indicate significant inundation of military, commercial, and industrial land uses and wetland land covers. Flooding will also impact residential land use in urban areas along the Elizabeth River and Hampton city, supporting the need for collaborative adaptation planning on hydrologically influenced spatial scales.

Social work post-disaster response in Iran: A case study of the 2019 mass flooding in Poldokhtar, Lorestan

Flash-flooding affected Iran in March 2019 causing the displacement of thousands of people. Social workers established a Child Friendly Space (CFS) and applied comprehensive case management to provide psychosocial support for people who were affected by flooding (PWAF) (n = 565) in a community in Poldokhtar, covering a period of 3 months. Outreach services, involving community-volunteers, providing counseling, establishing CFS, training PWAF for reducing violence, and preventing child abuse were essential social work post-disaster interventions to support vulnerable populations. The article reflects upon the often-neglected role of social workers in post-disaster settings, and brings new material for discussion from the unexplored field of Iranian social workers.

Media exposure, threat processing, and mitigation behaviors in Gulf Coast residents facing the co-occurring threats of COVID-19 and hurricanes

The 2020 hurricane season threatened millions of Americans concurrently grappling with COVID-19. Processes guiding individual-level mitigation for these conceptually distinct threats, one novel and chronic (COVID-19), the other familiar and episodic (hurricanes), are unknown. Theories of health protective behaviors suggest that inputs from external stimuli (e.g., traditional and social media) lead to threat processing, including perceived efficacy (self- and response) and perceived threat (susceptibility and severity), guiding mitigation behavior. We surveyed a representative sample of Florida and Texas residents (N = 1846) between April 14, 2020 and April 27, 2020; many had previous hurricane exposure; all were previously assessed between September 8, 2017 and September 11, 2017. Using preregistered analyses, two generalized structural equation models tested direct and indirect effects of media exposure (traditional media, social media) on self-reported (1) COVID-19 mitigation (handwashing, mask-wearing, social distancing) and (2) hurricane mitigation (preparation behaviors), as mediated through perceived efficacy (self- and response) and perceived threat (susceptibility and severity). Self-efficacy and response efficacy were associated with social distancing (p = .002), handwashing, mask-wearing, and hurricane preparation (ps < 0.001). Perceived susceptibility was positively associated with social distancing (p = 0.017) and hurricane preparation (p < 0.001). Perceived severity was positively associated with social distancing (p < 0.001). Traditional media exhibited indirect effects on COVID-19 mitigation through increased response efficacy (ps < 0.05), and to a lesser extent self-efficacy (p < 0.05), and on hurricane preparation through increased self-efficacy and response efficacy and perceived susceptibility (ps < 0.05). Social media did not exhibit indirect effects on COVID-19 or hurricane mitigation. Communications targeting efficacy and susceptibility may encourage mitigation behavior; research should explore how social media campaigns can more effectively target threat processing, guiding protective actions.

Investigating Flood Risks of Rainfall and Storm Tides Affected by the Parameter Estimation Coupling Bivariate Statistics and Hydrodynamic Models in the Coastal City

The public health risk caused by urban floods is a global concern. Flood risks are amplified by the interaction of rainfall and storm tides in coastal cities. In this study, we investigate the flood risks of rainfall and storm tides coupling statistical and hydrodynamic models and evaluate the influence of different parameter estimation methods and bivariate return periods (RPs) on flood risks in the coastal city. The statistical model is used to obtain the bivariate design of rainfall and storm tides with the integration of copula function, most-likely weight function and Monte Carlo simulation method. The bivariate designs are adopted as the input boundaries for the hydrodynamic model established by Personal Computer Storm Water Management Model (PCSWMM), and the flood risk is evaluated by the hydrodynamic model. Subsequently, the influence of different parameter estimation approaches (that is, parametric and non-parametric) and bivariate RPs (that is, co-occurrence RP, joint RP, and Kendall RP) on bivariate designs and flood risks are investigated. With Haikou coastal city in China as the case study, the results show that: (1) Gumbel copula is the best function to describe the correlation structure between rainfall and storm tides for the parametric and non-parametric approaches, and the non-parametric approach is a better fit for the observed data; (2) when the Kendall RP is large (more than 100 years), the flood risk is underestimated with an average of 17% by the non-parametric estimation, and the parametric estimation approach is recommended as it is considered the most unfavorable scenario; (3) the types of bivariate RP have the important impact on the flood risk. When there is no specific application need, the Kendall RP can be adopted as the bivariate design standard of flooding facilities since it can describe the dangerous areas more accurately for multivariate scenario. The results can provide references for reasonable flood risk assessment and flooding facility design in coastal cities.

A locally relevant framework for assessing the risk of sea level rise under changing temperature conditions: Application in New Caledonia, Pacific Ocean

Sea level rise is a key feature in a warmer world and its impact can be seen globally. Assessing climate change-induced sea level rise, therefore, is urgently needed particularly in small island nations, where the threats of sea level rise are immediate, but the level of preparedness is low. Here, we propose a stochastic simulator to link changes in Mean Annual Temperature (MAT) to Mean Annual Sea Level (MASEL) at the local scale. This is through what-if scenarios that are developed based on the association between local temperature and sea level. The model can provide a basis for a bottom-up impact assessment by addressing limitations of applying large-scale projections in small islands and facilitating the accessibility of the impact assessment to stakeholders. For this purpose, we decompose the MAT and MASEL signals into their linear trend and autocorrelation components as well as independent and identically distributed residual terms. We further explore the association between trend and residual terms of MAT and MASEL. If such dependencies exist, scenarios of sea level can be synthesized based on the trend and residual terms of temperature. We use linear regression to link trends of MAT and MASEL, and copulas to formulate dependencies between residuals. This allows stochastic sampling of MASEL conditioned to trend and random variability in MAT. This framework is used for retrospective and prospective simulations of MASEL in Nouméa, the capital city of New Caledonia, the Pacific. We set up six different model configurations for developing the stochastic sampler, each including various parametric options. By selecting the best setup from each configuration, we provide a multi-model stochastic projection of MASEL, assuming the persistence in current long-term trend in MAT and MASEL. We demonstrate how such simulations can be used for a risk-based impact assessments and discuss sources of uncertainty in future projections.

Parametric Vine Copula Framework in the Trivariate Probability Analysis of Compound Flooding Events

The interaction between oceanographic, meteorological, and hydrological factors can result in an extreme flooding scenario in the low-lying coastal area, called compound flooding (CF) events. For instance, rainfall and storm surge (or high river discharge) can be driven by the same meteorological forcing mechanisms, tropical or extra-tropical cyclones, resulting in a CF phenomenon. The trivariate distributional framework can significantly explain compound events’ statistical behaviour reducing the associated high-impact flood risk. Resolving heterogenous dependency of the multidimensional CF events by incorporating traditional 3D symmetric or fully nested Archimedean copula is quite complex. The main challenge is to preserve all lower-level dependencies. An approach based on decomposing the full multivariate density into simple local building blocks via conditional independence called vine or pair-copulas is a much more comprehensive way of approximating the trivariate flood dependence structure. In this study, a parametric vine copula of a drawable (D-vine) structure is introduced in the trivariate modelling of flooding events with 46 years of observations of the west coast of Canada. This trivariate framework searches dependency by combining the joint impact of annual maximum 24-h rainfall and the highest storm surge and river discharge observed within the time ±1 day of the highest rainfall event. The D-vine structures are constructed in three alternative ways by permutation of the conditioning variables. The most appropriate D-vine structure is selected using the fitness test statistics and estimating trivariate joint and conditional joint return periods. The investigation confirms that the D-vine copula can effectively define the compound phenomenon’s dependency. The failure probability (FP) method is also adopted in assessing the trivariate hydrologic risk. It is observed that hydrologic events defined in the trivariate case produce higher FP than in the bivariate (or univariate) case. It is also concluded that hydrologic risk increases (i) with an increase in the service design life of the hydraulic facilities and (ii) with a decrease in return periods.

Projecting the Impacts of a Changing Climate: Tropical Cyclones and Flooding

PURPOSE OF REVIEW

There is clear evidence that the earth's climate is changing, largely from anthropogenic causes. Flooding and tropical cyclones have clear impacts on human health in the United States at present, and projections of their health impacts in the future will help inform climate policy, yet to date there have been few quantitative climate health impact projections.

RECENT FINDINGS

Despite a wealth of studies characterizing health impacts of floods and tropical cyclones, many are better suited for qualitative, rather than quantitative, projections of climate change health impacts. However, a growing number have features that will facilitate their use in quantitative projections, features we highlight here. Further, while it can be difficult to project how exposures to flood and tropical cyclone hazards will change in the future, climate science continues to advance in its capabilities to capture changes in these exposures, including capturing regional variation. Developments in climate epidemiology and climate science are opening new possibilities in projecting the health impacts of floods and tropical cyclones under a changing climate.

Popular extreme sea level metrics can better communicate impacts

Estimates of changes in the frequency or height of contemporary extreme sea levels (ESLs) under various climate change scenarios are often used by climate and sea level scientists to help communicate the physical basis for societal concern regarding sea level rise. Changes in ESLs (i.e., the hazard) are often represented using various metrics and indicators that, when anchored to salient impacts on human systems and the natural environment, provide useful information to policy makers, stakeholders, and the general public. While changes in hazards are often anchored to impacts at local scales, aggregate global summary metrics generally lack the context of local exposure and vulnerability that facilitates translating hazards into impacts. Contextualizing changes in hazards is also needed when communicating the timing of when projected ESL frequencies cross critical thresholds, such as the year in which ESLs higher than the design height benchmark of protective infrastructure (e.g., the 100-year water level) are expected to occur within the lifetime of that infrastructure. We present specific examples demonstrating the need for such contextualization using a simple flood exposure model, local sea level rise projections, and population exposure estimates for 414 global cities. We suggest regional and global climate assessment reports integrate global, regional, and local perspectives on coastal risk to address hazard, vulnerability and exposure simultaneously.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s10584-021-03288-6.

An Insight into the Factors Controlling Delta Flood Events: The Case of the Evros River Deltaic Plain (NE Aegean Sea)

The present contribution aims to give an insight into the main terrestrial and marine processes leading to delta flooding in the case of the transboundary Evros delta, located at the microtidal NE Aegean Sea, on the basis of recorded flood events in the Evros deltaic plain. The prevailing weather conditions at the onset of the event, along with sea-level rise above the mean state, portray the mechanism for the development of compound flood events and subsequent riparian flooding. This system blocks the riverine water’s seaward exit, resulting in the flooding of the lower deltaic plain. The river discharge is recognized as a secondary factor acting mainly toward the persistence of the events. Several limitations restrict the quantification potential of the relative contribution of the key factors to the development, onset, and duration of a flood. Mitigation of the impacts of such flood events requires intercountry cooperation and a management plan based on a network of environmental monitoring.

Population exposure to compound extreme events in India under different emission and population scenarios

It is well understood that India is largely exposed to different climate extremes including floods, droughts, heat waves, among others. However, the exposure of co-occurrence of these events is still unknown. The present analysis, first study of its kind, provides the projected changeability of five different compound extremes under three different emission scenarios (SSP2-4.5, SSP3-7.0, and SSP5-8.5). These changes are combined with population projection under SSP2, SSP3, and SSP5 scenarios to examine the total exposure in terms of number of persons exposed during 2021-2060 (T1) and 2061-2100 (T2). Here, the outputs from thirteen GCMs are used under CMIP6 experiment. The findings from the study show that all the compound extremes are expected to increase in future under all the emission scenarios being greater in case of SSP5-8.5. The population exposure is highest (2.51- to 4.96-fold as compared to historical) under SSP3-7.0 scenario (2021-2100 i.e., T1 and T2) in case of coincident heat waves and droughts compound extreme. The total exposure in Central Northeast India is projected to be the highest while Hilly Regions are likely to have the lowest exposure in future. The increase in the exposure is mainly contributed from climate change, population growth and their interaction depending on different kinds of compound extremes. The findings would help in devising sustainable policy strategies to climate mitigation and adaptation.

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.

Projecting Climate Dependent Coastal Flood Risk With a Hybrid Statistical Dynamical Model

Numerical models for tides, storm surge, and wave runup have demonstrated ability to accurately define spatially varying flood surfaces. However these models are typically too computationally expensive to dynamically simulate the full parameter space of future oceanographic, atmospheric, and hydrologic conditions that will constructively compound in the nearshore to cause both extreme event and nuisance flooding during the 21st century. A surrogate modeling framework of waves, winds, and tides is developed in this study to efficiently predict spatially varying nearshore and estuarine water levels contingent on any combination of offshore forcing conditions. The surrogate models are coupled with a time-dependent stochastic climate emulator that provides efficient downscaling for hypothetical iterations of offshore conditions. Together, the hybrid statistical-dynamical framework can assess present day and future coastal flood risk, including the chronological characteristics of individual flood and wave-induced dune overtopping events and their changes into the future. The framework is demonstrated at Naval Base Coronado in San Diego, CA, utilizing the regional Coastal Storm Modeling System (CoSMoS; composed of Delft3D and XBeach) as the dynamic simulator and Gaussian process regression as the surrogate modeling tool. Validation of the framework uses both in-situ tide gauge observations within San Diego Bay, and a nearshore cross-shore array deployment of pressure sensors in the open beach surf zone. The framework reveals the relative influence of large-scale climate variability on future coastal flood resilience metrics relevant to the management of an open coast artificial berm, as well as the stochastic nature of future total water levels.

The Role of Intensifying Precipitation on Coastal River Flooding and Compound River-Storm Surge Events, Northeast Gulf of Mexico

Destructive coastal floods are commonly increasing in frequency and may be caused by global precipitation intensification. Such connections through climate, watershed, and river processes are poorly understood because of complex interactions in transitional fluvial-marine environments where flooding is caused by rivers, marine storm surge, or both in compound events. To better understand river floods along the fluvial-marine transition, we study watersheds of the northeastern Gulf of Mexico using long-term observations. Results show intensifying precipitation decreased precipitation-discharge lag times, increasing river-flood frequency and the likelihood of compound events in fluvial-marine transitions. This reduction in lag time occurred when the Atlantic Multidecadal Oscillation and El Niño Southern Oscillation began strongly affecting river discharge through the advection of moist air, intensifying precipitation. Along the fluvial-marine transition, compound events were largest in inland reaches. However, for inland reaches, compound event water levels did not exceed the floods caused solely by river flooding, the largest flood hazard in these systems. Our results demonstrate precipitation and river discharge play critical roles in coastal flooding and will likely escalate flooding as the climate continues to warm and intensify precipitation.

Estuarine tidal range dynamics under rising sea levels

How an estuary responds to sea level rise (SLR) is complex and depends on energy drivers (e.g., tides and river inflows), estuarine geometry (e.g., length and depth), intrinsic fluid properties (e.g., density), and bed/bank roughness. While changes to the tidal range under SLR can impact estuarine sediment transport, water quality, and vegetation communities, studies on the altered tidal range under SLR are often based on case studies with outcomes applicable to a specific site. As such, this study produced a large ensemble of estuarine hydrodynamic models (>1800) to provide a systematic understanding of how tidal range dynamics within different estuary types may change under various SLR and river inflow scenarios. The results indicated that SLR often amplifies the tidal range of different estuary types, except for short estuaries with a low tidal range at the mouth where SLR attenuates the tides. SLR alters the location of the points with minimum tidal range and overall tidal range patterns in an estuary. Variations in tidal range were more evident in converging estuaries, shallower systems, or in estuaries with strong river inflows. These findings provide an indication of how different estuary types may respond to estuaries and may assist estuarine managers and decision makers.

Sea level rise impacts on estuarine dynamics: A review

Sea level rise (SLR) poses a hazard to ecosystems and economies in low-lying coastal and estuarine areas. To better understand the potential impacts of SLR in estuaries, a comprehensive review of existing theory, literature, and assessment tools is undertaken. In addition, several conceptual models are introduced to assist in understanding interlinked estuarine processes and their complex responses to SLR. This review indicates that SLR impacts in estuaries should not be assessed via static (bathtub) approaches as they fail to consider important hydrodynamic effects such as tidal wave amplification, dampening, and reflection. Where hydrodynamic models are used, the existing literature provides a relatively detailed understanding of how SLR will affect estuarine hydrodynamics (e.g., tides and inundation regimes). With regards to the current understanding of, and ability to model, the connections between altered hydrodynamics (under SLR) and dependent geomorphic, ecological, and bio-geochemical processes, significant knowledge gaps remain. This is of particular concern as there is currently a paradigm shift towards more integrated and holistic management of estuaries. Estuarine management under accelerating SLR is likely to become increasingly complex, as decision-making will be undertaken with uncertainty. As such, this review highlights that there is a fundamental requirement for more sophisticated and interdisciplinary studies that integrate physical, ecological, bio-geochemical, and geomorphic responses of estuaries to SLR.

Flood Impacts on Critical Infrastructure in a Coastal Floodplain in Western Puerto Rico during Hurricane María

Flooding during extreme weather events damages critical infrastructure, property, and threatens lives. Hurricane María devastated Puerto Rico (PR) on 20 September 2017. Sixty-four deaths were directly attributable to the flooding. This paper describes the development of a hydrologic model using the Gridded Surface Subsurface Hydrologic Analysis (GSSHA), capable of simulating flood depth and extent for the Añasco coastal flood plain in Western PR. The purpose of the study was to develop a numerical model to simulate flooding from extreme weather events and to evaluate the impacts on critical infrastructure and communities; Hurricane María is used as a case study. GSSHA was calibrated for Irma, a Category 3 hurricane, which struck the northeastern corner of the island on 7 September 2017, two weeks before Hurricane María. The upper Añasco watershed was calibrated using United States Geological Survey (USGS) stream discharge data. The model was validated using a storm of similar magnitude on 11–13 December 2007. Owing to the damage sustained by PR’s WSR-88D weather radar during Hurricane María, rainfall was estimated in this study using the Weather Research Forecast (WRF) model. Flooding in the coastal floodplain during Hurricane María was simulated using three methods: (1) Use of observed discharge hydrograph from the upper watershed as an inflow boundary condition for the coastal floodplain area, along with the WRF rainfall in the coastal flood plain; (2) Use of WRF rainfall to simulate runoff in the upper watershed and coastal flood plain; and (3) Similar to approach (2), except the use of bias-corrected WRF rainfall. Flooding results were compared with forty-two values of flood depth obtained during face-to-face interviews with residents of the affected communities. Impacts on critical infrastructure (water, electric, and public schools) were evaluated, assuming any structure exposed to 20 cm or more of flooding would sustain damage. Calibration equations were also used to improve flood depth estimates. Our model included the influence of storm surge, which we found to have a minimal effect on flood depths within the study area. Water infrastructure was more severely impacted by flooding than electrical infrastructure. From these findings, we conclude that the model developed in this study can be used with sufficient accuracy to identify infrastructure affected by future flooding events.

Uncertainties in Riverine and Coastal Flood Impacts under Climate Change

Climate change can affect different drivers of flooding in low-lying coastal areas of the world, challenging the design and planning of communities and infrastructure. The concurrent occurrence of multiple flood drivers such as high river flows and extreme sea levels can aggravate such impacts and result in catastrophic damages. In this study, the individual and compound effects of riverine and coastal flooding are investigated at Stephenville Crossing located in the coastal-estuarine region of Newfoundland and Labrador (NL), Canada. The impacts of climate change on flood extents and depths and the uncertainties associated with temporal patterns of storms, intensity–duration–frequency (IDF) projections, spatial resolution, and emission scenarios are assessed. A hydrologic model and a 2D hydraulic model are set up and calibrated to simulate the flood inundation for the historical (1976–2005) as well as the near future (2041–2070) and far future (2071–2100) periods under Representative Concentration Pathways (RCPs) 4.5 and 8.5. Future storm events are generated based on projected IDF curves from convection-permitting Weather Research and Forecasting (WRF) climate model simulations, using SCS, Huff, and alternating block design storm methods. The results are compared with simulations based on projected IDF curves derived from statistically downscaled Global Climate Models (GCMs). Both drivers of flooding are projected to intensify in the future, resulting in higher risks of flooding in the study area. Compound riverine and coastal flooding results in more severe inundation, affecting the communities on the coastline and the estuary area. Results show that the uncertainties associated with storm hyetographs are considerable, which indicate the importance of accurate representation of storm patterns. Further, simulations based on projected WRF-IDF curves show higher risks of flooding compared to the ones associated with GCM-IDFs.

A global analysis of extreme coastal water levels with implications for potential coastal overtopping

Climate change and anthropogenic pressures are widely expected to exacerbate coastal hazards such as episodic coastal flooding. This study presents global-scale potential coastal overtopping estimates, which account for not only the effects of sea level rise and storm surge, but also for wave runup at exposed open coasts. Here we find that the globally aggregated annual overtopping hours have increased by almost 50% over the last two decades. A first-pass future assessment indicates that globally aggregated annual overtopping hours will accelerate faster than the global mean sea-level rise itself, with a clearly discernible increase occurring around mid-century regardless of climate scenario. Under RCP 8.5, the globally aggregated annual overtopping hours by the end of the 21^st-century is projected to be up to 50 times larger compared to present-day. As sea level continues to rise, more regions around the world are projected to become exposed to coastal overtopping.

As sea levels rise, coasts are being increasingly  threatened by overtopping caused by the combination of sea level rise, storm surge and wave runup. Here the authors find that global coastal overtopping has increased by over 50% in the last two decades, and under a RCP 8.5 scenario this could increase up to 50 times by 2100 compared to today.

Transformative climate adaptation in the United States: Trends and prospects

As climate change intensifies, civil society is increasingly calling for transformative adaptation that redresses drivers of climate vulnerability. We review trends in how U.S. federal government, private industry and civil society are planning for climate adaptation. We find growing divergence in their approaches and impacts. This incoherence increases maladaptive investment in climate-blind infrastructure, justice-blind reforms in financial and professional sectors, and greater societal vulnerability to climate impacts. If these actors were to proactively and deliberatively engage in transformative adaptation, they would need to address the material, relational and normative factors that hold current systems in place. Drawing on a review of transformation and collective impact literatures, we conclude with directions for research and policy engagement to support more transformative adaptation moving forward.

Incorporating climate change in flood estimation guidance

Research into potential implications of climate change on flood hazard has made significant progress over the past decade, yet efforts to translate this research into practical guidance for flood estimation remain in their infancy. In this commentary, we address the question: how best can practical flood guidance be modified to incorporate the additional uncertainty due to climate change? We begin by summarizing the physical causes of changes in flooding and then discuss common methods of design flood estimation in the context of uncertainty. We find that although climate science operates across aleatory, epistemic and deep uncertainty, engineering practitioners generally only address aleatory uncertainty associated with natural variability through standards-based approaches. A review of existing literature and flood guidance reveals that although research efforts in hydrology do not always reflect the methods used in flood estimation, significant progress has been made with many jurisdictions around the world now incorporating climate change in their flood guidance. We conclude that the deep uncertainty that climate change brings signals a need to shift towards more flexible design and planning approaches, and future research effort should focus on providing information that supports the range of flood estimation methods used in practice. This article is part of a discussion meeting issue 'Intensification of short-duration rainfall extremes and implications for flash flood risks'.

Quantifying the inundation impacts of earthquake-induced surface elevation change by hydrological and hydraulic modeling

Current estimates of flood hazards are often based on the assumption that topography is static. When tectonic and/or anthropogenic processes change the land surface elevation, the spatial patterns of floods might also change. Here, we employ the hydrological and hydraulic modeling to simulate floods in the Kujukuri Plain, Japan, in the years 2004 and 2013, when two severe floods occurred. In between the two floods, land surface elevations were changed by the 2011 Tohoku-Oki earthquake. The effects of land surface elevation changes on inundation areas were quantified by changing input topographies. Our results showed that, without taking into account land surface elevation changes, around 10% of inundation areas were underestimated at the time of flood events in the year 2013. The spatial distribution of inundation locations varied with local topographical features, for example, the areas with backmarsh and valley fill deposits were sensitive to the extent of inundation by land surface elevation changes. The sub-watershed near the coastal shoreline having below-zero meter elevation areas showed that the earthquake-induced land surface elevation changes exacerbated an additional 22% inundation area. This study suggests that the inundation areas will increase in catchments suffering severe settlements, which highlights the necessity of taking into account the spatio-temporal changes of land surface elevations on the assessment of flood hazards.

Global hotspots for the occurrence of compound events

Compound events (CEs) are weather and climate events that result from multiple hazards or drivers with the potential to cause severe socio-economic impacts. Compared with isolated hazards, the multiple hazards/drivers associated with CEs can lead to higher economic losses and death tolls. Here, we provide the first analysis of multiple multivariate CEs potentially causing high-impact floods, droughts, and fires. Using observations and reanalysis data during 1980–2014, we analyse 27 hazard pairs and provide the first spatial estimates of their occurrences on the global scale. We identify hotspots of multivariate CEs including many socio-economically important regions such as North America, Russia and western Europe. We analyse the relative importance of different multivariate CEs in six continental regions to highlight CEs posing the highest risk. Our results provide initial guidance to assess the regional risk of CE events and an observationally-based dataset to aid evaluation of climate models for simulating multivariate CEs.

Compound climate events such as floods and droughts together can cause severe socio-economic impacts. Here, the authors analyse global hazard pairs from 1980–2014 and find global hotspots for the occurrence of compound events.

More meteorological events that drive compound coastal flooding are projected under climate change

Compound flooding arises from storms causing concurrent extreme meteorological tides (that is the superposition of storm surge and waves) and precipitation. This flooding can severely affect densely populated low-lying coastal areas. Here, combining output from climate and ocean models, we analyse the concurrence probability of the meteorological conditions driving compound flooding. We show that, under a high emissions scenario, the concurrence probability would increase globally by more than 25% by 2100 compared to present. In latitudes above 40o north, compound flooding could become more than 2.5 times as frequent, in contrast to parts of the subtropics where it would weaken. Changes in extreme precipitation and meteorological tides account for most (77% and 20%, respectively) of the projected change in concurrence probability. The evolution of the dependence between precipitation and meteorological tide dominates the uncertainty in the projections. Our results indicate that not accounting for these effects in adaptation planning could leave coastal communities insufficiently protected against flooding.

Compound Inundation Impacts of Coastal Climate Change: Sea-Level Rise, Groundwater Rise, and Coastal Precipitation

The importance of considering the compound effects of multiple hazards has increased in recent years due to their catastrophic impacts on human lives and property. Compound effects correspond to events with multiple concurrent or consecutive drivers, e.g., heavy storms, coastal flooding, high tides, and sea level rise (SLR). There is a recent evidence on inundation caused by SLR-driven groundwater rise, and there is a distinct knowledge gap in understanding the compound inundation effects of this phenomenon considering the important hydrologic and hydraulic considerations under compound events. To fill this knowledge gap, we developed a novel analytical framework to understand the movements of the surface flow under typical precipitation events considering their interaction with uprising groundwater and SLR in a coastal watershed located in Oakland Flatlands, CA, USA, home to several disadvantaged communities. This modelling approach simulates the dynamics of compound flooding in two dimensions of the earth surface in a fine resolution, which is critical for devising proper flood management strategies. The reason to focus on disadvantaged coastal communities is that such communities typically encounter disproportionate environmental injustices due to the lack of sufficient drainage capacity in their infrastructure. Our results show that by considering the compound effect of SLR, groundwater inundation and precipitation flooding, the drainage capacity of infrastructure will be substantially exceeded, such that over 700 acres of the built infrastructure could be flooded. This is a considerable increase compared to scenarios that do not consider compound effect, or scenarios that consider inappropriate combinations of driving factors. In sum, our results highlight the significance of considering compound effects in the coastal inundation analyses, with a particular emphasis on the role of groundwater rise.

Sensitivity of Flood Hazard and Damage to Modelling Approaches

Combination of uncertainties in water level and wave height predictions for extreme storms can result in unacceptable levels of error, rendering flood hazard assessment frameworks less useful. A 2D inundation model, LISFLOOD-FP, was used to quantify sensitivity of flooding to uncertainty in coastal hazard conditions and method used to force the coastal boundary of the model. It is shown that flood inundation is more sensitive to small changes in coastal hazard conditions due to the setup of the regional model, than the approach used to apply these conditions as boundary forcing. Once the threshold for flooding is exceeded, a few centimetres increase in combined water level and wave height increases both the inundation and consequent damage costs. Improved quantification of uncertainty in inundation assessments can aid long-term coastal flood hazard mitigation and adaptation strategies, to increase confidence in knowledge of how coastlines will respond to future changes in sea-level.

Understanding of Contemporary Regional Sea-Level Change and the Implications for the Future

Global sea level provides an important indicator of the state of the warming climate, but changes in regional sea level are most relevant for coastal communities around the world. With improvements to the sea-level observing system, the knowledge of regional sea-level change has advanced dramatically in recent years. Satellite measurements coupled with in situ observations have allowed for comprehensive study and improved understanding of the diverse set of drivers that lead to variations in sea level in space and time. Despite the advances, gaps in the understanding of contemporary sea-level change remain and inhibit the ability to predict how the relevant processes may lead to future change. These gaps arise in part due to the complexity of the linkages between the drivers of sea-level change. Here we review the individual processes which lead to sea-level change and then describe how they combine and vary regionally. The intent of the paper is to provide an overview of the current state of understanding of the processes that cause regional sea-level change and to identify and discuss limitations and uncertainty in our understanding of these processes. Areas where the lack of understanding or gaps in knowledge inhibit the ability to provide the needed information for comprehensive planning efforts are of particular focus. Finally, a goal of this paper is to highlight the role of the expanded sea-level observation network-particularly as related to satellite observations-in the improved scientific understanding of the contributors to regional sea-level change.

Flood Risk Evaluation in Ungauged Coastal Areas: The Case Study of Ippocampo (Southern Italy)

The growing concentration of population and the related increase in human activities in coastal areas require numerical simulations to analyze the effects of flooding events that might occur in susceptible coastal areas in order to determine effective coastal management practices and safety measures to safeguard the inhabited coastal areas. The reliability of the analysis is dependent on the correct evaluation of key inputs such as return period of flooding events, vulnerability of exposed assets, and other risk factors (e.g., spatial distribution of elements at risk, their economic value, etc.). This paper defines a methodology to assess the effects of flooding events associated with basin run-off and storm surge in coastal areas. The assessment aims at quantifying in economic terms (e.g., loss of assets) the risk of coastal areas subject to flooding events. The methodology proposed in this paper was implemented to determine the areas subject to inundation on a coastal area in Southern Italy prone to hydrogeological instability and coastal inundation. A two-dimensional hydraulic model was adopted to simulate storm surges generated by severe sea storms coupled with intense rainfalls in order to determine the areas subject to inundation in the low-land area along the Adriatic coast object of this study. In conclusion, the economic risk corresponding to four different flooding scenarios was assessed by correlating the exceedance probability of each flooding scenario with the potential economic losses that might be realized in the inundated areas. The results of the assessment can inform decision-makers responsible for the deployment of risk mitigation measures.

Seasonality in a tidal reach: Existence, impact and a possible approach for design flood level estimation

Heterogeneity caused by seasonality could lead to the estimation error of the design flood level (DFL). This research intended to examine the existence of seasonality in the extreme water levels in a tidal reach and to quantify its impact on the DFL estimation. The mixed-distribution, a commonly used method for design value estimation with heterogeneous samples, was tested. A case study was carried out in the Pearl River Delta, South China. Results showed that a significant seasonality existed in the extreme water levels that were generated from the flood-tide interactions in the delta. If the DFL was estimated with a single distribution, the DFL with a return period smaller than 1.1 years would be underestimated and more information of the non-flood season would be lost. The mixed-distribution was superior in its consideration of seasonality, however, when the return period was over 10 years or smaller than 5 years, the DFL estimation results of this approach were only shifted by <1% from that of a single distribution.

Methodology and application of flood footprint accounting in a hypothetical multiple two-flood event

Multiple natural disasters are becoming ever more frequent around the world, with both climate change and rapid urbanization increasing the risk of such disasters in human society. Comprehensive analysis of the economic impact of multiple disasters on the industrial and economic system has become an urgent and essential part of urban recovery and sustainable development. However, there is a lack of studies that focus on assessing the indirect economic impacts resulting from such multiple events, and thereafter providing a common quantitative approach within their assessment. With the concept of flood footprint and input-output theory, this study proposes a more externally oriented methodology (flood footprint model) for indirect flood footprint (IFF) accounting and offers various extensive sensitivity analyses based on a hypothetical two-flood event. Based on the two-flood case, we conclude that the total flood footprint of a multi-flood within a given region is larger than the sum of individual flood footprints and this is the same for the IFF due to the combination effect, while the flood footprint is highly constrained by factors such as occurrence time and physical damage caused by the ensuing disaster. This article is part of the theme issue 'Urban flood resilience'.

Coastal Flooding and Inundation and Inland Flooding due to Downstream Blocking

Extreme atmospheric wind and precipitation events have created extensive multiscale coastal, inland, and upland flooding in United States (U.S.) coastal states over recent decades, some of which takes days to hours to develop, while others can take only several tens of minutes and inundate a large area within a short period of time, thus being laterally explosive. However, their existence has not yet been fully recognized, and the fluid dynamics and the wide spectrum of spatial and temporal scales of these types of events are not yet well understood nor have they been mathematically modeled. If present-day outlooks of more frequent and intense precipitation events in the future are accurate, these coastal, inland and upland flood events, such as those due to Hurricanes Joaquin (2015), Matthew (2016), Harvey (2017) and Irma (2017), will continue to increase in the future. However, the question arises as to whether there has been a well-documented example of this kind of coastal, inland and upland flooding in the past? In addition, if so, are any lessons learned for the future? The short answer is “no”. Fortunately, there are data from a pair of events, several decades ago—Hurricanes Dennis and Floyd in 1999—that we can turn to for guidance in how the nonlinear, multiscale fluid physics of these types of compound hazard events manifested in the past and what they portend for the future. It is of note that fifty-six lives were lost in coastal North Carolina alone from this pair of storms. In this study, the 1999 rapid coastal and inland flooding event attributed to those two consecutive hurricanes is documented and the series of physical processes and their mechanisms are analyzed. A diagnostic assessment using data and numerical models reveals the physical mechanisms of downstream blocking that occurred.

Extreme Coastal Water Levels Exacerbate Fluvial Flood Hazards in Northwestern Europe

Compound flooding, such as the co-occurrence of fluvial floods and extreme coastal water levels (CWL), may lead to significant impacts in densely-populated Low Elevation Coastal Zones. They may overstrain disaster management owing to the co-occurrence of inundation from rivers and the sea. Recent studies are limited by analyzing joint dependence between river discharge and either CWL or storm surges, and little is known about return levels of compound flooding, accounting for the covariance between drivers. Here, we assess the compound flood severity and identify hotspots for northwestern Europe during 1970–2014, using a newly developed Compound Hazard Ratio (CHR) that compares the severity of compound flooding associated with extreme CWL with the unconditional T-year fluvial peak discharge. We show that extreme CWL and stronger storms greatly amplify fluvial flood hazards. Our results, based on frequency analyses of observational records during 2013/2014’s winter storm Xaver, reveal that the river discharge of the 50-year compound flood is up to 70% larger, conditioned on the occurrence of extreme CWL, than that of the at-site peak discharge. For this event, nearly half of the stream gauges show increased flood hazards, demonstrating the importance of including the compounding effect of extreme CWL in river flood risk management.

Higher probability of compound flooding from precipitation and storm surge in Europe under anthropogenic climate change

In low-lying coastal areas, the co-occurrence of high sea level and precipitation resulting in large runoff may cause compound flooding (CF). When the two hazards interact, the resulting impact can be worse than when they occur individually. Both storm surges and heavy precipitation, as well as their interplay, are likely to change in response to global warming. Despite the CF relevance, a comprehensive hazard assessment beyond individual locations is missing, and no studies have examined CF in the future. Analyzing co-occurring high sea level and heavy precipitation in Europe, we show that the Mediterranean coasts are experiencing the highest CF probability in the present. However, future climate projections show emerging high CF probability along parts of the northern European coast. In several European regions, CF should be considered as a potential hazard aggravating the risk caused by mean sea level rise in the future.

Higher probability of compound flooding from precipitation and storm surge in Europe under anthropogenic climate change

In low-lying coastal areas, the co-occurrence of high sea level and precipitation resulting in large runoff may cause compound flooding (CF). When the two hazards interact, the resulting impact can be worse than when they occur individually. Both storm surges and heavy precipitation, as well as their interplay, are likely to change in response to global warming. Despite the CF relevance, a comprehensive hazard assessment beyond individual locations is missing, and no studies have examined CF in the future. Analyzing co-occurring high sea level and heavy precipitation in Europe, we show that the Mediterranean coasts are experiencing the highest CF probability in the present. However, future climate projections show emerging high CF probability along parts of the northern European coast. In several European regions, CF should be considered as a potential hazard aggravating the risk caused by mean sea level rise in the future.

Rising Sea Levels: Helping Decision-Makers Confront the Inevitable

Sea-level rise (SLR) is not just a future trend; it is occurring now in most coastal regions across the globe. It thus impacts not only long-range planning in coastal environments, but also emergency preparedness. Its inevitability and irreversibility on long time scales, in addition to its spatial non-uniformity, uncertain magnitude and timing, and capacity to drive non-stationarity in coastal flooding on planning and engineering timescales, create unique challenges for coastal risk-management decision processes. This review assesses past United States federal efforts to synthesize evolving SLR science in support of coastal risk management. In particular, it outlines the: (1) evolution in global SLR scenarios to those using a risk-based perspective that also considers low-probability but high-consequence outcomes, (2) regionalization of the global scenarios, and (3) use of probabilistic approaches. It also describes efforts to further contextualize regional scenarios by combining local mean sea-level changes with extreme water level projections. Finally, it offers perspectives on key issues relevant to the future uptake, interpretation, and application of sea-level change scenarios in decision-making. These perspectives have utility for efforts to craft standards and guidance for preparedness and resilience measures to reduce the risk of coastal flooding and other impacts related to SLR.

Quantifying uncertainties of sandy shoreline change projections as sea level rises

Sandy shorelines are constantly evolving, threatening frequently human assets such as buildings or transport infrastructure. In these environments, sea-level rise will exacerbate coastal erosion to an amount which remains uncertain. Sandy shoreline change projections inherit the uncertainties of future mean sea-level changes, of vertical ground motions, and of other natural and anthropogenic processes affecting shoreline change variability and trends. Furthermore, the erosive impact of sea-level rise itself can be quantified using two fundamentally different models. Here, we show that this latter source of uncertainty, which has been little quantified so far, can account for 20 to 40% of the variance of shoreline projections by 2100 and beyond. This is demonstrated for four contrasting sandy beaches that are relatively unaffected by human interventions in southwestern France, where a variance-based global sensitivity analysis of shoreline projection uncertainties can be performed owing to previous observations of beach profile and shoreline changes. This means that sustained coastal observations and efforts to develop sea-level rise impact models are needed to understand and eventually reduce uncertainties of shoreline change projections, in order to ultimately support coastal land-use planning and adaptation.

Coastal Flood Modeling Challenges in Defended Urban Backshores

Coastal flooding is a significant and increasing hazard. There are multiple drivers including rising coastal water levels, more intense hydrologic inputs, shoaling groundwater and urbanization. Accurate coastal flood event prediction poses numerous challenges: representing boundary conditions, depicting terrain and hydraulic infrastructure, integrating spatially and temporally variable overtopping flows, routing overland flows and incorporating hydrologic signals. Tremendous advances in geospatial data quality, numerical modeling and overtopping estimation have significantly improved flood prediction; however, risk assessments do not typically consider the co-occurrence of multiple flooding pathways. Compound flooding refers to the combined effects of marine and hydrologic processes. Alternatively, multiple flooding source–receptor pathways (e.g., groundwater–surface water, overtopping–overflow, surface–sewer flow) may simultaneously amplify coastal hazard and vulnerability. Currently, there is no integrated framework considering compound and multi-pathway flooding processes in a unified approach. State-of-the-art urban coastal flood modeling methods and research directions critical to developing an integrated framework for explicitly resolving multiple flooding pathways are presented.

Novel Hybrid Evolutionary Algorithms for Spatial Prediction of Floods

Adaptive neuro-fuzzy inference system (ANFIS) includes two novel GIS-based ensemble artificial intelligence approaches called imperialistic competitive algorithm (ICA) and firefly algorithm (FA). This combination could result in ANFIS-ICA and ANFIS-FA models, which were applied to flood spatial modelling and its mapping in the Haraz watershed in Northern Province of Mazandaran, Iran. Ten influential factors including slope angle, elevation, stream power index (SPI), curvature, topographic wetness index (TWI), lithology, rainfall, land use, stream density, and the distance to river were selected for flood modelling. The validity of the models was assessed using statistical error-indices (RMSE and MSE), statistical tests (Friedman and Wilcoxon signed-rank tests), and the area under the curve (AUC) of success. The prediction accuracy of the models was compared to some new state-of-the-art sophisticated machine learning techniques that had previously been successfully tested in the study area. The results confirmed the goodness of fit and appropriate prediction accuracy of the two ensemble models. However, the ANFIS-ICA model (AUC = 0.947) had a better performance in comparison to the Bagging-LMT (AUC = 0.940), BLR (AUC = 0.936), LMT (AUC = 0.934), ANFIS-FA (AUC = 0.917), LR (AUC = 0.885) and RF (AUC = 0.806) models. Therefore, the ANFIS-ICA model can be introduced as a promising method for the sustainable management of flood-prone areas.

Future Changes in Flood Hazards across Canada under a Changing Climate

Climate change has induced considerable changes in the dynamics of key hydro-climatic variables across Canada, including floods. In this study, runoff projections made by 21 General Climate Models (GCMs) under four Representative Concentration Pathways (RCPs) are used to generate 25 km resolution streamflow estimates across Canada for historical (1961–2005) and future (2061–2100) time-periods. These estimates are used to calculate future projected changes in flood magnitudes and timings across Canada. Results obtained indicate that flood frequencies in the northernmost regions of Canada, and south-western Ontario can be expected to increase in the future. As an example, the historical 100-year return period events in these regions are expected to become 10–60 year return period events. On the other hand, northern prairies and north-central Ontario can be expected to experience decreases in flooding frequencies in future. The historical 100-year return period flood events in these regions are expected to become 160–200 year return period events in future. Furthermore, prairies, parts of Quebec, Ontario, Nunavut, and Yukon territories can be expected to experience earlier snowmelt-driven floods in the future. The results from this study will help decision-makers to effectively manage and design municipal and civil infrastructure in Canada under a changing climate.

Modeling multiple sea level rise stresses reveals up to twice the land at risk compared to strictly passive flooding methods

Planning community resilience to sea level rise (SLR) requires information about where, when, and how SLR hazards will impact the coastal zone. We augment passive flood mapping (the so-called “bathtub” approach) by simulating physical processes posing recurrent threats to coastal infrastructure, communities, and ecosystems in Hawai‘i (including tidally-forced direct marine and groundwater flooding, seasonal wave inundation, and chronic coastal erosion). We find that the “bathtub” approach, alone, ignores 35–54 percent of the total land area exposed to one or more of these hazards, depending on location and SLR scenario. We conclude that modeling dynamic processes, including waves and erosion, is essential to robust SLR vulnerability assessment. Results also indicate that as sea level rises, coastal lands are exposed to higher flood depths and water velocities. The prevalence of low-lying coastal plains leads to a rapid increase in land exposure to hazards when sea level exceeds a critical elevation of ~0.3 or 0.6 m, depending on location. At ~1 m of SLR, land that is roughly seven times the total modern beach area is exposed to one or more hazards. Projected increases in extent, magnitude, and rate of persistent SLR impacts suggest an urgency to engage in long-term planning immediately.

A Copula-Based Bayesian Network for Modeling Compound Flood Hazard from Riverine and Coastal Interactions at the Catchment Scale: An Application to the Houston Ship Channel, Texas

Traditional flood hazard analyses often rely on univariate probability distributions; however, in many coastal catchments, flooding is the result of complex hydrodynamic interactions between multiple drivers. For example, synoptic meteorological conditions can produce considerable rainfall-runoff, while also generating wind-driven elevated sea-levels. When these drivers interact in space and time, they can exacerbate flood impacts, a phenomenon known as compound flooding. In this paper, we build a Bayesian Network based on Gaussian copulas to generate the equivalent of 500 years of daily stochastic boundary conditions for a coastal watershed in Southeast Texas. In doing so, we overcome many of the limitations of conventional univariate approaches and are able to probabilistically represent compound floods caused by riverine and coastal interactions. We model the resulting water levels using a one-dimensional (1D) steady-state hydraulic model and find that flood stages in the catchment are strongly affected by backwater effects from tributary inflows and downstream water levels. By comparing our results against a bathtub modeling approach, we show that simplifying the multivariate dependence between flood drivers can lead to an underestimation of flood impacts, highlighting that accounting for multivariate dependence is critical for the accurate representation of flood risk in coastal catchments prone to compound events.