case study for coastal flooding

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• Published: 25 November 2021

Assessing population exposure to coastal flooding due to sea level rise

• Mathew E. Hauer   ORCID: orcid.org/0000-0001-9390-5308 1 , 2 ,
• Dean Hardy   ORCID: orcid.org/0000-0003-4541-9430 3 , 4 ,
• Scott A. Kulp 5 ,
• Valerie Mueller   ORCID: orcid.org/0000-0003-1246-2141 6 , 7 ,
• David J. Wrathall 8 &
• Peter U. Clark 8 , 9  

Nature Communications volume  12 , Article number:  6900 ( 2021 ) Cite this article

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The exposure of populations to sea-level rise (SLR) is a leading indicator assessing the impact of future climate change on coastal regions. SLR exposes coastal populations to a spectrum of impacts with broad spatial and temporal heterogeneity, but exposure assessments often narrowly define the spatial zone of flooding. Here we show how choice of zone results in differential exposure estimates across space and time. Further, we apply a spatio-temporal flood-modeling approach that integrates across these spatial zones to assess the annual probability of population exposure. We apply our model to the coastal United States to demonstrate a more robust assessment of population exposure to flooding from SLR in any given year. Our results suggest that more explicit decisions regarding spatial zone (and associated temporal implication) will improve adaptation planning and policies by indicating the relative chance and magnitude of coastal populations to be affected by future SLR.

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Introduction

Sea-level rise (SLR) accompanying climate change will cause significant and costly impacts in the 21st century and beyond. Avoiding adverse consequences depends on our ability to undertake accurate assessments of the populations already affected, and those projected to be affected, to inform adaptation planning and the ability to adapt to such consequences 1 . Research has long sought to identify the impact of SLR on coastal areas 2 , 3 , 4 , with an increasing focus on estimating exposed populations and associated assets 2 , 5 , 6 . The human population is concentrated in the low-elevation coastal zone (LECZ; those <10 m above sea level) with more than 600 million people living in the LECZ globally 7 , and despite the increasing rate of SLR related flooding 8 , the global LECZ population is growing: more than 1 billion are forecast to live in the coastal zone by 2060 5 .

Scientific assessments estimating the populations affected by SLR date back at least four decades and are relatively common in the scientific literature. Intense interest in this topic is due to the magnitude and severity of SLR flooding as a climate impact, the clear potential implications for human migration 9 , the growing size of global coastal populations 5 , the relative simplicity of producing estimates 3 , and the increasing availability of both geophysical and population data products from local to global scales 7 . However, the magnitude of the population estimated to be affected ranges widely across studies. At the global level, the population estimated to be affected by SLR ranges from a low of 88 million 10 to a high of 1.4 billion 5 . These wide-ranging estimates can be attributed to several considerations, including: (1) differing spatial zones of “at-risk” that influence estimates of how many people will be affected by SLR, and (2) differing temporal horizons implied by any given spatial zone that affect estimates of when increased flooding and associated impacts due to SLR will occur. A third major consideration is the deployment of different datasets and methods to calculate exposure. Examining the contribution of different datasets and methods to wide-ranging estimates of SLR exposure is beyond the scope of this paper. For simplicity, we use the term “spatial zone” throughout the paper to describe inland areas relative to the coastline. However, we recognize that in some cases for these zones, we are discussing areas that are representative of “spatio-temporal zones” such as the 100-year flood plain, which while explicitly spatial, is dependent upon the temporal notion of a 1% chance of flooding in any given year.

The modeling choices around spatial zone of population affected by SLR imply a temporal horizon for when impacts will unfold. These temporal horizons can be forecast from as short as 100 years 11 to as long as 2000 years or longer 6 , 12 , pushing SLR impacts deep into the future. However, permanent inundation is not the most immediate impact of SLR. Regular daily to annual tidal flooding (e.g., nuisance flooding) events are likely to be the most disruptive to life in the near term 8 , and related impacts are already occurring in many parts of the world (e.g. coastal erosion 13 , coastal flooding 14 , and saltwater intrusion 15 ). Yet each individual spatial zone overlooks the spatio-temporal continuity of SLR impacts on a coastal landscape. Such spatio-temporal differences in assessments of populations affected highlight some of the limitations of singular, or limited, spatial zones for adaptation planning.

In a systematic review of research assessing populations affected by SLR, we identify 46 studies that meet our search criteria (See  Supplementary Material ). Each of these studies assessed populations affected by SLR using varied spatial zones of exposure. The spatial zone of SLR exposure assessed in these studies ranged from mean sea level (narrowest zone) to the LECZ (broadest zone). Twenty studies (43%) used more than one spatial zone, nine (20%) used more than two, and four (9%) used three or more. The most common three, from narrowest to broadest spatial zone, assessed populations affected as follows: (i) complete inundation or submergence under the future high-tide line ( n  = 20), (ii) extreme water levels such as storm surge via the 100-year floodplain ( n  = 17), and (iii) the LECZ ( n  = 11) (Supplementary Table  1 ). Of the seven spatial zones used in at least three studies, 61% ( n  = 28) of the studies used at least one of the three common zones, 13% used two, and only one study 16 used all three. These are the same three common spatial zones identified in previous studies 9 , 17 . McMichael et al. 17 referred to these three zones as Specified Levels of SLR , Coastal Floodplains, and the LECZ and we adopt those labels here.

Recurrent tidal flooding or flooding on an annual basis and flooding for a specified return level (Coastal Floodplains) is the exposure category where we expect impacts that are most immediate and severe 18 , 19 , 20 . Perigean spring tide events cause regularly recurring water levels well above high tide, and in many coastal areas these cause significant flooding, sometimes referred to as nuisance flooding or recurrent tidal flooding 8 , particularly when these tidal events are enhanced by significant onshore winds from tropical cyclones or storms.

Eventually, recurrent tidal flooding gives way to permanent inundation and submergence of coastal areas under future high tides. The future high-tide line (Specified Levels of SLR) is the narrowest delineation of exposure to SLR (i.e. transition from land to ocean), and commonly implies societal impacts that include permanent loss of settled areas, migration, and community relocation 9 . These areas are threatened by inundation and will ultimately be the most adversely affected locations, but societal losses will occur prior to permanent inundation and are dependent on adaptive measures that may be undertaken in advance. It is important to note that this strict delineation eschews other hazards associated with SLR.

Areas located beyond the Coastal Floodplains and within the upper bounds of the LECZ broaden populations exposed to coastal flooding and especially its extended impacts while carrying much less of a chance of flooding, but still include associated SLR hazards (such as soil salinization) 5 , 21 . Simply residing within the LECZ does not guarantee direct exposure to a SLR hazard such as a storm surge, but it does carry increased probability of exposure to the side effects of an extreme event through extended impacts on, for example, livelihood opportunities. For example, some populations residing within the 100-year floodplain may experience recurrent tidal flooding, permanent inundation, storm surges, and saltwater intrusion in coming decades, 8 , 22 , 23 , 24 while those beyond it are less likely to experience such effects. Broader zones such as the LECZ render any coastal area as “exposed” to SLR in nearly any time period, which makes it difficult to determine exactly who is exposed to SLR-related effects and when they are exposed. For our study, however, we do not assess these extended impacts, rather we specifically highlight the estimated populations that could be indirectly affected by flooding events triggering other impacts in the LECZ.

There are three main advantages to examining SLR exposure assessments across the three most common spatial zones. First, by accounting for the probability of flooding at locations between the LECZ to the mean higher high water (MHHW) mark into a single analytical framework, we better describe how possible differential impacts vary across space. Different populations within the coastal zone have differential exposure to flooding, allowing for more nuanced discussions of what it means to be “exposed” to SLR.

Second, each individual approach implies varying temporal windows. Specified levels of SLR assume SLR exposure only at the moment when areas are permanently submerged. Conversely, the LECZ represents the most inclusive estimate of exposure to SLR hazards potentially over millennia with high emissions and future sea level under high emissions will far exceed the LECZ if it is kept fixed relative to present sea level 12 . Between these two extremes lie Coastal Floodplains with a gradient of exposure to multiple hazards associated with the slow, continual rise in water levels.

Third, analyzing the most common approaches provides a framework for examining the entire range of population exposures to SLR impacts. Each spatial zone when applied often assumes homogeneity of impacts by identifying populations exposed based on their presence within each designated zone: people are either inside or outside the LECZ; inside or outside the 100-year floodplain; above or below specified levels of SLR. This equality of exposure within each chosen spatial zone ignores the variability within the actual zone itself. Those projected to live under the future high-tide line are exposed to virtually all SLR-associated hazards: soil salinization, recurrent tidal flooding, storm surge, livelihood impacts, shoreline erosion, etc. In contrast, those who live at higher elevations within coastal communities might only be exposed to storm surge and indirect livelihood impacts. Allowing for variation of exposure, as we have done here, permits an examination of multiple scenarios that might unfold along the spectrum of flood exposure in the coastal zone.

While we still use the above approaches and recognize their value in what population exposure assessments indicates related to the extended hazards beyond flooding, taken individually, no one approach to characterizing spatial zone is likely to accurately represent the heterogeneity of hazards associated with SLR nor quantify the spatial zone or timing of exposure to flooding. One approach to this problem is the EAE, a unifying spatio-temporal metric that characterizes exposure across all spatial zones using a finite time period (one year) for planning decisions. Unlike other approaches, the EAE indicates the population exposed to annual flooding by summing the range of annual exposure probabilities over space under any given Representative Concentration Pathway (RCP), an exposure profile that changes over time.

Furthermore, most previous assessments focus on assessing the populations exposed specifically to flooding disregard the annual probability of population exposure to flooding above the high-tide line spanning from the relatively frequent nuisance events (such as a spring/king tide) to 100-year floodplains and beyond. While some studies of SLR impacts have examined expected annual damages (e.g., 11 , 25 , 26 ), analysis of the expected annual exposure (EAE) of populations to flooding is relatively new 27 , 28 , 29 . EAE allows for integrating across the most common spatial zones into a single, continuous, model of populations annually exposed to flooding due to SLR from the high-tide line to the 10,000-year floodplain. Few assessments of EAE, however, apply projected estimates of future populations, which could serve as indicators of future impacts. Thus, we combine the EAE model with sub-county population projections in the United States to characterize SLR hazards between 2000 and 2100 under three of the IPCC’s Representative Concentration Pathways (RCP 2.6, 4.5, and 8.5) and all five Shared Socioeconomic Pathways.

In this work, based on our own review and previous work 9 , 17 , we analyze population exposure for the three most common spatial zones from the high-tide line to the LECZ. We show how this approach allows for better inter-model comparisons between estimates and, crucially, clarifies their differential exposure estimates related to SLR. Furthermore, we examine the EAE for the same areas and suggest that it benefits adaptation planning by showing the annual increase in populations likely to be directly affected by annual flooding events representing the leading edge of SLR impacts. Although adaptation will occur in the future, we do not account for adaptation measures in this analysis, instead interpreting potential future population exposure as an indicator of potential impacts. We emphasize that the EAE is not a replacement for the others, which have their own merits, but that it instead standardizes the broad coastal zone range into an all-inclusive spatial region centered on annual flood exposure; a metric that we suggest indicates the rate of change in populations exposed to annual flooding in a manner more easily interpreted for local level adaptation planning.

Overall Results

We find that in the year 2000, the expected number of people in the United States exposed to an annual flood event is just over 600 K people, 150 K people lived below the high-tide line, and 2.4 M people lived in the 100-year flood plain (Fig.  1 ). The combination of coastal population growth and SLR between 2000 and 2020 has already increased the EAE of the US coastal population by 60% (610 K to 980 K), increased the US coastal population living below the high-tide line by 60% (150 K to 240 K), and increased the US coastal population living in the 100-year floodplain by 45% (2.4 M to 3.5 M), despite just a 25% growth in the entire coastal population over the same period.

Uncertainty reflects the 95 th percentile prediction interval. Each spatial zone is not mutually exclusive, but cumulative. 9.2 million people in the Infrequent Flooding Effects zone under SSP1 is inclusive of those in the two preceding zones.

As the century progresses, SLR places the US coastal population at increasing chance of exposure to flooding (Fig.  1 and Table  1 ). Under the SSP2 and RCP 4.5 emission scenarios between 2020 and 2100, we project the EAE to increase 325% to 4.1 M people (2.3–6.4 M); we project the US coastal population below the high-tide line to increase more than 435% to 1.2 million people (0.3–5.1 M); and we project the US coastal population living in the 100-year flood plain to increase 160% to 9.0 million people (3.4 –22.3 M). Lower estimates use SSP3 and the 5 th percentile projection in RCP 4.5 while upper estimates use SSP5 and the 95th percentile projection from RCP 4.5 unless otherwise noted. At the same time, we project the population in the 406 coastal counties to increase by just over 40% (133–190 M). Importantly, this indicates that exposure to coastal flood hazards outpaces any increased exposure due to coastal population growth.

Uneven exposure

Exposure to SLR unfolds unevenly across the US (Fig.  2 ). In the year 2000, just two counties had over 100 K people in the 100-year floodplain. However, by 2100 and assuming no adaptation, we project nine counties with 100 K people annually exposed to flooding, and 13 counties with 100 K in the 100-year floodplain. In every single county and spatial zone, population exposure increases faster than population growth.

a shows the numeric distribution in 2100 and ( b ) shows the relative change in exposure between 2000 and 2100. Brown counties in ( b ) indicates declining exposure.

SLR Metrics Comparison

For any particular county, it is the combination of the three spatial zones (MHHW, 100-year Floodplain, and LECZ) together that capture the breadth of SLR impacts. Counties with similar exposure profiles along one metric (e.g., MHHW) may have vastly different exposure profiles along the other metrics. For example, despite having 100% of their populations in the LECZ, Currituck County, NC and Orange County, TX exhibit markedly different total exposure profiles. Figure  3 shows selected county pairs with one or more similar exposure metrics. Even if three of the four exposure metrics are similar, a fourth metric could still be quite different (e.g., McIntosh County, GA compared to Franklin County, FL).

Here we compare counties with similar exposures under different spatial zones to show similarity in one zone does not translate to similar exposure under a different spatial zone. MHHW is the Mean Higher High Water, EAE is the expected annual exposure, RL100 is the 100-year Floodplain, and LECZ is the Low-Elevation Coastal Zone.

The three most common spatial zones used to characterize the populations affected by SLR impacts have merits depending on data availability, temporal window analyzed, and amount of anticipated SLR. However, an exposure assessment using only one or two of these zones will fail to capture important heterogeneity in SLR exposure and could lead to misguided decision making. We suggest that the lack of consistency across studies and the imprecision of language related to flooding effects may relay a confusing and unclear message to the adaptation planning and policy-making community. For example, MHHW implies that populations below specified levels of SLR will experience imminent property loss and daily flooding. Those inhabiting the Coastal Floodplains zone will experience increasing risk of losses from nuisance flooding, storm surge, saltwater intrusion, soil salinization, etc. (see 9 for review). Populations in the LECZ can prepare for a broader set of socio-economic impacts to livelihoods due to the tangential effects of flooding extending beyond the water line over a large geographic area. For example, extended effects of flooding might include increasing costs of maintaining vulnerable infrastructure, job losses due to declining coastal industries and/or populations 30 , the extended effects of coastal property devaluation 31 , or climate gentrification 32 . Our more holistic approach allows for reimagining adaptation planning needs along a continuum of impacts. It is not just who will be affected, but when, and why they will be affected that must be accounted for in adaptation planning scenarios.

Our results indicate that geographic regions that have similar population exposure estimates under one or multiple spatial zones can be different under another zone—sometimes vastly different (Fig.  3 ). A key insight from our results is a more comprehensive picture for decision-makers who may not otherwise realize which zone in their jurisdiction has the greatest proportional change projected for the population affected by flooding and/or non-flooding related events. Such a misplaced judgement could have significant long-term ramifications for local populations as adaptation strategies may be misguided. For example, Glynn County, GA and Pasquotank County, NC have similar populations exposed to permanent inundation under specified levels of SLR, and therefore may have similar short-term adaptation responses for those expecting near-term losses (see Fig.  3 ). However, over the coming century, Pasquotank County, NC has double the population who can expect annual flooding (via EAE) and double who will live in the 100-year floodplain. This suggests that compared to Glynn County, GA, decision-makers in Pasquotank County, NC will need to prioritize long-term adaptation planning and financing, with an eye toward impacts associated with floodplain management over permanent inundation. For federal and state agencies, this type of high-level comparison could inform allocation of national adaptation funding to mitigate the most likely types of impacts aligned with predictions of a region’s largest population indicated to be affected. At the local level, it offers decision-makers who have limited resources to allocate toward mitigating impacts the critical details they need to inform tradeoffs in the adaptation planning process, such as the magnitude of populations to be affected, and when, how, and why they will be affected.

Moreover, none of the above spatial zones gives an estimate for the population that will be directly exposed to flooding annually, a useful statistic for planning. The EAE approach’s estimate of annual exposure from the 1-year to 10000-year flood plains provides decision-makers with this estimate of the population predicted to be directly affected by a flood event in any given year. Alongside estimates produced from the three most common spatial zones, the EAE allows for both a more nuanced understanding of the exposed population as well as a comparison with other previously published estimates that use the high-tide line, 100-year flood plain, or LECZ in their analysis.

How scientists—and crucially policymakers and planners on the frontlines—conceptualize population exposure to SLR will inform adaptation strategies for mitigating the impacts of SLR. Popular adaptation strategies for SLR include protection, accommodation, and retreat. Typical responses include shoreline armoring, elevating structures, and relocating buildings further from the encroaching shoreline, respectively. Aside from the equity issues of redistributing vulnerability or prioritizing adaptation of privileged populations near the shoreline 33 , 34 , typical adaptation strategies ignore the significant implications to populations and infrastructure located beyond the 100-year floodplain, who will nonetheless experience increasing flooding effects over time . Our study highlights the narrowness of popular spatial zones when used alone, but at the same time highlights their conceptual strengths when paired with each other for adaptation planning.

We come to three primary conclusions based on our analysis. Out of our review of 46 articles, scientists studying SLR routinely employ one ( n  = 26) or two ( n  = 12) spatial zones. As we show, restricting a SLR assessment to so few spatial zones overlooks the variation of estimated population exposure across zones, and crucially, does not identify the zones that are experiencing the most rapid change in numbers due to SLR. We suggest that scientists should utilize multiple zones to better quantify SLR impacts for adaptation planning, or at the least be more explicit with their choice of zone and its associated implications. Second, SLR increases anticipated impacts in coastal areas far faster than population growth in coastal areas. Third, using the EAE we show that nearly 1 million coastal residents in the US are presently exposed to an annual flood event. These are SLR impacts presently occurring rather than in the year 2100. Using EAE helps illuminate the contemporary impacts of SLR and its projected annual rate of change, underscoring the importance of immediate adaptation and mitigation needs but also where future efforts could focus on mitigating direct flooding impacts.

While we treat populations as homogeneous groups in our analysis, we recognize that successfully adapting to SLR will require a suite of adaptation planning responses that respond to the range of social and environmental variability within and across coastal systems. Adaptation planning must attend to the ways that social difference affects adaptive capacity under similar exposure levels 33 , 34 , 35 , 36 , 37 . Achieving equity in adaptation planning 38 , 39 requires tackling social difference, specifically how differential exposure affects different social groups differently. Moreover, holistic approaches to adaptation planning must also account for the varied range of SLR hazards beyond flooding and the consequent ramifications for socially differentiated populations. Future research might examine social heterogeneity of populations across the most commonly used spatial zones to better capture possible concerns with justice and equity in adaptation planning.

Expected annual exposure

To demonstrate the effect of spatial zone choice on estimates of populations exposed as an indicator of flooding impacts to SLR for coastal communities, we combined two primary models: a small-area demographic projection model and a flood risk probability model.

Small-area demographic projection model

Following 40 , we produced a set of small-area demographic projections using a proportional fitting algorithm to produce spatiotemporally consistent Census Block Groups (CBGs) for the period 1940–2010 and employed a mixed, linear/exponential projection for the period 2010–2100. We included only CBGS ( n  = 81,815) located in counties (n = 406) expected to experience any probability of flooding.

We produce these projections using the ‘Year Structure Built’ question, group quarters count ( GQ ), and persons-per household (PPHU) from the 2013–2018 Census Bureau’s American Community Survey and the count of housing units at the county-level from historic censuses. Following 41 , the population in time t in county i in CBG j is given as \({P}_{{tij}}=H* {{{{{{\rm{PPHU}}}}}}}+{GQ}\) .

We calculate H in the period 1940–2010 using \({\hat{H}}_{ij}^{v}=\frac{{C}_{j}^{v}}{{\sum }_{i=1}^{n}{\sum }_{t=1939}^{v-1}{H}_{ijt}^{v}}\) \(* {\sum }_{t=1939}^{v-1}{H}_{{ijt}}^{v}\) , where \({C}_{j}^{v}\) is the count of housing units from the historic census in the set of time periods \(v\in \{1940,1950,...,2010\}\) in county j and \({H}_{{ijt}}^{v}\) refers to the estimate of housing units in time t from the American Community Survey for block group i in county j . For example, to estimate the number of housing units in block group I in county j for the year 1960, the number counted in the 1960 census ( \({C}_{j}^{1960}\) ) is divided by the number of HUs in county j as estimated in the ACS for the period 1939–1959 \(({\sum }_{i=1939}^{1959}{H}_{j}^{1960})\) and multiplied by the number of HUs for each block group for the same period \(({\sum }_{i=1939}^{1959}{H}_{{ij}}^{1960})\) ,

We project H in the time periods 2020–2100 using \({H}_{{ij}}^{t+z}=\left(\alpha +\beta z\right)+[{H}^{t}-\left(\alpha +\beta t\right)]\) for any CBGs experiencing population growth and \({H}_{{ij}}^{t+z}={e}^{\beta }* {z}^{\alpha }+[{H}^{t}-\left({e}^{\alpha }* {t}^{\beta }\right)]\) for CBGs experiencing population decline. We subset our projections for the time periods 2000–2100.

We then control our projections to the Shared Socioeconomic Pathways (SSPs) 42 , 43 . Out of sample validations suggest reasonably good fit for this approach 40 , 42 . Controlling our projections to the SSPs allows a near direct translation of our small-area results to national-level SSPs and other nation-level SLR assessments.

Digital elevation model

To classify exposure categories, we employed airborne lidar-derived digital elevation models (DEMs) distributed by NOAA 44 supplemented with the USGS Northern Gulf of Mexico Topobathymetric DEM 45 in Louisiana and the USGS National Elevation Dataset 46 in the small fraction of land not covered by the other sources. These elevation data are vertically referenced to NAVD88 and converted to the MHHW datum using NOAA’s VDatum grid (version 2.3.5) 47 . Following a bathtub model, we assessed exposed land area using a given water height against the elevation model to generate binary inundation surfaces. The DEM data are high-resolution, high-accuracy, LiDAR-derived digital terrain (bare-earth) models with the lowest uncertainty associated with estimates of flood exposure 11 , 48 , 49 .

In past literature 28 , 50 , 51 , it is common to use connected components analysis on binary inundation surfaces to enforce hydrological connectivity to the ocean. While this approach works with a small number of elevation thresholds, it becomes computationally intractable when assessing tens of thousands of SLR scenarios (combinations of years + emissions scenarios + Monte Carlo simulations), as is done in this work. Instead, we follow the framework described in 52 to directly refine the DEMS. First, we generated inundation surfaces from 0–10 m above MHHW, at 0.25 m increments, denoting the i ’th such height in this sequence by \({h}_{i}\) , and denoting each such binary surface as \({{{{{{{\rm{ThresholdWaterSurface}}}}}}}}_{i}({{{{{{\rm{lat}}}}}}},{{{{{{\rm{lon}}}}}}})\) . For each pixel in the DEM below 10 m, we noted the minimum value of i for which \({{{{{{{\rm{ThresholdWaterSurface}}}}}}}}_{i}({{{{{{\rm{lat}}}}}}},{{{{{{\rm{lon}}}}}}})\) is 1 (i.e., where its elevation is below \({h}_{i}\) ), which we stored in a new index surface ThresholdIndexSurface(lat,lon) . We then incorporated levee data from the Mid-term Levee Inventory (FEMA/USACE, acquired September 2013) and used connected components analysis to remove isolated regions within each inundation surface, thus generating fully connected binary masks \({{{{{{{\rm{ConnectedWaterSurface}}}}}}}}_{i}({{{{{{\rm{lat}}}}}}},{{{{{{\rm{lon}}}}}}})\) . As before, for each pixel in the DEM below 10 m, we found the lowest value of i for which \({{{{{{{\rm{ConnectedWaterSurface}}}}}}}}_{i}({{{{{{\rm{lat}}}}}}},{{{{{{\rm{lon}}}}}}})\) , which we again stored in an index surface ConnectedIndexSurface(lat,lon).

We assumed that pixels where ThresholdIndexSurface(lat,lon)= ConnectedIndexSurface (lat,lon) are not isolated, and therefore their elevations in the refined DEM are unchanged. However, locations where ThresholdIndexSurface (lat,lon) < ConnectedIndexSurface’(lat,lon) were isolated. To ensure connectivity when thresholding against new water surfaces, we adjusted such pixels’ elevations to equal \({h}_{{{{{{\rm{ConnectedIndexSurface}}}}}}}\) \(({{{{{{\rm{lat}}}}}}},{{{{{{\rm{lon}}}}}}})\) .

Sea level rise projections and flood event probability surfaces

To produce an internally consistent model of flooding, given every pixel in the adjusted DEM, and any SLR projection, we calculated the annual probability that at least one nearby extreme flood event would exceed each pixels’ elevation. Here we used the probabilistic SLR projections published previously 53 , which incorporate local non-climatic factors such as isostatic adjustment and human-caused land subsidence, and are closely aligned with recent IPCC findings 54 , 55 .

We use historical storm surge records at individual tide stations to estimate their return level curves, and apply them to all pixels between the tide stations using a bathtub model. Unlike studies that perform hydrodynamic simulations on synthetic storms (e.g., FEMA’s base flood elevation maps), these curves do not consider factors such as local topography, rainfall, or waves. While the station-distance sensitivity analysis performed in 28 suggests that the distances between tide stations used in this work are sufficiently close to assess EAE in the US, exposure estimates at the <1% probability threshold may be particularly sensitive to these factors.

We specified our model following previous approaches 22 , 28 , 52 , 56 which hold storm surge constant, fitting the parameters of a generalized Pareto distribution (GPD) to historical heights and frequencies of extreme coastal flood events at NOAA tide stations along the US coastline with at least 30 years of hourly records through 2013. This specification allows us to estimate \(P(H\ge {E|Y}=2000)\) , the annual probability of the maximum water height, H , exceeding elevation, E , in the year 2000 (the baseline year, where SLR=0). We expanded a framework described previously 28 , 52 to estimate total per-pixel annual probability of exceedance of any water height in any year, unconditional to SLR sensitivity to emissions. Published SLR projections 53 are provided as a set of probabilistic distributions, each with 10,000 Monte Carlo samples of SLR for each tide-gauge station and for each year. Below we denote each sample as the function \({{{{{{\rm{SLR}}}}}}}_{j}(y)\) for \(j\in [1,\ldots ,10000]\) . From the law of total probability, we can estimate the annual probability of the maximum water height, H , exceeds elevation E in year Y from

We computed this function under each emissions pathway (RCPs 2.6, 4.5, and 8.5) for each decade (2000–2100), for elevations between 0 and 10 m at 0.1 m increments. We stored these probabilities in lookup tables for efficient queries.

For every pixel in the DEM with elevation E(lat,lon) , we determined its closest NOAA tide-gauge station and used the relevant lookup tables to estimate its annual water height exceedance probability for every SLR projection listed above. We stored the results in a large raster database, producing probability surfaces \(P(H\ge E({{{{{{\rm{lat}}}}}}},{{{{{{\rm{lon}}}}}}}){|Y}=y)\) for all three emissions scenarios and decades along the entire US coastline.

Recent studies suggest that the bathtub model employed here likely overestimates exposure, as it does not incorporate wave attenuation nor the time it takes for water to reach their full extent 57 , 58 . Given the high spatial resolution and wide distributions of water heights used in our EAE analysis, it is not yet computationally feasible to employ a hydrodynamic model to refine these results.

Exposure computation

To assess population exposure within a US Census Block Group under any water height (including all exposure approaches described above, namely, MHHW, LECZ, as well as 100-year storm surge adjusted for SLR), we generated a connected inundation surface. For the MHHW and LECZ layers, we simply thresholded the adjusted DEM to find pixels below SLR(y) and for (10+SLR(y)), respectively. For the 100-year storm layer, we thresholded the probability surface to find pixels where \(P\left(H\ge E,|,Y=y\right) < 0.01\) . For each block group, we counted the percentage of its pixels on dry land (as defined by the National Wetland Inventory 59 ) covered by the inundation surface, and multiplied by its total population, as predicted by each SSP. To compute expected annual exposure (EAE), defined as the expected number of people on land below the maximum local storm surge height in a given year 28 , we multiplied the value of each pixel within the probability surface \(P(H\ge E({{{{{{\rm{lat}}}}}}},{{{{{{\rm{lon}}}}}}}){|Y}=y)\) by the block group’s (per-pixel) population density and computed the sum.

Data availability

The data resulting from this study are deposited at https://doi.org/10.5281/zenodo.5562904 60 . The underlying data that support the findings of this study are available from Climate Central but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of Climate Central.

Code availability

Code to reproduce our analysis is available at https://doi.org/10.5281/zenodo.5562904 60 .

Howden, M. & Jacobs, K. L. in The US National Climate Assessment 157–171 (Springer, 2016).

Curtis, K. J. & Schneider, A. Understanding the demographic implications of climate change: estimates of localized population predictions under future scenarios of sea-level rise. Popul. Environ. 33 , 28–54 (2011).

Article   Google Scholar  

Schneider, S. H. & Chen, R. S. Carbon dioxide warming and coastline flooding: physical factors and climatic impact. Annu. Rev. Energy 5 , 107–140 (1980).

Wu, S.-Y., Yarnal, B. & Fisher, A. Vulnerability of coastal communities to sea-level rise: a case study of Cape May County, New Jersey, USA. Clim. Res. 22 , 255–270 (2002).

Neumann, B., Vafeidis, A. T., Zimmermann, J. & Nicholls, R. J. Future coastal population growth and exposure to sea-level rise and coastal flooding—a global assessment. PLoS ONE 10 , e0118571 (2015).

Article   PubMed   PubMed Central   Google Scholar  

Strauss, B. H., Kulp, S. & Levermann, A. Carbon choices determine US cities committed to futures below sea level. Proc. Natl Acad. Sci. 112 , 13508–13513 (2015).

Article   ADS   CAS   PubMed   PubMed Central   Google Scholar  

McGranahan, G., Balk, D. & Anderson, B. The rising tide: assessing the risks of climate change and human settlements in low elevation coastal zones. Environ. Urban. 19 , 17–37 (2007).

Sweet, W. V. & Park, J. From the extreme to the mean: Acceleration and tipping points of coastal inundation from sea level rise. Earths Future 2 , 579–600 (2014).

Article   ADS   Google Scholar  

Hauer, M. E. et al. Sea-level rise and human migration. Nat. Rev. Earth Environ. 1 , 28–39 (2020).

Desmet, K. et al. Evaluating the economic cost of coastal flooding. NBER Working Pap er 24918 (2018).

Hinkel, J. et al. Coastal flood damage and adaptation costs under 21st century sea-level rise. Proc. Natl Acad. Sci. 111 , 3292–3297 (2014).

Clark, P. U. et al. Consequences of twenty-first-century policy for multi-millennial climate and sea-level change. Nat. Clim. Change 6 , 360–369 (2016).

Hinkel, J. et al. A global analysis of erosion of sandy beaches and sea-level rise: An application of DIVA. Glob. Planet. Change 111 , 150–158 (2013).

Moftakhari, H. R. et al. Increased nuisance flooding along the coasts of the United States due to sea level rise: past and future. Geophys. Res. Lett. 42 , 9846–9852 (2015).

Chang, S. W., Clement, T. P., Simpson, M. J. & Lee, K.-K. Does sea-level rise have an impact on saltwater intrusion? Adv. Water Resour. 34 , 1283–1291 (2011).

Felsenstein, D. & Lichter, M. Social and economic vulnerability of coastal communities to sea-level rise and extreme flooding. Nat. Hazards 71 , 463–491 (2014).

McMichael, C., Dasgupta, S., Ayeb-Karlsson, S. & Kelman, I. A review of estimating population exposure to sea-level rise and the relevance for migration. Environ. Res. Lett. 15 , 123005 (2020).

Article   ADS   PubMed   PubMed Central   Google Scholar  

Carbognin, L., Teatini, P., Tomasin, A. & Tosi, L. Global change and relative sea level rise at Venice: what impact in term of flooding. Clim. Dyn. 35 , 1039–1047 (2010).

Dahl, K. A., Fitzpatrick, M. F. & Spanger-Siegfried, E. Sea level rise drives increased tidal flooding frequency at tide gauges along the US East and Gulf Coasts: Projections for 2030 and 2045. PloS ONE 12 , e0170949 (2017).

Spanger-Siegfried, E., Fitzpatrick, M. F. & Dahl, K. Encroaching tides: how sea level rise and tidal flooding threaten US East and Gulf Coast communities over the next 30 years. Cambridge, MA: Union of Concerned Scientists. (2014).

Hallegatte, S. et al. Assessing climate change impacts, sea level rise and storm surge risk in port cities: a case study on Copenhagen. Clim. Change 104 , 113–137 (2011).

Tebaldi, C., Strauss, B. H. & Zervas, C. E. Modelling sea level rise impacts on storm surges along US coasts. Environ. Res. Lett . 7 , 014032 (2012).

Woodruff, J. D., Irish, J. L. & Camargo, S. J. Coastal flooding by tropical cyclones and sea-level rise. Nature 504 , 44–52 (2013).

Article   ADS   CAS   PubMed   Google Scholar  

Vitousek, S. et al. Doubling of coastal flooding frequency within decades due to sea-level rise. Sci. Rep. 7 , 1399 (2017).

Vafeidis, A. T. et al. Water-level attenuation in global-scale assessments of exposure to coastal flooding: a sensitivity analysis. Nat. Hazards Earth Syst. Sci. 19 , 973–984 (2019).

Tiggeloven, T. et al. Global-scale benefit–cost analysis of coastal flood adaptation to different flood risk drivers using structural measures. Nat. Hazards Earth Syst. Sci. 20 , 1025–1044 (2020).

Koks, E. E. et al. A global multi-hazard risk analysis of road and railway infrastructure assets. Nat. Commun. 10 , 2677 (2019).

Kulp, S. & Strauss, B. H. Rapid escalation of coastal flood exposure in US municipalities from sea level rise. Clim. Change 142 , 477–489 (2017).

Article   CAS   Google Scholar  

Rasmussen, D. J., Oppenheimer, M., Kopp, R., Strauss, B. & Kulp, S. Physical extreme sea level metrics may misrepresent future flood risk. Earth Space Sci. Open Arch. https://doi.org/10.1002/essoar.10503428.1 (2020).

Hauer, M. E. Migration induced by sea-level rise could reshape the US population landscape. Nat. Clim. Change 7 , 321–325 (2017).

McAlpine, S. A. & Porter, J. R. Estimating recent local impacts of sea-level rise on current real-estate losses: a housing market case study in Miami-Dade, Florida. Popul. Res. Policy Rev. 37 , 871–895 (2018).

Keenan, J. M., Hill, T. & Gumber, A. Climate gentrification: from theory to empiricism in Miami-Dade County, Florida. Environ. Res. Lett. 13 , 054001 (2018).

Marino, E. Adaptation privilege and Voluntary Buyouts: perspectives on ethnocentrism in sea level rise relocation and retreat policies in the US. Glob. Environ. Change 49 , 10–13 (2018).

Atteridge, A. & Remling, E. Is adaptation reducing vulnerability or redistributing it? Wiley Interdiscip. Rev. Clim. Change 9 , 1–16 (2018).

Hardy, R. D., Milligan, R. A. & Heynen, N. Racial coastal formation: the environmental injustice of colorblind adaptation planning for sea-level rise. Geoforum 87 , 62–72 (2017).

Thomas, K. et al. Explaining differential vulnerability to climate change: a social science review. Wiley Interdiscip. Rev. Clim. Change 10 , e565 (2019).

Article   PubMed   Google Scholar  

Hardy, R. D. & Hauer, M. E. Social vulnerability projections improve sea-level rise risk assessments. Appl. Geogr. 91 , 10–20 (2018).

Graham, S., Barnett, J., Fincher, R., Hurlimann, A. & Mortreux, C. Local values for fairer adaptation to sea-level rise: A typology of residents and their lived values in Lakes Entrance, Australia. Glob. Environ. Change 29 , 41–52 (2014).

Graham, S., Barnett, J., Fincher, R., Mortreux, C. & Hurlimann, A. Towards fair local outcomes in adaptation to sea-level rise. Clim. Change 130 , 411–424 (2015).

Hauer, M. E., Evans, J. M. & Mishra, D. R. Millions projected to be at risk from sea-level rise in the continental United States. Nat. Clim. Change 6 , 691–695 (2016).

Hauer, M. E., Hardy, R. D., Mishra, D. R. & Pippin, J. S. No landward movement: examining 80 years of population migration and shoreline change in Louisiana. Popul. Environ. 40 , 369–387 (2019).

Hauer, M. E. Population projections for U.S. counties by age, sex, and race controlled to shared socioeconomic pathway. Sci. Data 6 , 190005 (2019).

O’Neill, B. C. et al. The roads ahead: narratives for shared socioeconomic pathways describing world futures in the 21st century. Glob. Environ. Change 42 , 169–180 (2017).

NOAA. Digital Coast Coastal Lidar. http://coast.noaa.gov/digitalcoast/data/coastallidar (2015).

U. S. G. S. Coastal National Elevation Database (CoNED) Project—Topobathymetric Digital Elevation Model (TBDEM). https://lta.cr.usgs.gov/coned_tbdem (2014).

Gesch, D. et al. The National Elevation Dataset. Photogramm. Eng. Remote Sens. 68 , 5–32 (2002).

Google Scholar  

Parker, B., Milbert, D., Hess, K. & Gill, S. National VDatum — The Implementation of a National Vertical Datum Transformation Database (NOAA, 2003).

Hooijer, A. & Vernimmen, R. Global LiDAR land elevation data reveal greatest sea-level rise vulnerability in the tropics. Nat. Commun. 12 , 3592 (2021).

Lichter, M., Vafeidis, A. T. & Nicholls, R. J. Exploring data-related uncertainties in analyses of land area and population in the “Low-Elevation Coastal Zone” (LECZ). J. Coast. Res. 27 , 757 (2010).

Strauss, B. H., Ziemlinski, R., Weiss, J. L. & Overpeck, J. T. Tidally adjusted estimates of topographic vulnerability to sea level rise and flooding for the contiguous United States. Environ. Res. Lett. 7 , 014033 (2012).

NOAA. Detailed Method for Mapping Sea Level Rise Inundation. Method Description. Office for Coastal Management (NOAA, 2017).

Buchanan, M. K. et al. Sea level rise and coastal flooding threaten affordable housing. Environ. Res. Lett. 15 , 124020 (2020).

Article   ADS   CAS   Google Scholar  

Kopp, R. E. et al. Probabilistic 21st and 22nd century sea-level projections at a global network of tide-gauge sites. Earths Future 2 , 383–406 (2014).

Church, J. A. et al. Sea level change . http://drs.nio.org/drs/handle/2264/4605 (2013).

Oppenheimer, M. et al. in IPCC Special Report on the Ocean and Cryosphere in a Changing Climate (eds Pörtner, H.-O. et al.) (Cambridge University Press, 2019).

Kulp, S. A. & Strauss, B. H. New elevation data triple estimates of global vulnerability to sea-level rise and coastal flooding. Nat. Commun. 10 , 4844 (2019).

Gallien, T. W., Sanders, B. F. & Flick, R. E. Urban coastal flood prediction: Integrating wave overtopping, flood defenses and drainage. Coast. Eng. 91 , 18–28 (2014).

Bates, P. D. et al. Combined modeling of US fluvial, pluvial, and coastal flood hazard under current and future climates. Water Resour. Res. 57 , e2020WR028673 (2021).

U.S. Fish and Wildlife Service. National Wetlands Inventory. https://www.fws.gov/wetlands/data/data-download.html (2012).

Hauer, M. et al. mathewhauer/SLR-typology-replication: Second Release (Zenodo, 2021). https://doi.org/10.5281/ZENODO.5562904 .

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Acknowledgements

This work was supported by the National Socio-Environmental Synthesis Center (SESYNC) under funding received from the US National Science Foundation DBI-1639145 (D.W., V.M., M.H., D.H., S.K., P.C.). We also would like to thank M. Oppenheimer for his insightful comments in developing this paper.

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Hauer, M.E., Hardy, D., Kulp, S.A. et al. Assessing population exposure to coastal flooding due to sea level rise. Nat Commun 12 , 6900 (2021). https://doi.org/10.1038/s41467-021-27260-1

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Can coastal cities turn the tide on rising flood risk?

Climate change is increasing the destructive power of flooding from extreme rain and rising seas and rivers. Many cities around the world are exposed. Strong winds during storms and hurricanes can drive coastal flooding through storm surge. As hurricanes and storms become more severe, surge height increases. Changing hurricane paths may shift risk to new areas. Sea-level rise amplifies storm surge and brings in additional chronic threats of tidal flooding. Pluvial and riverine flooding becomes more severe with increases in heavy precipitation. Floods of different types can combine to create more severe events known as compound flooding. With warming of 1.5 degrees Celsius , 11 percent of the global land area is projected to experience a significant increase in flooding, while warming of 2.0 degrees almost doubles the area at risk.

When cities flood, in addition to often devastating human costs, real estate is destroyed, infrastructure systems fail, and entire populations can be left without critical services such as power, transportation, and communications. In this case study we simulate floods at the most granular level (up to two-by-two-meter resolution) and explore how flood risk may evolve for Ho Chi Minh City (HCMC) and Bristol (See sidebar, “An overview of the case study analysis”). Our aim is to illustrate the changing extent of flooding, the landscape of human exposure, and the magnitude of societal and economic impacts.

An overview of the case study analysis

In Climate risk and response: Physical hazards and socioeconomic impacts , we measured the impact of climate change by the extent to which it could affect human beings, human-made physical assets, and the natural world. We explored risks today and over the next three decades and examined specific cases to understand the mechanisms through which climate change leads to increased socioeconomic risk.

In order to link physical climate risk to socioeconomic impact, we investigated cases that illustrated exposure to climate change extremes and proximity to physical thresholds. These cover a range of sectors and geographies and provide the basis of a “micro-to-macro” approach that is a characteristic of McKinsey Global Institute research. To inform our selection of cases, we considered over 30 potential combinations of climate hazards, sectors, and geographies based on a review of the literature and expert interviews on the potential direct impacts of physical climate hazards. We found these hazards affect five different key socioeconomic systems: livability and workability, food systems, physical assets, infrastructure services, and natural capital.

We ultimately chose nine cases to reflect these systems and to represent leading-edge examples of climate change risk. Each case is specific to a geography and an exposed system, and thus is not representative of an “average” environment or level of risk across the world. Our cases show that the direct risk from climate hazards is determined by the severity of the hazard and its likelihood, the exposure of various “stocks” of capital (people, physical capital, and natural capital) to these hazards, and the resilience of these stocks to the hazards (for example, the ability of physical assets to withstand flooding). We typically define the climate state today as the average conditions between 1998 and 2017, in 2030 as the average between 2021 and 2040, and in 2050 between 2041 and 2060. Through our case studies, we also assess the knock-on effects that could occur, for example to downstream sectors or consumers. We primarily rely on past examples and empirical estimates for this assessment of knock-on effects, which is likely not exhaustive given the complexities associated with socioeconomic systems. Through this “micro” approach, we offer decision makers a methodology by which to assess direct physical climate risk, its characteristics, and its potential knock-on impacts.

Climate science makes extensive use of scenarios ranging from lower (Representative Concentration Pathway 2.6) to higher (RCP 8.5) CO 2 concentrations. We have chosen to focus on RCP 8.5, because the higher-emission scenario it portrays enables us to assess physical risk in the absence of further decarbonization. (We also choose a sea-level rise scenario for one of our cases that is consistent with the RCP 8.5 trajectory). Such an "inherent risk" assessment allows us to understand the magnitude of the challenge and highlight the case for action. For a detailed description of the reason for this choice see the technical appendix of the full report.

Our case studies cover each of the five systems we assess to be directly affected by physical climate risk, across geographies and sectors. While climate change will have an economic impact across many sectors, our cases highlight the impact on construction, agriculture, finance, fishing, tourism, manufacturing, real estate, and a range of infrastructure-based sectors. The cases include the following:

• For livability and workability, we look at the risk of exposure to extreme heat and humidity in India and what that could mean for that country’s urban population and outdoor-based sectors, as well as at the changing Mediterranean climate and how that could affect sectors such as wine and tourism.
• For food systems, we focus on the likelihood of a multiple-breadbasket failure affecting wheat, corn, rice, and soy, as well as, specifically in Africa, the impact on wheat and coffee production in Ethiopia and cotton and corn production in Mozambique.
• For physical assets, we look at the potential impact of storm surge and tidal flooding on Florida real estate and the extent to which global supply chains, including for semiconductors and rare earths, could be vulnerable to the changing climate.
• For infrastructure services, we examine 17 types of infrastructure assets, including the potential impact on coastal cities such as Bristol in England and Ho Chi Minh City in Vietnam.
• Finally, for natural capital, we examine the potential impacts of glacial melt and runoff in the Hindu Kush region of the Himalayas; what ocean warming and acidification could mean for global fishing and the people whose livelihoods depend on it; as well as potential disturbance to forests, which cover nearly one-third of the world’s land and are key to the way of life for 2.4 billion people.

We chose these cities for the contrasting perspectives they offer: Ho Chi Minh City in an emerging economy, Bristol in a mature economy; Ho Chi Minh City in a regular flood area, Bristol in an area developing a significant flood risk for the first time in a generation.

We find the metropolis of Ho Chi Minh City can survive its flood risk today, but its plans for rapid infrastructure  expansion and continued economic growth could be incompatible with an increase in risk. The city has a wide range of adaptation options at its disposal but no silver bullet.

In the much smaller city of Bristol, we find a risk of flood damages growing from the millions to the billions, driven by high levels of exposure. The city has fewer adaptation options at its disposal, and its biggest challenge may be building political and financial support for change.

How significant are the flood risks facing Ho Chi Minh City and what can the city do?

Flooding is a common part of life in Ho Chi Minh City. This includes flooding from monsoonal rains, which account for about 90 percent of annual rainfall, tidal floods and storm surge from typhoons and other weather events. Of the city’s 322 communes and wards, about half have a history of regular flooding with 40 to 45 percent of land in the city less than one meter above sea level.

In our analysis, we quantify the possible impact on the city as floods hit real estate and infrastructure assets. 1 Flood modeling and expert guidance were provided by an academic consortium of Institute for Environmental Studies, Vrije Universiteit Amsterdam, and Center of Water Management and Climate Change, Vietnam National University. Infrastructure assets covered include both those currently available and those under construction, planned, or speculated. Knock-on effects are adjusted for estimates of economic and population growth. We simulate possible 1 percent probability flooding scenarios for the city for three periods: today, 2050, and a longer-term scenario of 180 centimeters of sea-level rise, which some infrastructure assets built by 2050 may experience as a worse-case in their lifetime (Exhibit 1).

• Today: We estimate that 23 percent of the city could flood, and a range of existing assets would be taken offline; infrastructure damage may total $200 million to $300 million. Knock-on effects would be significant, and we estimate could total a further $100 million to $400 million. Real estate damage may total $1.5 billion.
• 2050: A flood with the same probability in 30 years’ time would likely do three times the physical damage and deliver 20 times the knock-on effects. We estimate that 36 percent of the city becomes flooded. In addition, many of the 200 new infrastructure assets are planned to be built in flooded areas. As a result, the damage bill would grow, totaling $500 million to $1 billion. Increased economic reliance on assets would amplify knock-on effects, leading to an estimated $1.5 billion to $8.5 billion in losses. An additional $8.5 billion in real estate damages could occur.
• A 180 centimeters sea-level rise scenario: A 1 percent probability flood in this scenario may bring three times the extent of flood area. About 66 percent of the city would be underwater, driven by a large western area that suddenly pass an elevation threshold. Under this scenario, damage is critical and widespread, totaling an estimated $3.8 billion to $7.3 billion. Much of the city’s functionality may be shut down, with knock-on effects costing $6.4 billion to $45.1 billion. Real estate damage could total $18 billion.

While “tail” events may suddenly break systems and cause extraordinary impact, extreme floods will be infrequent. Intensifying chronic events are more likely to have a greater effect on the economy, with a mounting annual burden over time. We estimate that intensifying regular floods may rise from about 2 percent today to about 3 percent of Ho Chi Minh City’s GDP annually by 2050 (Exhibit 2).

Ho Chi Minh City has time to adapt, and the city has many options to avert impacts because it is relatively early in its development journey. As less than half of the city’s major infrastructure needed for 2050 exists today, many of the potential adaptation options could be highly effective. We outline three key steps:

• Better planning to reduce exposure and risk
• Investing in adaptation through hardening and resilience
• Financial mobilization to mitigate impacts on lower-income populations

For additional details on these actions, download the case study, Can coastal cities turn the tide on rising flood risk? (PDF–4MB).

Could Bristol’s flood risk grow from a problem to a crisis by 2065?

Bristol is facing a new flood risk. The river Avon, which runs through the city, has the second largest tidal range in the world, yet it has not caused a major flood since 1968, when sea levels were lower, and the city was smaller and less developed. During very high tides, the Avon becomes “tide locked” and limits/restricts land drainage in the lower reaches of river catchment area. As a result, the city is vulnerable to combined tidal and pluvial floods, which are sensitive to both sea-level rise and precipitation increase. Both are expected to climb with climate change . While Bristol is generally hilly and most of the urban area is far from the river, the most economically valuable areas of the city center and port regions are on comparatively low-lying land.

With the city’s support, we have modeled the socioeconomic impacts of 200-year (0.5 percent probability) combined tidal and fluvial flood risk, for today and for 2065. This considers the flood defenses in existence today; some of these were built after the 1968 flood, and many assumed a static climate would exist for their lifetime (Exhibit 3).

• Today: The consequences of a major flood today in Bristol would be small but are still material. We find that the flood area would be relatively minor, with small overflows on the edges of the port area and isolated floods in the center of the city. Our model estimates that damage to the city’s infrastructure could amount to $10 million to $25 million, real estate damage to $15 million to $20 million, and knock-on effects of $20 million to $150 million.
• 2065: In contrast, by 2065, an extreme flood event could be devastating. Water would exceed the city’s flood defenses at multiple locations, hitting some of its most expensive real estate, damaging arterial transportation infrastructure, and destroying sensitive critical energy assets. Our model estimates that damages to the city’s infrastructure could amount to between $180 million and $390 million. It may also cause $160 million to $240 million of property damage. Overall, considering economic growth, knock-on effects could total $500 million to $2.8 billion, and disruptions could last weeks or months (Exhibit 4).

Unlike many small and medium-size cities, Bristol has invested in understanding this risk. It has undertaken a detailed review of how the scale of flooding in the city will change in the future under different climate scenarios. This improved understanding of the risks is an example that other cities could learn from.

However, adaptation is unlikely to be straightforward. It is difficult to imagine Bristol’s infrastructure assets being in a position more exposed to the city’s flood risk. Yet the center of the city, formed in the 1400s, cannot be shifted overnight, nor would its leafy reputation be the same today if the city had not oriented the growth of the past 20 years to harness its existing Edwardian and Victorian architecture. Unlike in Ho Chi Minh City, most of the infrastructure the city plans to have in place in 2065 has already been built.

In the immediate future, Bristol’s hands are likely largely tied, and hard adaptation may be the most viable short-term solution. In the medium term, however, Bristol may be able to act to improve resilience through measures such as investing in sustainable urban drainage that may reduce the depth and duration of an extreme flood event.

Bristol is already taking a proactive approach to adaptation. A $130 million floodwall for the defense of Avonmouth was planned to begin in late 2019. The city is still scoping out a range of options to protect the city. As an outside-in estimate, based on scaling costs to build the Thames Barrier in 1982, plus additional localized measures that might be needed, protecting the city to 2065 may cost $250 million to $500 million (roughly 0.5 to 1.5 percent of Bristol’s GVA today compared to the possible flood impact we calculate of between 2 to 9 percent of the city’s GVA in 2065). However, the actual costs will largely depend on the final approach. 

Bristol has gotten ahead of the game by improving its own understanding of risk. Many other small cities are at risk of entering unawares into a new climatic band for which they and their urban areas are ill prepared. While global flood risk is concentrated in major coastal metropolises, a long tail of other cities may be equally exposed, less prepared, and less likely to bounce back.

For additional details, download the case study, Can coastal cities turn the tide on rising flood risk? (PDF–4MB).

About this case study:

In January 2020, the McKinsey Global Institute published Climate risk and response: Physical hazards and socioeconomic impacts . In that report, we measured the impact of climate change by the extent to which it could affect human beings, human-made physical assets, and the natural world over the next three decades. In order to link physical climate risk to socioeconomic impact, we investigated nine specific cases that illustrated exposure to climate change extremes and proximity to physical thresholds.

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Original research article, coastal flooding in the maldives induced by mean sea-level rise and wind-waves: from global to local coastal modelling.

• 1 Instituto Mediterráneo de Estudios Avanzados (UIB-CSIC), Esporles, Spain
• 2 Departament de Física, Universitat de les Illes Balears, Palma, Spain
• 3 French Geological Survey (BRGM), Orléans, France
• 4 Global Climate Forum, Berlin, Germany
• 5 Ministry of Environment, Malé, Maldives

The Maldives, with one of the lowest average land elevations above present-day mean sea level, is among the world regions that will be the most impacted by mean sea-level rise and marine extreme events induced by climate change. Yet, the lack of regional and local information on marine drivers is a major drawback that coastal decision-makers face to anticipate the impacts of climate change along the Maldivian coastlines. In this study we focus on wind-waves, the main driver of extremes causing coastal flooding in the region. We dynamically downscale large-scale fields from global wave models, providing a valuable source of climate information along the coastlines with spatial resolution down to 500 m. This dataset serves to characterise the wave climate around the Maldives, with applications in regional development and land reclamation, and is also an essential input for local flood hazard modelling. We illustrate this with a case study of HA Hoarafushi, an atoll island where local topo-bathymetry is available. This island is exposed to the highest incoming waves in the archipelago and recently saw development of an airport island on its reef via land reclamation. Regional waves are propagated toward the shoreline using a phase-resolving model and coastal inundation is simulated under different mean sea-level rise conditions of up to 1 m above present-day mean sea level. The results are represented as risk maps with different hazard levels gathering inundation depth and speed, providing a clear evidence of the impacts of the sea level rise combined with extreme wave events.

1. Introduction

Increased coastal flooding damages are among the potentially most hazardous and costliest aspects of global warming ( Hinkel et al., 2014 ), impacting populations, ecosystems and assets. Coastal flood exposure is currently increasing at rates higher than inland due to population growth, urbanisation and the coastward migration of people ( Merkens et al., 2018 ), and also due to coastal extreme water levels being raised by mean sea-level rise ( Marcos and Woodworth, 2017 ). The Special Report on the Ocean and Cryosphere in a Changing Climate (SROCC) of the Intergovernmental Panel on Climate Change (IPCC) projects that if greenhouse gas (GHG) emissions continue to rise unmitigated (i.e., RCP8.5) global-mean sea levels are likely to rise by 0.6–1.1 m by 2100, and 2.3–5.4 m by 2300 ( Oppenheimer et al., 2019 ). Projected mean sea-level rise during the twenty-first century and beyond ( Kopp et al., 2014 ) will inevitably increase the intensity of flood events and will thus exacerbate the exposure and vulnerability of coastal areas in the decades to come, with highest impacts expected in low-lying regions. Hinkel et al. (2014) estimated that, without adaptation, by 2100 almost 5% of the global population will be potentially flooded annually, with losses of up to 10% of the global GDP, under a 1.20 m mean sea-level rise. This will require the implementation of extensive and ubiquitous coastal adaptation solutions to avoid such large impacts ( Hinkel et al., 2019 ). But also if emissions are reduced to meet the goal of the Paris Agreement to limit global warming “well bellow 2°C” (i.e., RCP2.6), global mean sea-level is likely to rise by 0.3–0.6 m in 2100 and 0.6–1.1 m by 2300, which will still be a tremendous challenge, in particular for very low lying regions such as atoll states.

The threats of flooding events are particularly worrisome in low-lying coastal zones, including large deltas and sinking coastal mega-cities; but the regions with the largest expected relative impacts are small island states ( Nurse et al., 2014 ). The Maldivian archipelago is an iconic case of vulnerability to mean sea-level rise. Located in the equatorial region of the Tropical Indian Ocean, the Maldives consist of 1192 islands, dispersed across 860 km from 8° north to 1° south in latitude, of which 188 are inhabited ( NBS, 2017 ; Wadey et al., 2017 ) (see Figure 1 ). The resident population in 2014 was 437,000 people and is estimated to reach 557,000 in 2020, with 40% of the population living in the capital, Malé, and its surrounding islands Villimalé and Hulhumalé ( NBS, 2019 ). Average land elevations range from 0.5 m to 2.3 m above present-day mean sea level ( Woodworth, 2005 ), with 80% falling below 1 m. Since the 1950s several land reclamation projects have been carried out to address land scarcity, for example in the southern lagoon of Malé in 1954 ( Maniku, 1990 ). With the rapid economic development of the Maldives, land reclamation projects have also increased. The Maldivian government estimates that over 1300 hectares of reef or lagoon area have been reclaimed up until 2016 ( MEE, 2017 ). This new land is required to be elevated between 1.5 and 1.75 m above mean sea-level. However, this static approach to island elevation ignores the differing wave exposure across the archipelago.

Figure 1 . Map of the Indian Ocean with the Maldivian archipelago inside the black box. The four black arrows indicate the main wave direction identified. The red arrow indicates the location of the Haa Alif atoll in which is located Hoarafushi island (satellite image in the bottom-right corner extracted from Google Earth. Image © 2019 Maxar Technologies; Image © 2019 CNES/Airbus; Data SIO, NOAA, U.S. Navy, NGA, GEBCO).

A lot of land reclamation is taking place in the Maldives and a new long-term regional development strategy is currently being prepared that prioritises islands for development ( Gussmann and Hinkel, 2021 ). While it is known that wave exposure differs across islands, this has so far not been taken into account in land reclamation and regional development. The development of adaptation plans in the framework of coastline management aimed to address flood hazards requires accurate information and a deep understanding of the driving processes. Coastal flood events are caused by extreme coastal water levels that in turn result from the combination of relative mean sea-level, tides, storm surges, wind-waves, precipitation and/or river run-off ( Woodworth et al., 2019 ). The design of adaptation strategies therefore involves the knowledge of every individual driver and their future projections at the local scale, as well as their possible interactions ( Nicholls et al., 2014 ). In the case of the Maldives, the tidal range is relatively small (<1 m of maximum high waters) and the storm surge contribution is negligible, as corresponds to an equatorial region ( Wadey et al., 2017 ). Earlier studies have pointed at wind-waves as the primary mechanism causing flooding events in the Maldives, similarly to other Indian and Pacific islands ( Hoeke et al., 2013 ). One of the first works was presented by Harangozo (2013) , who investigated an event that occurred in April 1987 that flooded Mal?é, including reclaimed land below 1 m above mean sea-level and during which the hard structures designed to protect this land were destroyed. Based on altimetric wave measurements and in-situ sea-level observations, this event was attributed to prolonged swell waves originated in the Southern Indian Ocean and reaching the island during high tides. Similarly, in 2007, the Fares island, located in the southernmost atoll of the Maldives was flooded due to a series of remotely-generated swell events reaching the island ( Wadey et al., 2017 ; Beetham and Kench, 2018 ) which also affected other areas of the eastern Indian Ocean (e.g., Lecacheux et al., 2012 in La Réunion Island). This event was particularly hazardous as it flooded almost the entire island and affected more than 1500 people as well as the limited water resources of the island. An extensive study was carried out in response to this event and a protective offshore breakwater was built to avoid future damages. For a comprehensive list of flooding events in the Maldives, the reader is referred to Wadey et al. (2017) , where the available information of several flooding events has been collected from a number of sources.

Despite the recurrent flooding episodes associated with swells, overall, in the Maldivian archipelago a complete and accurate assessment on the wind-wave climate, including extreme waves, is hindered by the lack of observations and regionalisation of model runs. Numerical wind-wave simulations are available with a global coverage, including both re-analyses (i.e., Saha et al., 2010 ) and projections (i.e., Hemer and Trenham, 2016 ; Morim et al., 2019 ), although with a coarse resolution that prevents their use for many practical purposes, such as accurate local assessments. This work intends to fill this gap by providing the necessary information on waves to perform coastal studies along the Maldivian shorelines. The objectives of the present study are three-fold: first, we fully characterise the wave climate around the Maldives on the basis of global, coarse resolution numerical wave dynamical simulations for present-day, and we further evaluate the projected changes under climate change scenarios (section 3). Secondly, we downscale the extreme wave climate through propagation of the main extreme waves from the dominant directions toward the coastlines with a much higher resolution (section 4). And finally, we illustrate how this information can be translated into a flood hazard assessment in a selected location that is exposed to the largest incoming swell waves in the archipelago. To do so, we propagate wave conditions from the nearshore to the coastline under different mean sea-level rise scenarios and quantify the flooding extent with and without land reclamation (section 5). Data, methods and numerical models are described in section 2, while all the results are discussed together in section 6.

2. Data and Methods

This section describes the global wave data that is used to characterise and downscale wave information to the nearshore in the Maldives, together with the numerical models and their implementation. Local wave modelling is used as the basis of flood hazard assessment for a case study. To do so, waves are combined with a set of mean sea-level changes using a scenario-independent approach. That is, waves are propagated toward the shoreline under prescribed mean sea-level increments of 0.25, 0.5, 0.75, and 1 m with respect to present-day averaged value. Note that these values are not necessarily interpreted as climate-induced mean sea-level rise; they can also be associated to tidal oscillations or to a combination of tides and mean sea-level rise.

2.1. Global Wind-Wave Datasets

We have used the CAWCR Global wind-wave data set that is freely distributed through the CSIRO data server ( Hemer et al., 2015 ). This set, generated with the WaveWatch III wave model (version 3.14, Tolman, 2009 ) in a common 1° × 1° resolution global grid, consists of a hindcast, historical runs (late twentieth century), and projections for the twenty-first century. The hindcast has been forced with surface wind fields from the NCEP CFSR ( Saha et al., 2010 ) and covers the period from 1979 to 2009 with a temporal resolution of 1 h (this simulation is referred to as CFSR hereinafter). The historical runs and projections were generated using the output fields of 8 different CMIP5 models (ACCESS1.0, BCC-CSM1.1, CNRM-CM5, GFDL-ESM2M, HadGEM2-ES, INMCM4, MIROC5, and MRI-CGCM3), covering three different time periods with a temporal resolution of 6 h: historical runs for 1980-2005; and projected waves for mid-(2026–2045) and late-(2081–2100) twenty-first century. The projections for mid- and late-twenty-first century were run under two different emission scenarios, RCP4.5 and RCP8.5, although we will use only the latter. A detailed description of the wave climate dataset can be found in Hemer et al. (2013) .

Global wave models are used to characterise the present-day and future projected changes of wave climate around the Maldivian archipelago, with emphasis on the extreme wave climate. Return levels of Hs for a set of prescribed return periods are calculated by fitting the top 1% waves to a Generalised Pareto Distribution. Given the coarse spatial resolution of the model configuration, we do not expect the small islands as the Maldives to be accurately represented by these global simulations. Given that the wave fields are modified by the presence of the islands (see for example Supplementary Video 1 from Amores and Marcos, 2019 ), the global fields must be downscaled in order to be usable for practical purposes. This process is described in the following.

2.2. Regional Wave Modelling

Global waves have been dynamically downscaled in the Maldives using the WaveWatchIII wave model (version 4.18, Tolman, 2014 ). The model was implemented on an unstructured mesh with 33160 nodes and 64456 elements over a domain ranging from 71.5 to 75.5° E in longitude and from −1.5° N to 8.5° N in latitude (black rectangle surrounding the Maldives in Figure 1 ). The spatial resolution of the unstructured mesh varied from 50 km along the boundaries of the domain down to 500 m in the channels between the atolls. Only the external coasts of the atoll islands were considered due to the lack of bathymetric information inside the atolls. The regional bathymetry used to build the model grid was the GEBCO bathymetry 2014 in a global 30 arc-second interval grid ( https://www.gebco.net/ ). The wave spectrum was defined by a directional resolution of 10° and 24 frequency bands ranging non-linearly from 0.0373 to 1.1 Hz. Dynamical downscaling was preferred instead of statistical approaches because there is no local information on waves that can be used to calibrate the model.

2.3. Local Wave Modelling

Nearshore downscaled waves have been propagated toward the coastline for a case study site. The selected location corresponds to Haa Alif atoll (HA) at Hoarafushi island, located at the north of the archipelago ( Figure 1 ). Hoarafushi has a maximum length of 2,500 m and a maximum width of 500 m ( Figure 1 ). This site has been chosen for two main reasons: firstly, at the start of this study a land reclamation project to build a new airport next to the island was foreseen. The development of the regional airport on the newly reclaimed island on the reef of HA Hoarafushi is part of the government's regional development and decentralisation plans, which puts extra focus on the northernmost atoll Ihavandhippolhu. We therefore aimed at evaluating the exposure of this new reclaimed land to incoming waves and how its presence can alter the wave propagation over the reef and the exposure of the current island. The process of land reclamation was started on April 16th, 2019 ( https://edition.mv/news/10159 ) and finished almost 5 months later, on September 5th, 2019 ( https://edition.mv/news/12266 ; see Supplementary Figure 1 , to see the construction process on June 15th, 2019). Secondly, information on the local bathymetry and land elevation is available and allows to simulate the wave propagation. A bathymetry around the island was generated by combining measurements on the reef flat performed by the Maldives Transport and Contracting Company, who was in charge of the design of the land reclamation project. We completed these data with reef slope measurements taken during a field trip on February 2018 (using a single beam echosounder). Our measurements included a total of 10 profiles across-slope separated around 200–500 m between them as well as several along-slope transects. The minimum depth measured in the across-slope profiles was around 3 m, that was the closest the boat could get to the reef crest, and the maximum depth recorded, that was fixed by the maximum range of the echosounder, was around 50 m. Unfortunately, there is no detailed information on the topography of the island. Instead, a constant land height of 1.5 m above present-day mean sea level has been used, according to visual inspections and in accordance with existing regulations. The coastline of the island has been represented with a constant slope, given that there are not hard structures in the oceanward side. Two topo-bathymetries have been implemented, with and without the presence of the airport. Finally, it is worth mentioning that HA Hoarafushi island is exposed to the highest incoming waves around the archipelago, as will be shown below.

The local wave propagation has used the SWASH model ( Zijlema et al., 2011 , code available at http://swash.sourceforge.net/ ) in a 2D regular grid of 6,220 m in the W-E direction and 7,320 m in the S-N direction with 10 m of spatial resolution (see the domain in Figure 8 ). This model is suitable for our purposes as it is capable of simulating wave setup and runup and predicting infragravity waves in the nearshore ( Rijnsdorp et al., 2012 ), a relevant process that contributes to the amount of flooding by raising temporary the sea level near the coast. The West and South boundaries were considered active introducing the wave forcing in the domain with Jonswap spectra with a wave dispersion of 20° and a peak enhancement parameter γ of 3.3. A different combination of wave dispersion and γ was tested (5° and 10°, respectively), resulting in essentially the same results in terms of flooding. A 150 m sponge layer was placed in the eastern boundary and 1,000 m sponge layer in the northern boundary, to avoid unrealistic wave forcing from the interior of the atoll given by spurious wave reflection from the those boundaries. The lack of in-situ measurements of wave propagation and transformation along the domain made it impossible to calibrate the Manning's friction coefficient. The values for coral reefs found in the literature vary from 0.01 to 0.2. For example, Zijlema (2012) used 0.01, Prager (1991) used 0.05, Kraines et al. (1998) used 0.1, and Cialone and Smith (2007) used spatially-varying Manning's coefficient values of 0.02, 0.19, and 0.2 depending on the region of their domain. The Manning's coefficient value was finally fixed to 0.019 following Suzuki et al. (2018) , who investigated the most suitable value for SWASH model applied to overtopping computation along a beach profile with defined defenses. In our case, there is not a complete beach profile, but the overtopping, which is the process of interest here, is occurring at the shoreline of a sandy beach.

With this configuration, the total simulated time for each combination of parameters was 70 min, with an initial integration time of 0.05 s and having outputs every 5 s. This is computationally intensive but still feasible for the range of experiments and for the two topographies (with and without the airport).

3. Characterisation of Wave Climate Around the Maldives

3.1. present-day wave climate.

The outputs of the CFSR wave hindcast at 24 grid points around the Maldives are used to describe the large-scale present-day wave climate in the archipelago ( Figure 2 ). Wave roses in Figure 2 identify, for each grid point, the direction of the dominant wave regimes with their corresponding significant wave heights ( H s ) and peak periods ( T p ). One prominent feature is that the largest significant wave heights are usually accompanied by peak periods longer than 10–12 s (and reaching up to 24 s), which suggests that these are remotely generated waves, i.e, swell waves. This is in agreement with the location of the Maldives in the Equatorial region, where winds are weak, and in a region exposed to swell waves from the Southern Ocean ( Wadey et al., 2017 ; Amores and Marcos, 2019 ) and is further examined below.

Figure 2 . Distribution of the ocean wave climate around the Maldives from the CFSR Hindcast. Each wind-rose-plot corresponds to one point of the central map. The radial distance of each single point in each wind-rose indicates the wave height (m) while the azimutal value indicates the direction that the waves are coming from in nautical convention. The colour of each point shows the peak period. The continuous black (grey) line indicates the quantile 50 (99) for each direction while the dashed black like shows the quantile 50 averaging all the directions. Shadowed areas in the wave roses indicate the most frequent incoming wave directions in a 1° bins (% referred to the radial axis).

Waves from the south-west (~205°) are the most common with H s reaching values larger than 4 m (note that the angles follow the maritime convention, as indicated by the labels in the wave rose of point #1). This finding is in line with Amores and Marcos (2019) that demonstrated that between 80 and 90% of the swell events impacting along the Maldivian coastlines are from SW and originated in a region located between south of Africa and east of South America. The second most frequent direction is the south-east (~145°). These waves reach maximum values of H s around 3 m, thus smaller than ~205° waves, and with peak periods between 10 and 12 s. In addition to these two dominant swell wave directions, two other cases much less frequent but with non-negligible H s are detected. In the north of the archipelago the largest waves with H s of up to 5 m are from the west direction (~275°, see wave-rose #17 in Figure 2 ). And finally, waves from 60° are also found in the points of the northeastern side of the archipelago (see, for example, wave-rose #10 in Figure 2 ) with peak periods smaller than 10 s and H s smaller than around 2.5 m.

The characteristics of the incoming large-scale waves are further analysed in greater detail for three grid points capturing the entire range of directions: point #17 (northwest), point #3 (south), and point #10 (northeast). Figure 3 examines the annual and seasonal distribution of incoming waves for every direction and their classification in terms of wind-seas and swells, according to the spectral partitioning provided by the global wave models. These histograms, representing the number of events per year, have been constructed with wave events separated at least 3 days to avoid over-representation of the dominant directions and with a minimum peak prominence H s of 0.2 m to remove noise from smaller waves. The three points are representative of the four incoming wave directions identified above and all register a similar number of waves during the hindcasted period (between 45 and 50 per year, as listed in the title of the panels in Figure 3 ). Their distribution in directions is, however, different, and depends on their position. The most frequent wave direction, around 205°, is evident in points #17 and #3 and is equally likely throughout the entire year (see panels d, e, g, h for comparison among seasons). A composite of the wave and wind fields corresponding to these events is mapped in Supplementary Figure 2 , demonstrating that these waves indeed correspond to remotely generated southwestern swells, in line with the findings in Amores and Marcos (2019) . Waves from the west direction, around 275°, are the second most frequent in point #17 with a marked seasonal character, being only detected between May and October (panel d) and classified as a mixed sea+swell. These waves are generated by the Indian monsoon and only affect the northernmost area of the archipelago. The corresponding composites are shown in Supplementary Figure 3 . The presence of waves generated by the Indian monsoon likely has an impact on the wave type distribution of the southwestern swell at point #17, since its percentage of sea+swell is larger between May and October; also, the wind fields of the composites corresponding to both types of waves are identical (see last rows in Supplementary Figures 2, 3 ).

Figure 3 . Histograms of wave direction (in nautical convention) registered at the three points selected as being representative (point #17 in the first column ( a,d,g ), #3 in the second ( b,e,h ), and #10 in the third) ( c,f,i ). The first row shows the annual histograms ( a,b,c ), May-October histograms are shown in the second row ( d,e,f ) and November-April histograms are in the last row ( g,h,i ). Each pie chart indicates the spectral separation by type of wave produced by WaveWatch III model [pure sea ( H s of swell = 0), pure swell ( H s of sea = 0), sea + swell dominated by sea ( H s of sea > H s of swell) and swell + sea dominated by swell ( H s of swell > H s of sea)] corresponding to each wave component identified (grey shadows).

The second peak in point #3, seven times less frequent that the southwestern swell and also observed in point #10, corresponds to the direction around 145°, with waves detected throughout the entire year. According to the wave and wind fields composites ( Supplementary Figure 4 ) these are waves generated in the Southern Ocean, in a region off the southeastern coast of Australia ( Amores and Marcos, 2019 ). Finally, the fourth incoming direction, around 55°, is clearly detected in point #10, with a strong seasonal character. These waves correspond to the northeast monsoon ( Wadey et al., 2017 ) and are only relevant between November and April, contrasting with the Indian monsoon (panel f and i).

Return levels of H s for every direction and for the three grid points are shown in Figure 4 and listed in Supplementary Table 1 . Noteworthy are the flat tails for the southeastern swells evident in points #10 and #3. Independence among wave events is ensured with the 3-day declustering. A Generalised Pareto Distribution (GPD) has been fitted to the top 1% of the largest H s ; in the case that this subset is too small as to reliably fit the distribution, the 30 largest values (1 event per year on average) were used. The largest return levels correspond to waves generated by the Indian monsoon in point #17 ( Figure 4D ). This direction has a H s of 4.75 m for a 10 year return period that is larger than all the return levels for the 500 year return periods for all cases (with the exception of the southeastern swell affecting point #3 that has 4.99 m as H s associated with 500 year return period). On the other side, the lowest return levels correspond to the northeast monsoon affecting point #10 with a H s equal to 2.74 m for a return period of 500 years, around 1 m lower from the closest return level (3.61 m for 500 year return period for the swell coming from southeast in point #3).

Figure 4 . Return levels associated with each return period (thick continuous lines) for the three representative points (point #17 in the first column (a,d) , #3 in the second (b,e) , and #10 in the third) (c,f) for the wave directions identified at each point (for example, panel a corresponds to the first grey shadow in Figure 3a ). The uncertainty bands correspond to ±σ (dashed lines) and the 5−95 % intervals (dotted lines) and have been computed using the delta method. The return period indicated in the top of the panels (10, 20, 50, 100, and 500 years) are the ones selected to perform the regional downscaling with WaveWatch III. Note that the y -axis are different for each panel and do not allow a direct comparison.

3.2. Wave Projections During the Twenty-First Century

The same three grid points analysed above are used as proxies to evaluate the projected changes in waves around the Maldives, using the output of historical simulations and projections during the twenty-first century. Figure 5 represents changes in the frequency of arrival of waves for each direction of propagation by the end of the twenty-first century under RCP8.5 with respect to present-day values for each point. The median of the 8 climate model projections is shown in red (blue) when the projected changes indicate an increase (decrease) in the number of wave events and the grey area represents the model spread. Global models project an increase (~3%) of the southwestern swells, consistent with the findings in Amores and Marcos (2019) , who showed a greater activity in swell generation in the region of formation of these waves later in this century. For the waves generated by the Indian monsoon, models show a smaller decrease in the number of waves. Other directions do not show robust projected changes, as the model spread is larger than the median change. The same applies to projected variations in median and extreme H s in all directions of propagation ( Supplementary Figure 8 ).

Figure 5 . Projected changes in wave direction by the CMIP5 models described in section 2.1 for each one of the representative points selected ( a point #17, b point #3, and c point #10). Black line represents the CFSR hindcast histogram (same as the first row in Figure 3 ); grey shadow indicates the spread of the CMIP5 models (RCP8.5 - Historical); red (blue) shadows show where the models agree to project a frequency increase (decrease) of a given direction.

Overall, projected changes in H s are smaller than the multi-model spread even under RCP8.5 climate scenario. Variations are expected to be even smaller under RCP4.5. In consequence, present-day significant wave height is considered to be largely representative of future wave climate for the purposes of this work and only CFSR wave fields will be downscaled and propagated toward the shorelines. It is worth noting that changes in the frequency of each wave direction ( Figure 5 ) can be relevant to the transport of sediments and could modify current erosion patterns.

4. Regional Wave Downscaling

Global wave information needs to be downscaled to become representative and usable in the nearshore; however, downscaling the full hourly 30-year CFSR hindcast is computationally too intensive. On the other hand, in terms of coastal impacts assessments and, in particular when coastal flooding is concerned, it is extreme values that are the most relevant metric. Therefore, our approach consists of dynamically downscaling the return levels for H s calculated for 6 different return periods (namely, 10, 20, 50, 100, 500, and 1000 years) and the four main wave directions that were previously identified. To do so, we have used the Wave-WatchIII model configuration described in section 2.2. The H s return level associated with a given wave direction is defined at a reference grid point and propagated along the corresponding boundary. In order to insert consistently the H s at the rest of the grid points in the same boundary, the linear relationships between simultaneous events (±24 h ), arriving from the same direction and reaching the reference point and the other boundary points were computed. This procedure is illustrated in Supplementary Figures 9–12 , where also the reference grid points at each boundary are marked. The linear relationships between the reference grid point and the others are used to scale H s at each active boundary point. The boundary points where no simultaneous events with the reference point were found or, alternatively, for which there is no correlation (we set the limit value of R 2 of the linear adjustment to 0.2), were assigned a linear slope of 0.01 in order to avoid introducing spurious waves. The peak period ( T p ) of the incoming waves associated with each return level for H s , have been determined using a linear relationship between all the ( H s , T p ) events extracted at each reference point for each of the four directions of the incoming waves ( Supplementary Figure 13 ).

The resulting downscaled wave fields consist of a set of four return level curves at every coastal grid point with a spatial resolution of ~500 m. This resolution permits to model wave propagation at the scale of the archipelago. Although it is not accurate enough to perform local assessments inside an atoll, it provides, instead, the necessary boundary condition for the forcing. The full data set is provided at the Zenodo repository under this doi: 10.5281/zenodo.3886273 . Figure 6 shows the results for the 100-year return level of the four directions over the entire domain, sorted by decreasing H s . Note that the spatial patterns of different return levels will be the same for each wave direction and only the magnitude changes. Due to the limited resolution of the GEBCO topobathymetry (~1 km ), an accurate representation of the islands and the inner part of the atolls is not feasible. Thus, the atolls have been considered as whole entities. This assumption implies that the side of the islands that faces toward the atoll's interior is not solved by our regional downscaling. Nevertheless, it is not relevant at this scale because this side of the islands is not directly impacted by waves. We consider that, given the limited depth on the atoll rims (roughly 1 m), this assumption is reasonable, especially because a more accurate assessment would require tide-current local modelling to capture lagoon/ocean interactions.

Figure 6 . Regional downscaling of the 100 year return period wave event performed with WaveWatch III model (see section 2.2 for the details) for each one of the wave components identified [ (a) Indian Monsoon, (b) Southwestern Swell, (c) Southeastern swell, and (d) Northeastern Mosoon]. Black arrow in each panel indicated the wave direction. Black stars indicate the position of Malé, the main city of the Maldives, and Hoarafushi, the island where the local modelling was done.

Figure 6 shows that the largest waves in the Maldives ( H s >5 m ) are generated by the Indian monsoon (panel a) in the northwestern part of the archipelago, with values of H s exceeding 2 m in the area northwards from 3°N (note that the same colour scale is used for the four maps). One remarkable feature is that these waves, although attenuated, reach the western side of the Kaafu atoll, the most populated atoll in the Maldives and where the capital city Malé is located. Because of the absence of shadow effects, the western coast of the Kaafu atoll, is the inner region of the Maldives exposed to waves with larger H s , reaching values between 2.5 and 3.0 m. The southwestern swell (panel b), the second direction with largest H s after the Indian monsoon, is the component that spreads larger H s to a broader scale. More precisely, it generates ocean waves with H s >3 m (even than 3.5 m) to all the western sides of the atolls comprising the Maldives. The third ocean wave direction in terms of H s is the southeastern swell (panel c), that affects all the eastern side of the Maldives with H s ranging from 2 to 3.5 m. Finally, the northeastern monsoon (panel d) is the ocean wave component with smaller H s (<2 m ). Its effects are concentrated in the central region of the eastern side of the archipelago, from 2 to 6.5°N. It does not strongly affect the northernmost part of the Maldives because this region is located under the shadow of the Indian continent to the monsoon winds ( Supplementary Figure 6 ).

Combining the results of the four wave directions shown in Figure 6 , we can identify the wave component with greater H s at each grid point along the coastlines ( Figure 7a ), the value of this greater H s ( Figure 7b ), as well as how many different directions each coastal point is exposed to Figure 7c ; H s ≥1.5 m . In relative numbers, 33% of the coastlines are exposed to the large waves from the Indian monsoon, in 25% of them the highest waves arrive from the southwestern swell, in 28% from southeastern swell, and only 14% from the northeastern monsoon. As in the case of the Kaafu atoll mentioned above, a similar effect is found in the eastern part of the Faafu and Dhaalu atolls, also located in the interior of the Maldives. Here the dominant wave component reaching the eastern coast of these atolls is the southeastern swell that penetrates in the middle of the Maldives between Thaa Atoll and Meemu Atoll.

Figure 7 . Products derived by combining the regional downscaling for the 100 year return period of the 4 wave components in Figure 6 along the Maldivian coastlines. (a) shows the wave component causing the largest H s at each coastal point; (b) the maximum H s at the coast; and (c) , the number of wave directions with H s larger than 1.5 m that hit each coastal location for the 100 year return period.

In terms of maximum H s ( Figure 7b ), around 15% of the coastal points, most of them located in the interior of the archipelago, are affected by waves with 100-year return periods smaller than 1 m. The most common values are between 1 and 2 m, affecting 35% of the coastal locations. In 22% of the coasts, the 100-year return levels of H s vary between 2 and 3 m and in 17% H s between 3 and 4 m. The largest values, over 4 m, affect around 11% of the coastal points which are found, as expected, in regions where the Indian Monsoon dominates ( Figure 7b ).

Another metric for the exposure of the coasts to incoming waves is the number of swell directions reaching every coastal point. This is illustrated in Figure 7c , where we have quantified how many wave directions, from the 4 represented in Figure 6 , reach each coastal point with H s ≥1.5 m for the 100 year return period. The choice of the H s threshold and the return period selected is arbitrary and used only for illustration purposes; it is not determinant for the resulting map. We conclude that, in 32% of the coastal points, the 100-year return level of H s is always smaller than 1.5 m (grey points in Figure 7c ), with these areas located mainly in the interior of the archipelago. In 29% of the coastal points waves arrive from a single direction (blue points) and in 38% from two directions (yellow points), with the latter case mainly affecting the eastern and western side of the Maldives. In only 1% of the coastal grid points waves arrive from 3 directions (red points), but these are concentrated in the easternmost side of the Vaavu atoll.

5. Local Wave Modelling and Flood Hazard in Hoarafushi Island

The outputs of the regional wave downscaling developed in the previous section are used here in a local flood hazard assessment, illustrating its direct applicability. To do so, downscaled nearshore wave information in a coastal grid point next to Hoarafushi island is propagated toward the shoreline and used to assess coastal flooding under different mean sea-level rise scenarios. The are two reasons that make this location particularly interesting for local wave modelling: first, it is affected by the two largest wave components in the archipelago, i.e., the Indian Monsoon and the southwestern swell; and second, a new island was reclaimed to host a regional airport, which raises questions of present and future climate hazards (see section 2.3). The projected airport, that will have a length of around 1.5 km and a width of 300 m in its wider section, will be located in the reef of the island that faces toward the outer side of the atoll. This means that the shoreline of the airport will be substantially closer to the reef edge than the original island (150–200 m instead of 600 m), reducing the amount of wave energy that can be absorbed by the reef. This local-case study does not pretend to give any recommendation to stakeholders on the airport island height for this specific site. To do so, detailed local information, such as a high-resolution topo-bathymetry or ocean waves in-situ data to validate the model outputs would be required. This example illustrates the applicability of the regional wave downscaling developed here to a local study if precise local information was available.

Wave propagation with SWASH was carried out in the domains in Figure 8 . In total, 60 different runs were completed by combining 3 different return periods of H s (10, 50 and 100 years), two wave directions (Indian Monsoon and the southwestern swell), and 5 different mean sea levels (0, +0.25, +0.50, +0.75, and +1 m ) for the island configuration with and without airport. We have followed a scenario-independent approach for mean sea-level rise, with 0 m corresponding to present-day mean sea level. Mean sea-level changes with respect to the current situation may be interpreted in terms of projected mean sea-level rise (e.g., +0.50 m is the median projected mean sea-level rise in 2068 under RCP8.5 and 2088 under RCP2.6, according to Kopp et al., 2014 ) or as a combination of mean sea-level rise and high tides (e.g., +0.50 m is the mean rise in 2041 under RCP8.5 plus +0.25 m of tidal amplitude). The mean sea-level changes tested may also include, besides projected mean sea-level rise and tides, other physical processes that can cause mean sea-level variations from seasonal to decadal time scales. We recall here that tides in the Maldives reach a maximum range of around 1 m (0.7 m median range, Wadey et al., 2017 ). Note that precise geodetic references relating altitudes and tidal levels are lacking in the Maldives, so these values should be considered as an order of magnitude only. Four examples of selected simulations can be found in Supplementary Videos 1, 2 .

Figure 8 . Example of local downscaling simulations with SWASH model (see section 2.3 for details) for the 10 year return period southwestern swell event with 0.5 m of sea level rise (this increase in sea level could mean permanent sea level or high tide with present sea level) for the case without the airport (a) and with the new airport (b) . Grey (purple) stripe indicates the coastal region where the box plots from Figure 9 ( Supplementary Figure 15 ) are computed.

It has not been possible to validate the model outputs for the present-day situation due to the lack of observations. We are providing, nevertheless, a qualitative validation by comparing the velocity field obtained with the configuration that includes the airport to a satellite photography in which the airport is under construction ( Supplementary Figure 1 ). There is a consistency between higher current velocities in the model and the imprint of sediment transport from the new-built airport that are likely driven away by the currents.

The outputs of the first set of 30 model runs, that correspond to the spatial configuration without the airport ( Figure 8a ), are used to evaluate the exposure of the island in terms of the amount of flooding under different forcing conditions. The outputs along a 100-m wide coastal strip covering the western coast of the island (plotted as grey area in Figure 8a ) have been gathered together. To do so, the strip is divided in 25-m long sections resulting in 25 × 100 m boxes. Simulated water level time series were extracted for each box and used to compute median and maxima water levels for each model run in each of them. Figure 9 represents the boxplots along the entire coastal strip of these median (left panel) and maxima (right panel) values under all mean sea levels and return levels considered. The horizontal black thick line in both panels marks the height of the island and the two incoming directions are separated by vertical shadowed areas for comparison. Median values of total water level, that correspond to the superposition of the mean sea level and wave setup, do not reach the threshold of land elevation, indicating that there is no overflow at any point along the coastline under all the forcing conditions considered. The results also point at the southwestern swells as the potentially most hazardous waves, as these systematically induce higher water levels than the Indian monsoon waves (shadowed areas against blanked areas). The reason lies in the longer T p associated with the southwestern waves (~20 s ) in front of the monsoon waves ( ~12 s ). As expected, the larger wave setup for a given return period is obtained for the lowest mean sea level of 0 m : wave setup reaches almost 0.4 m under present-day mean sea level conditions and reduces to 0.3 m with an increase of 1 m . This is because in shallower waters the effects of wave shoaling and breaking leading to wave setup are larger. It is worth noting here that while an increased water level leads to a decreased setup, deeper water allows for larger H s on the reef flat and an increased run-up potential which could be relevant in terms of impact to infrastructures and erosion. On the other hand, maximum values along the coastal strip have been used to measure whether there has been overtopping generated by the incoming waves. Overtopping occurs whenever these values exceed the island elevation, with their magnitude indicating the severity of the flooding. The boxplots for the maximum values (right panel in Figure 9 ) point to the occurrence of overtopping under several forcing configurations. For example, 100-year return level waves from southwestern swell and +0.5 m mean sea level increase. Note that this may correspond to a 1 in 100-year events reaching the coast during the spring tides and under present-day mean sea level conditions. It also occurs for moderate extreme waves with a return period of 10-years in combination with +1 m of mean sea level (this case is also provided in the Supplementary Video 1 ) and for all the return periods for the southwestern swell with +0.75 m of mean sea level rise.

Figure 9 . Box plots along Hoarafushi coast without airport computed with time series of simulated water levels: (a) median values; (b) Maximum values. Box plot colours indicate the sea level of the simulation (dark blue 0 m; light blue 0.25 m; green 0.5 m; orange 0.75 m; dark red 1 m) and are referenced from the dashed line with the same colour. Box plots on grey (white) background are for the simulations for the southwestern swell (Indian Monsoon). The thicker line of the box plots shows the median values; the lower (upper) limit of the boxes indicates the 25th (75th) quantile; and the lower and upper whiskers indicate the minimum and maximum values. The horizontal thick black line indicates the island height.

With the construction of the airport connected to Hoarafushi ( Figure 8b ), the median and maximum water level values computed along the coast (grey area in Figure 8b ) slightly increased for all combinations of mean sea level, extreme waves and wave directions (see the equivalent figure to Figure 9 in Supplementary Figure 14 ). On average, the median values of water level along the coast increase around 0.05 m solely due to the presence of the airport, that partially blocks the channel between Hoarafushi and the island located southwards, leading to higher wave setup. The new reclaimed land is also exposed to incoming waves, and this exposure has been measured in a similar manner as for Hoarafushi, i.e., along a coastal strip on its western coast (blue area in Figure 8b ). We remark that the airport has been built 150-200 m away from the reef edge, reducing to a large extent the protection of the wave damping induced by the reef flat. Consequently, both median and maxima water level values are significantly higher than in Hoarafushi island ( Supplementary Figure 15 ). For example, with +1 m of increase in mean sea level, even moderate extreme waves would cause overtopping (e.g., 10-year return levels or less under high tide), and under current conditions a 50-year return level southwestern swell would partially flood the airport.

The flood hazard of the new reclaimed land is summarised in Figure 10 , using the set of 30 simulation runs with the airport. The flood hazard has been defined following the French standards, that define four different flooding hazard levels (low, moderate, high, and very high) that arise as combinations of inundation level and the water speed over land (see Supplementary Figure 16 ). The artificial island built for the airport is completely flooded with a high level of hazard for most part of the island for both wave directions and all return periods with an increase in mean sea level of ≥0.75 m (with the only exception of the Indian monsoon 10-year return period). It is foreseen that the reclaimed land suffers from partial flooding under a southwestern swell extreme of 50-year return period with current conditions of mean sea level. It is worth mentioning that, given the lack of topographic data for the new airport island, flooding hazard is possibly biased high. We simulated the island as being completely flat and without any coastal defenses. This is unlikely to be the case for a critical infrastructure. However, the actual defense height remains unknown, which is why we assume compliance to land reclamation regulations i.e., 1.5 m land elevation. Coastal defenses would only delay the impact of coastal flooding, but would not avoid it.

Figure 10 . Level of hazard on the new airport for all the SWASH simulations. The colourscale indicates the level of hazard defined by a combination of water height on the airport and water velocity (see Supplementary Figure 16 ). Different sea levels are represented at each row while the columns indicate return periods of H s (10, 50, and 100 years) as defined in the text for the regional wave climate. For each combination of sea level and return period, the result for the Indian Monsoon and Southwestern swell are shown at left and right, respectively.

6. Summary and Discussion

6.1. global to local coastal modelling.

Mean sea-level rise, despite having a global origin, has severe local coastal impacts, as it raises the baseline level on top of which extreme events reach the coastlines. Yet, projections of changes in mean sea-level as well as assessments of marine extremes are often provided on a large-scale basis (e.g., Vousdoukas et al., 2017 ), while understanding the causes of coastal flooding and anticipating the impacts require quantitative information at the local scale. This can be feasible to implement in regions where monitoring networks, forecasting and operational systems and development programs for sustainable coastlines are well established and mature (for example, the Flood and Coastal Erosion Risk Management Programme in the UK, or the Delta Programme in the Netherlands). In many cases, however, even local assessments rely on coarse resolution, large-scale global climate information.

In this work we have focused on the Maldivian archipelago, a region where recurrent flooding episodes occur driven by remotely generated waves. These events are, furthermore, projected to become more frequent as mean sea level rises due to the low elevation of the islands. Despite their exposure to waves, to our knowledge, the only source of wave climate information in the region so far are the outputs of global wave reanalysis with a spatial resolution of the order of a degree. Our work illustrates how these global wave fields from coarse resolution climate models can be translated into usable information for regional and local studies and how it can be combined with regionalised projections of mean sea-level rise and local topo-bathymetries.

The first step consisted of a detailed analysis and characterisation of the global wave climate around the Maldives using the closest grid points from the CFSR wave reanalysis (section 2.1). This is a prior mandatory step before the design of the regionalisation. We identified four dominant incoming wave directions from remotely generated waves: the two most common, that originate in the Southern Ocean ( Amores and Marcos, 2019 ), and swells generated by the Indian and Northwestern monsoons. In a second step, for each direction, extreme waves have been characterised in terms of H s and T p and a set of five return levels have been dynamically-downscaled using the spectral model WWIII (section 2.2). We have focused on extreme waves only because these are the most relevant for risk analyses; furthermore, the alternative of dynamically-downscaling a 35-year long reanalysis is unfeasible due to computational constraints (this worsens if historical runs and projections are considered). The regionalisation has resulted in a major product of the present work: a valuable data set of extreme waves along the Maldivian coasts with spatial resolutions down to 500 m in the points nearest to the coast. The data set is published at doi: 10.5281/zenodo.3886273 . The output of our regionalisation provides quantitative information on extreme waves, in the form of return level curves, at the regional scale in the Maldives and for the first time. This dataset is useful for coastal engineering studies, for feeding local coastal models of flooding hazards and for planning land reclamation and other regional developments. It also serves to compute the inundation potential at every location and for every incoming swell direction, that depends on wave energy, H s 2 · T p , in line with the “response approach” discussed in Sanuy et al. (2020) . Overall, it is expected to become a compelling source of scientific information that can be embedded in coastal climate services ( Le Cozannet et al., 2017 ; Kopp et al., 2019 ). The users should, nevertheless, ensure that the inherent uncertainties in the method and data are considered. This means that regional waves are representative of ocean swells in the vicinity of the atolls and that, for practical purposes, a detailed topobathymetry is needed is these regional outputs are to be used as boundary forcings. Also, the four main swell directions arriving to the archipelago are considered separately, since the generation mechanisms are independent; thus, every coastal location may be exposed to a different number of incoming wave directions, and all of them should be explored in a local case study, as illustrated in section 5 above.

There is a number of limitations in our regionalised wave fields. The bathymetry used in the regional wave model (GEBCO, see section 2.2) has a spatial resolution of ~ 1 km, which is not enough to resolve the features inside the atolls. We have therefore included every atoll as a single entity in the model domain, neglecting the wave propagation in the inner region and the exchanges between the lagoon of the atoll and the ocean. We consider, nevertheless, that this assumption is reasonable because our results provide evidence that shadow effects of the atolls to incoming waves are realistically simulated from all directions. That implies that we account for the waves that reach the external coast of the atolls everywhere in the Maldives. This limitation can be overcome in areas where mesoscale (~ 100 m resolution) bathymetric data sets exist, in which case the interactions with the inner lagoon can also be accounted for. Another caveat of the regional product is that only selected return periods of H s are provided, instead of an entire high-frequency time series at every coastal grid point. While the quantification of return levels is central to risk assessments, no information on averaged wave fields (useful for erosion studies, for example) is provided. Finally, it is worth pointing out that the regional product has not been validated against observations due to the lack of data.

6.2. Application for Coastal Flood Hazard

We have conducted a local flood hazard modelling experiment that demonstrates the applicability of the regionalised wave fields. Our case study is in the North of the archipelago, exposed to the largest incoming waves, and includes a land reclamation project. We have used the regionalised wave information to feed the wave propagation model SWASH around Hoarafushi island, where local bathymetry has been measured. We have estimated the flooding hazard under present-day conditions and also under projected future scenarios. Our analysis of the global wave climate revealed that projected changes in the large-scale wave characteristics during the twenty-first century are small in comparison to the multi-model spread even under the RCP8.5 scenario. Therefore, we rely on the downscaled regional wave reanalysis and assume that future changes in marine hazards will be driven only by mean sea-level rise. The local model does take into account the modification of the wave propagation due to higher mean sea levels, though. The set of the model experiments included the island configuration with and without the airport in order to determine how the presence of the new reclaimed land alters the flood hazard, the wave propagation and the associated currents.

Our results identified the southwestern swells as the potentially most hazardous waves in Hoarafushi, with 100-year return levels of H s up to 4 m and associated T p of ~20 s. This is, in addition, the most common wave direction that reaches this part of the archipelago, although not the one with largest H s (that are associated with the Indian monsoon). Our findings indicate that a moderate incoming southwestern swell corresponding to a return period of only 10 years will cause overtopping in Hoarafushi island if it reaches the shoreline under a mean sea level 0.75 m higher than its present-day value ( Figure 9 ). The presence of the reclaimed land slightly increases these impacts ( Supplementary Figure 14 ). The flood hazard is much stronger in the reclaimed land, that will experience overtopping episodes with sea levels only 0.25 m above present-day mean value ( Supplementary Figure 15 ). The reason is its location close to the reef that reduces the wave damping over the reef flat. We recall here that we have adopted a scenario-independent approach for mean sea level increases; this may be justified given that the range of mean sea level changes that we are considering (below 1 m) will be reached even under strong mitigation, as the maximum value lies within the committed global mean sea-level rise of past GHGs emissions ( Nauels et al., 2019 ). Thus, it is not about whether these higher mean sea levels will be reached, but when it will occur. Impact studies based on scenario-independent approaches in combination with ongoing monitoring of regional mean sea-level rise can facilitate the design of adaptive solutions to climate-induced hazards.

In addition, this approach also allows to evaluate the wave-induced flood hazard under particular tidal conditions. In the example above, mean sea level 0.75 m higher than present-day values can be interpreted as a combination of climate-induced mean sea-level rise and tidal oscillations. For instance, 0.75 m can be reached with 0.5 m of climate-induced mean sea level that, according to Kopp et al. (2014) , corresponds to the median projected value in 2068 under the RCP8.5 scenario, plus 0.25 m of tidal amplitude. In consequence, according to our estimates, the recently developed (in 2019) regional airport will be flooded under present-day mean sea-level conditions and 0.25 m of tidal amplitude if a moderate extreme swell event (10-year return period) reaches the area, that is, within the present decade. Note that we are not computing the likelihood of co-occurrence of extreme swells and high tides. The reasons for that are, firstly, that these two processes are uncorrelated (astronomical tides and remotely-generated swell events have independent driving mechanisms) which means that their joint probability could be computed as the product of their marginal probability distributions ( Pugh and Woodworth, 2014 ). However, this would require a complete set of time series of the two processes at every grid point. Although there are methods to generate a set of full synthetic time series from their statistical characterisation (e.g., Solari and Losada, 2011 ), this is a different type of product that is beyond the scope of the present work. Secondly, our approach is more flexible since it does not constrain the interpretation of the increments in mean sea level (either climate-induced sea level rise or tides or both), hence, allowing final users to tailor our approach to their needs, based on their respective risk-taking propensity.

The modification of the island configuration with the presence of the reclaimed land significantly modifies the patterns of the currents ( Supplementary Figure 1 ). Such changes are determinant for coastal erosion, as they control the sediment transport along the coastlines. Coastal erosion is considered a central problem in the Maldives, especially in densely populated islands ( Zahir et al., 2016 ; Duvat and Magnan, 2019 ). Erosion can be prevented or enhanced by many factors, including land reclamation, dredging and building coastal defenses. Here we demonstrate that our regional wave fields are a valuable tool also for anticipating possible erosion and changing spatial patterns in particular case studies.

The major limitation of our local coastal modelling exercise is the lack of a detailed topography of Hoarafushi and its nearby reclaimed airport. While we have measured bathymetric profiles during a field trip, the information on the topography is limited to the averaged elevation of the island. Likewise, the elevation of the reclaimed land (which was not yet built when the field trip took place) has been defined according to the national regulations. In consequence, we have not included coastal defenses and we have instead considered that both the island and the new reclaimed land are flat. This implies that our estimates of overtopping and flooding could be biased high; however, the presence of coastal defenses would not completely avoid the flood hazard, they would simple delay the impacts of mean sea-level rise.

Another point worthy of discussion is the assumption of static bathymetry and null reef response to changing climatic conditions. It is clear that reefs can change over time. For example, they can accrete following sea level rise ( Woodroffe and Murray-Wallace, 2012 ), they can degrade due to human activities (the construction of the airport is a good example) or they can die as a consequence of warmer temperatures ( Bruno and Selig, 2007 ) (indeed, warm reefs are projected to significantly decline even with global warming only 1.5°C above pre-industrial levels ( Bindoff et al., 2019 ) and to be virtually extinct with 2°C of warming ( Hoegh-Guldberg et al., 2018 ). In any of these cases, changes in the reef would imply changes in the wave propagation and level of protection of the island ( Sheppard et al., 2005 ). We disregard these potential changes in our local flood hazard modelling experiment because we analyse an artificially reclaimed island. Here, human activities generally have severe negative effects on the reef ( Duvat, 2020 ) and the island is protected with hard measures. This is also to urban atoll islands, as Hoarafushi, that are continuously adapting to increased hazard potential by building coastal infrastructures or artificially raising the land ( Duvat and Magnan, 2019 ; Esteban et al., 2019 ; Hinkel et al., 2019 ; Brown et al., 2020 ). This reduces the ability of the island to naturally increase its elevation by sediment deposition during overtopping events ( Kench and Beetham, 2019 ). Hence, we argue that in this case human interventions are probably more important for wave propagation than changes in the reef ( Duvat and Magnan, 2019 ). Contrastingly, in a natural island, assuming a static bathymetry and null reef response, would bias the results of model overtopping ( Beetham et al., 2017 ; Beetham and Kench, 2018 ). Nevertheless, the regional downscaling that we provide serves as a boundary condition for subsequent studies of wave-induced flooding under future conditions, which then have to account for these uncertainties of future reef responses.

7. Concluding Remarks

Our study provides the framework to fill the gap between global information of marine climate drivers, including mean sea level and extremes, and local coastal flood hazard modelling. In particular, we demonstrate the feasibility of using large-scale data sets (regionalised sea-level projections and global wind-waves simulations) to inform regional planning and local decision-making. Our work focused in the Maldives, but our technique can be applied to any coastal region, being most relevant where regional and local climate information is not available. Together with the outputs, we have discussed a number of uncertainties in regional as well as local coastal modelling that are inherent to the methodology. Some of the limitations, though, stem, to a large extent, from the lack of coastal observations (i.e., local topo-bathymetries). Our study thus advocates for improved monitoring systems and data collection to reduce uncertainties and better inform final users.

We have generated a valuable regional wave data set that fulfils the purposes of characterisation of the wave climate in a sparsely observed area. This dataset, in combination with detailed local information (e.g., high-resolution topo-bathymetries), serves as a milestone for informing adaptation policy and Maldivian decision-makers facing the challenge of adapting to rising sea-levels.

Data Availability Statement

The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://zenodo.org/record/3886273#.YCFTAOhKhPY .

Author Contributions

AA, MM, GL, and JH conceived the work. AA, MM, RP, and SL designed the numerical experiments. AA, MM, and JR analysed the return periods. AS and ZK retrieved the topobathimetric data. All authors contributed to the outline and writing of the manuscript.

This study was supported by the FEDER/Ministerio de Ciencia, Innovación y Universidades Agencia Estatal de Investigación through the MOCCA project (grant no. RTI2018-093941-B-C31) and by the INSeaPTION Project that is part of ERA4CS, an ERA-NET initiated by JPI Climate, and funded by Ministerio de Economía, Industria y Competitividad Agencia Estatal de Investigación (ES) (grant no. PCIN-2017-038), BMBF (DE), NOW (NL), and ANR (FR) with co-funding by the European Union (Grant 690462). This research has been also supported by the ANR project Storisk supported by the French Research Agency. AA was funded by the Conselleria d'Educació, Universitat i Recerca del Govern Balear through the Direcció General de Política Universitària i Recerca and by the Fondo Social Europeo for the period 2014–2020 (grant no. PD/011/2019). MM was supported by the Ministerio de Ciencia e Innovación and la Agencia Estatal de Investigación through grant no. IED2019-000985-I.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

We are grateful to Dr. Aurélie Maspataud for her support with bathymetric data and Dr. Fernando Méndez for advice on numerical wave modelling. We thank the Ministry of Environment, the Environmental Protection Agency, the Maldives Transport and Contracting Company and Water Solutions for their support during the field trip and data collection. AA is grateful to M. A. Blázquez for his support and comments during the preparation of the manuscript.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmars.2021.665672/full#supplementary-material

Amores, A., and Marcos, M. (2019). Ocean swells along the global coastlines and their climate projections for the 21st century. J. Clim. 33, 185–199. doi: 10.1175/JCLI-D-19-0216.1

CrossRef Full Text | Google Scholar

Beetham, E., and Kench, P. S. (2018). Predicting wave overtopping thresholds on coral reef-island shorelines with future sea-level rise. Nat. Commun. 9:3997. doi: 10.1038/s41467-018-06550-1

PubMed Abstract | CrossRef Full Text | Google Scholar

Beetham, E., Kench, P. S., and Popinet, S. (2017). Future reef growth can mitigate physical impacts of sea-level rise on atoll islands. Earths Fut. 5, 1002–1014. doi: 10.1002/2017EF000589

Bindoff, N. L., Cheung, W. W., Kairo, J. G., Arístegui, J., Guinder, V. A., Hallberg, R., et al. (2019). “Changing ocean, marine ecosystems, and dependent communities,” in IPCC Special Report on the Ocean and Cryosphere in a Changing Climate , eds H. -O. Pörtner, D. C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegría, M. Nicolai, A. Okem, J. Petzold, B. Rama, N. M. Weyer, 477–587, in press.

Google Scholar

Brown, S., Wadey, M. P., Nicholls, R. J., Shareef, A., Khaleel, Z., Hinkel, J., et al. (2020). Land raising as a solution to sea-level rise: an analysis of coastal flooding on an artificial island in the maldives. J. Flood Risk Manag. 13:e12567. doi: 10.1111/jfr3.12567

Bruno, J. F., and Selig, E. R. (2007). Regional decline of coral cover in the indo-pacific: timing, extent, and subregional comparisons. PLOS ONE 2:e711. doi: 10.1371/journal.pone.0000711

Cialone, M. A., and Smith, J. M. (2007). “Wave transformation modeling with bottom friction applied to southeast oahu reefs,” in 10th International Workshop on Wave Hindcasting and Forecasting & Coastal Hazard Assessment , (Citeseer), 1–12.

Duvat, V. K. (2020). Human-driven atoll island expansion in the maldives. Anthropocene 32:100265. doi: 10.1016/j.ancene.2020.100265

Duvat, V. K. E., and Magnan, A. K. (2019). Rapid human-driven undermining of atoll island capacity to adjust to ocean climate-related pressures. Sci. Rep. 9:15129. doi: 10.1038/s41598-019-54659-0

Esteban, M., Jamero, M. L., Nurse, L., Yamamoto, L., Takagi, H., Thao, N. D., et al. (2019). Adaptation to sea level rise on low coral islands: lessons from recent events. Ocean Coas. Manag. 168, 35–40. doi: 10.1016/j.ocecoaman.2018.10.031

Gussmann, G., and Hinkel, J. (2021). A framework for assessing the potential effectiveness of adaptation policies: coastal risks and sea-level rise in the maldives. Environ. Sci. Policy 115, 35–42. doi: 10.1016/j.envsci.2020.09.028

Harangozo, S. A. (2013). Flooding in the Maldives and Its Implications for The Global Sea Level Rise Debate (First Published: 01 January 1992) , (American Geophysical Union (AGU)), 95–99.

Hemer, M., Trenham, C., Durrant, T., and Greenslade, D. (2015). Cawcr global wind-wave 21st century climate projections (v2). CSIRO Service Collect . doi: 10.4225/08/55C991CC3F0E8

Hemer, M. A., Katzfey, J., and Trenham, C. E. (2013). Global dynamical projections of surface ocean wave climate for a future high greenhouse gas emission scenario. Ocean Model. 70, 221–245. doi: 10.1016/j.ocemod.2012.09.008

Hemer, M. A., and Trenham, C. E. (2016). Evaluation of a cmip5 derived dynamical global wind wave climate model ensemble. Ocean Model. 103, 190–203. doi: 10.1016/j.ocemod.2015.10.009

Hinkel, J., Church, J. A., Gregory, J. M., Lambert, E., Le Cozannet, G., Lowe, J., et al. (2019). Meeting user needs for sea level rise information: a decision analysis perspective. Earths Fut. 7, 320–337. doi: 10.1029/2018EF001071

Hinkel, J., Lincke, D., Vafeidis, A. T., Perrette, M., Nicholls, R. J., Tol, R. S. J., et al. (2014). Coastal flood damage and adaptation costs under 21st century sea-level rise. Proc. Natl. Acad. Sci. U.S.A. 111, 3292–3297. doi: 10.1073/pnas.1222469111

Hoegh-Guldberg, O., Jacob, D., Bindi, M., Brown, S., Camilloni, I., Diedhiou, A., et al. (2018). Impacts of 1.5°c Global Warming on Natural and Human Systems . Global warming of 1.5°C. An IPCC Special Report.

Hoeke, R. K., McInnes, K. L., Kruger, J. C., McNaught, R. J., Hunter, J. R., and Smithers, S. G. (2013). Widespread inundation of pacific islands triggered by distant-source wind-waves. Glob. Planet. Change 108, 128–138. doi: 10.1016/j.gloplacha.2013.06.006

Kench, P., and Beetham, E. P. (2019). Evidence of vertical building of reef islands through overwash and implications for island futures. Coastal Sediments 916–929. doi: 10.1142/9789811204487_0080

Kopp, R. E., Gilmore, E. A., Little, C. M., Lorenzo-Trueba, J., Ramenzoni, V. C., and Sweet, W. V. (2019). Usable science for managing the risks of sea-level rise. Earths Fut. 7, 1235–1269. doi: 10.1029/2018EF001145

Kopp, R. E., Horton, R. M., Little, C. M., Mitrovica, J. X., Oppenheimer, M., Rasmussen, D. J., et al. (2014). Probabilistic 21st and 22nd century sea-level projections at a global network of tide-gauge sites. Earths Fut. 2, 383–406. doi: 10.1002/2014EF000239

Kraines, S., Yanagi, T., Isobe, M., and Komiyama, H. (1998). Wind-wave driven circulation on the coral reef at bora bay, miyako island. Coral Reefs 17, 133–143. doi: 10.1007/s003380050107

Le Cozannet, G., Nicholls, R., Hinkel, J., Sweet, W., McInnes, K., Van de Wal, R., et al. (2017). Sea level change and coastal climate services: The way forward. J. Mar. Sci. Eng. 5, 49. doi: 10.3390/jmse5040049

Lecacheux, S., Pedreros, R., Le Cozannet, G., Thiébot, J., De La Torre, Y., and Bulteau, T. (2012). A method to characterize the different extreme waves for islands exposed to various wave regimes: a case study devoted to reunion island. Natural Hazards Earth Syst. Sci. 12, 2425–2437. doi: 10.5194/nhess-12-2425-2012

Maniku, H. A. (1990). Changes in the Topography of the Maldives . Forum of Writers on Environment (Maldives).

Marcos, M., and Woodworth, P. L. (2017). Spatiotemporal changes in extreme sea levels along the coasts of the north atlantic and the gulf of mexico. J. Geophys. Res. 122, 7031–7048. doi: 10.1002/2017JC013065

MEE. (2017). State of the Environment 2016. Ministry of Environment and Energy . Available online at: https://www.environment.gov.mv/v2/wp-content/files/publications/20170202-pub-soe-2016.pdf

Merkens, J.-L., Lincke, D., Hinkel, J., Brown, S., and Vafeidis, A. T. (2018). Regionalisation of population growth projections in coastal exposure analysis. Clim. Change 151, 413–426. doi: 10.1007/s10584-018-2334-8

Morim, J. L. M., Wang, H. X., Cartwright, N., Trenham, C., Semedo, A., Young, I., et al. (2019). Robustness and uncertainties in global multivariate wind-wave climate projections. Nat. Clim. Change 9, 711–718. doi: 10.1038/s41558-019-0542-5

Nauels, A., Gütschow, J., Mengel, M., Meinshausen, M., Clark, P. U., and Schleussner, C.-F. (2019). Attributing long-term sea-level rise to paris agreement emission pledges. Proc. Natl. Acad. Sci. U.S.A. 116, 23487–23492. doi: 10.1073/pnas.1907461116

NBS (2017). “Statistical yearbook of maldives 2016,” in National Bureau of Statistics Ministry of Finance and Treasury, Republic of Maldives (Malé).

NBS (2019). “Maldives population projections 2014-2054, assumption and results analysis,” in National Bureau of Statistics Ministry of Finance and Treasury, Republic of Maldives (Malé)

Nicholls, R. J., Hanson, S. E., Lowe, J. A., Warrick, R. A., Lu, X., and Long, A. J. (2014). Sea-level scenarios for evaluating coastal impacts. WIRES Clim. Change 5, 129–150. doi: 10.1002/wcc.253

Nurse, L., McLean, R., Agard, J., Bibruglio, L., Duvat, V., Pelesikoti, N., et al. (2014). “Small islands (chapter 29),” in Climate Change 2014 : Impacts, Adaptation, and Vulnerability. Part B : Regional Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change , eds V. R. Barros, C. B. Field, D. J. Dokken, M. D. Mastrandrea, K. J. Mach, T. E. Bilir, M. Chatterjee, K. L. Ebi, Y. O. Estrada, R. C. Genova, B. Girma, E. S. Kissel, A. N. Levy, S. MacCracken, P. R. Mastrandrea, and L. L.White (Cambridge, UK; New York, NY: Cambridge University Press), 1613–1654. Available online at: https://www.ipcc.ch/site/assets/uploads/2018/02/WGIIAR5-Chap29_FINAL.pdf

Oppenheimer, M., Glavovic, B., Hinkel, J., van de Wal, R., Magnan, A., Abd-Elgawad, A., et al. (2019). “Sea level rise and implications for low-lying islands, coasts and communities,” in IPCC Special Report on the Ocean and Cryosphere in a Changing Climate , eds H.-O. Pörtner, D. C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, A. Alegría, M. Nicolai, A. Okem, J. Petzold, B. Rama, N. M. Weyer. Available online at: https://www.ipcc.ch/srocc/chapter/chapter-4-sea-level-rise-and-implications-for-low-lying-islands-coasts-and-communities/

Prager, E. J. (1991). Numerical simulation of circulation in a caribbean-type backreef lagoon. Coral Reefs 10, 177–182. doi: 10.1007/BF00336771

Pugh, D., and Woodworth, P. (2014). Sea-Level Science: Understanding Tides, Surges, Tsunamis and Mean Sea-Level Changes . Cambridge: Cambridge University Press. doi: 10.1017/CBO9781139235778. Available online at: https://www.cambridge.org/core/books/sealevel-science/C5E551D95DA4E8AF116FED9F0DEB289B

Rijnsdorp, D. P., Smit, P. B., and Zijlema, M. (2012). Non-hydrostatic modelling of infragravity waves using swash. Coast. Eng. Proc. 1:currents.27. doi: 10.9753/icce.v33.currents.27

Saha, S., Moorthi, S., Pan, H.-L., Wu, X., Wang, J., Nadiga, S., et al. (2010). The ncep climate forecast system reanalysis. Bull. Am. Meteorol. Soc. 91, 1015–1058. doi: 10.1175/2010BAMS3001.1

Sanuy, M., Jiménez, J. A., Ortego, M. I., and Toimil, A. (2020). Differences in assigning probabilities to coastal inundation hazard estimators: Event versus response approaches. J. Flood Risk Manag. 13:e12557. doi: 10.1111/jfr3.12557

Sheppard, C., Dixon, D. J., Gourlay, M., Sheppard, A., and Payet, R. (2005). Coral mortality increases wave energy reaching shores protected by reef flats: examples from the seychelles. Estuarine Coast. Shelf Sci. 64, 223–234. doi: 10.1016/j.ecss.2005.02.016

Solari, S., and Losada, M. A. (2011). Non-stationary wave height climate modeling and simulation. J. Geophys. Res. 116:C09032. doi: 10.1029/2011JC007101

Suzuki, T., Altomare, C., De Roo, S., Vanneste, D., and Mostaert, F. (2018). Manning's Roughness Coefficient in Swash: Application to Overtopping Calculation . FHR Reports. Available online at: https://publicaties.vlaanderen.be/view-file/32905 .

Tolman, H. (2009). User manual and system documentation of wavewatch iii version 3.14. NOAA/NWS/NCEP/MMAB Technical Note 276 , (Appendices), 194.

Tolman, H. (2014). User manual and system documentation of wavewatch iii version 4.18. NOAA/NWS/NCEP/MMAB Technical Note 316 .

Vousdoukas, M. I., Mentaschi, L., Voukouvalas, E., Verlaan, M., and Feyen, L. (2017). Extreme sea levels on the rise along europe's coasts. Earths Fut. 5, 304–323. doi: 10.1002/2016EF000505

Wadey, M., Brown, S., Nicholls, R. J., and Haigh, I. (2017). Coastal flooding in the maldives: an assessment of historic events and their implications. Natural Hazards 89, 131–159. doi: 10.1007/s11069-017-2957-5

Woodroffe, C. D., and Murray-Wallace, C. V. (2012). Sea-level rise and coastal change: the past as a guide to the future. Quatern. Sci. Rev. 54, 4–11. doi: 10.1016/j.quascirev.2012.05.009

Woodworth, P. L. (2005). Have there been large recent sea level changes in the maldive islands? Glob. Planetary Change 49, 1–18. doi: 10.1016/j.gloplacha.2005.04.001

Woodworth, P. L., Melet, A., Marcos, M., Ray, R. D., Wöppelmann, G., Sasaki, Y. N., et al. (2019). Forcing factors affecting sea level changes at the coast. Surveys Geophys. 40, 1351–1397. doi: 10.1007/s10712-019-09531-1

Zahir, H., Asis, M., Rasheed, A., Musthafa, Z. M., Latheef, A. T., and Mohamed, I. (2016). “Second national communication of maldives to the united nations framework convention on climate change,” in Ministry of Environment and Energy. Republic of Maldives: Malé .

Zijlema, M. (2012). “Modelling wave transformation across a fringing reef using swash,” in ICCE 2012: Proceedings of the 33rd International Conference on Coastal Engineering, Santander, Spain, 1-6 July 2012 . Coastal Engineering Research Council. doi: 10.9753/icce.v33.currents.26. Available online at: https://journals.tdl.org/icce/index.php/icce/article/view/6479

Zijlema, M., Stelling, G., and Smit, P. (2011). Swash: an operational public domain code for simulating wave fields and rapidly varied flows in coastal waters. Coast. Eng. 58, 992–1012. doi: 10.1016/j.coastaleng.2011.05.015

Keywords: coastal flooding, wind-waves, sea-level rise, global-to-local modelling, climate services

Citation: Amores A, Marcos M, Pedreros R, Le Cozannet G, Lecacheux S, Rohmer J, Hinkel J, Gussmann G, van der Pol T, Shareef A and Khaleel Z (2021) Coastal Flooding in the Maldives Induced by Mean Sea-Level Rise and Wind-Waves: From Global to Local Coastal Modelling. Front. Mar. Sci. 8:665672. doi: 10.3389/fmars.2021.665672

Received: 08 February 2021; Accepted: 19 May 2021; Published: 21 June 2021.

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Copyright © 2021 Amores, Marcos, Pedreros, Le Cozannet, Lecacheux, Rohmer, Hinkel, Gussmann, van der Pol, Shareef and Khaleel. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Angel Amores, angel.amores@uib.es

This article is part of the Research Topic

Climate Services for Adaptation to Sea-Level Rise

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

• Published: 12 December 2022
• Volume 71 , pages 741–754, ( 2023 )

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• Allison Mitchell 1 ,
• Anamaria Bukvic   ORCID: orcid.org/0000-0001-7395-5383 2 ,
• Yang Shao 3 ,
• Jennifer L. Irish 4 &
• Daniel L. McLaughlin 5  

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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.

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Andres K, Savarese M, Bovard B, Parsons M (2019) Coastal wetland geomorphic and vegetative change: Effects of sea-level rise and water management on brackish marshes. Estuaries Coasts 42(5):1308–1327. https://doi.org/10.1007/s12237-019-00538-w

Article   Google Scholar  

Atkinson L, Ezer T, Smith E (2013) Sea level rise and flooding risk in Virginia. Sea Grant Law Policy J 5(2):3–14

Google Scholar  

Balica S, Dinh Q, Popescu I, Vo TQ, Pham DQ (2013) Flood impact in the Mekong Delta, Vietnam. J Maps 10:257–268. https://doi.org/10.1080/17445647.2013.859636

Blankespoor B, Dasgupta S, Laplante B (2014) Sea-level rise and coastal wetlands. Ambio 43(8):996–1005. https://doi.org/10.1007/s13280-014-0500-4

Boon JD (2012) Evidence of sea level acceleration at U.S. and Canadian tide stations, Atlantic Coast, North America. J Coast Res 28(6):1437–1445. https://doi.org/10.2112/JCOASTRES-D-12-00102.1

Borchert SM, Osland MJ, Enwright NM, Griffith KT (2018) Coastal wetland adaptation to sea level rise: Quantifying potential for landward migration and coastal squeeze. J Appl Ecol 55(6):2876–2887. https://doi.org/10.1111/1365-2664.13169

Bukvic A, Harrald J (2019) Rural versus urban perspective on coastal flooding: The insights from the U.S. Mid-Atlantic communities. Clim Risk Manag 23:7–18. https://doi.org/10.1016/j.crm.2018.10.004

Bukvic A, Rohat G, Apotsos A, de Sherbinin A (2020) A systematic review of coastal vulnerability mapping. Sustainability 12(7):1–26. https://doi.org/10.3390/su12072822

Cheng C, Yang YCE, Ryan R, Yu Q, Brabec E (2017) Assessing climate change-induced flooding mitigation for adaptation in Boston’s Charles River watershed, USA. Landsc Urban Plan 167:25–36. https://doi.org/10.1016/j.landurbplan.2017.05.019

Choden K, Keenan RJ, Nitschke CR (2020) An approach for assessing adaptive capacity to climate change in resource dependent communities in the Nikachu watershed, Bhutan. Ecol Indic 114:106293. https://doi.org/10.1016/j.ecolind.2020.106293

Cialone MA, Massey TC, Anderson ME, Grzegorzewski AS, Jensen RE, Cialone A, McAlpin TO (2015) North Atlantic Coast Comprehensive Study (NACCS) coastal storm model simulations: Waves and water levels. TR-15-14, U.S. Army Engineer Research and Development Center, Vicksburg, MS

City of Norfolk (2016) Norfolk Vision 2100. https://www.norfolk.gov/DocumentCenter/View/27768/Vision-2100---FINAL?bidId=

City of Portsmouth (2018) Portsmouth 2018 Comprehensive Plan. https://portsmouthva.gov/DocumentCenter/View/7623/Build-One-Portsmouth-Adopted-11-27-2018-PDF

Commonwealth of Virginia (2020) Virginia Coastal Coastal Resilience Master Planning Framework. https://www.governor.virginia.gov/media/governorvirginiagov/governor-of-virginia/pdf/Virginia-Coastal-Resilience-Master-Planning-Framework-October-2020.pdf

Considine C, Covi M, Yusuf JE (2017) Mechanisms for cross-scaling, flexibility and social learning in building resilience to sea level rise: Case study of Hampton Roads, Virginia. Am J Clim Change 6(2):385–402. https://doi.org/10.4236/ajcc.2017.62020

Dahal RP, Grala RK, Gordon JS, Petrolia DR, Munn IA (2018) Estimating the willingness to pay to preserve waterfront open spaces using contingent valuation. Land Use Policy 78:614–626. https://doi.org/10.1016/j.landusepol.2018.07.027

Dietrich JC, Zijlema M, Westerink JJ, Holthuijsen LH, Dawson C, Luettich Jr RA, Stone GW (2011) Modeling hurricane waves and storm surge using integrally-coupled, scalable computations. Coast Eng 58(1):45–65. https://doi.org/10.1016/j.coastaleng.2010.08.001

Dinh Q, Balica S, Popescu I, Jonoski A (2012) Climate change impact on flood hazard, vulnerability and risk of the Long Xuyen Quadrangle in the Mekong Delta. Int J River Basin Manag 10(1):103–120

Dudula J, Randhir T (2016) Modeling the influence of climate change on watershed systems: Adaptation through targeted practices. J Hydrol 541(6):703–713. https://doi.org/10.1016/j.jhydrol.2016.07.020.

Engelhart SE, Peltier WR, Horton BP (2011) Holocene relative sea-level changes and glacial isostatic adjustment of the U.S. Atlantic Coast. Geology 39(8):751–754. https://doi.org/10.1130/G31857.1

Enríquez-de-Salamanca Á (2019) Vulnerability reduction and adaptation to climate change through watershed management in St. Vincent and the Grenadines. GeoJournal 84(4):1107–1119. https://doi.org/10.1007/s10708-018-9914-z

Ezer T (2018) The increased risk of flooding in Hampton roads: On the roles of sea level rise, storm surges, hurricanes, and the gulf stream. Mar Technol Soc J 52(2):34–44. https://doi.org/10.4031/MTSJ.52.2.6

Ezer T (2022) A demonstration of a simple methodology of flood prediction for a coastal city under threat of sea level rise: The case of Norfolk, VA, USA. Earth’s Future 10:e2022EF002786.

Federal Emergency Management Agency (2019) Special Flood Hazard Area. https://www.fema.gov/special-flood-hazard-area

Habete D, Ferreira CM (2017) Potential impacts of sea-level rise and land-use change on special flood hazard areas and associated risks. Nat Hazards Rev 18(4):04017017. https://doi.org/10.1061/(asce)nh.1527-6996.0000262

Hampton Roads Planning District Commission (2017) Hampton Roads Hazard Mitigation Plan. https://www.hrpdcva.gov/uploads/docs/2017%20Hampton%20Roads%20Hazard%20Mitigation%20Plan%20Update%20FINAL.pdf

Hampton Roads Planning District Commission (2019a) Hampton Roads Regional Parcels [Data file]. https://www.hrgeo.org/pages/regional-parcels

Hampton Roads Planning District Commission (2019b) Norfolk and Virginia Beach Joint Land Use Study. https://www.hrpdcva.gov/uploads/docs/JLUS%20NOVB%20Exec%20Summary.pdf

HRPDC, see: Hampton Roads Planning District Commission (2021a) Portsmouth and Chesapeake Joint Land Use Fact Sheet. https://www.hrpdcva.gov/uploads/docs/Portsmouth%20%26%20Chesapeake%20Joint%20Land%20Use%20Fact%20Sheet2.pdf

HRPDC, see: Hampton Roads Planning District Commission (2021b) HRPDC Benchmarking 2021: Demographics. https://www.hrpdcva.gov/uploads/docs/08%20Benchmarking%20-%20Demographics.pdf

IPCC (2014) Climate Change 2014: Synthesis report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Core Writing Team, R.K. Pachauri and L.A. Meyer (eds.)]. IPCC, Geneva, Switzerland, pp 151

John BS, Yusuf JE (2019) Perspectives of the expert and experienced on challenges to regional adaptation for sea level rise: Implications for multisectoral readiness and boundary spanning. Coast Manag 47(2):151–168. https://doi.org/10.1080/08920753.2019.1564951

Joyce J, Chang NB, Harji R, Ruppert T, Singhofen P (2018) Cascade impact of hurricane movement, storm tidal surge, sea level rise and precipitation variability on flood assessment in a coastal urban watershed. Climate Dynamics 51(1):383–409

Kirwan ML, Gedan KB (2019) Sea-level driven land conversion and the formation of ghost forests. Nat Clim Change 9(6):450–457. https://doi.org/10.1038/s41558-019-0488-7

Kleinosky LR, Yarnal B, Fisher A (2007) Vulnerability of Hampton Roads, Virginia to storm-surge flooding and sea-level rise. Nat Hazards 40(1):43–70. https://doi.org/10.1007/s11069-006-0004-z

Kolok AS, Beseler CL, Chen XH, Shea PJ (2009) The watershed as a conceptual framework for the study of environmental and human health. Environ Health Insights 3(402):EHI.S1925. https://doi.org/10.4137/ehi.s1925

Lee ACK, Jordan HC, Horsley J (2015) Value of urban green spaces in promoting healthy living and wellbeing: prospects for planning. Risk Manag Healthc Policy 8:131

Lindsey R (2019) Climate change: Sea level rise. https://www.climate.gov/news-features/understanding-climate/climate-change-global-sea-level

Liu H, Behr JG, Diaz R (2016) Population vulnerability to storm surge flooding in coastal Virginia, USA. Integr Environ Assess Manag 12(3):500–509. https://doi.org/10.1002/ieam.170

Lyddon CE, Brown JM, Leonardi N, Plater AJ (2020) Sensitivity of flood hazard and damage to modelling approaches. J Mar Sci Eng 8(9):724

McFarlane B (2012) Climate change in Hampton Roads: Phase III: Sea level rise in Hampton Roads, Virginia. https://www.hrpdcva.gov/uploads/docs/HRPDC_ClimateChangeReport2012_Full_Reduced.pdf

Moftakhari HR, Salvadori G, AghaKouchak A, Sanders BF, Matthew RA (2017) Compounding effects of sea level rise and fluvial flooding. Proc Natl Acad Sci USA 114(37):9785–9790

Article   CAS   Google Scholar  

Mousavi ME, Irish JL, Frey AE, Olivera F, Edge BL (2011) Global warming and hurricanes: the potential impact of hurricane intensification and sea level rise on coastal flooding. Climatic Change 104(3):575–597

Nadal-Caraballo NC, Melby JA, Gonzalez VM, Cox AT (2015) Coastal storm hazards from Virginia to Maine. Engineer Research and Development Center Vicksburg MS Coastal and Hydraulics Lab. https://apps.dtic.mil/sti/citations/ADA627157

NOAA (2021) How to map open space for community rating system. https://coast.noaa.gov/data/digitalcoast/pdf/crs-gis-workflow.pdf

Oppenheimer M, Glavovic BC, Hinkel J, van de Wal R, Magnan AK, Abd-Elgawad A, Cai R, Cifuentes-Jara M, DeConto RM, Ghosh T, Hay J, Isla F, Marzeion B, Meyssignac B, Sebesvari Z (2019) Sea level rise and implications for low-lying islands, coasts and communities. In: Pӧrtner HO, Roberts DC, Masson-Delmotte V, Zhai P, Tignor M, Poloczanska E, Mintenbeck K, Alegría A, Nicolai M, Okem A, Petzold J, Rama B, Weyer NM (eds.), IPCC Special report on the ocean and cryosphere in a changing climate. Cambridge University Press, Cambridge, UK and New York, NY, USA, pp. 321–445. https://doi.org/10.1017/9781009157964.006

Parris A, Bromirski P, Burkett V, Cayan D, Culver M, Hall J, Weiss J (2012) Global sea level rise scenarios for the US National Climate Assessment. NOAA Tech Memo OAR CPO 1:37

Resio DT, Irish JL (2015) Tropical cyclone storm surge risk. Curr Clim Change Rep. 1(2):74–84

Rezaie AM, Ferreira CM, Walls M, Chu Z (2021) Quantifying the impacts of storm surge, sea level rise, and potential reduction and changes in wetlands in coastal areas of the Chesapeake Bay Region. Nat Hazards Rev 22(4):04021044

Schuerch M, Spencer T, Temmerman S, Kirwan ML, Wolff C, Lincke D, Brown S (2018) Future response of global coastal wetlands to sea-level rise. Nature 561(7722):231–234

Shannon PD, Swanston CW, Janowiak MK, Handler SD, Schmitt KM, Brandt LA, Butler-Leopold PR, Ontl T (2019) Adaptation strategies and approaches for forested watersheds. Clim Serv 13:51–64. https://doi.org/10.1016/j.cliser.2019.01.005

Siverd CG, Hagen SC, Bilskie MV, Braud DH, Gao S, Peele RH, Twilley RR (2019) Assessment of the temporal evolution of storm surge across coastal Louisiana. Coast Eng 150:59–78. https://doi.org/10.1016/j.coastaleng.2019.04.010

Smith JM, Cialone MA, Wamsley TV, McAlpin TO (2010) Potential impact of sea level rise on coastal surges in southeast Louisiana. Ocean Eng 37(1):37–47

Sweet WV, Kopp RE, Weaver CP, Obeysekera J, Horton RM, Thieler ER, Zervas C (2017) Global and regional sea level rise scenarios for the United States (No. CO-OPS 083). Silver Spring, Maryland, US

Union of Concerned Scientists (2016). The US military on front lines of rising seas. https://www.ucsusa.org/sites/default/files/attach/2016/07/front-lines-of-rising-seas-key-executive-summary.pdf

U.S. Army Corps of Engineers, Norfolk District (2015) City of Portsmouth, Virginia 2015 Floodplain Management and Repetitive Loss Plan Update. https://www.portsmouthva.gov/DocumentCenter/View/564/2015-Floodplain-Management-and-Repetitive-Loss-Plan-Update-PDF?bidId=

U.S Census Bureau (2019a) TIGER/Line Shapefiles [Data file]. https://www.census.gov/cgi-bin/geo/shapefiles/index.php

U.S. Census Bureau (2019b) 2019: ACS 5-Year Estimates Detailed Tables [Data File]. https://data.census.gov/

U.S. Geological Survey (2006) The Best Practices Data Model – Structures. https://services.nationalmap.gov/bestpractices/model/acrodocs/Poster_BPStructures_03_01_2006.pdf

U.S. Geological Survey (2011) National Land Cover Database [Data file]. https://datagateway.nrcs.usda.gov/GDGOrder.aspx

U.S. Geological Survey (2013) Watershed Boundary Dataset (v2.3) [Data file]. https://datagateway.nrcs.usda.gov/GDGOrder.aspx

U.S. Geological Survey (2014) National Elevation Dataset 30 meter [Data file]. https://datagateway.nrcs.usda.gov/GDGOrder.aspx

U.S. Geological Survey (n.d.) National Hydrography Dataset 1:24,000 [Data file]. https://datagateway.nrcs.usda.gov/GDGOrder.aspx

U.S. Geological Survey, National Geospatial Technical Operations Center, 20201210, USGS National Structures Dataset (NSD) for Virginia 20201210 State or Territory Shapefile: U.S. Geological Survey

USGCRP (2018) Impacts, risks, and adaptation in the United States: Fourth National Climate Assessment, Volume II. In: Reidmiller DR, Avery CW, Easterling DR, Kunkel KE, Lewis KLM, Maycock TK, Stewart BC (eds.) U.S. Global Change Research Program. Washington, DC, USA, pp 1515. https://doi.org/10.7930/NCA4.2018

Wright LD, Nichols CR (eds.) (2019) Tomorrow’s coasts: Complex and impermanent. Springer International Publishing. https://doi.org/10.1007/978-3-319-75453-6

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Acknowledgements

The probabilistic flood hazard and sea level nonlinearity data used herein are publicly available via the U.S. Army Corps of Engineers North Atlantic Coast Comprehensive Study’s Coastal Hazards System ( https://chswebtool.erdc.dren.mil/ ).

This material is based upon work supported by the National Science Foundation under Grants Nos. 1920478 and 1735139.

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Mitchell, A., Bukvic, A., Shao, Y. et al. Toward Collaborative Adaptation: Assessing Impacts of Coastal Flooding at the Watershed Scale. Environmental Management 71 , 741–754 (2023). https://doi.org/10.1007/s00267-022-01759-9

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A participatory community case study of periurban coastal flood vulnerability in southern Ecuador

Erica tauzer.

1 Institute for Global Health & Translational Science, SUNY Upstate Medical University, Syracuse, New York, United States of America

Mercy J Borbor-Cordova

2 Facultad de Ingeniería Marítima y Ciencias del Mar, Escuela Superior Politecnica del Litoral (ESPOL), Guayaquil, Guayas Province, Ecuador

Jhoyzett Mendoza

3 National Service for Risk Management and Emergencies, Guayaquil, Guayas Province, Ecuador

Telmo De La Cuadra

Jorge cunalata.

4 Universidad Tecnica de Machala, Machala, El Oro Province, Ecuador

Anna M Stewart-Ibarra

5 Department of Medicine, SUNY Upstate Medical University, Syracuse, New York, United States of America

6 InterAmerican Institute for Global Change Research (IAI), Montevideo, Department of Montevideo, Uruguay

Associated Data

All government data sources reported in the manuscript are open access and are cited in the manuscript. All coded interview data are presented in the results, figures, tables and supplementary files. There are restrictions for sharing the original audio files and transcripts from focus groups, as this would jeopardize the confidentiality of the participants and violate the protocol that was reviewed and deemed exempt by SUNY Upstate Medical University’s IRB. Relevant transcripts are available upon request. Please contact Lisa Ware, the Deputy Director of the Institute for Global Health and Translational Science at SUNY Upstate Medical University, with inquiries at ude.etatspu@LeraW .

Populations in coastal cities are exposed to increasing risk of flooding, resulting in rising damages to health and assets. Adaptation measures, such as early warning systems for floods (EWSFs), have the potential to reduce the risk and impact of flood events when tailored to reflect the local social-ecological context and needs. Community perceptions and experiences play a critical role in risk management, since perceptions influence people’s behaviors in response to EWSFs and other interventions.

We investigated community perceptions and responses in flood-prone periurban areas in the coastal city of Machala, Ecuador. Focus groups (n = 11) were held with community members (n = 65 people) to assess perceptions of flood exposure, sensitivity, adaptive capacity, and current alert systems. Discussions were audio recorded, transcribed, and coded by topic. Participatory maps were field validated, georeferenced, and digitized using GIS software. Qualitative data were triangulated with historical government information on rainfall, flood events, population demographics, and disease outbreaks.

Flooding was associated with seasonal rainfall, El Niño events, high ocean tides, blocked drainage areas, overflowing canals, collapsed sewer systems, and low local elevation. Participatory maps revealed spatial heterogeneity in perceived flood risk across the community. Ten areas of special concern were mapped, including places with strong currents during floods, low elevation areas with schools and homes, and other places that accumulate stagnant water. Sensitive populations included children, the elderly, physically handicapped people, low-income families, and recent migrants. Flood impacts included damages to property and infrastructure, power outages, and the economic cost of rebuilding/repairs. Health impacts included outbreaks of infectious diseases, skin infections, snakebite, and injury/drowning. Adaptive capacity was weakest during the preparation and recovery stages of flooding. Participants perceived that their capacity to take action was limited by a lack of social organization, political engagement, and financial capital. People perceived that flood forecasts were too general, and instead relied on alerts via social media.

Conclusions

This study highlights the challenges and opportunities for climate change adaptation in coastal cities. Areas of special concern provide clear local policy targets. The participatory approach presented here (1) provides important context to shape local policy and interventions in Ecuador, complimenting data gathered through standard flood reports, (2) provides a voice for marginalized communities and a mechanism to raise local awareness, and (3) provides a research framework that can be adapted to other resource-limited coastal communities at risk of flooding.

Introduction

Damages caused by flooding are growing in urban areas [ 1 , 2 ] due to increased population and assets, a changing climate [ 3 ], coastal subsidence [ 4 – 6 ], and deforestation [ 7 , 8 ]. In Latin America and the Caribbean (LAC), 7.5 million inhabitants and $299 billion USD in built capital are exposed to flooding from a 100-year event, without considering hurricanes [ 9 ]. This exposure will increase to 8.8 to 9.9 million inhabitants by mid-century, when taking into account extreme sea levels, increasing populations, and the historical trend in storm activity [ 9 ]. Flooding presents a high social and economic burden, particularly in low-income vulnerable populations—those who are least able to cope with the impacts and recover from the damages of flood events.

To address flooding and other disasters, the global Sendai Framework for Disaster Risk Reduction (2015–2030) identifies “understanding disaster risk” as a top priority [ 10 ]. A better understanding of local community perceptions of flood hazards can inform risk management planning that aims to reduce exposure to flood events while strengthening the resilience and adaptive capacity of communities [ 10 , 11 ].This understanding can inform the development of tailored climate services for disaster managers, such as early warning systems for floods (EWSFs) [ 10 , 12 , 13 ] When implemented effectively as part of a comprehensive risk management plan, a well-designed EWSF increases community and ecosystem resilience, reduces vulnerability and reduces damages to economies, health, property, infrastructure, and other assets of people, communities, nations, and the private sector [ 12 , 14 ].

Coastal Ecuador is particularly vulnerable to flooding due to an extensive, densely populated coastline along the Pacific Ocean [ 9 ]. This region experiences severe floods during El Niño events due to increased local rainfall [ 15 ]. A recent study identified southern coastal Ecuador as the location with the highest coastal risk in LAC due to a combination of coastal hazards, geographic exposure, and socioeconomic vulnerability [ 16 ]. When flood costs are measured as a percentage of GDP, the coastal city of Guayaquil, Ecuador, (population 2,644,891, [ 17 ]) ranks as the 3 rd most vulnerable city to flooding worldwide [ 18 ]. In these coastal cities, unstructured rapid urbanization has pushed the poor into low-lying areas along estuarine waterways prone to flooding [ 19 , 20 ].

In Ecuador, the Secretary of Risk Management (SNGR) has the primary responsibility to establish early warning systems with a multi-hazard approach in collaboration with national technical-scientific institutions, civil defense, and local governments [ 21 ]. EWSFs have been implemented in most of the river basins throughout the country using basic data; however, detailed hydrometeorological data are available in four hydrographic basins only. While these data are a key part of the national EWSF, it is widely understood that limited economic resources have impacted the operational capacity of EWSFs (A. Vaca, pers . comm .). The SNGR are also responsible for strengthening capacities for disaster prevention and recovery at municipal and local levels [ 22 ]. However, we hypothesize that a lack of engagement with community stakeholders has, in part, limited the local adaptive capacity and the implementation of adaptation actions, such as EWSFs.

Here we present a case study of community perceptions and responses to coastal urban flooding in LAC. Community perceptions and experiences play a critical role in risk management, since perceptions influence people’s behaviors in response to interventions and policies. We hypothesize that (1) flood exposure is multifactorial—driven by hydroclimatic events and local geographies, (2) flood risk is spatially heterogenous at the sub-neighborhood level, (3) populations are differentially sensitive to flooding, (4) impacts of flooding present a high social and economic burden in periurban communities, and (5) the community’s capacity to take actions in response to flooding depends on social, political, and financial assets. This study emerged in response to research priorities identified by the SNGR, an active partner in this investigation. Findings from this study inform the design and implementation of flood risk reduction actions, such as EWSFs, and adaptive capacity strengthening in Ecuador and in other regions with similar characteristics.

Ethics statement

This study was conducted in collaboration with the local municipal government of Machala and the SNGR. The investigation protocol was reviewed and deemed exempt by the Institutional Review Board (IRB) of SUNY Upstate Medical University. The study was also approved by the SNGR. All participants were over the age of 18 and no personal identifying information was collected. Due to the conversational approach used with the focus groups and potential literacy limitations, verbal consent was most appropriate and written consent was not deemed necessary by the IRB committee. Verbal consent was recorded in audio recordings of focus group conversations.

The midsized port city of Machala (population 279,887) is located on the southern coast of Ecuador, and is the capital of El Oro province [ 17 ]. In a recent analysis of urban coastal risks in LAC, El Oro was identified as the top risk hotspot due to the large at-risk population (mostly located in Machala), susceptible ecosystems, high rate of flood exposure from El Niño events, and high level of social vulnerability (i.e., high infant mortality, high malnutrition, low income and high inequality) [ 20 ]. The economy of Machala is based on agriculture (bananas, cacao, coffee), aquaculture (shrimp), mining, and commerce associated with a major port and proximity to the Peruvian border. The city was settled on lowland mangrove forests and has an estuarine inlet along the Gulf of Guayaquil [ 23 ]. Machala grew through a rapid unstructured process of mangrove deforestation for shrimp farms and urban settlements, resulting in modification of the local hydrology of the mangroves and flood-prone slums bordering the mangrove fragments at the urban periphery [ 24 ].

The tropical climate is marked by a hot rainy season from January to April (average maximum temp = 31.7° in April) during which 79% of total annual rainfall occurs. Heavy rainfall is associated with El Niño events [ 15 , 25 , 26 ], such as the exceptionally strong 1997–1998 event, when over 1800 mm of rainfall were recorded. El Niño events occur cyclically (every 2 to 7 years) when tropical central and eastern Pacific Ocean surface temperatures increase, resulting in local climate anomalies [ 27 ].

Three neighborhoods in the urban periphery were selected as study sites. These were identified as high flood risk zones through discussions with the municipal government and local SNGR officials ( Fig 1 ). The sites were located 1.5–3 km apart. Site 1 was Sauces 2, a neighborhood adjacent to an abandoned shrimp farm (pop. 1266). Site 2 was Urseza 2 Sector 3, a neighborhood adjacent to a local river system (pop. 498). Site 3 included Rayito de Luz and Riveras del Macho, two neighborhoods adjacent to a large drainage canal (combined pop. 2,258). Neighborhood characteristics from the most recent national census are presented in Table 1 . Generally, these communities lacked adequate access to urban infrastructure, such as paved streets, garbage collection, and municipal water/sewer connection.

The three participating study sites included areas of the cities noted by authorities as high flood risk areas. This map was created using freely available country boundary data from GADM.org and rendered in QGIS.

Demographic characteristics of study neighborhoods in Machala, Ecuador, from the most recent national census, conducted in 2010.

*Riveras de Macho and Rayito de Luz are treated as one study site, as they are geographically contiguous

Research framework

In this study, we utilize a research framework that is situated in the context of disaster risk reduction by encompassing the measures of hazard exposure, vulnerability, sensitivity, impact, and adaptive capacity [ 25 ] ( Fig 2 ). Risk is described as a measure of the probability and severity of a given hazard and the consequences of those hazards on the normal functioning of the community [ 28 – 30 ]. Risk analysis is often highly quantitative, overlooking contextual social, cultural and historic dimensions [ 28 , 30 – 32 ]. In response, researchers have called for systems thinking (e.g. Haimes 2009 [ 28 ]) to overcome narrow risk definitions and the inclusion of “qualitative” normative risk characterization. The framework presented here is designed to bring localized, qualitative information into broader-scale risk or hazard analysis (e.g. the Global Natural Disaster Hotspot framework by Dilley et al., 2005 [ 33 ]). We draw on previously proposed and more generalized frameworks (i.e., [ 34 – 38 ]), and adapt these frameworks to include indicators more usable in a context of flooding in LAC, as done in prior studies of urban heat [ 39 ].

The primary metrics in this study were flood hazards and vulnerabilities ( Fig 2 ). Primary hazards were defined as direct impacts from flooding events (e.g., high velocity flows destroying houses or eroding roads or bridges). Secondary hazards are those stemming from primary hazards, such as contaminated drinking water, power outages, or interruptions to communication or transportation systems. Flood vulnerability metrics include exposure, sensitivity and adaptive capacity [ 34 , 38 ]. Exposure to flooding refers to the presence of communities in places that could be adversely affected by flood hazards. Sensitivity, also called “susceptibility,” refers to the physical predisposition of exposed individuals or communities to be negatively affected by a flood event due to lack of resistance or predisposition to suffer harm as a consequence of a flood event [ 40 – 42 ]. Impacts (e.g., health, economic, social) result from an individual’s exposure combined with their unique sensitivities. Adaptive capacity refers to the ability of the system to respond or adjust to a flooding event, to moderate potential damage, to take advantage of opportunities, and to cope with the transformations that occur as a result of the flooding [ 40 ]. As a sub-category of adaptive capacity, this framework examines livelihood capitals [ 43 , 44 ]–the vital resource bases of communities and households (e.g., human, natural, financial, physical and social capitals).

Focus group methodology

Semi-structured focus group discussions were held in each community. Neighborhood residents were recruited to participate in focus group discussion in consultation with the presidents of each neighborhood council, and when applicable (in two communities), the presidents of the councils of women. When possible, groups were segmented by gender to elicit a more open dialogue, based on our prior experience. Each focus group met twice. Both meetings were led by a moderator and documented by a group note taker who summarized the discussion on poster paper in real-time. The first focus group included questions regarding people’s previous experiences with flooding, specifically the causes and impacts of flooding, frequency of flooding, areas flooded, and the relative intensity of flood exposure. With facilitated guidance from the moderator, participants generated a timeline of severe flood events in each community; they self-defined these severe events as floods that exceeded their perception of normal (shallower) annual flooding. They noted the depth of normal flooding and specific severe flood events, and they listed the causes and impacts of flooding. A mapping exercise was conducted to identify high-risk areas and flood extent in each area during normal years and during moderate and extreme flood events. Focus group participants divided into groups of 3–5 people and were provided printed aerial imagery maps of their neighborhood. To orient themselves, participants first marked the location of important neighborhood landmarks (e.g. their homes, schools, soccer fields). Participants then outlined the areas impacted by the flooding events identified in the timeline, and they identified other areas of special concern with respect to flooding (e.g., areas with inadequate infrastructure). Map elements were verified by researchers and community leaders who walked through the community and georeferenced key landmarks and locations using handheld GPS units.

Within three weeks the same groups reconvened for a second focus group to discuss flood-related actions taken in their community, specifically preparation actions taken before flood events, response actions during flooding, and recovery actions post-flooding. People were also asked to identify key institutional partners associated with the actions, community assets, and resource limitations.

All focus group discussions were held in the late afternoon or evening in a community meeting area and lasted between 60 and 90 minutes. Representatives from the SNGR were present at every meeting to answer questions once the focus group was over. One to two researchers facilitated the focus groups and were accompanied by local research assistants who were trained as observers and note takers. All discussions were tape-recorded and transcribed with permission from participants. Local research assistants transcribed the recordings of focus groups.

We analyzed transcripts using codebook and qualitative analysis software, Dedoose (Version 6.1.11), a program that is commonly used to organize transcripts and documents ( https://www.dedoose.com/ ) and to provide initial analysis of data. After creating a codebook, this software allowed us to tag pieces of text with the different codes. We then analyzed the text to identify when and where these codes appeared in the focus groups. Researchers later verified codes by assessing the context of the broader focus group conversation. The codebook was developed using the research framework described in Fig 2 ; codes are presented in S1 Table . Participant-generated histories of flood events (years and flood depth) were converted into bar charts for each site. Participant-generated maps of flood extent, areas of special concern, and community landmarks were digitized using Q-GIS and ArcGIS. Our findings were presented to communities and to the municipal government in January 2015 for feedback and validation.

Government data sources

Focus group data were triangulated with available government data. Daily rainfall was provided by the National Institute for Meteorology and Hydrology (INAMHI) for the Granja Santa Ines weather station in Machala (1986–2015, 3°17’26” S, 79°54’5” W, 10 m above sea level). This was the closest station to the study sites, located approximately 5 km from the city center. We calculated total annual rainfall and number of days with heavy rainfall, defined as days with greater than 50 mm of rainfall—the 99 th percentile of daily rainfall during rainy months (baseline January to June, 1986–2015). All flood events in Machala were extracted from the publically available Desinventar database from 1990 to 2013 (n = 45 reports) [ 45 ] ( https://online.desinventar.org/desinventar/#ECU-DISASTER ). We reviewed the description of each event and coded the flood causes and impacts. Infectious disease outbreaks in Machala were identified in the Desinventar database [ 45 ] and from a prior analysis of dengue fever case data from the Ministry of Health [ 46 ]. We assessed available indicators of population sensitivity from the most recent national census (2010) [ 47 ], and compared focus group results to previously mapped neighborhood-level census data [ 48 ]. We also examined flood hazard maps for the city produced by the SNGR in 2015; hazard calculations by the SNGR were derived from elevation, slope and rainfall [ 49 ].

Eleven semi-structured focus group discussions, with a total of 65 people, were held from September 2014 to November 2014 (three groups in Site 1, four groups each in Sites 2 and 3). Groups in Sites 2 and 3 were segmented by gender, while in Site 1, a single group met comprised of both men and women based on local recommendations (but featured mostly women). A third meeting was held in Sauces 2 to obtain additional information on the evaluation of a recent flood prevention activity—the organization of a local effort to clear debris from drainage canals. Six to 25 people participated in each focus group, with ages ranging from late teens to late 70s.

Exposure and flood hazards

Community members reported normal annual floods following heavy seasonal rains (floodwater depth range: 0.1 to 1 meter) ( Fig 3 ). They identified multiple severe floods over the last 30 years (range: two events at Site 1 to eight events at site 3). Floodwater depths in severe floods ranged from 0.5 to 3 meters, and floods lasted hours to months. The most severe floods occurred in 1982 and 1997/98, which coincided with strong El Niño events. When compared to rainfall data, at least one of the three sites reported severe flooding in all years with high rainfall; bolded years in Table 2 show years that exceeded the upper quartile (740 mm/year) of total annual rainfall from 1990–2013). However, they also noted serious flooding in low rainfall years such as Site 3 in 1994 and Site 1 in 2000, and Site 2 in 2005. Community-reported flooding coincided with flood events reported at the city level in the Desinventar database, except at Site 1 in 2000. Not surprisingly, heavy rainfall was identified a cause of flooding in 14 of 17 years with flooding in government reports.

A timeline of severe flood events and floodwater depth over the last 30 years was created by community members. The depth of floodwater in normal years is also noted.

Community members identified years with severe flood events (see sites with severe flooding). Rainfall data (annual rainfall and days > 50 mm) are from the Granja Santa Ines weather station in Machala. Years with high total rainfall are bolded; they exceeded the upper quartile (740 mm/year) of total annual rainfall from 1990–2013. Disease outbreaks (dengue, malaria, cholera, typhoid) at the city level were identified from the Desinventar database [ 45 ] and previously analyzed Ministry of Health case data [ 46 ]. Flood events (n = number of events), causes, and impacts at the city level were extracted from the Desinventar database.

*Annual days of rainfall that exceeded 50 mm/day, the 99th percentile of daily rainfall during the rainy season (January-June, 1986–2015 baseline).

1 Causes: rainfall = R, high tides = HT, El Niño = EN, canal or river overflow = OF, collapsed sewerage system = CSS, blocked drainage or garbage = BG

2 Impacts: T = transportation interrupted, C = crops damaged, H = homes/property damaged, S = schools damaged, E = people evacuated, P = loss of power, I = infrastructure damage, D = human deaths, HH = health hazard (stagnant water)

3 D = dengue fever, M = malaria, C = cholera, T = typhoid fever

People identified and mapped the sectors of their neighborhood that flooded annually and during severe floods ( Fig 4 ). This revealed spatial heterogeneity in perceived flood risk at the sub-neighborhood level. Each community mapped three to four areas of special concern such as places with strong currents during floods, canals that overflow, inadequate drainage systems, low lying areas with schools and homes, and other places that accumulate stagnant water ( Fig 4 ). Participants at Site 3 identified a berm that was built recently to isolate floodwaters from the adjacent low-lying residential area. The berm was built without a concrete cap and individuals were removing fill material from the berm illegally for their personal use, thereby weakening the berm’s protective capacity. Participants were concerned that the berm would collapse during the next flood event, as described in the following:

Maps generated by focus groups show the spatial extent of historic floods occurring within their communities. The ten areas of special concern included the following: (1) Places with strong currents during floods—these areas included police and fire stations and community health clinic; (2) inadequately-sized drainage pipes; (3) El Macho Canal—a tidal-influenced canal that acts as primary drainage canal for sewer and storm water systems and is a source of floodwater; (4) former shrimp farm—many parcels are unfilled and are full of water year-round; streets in this area have large persistent mud puddles that limit transit and pedestrians; (5) El Tigre Canal—a stagnant ditch that regularly overflows, flooding roads and private homes; (6) a primary road that remains dry during seasonal floods—this intersection also is used as a meeting area for community events; (7) inadequately-sized culverts; (8) a low-lying area that was formerly a brick quarry (known as “the hole”)—this site has an elementary school and private residences and endures annual flooding; (9) a berm constructed of uncapped material fill—material is being removed illegally and used as fill for private properties; and (10) an abandoned shrimp farm—stagnant pools collect water and periodically flood. This map was created using freely available street and neighborhood data from https://www.ecuadorencifras.gob.ec/geoportal/ , and all other data generated in focus group conversations, rendered in ArcGIS, and image files created using Adobe software.

“The government built this berm, but the people are not aware [of the purpose]: they remove the berm material to fill their own [properties]. They don’t know. The problem is that it endangers us all. Luckily it hasn’t rained hard recently” (Man from Site 3) .

Community maps were compared to flood hazard maps generated by the SNGR. Most of the city area in the SNGR maps was classified at the highest hazard level including all of the study areas. High hazard was defined as: (1) low lying zones with slopes between 0–5% that remain flooded for more than 6 months during the year, and (2) areas where the accumulation of water is caused by rainfall as well as rising river levels during the rainy season. The spatial resolution of the SNGR maps did not allow for an analysis of differential flood hazard within the study sites.

Community members reported that proximity to blocked drainage areas was the most important geographic determinant of flood exposure. Official reports also identified blocked drainages and garbage as causes of flooding in 2008 and 2013, respectively [ 45 ] ( Table 2 ). People explained that the blockages were caused by trash accumulation in areas that lacked regular municipal garbage collection and infrastructure (e.g. trash platforms). This resulted in a severe and perpetual litter problem when combined with the presence of stray dogs and illegal dumping at the city periphery. National census data confirmed that access to garbage collection was lowest in the urban periphery [ 48 ]; 63–83% of homes in the study had no garbage collection ( Table 1 ). A participant described the combined effect of inadequate infrastructure, garbage, and flooding.

“On the street where I live, there’s a canal… That canal is now pure garbage, which clogs [the culvert] and stagnates it. This year we are afraid that it’s going to rupture… the pipe is very small” (Woman from Site 1) .

Homes located near periurban estuarine canals were affected by tidal activity and overflowing canals, as were confirmed by official reports ( Table 2 ). People explained that high tides impeded rainwater runoff during heavy rainfall events, resulting in flooding. Machala’s largest estuarine canal, “El Macho”, is one of the two main canals that transport the city’s runoff and untreated sewage into the ocean. Participants from Site 1 reported that as recently as 30 years ago, El Macho canal was a naturally flowing mangrove inlet, with clear water used for swimming, bathing and fishing. Increasing urban settlements in recent decades have augmented sedimentation and flow rates in the canal and led to the proliferation of formal and informal sewer drains flowing into the canal.

Community members identified low elevation as another key geographic factor that increased flood exposure. They stated that unfilled parcels of land collected standing water that could linger for months, or in some cases, year-round. Participants from Site 2 ( Fig 4 ) identified year-round flood problems in properties that had not purchased material fill and lacked pumps and sewer systems to remove the pools of water.

“There is no [storm] sewer system…here it rains and it stagnates. It doesn’t have anywhere to go” (Woman from Site 2) .

From 2002 onward, official reports frequently identified collapsed sewer systems as a cause of flooding. Community members explained that financial resources determined the ability of a family to raise the property’s elevation, as described in the following,

“Sometimes the [economic] situation of some families is so low, and there is no fill [because] there is no money” (Woman from Site 2) .

Management of urban growth and development in low elevation flood plains was identified as a fundamental step to prevent flood exposure.

Sensitivity to flooding

Participants perceived that certain demographic groups were more sensitive to flood-related health problems and economic/material losses. Children, the elderly, and the physically handicapped were identified as the most sensitive populations due to lower immunity to infections; decreased mobility; and reliance on others for medication, food, and water. Participants reported that children were exposed to contaminated floodwaters while playing outdoors. Low-income households were also identified as highly sensitive, since they were less likely to have a second floor of the home where they could seek refuge or store valuables during floods. They were also less likely to have a rooftop cistern with ample freshwater, and were less able to purchase fill to raise the level of their parcel. Residents who relied on public transportation systems were affected because they had to wade through floodwaters and mud during their daily commutes. Car owners also faced financial burdens due to damages to their automobiles. Other sensitive groups included people with animals (cats, dogs, poultry) and recent migrants who did not have nearby relatives to help them.

Flood sensitivity was not specifically recorded by any governmental dataset; however, we compared focus group results to the spatial distribution of a subset of sensitive demographic groups that were previously mapped using data from the most recent national census data (2010) [ 47 , 48 ]. Flood-sensitive groups (e.g. younger households, households in poor condition without access to piped water) were concentrated in the northern periphery of the city—the location of two of the three study sites. Certain sensitivity metrics identified by communities were not available in the census, such as household income, reliance on public transportation, and social isolation (individuals without nearby family/friends) ( S2 Table ).

Impacts from flooding

People perceived that flooding presented a high social and economic burden on their community. Individuals reported that the primary impacts associated with flooding were damages to individual property (e.g., homes, cars, animals) and damages to people’s health (e.g., drowning, injury, and disease). Secondary impacts included the damages to roadways and destabilization of infrastructure and buildings. People’s livelihoods and daily activities were disrupted, and they struggled to pay for repairs to their homes. In official reports, damages to homes were the most frequently reported impact (13 of 17 years with flooding), followed by interruptions in transportation (8 years), evacuations (7 years), and crop damage (6 years) ( Table 2 ). Government reports also noted power outages and damages to schools, but did not mention damages to personal vehicles or animals.

People perceived that exposure to flood water and mud and the resulting health impacts were a chronic problems. Specific health impacts included infectious diseases diseases, injury/drowning, and venomous snakebites. Mosquito-borne diseases, such as dengue fever, were mentioned as a risk factor for residents who lived in proximity to pools of standing water. Cholera and typhoid fever were also associated with flooding. Participants reported skin infections due to contact with flood and wastewater. Government data sources confirmed outbreaks of infectious diseases (cholera, typhoid fever, malaria, dengue fever) following heavy rainfall associated with El Niño events in 1992–1993, 1997–1998, and 2010 ( Table 2 ). Of note, disease outbreaks were also documented in non-flood years, for example when the diseases were first emerging as new epidemics (e.g., dengue in 1990 and cholera in 1991). Thirteen deaths due to flooding were reported in 2002; however, causes were not specified. Additional information on drowning, injury, snakebites or skin infections was not available.

Adaptive capacity across flood stages

Adaptive capacity actions identified by community members were compared to existing initiatives, as reported in focus groups ( Table 3 ). In the response phase, the measures proposed by the community align with measures that were being implemented. Participants identified gaps in the recovery phase and prevention phase, as detailed in the following.

A comparison of actions identified by community members versus government interventions, as reported in focus groups.

During the flood preparation and prevention stage, participants identified limited economic resources (financial capital) and lack of community leadership (social capital) as underlying barriers to flood prevention. They also perceived that the community lacked the political voice and coordination (political capital) needed to obtain support for large-scale infrastructure improvements to reduce flood exposure, as expressed in the following:

There is no communication between authorities… People do not put pressure on them to do something good for the neighborhood. What people always want to do is to overthrow the government, not to put the pressure on them to do something good for the neighborhood” (Man from Site 3) .

A leader commented on community fatigue and the difficulty in organizing people after a prolonged campaign to legally incorporate the neighborhood ended successfully, and residents became complacent,

“The moment that the mayor legalized the properties… nobody came to meetings. That is what happens… when it comes to a workshop, [now] no one has time. They will tell you, ‘I am busy.’… Now it takes a lot of effort to organize us” (Woman from Site 2) .

Despite these challenges, participants from Site 3 had recently formed a neighborhood brigade that had been trained in flood response simulations by the SNGR, thereby increasing human capital. Most individuals, however, were unaware of this initiative, suggesting a lack of community engagement and the need for outreach and education. Another effective experience shared by community members was the recent tsunami EWS outreach campaign in Ecuador, where they received training and identified potential escape routes and meeting locations for their families in the case of a tsunami.

Participants discussed the effectiveness of the formal and informal flood warnings preceding the flood response stage. They indicated that formal communication outlets (e.g. television and radio) did provide general flood warnings based on forecasted rainfall as part of an existing official EWSF operated by the national government. However, they perceived that the forecasts were either too general or not timely enough. A participant described an ad-hoc warning system, based on upstream river observations,

“Sometimes, by chance, one travels from Guabo [a nearby town upstream from Machala] to see that the Jubones River is full. Then they will let us know that there is risk [of flooding] … It’s the only way [to know in advance], because the authorities do not warn us, and there are no alarms … nothing. The last time that the Jubones peaked and overflowed, [down] here was a scorching sun… It happens because the Jubones brings water from the mountains… and when the Jubones fills, and there is high tide here, we flood” (Woman from Site 3) .

Other instances of informal flood warnings occurred through community communication channels on social media, such as Facebook, Twitter or WhatsApp. Participants proposed greater use of these social media to disseminate flood warning and response information. However, they cautioned that informal communication chains might not reach members of the community equally during times of crisis. Accordingly, all groups identified sirens or loudspeakers as the most effective way to alert people for flood evacuations.

With respect to the flood response stage, people perceived community and government actions to be relatively effective. Community members were mobilized during times of crisis to help their neighbors in need. However, enforcing mandated evacuations was difficult, according to focus groups, due to the real concern of looting and the lack of law enforcement. When asked how one decided to stay or go during a flood evacuation, one participant responded,

“You have to stay because if not, you will be left without anything… the thieves do not care. They jump into the water and that’s it” (Woman from Site 3) .

During the flood recovery stage, community members struggled to take actions to repair the damages caused by flooding due to lack of financial capital. People said that they were responsible for bearing the cost of rebuilding their home, and they were unaware of flood insurance programs for private homeowners. Participants indicated that approximately one month of wages were lost during reconstruction efforts following severe flood events—a significant loss for a low-income family. Although they identified some programs that assist in rebuilding homes, these programs were highly competitive, middle class families were not eligible, and the construction was perceived as flimsy. The recovery stage was also limited by a lack of effective political engagement (political capital). They expressed distrust of local authorities whom they perceived to be more interested in political maneuvering than improving the welfare of the people.

“The authorities only come when there are votes and when it’s election time… They offer [funding] when there are elections… Or when there is a collapsed house or a drowning, but [even then] only that family is helped” (Man from Site 1) .

Despite these challenges, they did describe moments of unity (social capital), for example when people at Site 1 came together to repair a neighbor’s home that had collapsed into the Macho Canal.

Coastal flooding incurs a high social and economic burden worldwide, and the impacts are projected to increase in urban areas [ 1 , 2 ]. Community perceptions can inform the implementation of tailored flood risk reduction strategies, such as those of the United Nations Sendai Framework for Disaster Risk Reduction to prepare, respond, and “build back better” [ 10 ]. Machala is an ideal case study for coastal flooding given the high hazard level, long history of flooding, community concern, available government data, and interest in improving EWSFs. The findings and the participatory approach applied here can inform practitioners and community members seeking to implement interventions to reduce flood exposure, particularly in other resource-limited urban areas around the world.

This study revealed persistent social-ecological vulnerabilities that increase the risk of flooding in the urban periphery, expanding on prior studies in the LAC region [ 9 , 16 ]. Low levels of adaptive capacity in LAC [ 51 ], particularly in urban areas [ 52 ] have been attributed to poor housing conditions, a lack of infrastructure, lack of decision-maker access to local data, and national policies that focus on mitigation rather than adaptation [ 11 , 19 , 53 ]. In this study, adaptive capacity was limited, in part, by a lack of social and political capital, and a lack of engagement by the government with community stakeholders. Studies show that social networks, familiar ties, and traditions may be less supportive and stable in urban areas as compared to rural communities [ 54 , 55 ]. Prior studies from Machala also found that marginalized communities in the urban periphery lacked legal standing and political access, which limited their ability to engage effectively with government institutions [ 56 ].

This study provides local insights into the escalating social injustices associated with development in low-lying coastal areas. With high development costs to elevate homes out of the floodplain, matched with land use policies that drive low-income communities into periurban low-lying areas, we hypothesize that the communities in this study bear a proportionately greater burden of flooding than wealthier communities. Maps of census indicators clearly show the geographic stratification of vulnerability across the urban landscape, as confirmed in prior studies in Machala [ 48 ].

To understand the gaps in adaptive capacity and flood risk reduction, the political context in Ecuador must also be considered. Within the last decade, the SNGR and the Secretary of Water (SENAGUA) were created during a period of relative prosperity and economic growth. However, political distance between national and local governments during this period hampered efforts to increase adaptive capacities at city and community levels. One mega project, the Pasaje-Machala flood control and water diversion project, is located in the El Oro province near the site of this study [ 57 ]. However, mega-hydraulic infrastructures were designed primarily to protect economic interests (banana crops and shrimp farming), with limited protection of citizens in cities. Between the low-lying city conditions, the inadequately designed drainage canals, limited governance during the extreme events, and the lack of coordination between the key actors, inter-institutional relations remain weak and have arguably weakened in recent years. In the context of these precarious institutional structures, this study reveals that (1) social cohesion and empowerment of community leaders is essential to increase local capacities to reduce flood risk, and (2) citizens have precise knowledge and contextual information that can be shared with government allies, such as city planners and risk managers, when an open and trusting relationship is established.

By triangulating focus group information with existing governmental data, we were able to identify instances when the datasets were complimentary, concordant, or discordant. The discrepancies in community flood reports—as compared to rainfall levels and official flood reports—may be attributed to localized flooding in specific low-lying locations or faulty memory. While memory driven data is predisposed to inaccuracies and over-simplicity, we found that the community-generated timelines did provide a useful conversation tool as well as a gauge for the relative impacts of episodic flooding. The official causes of flooding identified by the SNGR at the city-level were similar to the causes identified in communities; however, the focus groups additionally identified ten areas of special concern and provided local context. These areas of concern are clear policy targets for local decision makers—allowing for focused and impactful investment of limited risk management resources.

The community timeline of flooding spanned more years than the available meteorological or flood event data, and community flood maps were more detailed than SNGR flood hazard maps. Focus group data can supplement existing historic data on flooding, depending on the age distribution of focus group participants and their length of residency in the community. In areas with little historical data or lack of coverage by hydro-meteorological stations, the reconstruction of historic flood timeline and community mapping can serve as an information source for historic flood events, as well as to determine locations of high vulnerability. In instances where data are non-existent, focus groups may provide insights on periodic flooding cycles and localized factors that contribute to flooding. Indeed, this and other studies suggest that participatory methods can generate accurate quantitative data while capturing local priorities [ 58 ].

Regarding health outcomes, communities accurately perceived the role of flooding in triggering outbreaks of infectious diseases. They identified children as the most vulnerable group, and this is supported by recent epidemiological studies in Machala, which found that children under 10 bear the greatest burden of dengue illness [ 59 ]. Prior local field and modeling studies also support their perception that mosquito vectors and dengue transmission increase following heavy rainfall events, in particular those associated with El Niño [ 46 , 60 ]. However, their perception that dengue fever risk increased due to pools of stagnant floodwater is a misconception, as the mosquito vector inhabits receptacles with filled with rainwater or tap water around the home (e.g., buckets, used tires, rubbish in the patio); this misconception has been documented previously [ 56 ].

With respect to sensitive population groups, it is notable that recent migrants were identified as a high-risk group. Since conducting this study, 1.2 million refugees and migrants from Venezuela have passed through Ecuador and over 200,000 have settled in the country [ 61 ]. Many migrants have settled in Machala due to proximity to the southern border crossing at Peru. Many migrants lack social and financial capital and are unfamiliar with local climate hazards. In Machala, migrants are settling in precarious periurban neighborhoods, increasing the population at risk of flooding. As census data are updated once per decade, focus groups can provide more recent insights into local population dynamics and sensitivities that are not captured by the census, such as social isolation of recent migration populations.

Opportunities and policy implications

Understanding local community perceptions is critical for policy makers interested in implementing flood risk reduction interventions. People’s perceptions of their risk or vulnerability influence their interest and ability to adopt risk reduction actions implemented by the government, as shown in prior studies of dengue fever [ 56 ]. Knowledge of the communities’ own risk perception is helpful for first responders to adequately communicate risk during times of disaster (e.g. emergency evacuations) or for officials who conduct outreach efforts to promote adaptive behaviors. Also, individuals who are aware of the impacts of flooding on their livelihood, assets, and health are more willing to take preventative measures rather than wait to take costly reactive measures [ 62 ]. Self-reported vulnerability may be indicative of a community’s motivation to self-initiate preparation actions for flooding events and adaptation to climate change [ 63 ]. The process of self-reporting further engages local communities in the process of education and raises awareness [ 39 ]. Policy makers should be concerned if communities have a low perception of the vulnerability relative to other communities in similarly vulnerable situations, as this turn amplifies overall social risk.

Local insights gathered in focus groups can assist in producing tailored solutions for a community, which in turn increases the feasibility and effectiveness of implementation actions. Focus groups can reveal less-obvious local issues that exacerbate flooding or render “expert-based” solutions ineffective. Without a corresponding effort to engage and gather data through integrated top-down and bottom- up approaches, interventions are unlikely to be sustainable [ 39 , 64 ].

This study has implications for the development of people-centered early warning systems. In 2006, the document “Developing Early Warning Systems: A Checklist” was developed to implement the early warning components of the World Conference on Disaster Reduction Hyogo Framework for Action 2005–2015: Building the Resilience of Nations and Communities to Disasters [ 65 ]. This document presents four key elements essential to people-centered early warning systems: 1) risk knowledge built upon systematic collection of data to address patterns and trends for a variety of hazards and vulnerability; 2) monitoring and warning services built upon accurate and timely scientific information; 3) clear and understandable dissemination and communication of risk information and early warnings to all members of the public; and 4) responses based on updated and tested plans that utilize local capacities and knowledge and are familiar to the public. The following findings and recommendations are elicited from this case study, and they link to the UN-ISDR Early Warning System checklist items.

• Improve monitoring and accuracy of warnings. Flood warnings in Ecuador currently incorporate only rainfall data. An integrated monitoring network is needed, linking ocean and atmospheric data and forecasts. Recent studies suggest the potential to forecast El Niño events up to two years in advance [ 66 ], and these models are being used with an ensemble of climate forecasts to predict climate conditions and dengue fever epidemics in southern coastal Ecuador [ 67 ]. The incorporation of El Niño events and tidal data in flood hazard modeling for coastal areas is a critical component of management efforts along the Pacific coast of Latin America [ 9 ].
• Develop clear flood warnings across a variety of communication channels. Our findings suggest that the use of both formal and informal communication networks may improve the delivery of EWSFs information. Flood forecasts issued via a system of sirens or loudspeakers, AM radio (used by taxi drivers) and individualized text alerts could complement existing television and radios channels. While sirens provide a warning to all individuals within hearing range, their effectiveness depends on whether community members know how to respond. Individualized text alerts could include detailed instructions on how to respond based on personalized information.
• Leverage and strengthen existing community flood response capacities. An EWSF can leverage community resources by being thoroughly integrated in community-based initiatives and organizations. Community members indicated that they were capable of participating in work brigades, childcare, or training each other in preparation simulations. Education interventions should aim to increase local capacities, should be adequately staffed and resourced, and can explore the possibility of a train the trainer model, thereby increasing the capacity of local leaders.
• Include principles of flood resiliency and risk prevention in comprehensive planning and land use regulations. Land-use zoning code and related land use policies should be enacted and enforced to dissuade residential development in hazardous areas like canal buffers and to use high-quality and appropriately-engineered flood management structures. Educational and collaborative planning opportunities exist at both local and municipal levels.

Limitations

Since this qualitative case study focused on high-risk periurban areas, these findings cannot be generalized to communities that face a lower overall risk of flooding. Also, participants who opted to participate in this study may have been more motivated to do so based on their personal experience with flooding. Historical intensity and frequency of flooding are subject to the collective community memories. This case study was not used to prioritize a particular group by their vulnerability, but rather to present a nuanced, self-reported characterization of flooding in high-risk communities. In this way, the methodology provides key insights necessary to inform potential flood risk reduction actions.

This study highlights the challenges and opportunities to reduce flood hazards in highly vulnerable coastal cities. The areas of special concern identified and mapped by community members are examples of clear local policy targets for flood risk reduction. The participatory approach presented here (1) provides important context to shape local policy and interventions in Ecuador, complimenting data gathered through standard flood reporting, (2) provides a voice for marginalized communities and a mechanism to raise local awareness, and (3) provides a research framework that can be adapted to other resource limited coastal communities at risk of flooding.

Supporting information

Codes were developed based on vulnerability framework ( Fig 2 ) and were used for qualitative analyses of transcripts.

This document compares the types of sensitive populations identified by focus groups to the data available from the national census.

Acknowledgments

We thank P. Romero-Lankao, M. Lemos, and investigators from National Center for Atmospheric Research (NCAR) for guidance during the project development, as well as collaborators from the SNGR and the municipal government of Machala.

Funding Statement

This study was supported by the Inter-American Institute for Global Change Research (IAI) Training Institute Seed Grants (TISG C2012/C2013) to AMSI and by SUNY Upstate Medical University. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Data Availability

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Coastal Flooding Hazard Use Case

The island communities off the coast of Maryland often face coastal flooding and other hazards. The islands are very remote; in some cases, there may be only one way to travel to and from an island. Those who may need to evacuate during a flood event are at higher risk. The Maryland Department of Emergency Management (MDEM) used Risk Index data to identify and prioritize communities with limited emergency access. MDEM will focus on these communities for evacuation and other emergency response planning efforts.  

• The MDEM Preparedness Branch wanted to identify communities with limited transportation access and high coastal flood risk. Having these data would help MDEM assist those most at risk and remote. 
• The isolated nature of these communities creates unique challenges. These challenges have led to gaps in assistance that MDEM is looking to address. These communities require specific planning scenarios to prepare for coastal flooding; a flood event could cut off the bridges that connect them to networks of aid. 
• It is hard for the state to collect data on local hazard events because not all communities have the emergency management staff or resources to maintain databases. Data are key for MDEM to better understand coastal flood risks. This way, MDEM can help communities to reduce coastal flood risks. 
• The MDEM Preparedness Branch worked with the Risk and Recovery Branch to find data sources on the island communities’ risk. They used the Expected Annual Loss and annualized frequency for Coastal Flooding from the National Risk Index. This data analysis showed the branches which communities faced the most coastal flood risk.   
• The branches merged the Risk Index Coastal Flooding data with transit, social vulnerability and resilience data. This helped them find the most remote and at-risk island areas. The data showed four communities with limited bridge, ferry and airfield access: St. George Island, Cobb Island, and Smith Island, and Stevensville.  
• The Preparedness Branch now has an initial list of islands that are the most at risk to coastal flooding and have the fewest ways to evacuate. The data will serve as a basis for emergency response planning and future outreach. This way, rescuers have the information and training they need to help these communities during a coastal flooding event.  

Key Takeaways

• Data help with making decisions to allot finite resources. The MDEM Preparedness Branch combined Risk Index Coastal Flooding data with other Maryland infrastructure data to find communities with the most vulnerability. Having this information is a valuable way to prioritize efforts with finite time and resources; it helps MDEM focus planning and outreach on those that need it most. 
• The FEMA National Risk Index fills data gaps. The Risk Index provides data at the local level for communities with few resources and mapping capacity. For places where there were no local risk data, the state could use the Risk Index to give a baseline of coastal flood risk.  
• FEMA National Risk Index data can be tailored to meet community needs. The data from the Risk Index are easily paired with those from other data sources. The MDEM Risk and Recovery Branch was able to choose what data worked best to reach its goal. Now, the Preparedness Branch has a unique analysis. It can use this to develop evacuation and emergency response plans to increase community resilience. 

U.S. Climate Resilience Toolkit

• Steps to Resilience
• Case Studies
• Minimizing the Impacts of Coastal Flooding Helps City Prepare for Sea Level Rise

Stressors and impacts

Residents of Charleston, South Carolina, are all too familiar with the periodic flooding that occurs during extreme high tides. During these events, salt water backs up through storm drains, resulting in hazardous road conditions. Traffic patterns are disrupted and motorists are forced to take alternate routes. Rain and onshore winds can push the tides even further inland. When extreme high tides occur, roads and businesses are sometimes forced to close and damage to buildings from repeated saltwater intrusion is a near certainty.

Today's floods are tomorrow's high tides

Water level data measured in Charleston Harbor by NOAA Tides & Currents since the early 1900s show the observed increase in relative sea level. Click the graph for a larger view and source information.

Water levels measured in Charleston Harbor since the early 1920s indicate a steady increase in sea level. One result of this increase is that high tides continue to reach ever higher elevations. Eventually, today’s occasional coastal floods will become regular events.

One example of the frequency of floods comes from 2010: predictions based on the regular cycle of moon phases for that year suggested that Charleston would experience five flood-producing high tides (defined as seven feet or higher). These types of predictions do not take into account the increased propensity for flooding during rainfall or onshore winds. By the end of the year, the effect of weather conditions on top of regular high tides had produced water levels seven feet or higher 19 times.

Infrastructure updates

Charleston has begun or completed several projects to reduce the impacts of occasional coastal flooding. By installing backflow preventers in drainage systems, the city has alleviated flooding on Colonial Street and at the intersection of Council and Tradd Streets. The city also reduced the threat of inundation by upgrading the Courtenay Drive stormwater pump station, constructing a pump station at Concord Street, and raising Hagood Avenue.

In addition, some one-way roads in downtown Charleston were modified to allow two-way traffic. While these lane reversals were not implemented to address flooding problems, the changes provide additional routes people can use to bypass flooded streets.

Additional strategies Charleston may consider to mitigate the impacts of tidal flooding include:

• Develop outreach materials to inform a variety of audiences, including businesses, industry, residences (permanent and temporary), and visitors of strategies that might reduce their risk
• Consider new signage that will caution people against driving through water-covered streets
• Prioritize drainage improvement projects based on benefits and affordability
• Consider ordinances that go above and beyond FEMA requirements for addressing impacts and hazards caused by more frequent flood conditions
• To supplement FEMA flood zone information (for planning and permitting purposes), develop an advisory level of flood zoning to indicate areas where frequent tidal flooding is a problem
• In redevelopment or new development projects, “do it right” by considering increases in the frequency of future flooding during the project design phase
• Account for climate change impacts, such as accelerated sea level rise and more frequent heavy rainfall, in future stormwater system upgrades

Taking steps to minimize impacts from coastal flooding is one of the best ways to build resilience and prepare for sea level rise.

To learn how the City of Charleston worked with partners to design and produce a simple handout to use as a communication tool that puts future sea level rise in the context of existing tidal flooding problems, read the case study  Designing a Communications Handout About Sea Level Rise .

Adapted from NOAA Digital Coast, "Today's Flood is Tomorrow's High Tide."

Animum, own work. GFDL ( http://www.gnu.org/copyleft/fdl.html ) or CC-BY-SA-3.0-2.5-2.0-1.0 ( http://creativecommons.org/licenses/by-sa/3.0 )], via Wikimedia Commons

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Kerala flood case study

Kerala flood case study.

Kerala is a state on the southwestern Malabar Coast of India. The state has the 13th largest population in India. Kerala, which lies in the tropical region, is mainly subject to the humid tropical wet climate experienced by most of Earth’s rainforests.

A map to show the location of Kerala

Eastern Kerala consists of land infringed upon by the Western Ghats (western mountain range); the region includes high mountains, gorges, and deep-cut valleys. The wildest lands are covered with dense forests, while other areas lie under tea and coffee plantations or other forms of cultivation.

The Indian state of Kerala receives some of India’s highest rainfall during the monsoon season. However, in 2018 the state experienced its highest level of monsoon rainfall in decades. According to the India Meteorological Department (IMD), there was 2346.3 mm of precipitation, instead of the average 1649.55 mm.

Kerala received over two and a half times more rainfall than August’s average. Between August 1 and 19, the state received 758.6 mm of precipitation, compared to the average of 287.6 mm, or 164% more. This was 42% more than during the entire monsoon season.

The unprecedented rainfall was caused by a spell of low pressure over the region. As a result, there was a perfect confluence of the south-west monsoon wind system and the two low-pressure systems formed over the Bay of Bengal and Odisha. The low-pressure regions pull in the moist south-west monsoon winds, increasing their speed, as they then hit the Western Ghats, travel skywards, and form rain-bearing clouds.

Further downpours on already saturated land led to more surface run-off causing landslides and widespread flooding.

Kerala has 41 rivers flowing into the Arabian Sea, and 80 of its dams were opened after being overwhelmed. As a result, water treatment plants were submerged, and motors were damaged.

In some areas, floodwater was between 3-4.5m deep. Floods in the southern Indian state of Kerala have killed more than 410 people since June 2018 in what local officials said was the worst flooding in 100 years. Many of those who died had been crushed under debris caused by landslides. More than 1 million people were left homeless in the 3,200 emergency relief camps set up in the area.

Parts of Kerala’s commercial capital, Cochin, were underwater, snarling up roads and leaving railways across the state impassable. In addition, the state’s airport, which domestic and overseas tourists use, was closed, causing significant disruption.

Local plantations were inundated by water, endangering the local rubber, tea, coffee and spice industries.

Schools in all 14 districts of Kerala were closed, and some districts have banned tourists because of safety concerns.

Maintaining sanitation and preventing disease in relief camps housing more than 800,000 people was a significant challenge. Authorities also had to restore regular clean drinking water and electricity supplies to the state’s 33 million residents.

Officials have estimated more than 83,000km of roads will need to be repaired and that the total recovery cost will be between £2.2bn and $2.7bn.

Indians from different parts of the country used social media to help people stranded in the flood-hit southern state of Kerala. Hundreds took to social media platforms to coordinate search, rescue and food distribution efforts and reach out to people who needed help. Social media was also used to support fundraising for those affected by the flooding. Several Bollywood stars supported this.

Some Indians have opened up their homes for people from Kerala who were stranded in other cities because of the floods.

Thousands of troops were deployed to rescue those caught up in the flooding. Army, navy and air force personnel were deployed to help those stranded in remote and hilly areas. Dozens of helicopters dropped tonnes of food, medicine and water over areas cut off by damaged roads and bridges. Helicopters were also involved in airlifting people marooned by the flooding to safety.

More than 300 boats were involved in rescue attempts. The state government said each boat would get 3,000 rupees (£34) for each day of their work and that authorities would pay for any damage to the vessels.

As the monsoon rains began to ease, efforts increased to get relief supplies to isolated areas along with clean up operations where water levels were falling.

Millions of dollars in donations have poured into Kerala from the rest of India and abroad in recent days. Other state governments have promised more than $50m, while ministers and company chiefs have publicly vowed to give a month’s salary.

Even supreme court judges have donated $360 each, while the British-based Sikh group Khalsa Aid International has set up its own relief camp in Kochi, Kerala’s main city, to provide meals for 3,000 people a day.

International Response

In the wake of the disaster, the UAE, Qatar and the Maldives came forward with offers of financial aid amounting to nearly £82m. The United Arab Emirates promised $100m (£77m) of this aid. This is because of the close relationship between Kerala and the UAE. There are a large number of migrants from Kerala working in the UAE. The amount was more than the $97m promised by India’s central government. However, as it has done since 2004, India declined to accept aid donations. The main reason for this is to protect its image as a newly industrialised country; it does not need to rely on other countries for financial help.

Google provided a donation platform to allow donors to make donations securely. Google partners with the Center for Disaster Philanthropy (CDP), an intermediary organisation that specialises in distributing your donations to local nonprofits that work in the affected region to ensure funds reach those who need them the most.

Google Kerala Donate

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Future Strategies

State Case Studies

Federal Review

Management Goals

• Holistic Approach to Coastal FRM

CONTENTS ≡

CONTENTS ✕

Coastal Management Program

Shoreline regulations, floodplain management, wetland management, building codes, community planning, stormwater and runoff management, erosion management, climate adaptation initiatives, state management capacity, alternatives to structural mitigation, long-term planning, balance of mitigation and disaster recovery, holistic management approach, new york coastal flood risk management case study.

Policies and Programs

The New York Coastal Management Program , established in 1982, is housed within the New York Department of State’s Office of Planning, Development, and Community Infrastructure . Much of the program’s legislative authority is drawn from the state Waterfront Revitalization of Coastal Areas and Inland Waterways law as well as the Coastal Erosion Hazard Areas law . The program pursues goals related to coastal resources protection and development, local waterfront revitalization, coordination of major activities affecting coastal resources, public awareness of coastal issues, and federal consistency with state coastal management policies. Within New York, the Department of State administers the program and coordinates its implementation in cooperation with the state Department of Environmental Conservation as well as other state agencies.

Coastal program boundaries extend along the coast of Long Island, New York City, Hudson River estuary, both Great Lakes that border New York, and the Niagara River. Specific landward boundaries of the coastal program vary by region and locality due to initial delineation proposals from local government agencies. All barrier and coastal islands on Long Island are included within program boundaries along with areas 1,000 feet landward of the shoreline, extending further in some cases. The New York City program boundary generally extends 500 to 1,000 feet inland from the shoreline, with select areas along major tributaries also extending further. Within the Hudson River Valley the landward boundary is generally 1,000 feet but may extend up to 10,000 feet in areas that possess high aesthetic, agricultural, or recreational value. In the Great Lakes region the boundary is also generally 1,000 feet, though urbanized areas or transportation infrastructure parallel to shore limit the boundary to 500 feet or less in some cases.

Coastal management program consistency reviews require federal actions in the state coastal zone to be consistent with the enforceable policies of the state program or the policies of an approved local waterfront revitalization program. The program also contains provisions to ensure consistency of state actions in coastal areas. Of the 44 coastal management program enforceable policies in New York, seven specifically address flooding and erosion hazards. These policies touch on a number of aspects of coastal flood risk management including the siting of buildings in coastal areas to minimize risk to property and human lives, protection of natural features that mitigate coastal flood risk, construction of erosion control structures to to meet long-term needs, prevention of flood level increases due to coastal activities or development, prevention of coastal mining or dredging from interfering with natural coastal processes, use of public funds for erosion protection structures, and use of non-structural mitigation measures when possible. Additional enforceable policies address coastal development, fish and wildlife resources, public access, recreation, historic and scenic resources, agricultural lands, energy and ice management, water and air resources, and wetlands management.

At the state level, aspects of New York’s Environmental Conservation Law , Local Waterfront Revitalization Program , and State Environmental Quality Review permitting program influence coastal zoning and development decisions. Article 34 of the Environmental Conservation Law requires the identification of coastal erosion hazard areas and rates of recession of coastal lands. Shoreline setbacks must then be implemented at a distance that is sufficient to minimize damage from erosion. Article 36 of the Environmental Conservation Law , the state Flood Plain Management Act, also addresses coastal hazards, requiring walled and roofed buildings to be sited landward of mean high tide and prohibiting mobile homes within coastal high hazard areas, among other restrictions. Article 15, Water Resources Law , regulates the placement of coastal structures such as docks or piers and also addresses the placement of fill in coastal areas. Together these elements of the Environmental Conservation Law provide much of the legal basis for zoning decisions that can affect coastal flood risk at the municipal and local level.

Participation in the Local Waterfront Revitalization Program can also influence a local government’s coastal zoning decisions. In the process of preparing and adopting a revitalization program, local governments provide a more specific implementation of the state Coastal Management Program, taking advantage of local regulatory powers such as zoning ordinances and site plan review. Upon approval of a Local Waterfront Revitalization Program, state actions must then be consistent with the local program. In this way the enforceable policies of the Coastal Management Program, including those that relate to coastal flooding and erosion, are incorporated into local zoning decisions. Elements of enforceable policies are also incorporated into environmental permitting through the State Environmental Quality Review Program, which requires state agencies and local governments to prepare an environmental impact statement for any action that may have a significant impact on the environment. If an action in a coastal area requires the preparation of an impact statement, it must also be determined that the action is consistent with any relevant coastal enforceable policies. Consistency reviews must also be applied to NYS SEQRA type 1 actions as well as unlisted actions.

Floodplain management activities within New York are primarily conducted through the National Flood Insurance Program . Any regulations developed by the state must be at a minimum as strict as those prescribed by FEMA. Beyond the state level communities may adopt more restrictive floodplain management regulations. Within the state, local communities largely regulate development within federally designated Special Flood Hazard Areas, with state assistance provided by the New York State Department of Environmental Conservation. Local development permits govern private development within floodplains as well as development by a county, city, town, village, school district, or public improvement district, as specified in the state Environmental Conservation Law.

State standards for floodplain development permits in all designated special flood hazard areas require adequate anchorage and use of flood resistant material for all new construction and substantial improvement to existing structures. Utilities must also be designed in a manner that minimizes or eliminates risk of damage or failure during flood events. In areas where base flood elevation data exists, new construction or substantially improved residential structures must have the lowest floor at two feet above the BFE, including basements and cellars. Nonresidential structures may employ floodproofing to provide protection. Any enclosed areas below the base flood elevation must be designed to allow for the equalization of hydrostatic forces on exterior walls during a flood event. If no base flood elevation has been determined, new construction or substantially improved residential structures must be elevated above grade to the depth specified on the corresponding flood insurance rate map or two feet if no number is specified, with nonresidential structures again able to employ floodproofing measures. All state agency activities, whether directly undertaken, funded, or approved by an agency, must also be evaluated in terms of significant environmental impacts under the State Environmental Quality Review program, which includes a substantial increase in flooding as a criteria of significance. An environmental impact statement must be prepared if it is determined that an action may have a potential significant adverse impact.

All structures must be located landward of mean high tide levels within coastal high hazard areas, and all new construction or substantially improved structures must be elevated on pilings or columns so that the bottom of the lowest horizontal structural member of the lowest flood is elevated to or above the BFE. Pilings or column foundations must be adequately anchored, and fill is prohibited for use as a structural support for any new structure or substantial improvement. Space below the lowest floor may not contain obstructions to flood flows or otherwise be enclosed with non-breakaway walls. Any such space below the lowest structural floor may not be used for human habitation. New development or substantial improvement to structures must also not affect sand dunes in any way that increases potential flood damages.

The New York State Department of Environmental Conservation is also responsible for wetland management within the state. Statutory authority for wetland regulations stems from the Tidal Wetlands Act and Freshwater Wetlands Act , part of the larger state Environmental Conservation Law . Wetlands and wetland regulations are divided into either tidal or freshwater, and wetlands are further classified within each category. State wetland inventories containing information on delineated areas and classifications are made available for public use as part of the state wetland mapping program. Activities within wetland areas are regulated through a permit system.

Tidal wetlands regulations are designed to allow uses of wetlands that are compatible with the preservation, protection, and enhancement of ecological values including flood protection and storm control. Development restrictions require that all buildings and structures in excess of 100 square feet be located a minimum of 75 feet landward from tidal wetland edges, with less stringent setbacks in place for buildings located within New York City. Similar setback requirements exist for impervious surfaces exceeding 500 square feet. On-site sewage systems must have a setback of at least 100 feet, and a minimum of two feet of soil must be between the bottom of a system and the seasonal high groundwater level.

Permit standards for activities within tidal wetlands require that any proposed activity be compatible with the overall state policy of preserving and protecting tidal wetlands, and as such any activity may not cause any undue adverse impact on the ecological value of an affected wetland area or any adjoining areas. Standards also require that any activity within tidal wetlands be compatible with public health and welfare, be reasonable and necessary, and take into account both alternative actions and the necessity of water access or dependence for the proposed action. The state also publishes compatible use guidelines for activities within wetlands based on wetland type. If any activity is presumed to be incompatible with state tidal wetland use guidelines, an applicant must overcome the presumption of incompatible use and demonstrate that the activity is compatible with the preservation, protection, and enhancement of wetland values. If a use is specifically listed as incompatible within guidelines the use is then prohibited. Permitted activities in areas adjacent to tidal wetlands must also be compatible with public health and welfare, have no undue adverse impact on wetland ecological values, and comply with use guidelines.

State flood-resistant construction requirements are listed in the International Residential Code as adopted by New York State . Regulations apply to new residential buildings and structures located fully or partially within flood hazard areas as well as any substantially improved or restored structures within flood hazard areas. Construction requirements are based on the design flood elevation, which at a minimum must be the higher of either the peak elevation of a 1% annual chance flood event or the elevation of the design flood event as adopted on community flood hazard maps. Structures within flood hazard areas must generally be designed and anchored to resist the flood forces associated with the design flood elevation, and methods and practices to minimize flood damage must also be employed.

For the purposes of determining appropriate structural elevations, the lowest floor of a structure is defined as the lowest floor of any enclosed area, including basements. Within flood hazard areas not subject to high-velocity wave action, structures must have the lowest floor elevated to two feet above the base flood elevation or design flood elevation, whichever is higher. Utility systems must also be elevated to this standard. If no depth number is specified structures must be elevated not less than three feet above the highest adjacent grade. Any enclosed area below the design flood elevation must be used only for building access, parking, or storage and must contain flood openings sufficient to equalize hydrostatic forces on exterior walls.

For buildings and structures located in coastal high-hazard areas, including both V zones and Coastal A zones, the lowest floor must be elevated so that the lowest horizontal structural members are elevated to either the base flood elevation plus two feet or the design flood elevation, whichever is higher. Any walls below the design flood elevation must be designed to break away without causing damage to the elevated portion of the building, and again may be used only for parking, building access, or storage. Structures must be elevated using adequately anchored pilings or columns, with select exceptions in Coastal A zones. The use of fill for structural support and any construction of basement floors below grade are prohibited. New buildings and any substantially improved structures in coastal high-hazard areas must be located landward of the mean high tide, and any alteration of sand dunes must not result in any increased potential for flood damage in surrounding areas.

Planning at the state level is guided by the State Smart Growth Public Infrastructure Policy Act , an article within the larger Environmental Conservation Law. The act outlines criteria for public infrastructure projects that are either approved, directly undertaken, or financed by state infrastructure agencies. Among these criteria is a requirement that future public infrastructure projects mitigate future climate risk due to sea level rise, storm surge, or flood events based on available data or predictions of future extreme weather conditions. This and other criteria must be met to a practicable extent, and if deemed impracticable an agency must provide a detailed statement of justification.

The Office of Planning, Development, and Community Infrastructure within the Department of State administers several programs involved in community planning. The New York Rising Community Reconstruction Program provides recovery and resiliency planning assistance to communities affected by severe storm events, including hurricanes Sandy and Irene. The program is operated through the Governor’s Office of Storm Recovery and involves collaborations between state teams and community members to develop reconstruction plans and strategies to increase physical, economic, and social resilience, often including elements related to mitigating future flood risk. State Waterfront Revitalization Programs are also involved in community redevelopment planning. These programs establish land and water use policies and identify revitalization projects at a local level to allow for sustainable use of coastal resources, including planning for coastal flood risk resilience. Local Waterfront Revitalization Programs can also be a conduit for technical assistance and grant funding to facilitate climate change adaptation through the New York State Environmental Protection Fund grant program , a permanent fund addressing a broad range of environmental and community development needs.

The majority of stormwater regulations in New York focus on water quality issues as part of the State Pollutant Discharge Elimination System , a state program that has been approved by the EPA as part of the National Pollutant Discharge Elimination System . The program regulates point source discharges to both groundwater and surface waters and also conducts permitting for stormwater runoff from industrial activities, municipal sewer systems in urbanized areas, and construction activities. The program is administered by the state Department of Environmental Conservation.

While water quality is the focus of stormwater programs within the state, the state stormwater design manual lists best practices that include measures to reduce overbank flooding in order to maintain pre-development peak discharge rates for two and ten-year frequency storm events following development. The design manual also addresses risks due to potential floodplain expansion following development as well as green infrastructure strategies. These green infrastructure strategies are presented as a means to meet runoff reduction standards, which require that post-development conditions replicate pre-development hydrology. Stormwater projects, like all activities undertaken, funded, or approved by state agencies, are also under the purview of the State Environmental Quality Review Act , which requires preparation of an environmental impact statement if a project is likely to cause a significant increase in flood risk.

Coastal erosion in New York is managed within designated coastal erosion hazard areas. Areas are designated as per requirements of the state Coastal Erosion Hazard Areas Act , part of the larger state Environmental Conservation Law. Regulatory programs within identified hazard areas are administered by the state Department of Environmental Conservation. Programs may also be established at a local level if minimum state standards and criteria are met. The objectives of the program, as outlined in the state administrative code, are to ensure that activities in coastal areas subject to flooding minimize or prevent damage to property and natural features, that structures are placed at a safe distance from hazard areas to prevent premature damage to both structures and natural features, that public investment likely to encourage development within erosion hazard areas is restricted, and that publicly financed structures are only used when necessary and effective. Sections of the state administrative code also describe the erosion protection functions of natural protective features in order to guide the review of permit applications.

Coastal erosion management permits are required for any regulated activity conducted within a designated coastal erosion hazard area. Coastal erosion management permit standards require that any proposed activity be reasonable and necessary, with consideration of proposed alternatives, and that an activity will not likely lead to a measurable increase in erosion at the proposed site or other locations. Standards also require activities to prevent or minimize adverse effects to natural protective features, existing erosion protection structures, or natural resources such as fish and wildlife habitat.

Regulations within structural hazard areas allow for placement of movable structures, with construction restrictions, if a permit has been granted. Construction or placement of nonmovable structures is prohibited. Any public utility systems within structural hazard areas also require a coastal erosion management permit. Additional restrictions on regulated activities are present within natural protective feature areas, including nearshore areas, beaches, bluffs, primary dunes, and secondary dunes. Construction of erosion protection structures is allowed within such areas provided the structure meets permitting requirements and is designed to prevent or minimize damage to property and natural features in a cost-effective manner. Structures must be designed to control erosion on site for a minimum of 30 years.

New York has put forth several climate adaptation measures at the state level, led primarily by the state Department of Environmental Conservation. Sea-level rise projections for threatened coastal areas are currently published within the state administrative code, a recommendation from the previously convened NYS Sea Level Rise Task Force . The projections formally establish sea-level rise levels throughout Long Island, New York City, and the Hudson River, providing information based on five risk scenarios and extending out to 2100. The Department of Environmental Conservation has also formally acknowledged its role in climate change adaptation through Commissioner’s Policy 49: Climate Change and DEC Action . The policy outlines methods by which climate change considerations may be integrated into current DEC activities and programs, including making greenhouse gas reductions a primary goal, creating specific mitigation objectives for existing and future programs, incorporating adaptation strategies into programs and activities, considering climate change implications in daily department activities, and identifying specific actions to further climate change goals and objectives as part of annual planning processes. The policy goes on to establish mitigation and adaptation objectives as well as departmental responsibilities and implementation procedures.

The 2014 Community Risk and Resiliency Act (CRRA) forms the basis for a number of climate adaptation initiatives within New York from a legislative standpoint. The previously mentioned sea-level rise projections were a product of the CRRA, as the act amended the state Environmental Conservation Law to include a requirement that the DEC adopt science-based projections. The CRRA also amended additional sections of the Environmental Conservation Law to require applicants for identified funding and permitting programs to demonstrate that risk due to sea-level rise, storm surge, and flooding have been considered in project design and requires the DEC to incorporate similar considerations into facility-siting regulations. The sea-level rise, storm surge, and flood risk mitigation components of the Smart Growth Public Infrastructure Policy Act are also tied to the CRRA. The CRRA also directs the Department of State and Department of Conservation to develop model local laws that consider data-based future risk due to sea-level rise, storm surge, and flooding as well as guidance on the use of natural resources and natural processes to enhance community resilience to such hazards.

Elements of Policy Goals/Management Principles

• State management capacity is bolstered by the New York Coastal Management Program’s federal consistency review process, which requires that federal activities within the state coastal zone be consistent with the program’s enforceable policies. The New York program has 44 enforceable policies in total, with 7 specifically addressing flood and erosion hazards.
• Local governments can implement the state Coastal Management Program at a smaller scale through the Local Waterfront Revitalization Program, extending the influence of state program goals and enforceable policies.
• The enforceable policies of the state coastal management program address the protection of natural features that mitigate coastal flood risk and the use of non-structural mitigation measures where feasible.
• Shoreline setbacks must be established within identified coastal erosion hazard areas, and setbacks must be at a distance sufficient to minimize damage from erosion considering the rate of recession of coastal lands.
• Floodplain management regulations require that any new development or substantial improvement to structures in coastal areas not affect sand dunes in any way that might increase potential flood damages.
• Wetland management regulations require that structures be located a minimum of 75 feet landward from the edges of tidal wetlands, preserving natural flood risk mitigation functions.
• Sections of the state administrative code related to erosion management include descriptions of the erosion protection functions of natural features to guide permit applications, and permit standards require that erosion management activities prevent or minimize adverse impacts on natural protective features.
• The state stormwater management design manual includes information on green infrastructure strategies, which are presented as a means to meet runoff reduction standards and maintain pre-development hydrology for project areas.
• The state building code requires structures not subject to wave action to have the lowest floor elevated a minimum of one foot above the base flood elevation. This rule applies to the lowest horizontal structural members of structures that are subject to wave action.
• State regulations require that erosion protection structures in coastal areas be designed to control erosion on site for a minimum of 30 years.
• Public infrastructure projects approved, undertaken, or financed by state agencies must account for and mitigate risk due to future climate risk factors such as sea-level rise, storm surge, and flood events. Mitigation efforts must be based on available data as well as projections of future conditions.
• The state has published sea-level rise projections for threatened coastal areas within the state administrative code, formally establishing risk based on five scenarios and extending to 2100.
• Commissioner’s Policy 49: Climate Change and DEC Action identifies ways that climate change considerations could be incorporated into current state programs and activities and defines departmental responsibilities and procedures for implementing the climate adaptation goals of the policy.
• The state Community Risk and Resiliency Act formally establishes a number of climate adaptation initiatives within the state, including the requirement that the state Department of Environmental Conservation adopt science-based sea-level rise projections and that applicants to funding and permitting programs demonstrate that climate risk has been incorporated into the siting of facilities.
• The enforceable policies of the state coastal management program address the siting of buildings in coastal areas to reduce risk and well as restrictions on the use of public funds for erosion protection structures.
• One of the objectives of the state erosion management program as described in the state administrative code is to restrict public investment that could encourage development within coastal erosion hazard areas. An additional objective is to use publicly financed erosion control structures only when necessary and effective.
• The New York Rising Community Reconstruction program works to develop reconstruction plans and strategies to increase coastal community resilience following severe storm events, often involving the mitigation of future flood risk.
• The New York Coastal Management Program lists coordination of major activities affecting coastal resources as one of the program goals, and multiple state agencies are involved in implementing the program’s broad suite of enforceable policies.
• If an action requires preparation of an environmental impact statement as part of the State Environmental Quality Review Program it must also be consistent with the enforceable policies of the state coastal program, including policies related to coastal hazards.
• State wetland regulations are based on the preservation, protection, and enhancement of ecological values as opposed to acreage, with flood control and storm protection listed among the functions provided.
• The State Environmental Quality Review Program includes the potential for a substantial increase in flooding as a criteria of significance, which then triggers the preparation on an environmental impact statement for state agency activities.
• State Waterfront Revitalization Programs establish land and water use policies that incorporate coastal resilience into revitalization projects and community redevelopment planning.

View the other State Coastal Flood Risk Management Case Studies:

Urban habitats lessen coastal flooding impacts

Lawns and stormwater ponds remove nitrogen and improve water quality, a Carolina study says.

As climate change continues, some storm models are predicting an increase in hurricane intensity and an increase in precipitation, causing flooding and devastating coastal communities, water quality and daily life.

A new study by researchers at UNC-Chapel Hill assessed water quality regulation by flooded landscapes in the upper Neuse River Estuary along the North Carolina coast. They found urban estuarine habitats to be effective at permanently removing nitrogen through denitrification — a natural process that improves water quality.

“Urban landscapes like lawns and stormwater ponds are becoming more common as coastlines develop, so understanding the role they play in maintaining water quality is increasingly important,” said Anne Smiley, lead researcher and doctoral student in the College of Arts and Sciences’  earth, marine and environmental sciences department . Smiley also belongs to the  Piehler Lab .

“Coastal landscapes are a patchwork of natural habitats, like marshes and forests, and urban habitats. Denitrification capacity and distributions of these landscapes within a floodplain can influence water quality regulation capacity at the watershed scale. As coastal development continues, it is important to understand the beneficial functions of urban landscapes, like lawns and stormwater ponds, as well as the functioning of natural habitats, like marshes, in the context of a built environment,” Smiley said.

Previous studies have documented effective denitrification in wetlands and oyster reefs, but this study focused on urban and natural landscape elements, such as stormwater ponds, undeveloped open space, forested wetlands and subtidal sediment. It also accounted for different storm characteristics such as high-speed sustained winds or high amounts of rainfall, which affect the water chemistry differently. The team used data from 37 hurricanes that affected the study region ranging from 1996 to 2019.

The results of the study, published in  Natural Hazards and Earth System Sciences , an  EGUsphere journal , show that both urban landscapes and natural habitats play an important role in regulating water quality during flooding events, although nitrogen is processed differently due to storm characteristics.

Storm characteristics, habitat type and habitat coverage within a storm’s floodplain influence overall contribution to nitrogen removal. Flooded landscapes were less effective at removing nitrogen during high-wind, high-precipitation events. In general, natural habitats like marshes and swamp forests were more effective at removing nitrogen than urban habitats like lawns and stormwater ponds.

Although the importance of these urban habitats cannot be discounted, the stormwater pond sampled in this study was part of a restored wetland and likely removes a considerable amount of nitrogen during low-precipitation and low-wind storm events. Swamp forests, an abundant floodplain habitat, made the largest contribution to removing nitrogen from the water during every type of storm, making them a valuable habitat for maintaining water quality.

“These differences in processing suggest that abundance and spatial distributions of these habitats within a floodplain can influence overall nitrogen removal capacity at the watershed scale,” said Smiley. “Understanding nitrogen removal capabilities and limitations of flooded natural coastal habitats as well as those urban landscapes that will become more and more prevalent, will enable us to make informed management decisions to benefit the integrity of our coastal waters.”

“Anne’s work has advanced our understanding of the value of natural systems like marshes in urbanized areas,” said  Mike Piehler , a co-author and professor, director of the UNC Institute for the Environment and chief sustainability officer at Carolina. “This information is already informing land use decisions in North Carolina’s coastal communities and has broad application beyond our state. I am so excited for Anne to have this great paper from her dissertation published in an excellent journal.”

Read the full paper in  Natural Hazards and Earth System Sciences .

See climate’s impact on algae to zoos in Carolina Digital Repository’s curation of open access articles.

Robert Hawkins named new SSW vice dean

An associate dean at NC State, he will take on his role at the UNC School of Social Work on July 1.

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After earning a master’s degree, Kat Goodpaster became assistant director of Carolina’s Russian Flagship Program.

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Trash Force picks up after campus

What started as an extra credit opportunity grew into a club who has fun keeping Carolina clean.

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East Coast flooding in UK – 5th December 2013

On Thursday 5th December 2013 large areas of the east coast of England were affected by coastal flooding on a scale not seen since the  Great floods of 1953 . A combination of factors led to the storm surge that was responsible for flooding. This included a high spring tide, an area of low pressure and high northerly winds.

Tidal surge graph spurn 2013 – source: http://www.tides4fishing.com/uk/england/spurn-head

The  tidal coefficient  was 94(very high). The  tide heights  were 0.9 m, 7.1 m, 1.1 m and 7.2 m. We can compare these levels with the maximum high tide recorded in the tide tables for Spurn Head which is of 7.6 m and a minimum height of 0.3 m.

What is a storm surge and why does it happen?

Video from the BBC –  http://www.bbc.co.uk/news/uk-25229885  Met Office Article –  http://www.metoffice.gov.uk/learning/learn-about-the-weather/weather-phenomena/storm-surge  The Atlantic storm, which brought the coastal flooding and gale-force winds of up to 100mph, caused widespread disruption across the UK and claimed the lives of two men – in West Lothian, Scotland, and in Retford, Nottinghamshire.

The Environment Agency said 800,000 homes in England had been protected by flood defences and better forecasting had given people “vital time” to prepare. The agency said sea levels had peaked at 5.8m (19ft) in Hull – the highest seen by the East Yorkshire city since 1953 – and 4.7m (15ft) in Dover, Kent, the highest recorded there in more than 100 years.

Preparing for the 2013 storm surge:

Video report:  http://www.bbc.co.uk/news/uk-25237082 Thousands evacuated:  http://www.bbc.co.uk/news/uk-england-norfolk-25228834

Warnings issued:  http://www.metoffice.gov.uk/news/releases/archive/2013/storm-surge Preparing your home:  http://www.bbc.co.uk/news/science-environment-20497598 Thames Barrier raised:  http://now-here-this.timeout.com/2013/12/05/thames-barrier-to-close-tonight-as-forcasts-predict-the-biggest-storm-surge-for-30-years/ How the barrier works:   http://www.environment-agency.gov.uk/homeandleisure/floods/38359.aspx  The Environment Agency estimates that 800,000 homes and businesses were saved due to flood defence schemes.

Impacts of December 2013 floods:   Social Impacts

Thousands of people were evacuated from Britain’s east coast of England. Victims of the most serious tidal surge in 60 years have been warned to avoid direct contact with floodwater and beware of rats moving into homes.More detail and pictures here:  http://www.dailymail.co.uk/news/article-2519891/Beware-invasion-flood-rats-Homeowners-hit-tidal-surge-told-avoid-contact-water-amid-fears-pest-invasion.html

People Urged to remain vigilant:  http://www.independent.co.uk/news/uk/uk-weather-warnings-scotland-and-north-battered-by-100mph-winds-as-biggest-tidal-surge-in-60-years-threatens-east-coast-8984542.html

Impacts of the December 2013 floods:  Economic Impacts

Economic impact in North Norfolk –  http://www.northnorfolk.org/files/Tidal-surge-combined-info.pdf Seven Cliff Top Homes Collapse in Hemsby –  http://www.bbc.co.uk/news/uk-england-norfolk-25254808

1,400 homes were flooded, including 300 in Boston, Lincolnshire, according to Environment Agency (EA) figures.

Some good images and videos on the Daily Mail website:  http://www.dailymail.co.uk/news/article-2518340/Britain-battered-worst-tidal-surge-60-years-Sea-walls-breached-20ft-waves-smash-string-east-coast-towns.html

VIDEO – Bungalow falls off cliff in Norfolk  http://www.bbc.co.uk/news/uk-25258149 VIDEO – Thousands evacuated:  http://www.bbc.co.uk/news/uk-25253733 VIDEO – Homes in Whitby flooded:  http://www.bbc.co.uk/news/uk-25257747  plus: http://www.bbc.co.uk/news/uk-25258150 VIDEO – Cleaning up after the floods:  http://www.bbc.co.uk/news/uk-25257754 VIDEO – Aerial footages of flood aftermath:  http://www.bbc.co.uk/news/uk-25260863

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Dubai’s Extraordinary Flooding: Here’s What to Know

Images of a saturated desert metropolis startled the world, prompting talk of cloud seeding, climate change and designing cities for intensified weather.

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By Raymond Zhong

Scenes of flood-ravaged neighborhoods in one of the planet’s driest regions have stunned the world this week. Heavy rains in the United Arab Emirates and Oman submerged cars, clogged highways and killed at least 21 people. Flights out of Dubai’s airport, a major global hub, were severely disrupted.

The downpours weren’t a freak event — forecasters anticipated the storms several days out and issued warnings. But they were certainly unusual. Here’s what to know.

Heavy rain there is rare, but not unheard-of.

On average, the Arabian Peninsula receives a scant few inches of rain a year, although scientists have found that a sizable chunk of that precipitation falls in infrequent but severe bursts, not as periodic showers.

U.A.E. officials said the 24-hour rain total on Tuesday was the country’s largest since records there began in 1949 . But parts of the nation had experienced an earlier round of thunderstorms just last month.

Oman, with its coastline on the Arabian Sea, is also vulnerable to tropical cyclones. Past storms there have brought torrential rain, powerful winds and mudslides, causing extensive damage.

Global warming is projected to intensify downpours.

Stronger storms are a key consequence of human-caused global warming. As the atmosphere gets hotter, it can hold more moisture, which can eventually make its way down to the earth as rain or snow.

But that doesn’t mean rainfall patterns are changing in precisely the same way across every corner of the globe.

In their latest assessment of climate research , scientists convened by the United Nations found there wasn’t enough data to have firm conclusions about rainfall trends in the Arabian Peninsula and how climate change was affecting them. The researchers said, however, that if global warming were to be allowed to continue worsening in the coming decades, extreme downpours in the region would quite likely become more intense and more frequent.

The role of cloud seeding isn’t clear.

The U.A.E. has for decades worked to increase rainfall and boost water supplies by seeding clouds. Essentially, this involves shooting particles into clouds to encourage the moisture to gather into larger, heavier droplets, ones that are more likely to fall as rain or snow.

Cloud seeding and other rain-enhancement methods have been tried across the world, including in Australia, China, India, Israel, South Africa and the United States. Studies have found that these operations can, at best, affect precipitation modestly — enough to turn a downpour into a bigger downpour, but probably not a drizzle into a deluge.

Still, experts said pinning down how much seeding might have contributed to this week’s storms would require detailed study.

“In general, it is quite a challenge to assess the impact of seeding,” said Luca Delle Monache, a climate scientist at the Scripps Institution of Oceanography in La Jolla, Calif. Dr. Delle Monache has been leading efforts to use artificial intelligence to improve the U.A.E.’s rain-enhancement program.

An official with the U.A.E.’s National Center of Meteorology, Omar Al Yazeedi, told news outlets this week that the agency didn’t conduct any seeding during the latest storms. His statements didn’t make clear, however, whether that was also true in the hours or days before.

Mr. Al Yazeedi didn’t respond to emailed questions from The New York Times on Thursday, and Adel Kamal, a spokesman for the center, didn’t immediately have further comment.

Cities in dry places just aren’t designed for floods.

Wherever it happens, flooding isn’t just a matter of how much rain comes down. It’s also about what happens to all that water once it’s on the ground — most critically, in the places people live.

Cities in arid regions often aren’t designed to drain very effectively. In these areas, paved surfaces block rain from seeping into the earth below, forcing it into drainage systems that can easily become overwhelmed.

One recent study of Sharjah , the capital of the third-largest emirate in the U.A.E., found that the city’s rapid growth over the past half century had made it vulnerable to flooding at far lower levels of rain than before.

Omnia Al Desoukie contributed reporting.

Raymond Zhong reports on climate and environmental issues for The Times. More about Raymond Zhong

What caused Dubai floods? Experts cite climate change, not cloud seeding

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DID CLOUD SEEDING CAUSE THE STORM?

CAN'T CREATE CLOUDS FROM NOTHING

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Assessment of cyclone risk and case study of Gaja cyclone using GIS techniques and machine learning algorithms in coastal zone of Tamil Nadu, India

• Thenmozhi, M.
• Sujatha, M.
• Kavitha, M.
• Senthilraja, S.

Cyclones can cause devastating impacts, including strong winds, heavy rainfall, storm surges, and flooding. The aftermath includes infrastructure damage, loss of life, displacement of communities, and ecological disruptions. Timely response and recovery efforts are crucial to minimize the socio-economic and environmental consequences of cyclones. To accelerate the time-consuming risk assessment process, particularly in geographically diverse regions, a blend of multi-criteria decision-making and machine learning models was utilized. This novel approach swiftly assessed cyclone risk and the impact of the Gaja cyclone in Nagapattinam, India. The method involved assigning weights to distinct criteria, unveiling notable vulnerability aspects like elevation, slope, proximity to the coast, distance from cyclone tracts, Lu/Lc, population density, proximity to cyclone shelters, household density, accessibility to healthcare facilities, NDVI, and levels of awareness. Daddavari, Ettugudi, Kodikarai, Vedharanyam, Velankanni, and Thirupoondi face high/extreme cyclone risk. Nagore, Nagapattinam, Pillai, Enangudi, and Sannanllur have low/no threat. To further enhance the precision of the study, machine learning algorithms like SVM, SAM, and MLC were deployed. These models were instrumental in generating pre- and post-cyclone land use maps. The influence of Gaja cyclones effects shows decreasing of agriculture land from 34% to 30%, aquaculture increase 1%, barren land decrease from 8% to 6%, Built-up land decrease from 15% to 13%, land with scrub and salt pan also decrease from 21% to 17% and 10%-8%. Mostly effect of Gaja cyclone is dramatic increase of water body from 8% to 21%. Conducting cyclone risk zone analysis and pre/post-cyclone Land Use Land Cover (LULC) detection in Nagapattinam offers valuable insights for disaster preparedness, infrastructure planning, and climate resilience. This study can enhance understanding of vulnerability and aid in formulating strategies to mitigate cyclone impacts, ensuring sustainable development in the region.

• Cyclone vulnerability;
• Cyclone hazards;
• Mitigation capacity;
• Gaja cyclones