Sea level rise (SLR) increases the risk of flooding at coastal sites that use and produce hazardous substances. We assess whether socially marginalized populations in the United States are more likely to be impacted by projected SLR-related flooding of hazardous sites that could result in contaminant releases. We identify 5500 facilities at risk of a 1-in-100-year flood event by 2100 under a scenario of continued high greenhouse gas emissions, including coastal power plants, sewage treatment facilities, fossil fuel infrastructure, industrial facilities, and formerly used defense sites. Seven states (Louisiana, Florida, New Jersey, Texas, California, New York, and Massachusetts) account for nearly 80% of projected at-risk facilities. Controlling for population density and county, a one standard deviation increase in the proportion of linguistically isolated households, neighborhood residents identifying as Hispanic, households with incomes below twice the federal poverty line, households without a vehicle, non-voters, and renters is associated with 19-41% higher likelihood of having a site at risk of SLR-related flooding within 1 kilometer (odds ratios [95% confidence intervals]: 1.19 [1.09, 1.31], 1.22 [1.08, 1.37], 1.27 [1.16, 1.39], 1.35 [1.21-1.51], 1.36 [1.21, 1.53], and 1.41 [1.32, 1.52], respectively). Results elucidate the need for disaster planning, land-use decision-making, as well as mitigation strategies that address the inequitable hazards and potential health threats posed by SLR.

The impacts of climate change and sea level rise are posing substantial threats to the long-term habitability of low-lying atolls. As of today, the sparse data coverage of these islands limits the ability to assess and respond to climate change related risks. Advances in coastal digital elevation models provide data for very remote coastal regions with low vertical bias. Here, we combine the Intergovernmental Panel on Climate Change regional sea level rise projections under its illustrative emissions scenarios, with the coastal digital elevation model CoastalDEM and COAST-RP, a dataset of storm tide return periods to assess the exposure to rising sea levels and coastal flooding of 166 atolls. Our results show that in 2050 and under a very low emissions scenario (SSP1-1.9), atoll area exposure to SLR and coastal flooding will amount to 35% [34-36%] and that only 64% of atoll area can still be considered safe. By the end of century and under the same scenario, only 61% can be considered safe. Under an intermediate emissions scenario (SSP2-4.5), a scenario roughly capturing projected warming under current policies and actions, the share of safe land further reduces to 58% by 2100. By 2150, only 58% or 51% of the land can still be considered safe under the very low and intermediate emissions scenario respectively. Our results show that the habitability of atolls is already threatened in the near future, but that near-term mitigation can limit the pace at which atolls are flooded in particular beyond 2100. Our results imply that in addition to immediate and rapid emission reductions in line with the Paris Agreement, remaining adaptation options must be enabled and implemented today to reduce the future exposure of atolls.

The planned, permanent relocation of entire communities away from sea level rise (SLR) and coastal floods is an already occurring climate change adaptation strategy. Yet, planned relocations are fraught undertakings with multiple goals, and may or may not achieve their most basic objective: to reduce risk. Here we assess risk of future coastal flooding before and after moving, for three dates and three emissions scenarios, for 17 communities from a global dataset. Most communities achieved exposure reduction with less future inundation in destinations than origin sites, but the extent varies across time and emissions scenario. In all cases, origin sites have projected exposure to SLR plus a once-per-year flood, with increasing exposure under high emissions scenarios and towards 2100. In nine cases, even destination sites have projected inundation exposure under some scenarios. Small-island-to-small-island relocations had more projected inundation in destinations than moves from a small-island-to-mainland, or from mainland-to-mainland. Planned relocations reduce communities' risk of future coastal floods, but they do not eliminate it entirely - especially under high emissions scenarios and for moves with small-island destinations, according to an analysis that combines data on relocation sites and inundation projections.

The warnings of potential climate migration first appeared in the scientific literature in the late 1970s when increased recognition that disintegrating ice sheets could drive people to migrate from coastal cities. Since that time, scientists have modelled potential climate migration without integrating other population processes, potentially obscuring the demographic amplification of this migration. Climate migration could amplify demographic change – enhancing migration to destinations and suppressing migration to origins. Additionally, older populations are the least likely tomigrate and climate migration could accelerate population aging in origin areas. Here, we investigate climate migration under sea-level rise (SLR), a single climatic hazard, and examine both the potential demographic amplification effect and population aging by combining matrix population models,flood hazard models, and a migration model built on 40 years of environmental migration in the US to project the US population distribution of US counties. We find that the demographic amplification of SLR for all feasible RCP-SSP scenarios in 2100 ranges between 8.6M - 28M [5.7M - 53M] – 5.3 to 18 times the number of migrants (0.4M - 10M). We also project a significant aging of coastal areas as youthful populations migrate but older populations remain, accelerating population aging in origin areas. As the percentage of the population lost due to climate migration increases, the median age also increases – up to 10+ years older in some highly impacted coastal counties. Additionally, our population projection approach can be easily adapted to investigate additional or multiple climate hazards.

The planned, permanent relocation of entire communities away from areas facing sea level rise (SLR) and coastal floods is an increasingly recognized strategy for climate change adaptation. Yet, planned relocations may or may not reduce risk. We assess projections of future coastal flooding in all the completed or underway relocations that met our criteria for inclusion from a global dataset. Most of the 17 cases achieved exposure reduction with less future inundation in destinations than origin sites, but the extent varies across time and emissions scenario. In all cases, origin sites are projected to be exposed to SLR combined with a once-per-year flooding event. In nine cases, even destination sites are projected to be exposed to SLR plus a once-a-year flooding event under some scenarios. Small island-to-small island relocations had more projected inundation in destinations than moves from a small island-to-mainland, or from mainland-to-mainland.

Rising sea level increases the exposure to flooding and related damage in coastal areas with high population density and substantial economic activity. As global temperatures continue to rise due to climate change, sea levels have been consistently increasing and are projected to continue this upward trend. This study assesses the future exposure at provincial and city levels populations coastal mainland China coast to local sea level changes under five greenhouse gas (GHG) emission scenarios from IPCC-AR6, as well as two low-confidence scenarios accounting for the potential impact of uncertain ice sheet processes with low- and high-GHG emissions. We incorporate spatial heterogeneity into regional sea level projections and population projections from 2020 to 2100, extreme sea levels (ESLs) of 10-, 50-, and 100 year return periods (RP), and local coastal protection standards. Our findings indicate that the inundated areas expand continuously within the century with heightened exposure under higher emission scenarios. Although the coastal population is projected to decline, the fraction of the coastal population exposed to flooding increases across all scenarios, with accelerated growth under higher GHG emissions and higher ESLs. Zhejiang and Jiangsu emerge as the provinces most exposed to sea-level rise, whereas Taizhou, Nantong, Wuxi, Panjin, and Huzhou are identified as the top five cities with the highest population exposure to local sea level rise (SLR). Transitioning towards a sustainable scenario (i.e. SSP1-2.6) rather than a fossil fuel-intensive one (i.e. SSP5-8.5) can reduce the local SLR and substantially mitigate these exposures. Compared to the median projections under SSP5-8.5, aligning GHG emissions with SSP1-2.6 could reduce population exposure substantially in all coastal provinces, especially in Jiangsu, where population exposure to 100 year RP coastal floods would be reduced by similar to 1.6 M in 2050 and by similar to 5.4 M in 2100.

Sea level rise (SLR) and heavy precipitation events are increasing the frequency and extent of coastal flooding, which can trigger releases of toxic chemicals from hazardous sites, many of which are in low-income communities of color. We used regression models to estimate the association between facility flood risk and social vulnerability indicators in low-lying block groups in California. We applied dasymetric mapping techniques to refine facility boundaries and population estimates and probabilistic SLR projections to estimate facilities' future flood risk. We estimate that 423 facilities are at risk of flooding in 2100 under a high emissions scenario (RCP 8.5). One unit standard deviation increases in nonvoters, poverty rate, renters, residents of color, and linguistically isolated households were associated with a 1.5-2.2 times higher odds of the presence of an at-risk site within 1 km (ORs [95% CIs]: 2.2 [1.8, 2.8], 1.9 [1.5, 2.3], 1.7 [1.4, 1.9], 1.5 [1.2, 1.9], and 1.5 [1.2, 1.9], respectively). Among block groups near at least one at-risk site, the number of sites increased with poverty, proportion of renters and residents of color, and lower voter turnout. These results underscore the need for further research and disaster planning that addresses the differential hazards and health risks of SLR.

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

Coastal wetlands provide a wide array of ecosystem services, valued at trillions of dollars per year globally. Although accelerating sea level rise (SLR) poses the long-term threat of inundation to coastal areas, wetlands may be sustained in two ways: by positive net surface-elevation change (SEC) from sediment and organic matter buildup and by accumulation, or horizontal migration into refugia—low-lying, undeveloped upland areas that become inundated. Using a simple model together with high-resolution elevation data, we provide, across the contiguous United States, analysis of the local effects of SLR, maximum SEC rates, and coastal development on the long-term resilience of coastal wetlands. We find that protecting current refugia is a critical factor for retaining wetlands under accelerating SLR. If refugia are conserved under an optimistic scenario (a high universal maximum SEC rate of 8 mm/yr and low greenhouse gas emissions), wetlands may increase by 25.0% (29.4%–21.5%; 50th, 5th–95th percentiles of SLR) by the end of the century. However, if refugia are developed under a more pessimistic scenario (a moderate universal maximum SEC rate of 3 mm/yr, high greenhouse gas emissions, and projections incorporating high ice-sheet contributions to SLR), wetlands may decrease by −97.0% (−82.3%–99.9%). These median changes in wetland area could result in an annual gain of ∼$222 billion compared to an annual loss of ∼$732 billion in ecosystem services in the US alone. Focusing on key management options for sustaining wetlands, we highlight areas at risk of losing wetlands and identify the benefits possible from conserving refugia or managing SEC rates.

Sea-level rise and ensuing permanent coastal inundation will cause spatial shifts in population and economic activity over the next 200 years. Using a highly spatially disaggregated, dynamic model of the world economy that accounts for the dynamics of migration, trade, and innovation, this paper estimates the consequences of probabilistic projections of local sea-level changes under different emissions scenarios. Under an intermediate greenhouse gas concentration trajectory, permanent flooding is projected to reduce global real GDP by an average of 0.19% in present value terms, with welfare declining by 0.24% as people move to places with less attractive amenities. By the year 2200 a projected 1.46% of world population will be displaced. Losses in many coastal localities are more than an order of magnitude larger, with some low-lying urban areas particularly hard hit. When ignoring the dynamic economic adaptation of investment and migration to flooding, the loss in real GDP in 2200 increases from 0.11% to 4.5%. This shows the importance of including dynamic adaptation in future loss models.

In 2012, Hurricane Sandy hit the East Coast of the United States, creating widespread coastal flooding and over $60 billion in reported economic damage. The potential influence of climate change on the storm itself has been debated, but sea level rise driven by anthropogenic climate change more clearly contributed to damages. To quantify this effect, here we simulate water levels and damage both as they occurred and as they would have occurred across a range of lower sea levels corresponding to different estimates of attributable sea level rise. We find that approximately $8.1B ($4.7B-$14.0B, 5th-95th percentiles) of Sandy's damages are attributable to climate-mediated anthropogenic sea level rise, as is extension of the flood area to affect 71 (40-131) thousand additional people. The same general approach demonstrated here may be applied to impact assessments for other past and future coastal storms.

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. The exposure of populations to sea-level rise is a leading indicator assessing the impact of future climate change on coastal regions. The authors identify three spatial zones of flooding such as mean higher water, the 100 year floodplain and the low-elevation coastal zone and show population exposure can differ between those zones.

A portion of human-caused carbon dioxide emissions will stay in the atmosphere for hundreds of years, raising temperatures and sea levels globally. Most nations’ emissions-reduction policies and actions do not seem to reflect this long-term threat, as collectively they point toward widespread permanent inundation of many developed areas. Using state-of-the-art new global elevation and population data, we show here that, under high emissions scenarios leading to 4 ∘ C warming and a median projected 8.9 m of global mean sea level rise within a roughly 200- to 2000-year envelope, at least 50 major cities, mostly in Asia, would need to defend against globally unprecedented levels of exposure, if feasible, or face partial to near-total extant area losses. Nationally, China, India, Indonesia, and Vietnam, global leaders in recent coal plant construction, have the largest contemporary populations occupying land below projected high tide lines, alongside Bangladesh. We employ this population-based metric as a rough index for the potential exposure of the largely immovable built environment embodying cultures and economies as they exist today. Based on median sea level projections, at least one large nation on every continent but Australia and Antarctica would face exceptionally high exposure: land home to at least one-tenth and up to two-thirds of current population falling below tideline. Many small island nations are threatened with near-total loss. The high tide line could encroach above land occupied by as much as 15% of the current global population (about one billion people). By contrast, meeting the most ambitious goals of the Paris Climate Agreement will likely reduce exposure by roughly half and may avoid globally unprecedented defense requirements for any coastal megacity exceeding a contemporary population of 10 million.

To date, projections of human migration induced by sea-level change (SLC) largely suggest large-scale displacement away from vulnerable coastlines. However, results from our model of Bangladesh suggest counterintuitively that people will continue to migrate toward the vulnerable coastline irrespective of the flooding amplified by future SLC under all emissions scenarios until the end of this century. We developed an empirically calibrated agent-based model of household migration decision-making that captures the multi-faceted push, pull and mooring influences on migration at a household scale. We then exposed ~4800 000 simulated migrants to 871 scenarios of projected 21st-century coastal flooding under future emissions pathways. Our model does not predict flooding impacts great enough to drive populations away from coastlines in any of the scenarios. One reason is that while flooding does accelerate a transition from agricultural to non-agricultural income opportunities, livelihood alternatives are most abundant in coastal cities. At the same time, some coastal populations are unable to migrate, as flood losses accumulate and reduce the set of livelihood alternatives (so-called 'trapped' populations). However, even when we increased access to credit, a commonly-proposed policy lever for incentivizing migration in the face of climate risk, we found that the number of immobile agents actually rose. These findings imply that instead of a straightforward relationship between displacement and migration, projections need to consider the multiple constraints on, and preferences for, mobility. Our model demonstrates that decision-makers seeking to affect migration outcomes around SLC would do well to consider individual-level adaptive behaviors and motivations that evolve through time, as well as the potential for unintended behavioral responses.

Extreme flood events frequently threaten coastal and river communities, and communicating the potential impacts of such forecasts to their populations is crucial to protect property and human life. However, traditional methods to warn residents of forecasted flood events are often ignored or not fully understood. Recent works have produced 3D visualizations of flooding to better capture viewers’ attentions but tend to be expensive, visually unrealistic, or incapable of parameterizing water height. Here we propose an efficient and scientifically-grounded approach to generate realistic images and animations of a flood at any height composited with a photograph taken at street level. Using vehicular LIDAR point cloud and color photo data, we employ a convolutional neural network to generate a dense depth map across an image. We use 3D modeling software to automatically generate and render a water surface, along with its own depth map, at the appropriate height and orientation. The depth maps are used to composite the photo with the rendered water surface to generate the final images, and this process can be repeated to generate videos of rising coastal floodwaters with animated waves within minutes. These visualizations are striking, and the overall framework can be supported by any particular image collection or depth map construction methodology, making this an affordable and achievable approach to flood risk communication.

In Brief In 2018, Climate Central released CoastalDEM v1.1, a near-global coastal digital elevation model (DEM) that used an artificial neural network to reduce errors present in a DEM derived from NASA’s Shuttle Radar Topography Mission (SRTM). CoastalDEM v1.1 was tested against lidar-derived elevation data in the US and Australia, and showed greatly reduced vertical bias and root mean square error (RMSE) compared to SRTM in both forests and cities.

Code supporting Strauss et al. (2021) published in Nature Communications. If you use any original data from this archive, please cite the study as:Strauss, B.H., Orton, P.M., Bittermann, K. et al. Economic damages from Hurricane Sandy attributable to sea level rise caused by anthropogenic climate change. Nat Commun 12, 2720 (2021). https://doi.org/10.1038/s41467-021-22838-1If you have any questions or comments, please contact Daniel Gilford at dgilford@climatecentral.org Included are Input, Output, and Source files (compressed) used in the publication; data files are primarily in txt, csv, xlsx, and mat formats. In the absence of a MATLAB license, mat files may be read with open access software such as SciPy. Code supporting this publication may be found at https://github.com/climatecentral/cc_sandy_matlab. Archived Data Short Descriptions: INPUT -- Input semi-empirical model, hydrodynamic, and observational data files used to create distributions/analyses in this study. 8518750_meantrend.csv: The Battery, NY monthly mean sea levels and trends/uncertainty, accessed from https://tidesandcurrents.noaa.gov/sltrends/sltrends_station.shtml?id=8518750 on 29 July 2020. cmip5.zip: CMIP5 semi-empirical model analyses for each individual model and scenarios (historical and counterfactual), and index files for reference. hadcrut.zip: HadCRUT4 semi-empirical model analyses for each individual HadCRUT4 scenario (historical and counterfactuals) Dangendorf2019_GMSL.txt: Monthly mean global mean sea level rise from Dangendorf et al. (2019). Also included are datum information, block damages (/damage/ directory), hydrodynamic simulations (/simulations_july_2016/ directory), and additional auxiliary files required to run the accompanying repository analyses. OUTPUT -- Code outputs supporting this publication fig1_data.mat: Quick access source data file which may be used to recreate Fig. 1 in the manuscript SEanalysis.mat: The full output semi-empirical model analyses in this study summary_samps.mat: Summary/ensemble analyses in this study SOURCE -- Individual source data files for each Figure (1, 2, 3a-b), Table (1-2), Supplementary Figure (S1-4), and Supplementary Table (S1-6) in this study. Included is a readme.txt with full descriptions of source data files. We acknowledge funding from NSF grant ICER-1663807, NASA grant 80NSSC17K0698,

Earth and Space Science Open Archive This is a preprint and has not been peer reviewed. ESSOAr is a venue for early communication or feedback before peer review. Data may be preliminary.Learn more about preprints preprintOpen AccessYou are viewing an older version [v1]Go to new versionPhysical extreme sea level metrics may misrepresent future flood riskAuthors D.J. Rasmussen iD Michael Oppenheimer iD Robert Kopp iD Benjamin Strauss Scott Kulp See all authors D.J. RasmusseniDCorresponding Author• Submitting AuthorPrinceton UniversityiDhttps://orcid.org/0000-0003-4668-5749view email addressThe email was not providedcopy email addressMichael OppenheimeriDPrinceton UniversityiDhttps://orcid.org/0000-0002-9708-5914view email addressThe email was not providedcopy email addressRobert KoppiDRutgers UniversityiDhttps://orcid.org/0000-0003-4016-9428view email addressThe email was not providedcopy email addressBenjamin StraussClimate Centralview email addressThe email was not providedcopy email addressScott KulpClimate Centralview email addressThe email was not providedcopy email address

The frequency of coastal floods around the United States has risen sharply over the last few decades, and rising seas point to further future acceleration. Residents of low-lying affordable housing, who tend to be low-income persons living in old and poor quality structures, are especially vulnerable. To elucidate the equity implications of sea level rise (SLR), we provide the first nationwide assessment of recent and future risks to affordable housing from SLR and coastal flooding in the United States. By using high-resolution building footprints and probability distributions for both local flood heights and SLR, we identify the coastal states and cities where affordable housing—both subsidized and market-driven—is most at risk of flooding. We provide estimates of both the expected number of affordable housing units exposed to extreme coastal water levels and of how often those units may be at risk of flooding. The number of affordable units exposed in the United States is projected to more than triple by 2050. New Jersey, New York, and Massachusetts have the largest number of units exposed to extreme water levels both in absolute terms and as a share of their affordable housing stock. Some top-ranked cities could experience numerous coastal floods reaching higher than affordable housing sites each year. As the top 20 cities account for 75% of overall exposure, limited, strategic and city-level efforts may be able to address most of the challenge of preserving coastal-area affordable housing stock.