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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. 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.

Flood exposure has been linked to shifts in population sizes and composition. Traditionally, these changes have been observed at a local level providing insight to local dynamics but not general trends, or at a coarse resolution that does not capture localized shifts. Using historic flood data between 2000-2023 across the Contiguous United States (CONUS), we identify the relationships between flood exposure and population change. We demonstrate that observed declines in population are statistically associated with higher levels of historic flood exposure, which may be subsequently coupled with future population projections. Several locations have already begun to see population responses to observed flood exposure and are forecasted to have decreased future growth rates as a result. Finally, we find that exposure to high frequency flooding (5 and 20-year return periods) results in 2-7% lower growth rates than baseline projections. This is exacerbated in areas with relatively high exposure to frequent flooding where growth is expected to decline over the next 30 years. Using historical data across the U.S., the authors find that population declines are associated with flood exposure. Projecting this relationship to 2053, the authors find that flood risk may result in 7% lower growth than otherwise expected.

Sea-level rise will impact heavily populated coastal areas, necessitating adaptation or migration. This study considers how potential migration away from affected areas will have a broader effect on the US population landscape. Many sea-level rise (SLR) assessments focus on populations presently inhabiting vulnerable coastal communities 1 , 2 , 3 , but to date no studies have attempted to model the destinations of these potentially displaced persons. With millions of potential future migrants in heavily populated coastal communities, SLR scholarship focusing solely on coastal communities characterizes SLR as primarily a coastal issue, obscuring the potential impacts in landlocked communities created by SLR-induced displacement. Here I address this issue by merging projected populations at risk of SLR 1 with migration systems simulations to project future destinations of SLR migrants in the United States. I find that unmitigated SLR is expected to reshape the US population distribution, potentially stressing landlocked areas unprepared to accommodate this wave of coastal migrants—even after accounting for potential adaptation. These results provide the first glimpse of how climate change will reshape future population distributions and establish a new foundation for modelling potential migration destinations from climate stressors in an era of global environmental change.

As Puerto Rico ('PR') makes long-term investments in the reconstruction of its built environment following Hurricanes Maria and Irma, a fundamental research question remains unanswered: who will benefit from these recovery and resilience efforts? The article presents 30-year demographic projections (2017-2047) that show current fiscal and infrastructure planning efforts overestimate the size and composition of the future PR populations who may be the direct and indirect beneficiaries of post-Hurricane recovery and resilience investments in the built environment. Our projections suggest long-term projected depopulation are inconsistently applied in the fiscal and infrastructure planning, shaping both recovery and resilience efforts. As PR moves forward with long-term plans and capital investments, consistently deployed, long-range population projections are critical for determining the optimal stewardship of public resources and as a check on the construction of a built environment that might be beyond the sustainable capacity of PR to utilize, maintain, and pay for.

Scholars rely on accurate population and mortality data to inform efforts regarding the coronavirus disease 2019 (COVID-19) pandemic, with age-specific mortality rates of high importance because of the concentration of COVID-19 deaths at older ages. Population counts, the principal denominators for calculating age-specific mortality rates, will be subject to noise infusion in the United States with the 2020 census through a disclosure avoidance system based on differential privacy. Using empirical COVID-19 mortality curves, the authors show that differential privacy will introduce substantial distortion in COVID-19 mortality rates, sometimes causing mortality rates to exceed 100 percent, hindering our ability to understand the pandemic. This distortion is particularly large for population groupings with fewer than 1,000 persons: 40 percent of all county-level age-sex groupings and 60 percent of race groupings. The U.S. Census Bureau should consider a larger privacy budget, and data users should consider pooling data to minimize differential privacy’s distortion.

Small area and subnational population projections are important for understanding long-term demographic changes. I provide county-level population projections by age, sex, and race in five-year intervals for the period 2020-2100 for all U.S. counties. Using historic U.S. census data in temporally rectified county boundaries and race groups for the period 1990-2015, I calculate cohort-change ratios (CCRs) and cohort-change differences (CCDs) for eighteen five-year age groups (0-85+ ), two sex groups (Male and Female), and four race groups (White NH, Black NH, Other NH, Hispanic) for all U.S counties. I then project these CCRs/CCDs using ARIMA models as inputs into Leslie matrix population projection models and control the projections to the Shared Socioeconomic Pathways. I validate the methods using ex-post facto evaluations using data from 1969-2000 to project 2000-2015. My results are reasonably accurate for this period. These data have numerous potential uses and can serve as inputs for addressing questions involving sub-national demographic change in the United States.

Ongoing population growth could greatly exacerbate the human impact of sea-level rise in coastal areas of the continental US this century, with the potential to induce mass population movements unless protective measures are taken. Sea-level rise (SLR) is one of the most apparent climate change stressors facing human society 1 . Although it is known that many people at present inhabit areas vulnerable to SLR 2 , 3 , few studies have accounted for ongoing population growth when assessing the potential magnitude of future impacts 4 . Here we address this issue by coupling a small-area population projection with a SLR vulnerability assessment across all United States coastal counties. We find that a 2100 SLR of 0.9 m places a land area projected to house 4.2 million people at risk of inundation, whereas 1.8 m affects 13.1 million people—approximately two times larger than indicated by current populations. These results suggest that the absence of protective measures could lead to US population movements of a magnitude similar to the twentieth century Great Migration of southern African-Americans 5 . Furthermore, our population projection approach can be readily adapted to assess other hazards or to model future per capita economic impacts.

Subcounty housing unit counts are important for studying geo-historical patterns of (sub)urbanization, land-use change, and residential loss and gain. The most commonly used subcounty geographical unit for social research in the United States is the census tract. However, the changing geometries and historically incomplete coverage of tracts present significant obstacles for longitudinal analysis that existing datasets do not sufficiently address. Overcoming these barriers, we provide housing unit estimates in consistent 2010 tract boundaries for every census year from 1940 to 2010 plus 2019 for the entire continental US. Moreover, we develop an “urbanization year” indicator that denotes if and when tracts became “urbanized” during this timeframe. We produce these data by blending existing interpolation techniques with a novel procedure we call “maximum reabsorption.” Conducting out-of-sample validation, we find that our hybrid approach generally produces more reliable estimates than existing alternatives. The final dataset, Historical Housing Unit and Urbanization Database 2010 (HHUUD10), has myriad potential uses for research involving housing, population, and land-use change, as well as (sub)urbanization.Measurement(s)human dwellingTechnology Type(s)Geographic Information SystemFactor Type(s)housing units • urbanizationSample Characteristic - OrganismHomo sapiensSample Characteristic - Environmentanthropogenic environmentSample Characteristic - Locationcontiguous United States of America

We generate datasets quantifying extreme temperature exposure in Europe using a variety of metrics at two sub-national spatial scales (NUTS 2 and NUTS 3) and three temporal scales (daily, extreme temperature wave, and yearly) from 1980-2024. These datasets capture the breadth of temperature metrics used in epidemiology, demography and environmental literature with 67 different metrics: including regionally-unusual temperature events (defined as temperatures above/below the 95 t h /5 t h percentile of historical temperatures) and periods of sustained (consecutive day) exposure to extreme temperatures. Although publicly available, climate data format and spatial resolution rarely matches the structure, scale, and extent used to disseminate government statistics on health, economic, and demographic variables, and manipulating raw data is computationally expensive. Here we provide temperature data in a user-friendly format which can easily be linked to EuroStat. Our open-sourced code and reproducible methods can be extended to produce similar datasets at the global scale.

Prospective demographic information of the United States is limited to national-level analyses and subnational analyses of the total population. With nearly 40% of the U.S. population being residents of coastal areas, understanding the anticipated demographic changes in coastal counties is important for long-range planning purposes. In this research note, we use long-range, county-level population projections based on a simplified cohort-component method to discuss demographic changes by age, sex, and race and ethnicity for coastal counties between 2020 and the end of the century, and we compare these changes to inland counties. Presently, coastal counties are statistically significantly different from inland counties by race and ethnicity (more diverse) and sex (more women) but not by age, yet by 2025, we expect coastal counties to become significantly older than inland counties. We note several important trajectories of predicted demographic outcomes in coastal counties across the remainder of the century: (1) the non-Hispanic White population is expected to decrease, both numerically and as a percentage of the population; (2) the population older than 65 is projected to increase, both numerically and as a percentage of the population; and (3) the ratio of women to men remains constant over the century at 1.03. These trends combine to suggest that the future U.S. coastline will likely be both increasingly diverse racially and ethnically and significantly older than it is today.