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Migration induced by sea-level rise could reshape the US population landscape



Many sea-level rise (SLR) assessments focus on populations presently inhabiting vulnerable coastal communities, 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 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. © 2017 Macmillan Publishers Limited, part of Springer Nature. All rights reserved.
Migration induced by sea-level rise could reshape
the US population landscape
Mathew E. Hauer
Many sea-level rise (SLR) assessments focus on populations
presently inhabiting vulnerable coastal communities1–3, 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 character-
izes 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 SLR1with 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.
It is generally understood that sea-level rise (SLR) of 1–2m
(refs 4–6) could lead to widespread human migration2,7 as residents
of highly vulnerable coastal communities look to escape rising
water levels. With up to 180 million people directly at risk to
SLR in the world and over 1 billion living in the lower-elevation
coastal zone8,9, understanding the ramifications of these potential
migrants on destination communities is a priority for climate
change research10–13.
SLR assessments, identifying both the number and locations of
potentially displaced persons, fill the literature1,2,14 and are useful
for the deployment of critical infrastructure in coastal areas. Yet
questions of where the millions of potentially displaced persons
will go remain unanswered despite a general understanding that
SLR displaced persons are likely to have profound effects on
future population landscapes11,14. Only a few studies have put forth
general hypotheses regarding SLR migration11,15 , and this void has
prompted recent calls for additional migration modelling16,17. To
date, no studies modelling precisely how SLR-induced migration
will affect the population distribution exist. By focusing solely
on coastal communities without directly addressing SLR-induced
migration, we probably underestimate the scale and magnitude of
these impacts.
Relationships between environmental stressors and migration
are highly complex as press and pulse events trigger migration
responses that range from short-distance temporary migration to
permanent long-distance migration; some will move and others
will not18–23. SLR is unique among environmental stressors as the
conversion of habitable land to uninhabitable water is expected
to lead to widespread human migration without the deployment
of costly protective infrastructure2,7,11,15. It is unclear, however,
what will actually trigger future climate migrants: press events,
such as drought or SLR, or pulse events, such as tropical
cyclones. When climate effects are integrated over long periods
of time, it is likely that a combination of press and pulse events
will spur migration24 across pre-existing migration pathways19,21 ,
leveraging established networks of social capital and kin networks in
destination decisions25. This is because press and pulse events that
spur migration operate mostly independently of the kin networks
and social capital that drive destination decisions10. Thus, climate
migrants resulting from press stressors will probably constitute
enhanced’, or extra, normal out-migration.
I combine estimates of the populations at risk to SLR1within
a migration systems simulation to estimate both the number and
destinations of potential SLR migrants in the United States (US) over
the coming century. By focusing on the destinations of SLR migrants
I am able to more holistically describe the impacts of SLR. This study
aims to answer one fundamental question regarding SLR-induced
migration: What areas are likely to see the greatest in-migration
due to SLR? Local officials in landlocked communities can use these
results to plan for potential infrastructure required to accommodate
an influx of coastal migrants and could shift the conceptualization
of SLR from a coastal issue to a more ubiquitous issue.
To answer these questions, first I use published estimates of
county-level projected populations at risk to SLR for the years 2010
through 21001under the 1.8 m SLR scenario for 319 coastal US
counties. Hauer et al.1simulated expected changes in the mean
higher high water (MHHW) mark on areas that are hydrologically
connected to coastal areas without taking into account additional
land loss caused by other natural factors such as erosion or land
subsidence. They then projected the populations exposed to SLR
over the coming century in a dynamically assessed, spatially explicit
small-area population-environment projection model, based on
growth in the period 1940–2010, where populations under the
projected MHHW mark are assumed to be at risk of displacement.
They estimated a potential 13.1 million persons could be at
risk of migrating due to a SLR of 1.8m by 2100. These data
provide the number of persons likely to migrate, but not the
migration destinations.
I then integrated the projected populations within a projected
migration system for all coastal counties affected by SLR (n=319)
and possible destinations (n=3,113) based on the Internal Revenue
Services’ annual series of county-to-county migration flow data
for the years 1990–201326. This is the largest dataset of county-to-
county migration in the US and includes data for 95 to 98 per cent
of tax filers and their dependents. To capture the temporal variability
of the county-to-county migration flows I used unobserved
component modelling (UCM)27 for each individual dyad origin–
destination pair (n=46,203) to project shifts and changes in
the migration system over the coming century. Populations are
migrated based on the proportion of total outflows from the
originating county in the projected system.
Department of Geography, University of Georgia, Athens, Georgia 30602, USA. e-mail:
© 2017 Macmillan Publishers Limited, part of Springer Nature. All rights reserved.
−199,999 to −50,000
−449,999 to −200,000
−199,999 to −50,000
−449,999 to −200,000
New York
Los Angeles
San Francisco
Figure 1 | Estimated SLR net migrants (in-migrants minus out-migrants) for counties and core based statistical areas under the 1.8 m scenario and no
adaptation. a, US counties. b, Core based statistical areas. For b, I considered only counties located in CBSAs. Counties and CBSAs without expected SLR
in-migration are in white. States are abbreviated to standard two-letter codes.
Figure 2 | Net change in population due to sea-level rise under the 1.8m scenario and no adaptation. I considered migration destinations for all 50 states
and the District of Columbia (DC) and migration origins for 22 states and the District of Columbia. These are the net changes in population due to both in-
and out-migration due to sea-level rise. States are abbreviated to standard two-letter codes.
It is possible that populations escaping SLR might migrate
to inland areas completely unaffected by SLR, as hypotheses
suggest11,15. However, not all coastal counties will be completely
inundated and many areas will still be suitable for human settlement
even with 1.8 m of SLR. To capture these possibilities, projected
migrants to each possible destination county are dynamically
adjusted based on the unaffected populations remaining in each
coastal county. In this way I model both migrations to inland areas
completely unaffected by SLR and migrations to coastal areas still
suitable for habitation. A detailed technical description is available
in the Methods.
It is likely that many communities will deploy a wide variety
of adaptation measures, including sea walls, beach and marsh
nourishment, pumps, or elevate homes and roads to protect
both people and property, and IPCC reports have increasingly
emphasized adaptation when discussing SLR28. Global estimates of
adaptive infrastructure for SLR could reach US$421 billion (2014
values) per year8and could cost upwards of US$1.1 trillion in the
US29. However, the deployment of adaptation measures is driven
by wealth for both cities30 and individuals10,31 . To approximate
this dynamic, I assume that households earning greater than
US$100,000 per year are likely to adapt to SLR in some manner,
and thus unlikely to migrate. This income threshold represents
approximately the top quartile and double the US median household
income, and is neither too restrictive nor too broad to capture the
range of individual adaptive measures. Detailed projections of SLR
© 2017 Macmillan Publishers Limited, part of Springer Nature. All rights reserved.
7,0 00
Figure 3 | Circular plot of bi-lateral SLR migration flows for US States under the 1.8 m scenario and no adaptation. Tick marks show the number of
migrants (inflows and outflows) in thousands. States are ordered clockwise by the size of inflows. The top ten outflow states are coloured; all other states
are in grey. States are abbreviated to standard two-letter codes.
migrants for all destination counties under both a ‘no adaptation’
scenario and a wealth-based adaptation scenario are also found in
the Supplementary Dataset.
I find that in the US, every state, 86% of US Core Based Statistical
Areas (CBSAs) (791 out of 915), and 56% of counties (1,735 out of
3,113) could be affected in some way by net migration (in-migration
minus out-migration) associated with 1.8 m of SLR (Fig. 1). Florida
could lose more than 2.5 million residents due to 1.8m of SLR,
while Texas could see nearly 1.5 million additional residents (Fig. 2).
Additionally, nine states could see net losses in their populations
due to SLR. Figure 3 demonstrates all origin–destination flows at
the US state level, demonstrating that the sheer magnitude of places
affected could alter the US population landscape. SLR migrants are
expected to comprise both intra- and inter-state migrations, and no
state is left untouched by SLR migration. Even accounting for the
deployment of adaptive infrastructure, millions of people could still
migrate (Table 1 and Supplementary Dataset).
My results also suggest that CBSAs such as Austin Texas,
Orlando Florida, Atlanta Georgia, and Houston Texas could
see more than 250,000 previously unforeseen future SLR net
migrants each (Table 1 and Supplementary Dataset). Thirteen
CBSAs could see more than 100,000 SLR net migrants by 2100
with 1.8 m of SLR. Conversely, ten CBSAs could lose more
than 100,000 residents due to SLR, with Miami Florida losing
over 2.5 million residents. Even accounting for those who could
adapt in place, many inland communities could see tens of
thousands of SLR in migrants, and many coastal communities
could lose tens of thousands of residents. Extended results for all
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Table 1 | Select core based statistical area (CBSA) destinations of SLR net migration (in-migration minus out-migration) in 2100
with and without adaptation under the 1.8 m scenario.
CBSA No adaptation With adaptation
Net migration +/Rank Net migration +/Rank
Austin-Round Rock, TX 818,938 243,821 1 625,627 179,186 1
Orlando-Kissimmee-Sanford, FL 461,411 62,665 2 369,120 38,834 2
Atlanta-Sandy Springs-Roswell, GA 320,937 131,984 3 248,684 68,868 3
Phoenix-Mesa-Scottsdale, AZ 100,524 12,851 13 73,935 1,949 13
Myrtle Beach-Conway-N Myrtle Beach, SC-NC 12,146 13,855 78 3,142 9,389 141
North Port-Sarasota-Bradenton, FL 208 25,057 727 2,128 21,717 168
Los Angeles-Long Beach-Anaheim, CA 3,140 51,590 737 13,181 22,200 61
New York-Newark-Jersey City, NY-NJ-PA 50,804 494,625 775 15,808 194,047 50
New Orleans-Metairie, LA 500,011 24,053 795 373,283 10,733 795
Miami-Fort Lauderdale-West Palm Beach, FL 2,509,978 155,119 796 2,009,263 95,845 796
+/represents the 80th confidence interval. States are abbreviated to standard two-letter codes.
counties and CBSAs are available in the Supplementary Methods
(Supplementary Dataset).
With many projected migrants remaining in coastal communities
(Table 1 and Supplementary Dataset), SLR could generate millions
of ‘trapped’ people32 . Trapped populations are sometimes discussed
through the concept of involuntary immobility32, but there is also
those who do not desire to move and thus constitute voluntary
immobility. These results suggest that many people displaced by SLR
could find themselves or their descendants exposed to SLR, even
with migration as an adaptation, as sea levels continue to rise past
the year 2100 with migration that constitutes relocation to presently
safe, but ultimately vulnerable, coastal communities.
Additionally, infrastructure challenges required to protect
coastal communities are well documented1,2, but the infrastructure
challenges of accommodating millions of SLR migrants in largely
unprepared inland municipalities is virtually unexplored. For many
destinations, such as Riverside California, Phoenix Arizona, Las
Vegas Nevada, and Atlanta Georgia, already experiencing water
management and growth management challenges, the SLR migrants
who wash across the landscape over the coming century could place
undue burden in these places if accommodation strategies are left
unplanned. Studies of migration impacts do not solve the challenges
in these areas, but rather reveal a more holistic understanding of
SLR impacts and needed interventions.
SLR has been broadly conceptualized as a coastal issue or haz-
ard, as assessments have focused on the effects in coastal com-
munities1,2,8,9. With millions of potential future migrants in heavily
populated coastal communities, SLR scholarship focusing solely on
coastal communities endorses a narrative that characterizes SLR
as primarily a coastal issue, obscuring the potential impacts in
landlocked communities created by SLR-driven migration. My work
shows that this coastal conceptualization of SLRcreates a deceptively
small area of affect if relocation is left unaccounted. This work offers
the first glimpse of how SLR could alter the population distribution
of the US as both coastal and landlocked communities are likely
to be affected by SLR: directly in coastal areas due to SLR itself
and indirectly in landlocked areas through the influx of people
escaping SLR.
Furthermore, the migration approach for examining destinations
associated with climate change shown here allows for modelling
migration destinations of other climate change stressors. For
instance, it has been estimated that parts of the Middle East
and North Africa (MENA) could become uninhabitable by the
end of the century, potentially spurring an exodus of 500 million
people3. Future scholars could employ my approach to model
the destinations of these potential MENA migrants. There is
tremendous potential in coupling migration systems information
with climate change models to examine the implications of climate-
change-induced migration. This type of modelling requires detailed
origin–destination migration information, limiting the areas where
this approach could be used to those where data are available.
Migration models are only as good as the data underlying them.
The suppression of IRS migration data of flows with fewer than 10
migrants could systematically bias my results against rural areas far
from coastal communities, and the modelling approach undertaken
does not allow for new unforeseen migration pairs to emerge in
the future. However, the destination counties cover 93.6% of the
US population and over 250 destination counties are outside of
CBSAs (the typical distinction of urban/rural), limiting the scope
of the rural bias to mainly sparsely populated communities far from
coastal areas. New destinations for SLR migrants could still emerge
in rural areas, and if they do, my results of the geographic spread
of SLR migrants could be considered conservative. Environmental
migration scholars could further investigate the likelihood of
emerging rural destinations related to climate change migration to
better inform future modelling efforts.
Previous trends are not always indicative of future results.
Societal and economic shifts, population ceilings, local growth
ordinances, adaptive behaviour, and climate change itself could
all change future migrant destinations. Although I model the
potential destinations of SLR migrants, I do not precisely model
how other climate stressors or other factors might influence the
future migration systems. Our current understanding of the location
decisions of environmental migrants is still limited, and there
have been recent calls to better understand migration flows16.
My approach builds on the growing literature concerning migrant
destinations and environmental change10,11,15,25,33–35, and accounts for
potential future migration systems.
High potential exists for development of deeper and more
integrated examinations of the role of adaptation and other climate
stressors on SLR migration. Neglecting to account for adaptive
behaviour could lead to an overestimation of either flood risk36
or migration37. Future studies could examine varying adaptation
scenarios related to the 100-year flood plain or the lower-elevation
coastal zone creating differing scenarios of ‘stayers.
If future migration pathways mimic past pathways, SLR is
expected to reshape the US population distribution and could
stress some landlocked areas unprepared for these migrations while
revitalizing others. SLR is currently framed as a coastal hazard,
but the migratory effects could ripple far inland. My results show
the importance of accounting for future migrations associated
with climate change in long-range planning processes for disaster
management, transportation infrastructure, land-use decisions, and
so on.
© 2017 Macmillan Publishers Limited, part of Springer Nature. All rights reserved.
Methods, including statements of data availability and any
associated accession codes and references, are available in the
online version of this paper.
Received 23 August 2016; accepted 13 March 2017;
published online 17 April 2017
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I am grateful for the constructive comments from J. M. Byars, S. Holloway,
J. M. Shepherd, J. Evans, J. S. Pippin and J. Véron.
Additional information
Supplementary information is available in the online version of the paper. Reprints and
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Springer Nature remains neutral with regard to jurisdictional claims in published maps
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Competing financial interests
The author declares no competing financial interests.
© 2017 Macmillan Publishers Limited, part of Springer Nature. All rights reserved.
The methodology for projecting sea-level rise (SLR) migrant destinations is
outlined in this section. First, I describe the datasets and basic methodology for
creating my migration matrices. Second, the methodology for capturing migration
system uncertainty is discussed.
Data. I utilize two primary sources of data concerning the magnitude of flows in
the migration system and the migration system itself. The first, the magnitude of
flows, comes from published populations projected to be at risk to SLR1.
Hauer et al.1used the National Oceanic and Atmospheric Administration’s
(NOAA) 0 m, 0.9 m (3 feet) and 1.8m (6 feet) SLR datasets for twenty-two coastal
states and the District of Columbia. These datasets simulate expected changes in
the mean higher high water (MHHW) mark on areas that are hydrologically
connected to coastal areas without taking into account additional land loss caused
by other natural factors such as erosion. They projected populations using a
modified Hammer Method38 combined with the Housing Unit Method39 for
population estimation to create temporally contiguous sub-county boundaries over
a 70-year base period from 1940 to 2010, which were then used to project
populations at these same sub-county geographies through the use of a
linear/exponential extrapolation approach for projecting census block groups
(CBG) from 2010 to 2100. The populations at risk to SLR, aggregated to the US
county and available in their Supplementary Table 21, provide the magnitude of
out-migrants from 319 coastal counties.
For this research, I used the annual series of county-to-county migration
datasets26, produced by the Internal Revenue Service (IRS) in conjunction with the
US Census Bureau, as the basis for the migration system. The IRS datasets utilize
the IRS Individual Master File containing every Form 1040, 1040A and 1040EZ
processed by the IRS, and includes 95 to 98 percent of all individual tax filers and
their dependents. The Census Bureau identifies migrants when a current filing
years’ return is from a different location than the matched preceding years’ return.
These data capture only the tax-filing universe, but the spatio-temporal
stability40–42 coupled with the very large administrative sample make them
attractive for modelling large-scale migration patterns. The IRS does suppress
migration flows comprising fewer than 10 individual migrants, systemically
suppressing small rural migration flows. However, the long-term trend of rural
out-migration to urban areas43 is expected to continue in this century44.
Migration systems theory (MST) has been tied to environmental migration in
recent years45–47. MST is a branch of migration research that holistically examines
migration options by studying all origin–destination combinations rather than any
single origin–destination combination45,46,48. Migration decisions—not just the
decision to migrate, but also decisions on where to migrate—are often driven by
kin networks, employment opportunities, amenities, both natural and economic,
economic vitality, and so forth10,22,46,49–51 . This network of ‘pull’ factors embedded
within the migration system tends to drive locational decision-making due to
environmental, or other, ‘push’ factors10,13,17,18,25,52,53 .
To describe the complete migration system in the United States, let matrix M(x)
represent all possible county-level origin–destination combinations. The sum of
any given column and row in the matrix will equal the total number of migrants
into or out of any given county. For this analysis I am concerned only with the 319
coastal counties expected to experience some form of SLR inundation under the
1.8 m scenario and their connections to the other 3,113 US counties
(n=993,047 matrix cells). I created these matrices for each year of the IRS
migration data between 1990 and 2013. Supplementary Fig. 1 shows examples of
these systems into and out of three sample counties.
m1,1 ··· m1,3113
m319,1 ··· m319,3113
o{1, ..., 319}
d{1, ..., 3113}
Migration system projection approach. I employed the use of an unobserved
components model (UCM) for forecasting equally spaced univariate time series
data27. UCMs decompose a time series into components such as trends, seasons,
cycles and regression effects, and are designed to capture the features of the series
that explain and predict its behaviour. UCMs are similar to dynamic models in
Bayesian time series forecasting54. All projections were undertaken in SAS 9.4
using the PROC UCM procedure.
The basic structural model (BSM) is the sum of its stochastic components. Here
I use a trend component µtand a random error componentεt, and it can be
described as:
Each of the model components are modelled separately with the random error εt
modelled as a sequence of independent, identically distributed zero-mean
Gaussian random variables. The trend component is modelled using the
following equations:
ηtN(0, σ2
ξtN(0, σ2
These equations specify a trend where the level µtand the slope βtvary over time,
governed by the variance of the disturbance terms ηtand ξtin their equations. Here
all origin–destination dyadic pairs containing any migration information over the
series were modelled (n=46,203) in individual UCM models.
This approach allows for the projected evolution of the migration system as
dyadic pairs either strengthen or weaken over time, allowing for migration links to
wax or wane over the projection horizon. Empirical simulations of environmental
migration have proven very fruitful in the modelling of climate-change-induced
migration15,55,56, and here I build upon those efforts by projecting future
climate migration.
Our current understanding of the migratory response to sea-level rise is still
underdeveloped. Will displaced coastal populations relocate into the parts of
coastal communities unaffected by sea-level rise? Or will displaced persons migrate
to more inland areas free from the challenges of sea-level rise11,15? Many areas in
threatened coastal communities are still eligible for human settlement and could be
possible destinations for future SLR migrants. To capture both possibilities, I
employ a raking procedure to proportionally adjust in-migrants based on the
inverse of the proportion of the population affected by SLR and a redistribution of
those migrants to unaffected counties.
od =Mt
od (1Dt
The adjusted number of in-migrants Mto destination county dfrom origin county
oat time tis equal to the number of migrants multiplied by one minus the
proportion of the population impacted by SLR in the destination county at time t.
For inland counties, the right-hand side of the equation equals one, yielding
no adjustment.
o ˆ
However, the out-migrants from each affected coastal county must be raked to
equal the total population at risk to SLR expected to be displaced. This is
accomplished by redistributing the difference of the unadjusted migrants Mt
origin ofrom the adjusted migrants from origin oand multiplying it by the
proportion of adjusted migrants from origin oto destination dto the unaffected
counties (i=U) from the total adjusted migrants from origin o. In this way, the
underlying migration system from each individual origin is preserved in the
raking procedure.
To assess how adaptation might impact future migration streams, the
proportion of the population in households earning greater than $100,000 per year
in the 2011–2015 American Community Survey data were assumed to
be non-migrants.
Projection uncertainty. Evaluation of migration system projection. Projection
intervals allow us to examine the feasibility of the future projected migration
systems and are typically employed in the evaluation of population projections57 .
Demographers have typically used the 2/3 or 66% projection interval to assess the
accuracy of a population projection58,59, representing ‘low’ and ‘high’ scenarios
that are ‘neither so wide as to be meaningless nor too narrow to be
overly-restrictive60 .
To examine the feasibility of the migration system projections, I produce
projections based on the equations in the preceding section with the base period
1990–2003 and an evaluation period of 2004–2013. If less than 2/3 of the IRS
migration counts fall within the 2/3 projection interval then the results would
suggest less than ideal accuracy. However, if more than 2/3 of the IRS migration
counts fall within the 2/3 projection interval, it would suggest an ideal amount of
accuracy. I assessed the 2/3 interval for 10 years of projections for an evaluation of
base period 1990–2003 and a projection period of 2004–2013.
Supplementary Table 1 shows the overall number of IRS migration counts that
fall within the 2/3 projection interval for each year of the evaluation period.
Overall, the UCMs produce robust projections, as all projection years are above the
2/3 projection interval.
Data availability. The data that support the findings of this study have been
deposited in openICPSR ( .
© 2017 Macmillan Publishers Limited, part of Springer Nature. All rights reserved.
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... While there is an extensive literature on climate and migration that examines the potential for past or current influences and their causes (Piguet, Models of climate effects on US domestic migration and population change in the demographic literature have largely focused on the effects of sea level rise (SLR) as a driver (Curtis and Schneider, 2011;Hauer, 2017;Robinson et al., 2020). They combine projected inundation of coastal areas with spatially resolved population projections and assume that people in inundated areas migrate. ...
... Migration away from coastal areas is modeled either assuming current spatial patterns remain constant or are allowed to change as a function of changing population densities. Total migration reaches 4-13 million people per year nationally by 2100 (Hauer, 2017) for SLR scenarios that are moderate to very high. Most out-migration is from Florida, while destinations vary by study, concentrated in Texas (Hauer, 2017) or the east and southeast regions (Robinson et al., 2020). ...
... Total migration reaches 4-13 million people per year nationally by 2100 (Hauer, 2017) for SLR scenarios that are moderate to very high. Most out-migration is from Florida, while destinations vary by study, concentrated in Texas (Hauer, 2017) or the east and southeast regions (Robinson et al., 2020). One study (Feng et al, 2012) that went beyond SLR impacts focused on domestic migration in response to climate-driven changes in crop yields. ...
Full-text available
Climate change is a potentially important driver of migration within the US but its impact on future population distribution remains under-explored. We project the impact of climate change over the 21st century on state-level population by integrating a detailed demographic model with a model of migration responses to changes in temperature and their consequences for regional economic conditions. We find that climate change is unlikely to fundamentally alter the large-scale distribution of people across the US, but it could have important consequences for some states. End-of-century population in states projected to experience either large, direct climate impacts or large inflows of climate migrants is altered by 10-30% relative to comparable projections where this influence is omitted. The dominant pattern of climate-induced migration is from the South to the Northeast and West with the pattern driven by the relative change in climate experienced across locations, distance, and the changes in regional economic conditions wrought by migration itself. The follow-on effects of migration on natural growth also influence state population change. Climate scenarios which produce the greatest degree of warming do not unambiguously produce the largest changes in population, because widespread warming results in relatively few safe havens, rendering migration an ineffective form of adaptation to climate change.
... The insight of interconnectivity from MST resonates with that of social network analysis. Indeed, past research has used social network analysis to study migration systems (Charyyev and Gunes 2019;Desmarais and Cranmer 2012;DeWaard et al. 2020;DeWaard and Ha 2019;DeWaard, Kim, and Raymer 2012;Hauer 2017;Leal 2021;Liu, Andris, and Desmaris 2019;Nogle 1994;Vögtle and Windzio 2022;Windzio 2018;Windzio, Teney, and Lenkewitz 2019). This school of MST, which Bakewell (2014) calls the "abstract system," interrogates macrolevel migration patterns by analyzing migration networks consisting of localities (in network terms, nodes) and migration flows between each directed pairs of localities (in network terms, edges). ...
... Mueller and Gasteyer 2023;Schroeder and Pacas 2021). Movement across a county boundary is a frequently-used definition of internal migration in the literature(Brown and Bean 2016;DeWaard et al. 2020;Hauer 2017;Partridge et al. 2012). Administered by the U.S. Census Bureau, ACS surveys respondents' location of residence one year ago and estimates the population size that migrated between each pair of counties each year. ...
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Despite the popular narrative that the United States is a “land of mobility,” the country may have become a “rooted America” after a decades-long decline in migration rates. This article interrogates the lingering question about the social forces that limit migration, with an empirical focus on internal migration in the United States. We propose a systemic, network model of migration flows, combining demographic, economic, political, and geographic factors and network dependence structures that reflect the internal dynamics of migration systems. Using valued temporal exponential-family random graph models, we model the network of intercounty migration flows from 2011 to 2015. Our analysis reveals a pattern of segmented immobility, where fewer people migrate between counties with dissimilar political contexts, levels of urbanization, and racial compositions. Probing our model using “knockout experiments” suggests one would have observed approximately 4.6 million (27 percent) more intercounty migrants each year were the segmented immobility mechanisms inoperative. This article offers a systemic view of internal migration and reveals the social and political cleavages that underlie geographic immobility in the United States.
... To date, some studies generalize the Katrina experience by examining other severe ood events, 3,4,5,6 while others subordinate a focus on race to make more general claims about the relationship between climate and migration. 7,8,9,10 Research examining the effect of oods, generally, on internal migration broken down by race does not exist; this paper lls that gap. ...
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Floods are increasingly frequent and severe due to climate change, thereby impacting migration within the United States. Considering that Black and Brown populations are disproportionately exposed to floods, less likely to receive disaster-related government funds, and vulnerable during subsequent displacement, an examination of differences in migration patterns across racial/ethnic groups is critical. The prevailing conjecture is that after floods, Black and Brown populations will migrate while White ones remain in place. We test this hypothesis by examining the effect of floods on migration across all U.S. county-pairs between 2006-2016 and find that this hypothesis is incorrect: generally, after floods Black populations remain in place and White populations migrate. However, this pattern reverses when the Federal Emergency Management Agency provides financial support. Notably, migration by Hispanic and Asian populations is not significantly affected by floods. These results provide the first evidence of racial disparities in climate migration.
... Studies have mainly focused on the direct impacts of SLR, assessed by modeling future spatial patterns of permanent inundation and/or extreme event flood zones and then intersecting them with spatially distributed social data (e.g. population density, racial and ethnic population fractions) (Hauer et al 2016, Hauer 2017, whereas capturing indirect impacts is more complex. Wealthier, higher-capacity neighborhoods are generally understood to have greater ability to handle the shocks and stressors of climate change than socioeconomically vulnerable or marginalized communities (Fussell et al 2010(Fussell et al , 2014. ...
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Exposure to sea-level rise (SLR) and flooding will make some areas uninhabitable, and the increased demand for housing in safer areas may cause displacement through economic pressures. Anticipating such direct and indirect impacts of SLR is important for equitable adaptation policies. Here we build upon recent advances in flood exposure modeling and social vulnerability assessment to demonstrate a framework for estimating the direct and indirect impacts of SLR on mobility. Using two spatially distributed indicators of vulnerability and exposure, four specific modes of climate mobility are characterized: (1) minimally exposed to SLR (Stable), (2) directly exposed to SLR with capacity to relocate (Migrating), (3) indirectly exposed to SLR through economic pressures (Displaced), and (4) directly exposed to SLR without capacity to relocate (Trapped). We explore these dynamics within Miami-Dade County, USA, a metropolitan region with substantial social inequality and SLR exposure. Social vulnerability is estimated by cluster analysis using 13 social indicators at the census tract scale. Exposure is estimated under increasing SLR using a 1.5m resolution compound flood hazard model accounting for inundation from high tides and rising groundwater and flooding from extreme precipitation and storm surge. Social vulnerability and exposure are intersected at the scale of residential buildings where exposed population is estimated by dasymetric methods. Under 1m sea-level rise, 56% of residents in areas of low flood hazard may experience displacement, whereas 26% of the population risks being trapped (19%) in or migrating (7%) from areas of high flood hazard, and concerns of depopulation and fiscal stress increase within at least 9 municipalities where 50% or more of their total population is exposed to flooding. As SLR increases from 1 to 2m, the dominant flood driver shifts from precipitation to inundation, with population exposed to inundation rising from 2.8% to 54.7%. Understanding shifting geographies of flood risks and the potential for different modes of climate mobility can enable adaptation planning across household-to-regional scales.
... The international communities, including policymakers and researchers, are now tossing light on the migration and climate change nexus to make more effective policies; however, the knowledge is still fragmented (Piguet et al., 2011). The literature argues that climate change effects such as sea-level rise often produce climate refugees (Farbotko & Lazrus, 2012;Hauer, 2017;McLeman, 2014). Migration-induced human resettlement may happen due to climate change which is often treated as a push factor (Piguet et al., 2011). ...
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Literature argues that numerous climatic factors contribute to migration decisions. To understand the complex interplay between climate change impacts and migration-decision, we need to analyse how the factors affect the said decision. This bibliometric review aims to analyse the climate change and migration literature and assess future research opportunities for exploring climate-induced migration. This review considers 4658 documents extracted from Scopus by performing a search with the words 'migration', 'climate change', 'climatic hazard' and 'coastal region' covering journal articles, review papers, book chapters, books, and conference papers from 2011 to 2020. This study applied VOSViewer for analysis. Results reveal that climate change is a dominant driver of migration, and the literature is deeply rooted in the United States and the United Kingdom. The lexical network shows that the developed countries which are less vulnerable to climatic hazards produce more co-authored documents. Furthermore, in the migration discourse, the co-authors from developed countries have strong ties exhibiting migration and climate change research, mainly concentrated among the collaborative framework of developed countries’ researchers. Therefore, more research on migration and climate change issues in collaboration with the global south and north is highly demanding, providing further insights into the existing research arena.
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Within coastal communities, sea level rise (SLR) will result in widespread intermittent flooding and long-term inundation. Inundation effects will be evident, but isolation that arises from the loss of accessibility to critical services due to inundation of transportation networks may be less obvious. We examine who is most at risk of isolation due to SLR, which can inform community adaptation plans and help ensure that existing social vulnerabilities are not exacerbated. Combining socio-demographic data with an isolation metric, we identify social and economic disparities in risk of isolation under different SLR scenarios (1-10 ft) for the coastal U.S. We show that Black and Hispanic populations face a disproportionate risk of isolation at intermediate levels of SLR (4 ft and greater). Further, census tracts with higher rates of renters and older adults consistently face higher risk of isolation. These insights point to significant inequity in the burdens associated with SLR.
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Impacts of sea level rise will last for centuries; therefore, flood risk modeling must transition from identifying risky locations to assessing how populations can best cope. We present the first spatially interactive (i.e., what happens at one location affects another) land change model (FUTURES 3.0) that can probabilistically predict urban growth while simulating human migration and other responses to flooding, essentially depicting the geography of impact and response. Accounting for human migration reduced total amounts of projected developed land exposed to flooding by 2050 by 5%–24%, depending on flood hazard zone (50%–0.2% annual probability). We simulated various “what-if” scenarios and found managed retreat to be the only intervention with predicted exposure below baseline conditions. In the business-as-usual scenario, existing and future development must be either protected or abandoned to cope with future flooding. Our open framework can be applied to different regions and advances local to regional-scale efforts to evaluate potential risks and tradeoffs.
For residential relocation as an adaptation measure to sea level rise due to climate change, this study estimated costs, land-use changes and transfer distances for the Japanese coasts. A simulation was carried out to allocate affected households to available lands in ascending order of geographical distance from the inundation area. The resulting estimated relocation costs in 2050 and 2070 were 117-118 trillion yen and 150-151 trillion yen for SSP5-RCP8.5, and 100-101 trillion yen and 108-109 trillion yen for SSP1-RCP2.6. These amounts were close to the lower ends of the ranges of relocation costs in previous studies. With regard to land-use changes, the use of wastelands increased over time, whereas the uses of agricultural land and forests decreased over time. Transfer distances decreased over time as a population decline led to more wasteland being closer to the inundation area.
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This paper proposes a new method for short-term county population projections. It is based on a modification of the ratio-correlation method of population estimation. The modified ratio-correlation method can produce projections with a high potential for accuracy without requiring substantial data and intensive intellectual labor inputs. Tests of accuracy are examined for the modified ratio-correlation method and two currently available alternatives using data from Wash-ington state. The tesls suggest that the new method performs well. Advantages of the modified ratio-correlation method are discussed, with particular attention given to the formal measurement of uncertainty. Forecast intervals are constructed and examined for the projections constructed for counties in Washington state. Given certain limitations, the forecast intervals appear to be robust in terms of providing accurate assessments of the precision associated with county population projections made using the modified ratio-correlation method.
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The global reach of human activities affects all natural ecosystems, so that the environment is best viewed as a social–ecological system. Consequently, a more integrative approach to environmental science, one that bridges the biophysical and social domains, is sorely needed. Although models and frameworks for social–ecological systems exist, few are explicitly designed to guide a long-term interdisciplinary research program. Here, we present an iterative framework, “Press–Pulse Dynamics” (PPD), that integrates the biophysical and social sciences through an understanding of how human behaviors affect “press” and “pulse” dynamics and ecosystem processes. Such dynamics and processes, in turn, influence ecosystem services –thereby altering human behaviors and initiating feedbacks that impact the original dynamics and processes. We believe that research guided by the PPD framework will lead to a more thorough understanding of social–ecological systems and generate the knowledge needed to address pervasive environmental problems.
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The ensemble results of CMIP5 climate models that applied the RCP4.5 and RCP8.5 scenarios have been used to investigate climate change and temperature extremes in the Middle East and North Africa (MENA). Uncertainty evaluation of climate projections indicates good model agreement for temperature but much less for precipitation. Results imply that climate warming in the MENA is strongest in summer while elsewhere it is typically stronger in winter. The summertime warming extends the thermal low at the surface from South Asia across the Middle East over North Africa, as the hot desert climate intensifies and becomes more extreme. Observations and model calculations of the recent past consistently show increasing heat extremes, which are projected to accelerate in future. The number of warm days and nights may increase sharply. On average in the MENA, the maximum temperature during the hottest days in the recent past was about 43 °C, which could increase to about 46 °C by the middle of the century and reach almost 50 °C by the end of the century, the latter according to the RCP8.5 (business-as-usual) scenario. This will have important consequences for human health and society.
Recent theoretical interest in migration systems calls attention to the functions of diverse linkages between countries in stimulating, directing and maintaining international flows of people. This article proposes a conceptual framework for the nonpeople linkages in international migration systems and discusses the implications for population movement of the four categories and three types of linkages that define the framework.
This unique book introduces an essential element in applied demographic analysis: a tool-kit for describing, smoothing, repairing and - in instances of totally missing data - inferring directional migration flows. Migration rates combine with fertility and mortality rates to shape the evolution of human populations. Demographers have found that all three generally exhibit persistent regularities in their age and spatial patterns, when changing levels are controlled for. Drawing on statistical descriptions of such regularities, it is often possible to improve the quality of the available data by smoothing irregular data, imposing the structures of borrowed and related data on unreliable data, and estimating missing data by indirect methods. Model migration schedules and log-linear models are presented as powerful methods for helping population researchers, historical demographers, geographers, and migration analysts work with the data available to them.
Recent studies showed that climate change and socioeconomic trends are expected to increase flood risks in many regions. However, in these studies, human behavior is commonly assumed to be constant, which neglects interaction and feedback loops between human and environmental systems. This neglect of human adaptation leads to a misrepresentation of flood risk. This article presents an agent-based model that incorporates human decision making in flood risk analysis. In particular, household investments in loss-reducing measures are examined under three economic decision models: (1) expected utility theory, which is the traditional economic model of rational agents; (2) prospect theory, which takes account of bounded rationality; and (3) a prospect theory model, which accounts for changing risk perceptions and social interactions through a process of Bayesian updating. We show that neglecting human behavior in flood risk assessment studies can result in a considerable misestimation of future flood risk, which is in our case study an overestimation of a factor two. Furthermore, we show how behavior models can support flood risk analysis under different behavioral assumptions, illustrating the need to include the dynamic adaptive human behavior of, for instance, households, insurers, and governments. The method presented here provides a solid basis for exploring human behavior and the resulting flood risk with respect to low-probability/high-impact risks.
Although humanitarian crises, such as the ongoing mass exodus from Syria toward Europe, tend to focus global attention on migration, each year millions of people migrate to and from affected countries throughout the world. Progress has been made in understanding drivers of migration, and we have relatively good data on immigrant populations, but we lack information on how many people leave their country each year to settle elsewhere and who these emigrants are. The impact of migration on the individual and on sending and receiving communities and countries is only partly understood. Economic effects can be very different from the impacts on society and culture; some gain from migration, whereas others lose. The lack of knowledge creates systemic risks and uncertainties and frustrates public debate and the formation of effective policies. As high-level leaders convene to discuss such issues at the first United Nations World Humanitarian Summit, we outline priorities for migration data collection, research, and training.