Climate Change and Children's Health and Well-Being in the United States
Appendix E: Supplemental Information for
Analyses in Flooding Chapter
This appendix describes methods, data sources, and assumptions for the flooding
analyses presented in Chapter 6 of the main report. First is the information for our detailed analysis
of coastal flooding and children's homes. Second is information required for the discussion of
emerging literature linking inland flooding with analogous effects on children's homes.
Detailed Analysis of Coastal Flooding and
Children's Homes
This section provides supporting information for the detailed analysis of coastal flooding
impacts to children's homes presented in Chapter 6 of the main report. It starts with a discussion of
the National Coastal Properties Model (NCPM) and recent research that has relied on its methods
and functionality. Then, the analytic steps in the report's future projection of coastal flooding risks to
children are described in detail. After presenting the results of the analysis, this section concludes
with limitations of the approach.
NATIONAL COASTAL PROPERTIES MODEL AND RELATED
RESEARCH
The NCPM is a well-established tool for evaluating site-specific risks to coastal properties in the
contiguous United States associated with sea level rise and storm surge. The model determines
inundated areas at the 150 m grid resolution for each coastal county and estimates property losses
and expected damage, considering local characteristics like elevation and proximity to tidally
influenced waterbodies. Permanent inundation associated with sea level rise is modeled using a
"modified bathtub" approach that ensures a hydraulic connection as sea levels rise. It assumes
complete loss of structure value once the mean high or higher water level reaches the property, or if
repeated storm surge damage reaches a threshold of a persistent 10% annual economic damage.
Storm surge is modeled using historical tide gauge measurements from NOAA. Details and relevant
data sources are provided in Table 1 below and in Neumann et al., referenced below.
The model also differentiates between "with" and "no additional" adaptation scenarios. Under the
"with adaptation" scenario, properties are protected (e.g., via sea walls, beach nourishment, and
elevating properties) when the avoided property damages associated with protection outweigh the
costs of implementing the mitigation measures. Alternatively, the "no additional adaptation"
scenario assumes no protective measures beyond those currently in place. More information about
the NCPM can be found in Neumann et al., particularly the supplemental materials; and at
www.epa.gov/cira, particularly the impacts by units of sea-level rise in Appendix B of the FrEDI
documentation and in Technical Appendix H of EPA's report, Climate Change and Social Vulnerability
in the United States: A Focus on Six Impacts.
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Climate Change and Children's Health and Well-Being in the United States
Over the last decade, several published studies have used the NCPM to analyze coastal flooding risks,
including Lorie et al.,1 Martinich et al.,2 and Neumann et al.3,4' 5 Most recently, Neumann et a 1.6
employed the NCPM to estimate the monetary value of damages to existing coastal infrastructure
under different climate change scenarios. The authors found that the average annual damages to
coastal properties could reach $44.1 billion (2018 dollars) by 2090 relative to a 2005 baseline with
high emissions (RCP8.5) and no additional adaptation. The analysis in this report revisits the NCPM
parameters used in Neumann et al. to identify the census blocks susceptible to flooding impacts and
estimates the number of children in those census blocks that may experience the various risks
associated with flooded homes.
ANALYSIS STEPS
Table 1 details the analytic steps, data sources, and assumptions used to estimate the number of
children at risk of "temporary home damage and displacement" and "complete home loss"
associated with coastal flooding (see Chapter 6 for details). As described in the table, this analysis
summarizes impacts by increments of global mean sea level (GMSL) rise. For more information on
how the analysis applies thresholds of GMSL, see methods described in Chapter 2 of the main report
or Appendix A.
This analysis is limited to coastal counties. The report defines a county as "coastal" using the National
Oceanic and Atmospheric Administration's (NOAA) definition of Coastal Watershed Counties.7
However, this definition is limited in this analysis to the contiguous U.S. and excludes those counties
incorporated in NOAA's list solely based on proximity to or bordering on the Great Lakes. 302
counties meet this definition.
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Climate Change and Children's Health and Well-Being in the United States
Table 1: Analytic Steps in Climate Change Impacts on Coastal Flooding and Children's Homes Analysis
2
(0
00
1. Establish the number
of children living in
homes experiencing
damage from coastal
flooding conditions in
the baseline
Various property value, property type
(e.g., residential) and undeveloped land
data housed in NCPM and used in
Neumann et al. (2021)
Methods, Assumptions, and Notes
The baseline represents coastal flooding impacts to children without sea
level rise and is centered on the year 2000.
Temporary displacement: based on annual expected damage from storm
surge that exceeds 2% (see Step 3a below for more detail) without
changes in mean sea level.
Home loss: based on an annual expected damage from storm surge that
exceeds 10% (see Step 3a for more details) based on 2005 era mean sea
level. The baseline excludes loss of home when the grid cell area is below
the mean high or higher water (MHHW) in 2005 (that is, if the cell has
been inundated already at the start of the simulation)
o
a>
IS)
0)
(0
u
0)
2. Forecast future
coastal flooding from
sea level rise and storm
surge exceedance
curves assuming "no
additional adaptation"
and "with adaptation"
Sweet et al.8 for local relative rise in sea
level to 2100, and Lorie et al. for
historical storm surge exceedance
curves. Both data sources are used in
Neumann et al. and incorporated into
the NCPM.
Local sea level rise accounts for local factors such as vertical land
movement and effects of climate on ocean currents among others (see
Sweet et al.). Storm surge height portfolios are estimated from tide
gauge data. A distance weighting procedure for interpolating between
individual tide gauges is employed to attribute tide gauge-level results to
each coastal county.
The NCPM simulates flooding in 302 coastal counties at the 150 m
square grid between 2000 and 2100 assuming "no additional adaptation"
and "with adaptation." Sea walls and beach nourishment protect areas in
the landward direction, while the action of elevating buildings within a
grid only protects the elevated building itself.
Using the annual model output, the methodology averages across the six
climate change projections around 11-year windows centered on arrival
years of sea level points (25 cm to 125 cm, at 25 cm increments), as
described in Chapter 2 of the main report and Appendix A.
a>
o
l/l
0)
.01 IE
u
3a. Evaluate the number
of homes at risk of
temporary damage and
displacement or
complete loss
Various data housed in NCPM and used
in Neumann et al.
Temporary displacement: This scenario assumes an annual expected
property damage threshold of 2%.'This would mean annual repair costs
would be roughly equivalent to the cost of the residential structure after
a 50-year period. This level of damage is designed to represent a
significant, but not necessarily fully threatening, level of risk; 2% average
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Climate Change and Children's Health and Well-Being in the United States
Step
Data
Methods, Assumptions, and Notes
expected damages (AED) is roughly twice the standard procedure for
maintenance used by property managers as the expected routine
maintenance cost for a structure.
Home loss: Consistent with assumptions in Lorie et al. and Neumann et
al., which recently revised and updated the adaptation and property at
risk of exclusion from adaptation decision rules for the NCPM, this
scenario assumes an individual 150 m grid cell is at high risk of complete
loss if either (i) once it reaches an annual expected damage threshold of
10%, which indicates the cost of repair is higher than the value of the
residential structure over a 10-year period or (ii) the property is below
the MHHW level driven by sea level rise.
3b. Estimate the
number of children
living in homes at risk of
temporary damage and
displacement or
complete loss
See Chapter 2 of the main report and
Appendix A for details on our population
projection methods and data sources
used throughout the analysis.
To determine the number of children living in the homes identified in
Step 3a, this analysis maps each 150 m to census block groups, then
applies the population at the census block group level (see Chapter 2 and
Appendix A). There are roughly 220 grids per census block group in the
coastal counties. The intermediate SLR scenario (1 meter of global mean
rise by end of century) is used to determine the year from the population
projection to use for the 50 cm and 100 cm scenarios.
Within a census block group, the data do not distinguish between
residential and non-residential structures, or determine which homes
include children. To address this, the analysis assumes the number of
children impacted relates to the proportion of property impacted within
the census block group. This process assumes an even spatial distribution
of children living across the census block group.
For the temporary displacement analysis, the number of children
affected are displayed in annual terms. For the home loss scenario, the
analysis presents the cumulative number of children affected at or below
the sea level rise threshold.
Notes: *1% and 3 % AED thresholds were also considered. At 100 cm of global mean sea level rise, the number of children impacted by a 1% AED is about
10% higher than at a 2% AED. At a 3% AED, the number of children impacted is about 15% fewer than at a 2% AED.
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Climate Change and Children's Health and Well-Being in the United States
EFFECTS ON CHILDREN RESULTS
Table 2 describes the overall results, assuming population growth consistent with EPA's ICLUSv2
population tool (see Chapter 2 and Appendix A). Table 3 demonstrates the influence of climate
change by assuming a consistent U.S. population size using 2010 levels.
For coastal flooding, which is driven by sea-level rise, a G CM -specific approach to uncertainty
analysis (i.e., high- and low- end estimates to accompany the average across climate models) used
for other analyses is not applicable. Therefore, the uncertainty estimates for this analysis rely on
Sweet et al. that reports global mean projections as well as the 17th and 83rd percentiles. This "likely
range" of sea level rise is used in the flooding analysis to estimate the number of children impacted
in the contiguous United States (U.S.) for each increment of sea level rise from 25 cm to 100 cm. The
technique works well for the "Home Loss" scenario because the estimates are permanent and
cumulative, and therefore increase as sea level rises increases. However, uncertainty bounds are not
included for the "Temporary Displacement" scenario. This is because temporary displacement
represents a shifting zone of influence that migrates landward as sea levels rise but excludes the
zone where homes have already been lost. For this reason, the number of children impacted by
temporary displacement fluctuates as seas rise by about +/- 20% but generally does not increase
with higher sea levels.
Table 2: Total Number of Children Living in Coastal Areas with Homes Projected to Be Affected
by Flooding (with Population Growth)
Global Mean
Temporary Displacement
Home Loss
Sea Level Rise
No Additional
With
No Additional
With
Scenario
Adaptation
Adaptation
Adaptation
Adaptation
25 cm
1,070,000
705,000
121,000
119,000
(106,000 to 136,000)
(105,000 to 131,000)
50 cm
1,014,000
629,000
185,000
169,000
(159,000 to 437,000)
(149,000 to 216,000)
75 cm
1,129,000
833,000
517,000
230,000
(358,000 to 1,082,000)
(201,000 to 294,000)
100 cm
1,079,000
956,000
1,133,000
300,000
(477,000 to 2,962,000)
(223,000 to 603,000)
125 cm
818,000
696,000
1,724,000
392,000
Notes: Number of children impacted relative to baseline risks: 662,600 children for temporary displacement and
48,800 children for home loss. The number of children affected by temporary displacement are annual estimates
whereas the number of children affected by home loss are cumulative (i.e., once a home is lost, the number of
affected children is included in the affected population in all subsequent sea level rise thresholds). The low and
high estimates for home loss are characterized by uncertainty bounds from Sweet et al. that reports global mean
projections as well as the 17th and 83rd percentiles. All other estimates in the table reflect averages across
climate models.
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Climate Change and Children's Health and Well-Being in the United States
Table 3: Total Number of Children Living in Coastal Areas with Homes Projected to be Affected
by Flooding (2010 Population)
Global Mean
Temporary Displacement
Home Loss
Sea Level Rise
Scenario
No
Additional
Adaptation
With
Adaptation
No Additional
Adaptation
With
Adaptation
25 cm
948,000
630,000
108,000
107,000
(95,400 to 119,000)
(94,300 to 115,000)
50 cm
758,000
502,000
151,000
138,000
(134,000 to 298,000)
(126,000 to 171,000)
75 cm
804,000
613,000
345,000
181,000
(252,000 to 661,000)
(161,000 to 227,000)
100 cm
795,000
699,000
690,000
231,000
(322,000 to 1,886,000)
(176,000 to 469,000)
125 cm
589,000
500,000
1,062,000
302,000
Notes: See Table 2.
The next several tables and figures describe the spatial distribution of these impacts on children.
Table 4 summarizes the number of children affected and the number of children per 100,000 living in
coastal counties for both flooding impact severity, assuming no additional adaptive action. There is
substantial variation across and within states in part because of differences in topography, and in
part because of differences in the location of homes relative the projected future coastal flood plain.
Figures 1 through 5 that follow highlight the distribution aggregated to census tracts from census
block groups, recognizing this heterogeneity within and between states.
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Climate Change and Children's Health and Well-Being in the United States
Table 4: Number of Children Living in Coastal Areas with Homes Projected to Be Affected by
Flooding by State, Assuming No Additional Adaptation at 100 cm of Global Sea Level Rise
State
Expected Annual Temporary
Displacement
Cumulative Home Loss
Total Children
Per 100,000
Total Children
Per 100,000
Alabama
5,000
3,100
2,000
1,600
California
61,000
500
70,000
600
Connecticut
21,000
2,600
2,000
300
Delaware
2,000
1,000
8,000
3,300
Washington, DC
1,000
500
0
0
Florida
263,000
4,700
731,000
12,900
Georgia
14,000
5,500
10,000
4,000
Louisiana
134,000
19,600
67,000
9,900
Maine
3,000
2,200
4,000
2,900
Maryland
21,000
2,100
16,000
1,600
Massachusetts
34,000
2,500
6,000
400
Mississippi
40,000
27,100
5,000
3,300
New Hampshire
1,000
900
2,000
1,500
New Jersey
90,000
3,100
21,000
700
New York
180,000
3,500
4,000
100
North Carolina
47,000
15,000
55,000
17,600
Oregon
7,000
2,200
6,000
1,700
Pennsylvania
1,000
100
1,000
100
Rhode Island
4,000
1,600
1,000
500
South Carolina
26,000
9,700
17,000
6,500
Texas
28,000
1,000
42,000
1,500
Virginia
76,000
5,800
45,000
3,500
Washington
21,000
1,500
18,000
1,200
Notes: Total number of children affected, and number of children affected per 100,000 children, living in coastal
counties for each state. These assume no additional adaptation at 100 cm of global sea level rise.
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Climate Change and Children's Health and Well-Being in the United States
Figure 1: Children Projected to Be Affected by Home Loss Per 100,000 Living in Coastal
Counties Affected at 50 cm of Global Sea Level Rise (Assuming No Additional Adaptation)
7'
t,
(
Top five states: NC (13,000), LA (4,000), SC (3,000), VA (2,000), and GA (1,000)
No Impact >0 - 1,000 1,000 - 3,000 "3,000 - 8,000 "8,000 - 20,000 "20,000 - 100,000
Note: This map describes the number of children per 100,000 projected to be affected by complete home
loss from coastal flooding at 50 cm of global sea level rise assuming no additional adaptation measures
are taken. Darker shading conveys higher impacts. The five states with the highest average impacts per
100,000 children are outlined in black. Only coastal counties are considered in this analysis, see Table 1.
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Climate Change arid Children's Health and Well-Being in the United States
Figure 2: Children Projected to be Affected by Temporary Home Displacement Per 100,000
Living in Coastal Counties Affected at 100 cm of Global Sea Level Rise
No Additional Adaptation
*
w-
v:*
Top five states: MS (27,000), LA (20,000), NC (15,000), SC (10,000), and VA (6,000),
With Adaptation
k
r ^ \
Top five states: MS (25,000), LA (18,000), NC (15,000), SC (9,000), and GA (5,000),
No Impact >0- 1,000 1,000- 3,000 3,000-8,000 ¦ 8,000 - 20,000 " 20,000 - 100,000
Note: These maps describe the number of children per 100,000 projected to be affected by temporary
home displacement from coastal flooding at 100 cm of global sea level rise assuming both "no additional
adaptation" and "with adaptation" scenarios. Darker shading conveys higher impacts. The five states
with the highest average impacts per 100,000 children are outlined in black. Only coastal counties are
considered in this analysis, see Table 1.
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Climate Change arid Children's Health and Well-Being in the United States
Figure 3: Children Projected to be Affected by Home Loss Per 100,000 Living in Coastal
Counties Affected at 100 cm of Global Sea Level Rise
w
I;
No Additional Adaptation
V.
Top five states: NC (18,000), FL (13,000), LA (10,000), SC (7,000), and GA (4,000).
With Adaptation
/
u
* „
Top five states: NC (15,000), LA (6,000), SC (4,000), DE (3,000), and ME (3,000).
No impact >0- 1,000 1,000-3,000 3,000-8,000 M 8,000 - 20,000 M 20,000 - 100,000
Note: These maps describe the number of children per 100,000 projected to be affected by complete
home loss from coastal flooding at 100 cm of global sea level rise assuming both "no additional
adaptation" and "with adaptation" scenarios. Darker shading conveys higher impacts. The five states
with the highest average impacts per 100,000 children are outlined in black. Only coastal counties are
considered in this analysis, see Table 1.
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Climate Change arid Children's Health and Well-Being in the United States
Figure 4: Total Number of Children Projected to be Affected by Temporary Home
Displacement in Coastal Counties at 100 cm of Global Sea Level Rise
No Additional Adaptation
a
VI
-J{./
im
Top five states: FL (263,000), NY (180,000), LA (42,000), NJ (90,000), and VA (96,000).
With Adaptation
i
Top five states: FL (221,000), NY (176,000), LA (120,000), NJ (86,000), and VA (63,000).
No Impact >0-5 5-30 30- 100 ¦ 100 - 500 "500 - 9,700
Note: These maps describe the total number of children projected to be affected by temporary home
displacement from coastal flooding at 100 cm of global sea level rise assuming both "no additional
adaptation" and "with adaptation" scenarios. Darker shading conveys higher impacts. The five states
with the highest total impacts are outlined in black. Only coastal counties are considered in this analysis,
see Table 1.
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Climate Change arid Children's Health and Well-Being in the United States
Figure 5: Total Number of Children Projected to be Affected by Home Loss in Coastal Counties
at 100 cm of Global Sea Level Rise
No Additional Adaptation
jjf
No Impact >0-5 5-30 30- 100 "100-500 "500- 12,000
Top five states: FL (731,000), CA (70,000), LA (67,000), NC (55,000), and VA (45,000).
With Adaptation
fl— dflt
» ¦ n / \
Iv- 11 n—^, , v
No Impact >0-5 5-30 30- 100 "100-500
Top five states: FA (52,000), NC (48,000), LA (42,000), CA (32,000), and VA (32,000).
Note: These maps describe the total number of children projected to be affected by complete home loss
from coastal flooding at 100 cm of global sea level rise assuming both "no additional adaptation" and
"with adaptation" scenarios. Darker shading conveys higher impacts. The five states with the highest
total impacts are outlined in black. Only coastal counties are considered in this analysis, see Table 1.
Each map in this figure includes a different legend.
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Climate Change and Children's Health and Well-Being in the United States
Figures 6 through 9 describe the results of the social vulnerability analysis (see Chapter 2 and
Appendix A for methods, data sources, and assumptions). These results are presented for the
"Complete Home Loss" and "Temporary Home Displacement" scenarios at both 50 cm and 100 cm of
global sea level rise assuming both "no additional adaptation" and "with adaptation" for comparison.
The estimated risks for each socially vulnerable group are presented relative to each group's
"reference" population, defined as all individuals other than those in the group analyzed. Positive
numbers indicate the group is disproportionately affected by the referenced impact. Negative
numbers indicate the group is less likely to live in the areas with the highest projected impacts.
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Climate Change arid Children's Health and Well-Being in the United States
Figure 6: Social Vulnerability Analysis Results for Children Projected to be Affected by
Temporary Home Displacement from Coastal Flooding
No Additional Adaptation With Adaptation
50 cm
Limited English Speaking
Low Income
BIPOC
No Health Insurance
Limited English Speaking
Low Income
BIPOC
No Health Insurance
-3%
0%
-11%
32%
100 cm
14%
-3%
10%
-2%
20%
13%
-2%
1%
-13%
Figure 7: Social Vulnerability Analysis Results for Children Projected to be Affected by
Temporary Home Displacement from Coastal Flooding, by Racial and Ethnic Group
No Additional Adaptation With Adaptation
50 cm
American Indian or Alaska Native
Asian
Black or African American
Pacific Islander
Hispanic or Latino
White, non-Hispanic
100 cm
24%
American Indian or Alaska Native
-7%
-6%
Asian
1 7% 15%
Black or African American
| 3%
-4%
Pacific Islander
-10%
-14%
Hispanic or Latino
| 33% 18
White, non-Hispanic
-17%
-9%
21%
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Climate Change arid Children's Health and Well-Being in the United States
Figure 8: Social Vulnerability Analysis Results for Children Projected to Be Affected by Home
Loss from Coastai Flooding
No Additional Adaptation
With Adaptation
Limited English Speaking
Low Income
BIPOC
No Health Insurance
Limited English Speaking
Low Income
BIPOC
No Health Insurance
-28%
-7%
-9%
11%
50 cm
100 cm
64%
57%
-26%
-5%
-8%
11%
-22%
-10%
-7%
9%
Figure 9: Social Vulnerability Analysis Results for Children Projected to Be Affected by Home
Loss from Coastal Flooding, by Racial and Ethnic Group
No Additional Adaptation With Adaptation
50 cm
American Indian or Alaska Native
| 56%
Asian
-14%
-12%
Black or African American
| 5%
| 8%
Pacific Islander
-5%
-5%
Hispanic or Latino
-22%
-22%
White, non-Hispanic
¦8%
| 5%
100 cm
American Indian or Alaska Native
-15%
Asian
-4%
-18%
Black or African American
| 15%
-1%
Pacific Islander
-16%
-10%
Hispanic or Latino
I 70%
-18%
White, non-Hispanic
-36%
11%
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Climate Change and Children's Health and Well-Being in the United States
LIMITATIONS
Below are several limitations of our analysis. See Neumann et al. and Lorie et a I. for additional
descriptions of limitations of the NCPM.
1. Although the NCPM evaluates impacts at grid cells that are 150 m square, the property
characteristics in the NCPM are at the census block group level and not at the parcel level.
Because of this, the methodology approximates the population affected by taking the portion
of upland property area that is impacted and multiplying that by population for the block
group. As such, the analysis does not consider different lot sizes within the block group,
vacant lots, or houses with solely seasonal occupation.
2. The NCPM models risks to infrastructure, and the methodology makes several assumptions to
link those risks to potential impacts on children. For instance, all 150m grids are assumed to
contain residential structures and an even spatial distribution of children living in the 150m
grids overlapping census blocks, the level at which population information is available.
3. Uncertainty surrounding the less-severe flooding scenario. As described in Table 1, the
"temporary damage and displacement" flooding scenario is defined using an AED threshold
of 2% with a 10-year planning horizon. This level was chosen based on expert judgment
among the referenced study authors researchers well-acquainted with the NCPM, although it
is not necessarily data-driven (see earlier in this Appendix for results of sensitivity analysis on
this parameter).
4. Adaptation is a complex process and difficult to forecast. Ma ny ada ptation response
decisions of this type in the coastal zone are not made with strict cost-benefit decision rules,
particularly at local levels. Other factors may include local zoning bylaws or similar policies,
future land-use plans, the presence of development-supporting infrastructure, or proximity
to sites with high cultural value. However, the analytical framework of the NCPM provides a
simple, benefit-cost decision framework that can be applied consistently for regional and
national-scale analyses.
5. increasing degrees of sea level rise and storm surge risks over time are likely to trigger
changes in the demographics of populations at risk of facing flooding effects. For example,
the owners of properties that are repeatedly damaged by storm surge may choose to sell.
Those who have limited access to information regarding risks of purchasing near-coast
property, who have strong sociocultural ties to a location, or who value coastal property may
move into these areas once the property values drop, changing the demographics of the
properties at risk, especially at higher rates of sea level rise. Such demographic changes are
not accounted for in the modeling approach used in this analysis.
6. The distribution of demographics (e.g., age, race or ethnicity, income/poverty status, etc.)
within the census block groups are not considered because that information is not a va liable.
Flowever, there likely are differences in demographics for which this analysis is unable to
account, and which may be relevant. For instance, the data may include the presence or
absence of children in the households of shore-front property-owners and property-owners a
few streets away from the beach.
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Climate Change and Children's Health and Well-Being in the United States
7. Storm modeling in the NCPM is simplified. The NCPM uses a modified bathtub approach for
simulating sea level rise-related inundation and storm surge flood zones and depth. While
this was the only feasible approach at the national scale, local dynamic flood modeling
techniques may show different patterns and depths of inundation and storm surge flood.
Additionally, changes in topography, ground cover, and hydrology over time likely will alter
flood and inundation patterns, especially later in the century. These details are not captured
in the NCPM modeling featured in this section of the report.
8. Children will experience the impacts of flooding differently. Th is a na lysis h igh I ights the
number of children at risk of losing their home permanently and temporarily but cannot
extend to how loss of home affects other aspects of public health.
DATA SOURCES
Table 5: Summary of Data Sources Used in the Coastal Flooding and Children's Homes Analysis
Data Type
Description
Data Documentation and Availability
Sea level rise
and tide gauge
levels
Sea level rise projections and tide
gauge levels used to develop
storm surge heights and
probabilities.
National Oceanographic and Atmospheric
Administration. 2017. Global and regional sea level
rise scenarios for the United States. NOAA Center
for Operational Oceanographic Products and
Services, Technical Report NOS CO-OPS 083.
Domestic
economic
growth
Projection of future gross
domestic product (GDP) from the
Emissions Predictions and Policy
Analysis (EPPA, v6) model. The
projection of GDP growth through
2040 was taken from the 2016
Annual Energy Outlook reference
case, combined with EPPA-6
baseline assumptions for other
regions and time periods.
Chen, Y.-H. H., et al. The MIT EPPA6 Model:
Economic Growth, Energy Use, and Food
Consumption. MIT Joint Program on the Science and
Policy of Global Change, Report 278, Cambridge, MA
2015. Available online at
http://globalchange.mit.edu/research/publications/
2892
U.S. Energy Information Administration, 2016:
Annual Energy Outlook. Available online at
https://www.eia.gov/outlooks/aeo
Infrastructure
inventory data
Property value for each 150 m X
150 m coastal county grid cell is
derived from compiled tax
assessment values for land and
structure, and address residential,
commercial, industrial,
institutional, and most categories
of public land (excluding military
installations).
Updates from Neumann et al. 2010.Available by
county at CIRA2.0 sectoral impact data repository.
Available at:
https://www.indecon.com/proiects/benefits-of-
global-action-on-climate-change/
Elevation, land
cover, land use,
mean tidal levels
Various land and tidal
characteristics for all coastal areas
including elevation, land cover,
land use, MHHW historical levels,
compiled for use in the NCPM.
Neumann, J.E., P. Chinowsky, J. Helman, M. Black, C.
Fant, K. Strzepek, and J. Martinich. 2021. Climate
effects on US infrastructure: The economics of
adaptation for rail, roads, and coastal development.
Climatic Change, 167(44), doi:10.1007/sl0584-021-
03179-w. Available online at
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Climate Change and Children's Health and Well-Being in the United States
Data Type
Description
Data Documentation and Availability
https://link. springer, com/article/10.1007/sl0584-
021-03179-w
Global sea level
rise scenarios
See Appendix A for data sources
Future
population of
children
See Appendix A for data sources
Demographics
for social
vulnerability
analysis
See Appendix A for data sources
Inland Flooding and Children's Homes
Chapter 6 highlights research about the possible effects of inland flooding on
children's homes. As outlined in the chapter, the method relies on results from
Wobus et al.9 and data by census tract and block group developed for U.S. EPA10. Note that the study
also considered adaptation to flood risk in the form of floodproofing, property elevation, and
property acquisition, but overall did not find a strong effect between climate change and the uptake
of these measures relative to the present day. Figures 10 and 11 show impacts by census tract, but
the analysis of children at risk was performed at the finer block group level. Block-group-level
analysis is less precise than the National Coastal Property Model 150m grid analysis, which is the
focus of the Chapter 6 detailed analysis, as flood zones tend to be smallerthan block groups. The
analysis of inland flooding nonetheless is informative of the number of children who might be
affected by this additional stressor.
For temporary evacuation, the analysis uses a threshold 2% AED ratio for the block group. For more
severe residential damage, a threshold 5% AED for the block group is applied. The use of lower
thresholds for the inland flooding analysis compared to coastal flooding analysis reflects the high
likelihood that not all homes in a block group may be affected by the same flood, and that more
concentrated areas (of unknown dimensions) may be affected acutely at a property or neighborhood
level. It also reflects that damages may represent a smaller proportion of the total block group
structure value. Table 6 provides further details on the number of affected children at various AED
thresholds for degrees of global warming.
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Climate Change arid Children's Health and Well-Being in the United States
Figure 10: Projected Annual Expected Damage Ratios for Inland Flooding at 2°C of Global
Mean Temperature Rise
Ratio
Source: Analysis of data from Wobus et al. (2021) and data by census block group
Figure ix: Projected Annual Expected Damage Ratios for Inland Flooding at 4°C of Global
Mean Temperature Rise
Ratio
Source: Analysis of data from Wobus et al. (2021) and data by census block group
Change in Damage
(% of value I year)
H Less than -0.20%
-0.20%--0.01%
-0.01% - +0.01%
+0.01% -+0.20%
+0.20% - +0.40%
M +0.40% - +0.60%
M More than +0.60%
Change in Damage
(% of value/year)
¦I Less than -0.20%
-0.20% - -0.01%
-0.01%-+0.01%
+0.01%-+0.20%
M +0.20% - +0.40%
M +0.40% - +0.60%
¦¦ More than +0.60%
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Climate Change and Children's Health and Well-Being in the United States
Table 6: Projected Number of Children Potentially Affected by Inland Flooding at Varying AED
Thresholds
Annual Expected Damage (AED)
Ratio Threshold
Number of Children Affected
at 2°C of Global Warming
Number of Children Affected
at 4°C of Global Warming
Greater than 0.5%
156,000
880,000
Greater than 1%
237,000
894,000
Greater than 2%
199,000
569,000
Greater than 3.5%
97,000
233,000
Greater than 5%
17,000
56,000
Greater than 10%
0
876
Notes: These are the additional number of children affected as a result of increased global mean temperatures,
relative to a 1986-2005 baseline. These are characteristically different from the coastal flooding analysis above
which relies on more spatially resolved flood depths and therefore flood damage to estimate the number of
children that experience these flood damage thresholds. The AED expressed here is an average across the entire
census block group, where it's likely that many homes are unaffected, i.e., not in thefloodplain.
DATA SOURCES
Table 7: Summary of Data Sources Used in the Coastal Flooding and Children's Homes Analysis
Data Type
Description
Data Documentation and Availability
Downscaled
Hyd rology
Dataset
Daily routed flows at
approximately 57,000 stream
reaches across the CONUS for
an ensemble of GCMs
downscaled using the bias
correction and spatial
disaggregation method.
Reclamation: Downscaled CMIP3 and CMIP5 Climate and
Hydrology Projections: Release of Hydrology Projections,
Comparison with Preceding Information, and Summary of User
Needs, Prepared by the US Department of the Interior, Bureau of
Reclamation, Technical Services Center, Denver, CO, 2014.
Documentation and VIC hvdroloev data are available at ftp://edo-
dcD.ucllnl.org/Dub/dcD/archive/cmiD5/hvdro/BCSD dailv VIC nc/
Property
Flood Risk
Data
Property-level flood risk
dataset and model for the U.S.
First Street Foundation, 2020. First Street Foundation Flood Model
(FSF-FM): Technical Documentation. Brooklyn, NY. Published
06/17/2020.
httDs://assets.firststreet.org/uDloads/2020/06/FSF Flood Model
Technical Documentation.odf
Bates, P.D., Quinn, N., Sampson, C., Smith, A., Wing, O., Sosa, J.,
Savage, J., Olcese, G., Neal, J., Schumann, G. and Giustarini, L.,
2020. Combined modelling of US fluvial, pluvial and coastal flood
hazard under current and future climates. Water Resources
Research, p.e2020WR028673.
Armal, S., Porter, J. R., Lingle, B., Chu, Z., Marston, M. L, & Wing,
O. E. (2020). Assessing Property Level Economic Impacts of
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Climate Change and Children's Health and Well-Being in the United States
Data Type
Description
Data Documentation and Availability
Climate in the US, New Insights and Evidence from a
Comprehensive Flood Risk Assessment Tool. Climate, 8(10), 116.
First Street data can be accessed on the Foundation's website:
https://firststreet.org/flood-factor/
Depth-
damage
curves
Depth-damage curves for
different occupancy classes of
properties
Federal Emergency Management Agency. 2016. Multi-hazard Loss
Estimation Methodology Flood Model HAZUS®MH MR3 Technical
Manual. Developed by: Department of Homeland Security,
Federal Emergency Management Agency-Mitigation Division.
Washington, D.C. Under a contract with: National Institute of
Building Sciences Washington, D.C.
https://www.hsdl.org/?abstract&did=480580
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Climate Change and Children's Health and Well-Being in the United States
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O'Grady, M. and Gentile, L.E., 2020. Modeling coastal flood risk and adaptation response under future climate
conditions. Climate risk management, 29, p.100233.
2 Martinich, J., Neumann, J., Ludwig, L and Jantarasami, L, 2013. Risks of sea level rise to disadvantaged
communities in the United States. Mitigation and Adaptation Strategies for Global Change, 18, pp.169-185.
3 Neumann, J.E., Emanuel, K., Ravela, S., Ludwig, L, Kirshen, P., Bosma, K. and Martinich, J., 2015. Joint effects of
storm surge and sea-level rise on US Coasts: new economic estimates of impacts, adaptation, and benefits of
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6 Neumann, J.E., Chinowsky, P., Helman, J., Black, M., Fant, C., Strzepek, K. and Martinich, J., 2021. Climate effects
on US infrastructure: the economics of adaptation for rail, roads, and coastal development. Climatic change,
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coastal-counties.html
8 Sweet, W.V., Horton, R., Kopp, R.E., and Romanou, A. 2017. Sea level rise. In: Climate Science Special Report: A
Sustained Assessment Activity of the U.S. Global Change Research Program [Wuebbles, D.J., D.W. Fahey, K.A.
Hibbard, D.J. Dokken, B.C. Stewart, and T.K. Maycock (eds.)]. U.S. Global Change Research Program, Washington,
DC, USA. pp. 333-363,
9 Wobus, C., Porter, J., Lorie, M., Martinich, J. and Bash, R., 2021. Climate change, riverine flood risk and adaptation
for the conterminous United States. Environmental Research Letters, 16(9), p.094034.
10 U.S. Environmental Protection Agency. 2021. "Climate Change and Social Vulnerability in the United States: A
Focus on Six Impacts." U.S. EPA430-R-21-003. Data available at: https://www.epa.gov/cira/technical-
appendices-and-data (Appendix I)
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