Question:
You are given the full text of a scientific research paper.

    Your task is to generate a structured **policy brief** that includes the following components:

    - A concise, descriptive **title** that captures the central theme or policy issue addressed in the paper (do not label it with “Title:”).
    - **Policy Problem** - What societal or strategic challenge is the paper addressing?
    - **Scientific Research Findings** - What are the key results relevant to this issue?
    - **Scientific Research Study Methods** - What methodology or data supports these findings?
    - **Policy Implications** - How might the findings guide or influence policy?

    Write each section in a clear, accessible style suitable for policymakers. Avoid overly technical language, but use essential terms when necessary. Structure the output as a labeled five-part policy brief document.

    Scientific Research Paper:
    Nature Climate Change | Volume 12 | November 2022 | 995–998
995
nature climate change
https://doi.org/10.1038/s41558-022-01501-7
Article
How the USA can benefit from risk-based
premiums combined with flood protection
Lars T. de Ruig1,2
, Toon Haer
  1, Hans de Moel
  1, Samuel D. Brody3,
W. J. Wouter Botzen1,4,5, Jeffrey Czajkowski6 and Jeroen C. J. H. Aerts1,7
Flood risk management in the USA is largely embedded in the National Flood
Insurance Program (NFIP). Climate change and increasing exposure in flood
plains pose a challenge to flood risk managers and make it vital to reduce
risk in the future. The proposed reforms are steering the NFIP to risk-based
premiums, but it is uncertain if the reforms will result in unaffordability
and incentivize risk-reduction investments or how the NFIP is affected by
large-scale adaptation efforts. Using an agent-based model approach for
current and future scenarios, we demonstrate that risk-based premiums will
yield a positive societal benefit (US$10 billion) because they will incentivize
household risk-reduction investments. Moreover, our results show that
proactive investment in large-scale adaptation measures complements a
transition to risk-based premiums to yield a higher overall societal benefit
(US$26 billion). We suggest that transitioning the NFIP to risk-based
premiums can only be secured by additional investments in large-scale flood
protection infrastructure.
Flooding is a devastating natural hazard, causing an average
>US$100 billion of damage every year1. Recent events of coastal and
river flooding in Europe and Asia have shown the huge impact of such
events on communities and policy-makers are struggling with how
to anticipate future increase in flood risk due to climate change and
population growth2. Without adaptation investments under the repre-
sentative concentration pathway (RCP) 4.5 and shared socio-economic
pathway (SSP) 2 scenarios, fluvial flood risk for the USA is expected to
increase from about US$27 billion to US$66 billion per year (refs. 3,4),
while coastal flood risk cost is expected to increase from US$1.8 billion
to US$189 billion5. In the USA, the National Flood Insurance Program
(NFIP) is the main program for managing flood risk. The NFIP pro-
vides almost 5 million policies to homeowners and businesses in the
USA, covering US$1.2 trillion in assets and making it the largest flood
insurance market worldwide6. The program requires households in a
participating community with a bank-backed mortgage living within a
100-year flood zone to purchase mandatory flood insurance coverage.
It also requires that new developments in these zones meet certain
building codes. These low-lying flood zones are mapped by the Federal
Emergency Management Agency (FEMA). However, the program has a
US$20.5 billion debt due to, amongst other things, the setting of pre-
miums on the basis of national averages that do not reflect local risk,
new development in flood-impacted areas, and the lack of incentives
for homeowners to implement flood adaptation measures other than
building elevation7.
Several reforms have been introduced to solve some of the issues.
These include the new Risk Rating 2.0 program, which more accurately
sets premiums that reflect yearly risk for individual buildings8. While
the reforms are expected to increase mean insurance uptake and solve
financial burden on the program, they are also likely to put pressure on
affordability for low-income households living in high-risk flood zones.
Moreover, there is uncertainty how the NFIP will perform under future
climate change conditions. However, existing studies on NFIP reforms
only focus on a specific region or on individual elements of the program
Received: 12 November 2021
Accepted: 15 September 2022
Published online: 24 October 2022
 Check for updates
1Institute for Environmental Studies (IVM), VU University Amsterdam, Amsterdam, the Netherlands. 2Royal HaskoningDHV, Amersfoort, the Netherlands.
3Institute for a Disaster Resilient Texas (IDRT), Texas A&M University Galveston Campus, Galveston, TX, USA. 4Risk Management and Decision Processes
Center, The Wharton School, University of Pennsylvania, Philadelphia, PA, USA. 5Utrecht University School of Economics, Utrecht University, Utrecht, the
Netherlands. 6Center for Insurance Policy and Research, National Association of Insurance Commissioners (NAIC), Kansas City, MO, USA. 7Deltares, Delft,
the Netherlands.
 e-mail: Lars.de.Ruig@vu.nl; Jeroen.Aerts@vu.nl

Nature Climate Change | Volume 12 | November 2022 | 995–998
996
Article
https://doi.org/10.1038/s41558-022-01501-7
households, governments and insurance markets is key in gaining a
comprehensive understanding of the NFIP and the effects of govern-
ment policies. Recent literature demonstrates the applicability of
agent-based models (ABMs) for topics related to flood risk and the
effects of individual decision-making, such as evacuation14, housing
markets15, climate change migration16, community mitigation17,18 and
insurance markets19.
This study is based on a new flood risk and agent-based modelling
framework (Methods; Supplementary Information), which simulates
dynamic decisions by homeowners regarding whether to implement
DRR (for example, elevation or flood-proofing of buildings) and
purchase flood insurance. We account for heterogeneous consumer
behaviour and individual bounded rationality in risk perceptions20,21.
Furthermore, governments can decide either proactively (using cost–
benefit analysis based on yearly risk projections) or reactively (after a
flood event) to invest in regional flood protection infrastructure. In
turn, governmental proactive or reactive decision-making will influ-
ence the behaviour of homeowners. All the agents are provided with
yearly risk projections from an underlying flood risk model, which
is driven by climate change scenarios (RCP 4.5) and socio-economic
projections (SSP 2) until 2050 (Supplementary Information). We apply
this method to the conterminous US on a grid resolution of 30 arcsec.
Transition towards risk-based premiums
The effects of a transition from the current NFIP to risk-based premiums
for 2050 is illustrated in Fig. 1, in line with the patterns found for the
short-term effects in 2030 (Supplementary Fig. 2). Such transition leads
to higher geographical heterogeneity of premium levels on a local scale.
On a national scale, the model predicts that premiums will become
relatively lower in coastal areas and higher in fluvial areas (although
all premiums increase over time due to the effects of climate change
and are limited to assessing the current conditions. Recent debates on
the restoration of aging infrastructure and studies on flood manage-
ment suggest that complementary government-based investments
in large-scale flood protection infrastructure (for example, dikes) are
required to anticipate future climate risks and the increasing exposure
of assets in flood-prone areas2,3,9,10. There is a lack of a comprehensive
US-scale analysis of the NFIP that addresses how policy-holders will be
impacted in the future by reforms, investments in governmental flood
infrastructure and the ability of the NFIP to cope with climate change.
While the new reforms have been assessed in various studies8,
this study aims to complement the upcoming reforms by showcasing
household behaviour patterns under different market structures and
with different governmental adaptation investment efforts under
climate change scenarios for fluvial and coastal flood risk. This is done
by studying four indicators over time (2020–2050): insurance pen-
etration rates, unaffordability of premiums and investment costs of
risk-reduction measures, incentivization rate of building-scale disaster
risk reduction (DRR) and the program’s debt. The sensitivity of these
indicators under future changes provides an indication of whether the
programme will be financially healthy in the future. It also indicates
the challenges that policy-holders may face. It should be noted that
this paper does not aim to replicate or fully assess the Risk Rating 2.0
program as the NFIP Risk Rating 2.0 Delay Act of 2021 has delayed
roll-out to 30 September 2022. We also show how households are incen-
tivized to invest in DRR at the local level and highlight the importance
of proactive governmental large-scale flood protection measures to
complement the performance of the NFIP.
Conventional flood risk assessment methods are often
ill-equipped to address flood management policy changes as they
address neither interactions between key stakeholders nor house-
hold decision-making11–13. The spatial–temporal interplay between
Percentage change (%)
a
b
d
e
c
Percentage change (%)
Percentage change (%)
–100 –50
–50 –25
0
25
50
0
50
100
–100 –50
0
50
100
25
20
15
10
Percentage (%)
5
0
100
80
60
40
20
0
–20
Percentage (%)
Coastal
Fluvial
2030
2050
Scenario
Current NFIP
RCP 4.5, SSP 2
Risk-based NFIP
Fig. 1 | Effects of NFIP reform 2050 (RCP 4.5 + SSP 2). a–c, Effects of
NFIP premiums to risk-based premiums in terms of market penetration (a),
unaffordability (b) and risk (c) are visualized as the mean percentage change on
a county level for 2050. d, Mean changes in penetration rates between coastal
and fluvial risk areas. e, Mean insurer debt showing that debt might still increase
despite risk-based premiums (although at a lower rate than under current
conditions). For d and e, error bars indicate the standard deviation (n = 250 per
scenario).

Nature Climate Change | Volume 12 | November 2022 | 995–998
997
Article
https://doi.org/10.1038/s41558-022-01501-7
compared to present day). These effects result in a decrease of insur-
ance penetration rates in fluvial regions from 24.7% to 13.2% (partly
due to an increase in unaffordability). This implies that (on average)
the NFIP is currently underpricing these regions. Conversely, coastal
regions display an increase from 10.2% to 17.1% in penetration rates
due to more attractive premiums under risk-based insurance pricing
for a share of households due to spatial variation in premiums. Still, a
subset of policy-holders will experience a steep increase of their pre-
mium in these regions. Despite the lower penetration rates in fluvial
regions, introducing risk-based premiums and offering premium dis-
counts based on the actual reduction of risk achieved by flood-proofing
buildings is expected to decrease the total average residential flood
risk (coastal and fluvial) across the USA by about US$1 billion (−7.3%)
by 2050. These results highlight the effectiveness of offering a pre-
mium discount to implement a variety of DRR types (wet and dry
flood-proofing), including through retrofitting.
Our model demonstrates that unaffordability (Methods) is
expected to decrease from 4.5 million households (23.8%) to 1 million
households (5.6%) for the 18.8 million households at risk of floods
nationwide in 2050 following a transition to risk-based premiums.
However, the magnitude of the remaining unaffordability increases
substantially to an average of US$2,000 per year per household. These
findings indicate that although risk-based premiums are lower than
current NFIP premiums for many households, moving towards
risk-based premiums implies a sharp increase in premiums and
unaffordability for a subgroup of households living in high-risk
areas. Unaffordability issues can potentially be overcome by offering
insurance vouchers and providing inexpensive accessible loans for
financing DRR measures by low-income households currently living
in high flood risk zones7. Alternatively, further incentivizing DRR by
homeowners would make more people eligible for premium discounts.
Relocation or managed retreat might also become necessary2. As seen
in Fig. 1e, continuation of the program without anticipating climate
change or socio-economic development will further increase the
debt of the NFIP by ~60% (from US$20.5 billion to US$32.8 billion).
Introducing risk-based premiums will significantly limit the rise in
future debt but will not solve the problem entirely (debt will increase
by 28% instead of 60%). An additional markup of premiums might be
required to pay off current debt and make the program financially
sound in the future.
Insurance schemes must be interlinked with flood
adaptation
Our results show that additional large-scale flood adaptation investment
complements a transition to risk-based premiums and household-level
DRR measures22. Table 1 shows four policy scenarios: a baseline scenario
(Sc1, current NFIP, reactive government) and three scenarios (Sc2, 3
and 4) with current and risk-based NFIP schemes and proactive or reac-
tive government policies combinations. These are evaluated relative to
the baseline Sc1 (see Methods for scenario descriptions and Supplemen-
tary Information for additional results). Table 1 displays the evaluation
of these four scenarios for 2020–2050. It shows the present values of
categories that address the total societal costs of flood risk and flood
management. Total societal costs (F) are the expected cost of uncovered
flood damage to properties and public assets (covered residential risk
B subtracted from total flood risk A), insurance premium payments
(C; costs of covered risk) and the costs of flood risk-reduction measures
incurred by governments (D) and households (E). The results show that
transitioning to risk-based premiums has a positive net present societal
benefit of about US$10 billion for the period from 2020 to 2050, even
when governments remain reactive towards investments in adaptation
infrastructure. If the government acts proactively alongside risk-based
premiums, the net present societal benefit increases to US$26 billion
for the same period. Large-scale flood protection measures also have
risk-reduction benefits over a longer lifespan than the 30-year period
evaluated (2020–2050); hence, extending the analysis to the far future
(for example, 2100) will favour proactive government policies even
more. Accordingly, the government can reduce a large share of (future)
flood risk by increasing flood protection through the installation of
levees, the adoption of nature-based solutions or the implementation
of other measures.
The community rating system (CRS) is a voluntary programme that
incentivizes communities to actively engage in floodplain management
activities that exceed the minimum programme requirement. The CRS
could be used to further promote risk awareness and comprehensive
floodplain management by communities and local governments in
exchange for NFIP premium discounts to policy-holders in the com-
munity22. However, the full potential of the current implementation
of the CRS is not yet used because only a small share of participating
communities are actively reducing risk and the system does not con-
sistently reward measures that consider future climate change risks23.
As demonstrated by ref. 23, using the CRS to focus on regional flood
protection goes hand-in-hand with the aforementioned NFIP reforms,
since reducing flood risk through flood protection investments helps
to keep insurance premiums affordable.
Risk-based premiums and flood adaptation
infrastructure
Our analysis suggests that either moving towards risk-based premi-
ums or proactively investing in large-scale flood protection yields
Table 1 | Total societal costs related to flood risk
A. Total flood risk
(of which total
residential risk)
B. Covered
residential risk
C. Insurance
premium
expenses
D. Government
flood protection
investment costs
E. Household
flood-proofing
investment costs
F. Total
societal costs
A − B + C + D + E
Policy scenarios
(US$ billion)
(US$ billion)
(US$ billion)
(US$ billion)
(US$ billion)
(US$ billion)
Sc1: Baseline, current NFIP and
reactive government
US$497 [34] (US$198) [15]
US$44 [3]
US$60 [2]
US$124 [57]
US$5.2 [4]
US$643 [74]
Sc2: Current NFIP + proactive
government (relative to baseline)
−US$88 (−US$31)
−US$3.2
−US$2.5
+US$75
−US$1.2
−US$15
Sc3: Risk-based NFIP + reactive
government (relative to baseline)
−US$8.9 (−US$9.0)
−US$19
−US$23
+US$0.0
+US$3.2
−US$10
Sc4: Risk-based NFIP + proactive
government (relative to baseline)
−US$94 (−US$37)
−US$23
−US$28
+US$74
+US$1.0
−US$26
Results are based on RCP 4.5 and SSP 2 for the period 2020–2050. Cost categories are: F, cumulative cost from 2020 to 2050, which is the sum of uncovered total risk (covered residential risk
(B) subtracted from total risk (A)); C, insurance premium expenses; D, governmental flood protection investment costs; and E, household flood-proofing investment costs. Values are expressed
as present values (US$ billion, 2020 at 4% discount rate) relative to the baseline scenario Sc1. Negative values denote a societal improvement and positive values indicate a cost for society
compared with the baseline (note: numbers are rounded). Standard deviations are indicated in square brackets (n = 250 per scenario).

Nature Climate Change | Volume 12 | November 2022 | 995–998
998
Article
https://doi.org/10.1038/s41558-022-01501-7
significantly higher societal benefits. The highest societal benefits
are achieved when adopting both strategies. Despite high upfront
investment costs for large-scale adaptation of ~US$75 billion (compared
to the baseline scenario), proactive investments are complementary
to risk-based premiums. Together, they yield high average societal
benefits ($26 billion, net present value for 2020–2050, Table 1). While
the investment costs for large-scale adaptation seem high, the total
societal benefits of US$26 billion with adaptation investments sig-
nificantly surpasses the expected societal benefit of US$10 billion
when only transitioning to risk-based premiums (Table 1). Further-
more, investing in flood protection infrastructure will reduce some
of the equity issues (unaffordability) that arise when solely moving to
risk-based premiums (columns C and E in Table 1). It will also reduce the
risk to other governmental assets (for example, energy infrastructure
and low-lying port areas). The remaining unaffordability issues for the
subset of households with a major increase in insurance premiums
could be addressed by offering insurance vouchers and providing
low-interest accessible loans to further incentivize the implementa-
tion of DRR7 or relocation2.
The results of this study offer a timely contribution to both the
Risk Rating 2.0 transition and the infrastructure bill. We show that
there is significant synergy to be achieved by moving towards both a
risk-based premium and a proactive strategy on large-scale adaptation.
Our findings demonstrate that investing in large-scale adaptation
does not reduce the effectiveness of risk-based premiums; in fact,
it contributes to the reduction of unaffordability.
Online content
Any methods, additional references, Nature Research reporting sum-
maries, source data, extended data, supplementary information,
acknowledgements, peer review information; details of author con-
tributions and competing interests; and statements of data and code
availability are available at https://doi.org/10.1038/s41558-022-01501-7.
References
1.
Merz, B. et al. Causes, impacts and patterns of disastrous river
floods. Nat. Rev. Earth Environ. 2, 592–609 (2021).
2.
Mills, E. Insurance in a climate of change. Science 309,
1040–1044 (2005).
3.
Ward, P. J. et al. A global framework for future costs and benefits
of river-flood protection in urban areas. Nat. Clim. Change 7,
642–646 (2017).
4.
Winsemius, H. C. et al. Global drivers of future river flood risk. Nat.
Clim. Change 6, 381–385 (2016).
5.
Tiggeloven, T. et al. Global-scale benefit–cost analysis of
coastal flood adaptation to different flood risk drivers using
structural measures. Nat. Hazards Earth Syst. Sci. 20, 1025–1044
(2020).
6.
Horn, D. P. & Webel, B. W. Introduction to the National Food
Insurance Program (NFIP, 2019).
7.
Michel-Kerjan, E. O. & Kunreuther, H. Redesigning flood
insurance. Science 333, 408–409 (2011).
8.
Risk Rating 2.0 Overview (FEMA, 2019).
9.
Mechler, R. & Schinko, T. Identifying the policy space for climate
loss and damage. Science 354, 290–292 (2016).
10.	 Di Baldassarre, G., Kooy, M., Kemerink, J. S. & Brandimarte, L.
Towards understanding the dynamic behaviour of floodplains
as human-water systems. Hydrol. Earth Syst. Sci. 17, 3235–3244
(2013).
11.
Di Baldassarre, G. et al. Debates—perspectives on
socio-hydrology: capturing feedbacks between physical and
social processes. Water Resour. Res. 51, 4770–4781 (2015).
12.	 Di Baldassarre, G. et al. Socio-hydrology: conceptualising human-
flood interactions. Hydrol. Earth Syst. Sci. 17, 3295–3303 (2013).
13.	 Grames, J., Prskawetz, A., Grass, D., Viglione, A. & Blöschl, G.
Modeling the interaction between flooding events and economic
growth. Ecol. Econ. 129, 193–209 (2016).
14.	 Dawson, R. J., Peppe, R. & Wang, M. An agent-based model for
risk-based flood incident management. Nat. Hazards 59, 167–189
(2011).
15.	 Filatova, T. Empirical agent-based land market: integrating
adaptive economic behavior in urban land-use models. Comput.
Environ. Urban Syst. 54, 397–413 (2015).
16.	 Hassani-Mahmooei, B. & Parris, B. W. Climate change and internal
migration patterns in Bangladesh: an agent-based model.
Environ. Dev. Econ. 17, 763–780 (2012).
17.	 Tonn, G. L. & Guikema, S. D. An agent-based model of evolving
community flood risk. Risk Anal. 38, 1258–1278 (2018).
18.	 Tonn, G. L., Guikema, S. & Zaitchik, B. Simulating behavioral
influences on community flood risk under future climate
scenarios. Risk Anal. 40, 884–898 (2019).
19.	 Han, Y. & Peng, Z. R. The integration of local government,
residents, and insurance in coastal adaptation: an agent-based
modeling approach. Comput. Environ. Urban Syst. 76, 69–79
(2019).
20.	 Haer, T., Botzen, W. J. W. & Aerts, J. C. J. H. Advancing disaster
policies by integrating dynamic adaptive behaviour in risk
assessments using an agent-based modelling approach. Environ.
Res. Lett. 14, 044022 (2019).
21.	 Aerts, J. C. J. H. et al. Integrating human behaviour dynamics into
flood disaster risk assessment. Nat. Clim. Change 8, 193–199
(2018).
22.	 Sadiq, A. A., Tyler, J. & Noonan, D. S. A review of community flood
risk management studies in the United States. Int. J. Disaster Risk
Reduct. 41, 101327 (2019).
23.	 Blessing, R., Brody, S. D. & Highfield, W. E. Valuing floodplain
protection and avoidance in a coastal watershed. Disasters 43,
906–925 (2019).
Publisher’s note Springer Nature remains neutral with regard to
jurisdictional claims in published maps and institutional affiliations.
Springer Nature or its licensor holds exclusive rights to this
article under a publishing agreement with the author(s) or other
rightsholder(s); author self-archiving of the accepted manuscript
version of this article is solely governed by the terms of such
publishing agreement and applicable law.
© The Author(s), under exclusive licence to Springer Nature Limited
2022

Nature Climate Change
Article
https://doi.org/10.1038/s41558-022-01501-7
Methods
Modelling framework
The core of the modelling framework was developed for the contermi-
nous US, including the entire USA coastline and all main river basins. It
simulated flood risk at a yearly time step with representative household
adaptation at a resolution of 30ʺ × 30ʺ and government adaptation at
the county level. Homeowners could invest in DRR measures (eleva-
tion or flood-proofing of buildings) or take out/cancel insurance and
governments could invest in elevating dikes. Both these adaptations
and the proposed NFIP reform policies were captured in four policy sce-
narios. The model was run 50 times for each of these policy scenarios
while also assuming different climate scenarios and socio-economic
scenarios. The framework builds upon earlier versions of DYNAMO20,24.
New components are the flood insurance market module, the address-
ing of coastal flood risk in addition to fluvial flood risk and the nation-
wide application of the model. For the method description of the
core model, please refer to ref. 20. See also Extended Data Fig. 1 and
Supplementary Information.
Flood risk model
The GLOFRIS flood risk model follows a commonly applied hazard–
exposure–vulnerability model25,26. Coastal and fluvial inundation maps
were combined with land use to simulate the (future) exposure of assets
and their values in flood zones. Depth–damage curves were used to
combine hazard and exposure data to simulate flood risk (expected
annual damage, EAD, in US$ per yr) for each individual grid cell and
county. Floods can stochastically occur every year in each county on
the basis of their return period. See also Extended Data Fig. 1a and
Supplementary Information.
Scenarios
Fluvial and coastal inundation maps are available for the current and
future climate following the RCP 4.5 and RCP 8.5. The SSP scenarios27–32
were used to represent the initial population numbers and to project
population growth, income and economic growth for 2050. We applied
the SSP 2 and SSP 5 scenarios as they matched well with RCP 4.5 and
RCP 8.5, respectively. SSP 2 was a middle-of-the-road scenario, while
SSP 5 was an energy-intensive and resource-intensive scenario. The
former was used throughout the paper and the results of the SSP 5
scenario can be found in the Supplementary Information. See also
Extended Data Fig. 1c.
Input data
Fluvial flood hazard. The GLOFRIS fluvial inundation maps are based
on existing research3,33. In brief, daily time series of flood volumes
were constructed using hydrological and hydrodynamic model-
ling at a 0.5° × 0.5° resolution. The GLOFRIS model was forced with
EU-WATCH data for the period 1960–1999, representing historic con-
ditions. For future conditions, the GLOFRIS model was forced with
five global climate models (GCMs): HadGEM2-ES, IPSL-CM5A-LR,
MIROC-ESM-CHEM, GFDL-ESM2M and NorESM1-M. Yearly maximum
hydrological time series were extracted from the daily gridded flood
volumes and a Gumbel distribution was fitted accordingly. The result-
ing Gumbel parameters were used to estimate (future) return peri-
ods for each grid cell: 5, 10, 25, 50, 100, 250, 500 and 1,000 yr. Finally,
GLOFRIS distributed flood volumes to a digital elevation model to
create high-resolution (30ʺ × 30ʺ) inundation maps and their return
periods3,33. See also Extended Data Fig. 1b.
Coastal flood hazard. For coastal inundation, as described in detail
in ref. 5, the extreme sea levels are taken from the Global Tide and
Surge Reanalysis (GTSR) dataset34. GTSR is a global dataset of daily
sea levels (tide and storm surge) for 1979–2014, which is based on
the Global Tide and Surge Model (GTSM). Within GTSM, tides are
simulated separately using the Finite Element Solution (FES2012)
hydrodynamic model35. Surges are simulated using metrological data
from the ERA-Interim global atmospheric reanalysis36. The GTSR data-
set is enhanced with historical tropical cyclone tracks over the period
of 1979–2004, using the International Best Track Archive for Climate
Stewardship archive, as GTSR is known for under-representing tropical
cyclones. The extremes are subsequently calculated using a Gumbel
fit of annual maxima using the maximum-likelihood method and
validated in refs. 34,37.
To calculate coastal inundation, overland inundation from
near-shore tide and surge levels were computed, after which the
nearest GTSR location are projected at the coastline. A resistance
factor is used to simulate the reduction of flooding land inwards,
as tides and storm surges have a limited time span and therefore
their flood peak and associated volume can only penetrate inland
to a certain degree5. Coastal flood maps were made on a resolution
of 30ʺ × 30ʺ for the same return periods as the fluvial flood maps:
5, 10, 25, 50, 100, 250, 500 and 1,000 yr, respectively33. For future
conditions, mean sea level rise conditions were obtained from the
RISES-AM project38 and simulated as a range of probabilistic outcomes.
For this paper, the 50th percentile was used. Future subsidence rates
from the SUB_CR model and are included in the inundation model by
adding the subsidence estimates to the MERIT digital terrain model39.
In the areas where fluvial and coastal flood cells overlapped, we applied
a simple method proposed by FEMA40 and selected the highest inunda-
tion value.
For the continental scale and purpose of the study, the GLOFRIS
model is sufficient. However, future research should focus on coupling
ABMs with higher resolution flood hazard models (for example, ref. 41)
to increase the accuracy of risk simulations and to allow for improved
analyses on the local scale.
Exposure. The GlobalLand30 (ref. 42) database was used to estimate
the exposure of urban assets. Urban grid cells within the GlobalLand30
dataset were set to 75% residential, 15% commercial and 10% industrial
(with an assumed building density of 20% for residential and 30% for
commercial or industrial)5,43. Future changes in residential surface
area per cell were derived from the correlation between population
growth and residential building surface growth20. The gross domestic
product (GDP) growth from the SSPs was used to increase the value of
properties over time.
Vulnerability. The vulnerability was represented by depth–dam-
age curves (the relationship between inundation and the share of
damage per land use type) and maximum damage values (the total
amount of damage per land use type). The HAZUS Multi-Hazard
model44 of FEMA was used for the curves and the maximum damage
values were taken from ref. 43 following existing methods3,5. The
depth–damage curves were altered to simulate the effect of household
DRR measures: dry flood-proofing (preventing flood water from
entering a building) or elevation of new buildings45. Dry flood-proofing
has a lifespan of 75 yr (ref. 46), costs US$100 m−2 (Supplementary
Table 5) and was implemented for water levels up to 1 m (FEMA47)
reducing damage by 85%48,49. However, overtopping due to higher
water levels resulted in full damage. For elevation, the vulnerability
curve was shifted so that damage would only occur above 1 m of
inundation.
Social vulnerability is represented by accounting for affordability
in the adaptation choices of households. It is recommended for future
research to also include other social vulnerability factors.
Current flood protection. Not all low-lying flood zones have the poten-
tial to be flooded due to the existing flood protection infrastructure
(for example, dikes). The initial protection standards of levees in flood
zones were based on the FLOPROS dataset50. Tiggeloven et al.5 and
others used an approach where protection levels were based on GDP

Nature Climate Change
Article
https://doi.org/10.1038/s41558-022-01501-7
but this resulted in very high protection levels for the USA (while obser-
vations suggested otherwise)51,52. Therefore, the coastal protection
standards were set to 30-year exceedance levels51,52, which can be
changed by the governmental agents (equation (4)).
Policy scenarios
A total of four policy scenarios (Sc1–4) were simulated. These consisted
of combinations of the following NFIP insurance and governmental
policies:
•
Sc1: current NFIP. This scenario simulated the current NFIP
market structure as closely as possible. Observed premiums for
100-year flood zones and low-risk flood zones were used.
•
Sc2: NFIP with risk-based premiums. Risk-based premiums were
calculated by the flood risk model plus a loading factor of the
current NFIP premium setting (estimated at 49.7%).
•
Sc3: reactive government. Re-evaluation of protection standards
of levees only occurred after a flood event in a county. Measures
were implemented on the basis of a cost–benefit analysis using
risk information from the model.
•
Sc4: proactive government. Re-evaluation of protection
standards occurred either every 6 years or after a flood event
in the respective county. Measures were implemented on the
basis of a cost–benefit analysis using risk information from the
model.
Each policy scenario per RCP/SSP scenario was run for 50
repetitions for each RCP/SSP scenario per five GCMs, for a total of
250 repetitions each (n = 250).
Insurance market (ABM)
Premiums. Household decisions were based on yearly premiums
(building and content coverage), which were simulated per grid cell of
30ʺ × 30ʺ. See also Extended Data Fig. 1a. Start values of the premiums in
Sc1 and Sc2 (current NFIP) were 2014 county averages for 100-year flood
zones and low-risk flood zones53. State averages for premiums were
used if county-level information was lacking. These 2014 premiums
were adjusted to 2000 values at the start of the simulations. Next, these
premiums were multiplied by the percentage change in yearly EAD
simulated by GLOFRIS. It was assumed that yearly premiums changed
before the yearly government decision on protection standards and
the risk increase at that time step.
Premium discounts. For the scenarios with risk-based premiums
(Sc3 and Sc4), unloaded premiums were initially calculated by the
flood risk model. Subsequently, average NFIP loading factors (49.7%)
were added54,55. Finally, a premium discount was applied using the CRS
(Supplementary Information). Different discounts were applied for
the 100-year and the low-risk flood zones (based on the ref. 53 dataset).
Along with the CRS discounts, households that implemented DRR
(elevation or flood-proofing) received a percentage premium discount
that reflected the risk reduction obtained from implementing the DRR
measure for the risk-based scenarios.
Mandatory insurance. Part of the NFIP is a mandatory purchase
requirement for federally funded mortgages in a 100-year flood zone.
Estimates show that, on average, 55% of the properties within a 100-year
flood zone in participating communities are bound to the mandatory
purchase requirement. However, research shows that an average of
only 78% of those households comply56. We applied the 55% mandatory
share for 100-year flood zones for participating communities and we
benchmarked the compliance rate on affordability and the expenditure
cap (that is, we assumed that if a households could not afford the policy
premium, then they would not comply with the mandatory purchase
requirement, resulting in an average compliance rate of 78%). After
the initial model setup, households in non-participating communities
can voluntarily adopt insurance over time but no mandatory require-
ment is enforced. It should be noted that this could lead to a higher
insurance demand for inland communities that are non-participatory
in the present.
Expenditure cap insurance. Following ref. 57 and ref. 58, we applied
an expenditure cap definition for unaffordability. It was assumed
that households could afford flood insurance if their annual pre-
mium was within the expenditure cap of their annual income, which
was benchmarked at 7.5%. Income was distributed per county
through a log-normal distribution based on mean and median
income from the US Census Bureau 201059. See also Supplementary
Information.
Homeowner behaviour (ABM). Households that were not bound to
the mandatory requirement had a yearly decision to take or cancel
insurance. See also Extended Data Fig. 1a. First, (un)affordability was
tested through the expenditure cap for insurance of 7.5%. Second,
two strategies were compared following a subjective expected utility
(EU)20,60,61 model:
Strategy 1: take insurance, accepting the deductible
Strategy 2: do not take or cancel insurance
The strategy that yielded the highest EU was chosen. The subjec-
tive EU equation is as follows:
EUs =
PI
∫
Pi
βPiU (Wt −γDi,t × δs −Cpremium,t −dpremium,t) dP
(1)
Equation (1) calculates EUs for each strategy s. Each event i has a
probability Pi of occurring with a factor β as perceived probabilities
(see below). The total set of events I is the return periods of each flood
event (with return periods of 5, 10, 25, 50, 100, 250, 500, 1,000 and
10,000 years, respectively) and the probability of no flood event. The
EUs is subsequently calculated as the approximation of the integral over
I. Utility is calculated as a function of wealth W, uncovered damage D,
factor γ as perceived damage (see below), premium C and a premium
discount d (if applicable to the scenario). Damage D per event i for
year t is calculated using the hazard–exposure–vulnerability model.
Risk aversion. A general utility function following constant relative
risk aversion62–64 was assumed. In line with common findings62,63, house-
holds were assumed to be slightly risk-averse in which case U(x) = ln(x).
Deductibles. For strategy 1, homeowners had to pay a deductible
δ of 10% of the incurred damages, while strategy 2 had full damages
(no damage was covered, so δ2 = 1, C = 0 and d = 0).
Perception. Individuals act with bounded rationality in their decisions
on buying flood insurance. This was represented by the perceived prob-
abilities β and perceived damages γ. Both factors were benchmarked
on the basis of empirical data by ref. 24 and simulated households over
estimating their risk after a flood event while underestimating their risk
after a period of no floods. Mathematically this is shown as:
β = 12.0639 × α3.71657
t
+ 0.08233
(2a)
γ = 0.442774 × α1.1671
t
+ 0.802826
(2b)
where αt = 1 if a flood occurs and αt = αt−1 /1.6 if no flood occurs. This
expression results in an increase during a flood event. In years with no
storm events (the grid cell experiences no inundation), β and γ will sub-
sequently decay to the inverse of the observed values in ~6 years after
the storm event, in line with empirical evidence65–67. While homeowners
were aware of increasing risk over time, it is assumed that they were not

Nature Climate Change
Article
https://doi.org/10.1038/s41558-022-01501-7
fully informed on flood risk due to their bounded rationality. Therefore,
at the start of the simulations, each agent is assigned a different risk
increase value picked from either a random-uniform distribution of the
objective risk increase (simulated by the risk model) or no increase at
all. It should be noted that individual risk perception is the main driver
for changing behaviour but the decisions are also strongly influenced
by the effectiveness of the adaptation measure and the income and
wealth of the household, in line with coping appraisals68. For future
work, it is recommended to assess the effects of including other drivers
such as influences by neighbourhood behaviour.
Homeowner DRR and affordability. Affordability was tested before
households could consider investing in individual adaptation meas-
ures (elevation for new properties and dry flood-proofing for exist-
ing properties). See also Supplementary Information. Elevation will
prevent damages until the implementation height. Dry flood-proofing
is preventing water from entering the property, which will lead to a
decrease in damages up to 85% for implementation height48,49. However,
inundation higher than the implementation height will cause overtop-
ping and will result in full damages.
An expenditure cap for DRR was set to 2.5% (Supplementary
Information) to define affordability (similar to the cap for insurance).
Adjustments were made as these are long-term investments that have
benefits over time but have high initial investment costs. Therefore,
it was assumed that households could fund the investment through a
personal loan with 15% interest over 5 years. The annual loan payment
was used to test the affordability24.
If the DRR options were affordable, each group (existing and new
unprotected households) had the choice between two strategies:
Strategy 1: implement disaster risk reducing measures
Strategy 2: do nothing
This choice was determined on the basis of equation (3), which
calculates the subjective discounted expected utility (DEU) as follows:
DEUs =
PI
∫
Pi
βPiU (NPVs) dP =
PI
∫
Pi
βPiU (
T
∑
t=1
Wt−γDi,t,s
(1+r)t
−
L
∑
t=0
Cannual,s
(1+r)t ) dP
=
PI
∫
Pi
βPiU (
T
∑
t=1
Wt−γDi,t,s
(1+r)t
−
L
∑
t=0
n∗C0,s
1−(1+n)L
(1+r)t ) dP
(3)
The DEU model is calculated for strategy s. The variables D, β, γ, W, P
and i and the general utility function U(x) are similar to those in equation
(1). The net present value (NPVs) is the sum of wealth Wt and the (reduced)
damages Di,t,s over the lifespan of either measure T, discounted to the pre-
sent value using discount rate r. The discount rate is the pure time prefer-
ence of residents and is assumed to be 3%, following ref. 69. The investment
costs C0,s are US$100 m−2 for dry flood-proofing and US$45 m−2 for
elevation (considering it only applied to new buildings). Following
up the affordability metric, the investments are spread over 5 years
through a personal loan with 15% interest (Supplementary Table 5),
as an annual loan payment Cannual,s. For strategy 2 (without action), the
NPVs contains full perceived damages and no investment costs.
It should be noted that DRR investments and the uptake/cancella-
tion of flood insurance are not mutually exclusive, nor does one lead to
another. Each decision is simulated yearly and made decisions (such as
investing in DRR) will impact future decisions during a model simulation.
Government large-scale adaptation. Governments had the ability
to adapt by raising protection standards (5-, 10-, 25-, 50-, 100-, 250-,
500- and 1,000-year) each year per county. The initial fluvial protec-
tion standards were taken from the FLOPROS database50. A 30-year
protection level was assumed for the coastal protection standards.
(While some have aimed to determine coastal protection standards
on the basis of GDP correlations, this is often not realistic for the USA
as found by ref. 51 and others52,70 (Supplementary Table 5).) Adaptation
measures simulate increasing dike heights and all necessary additional
adaptation measures (for example, beach nourishment and revet-
ments), although some areas might be overestimated whereas other
will be underestimated due to the scale of the model. During the initial
modelling setup, these protection levels were matched with water
levels to estimate an initial dike height. Protection standards could be
increased by increasing dike heights for rivers and coasts for a county.
The height increase was determined by using water levels associated
with different return periods to reach the necessary protection level.
If dikes were not upgraded over time, protection levels could decrease
due to sea level rise and climate change effects.
The decision to increase dike heights was based on a cost–benefit
analysis approach, whereby the present value of investment and main-
tenance costs was weighted against the present value of benefits from
adaptation over time or mathematically:
NPVPSi =
N
∑
n=1
L
∑
t=1
Bt,PSi,n−Ct,PSi,n
(1+r)t
−C0,PSi,n
=
N
∑
n=1
L
∑
t=1
(EADredt,PSi,n−EADredt,PScurrent,n)−(Ct,PSi,n−Ct,PScurrent,n)
(1+r)t
−C0,PSi,n
(4)
Here, NPVPSi is the net present value of investing in dike heights
associated with a protection standard PSi, calculated as the sum of each
grid cell n for a specific county and over the lifespan of a dike L (assumed
at 100 years)71. The benefits over time Bt are the difference between the
reduction of EAD (EADred) between the new protection standard and
the current protection standard PScurrent. Similarly, the maintenance
costs Ct are the difference between the new and current protection
standard. Lastly, C0 are the investment costs, the discount rate r
(assumed at 4%) and time t in years. The maintenance costs are assumed
at US$0.1 × 106 per km and investment costs are assumed at US$8 × 106
per length (km) per height (m)48. The protection standard with the high-
est NPV is chosen. If none of the protection standard has a positive NPV,
nothing is done. For the reactive and proactive scenarios, we assume
that when the government decides on adaptation, it makes decisions
on the basis of perfect information on future developments of risk. As
the proactive government takes adaptation decisions frequently and
the reactive government very infrequently, we do capture an approxi-
mate upper- and lower-bound of government decision-making.
Agent interactions. Household and governmental agents interact
through investing in either DRR measures or large-scale adaptation
measures, respectively, reducing risk over time. Whenever a house-
hold reduces their flood risk by implementing DRR measures, this will
directly influence their decision on insurance, through changes in the
perceived probability of floods or the associated damage and a lower
insurance premium for the risk-based insurance market scenarios. In
addition, they reduce a (minor) share of regional flood risk, impacting
the decisions by the regional government.
Similarly, if governments reduce risk by implementing
regional-scale adaptation measures, then this will be reflected in a
lower perceived probability and damage by households depending
on the level of government protection. Via this mechanism, flood
protection by the government thus influences household’s decisions
on adaptation investments and insurance uptake or cancellation.
Modelling robustness. We aimed to assess the sensitivity of proposed
NFIP reforms under future conditions. To maximize the reliability of our
model for this sensitivity analysis, we followed a three-step approach72
(Supplementary Information): (1) benchmarking, (2) validation and
(3) sensitivity analysis.
Benchmarking. Benchmarking of the GLOFRIS model has been exten-
sively described by ref. 3,33. With a hit rate of 70%, the global model
performs well for the USA; however, it sometimes overestimates or

Nature Climate Change
Article
https://doi.org/10.1038/s41558-022-01501-7
underestimates inundations (Supplementary Information). This
uncertainty is apparent when comparing the damage simulations to
observed events (2000–2018): GLOFRIS underestimates three extreme
hurricanes (Katrina, Sandy and Harvey) and overestimates other events
(albeit to a lesser extent). We therefore applied a scaling factor (Supple-
mentary Information) to better match the average observed damage.
The underestimation of extremes can be explained by the dominant
flooding processes during these events, which are not captured by our
modelling setup (for example, levee breach; Supplementary Informa-
tion) and also by the fact that tropical cyclones are not well-represented
in the GTSR dataset34.
Furthermore, the ABM model is upscaled from a benchmarked
ABM for New York City using the same modelling decision rules and
theory. However, the benchmarked parameters (risk aversion, protec-
tion standard, investment costs of dry flood-proofing, expenditure cap
of dry flood-proofing investments, loan interest rate and loan dura-
tion) for New York City do not apply for the whole USA, which is why
we applied standardized values of the parameters in the behavioural
rules, as mentioned above. In addition, we benchmarked the parameter
‘expenditure cap for insurance’, by testing different values (2.5%, 5% and
7.5%) for this parameter and then ran the model 50 times for each of the
parameter values. By comparing the number of policies per county as
simulated by the model with the nationwide database (Supplementary
Information), through a Spearman’s Rho correlation test, the cap was
set to 7.5% (ρ = 0.679; P = 0.000).
Validation. Modelling outputs for the years 2000–2018 were compared
to FEMA data (Supplementary Information) using two indicators (insur-
ance damage payouts and premium income). The model was run for 200
repetitions to account for uncertainty. Supplementary Table 6 shows a
slight underestimation of mean yearly annual insurance damage pay-
outs (US$2.8 billion versus US$3.0 billion) and a slight overestimation of
premium income (US$1.7 billion versus US$1.3 billion) when comparing
the model with the observed values, respectively. However, the standard
deviation of the observed values was much higher, especially for the
damages (0.9 versus 4.4 for the model and observed values). This last dif-
ference can be attributed to the underestimation of the damage by flood
risk model for the three extreme hurricanes in the period 2000–2018.
Sensitivity analysis. The results from the validation provide confidence
that, except for the three extreme events in the years 2000–2018, our
modelled values of both damage payouts and premium income are
close to their actual counterparts. However, to further test modelling
uncertainty, a sensitivity analysis was conducted (Supplementary
Tables 7–10), varying four key decision variables in the ABM:
	(1)	 Reducing the expenditure cap for insurance of 7.5% decreases the
share of policy-holders up to 15% and 42% for an expenditure cap
of 5.0% and 2.5%, respectively. This is largely caused by an increase
in household affordability. However, since other variables are
only slightly influenced and the expenditure cap is benchmarked
on the observed data, the used benchmark of 7.5% seems valid.
	(2)	 Increasing the expenditure cap for DRR investments from 2.5%
to 7.5% results in more households investing in DRR (up to
54% for an expenditure cap of 7.5%) and consequently reduces
residential risk (up to 20%). However, the changes in results are
relatively uniform between scenarios and do not affect the main
conclusions of the paper.
	(3)	 Increasing or decreasing the loan interest rates either decreases
or increases the share of households that invest in DRR. How-
ever, this has a relatively low impact on results (with residential
risk only changing by up to 4%).
	(4)	 Varying the governmental investment costs by ±20% has only
a minor impact on overall modelling results (for example, risk
fluctuates by ±5% uniformly between scenarios).
Data availability
Land-cover data were obtained from GlobeLand30,42. Inundation data
were obtained from the GLOFRIS cascade model33. Vulnerability curves
were obtained from HAZUS-MH model44. Maximum damage values are
available at ref. 43. NFIP insurance data are available at ref. 55. Income
data were obtained from the US Census Bureau59. Protection standard
database was obtained from FLOPROS50. NFIP Redacted Claims dataset is
available from FEMA73. FEMA and the Federal Government cannot vouch
for the data or analyses derived from these data after the data have been
retrieved from the Agency’s website(s) and/or Data.gov. Socio-economic
data were obtained from the International Institute for Applied Systems
Analysis74. The generated data that support the finding of this study
are available in figshare with the identifiers: https://doi.org/10.6084/
m9.figshare.17049416.v1. There are no restrictions on data availability.
Code availability
The code for DYNAMO is available in Zenodo with the identifiers:
https://doi.org/10.5281/zenodo.7025225. There are no restrictions
on code availability.
References
24.	 de Ruig, L. T. et al. An agent-based model for evaluating reforms
of the National Flood Insurance Program: a benchmarked model
applied to Jamaica Bay, NYC. Risk Anal. https://doi.org/10.1111/
risa.13905 (2022).
25. Kron, W. Flood risk = hazard • values • vulnerability. Water Int. 30,
58–68 (2005).
26.	 de Moel, H., van Vliet, M. & Aerts, J. C. J. H. Evaluating the
effect of flood damage-reducing measures: a case study of the
unembanked area of Rotterdam, the Netherlands. Reg. Environ
Change 14, 895–908 (2014).
27.	 Riahi, K. et al. The Shared Socioeconomic Pathways and their
energy, land use, and greenhouse gas emissions implications: an
overview. Glob. Environ. Change 42, 153–168 (2017).
28.	 Samir, K. C. & Lutz, W. The human core of the shared
socioeconomic pathways: population scenarios by age, sex and
level of education for all countries to 2100. Glob. Environ. Change
42, 181–192 (2017).
29.	 Dellink, R., Chateau, J., Lanzi, E. & Magné, B. Long-term economic
growth projections in the Shared Socioeconomic Pathways.
Glob. Environ. Change 42, 200–214 (2017).
30.	 Crespo Cuaresma, J. Income projections for climate change
research: a framework based on human capital dynamics.
Glob. Environ. Change 42, 226–236 (2017).
31.	 Leimbach, M., Kriegler, E., Roming, N. & Schwanitz, J. Future
growth patterns of world regions—a GDP scenario approach.
Glob. Environ. Change 42, 215–225 (2017).
32.	 Jiang, L. & O’Neill, B. C. Global urbanization projections for the
Shared Socioeconomic Pathways. Glob. Environ. Change 42,
193–199 (2017).
33.	 Ward, P. J. et al. Aqueduct Floods Methodology (WRI, 2020);
www.wri.org/publication/aqueduct-floods-methodology
34.	 Muis, S., Verlaan, M., Winsemius, H. C., Aerts, J. C. J. H. & Ward, P.
J. A global reanalysis of storm surges and extreme sea levels.
Nat. Commun. 7, 11969 (2016).
35.	 Carrère, L. & Lyard, F. Modeling the barotropic response of
the global ocean to atmospheric wind and pressure forcing—
comparisons with observations. Geophys. Res. Lett. https://doi.
org/10.1029/2002GL016473 (2003).
36.	 Dee, D. P. et al. The ERA-Interim reanalysis: configuration and
performance of the data assimilation system. Q. J. R. Meteorol.
Soc. 137, 553–597 (2011).
37.	 Muis, S. et al. A comparison of two global datasets of extreme
sea levels and resulting flood exposure. Earths Future 5, 379–392
(2017).

Nature Climate Change
Article
https://doi.org/10.1038/s41558-022-01501-7
38.	 Jevrejeva, S., Grinsted, A. & Moore, J. C. Upper limit for sea
level projections by 2100. Environ. Res. Lett. 9, 104008
(2014).
39.	 Kooi, H., Bakr, M., de Lange, G., den Haan, E. & Erkens, G. User
Guide to SUB-CR a MODFLOW Package for Land Subsidence and
Aquifer System Compaction that Includes Creep Deltares internal
report 11202275-008 (Deltares, 2018).
40.	 Guidance for Flood Risk Analysis and Mapping Combined Coastal
and Riverine (FEMA, 2015).
41.	 Bates, P. D. et al. Combined modeling of US fluvial, pluvial, and
coastal flood hazard under current and future climates. Water
Resour. Res. 57, e2020WR028673 (2021).
42.	 Jun, C., Ban, Y. & Li, S. Open access to Earth land-cover map.
Nature 514, 434 (2014).
43.	 Huizinga, J., de Moel, H. & Szewczyk, W. Global Flood Depth–
Damage Functions: Methodology and the Database with
Guidelines (Publications Office of the European Union, 2017);
https://doi.org/10.2760/16510
44.	 Multi-Hazard Loss Estimation Methodology, Flood Model, HAZUS,
Technical Manual (FEMA, 2013).
45.	 de Ruig, L. T., Haer, T., de Moel, H., Botzen, W. J. W. & Aerts, J. C.
J. H. A micro-scale cost–benefit analysis of building-level flood
risk adaptation measures in Los Angeles. Water Resour. Econ. 32,
100147 (2020).
46.	 Aerts, J. C. J. H. & Botzen, W. J. W. Flood-resilient waterfront
development in New York City: bridging flood insurance,
building codes, and flood zoning. Ann. N. Y. Acad. Sci. 1227, 1–82
(2011).
47.	 Homeowner’s Guide to Retrofitting—Six Ways to Protect your Home
from Flooding (FEMA, 2014).
48.	 Aerts, J. C. J. H., Botzen, W. J. W., de Moel, H. & Bowman, M.
Cost estimates for flood resilience and protection strategies in
New York City. Ann. N. Y. Acad. Sci. 1294, 1–104 (2013).
49.	 de Ruig, L. T. et al. An economic evaluation of adaptation
pathways in coastal mega cities: an illustration for Los Angeles.
Sci. Total Environ. 678, 647–659 (2019).
50.	 Scussolini, P. et al. FLOPROS: an evolving global database of
flood protection standards. Nat. Hazards Earth Syst. Sci. 16,
1049–1061 (2016).
51.	 Hallegatte, S., Green, C., Nicholls, R. J. & Corfee-Morlot, J. Future
flood losses in major coastal cities. Nat. Clim. Change 3, 802–806
(2013).
52.	 Nicholls, R. & Cazenave, A. Sea-level rise and its impact on coastal
zones. Science 328, 1517–1520 (2010).
53.	 Czajkowski, J., Villarini, G., Montgomery, M., Michel-Kerjan, E. O. &
Goska, R. Assessing current and future freshwater flood risk from
North Atlantic tropical cyclones via insurance claims. Sci. Rep. 7,
41609 (2017).
54.	 Technical Documentation of NFIP Actuarial Assumptions and
Methods (FEMA, 2013).
55.	 The National Flood Insurance Program: Financial Soundness and
Affordability (Congressional Budget Office, 2017).
56.	 Dixon, L. & Clancy, N. The National Flood Insurance Program’s
Market Penetration Rate: Estimates and Policy Implications (RAND
Corporation, 2006); https://www.rand.org/pubs/technical_
reports/TR300.html
57.	 Kousky, C. & Kunreuther, H. Addressing affordability in the
National Flood Insurance Program. J. Extreme Events 1, 1450001
(2014).
58.	 Hudson, P. A comparison of definitions of affordability for flood
risk adaption measures: a case study of current and future
risk-based flood insurance premiums in Europe. Mitig. Adapt.
Strateg. Glob. Change 23, 1019–1038 (2018).
59.	 Income Data per County (US Census Bureau, 2010); https://www2.
census.gov/geo/tiger/TIGER_DP/2010ACS/
60.	 Von Neumann, J. & Morgenstern, O. Theory of Games and
Economic Behavior (Princeton Univ. Press, 1947).
61.	 Hudson, P., Botzen, W. J. W. & Aerts, J. C. J. H. Flood insurance
arrangements in the European Union for future flood risk under
climate and socioeconomic change. Glob. Environ. Change 58,
101966 (2019).
62.	 Harrison, G. W., List, J. A. & Towe, C. Naturally occurring
preferences and exogenous laboratory experiments:
a case study of risk aversion. Econometrica 75, 433–458
(2007).
63.	 Bombardini, M. & Trebbi, F. Risk aversion and expected utility
theory: an experiment with large and small stakes. J. Eur. Econ.
Assoc. 10, 1348–1399 (2012).
64.	 Wakker, P. P. in Wakker, P. P. (ed) Prospect Theory for Risk and
Ambiguity Ch. 3 (Cambridge Univ. Press, 2008).
65.	 Kunreuther, H. Mitigating disaster losses through insurance.
J. Risk Uncertain. 12, 171–187 (1996).
66.	 Bin, O. & Landry, C. E. Changes in implicit flood risk premiums:
empirical evidence from the housing market. J. Environ. Econ.
Manag. 65, 361–376 (2013).
67.	 Kunreuther, H., Sanderson, W. & Vetschera, R. A behavioral model
of the adoption of protective activities. J. Econ. Behav. Organ. 6,
1–15 (1985).
68.	 Bubeck, P., Botzen, W. J. W. & Aerts, J. C. J. H. A review of risk
perceptions and other factors that influence flood mitigation
behavior. Risk Anal. 32, 1481–1495 (2012).
69.	 Tol, R. S. J. The social cost of carbon. Annu. Rev. Resour. Econ. 3,
419–443 (2011).
70.	 Nicholls, R. J. Coastal flooding and wetland loss in the
21st century: changes under the SRES climate and socio-
economic scenarios. Glob. Environ. Change 14, 69–86
(2004).
71.	 Aerts, J. C. J. H. A review of cost estimates for flood adaptation.
Water 10, 1646 (2018).
72.	 Cirillo, P. & Gallegati, M. The empirical validation of an
agent-based model. East Econ. J. 38, 525–547 (2012).
73.	 OpenFEMA Dataset: FIMA NFIP Redacted Claims (FEMA, accessed
23 June 2020); https://www.fema.gov/openfema-data-page/
fima-nfip-redacted-claims
74.	 Keywan, R. et al. The Shared Socioeconomic Pathways and
their energy, land use, and greenhouse gas emissions
implications: an overview. Global Environmental Change 42,
153–168 (2017).
Acknowledgements
We would like to thank M. Montgomery for sharing data and
M. Tesselaar for his feedback. This research received funding from
the Netherlands Organization for Scientific Research VIDI (45214005
to W.J.W.B.) and VICI (016140067 and 453-13-006 to J.A.) grant
programmes and ERC advanced grant (884442 to J.A.).
Author contributions
The work was conceptualized by L.T.de R., T.H., W.J.W.B. and J.A. The
methodology was developed by L.T.de R., T.H. and H.de M. Formal
analysis was undertaken by L.T.de R. and T.H. Visualization was by
L.T.de R. and H.de M. Supervision and funding acquisition were by
J.A. The article was written by L.T.de R., H.de M., S.D.B., W.J.W.B., J.C.
and J.C.J.H.A.
Competing interests
The authors declare no competing interests.
Additional information
Extended data is available for this paper at
https://doi.org/10.1038/s41558-022-01501-7.

Nature Climate Change
Article
https://doi.org/10.1038/s41558-022-01501-7
Supplementary information The online version
contains supplementary material available at
https://doi.org/10.1038/s41558-022-01501-7.
Correspondence and requests for materials should be addressed to
Lars T. de Ruig or Jeroen C. J. H. Aerts.
Peer review information Nature Climate Change thanks Dylan Turner,
Yu Han, Brett Sanders and Brayton Noll for their contribution to the
peer review of this work.
Reprints and permissions information is available at
www.nature.com/reprints.

Nature Climate Change
Article
https://doi.org/10.1038/s41558-022-01501-7
Extended Data Fig. 1 | A schematic overview of the primary modelling
steps. A schematic overview of the primary modelling steps, showing the main
buildings blocks of the DYNamic climate impact Adaptation Model (DYNAMO):
(a) modelling schematic: a flood risk model and an agent-based model, (b) input
data: flood maps, exposure data, flood protection data, income, (c) scenarios:
socio-economic and climate change scenarios until 2050, (d) policy scenarios:
governmental adaptation policies and NFIP market structures, (e) outputs: flood
risk (EAD), insurance penetration rates, affordability, disaster risk reduction
(DRR), and flood protection standards, (f) validation: damage and premium
income. The framework builds upon earlier applications and version of DYNAMO
by Haer et al.21 for the EU and de Ruig et al.28 for New York City.

    Policy Brief:

Answer:
**Climate-proofing the National Flood Insurance Program**

**Policy Problem**Flood damage in the United States is expected to rise in the future due to climate change, population growth and economic growth in floodplains. One way to offset future flood damages is through proper insurance coverage. The main provider of flood insurance in the United States is the federally run National Flood Insurance Program (NFIP), a currently financially unsustainable insurance programme in substantial debt. Moreover, the NFIP insufficiently incentivizes policyholders to undertake flood risk reduction measures to limit flood damages, because insurance premiums insufficiently reflect flood risk and do not reward risk reduction actions satisfactorily. To address these issues, the NFIP is underway with implementing the Risk Rating 2.0 program, which aims at better premium differentiation through risk-based premiums and enhanced incentives for individual policyholder flood risk reduction. It is unknown whether these reforms are sufficient for future ‘climate-proofing’ of the NFIP or whether additional investments in flood protection are needed.

**Scientific Research Findings**We show that risk-based premiums, which are spatially explicit on a household level, result in decreases for some households but substantial increases for others. Importantly though, we observe that offering premium discounts to incentivize building-level risk reduction measures decreases total residential flood risk across the United States by approximately US$1 billion per year (−7.3%) by 2050. Combined, introducing risk-based NFIP premiums that incentivize household risk reduction will yield a positive societal net benefit (US$10 billion in 30 years). Complementing this reform with pro-active government investments in large-scale flood protection yields an even higher overall societal net benefit (US$26 billion in 30 years). In conclusion, to fully minimize future flood risk, investments in large-scale flood protection are required in addition to NFIP rating reforms. Whereas our study specifically focusses on the United States, the concept of incentivizing risk reduction through risk-based pricing is highly relevant for adaptation planning globally.

**Scientific Research Study Methods**The study applies an innovative flood risk model that not only simulates coastal and fluvial flood occurrences and damages moving forward in time to 2050 for the conterminous United States, but also simulates government and household adaptation decisions interacting with the dynamic flood risk through their decisions on insurance purchase, building-level risk reduction and larger-scale protection for different climate scenarios. Floods are simulated stochastically based on their return period, influencing how households perceive their risk, which might deviate from true risk, and their perceived value of adaptive measures or buying insurance. Furthermore, governments can decide either to proactively (every six years or after a flood event) or reactively (only after a flood event) invest in regional flood protection infrastructure. In turn, these government investments will influence the flood insurance demand and risk reduction actions of homeowners.

**Policy Implications**• Charging flood insurance premiums that reflect true flood risk incentivizes flood risk reduction by policyholders, thereby reducing residential flood risk across the United States moving forward in time.
• Risk-based premiums introduce substantial heterogeneity in premiums at a localized flood risk-scale, which is beneficial for some households but implies unaffordability for others.
• The combined effects from introducing risk-based premiums to incentivize individual building risk reduction results in a positive societal impact.
• Complementing risk-based premiums with large-scale, regional adaptation investments by the government results in the highest societal benefit.
• Investing in flood protection infrastructure will reduce some of the equity issues that arise when solely moving to risk-based premiums.