Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

How the USA can benefit from risk-based premiums combined with flood protection

Nature Climate Change volume 12, pages 995–998 (2022)Cite this article

Subjects

Abstract

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.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Effects of NFIP reform 2050 (RCP 4.5 + SSP 2).

Similar content being viewed by others

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

  1. Merz, B. et al. Causes, impacts and patterns of disastrous river floods. Nat. Rev. Earth Environ. 2, 592–609 (2021).

    Article  Google Scholar 

  2. Mills, E. Insurance in a climate of change. Science 309, 1040–1044 (2005).

    Article  CAS  Google Scholar 

  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).

    Article  Google Scholar 

  4. Winsemius, H. C. et al. Global drivers of future river flood risk. Nat. Clim. Change 6, 381–385 (2016).

    Article  Google Scholar 

  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).

    Article  Google Scholar 

  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).

    Article  CAS  Google Scholar 

  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).

    Article  CAS  Google Scholar 

  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).

    Article  Google Scholar 

  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).

    Article  Google Scholar 

  12. Di Baldassarre, G. et al. Socio-hydrology: conceptualising human-flood interactions. Hydrol. Earth Syst. Sci. 17, 3295–3303 (2013).

    Article  Google Scholar 

  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).

    Article  Google Scholar 

  14. Dawson, R. J., Peppe, R. & Wang, M. An agent-based model for risk-based flood incident management. Nat. Hazards 59, 167–189 (2011).

    Article  Google Scholar 

  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).

    Article  Google Scholar 

  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).

    Article  Google Scholar 

  17. Tonn, G. L. & Guikema, S. D. An agent-based model of evolving community flood risk. Risk Anal. 38, 1258–1278 (2018).

    Article  Google Scholar 

  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).

    Article  Google Scholar 

  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).

    Article  Google Scholar 

  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).

    Article  Google Scholar 

  21. Aerts, J. C. J. H. et al. Integrating human behaviour dynamics into flood disaster risk assessment. Nat. Clim. Change 8, 193–199 (2018).

    Article  Google Scholar 

  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).

    Article  Google Scholar 

  23. Blessing, R., Brody, S. D. & Highfield, W. E. Valuing floodplain protection and avoidance in a coastal watershed. Disasters 43, 906–925 (2019).

    Article  Google Scholar 

  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).

    Article  Google Scholar 

  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).

    Google Scholar 

  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).

    Article  Google Scholar 

  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).

    Article  Google Scholar 

  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).

    Article  Google Scholar 

  30. Crespo Cuaresma, J. Income projections for climate change research: a framework based on human capital dynamics. Glob. Environ. Change 42, 226–236 (2017).

    Article  Google Scholar 

  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).

    Article  Google Scholar 

  32. Jiang, L. & O’Neill, B. C. Global urbanization projections for the Shared Socioeconomic Pathways. Glob. Environ. Change 42, 193–199 (2017).

    Article  Google Scholar 

  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).

    Article  Google Scholar 

  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).

    Article  Google Scholar 

  38. Jevrejeva, S., Grinsted, A. & Moore, J. C. Upper limit for sea level projections by 2100. Environ. Res. Lett. 9, 104008 (2014).

    Article  Google Scholar 

  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).

    Article  Google Scholar 

  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).

    Article  Google Scholar 

  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).

    Article  Google Scholar 

  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).

    Article  Google Scholar 

  50. Scussolini, P. et al. FLOPROS: an evolving global database of flood protection standards. Nat. Hazards Earth Syst. Sci. 16, 1049–1061 (2016).

    Article  Google Scholar 

  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).

    Article  Google Scholar 

  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).

    Article  Google Scholar 

  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).

    Article  Google Scholar 

  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).

    Article  Google Scholar 

  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).

    Article  Google Scholar 

  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).

    Article  Google Scholar 

  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).

    Article  Google Scholar 

  67. Kunreuther, H., Sanderson, W. & Vetschera, R. A behavioral model of the adoption of protective activities. J. Econ. Behav. Organ. 6, 1–15 (1985).

    Article  Google Scholar 

  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).

    Article  CAS  Google Scholar 

  69. Tol, R. S. J. The social cost of carbon. Annu. Rev. Resour. Econ. 3, 419–443 (2011).

    Article  Google Scholar 

  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).

    Article  Google Scholar 

  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).

    Article  Google Scholar 

  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).

Download references

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 information

Authors and Affiliations

  1. Institute for Environmental Studies (IVM), VU University Amsterdam, Amsterdam, the Netherlands

    Lars T. de Ruig, Toon Haer, Hans de Moel, W. J. Wouter Botzen & Jeroen C. J. H. Aerts

  2. Royal HaskoningDHV, Amersfoort, the Netherlands

    Lars T. de Ruig

  3. Institute for a Disaster Resilient Texas (IDRT), Texas A&M University Galveston Campus, Galveston, TX, USA

    Samuel D. Brody

  4. Risk Management and Decision Processes Center, The Wharton School, University of Pennsylvania, Philadelphia, PA, USA

    W. J. Wouter Botzen

  5. Utrecht University School of Economics, Utrecht University, Utrecht, the Netherlands

    W. J. Wouter Botzen

  6. Center for Insurance Policy and Research, National Association of Insurance Commissioners (NAIC), Kansas City, MO, USA

    Jeffrey Czajkowski

  7. Deltares, Delft, the Netherlands

    Jeroen C. J. H. Aerts

Authors
  1. Lars T. de Ruig
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Toon Haer
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hans de Moel
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Samuel D. Brody
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. W. J. Wouter Botzen
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Jeffrey Czajkowski
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Jeroen C. J. H. Aerts
    View author publications

    You can also search for this author in PubMed Google Scholar

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.

Corresponding authors

Correspondence to Lars T. de Ruig or Jeroen C. J. H. Aerts.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

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.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

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.

Supplementary information

Supplementary Information

Supplementary Methods, Results, benchmarking, validation and sensitivity analyses, Figs. 1–5 and Tables 1–12.

Rights and permissions

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.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

de Ruig, L.T., Haer, T., de Moel, H. et al. How the USA can benefit from risk-based premiums combined with flood protection. Nat. Clim. Chang. 12, 995–998 (2022). https://doi.org/10.1038/s41558-022-01501-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41558-022-01501-7

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing