Vulnerability assessment of future flood impacts for populations on private wells: utilizing climate projection data for public health adaptation planning

  • Brendalynn O. Hoppe
  • Kristin K. Raab
  • Kenneth A. Blumenfeld
  • James Lundy
Article

Abstract

Climate change hazards, like extreme precipitation and flooding, are expected to adversely impact drinking water sources. People dependent on private wells are particularly vulnerable. Increasing availability of climate projection data can facilitate assessments of future vulnerability; however, there are challenges for end users. We developed a novel yet accessible approach for applying climate projection data (2050–2074) to the estimation of flood risk to Minnesota populations dependent on private wells for drinking water. Results were compiled in a geographic information system (GIS)-based overlay analysis with data representing potential nitrate contamination and infant population projections resulting in a county-level composite vulnerability index (CVI). Our findings show that by mid-century, 80% of Minnesota counties with over 20,000 flood-sensitive private wells will experience June extreme rainfall levels historically associated with disaster-level flooding. Counties with very high to high CVI are located mainly in central and southern regions and will account for over 60% of the state’s overall population growth by mid-century underscoring the need to expand private well protections. Climate projection data from global climate models can be used by public health professionals to determine future precipitation extremes and applied to population vulnerability assessments to inform public health planning and response to future climate changes.

Notes

Funding information

Funding for a portion of this project came from the Centers for Disease Control and Prevention, award number CDC-RFA-EH16-1602.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no competing interests.

Supplementary material

10584_2018_2207_MOESM1_ESM.pptx (464 kb)
ESM 1 (PPTX 463 kb)

References

  1. AASC, n.d. American Association of State Climatologists, State Programs. Available at: https://www.stateclimate.org/state_programs. Accessed January 10, 2018
  2. Bao J, Li X, Yu C (2015) The construction and validation of the heat vulnerability index, a review. Int J Environ Res Public Health 12:7220–7234CrossRefGoogle Scholar
  3. Carlsen H, Drebog K, Wikman-Svahn P (2013) Tailor-made scenario planning for local adaptation to climate change. Mitig Adapt Strateg Glob Change 18:1239–1255CrossRefGoogle Scholar
  4. Cheng L, AghaKouchak A (2014) Nonstationary precipitation intensity-duration-frequency curves for infrastructure design in a changing climate. Sci Rep 4:7093CrossRefGoogle Scholar
  5. Chuang W, Gober P (2015) Predicting hospitalization for heat-related illness at the census-tract level: accuracy of a generic heat vulnerability index in Phoenix, Arizona (USA). Environ Health Perspect 123:606–612Google Scholar
  6. Conlon K, Kintziger K, Jagger M, Stefanova L, Uejio C, Konrad C (2016) Working with climate projections to estimate disease burden: perspectives from public health. Int J Environ Res Public Health 13:1–23CrossRefGoogle Scholar
  7. Downing T, Butterfield R, Cohen S, Huq S, Moss R, Rahman A, Sokona Y, Stephen L. 2001. Vulnerability indices: climate change impacts and adaptation. UNEP Policy Series. Nairobi: United Nations Environment ProgrammeGoogle Scholar
  8. Ebi K, Hess J, Isaksen T (2016) Using uncertain climate and development information in health adaptation planning. Curr Environ Health Rep 3:99–105CrossRefGoogle Scholar
  9. Federal Emergency Management Agency (FEMA). (2001). Modernizing FEMA’s flood hazard mapping program. Available at: https://www.fema.gov/media-library-data/20130726-1545-20490-3997/frm_frpt.pdf. Accessed 31 March 2017
  10. Federal Emergency Management Agency (FEMA). (2003). How to read a flood insurance rate map tutorial. Available at: https://www.fema.gov/media-library-data/20130726-1550-20490-1950/ot_firm.pdf. Accessed 31 March 2017
  11. Flanagan S, Marvinney R, Johnston R, Yang Q, Zheng Y (2015) Dissemination of well water arsenic results to homeowners in Central Maine: influences on mitigation behavior and continued risks for exposure. Sci Total Environ 505:1282–1290CrossRefGoogle Scholar
  12. Harding K, Snyder P, Liess S (2003) Use of dynamical downscaling to improve the simulation of Central U.S. warm season precipitation in CMIP5 models. J Geophys Res Atmos 118:12,522–12,536CrossRefGoogle Scholar
  13. Hess J, Ebi K (2016) Iterative management of heat early warning systems in a changing climate. Ann N Y Acad Sci 1382:21–30CrossRefGoogle Scholar
  14. Homer C, Dewitz J, Yang L, Jin S, Danielson P, Xian G, Coulston J, Herold N, Wickham J, Megown K (2015) Completion of the 2011 National Land Cover Database for the conterminous United States—representing a decade of land cover change information. Photogramm Eng Remote Sens 81:345–354Google Scholar
  15. IPCC, 2007. Climate change 2007: impacts, adaptation and vulnerability. Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, M.L. Parry, O.F. Canziani, J.P. Palutikof, P.J. van der Linden and C.E. Hanson, Eds., Cambridge University Press, Cambridge, UK, 976ppGoogle Scholar
  16. Karl T, Melillo J, Peterson T (2009) Global climate change impacts in the United States. Cambridge University Press, Cambridge, MAGoogle Scholar
  17. Kreutzwiser R, de Loë R, Imgrund K, Conboy MJ, Simpson H, Plummer R (2011) Understanding stewardship behaviour: factors facilitating and constraining private water well stewardship. J Environ Manag 92:1104–1114CrossRefGoogle Scholar
  18. Kunkel K, Karl T, Easterling D, Redmond K, Young J, Yin X, Hennon P (2013) Probable maximum precipitation and climate change. Geophys Res Lett 40:1402–1408CrossRefGoogle Scholar
  19. Lockhart KM, King AM, Harter T (2013) Identifying sources of groundwater nitrate contamination in a large alluvial groundwater basin with highly diversified intensive agricultural production. J Contam Hydrol 151:140–154CrossRefGoogle Scholar
  20. Manangan A, Uejio C, Saha S, Schramm P, Marinucci G, Langford Brown C, Hess J, Luber G (2015) Assessing health vulnerability to climate change: a guide for health departments. Centers for Disease Control and Prevention, AtlantaGoogle Scholar
  21. McMichael A (2013) Impediments to comprehensive research on climate change and health. Int J Environ Res Public Health 10:6096–6105CrossRefGoogle Scholar
  22. Melillo J, Richmond T, Yohe G. Highlights of climate change impacts in the United States: the third national climate assessment. Available at: http://s3.amazonaws.com/nca2014/low/NCA3_Highlights_LowRes.pdf?download=1. Accessed March 31, 2017
  23. Minnesota Department of Health (MDH). Drinking water by the Numbers, 2017a. Available at: http://www.health.state.mn.us/divs/eh/water/com/dwar/waternumbersfy17.pdf. Accessed January 10, 2018
  24. Minnesota Department of Health (MDH). Nitrate-nitrogen risk ranking methods and results, 2017b. Available at: http://www.health.state.mn.us/divs/eh/water/swp/maps/no3risk.pdf. Accessed January 10, 2018
  25. Minnesota Department of Natural Resources (MNDNR). Minnesota facts and figures. Available online: http://www.dnr.state.mn.us/faq/mnfacts/index.html. Accessed January 10, 2018
  26. Minnesota Department of Natural Resources (MNDNR). Historic mega-rain events in Minnesota, 2018. Available online: http://www.dnr.state.mn.us/climate/summaries_and_publications/mega_rain_events.html. Accessed January 24, 2018
  27. Minnesota Pollution Control Agency (MPCA). Groundwater protection recommendations report, 2016. Available at: https://www.pca.state.mn.us/sites/default/files/lrwq-gw-1sy16.pdf. Accessed March 31, 2017
  28. Neiner J, Howze E, Greaney M (2004) Using scenario planning in public health: anticipating alternative futures. Health Promot Pract 5:69–79CrossRefGoogle Scholar
  29. Nolan B, Hitt K, Ruddy B (2002) Probability of nitrate contamination of recently recharged groundwaters in the conterminous United States. Environ Sci Technol 36:2138–2145CrossRefGoogle Scholar
  30. Pastén-Zapata E, Ledesma-Ruiz R, Harter T, Ramírez AI, Mahlknecht J (2014) Assessment of sources and fate of nitrate in shallow groundwater of an agricultural area by using a multi-tracer approach. Sci Total Environ 470-471:855–864CrossRefGoogle Scholar
  31. Puckett L, Cowdery T (2002) Transport and fate of nitrate in a glacial outwash aquifer in relation to ground water age, land use practices, and redox processes. J Environ Qual 31:782–796CrossRefGoogle Scholar
  32. Riahi K, Rao S, Krey V, Cho C, Chirkov V, Fischer G, Kindermann G, Nakicenovic N, Rafaj P (2011) RCP 8.5—a scenario of comparatively high greenhouse gas emissions. Clim Chang 109:33CrossRefGoogle Scholar
  33. Rogan W, Brady M, Committee on Environmental Health, Committee on Infectious Diseases (2009) Drinking water from private wells and risks to children. Pediatrics 123:e1123–e1137CrossRefGoogle Scholar
  34. Scoccimarro E, Gualdi S, Bellucci A, Sanna A, Fogli P, Manzini E, Vichi M, Oddo P, Navarra A (2011) Effects of tropical cyclones on ocean heat transport in a high resolution coupled general circulation model. J Clim 24:4368–4384CrossRefGoogle Scholar
  35. Shah P, Mallory M, Ando A, Guntenspergen G. 2016. Fine-resolution conservation planning with limited climate-change information. Conserv Biol 27Google Scholar
  36. Sterk A, Schijven J, de Roda HA, de Nijs T (2016) Effect of climate change on runoff of Campylobacter and Cryptosporidium from land to surface water. Water Res 95:90–102CrossRefGoogle Scholar
  37. Tong S, Confalonieri U, Ebi K, Olsen J (2016) Managing and mitigating the health risks of climate change: calling for evidence-informed policy and action. Environ Health Perspect 124:A176–A179CrossRefGoogle Scholar
  38. United States Agency for International Development (USAID). Design and use of composite indices in assessments of climate change vulnerability and resilience. Available at: http://www.ciesin.org/documents/Design_Use_of_Composite_Indices.pdf. Accessed March 21, 2017
  39. USDA National agricultural statistics service cropland data layer, Minnesota, 2015. Available at: https://gisdata.mn.gov/dataset/agri-cropland-data-layer-2015. Accessed March 31, 2017
  40. Viglione A, Merz B, Viet Dung N, Parajka J, Nester T, Blöschl G (2016) Attribution of regional flood changes based on scaling fingerprints. Water Resour Res 52:5322–5340CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V., part of Springer Nature 2018

Authors and Affiliations

  • Brendalynn O. Hoppe
    • 1
  • Kristin K. Raab
    • 1
  • Kenneth A. Blumenfeld
    • 2
  • James Lundy
    • 1
  1. 1.Minnesota Department of HealthSt. PaulUSA
  2. 2.State Climatology Office, Minnesota Department of Natural ResourcesSt. PaulUSA

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