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Natural Hazards

, Volume 80, Issue 1, pp 623–637 | Cite as

Projection of heat wave mortality related to climate change in Korea

  • Do-Woo Kim
  • Ravinesh C. Deo
  • Jea-Hak Chung
  • Jong-Seol Lee
Original Paper

Abstract

Heat waves associated with climate change are a significant future concern. Although deaths from heat disorders are a direct effect of heat wave incidences, only a few studies have addressed the causal factors between heat wave incidences and deaths from heat disorder. This study applies regression analysis to the time series data in order to deduce the causal factors that affect the number of deaths from heat disorders (NDHD) in Korea using observational dataset from 1994–2012. The duration of a heat wave and the age of the population are highly correlated with the magnitude of the NDHD. Based on this correlation we also analyze heat wave projections to the climate change scenarios produced using the Hadley Centre Global Environmental Model version 3 under the Representative Concentration Pathways (RCP 4.5 and RCP 8.5) and to the single aging population scenario till 2060. The magnitude of the NDHD is expected to elevate by approximately fivefold under the RCP4.5 and 7.2-fold under the RCP 8.5 scenarios compared to the current baseline value (≈23 people per summer). Of greater concern is that the steady death rate increase is expected to be intercepted by the more severe events in future compared to the present period. Under both RCP scenarios considered, the extreme cases are projected to eventuate around the 2050s with approximately 250 deaths. We find that in spite of the greenhouse gas policy proposed to meet reductions under the RCP 4.5 scenario; serious heat wave damage in terms of human mortality may still be unavoidable in Korea.

Keywords

Heat waves projection in Korea RCP 4.5 and 8.5 Heat deaths Heat wave trends 

Acronyms

AR

Aging ratio

ANN

Artificial neural network

Β

Standardized coefficient

BP

Breusch-Pagan

DHW

Maximum duration of heat wave days

DT

Daily maximum temperature

HadGEM3-RA

Hadley Centre Global Environmental Model 3

MES

Mean Squared Error

MK-PRISM

Modified Korean-Parameter-elevation Regressions an Independent Slopes Model

MSE

Mean square error

MT

Mean of maximum temperature

NDHD

Number of deaths from heat disorders

NHW

Number of heat wave days

p value

Probability value

R2

Coefficient of determination

RCP4.5

Representative concentration pathway 4.5

RCP8.5

Representative concentration pathway 8.5

SVR

Support vector regressions

Notes

Acknowledgments

This research was primarily supported by the National Disaster Management Institute (Korea). Also, Dr RC Deo held a ‘RAIS’ Grant from the Academic Division (University of Southern Queensland) that assisted in the collaboration with Dr Do-Woo Kim (Korea).

References

  1. Alho JM (1990) Stochastic methods in population forecasting. Int J Forecast 6(40):521–530CrossRefGoogle Scholar
  2. Amengual A, Homar V, Romero R, Brooks HE, Ramis C, Gordaliza M, Alonso S (2014) Projections of heat waves with high impact on human health in Europe. Glob Planet Change 19:71–84CrossRefGoogle Scholar
  3. Aruga T (2011) Heat stroke environmental health manual, Japanese Ministry of Environment, Tokyo, p 6 (In Japanese)Google Scholar
  4. Baccini M, Kosatsky T, Analitis A, Anderson HR, D’Ovidio M, Menne B, Michelozzi P, Biggeri A (2011) Impact of heat on mortality in 15 European cities: attributable deaths under different weather scenarios. J Epidemiol Community Health 65(1):64–70CrossRefGoogle Scholar
  5. Bai H, Islam MN, Kuroki H, Honda K, Wakasugi C (1995) Deaths due to heat waves during the summer of 1994 in Osaka Prefecture, Japan. Nippon Hoigaku Zasshi 49(4):265–274Google Scholar
  6. Brücker G (2003) Impact sanitaire de la vague de chaleur d’août 2003: premiers résultats et travaux à mener. Bull Épidémiol Hebdomad 45–46:217Google Scholar
  7. Cheng CS, Campbell M, Li Q, Li G, Auld H, Day N, Pengelly D, Cingrich S, Klaassen J, Maclver D, Comer N, Mao Y, Thompson W, Lin H (2009) Differential and combined impacts of extreme temperatures and air pollution on human mortality in south-central Canada. Part II: future estimates. Air Qual Atmos Health 1(4):223–235CrossRefGoogle Scholar
  8. Cowan T, Purich A, Perkins S, Pezza A, Boschat G, Sadler K (2014) More frequent, longer, and hotter heat waves for Australia in the twenty-first century. J Clim 27:5851–5871CrossRefGoogle Scholar
  9. Díaz J, Linares C, Tobías A (2006) Impact of extreme temperatures on daily mortality in Madrid (Spain) among the 45–64 age-group. Int J Biometeorol 50(6):342–348CrossRefGoogle Scholar
  10. Doherty RM, Heal MR, Wilkinson P, Pattenden S, Vieno M, Armstrong B, Atkinson R, Chalabi Z, Kovats S, Milojevic A, Stevenson DS (2009) Current and future climate-and air pollution-mediated impacts on human health. Environ Health 8(suppl 1):S8CrossRefGoogle Scholar
  11. Field CB, Barros VR, Mach K, Mastrandrea M (2014) Climate change 2014: impacts, adaptation, and vulnerability. In: Contribution of working group II to the fifth assessment report of the intergovernmental panel on climate changeGoogle Scholar
  12. Gobakis K, Kolokotsa D, Synnefa A, Saliari M, Giannopoulou K, Santamouris M (2011) Development of a model for urban heat island prediction using neural network techniques. Sustain Cities Soc 1:104–115CrossRefGoogle Scholar
  13. Guest CS, Willson K, Woodward AJ, Hennessy K, Kalkstein LS, Skinner C, McMichael AJ (1999) Climate and mortality in Australia: retrospective study, 1979–1990, and predicted impacts in five major cities in 2030. Clim Res 13:1–15CrossRefGoogle Scholar
  14. Hajat S, Armstrong BG, Gouveia N, Wilkinson P (2005) Mortality displacement of heat-related deaths: a comparison of Delhi, Sao Paulo, and London. Epidemiology 16(5):613–620CrossRefGoogle Scholar
  15. Hajat S, Kovats RS, Lachowycz K (2007) Heat-related and cold-related deaths in England and Wales: who is at risk? Occup Environ Med 64(2):93–100CrossRefGoogle Scholar
  16. Hajat S, Vardoulakis S, Heaviside C, Eggen B (2014) Climate change effects on human health: projections of temperature-related mortality for the UK during the 2020s, 2050s and 2080s. J Epidemiol Community Health 68(7):641–648CrossRefGoogle Scholar
  17. Hoshi A, Inaba Y (2007) Prediction of Heat Disorders in Japan. Glob Environ Res 11:45–50Google Scholar
  18. Huang C, Barnett AG, Wang X, Vaneckova P, FitzGerald G, Tong S (2011) Projecting future heat-related mortality under climate change scenarios: a systematic review. Environ Health Perspect 119:1681–1690CrossRefGoogle Scholar
  19. Huynen MM, Martens P, Schram D, Weijenberg MP, Kunst AE (2001) The impact of heat waves and cold spells on mortality rates in the Dutch population. Environ Health Perspect 109(5):463–470CrossRefGoogle Scholar
  20. Jackson JE, Yost MG, Karr C, Fitzpatrick C, Lamb BK, Chung SH, Chen J, Avise J, Rosenblatt RA, Fenske RA (2010) Public health impacts of climate change in Washington State: projected mortality risks due to heat events and air pollution. Clim Change 102:1–28CrossRefGoogle Scholar
  21. Katsouyanni K, Trichopoulos D, Zavitsanos X, Touloumi G (1988) The 1987 Athens heatwave. Lancet 332(8610):575CrossRefGoogle Scholar
  22. Keatinge WR, Donaldson GC, Cordioli E, Martinelli M, Kunst AE, Mackenbach JP, Nayha S, Vuori I (2000) Heat related mortality in warm and cold regions of Europe: observational study. BMJ 321(7262):670–673CrossRefGoogle Scholar
  23. Kim JY, Lee DG, Kysely J (2008) A synoptic and climatological comparison of record-breaking heat waves in Korea and Europe. Atmosphere 18:355–365 (In Korean with English abstract) Google Scholar
  24. Kim MK, Lee DH, Kim J (2013) Production and validation of daily grid data with 1 km resolution in South Korea. Clim Res 8:13–25 (In Korean with English abstract) Google Scholar
  25. Kim DW, Chung JH, Lee JS, Lee JS (2014) Characteristics of heat wave mortality in Korea. Atmosphere 24:225–234 (In Korean with English abstract) CrossRefGoogle Scholar
  26. Koppe C, Kovats S, Jendritzky G, Menne B (2004) Heat-waves: risks and responses. Health and Global Environmental Change Series, no. 2. World Health OrganizationGoogle Scholar
  27. Korean Metrological Agency (KMA) (2011) How to use the local climate change information?Google Scholar
  28. Kysely J, Kim JY (2009) Mortality during heat waves in South Korea, 1991 to 2005: How exceptional was the 1994 heat wave? Clim Res 38:105–116CrossRefGoogle Scholar
  29. Laaidi M, Laaidi K, Besancenot JP (2006) Temperature-related mortality in France, a comparison between regions with different climates from the perspective of global warming. Int J Biometeorol 51(2):145–153CrossRefGoogle Scholar
  30. McMichael AJ, Anderson HR, Brunekree B, Cohen AJ (1998) Inappropriate use of daily mortality analyses to estimate longer-term mortality effects of air pollution. Int J Epidemiol 27(3):450–453CrossRefGoogle Scholar
  31. Meehl GA, Tebaldi C (2004) More intense, more frequent, and longer lasting heat waves in the 21st century. Science 305(5686):994–997CrossRefGoogle Scholar
  32. Mihalakakou G, Flocas HA, Santamouris M, Helmis CG (2002) Application of neural networks to the simulation of the heat island over Athens, Greece, using synoptic types as a predictor. J Appl Meteorol 41:519–527CrossRefGoogle Scholar
  33. National Emergency Management Agency (NEMA) (2013). Annual report on natural disaster, p 23Google Scholar
  34. Park JG, Jung WS, Kim UB, Song JH, Lee JU (2007) Study on the extreme heat health watch warning system. National Institute of Meteorological Research, p 93 (In Korean with English abstract)Google Scholar
  35. Revich B, Shaposhnikov D (2008) Temperature-induced excess mortality in Moscow, Russia. Int J Biometeorol 52(5):367–374CrossRefGoogle Scholar
  36. Salcedo-Sanz S, Deo RC, Carro-Calvo L, Saavedra-Moreno B (2015) Monthly prediction of air temperature in Australia and New Zealand with machine learning algorithms. Theor Appl Climatol. doi: 10.1007/s00704-015-1480-0070410.1007/s00704-015-1480-4 Google Scholar
  37. Schuman SH (1972) Patterns of urban heat-wave deaths and implications for prevention: data from New York and St. Louis during July, 1966. Environ Res 5(1):59–75CrossRefGoogle Scholar
  38. Stocker TF, Qin D, Plattner GK, Tignor M, Allen SK, Boschung J, Nauels A, Xia Y, Bex V, Midgley PM (2013). Climate change 2013: The physical science basis. In: Intergovernmental panel on climate change, working group I contribution to the IPCC fifth assessment report (AR5). Cambridge University Press, New YorkGoogle Scholar
  39. Takahashi K, Honda Y, Emori S (2007) Assessing mortality risk from heat stress due to global warming. J Risk Res 10(3):339–354CrossRefGoogle Scholar
  40. USA National Weather Service Hazard Statistics, www.nws.noaa.gov/om/hazstats.shtml. Accessed 6 Dec 2014
  41. Vouterakos P, Moustris K, Bartzokas A, Ziomas I, Nastos P, Paliatsos A (2012) Forecasting the discomfort levels within the greater Athens area, Greece using artificial neural networks and multiple criteria analysis. Theor Appl Climatol 110:329–343CrossRefGoogle Scholar
  42. Whelpton PK (1936) An empirical method for calculating future population. J Am Stat As 31:457–473CrossRefGoogle Scholar
  43. Zhang K, Li Y, Schwartz JD (2014) What weather variables are important in predicting heat-related mortality? A new application of statistical learning methods. Environ Res 132:350–359CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  • Do-Woo Kim
    • 1
  • Ravinesh C. Deo
    • 2
  • Jea-Hak Chung
    • 1
  • Jong-Seol Lee
    • 1
  1. 1.National Disaster Management InstituteSeoulKorea
  2. 2.School of Agricultural, Computational and Environmental Sciences, International Centre for Applied Climate Sciences (ICACS)University of Southern QueenslandSpringfieldAustralia

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