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Cited by 673Climate variability and modelsClimate Change and Health ImpactsClimate Change and Environmental Impact

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Deadly heat waves projected in the densely populated agricultural regions of South Asia

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Im, Eun-Soon, Pal, Jeremy S., Eltahir, Elfatih A. B.·2017·Science Advances
DOI 10.1126/sciadv.1603322PMC PMC5540239
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Abstract

Climate change poses a deadly heat wave risk to South Asia due to population density, poverty, and outdoor working conditions.

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Figures

Fig. 1 Spatial distribution of highest daily maximum wet-bulb temperature, TWmax (°C), in modern record (1979–2015). Global distribution of TWmax is computed using ERA-Interim 3-hourly data with 0.75° × 0.75° horizontal resolution. The enlarged domain highlights three distinguishable regions with higher TWmax: southwest Asia around the Persian/Arabian Gulf and Red Sea, South Asia in the Indus and Ganges river valleys, and eastern China. TWmax values below 27°C are not shown.
Fig. 1 Spatial distribution of highest daily maximum wet-bulb temperature, TWmax (°C), in modern record (1979–2015). Global distribution of TWmax is computed using ERA-Interim 3-hourly data with 0.75° × 0.75° horizontal resolution. The enlarged domain highlights three distinguishable regions with higher TWmax: southwest Asia around the Persian/Arabian Gulf and Red Sea, South Asia in the Indus and Ganges river valleys, and eastern China. TWmax values below 27°C are not shown.CC BY-NC · PubMed Central
Fig. 2 Histograms of daily maximum wet-bulb temperature, TWmax (°C), and maps of the ensemble averaged 30-year TWmax. (A) The histograms are generated for the most populous cities in the selected regions for each scenario: HIST (blue), RCP4.5 (green), and RCP8.5 (red). Values within each panel correspond to the 2- and 25-year return period of the bias-corrected annual maxima of TWmax, and the x and y axes indicate TWmax (°C) and the number of occurrences on a logarithmic scale, respectively. The background image was obtained from NASA Visible Earth. (B to D) The spatial distributions of bias-corrected ensemble averaged 30-year TWmax for each GHG scenario: HIST (1976–2005) (B), RCP4.5 (2071–2100) (C), and RCP8.5 (2071–2100) (D).
Fig. 2 Histograms of daily maximum wet-bulb temperature, TWmax (°C), and maps of the ensemble averaged 30-year TWmax. (A) The histograms are generated for the most populous cities in the selected regions for each scenario: HIST (blue), RCP4.5 (green), and RCP8.5 (red). Values within each panel correspond to the 2- and 25-year return period of the bias-corrected annual maxima of TWmax, and the x and y axes indicate TWmax (°C) and the number of occurrences on a logarithmic scale, respectively. The background image was obtained from NASA Visible Earth. (B to D) The spatial distributions of bias-corrected ensemble averaged 30-year TWmax for each GHG scenario: HIST (1976–2005) (B), RCP4.5 (2071–2100) (C), and RCP8.5 (2071–2100) (D).CC BY-NC · PubMed Central
Fig. 3 Vulnerability due to population density, poverty, and outdoor working conditions. (A to C) Maps of population density in people/km2 (A), GDP in U.S. dollars per capita (B), and agricultural land fraction (C). (D to F) Population fraction exposed at least once in the simulations to TWmax (D) and exposed to median of annual maxima of TWmax (E), and agricultural land fraction (crop and pasture) exposed at least once in the simulations to TWmax (F). Blue, green, and red lines represent the HIST (1976–2005), RCP4.5 (2071–2100), and RCP8.5 (2071–2100) scenarios, respectively. Data for 2000 population density (43), 2014 GDP (Indian Ministry of Statistics and Programme Implementation), and 2000 agricultural intensity (44) are regridded onto the MRCM grid.
Fig. 3 Vulnerability due to population density, poverty, and outdoor working conditions. (A to C) Maps of population density in people/km2 (A), GDP in U.S. dollars per capita (B), and agricultural land fraction (C). (D to F) Population fraction exposed at least once in the simulations to TWmax (D) and exposed to median of annual maxima of TWmax (E), and agricultural land fraction (crop and pasture) exposed at least once in the simulations to TWmax (F). Blue, green, and red lines represent the HIST (1976–2005), RCP4.5 (2071–2100), and RCP8.5 (2071–2100) scenarios, respectively. Data for 2000 population density (43), 2014 GDP (Indian Ministry of Statistics and Programme Implementation), and 2000 agricultural intensity (44) are regridded onto the MRCM grid.CC BY-NC · PubMed Central

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Cites

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Research works printed but not linked: 3

  1. K. R. Smith A. Woodward D. Campbell-Lendrum D. D. Chadee Y. Honda Q. Liu J. Olwoch B. Revich R. Sauerborn Human health: Impacts adaptation and co-benefits in Climate Change 2014: Impacts Adaptation and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change C. B. Field V. R. Barros D. J. Dokken K. J. Mach M. D. Mastrandrea T. E. Bilir M. Chatterjee K. L. Ebi Y. O. Estrada R. C. Genova B. Girma E. S. Kissel A. N. Levy S. MacCracken P. R. Mastrandrea L. L. White Eds. (Cambridge Univ. Press 2014) pp. 709–754.
  2. K. R. Smith A. Woodward D. Campbell-Lendrum D. D. Chadee Y. Honda Q. Liu J. M. Olwoch B. Revich R. Sauerborn Human health: Impacts adaptation and co-benefits in Climate Change 2014: Impacts Adaptation and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel of Climate Change C. B. Field V. R. Barros D. J. Dokken K. J. Mach M. D. Mastrandrea T. E. Bilir M. Chatterjee K. L. Ebi Y. O. Estrada R. C. Genova B. Girma E. R. Kissel A. N. Levy S. MacCracken P. R. Mastrandrea L. L. White Eds. (Cambridge Univ. Press 2014) pp. 709–754.
  3. Disasters in Asia and the Pacific: 2015 Year in Review (United Nations Economic and Social Commission for Asia and the Pacific 2016); www.unescap.org/sites/default/files/2015_Year%20in%20Review_final_PDF_1.pdf.

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Metadata

Authors
Im, E.-S., Pal, J. S., & Eltahir, E. A. B.
Affiliations
  • Hong Kong University of Science and Technology (HK)
  • Loyola Marymount University (US)
  • Massachusetts Institute of Technology (US)
Corresponding author
  • Im, Eun-Soon
  • Pal, Jeremy S.
  • Eltahir, Elfatih A. B.
ORCID
Venue
Science Advances
Publisher
American Association for the Advancement of Science (AAAS)
Published
2017-08-04
Volume
3
Issue
8
Pages
e1603322-e1603322
ISSN
2375-2548 (ISSN-L)
DOI
10.1126/sciadv.1603322
PMC
PMC5540239
OpenAlex
W2740973936
Cited by
673
References
42
Citations by year
2026: 75 · 2025: 78 · 2024: 100 · 2023: 80 · 2022: 78 · 2021: 105 · 2020: 60 · 2019: 51 · 2018: 38 · 2017: 7
FWCI
29.52
Type
journal article
Language
en
Open access
Licence
cc-by-nc
Locations
  • Science Advances (publishedVersion, cc-by-nc)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by-nc)
  • PubMed Central (publishedVersion, cc-by-nc)
  • PubMed (publishedVersion)
  • DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc)
  • Rare & Special e-Zone (The Hong Kong University of Science and Technology) (submittedVersion)
Funders
National Research Foundation Singapore (award332065)
Topics
  • Physical Sciences › Environmental Science › Global and Planetary Change › Climate variability and models
  • Physical Sciences › Environmental Science › Health, Toxicology and Mutagenesis › Climate Change and Health Impacts
  • Physical Sciences › Environmental Science › Global and Planetary Change › Climate Change and Environmental Impact
Keywords
heat waves, wet-bulb temperature, climate change projections, agricultural land, Ganges basin, Indus Basin, human vulnerability, natural hazards, greenhouse gas emissions, South Asia
MeSH terms
Agriculture, Asia, Climate, Hot Temperature, Humans, Models, Theoretical, Population Density, Poverty, Temperature, Vulnerable Populations, Climate Change, Spatio-Temporal Analysis

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