Articles | Volume 21, issue 6
https://doi.org/10.5194/nhess-21-1807-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-21-1807-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Predicting social and health vulnerability to floods in Bangladesh
Department of Civil and Environmental Engineering, University of Wisconsin-Madison, Wisconsin, USA
Climate Hazards Center, Department of Geography, University of California, Santa Barbara, California, USA
Hassan Ahmadul
Red Cross Red Crescent Climate Centre, the Hague, the Netherlands
Jonathan Patz
Global Health Institute, Nelson Institute Center for Sustainability and the Global Environment, and Department of Population Health Sciences, University of Wisconsin-Madison, Wisconsin, USA
Paul Block
Department of Civil and Environmental Engineering, University of Wisconsin-Madison, Wisconsin, USA
Related authors
Donghoon Lee, Jia Yi Ng, Stefano Galelli, and Paul Block
Hydrol. Earth Syst. Sci., 26, 2431–2448, https://doi.org/10.5194/hess-26-2431-2022, https://doi.org/10.5194/hess-26-2431-2022, 2022
Short summary
Short summary
To fully realize the potential of seasonal streamflow forecasts in the hydropower industry, we need to understand the relationship between reservoir design specifications, forecast skill, and value. Here, we rely on realistic forecasts and simulated hydropower operations for 753 dams worldwide to unfold such relationship. Our analysis shows how forecast skill affects hydropower production, what type of dams are most likely to benefit from seasonal forecasts, and where these dams are located.
Colin Keating, Donghoon Lee, Juan Bazo, and Paul Block
Nat. Hazards Earth Syst. Sci., 21, 2215–2231, https://doi.org/10.5194/nhess-21-2215-2021, https://doi.org/10.5194/nhess-21-2215-2021, 2021
Short summary
Short summary
Disaster planning has historically underallocated resources for flood preparedness, but evidence supports reduced vulnerability via early actions. We evaluate the ability of multiple season-ahead streamflow prediction models to appropriately trigger early actions for the flood-prone Marañón River and Piura River in Peru. Our findings suggest that locally tailored statistical models may offer improved performance compared to operational physically based global models in low-data environments.
Donghoon Lee, Jia Yi Ng, Stefano Galelli, and Paul Block
Hydrol. Earth Syst. Sci., 26, 2431–2448, https://doi.org/10.5194/hess-26-2431-2022, https://doi.org/10.5194/hess-26-2431-2022, 2022
Short summary
Short summary
To fully realize the potential of seasonal streamflow forecasts in the hydropower industry, we need to understand the relationship between reservoir design specifications, forecast skill, and value. Here, we rely on realistic forecasts and simulated hydropower operations for 753 dams worldwide to unfold such relationship. Our analysis shows how forecast skill affects hydropower production, what type of dams are most likely to benefit from seasonal forecasts, and where these dams are located.
Colin Keating, Donghoon Lee, Juan Bazo, and Paul Block
Nat. Hazards Earth Syst. Sci., 21, 2215–2231, https://doi.org/10.5194/nhess-21-2215-2021, https://doi.org/10.5194/nhess-21-2215-2021, 2021
Short summary
Short summary
Disaster planning has historically underallocated resources for flood preparedness, but evidence supports reduced vulnerability via early actions. We evaluate the ability of multiple season-ahead streamflow prediction models to appropriately trigger early actions for the flood-prone Marañón River and Piura River in Peru. Our findings suggest that locally tailored statistical models may offer improved performance compared to operational physically based global models in low-data environments.
Cited articles
Ahern, M., Kovats, R. S., Wilkinson, P., Few, R., and Matthies, F.: Global
health impacts of floods: Epidemiologic evidence, Epidemiol. Rev., 27,
36–46, https://doi.org/10.1093/epirev/mxi004, 2005. a
Ahsan, M. N. and Warner, J.: The socioeconomic vulnerability index: A
pragmatic approach for assessing climate change led risks – A case study in the south-western coastal Bangladesh, Int. J. Disast. Risk Reduct., 8, 32–49, https://doi.org/10.1016/j.ijdrr.2013.12.009, 2014. a, b, c
Akanda, A. S., Jutla, A. S., Alam, M., De Magny, G. C., Siddique, A. K., Sack, R. B., Huq, A., Colwell, R. R., and Islam, S.: Hydroclimatic influences on seasonal and spatial cholera transmission cycles: Implications for public health intervention in the Bengal Delta, Water Resour. Res., 47, 1–11, https://doi.org/10.1029/2010WR009914, 2011. a
Alderman, K., Turner, L. R., and Tong, S.: Floods and human health: A
systematic review, Environ. Int., 47, 37–47, https://doi.org/10.1016/j.envint.2012.06.003, 2012. a, b
Alfieri, L., Burek, P., Dutra, E., Krzeminski, B., Muraro, D., Thielen, J., and Pappenberger, F.: GloFAS – global ensemble streamflow forecasting and
flood early warning, Hydrol. Earth Syst. Sci., 17, 1161–1175,
https://doi.org/10.5194/hess-17-1161-2013, 2013. a
Batterman, S., EIsenberg, J., Hardin, R., Kruk, M. E., Lemos, M. C., Michalak, A. M., Mukherjee, B., Renne, E., Stein, H., Watkins, C., and Wilson, M. L.: Sustainable control of water-related infectious diseases: A review and proposal for interdisciplinary health-based systems research, Environ. Health Perspect., 117, 1023–1032, https://doi.org/10.1289/ehp.0800423, 2009. a
BBS: Poverty Maps of Bangladesh 2010: Technical Report, Tech. Rep. 22062,
World Bank, World Food Programme, Bangladesh Bureau of Statistics, available at: http://hdl.handle.net/10986/20785 (last access: 10 June 2021), 2010. a
Birkmann, J.: Danger need not spell disaster But how vulnerable are we?, Tech. Rep. 1, available at: https://collections.unu.edu/view/UNU:3105
(last access: 10 June 2021), 2005. a
Birkmann, J.: Measuring vulnerability to natural hazards: Towards disaster
resilient societies, in: vol. 02, United Nations University, New York, 2006. a
CEGIS: Analytical framework for the planning of Integrated Water Resources
Management, December, CEGIS – Center for Environmental and Geographic Information Systems, Dhaka, Bangladesh, 2003. a
Colston, J., Olortegui, M. P., Zaitchik, B., Yori, P. P., Kang, G., Ahmed, T., Bessong, P., Mduma, E., Bhutta, Z., Shrestha, P. S., Lima, A., and Kosek, M.: Pathogen-specific impacts of the 2011–2012 La Niña-associated floods
on enteric infections in the MAL-ED Peru Cohort: A comparative interrupted
time series analysis, Int. J. Environ. Res. Publ. Health, 17, 487, https://doi.org/10.3390/ijerph17020487, 2020. a
Coughlan de Perez, E., van den Hurk, B., van Aalst, M. K., Jongman, B., Klose, T., and Suarez, P.: Forecast-based financing: an approach for catalyzing humanitarian action based on extreme weather and climate forecasts, Nat. Hazards Earth Syst. Sci., 15, 895–904,
https://doi.org/10.5194/nhess-15-895-2015, 2015. a
Cutter, S. L., Boruff, B. J., and Shirley, W. L.: Social Vulnerability to
Environmental Hazards, Social Sci. Quart., 84, 242–261,
https://doi.org/10.1111/1540-6237.8402002, 2003. a, b, c
Dewan, A. M.: Floods in a megacity: Geospatial techniques in assessing
hazards, risk and vulnerability, in: Floods in a Megacity: Geospatial
Techniques in Assessing Hazards, Risk and Vulnerability, chap. 6, Springer Netherlands, Dordrecht, 1–199, https://doi.org/10.1007/978-94-007-5875-9, 2013. a, b, c, d
DGHS: Bangladesh Health-National Adaptation Plan (HNAP), Tech. rep., Ministry
of Health and Family Welfare, Government of the People's Republic of
Bangladesh, Bangladesh, 2018. a
Doocy, S., Daniels, A., Murray, S., and Kirsch, T. D.: The human impact of
floods: a historical review of events 1980–2009 and systematic literature
review, PLoS Currents, available at: https://apps.who.int/iris/handle/10665/268903 (last access: 10 June 2021), 2013. a
Du, W., Fitzgerald, G. J., Clark, M., and Hou, X. Y.: Health impacts of floods, Prehospit. Disast. Med., 25, 265–272, https://doi.org/10.1017/S1049023X00008141, 2010. a
Emerton, R., Zsoter, E., Arnal, L., Cloke, H. L., Muraro, D., Prudhomme, C.,
Stephens, E. M., Salamon, P., and Pappenberger, F.: Developing a global
operational seasonal hydro-meteorological forecasting system: GloFAS-Seasonal v1.0, Geosci. Model Dev., 11, 3327–3346,
https://doi.org/10.5194/gmd-11-3327-2018, 2018. a
Fekete, A.: Validation of a social vulnerability index in context to river-floods in Germany, Nat. Hazards Earth Syst. Sci., 9, 393–403, https://doi.org/10.5194/nhess-9-393-2009, 2009. a, b
Gain, A. K., Mojtahed, V., Biscaro, C., Balbi, S., and Giupponi, C.: An
integrated approach of flood risk assessment in the eastern part of Dhaka
City, Nat. Hazards, 79, 1499–1530, https://doi.org/10.1007/s11069-015-1911-7, 2015. a, b, c
Hashizume, M., Wagatsuma, Y., Faruque, A. S., Hayashi, T., Hunter, P. R.,
Armstrong, B., and Sack, D. A.: Factors determining vulnerability to
diarrhoea during and after severe floods in Bangladesh, J. Water Health, 6, 323–332, https://doi.org/10.2166/wh.2008.062, 2008. a, b, c
Hoque, M. A. A., Tasfia, S., Ahmed, N., and Pradhan, B.: Assessing spatial
flood vulnerability at kalapara upazila in Bangladesh using an analytic
hierarchy process, Sensors, 19, 1302, https://doi.org/10.3390/s19061302, 2019. a, b, c
Islam, A. N., Deb, U. K., Al Amin, M., Jahan, N., Ahmed, I., Tabassum, S.,
Ahamad, M. G., Nabi, A., Singh, N. P., Kattarkandi, B., and Bantilan, C.:
Vulnerability to Climate Change: Adaptation Strategies and Layers of
Resilience Quantifying Vulnerability to Climate Change in Bangladesh, Tech. rep., ICRISAT – International Crops Research Institute for the Semi-Arid Tropics, Telangana, India, available at: http://oar.icrisat.org/8117/ (last access: 10 June 2021), 2013. a, b, c
Jonkman, S. and Vrijling, J.: Loss of life due to floods, J. Flood Risk Manage., 1, 43–56, 2008. a
Kaiser, H. F.: The Application of Electronic Computers to Factor Analysis,
Educ. Psychol. Meas., 20, 141–151, https://doi.org/10.1177/001316446002000116, 1960. a
Kienberger, S., Lang, S., and Zeil, P.: Spatial vulnerability units –
Expert-based spatial modelling of socio-economic vulnerability in the Salzach
catchment, Austria, Nat. Hazards Earth Syst. Sci., 9, 767–778,
https://doi.org/10.5194/nhess-9-767-2009, 2009. a
Kosek, M., Bern, C., and Guerrant, R. L.: The global burden of diarrhoeal
disease, as estimated from studies published between 1992 and 2000, Bull.
World Health Organiz., 81, 197–204, 2003. a
Kovats, R. S., Bouma, M. J., Hajat, S., Worrall, E., and Haines, A.: El Niño and health, Lancet, 362, 1481–1489, https://doi.org/10.1016/S0140-6736(03)14695-8, 2003. a
Kundzewicz, Z. W. and Kundzewicz, W. J.: Mortality in Flood Disasters, in: Extreme Weather Events and Public Health Responses, edited by: Kirch, W., Bertollini, R., and Menne, B., Springer, Berlin, Heidelberg, https://doi.org/10.1007/3-540-28862-7_19, 2005. a
Kundzewicz, Z. W. and Takeuchi, K.: Flood protection and management: quo
vadimus?, Hydrolog. Sci. J., 44, 417–432, 1999. a
Kunii, O., Nakamura, S., Abdur, R., and Wakai, S.: The impact on health and
risk factors of the diarrhoea epidemics in the 1998 Bangladesh floods, Publ. Health, 116, 68–74, https://doi.org/10.1038/sj.ph.1900828, 2002. a, b
Lee, D., Ahmadul, H., Patz, J., and Block, P.: Supplementary data to “Predicting social and health vulnerability to floods in Bangladesh” (Version 1.0) [Data set], Zenodo, https://doi.org/10.5281/zenodo.4718085, 2021. a
Mahhzab, M.: Lagging Districts Development: Background Study Paper for
Preparation of the Seventh Five-Year Plan, Tech. rep., BIDS – Bangladesh Institutue of Development Studies, Dhaka, Bangladesh, 2015. a
Mani, M. S. and Wang, L.: Climate Change and Health Impacts: How Vulnerable is Bangladesh and What Needs to be Done?, Tech. rep., World Bank Group, Washington, DC, available at: http://hdl.handle.net/10986/21820
(last access: 10 June 2021), 2014. a
Mazumder, A., Hassan, A., Islam, S., Islam, S., and Ragib Ahsan, M.: Spatial and Temporal Change in Disease Distribution for Climate Change in Bangladesh, in: Climate Change Risk and Adaptation in Bangladesh, chap. 7, edited by: Ahmadul, H. and Parsons, L., Green University Press, Dhaka, 109–115, 2015. a
MHFW: Facility Registry, available at: http://facilityregistry.dghs.gov.bd, last access: 24 November 2020. a
Nahar, N., Blomstedt, Y., Wu, B., Kandarina, I., Trisnantoro, L., and Kinsman, J.: Increasing the provision of mental health care for vulnerable,
disaster-affected people in Bangladesh, BMC Publ. Health, 14, 708, https://doi.org/10.1186/1471-2458-14-708, 2014. a
Netherlands Red Cross: Bangladesh Floods – August 2017 – Flooding levels & Vulnerability, available at:
https://data.humdata.org/dataset/bangladesh-floods-august-2017-vulnerability-population-density
(last access: 10 June 2021), 2017. a
Patnaik, U. and Narayanan, K.: Vulnerability and Climate Change: An Analysis of the Eastern Coastal Districts of India, MPRA Paper 22062, University Library of Munich, Munich Germany, available at: https://ideas.repec.org/p/pra/mprapa/22062.html (last access: 10 June 2021), 2009. a
Rabby, Y. W., Hossain, M. B., and Hasan, M. U.: Social vulnerability in the
coastal region of Bangladesh: An investigation of social vulnerability index
and scalar change effects, Int. J. Disast. Risk Reduct., 41, 101329, https://doi.org/10.1016/j.ijdrr.2019.101329, 2019. a, b, c
Roy, D. C. and Blaschke, T.: Spatial vulnerability assessment of floods in the coastal regions of Bangladesh, Geomat. Nat. Hazards Risk, 6, 21–44, https://doi.org/10.1080/19475705.2013.816785, 2015. a, b, c, d
Rufat, S., Tate, E., Emrich, C. T., and Antolini, F.: How Valid Are Social
Vulnerability Models?, Ann. Am. Assoc. Geogr., 109, 1131–1153, https://doi.org/10.1080/24694452.2018.1535887, 2019. a, b
Saaty, T. L. and Vargas, L. G.: Models, Methods, Concepts & Applications of
the Analytic Hierarchy Process, in: vol. 175 of nternational Series in
Operations Research & Management Science, Springer US, Boston, MA,
https://doi.org/10.1007/978-1-4614-3597-6, 2012. a
Schwartz, B. S., Harris, J. B., Khan, A. I., LaRocque, R. C., Sack, D. A.,
Malek, M. A., Faruque, A. S., Qadri, F., Calderwood, S. B., Luby, S. P., and
Ryan, E. T.: Diarrheal epidemics in Dhaka, Bangladesh, during three consecutive floods: 1988, 1998, and 2004, Am. J. Trop. Med. Hyg., 74, 1067–1073, https://doi.org/10.4269/ajtmh.2006.74.1067, 2006. a, b, c, d
Shahid, S.: Probable impacts of climate change on public health in Bangladesh, Asia-Pacific journal of public health/Asia-Pacific Academic
Consortium for Public Health, 22, 310–319, https://doi.org/10.1177/1010539509335499,
2010. a, b, c, d
Start Network: Bangladesh Flooding Disaster Summary Sheet, Tech. rep., available at:
https://reliefweb.int/report/bangladesh/bangladesh-flooding-disaster-summary-sheet-20-may-2018
(last access: 10 June 2021), 2018. a
Tapsell, S. M., Penning-Rowsell, E. C., Tunstall, S. M., and Wilson, T. L.:
Vulnerability to flooding: health and social dimensions, Philos. T. Roy. Soc. A, 360, 1511–1525, https://doi.org/10.1098/rsta.2002.1013, 2002. a
Uddin, K., Matin, M. A., and Meyer, F. J.: Operational flood mapping using
multi-temporal Sentinel-1 SAR images: A case study from Bangladesh, Remote
Sens., 11, 1581, https://doi.org/10.3390/rs11131581, 2019. a
UNISDR: Sendai Framework for Disaster Risk Reduction 2015–2030, Tech. rep.,
available at: http://www.preventionweb.net/publications/view/43291
(last access: 10 June 2021), 2015. a
Villagrán de Léon, J. C.: Rapid assessment of potential impacts
of a tsunami: lessons from the Port of Galle in Sri Lanka, UNU-EHS – UNU Institute for Environment and Human Security, available at:
https://collections.unu.edu/view/UNU:1876 (last access: 10 June 2021), 2008. a
Ward, P. J., Blauhut, V., Bloemendaal, N., Daniell, E. J., De Ruiter, C. M.,
Duncan, J. M., Emberson, R., Jenkins, F. S., Kirschbaum, D., Kunz, M., Mohr,
S., Muis, S., Riddell, A. G., Schäfer, A., Stanley, T., Veldkamp, I. E., and Hessel, W. C.: Review article: Natural hazard risk assessments at the global scale, Nat. Hazards Earth Syst. Sci., 20, 1069–1096,
https://doi.org/10.5194/nhess-20-1069-2020, 2020. a
WHO: Floods in the WHO European Region: health effects and their prevention,
WHO Regional Office for Europe, Copenhagen, available at:
http://www.euro.who.int/en/health-topics/environment-and-health/Climate-change/publications/2013/floods-in-the-who-european-region-health-effects-and-their-
prevention
(last access: 10 June 2021), 2013. a, b
World Bank: Population – Bangladesh, World Development Indicators, available at: https://data.worldbank.org/indicator/SP.POP.TOTL?locations=BD
(last access: 10 June 2021), 2018. a
Worldpop, Department of Geography and Geosciences University of Louisville,
Department de Geographie Universite de Namur, and CIESIN Columbia University: Global High Resolution Population Denominators Project, https://doi.org/10.5258/SOTON/WP00645, 2018. a
Short summary
This article assesses the thematic and composite social and health vulnerability of Bangladesh to floods. Tailored vulnerability, weighted by flood forecast and satellite inundation, can be used to predict the massive impacts of the August 2017 flood event. This approach has several advantages and practical implications, including the potential to promote targeted and coordinated disaster management and health practices.
This article assesses the thematic and composite social and health vulnerability of Bangladesh...
Altmetrics
Final-revised paper
Preprint