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New estimates of flood exposure in developing countries using high-resolution population data

Current estimates of global flood exposure are made using datasets that distribute population counts homogenously across large lowland floodplain areas. When intersected with simulated water depths, this results in a significant mis-estimation. Here, we use new highly resolved population information...

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Autores principales: Smith, Andrew, Bates, Paul D., Wing, Oliver, Sampson, Christopher, Quinn, Niall, Neal, Jeff
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6472407/
https://www.ncbi.nlm.nih.gov/pubmed/31000721
http://dx.doi.org/10.1038/s41467-019-09282-y
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author Smith, Andrew
Bates, Paul D.
Wing, Oliver
Sampson, Christopher
Quinn, Niall
Neal, Jeff
author_facet Smith, Andrew
Bates, Paul D.
Wing, Oliver
Sampson, Christopher
Quinn, Niall
Neal, Jeff
author_sort Smith, Andrew
collection PubMed
description Current estimates of global flood exposure are made using datasets that distribute population counts homogenously across large lowland floodplain areas. When intersected with simulated water depths, this results in a significant mis-estimation. Here, we use new highly resolved population information to show that, in reality, humans make more rational decisions about flood risk than current demographic data suggest. In the new data, populations are correctly represented as risk-averse, largely avoiding obvious flood zones. The results also show that existing demographic datasets struggle to represent concentrations of exposure, with the total exposed population being spread over larger areas. In this analysis we use flood hazard data from a ~90 m resolution hydrodynamic inundation model to demonstrate the impact of different population distributions on flood exposure calculations for 18 developing countries spread across Africa, Asia and Latin America. The results suggest that many published large-scale flood exposure estimates may require significant revision.
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spelling pubmed-64724072019-04-19 New estimates of flood exposure in developing countries using high-resolution population data Smith, Andrew Bates, Paul D. Wing, Oliver Sampson, Christopher Quinn, Niall Neal, Jeff Nat Commun Article Current estimates of global flood exposure are made using datasets that distribute population counts homogenously across large lowland floodplain areas. When intersected with simulated water depths, this results in a significant mis-estimation. Here, we use new highly resolved population information to show that, in reality, humans make more rational decisions about flood risk than current demographic data suggest. In the new data, populations are correctly represented as risk-averse, largely avoiding obvious flood zones. The results also show that existing demographic datasets struggle to represent concentrations of exposure, with the total exposed population being spread over larger areas. In this analysis we use flood hazard data from a ~90 m resolution hydrodynamic inundation model to demonstrate the impact of different population distributions on flood exposure calculations for 18 developing countries spread across Africa, Asia and Latin America. The results suggest that many published large-scale flood exposure estimates may require significant revision. Nature Publishing Group UK 2019-04-18 /pmc/articles/PMC6472407/ /pubmed/31000721 http://dx.doi.org/10.1038/s41467-019-09282-y Text en © The Author(s) 2019 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Smith, Andrew
Bates, Paul D.
Wing, Oliver
Sampson, Christopher
Quinn, Niall
Neal, Jeff
New estimates of flood exposure in developing countries using high-resolution population data
title New estimates of flood exposure in developing countries using high-resolution population data
title_full New estimates of flood exposure in developing countries using high-resolution population data
title_fullStr New estimates of flood exposure in developing countries using high-resolution population data
title_full_unstemmed New estimates of flood exposure in developing countries using high-resolution population data
title_short New estimates of flood exposure in developing countries using high-resolution population data
title_sort new estimates of flood exposure in developing countries using high-resolution population data
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6472407/
https://www.ncbi.nlm.nih.gov/pubmed/31000721
http://dx.doi.org/10.1038/s41467-019-09282-y
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