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Global assessment of storm disaster-prone areas
BACKGROUND: Advances in climate change research contribute to improved forecasts of hydrological extremes with potentially severe impacts on human societies and natural landscapes. Rainfall erosivity density (RED), i.e. rainfall erosivity (MJ mm hm(-2) h(-1) yr(-1)) per rainfall unit (mm), is a meas...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9401149/ https://www.ncbi.nlm.nih.gov/pubmed/36001546 http://dx.doi.org/10.1371/journal.pone.0272161 |
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author | Diodato, Nazzareno Borrelli, Pasquale Panagos, Panos Bellocchi, Gianni |
author_facet | Diodato, Nazzareno Borrelli, Pasquale Panagos, Panos Bellocchi, Gianni |
author_sort | Diodato, Nazzareno |
collection | PubMed |
description | BACKGROUND: Advances in climate change research contribute to improved forecasts of hydrological extremes with potentially severe impacts on human societies and natural landscapes. Rainfall erosivity density (RED), i.e. rainfall erosivity (MJ mm hm(-2) h(-1) yr(-1)) per rainfall unit (mm), is a measure of rainstorm aggressiveness and a proxy indicator of damaging hydrological events. METHODS AND FINDINGS: Here, using downscaled RED data from 3,625 raingauges worldwide and log-normal ordinary kriging with probability mapping, we identify damaging hydrological hazard-prone areas that exceed warning and alert thresholds (1.5 and 3.0 MJ hm(-2) h(-1), respectively). Applying exceedance probabilities in a geographical information system shows that, under current climate conditions, hazard-prone areas exceeding a 50% probability cover ~31% and ~19% of the world’s land at warning and alert states, respectively. CONCLUSION: RED is identified as a key driver behind the spatial growth of environmental disruption worldwide (with tropical Latin America, South Africa, India and the Indian Archipelago most affected). |
format | Online Article Text |
id | pubmed-9401149 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-94011492022-08-25 Global assessment of storm disaster-prone areas Diodato, Nazzareno Borrelli, Pasquale Panagos, Panos Bellocchi, Gianni PLoS One Research Article BACKGROUND: Advances in climate change research contribute to improved forecasts of hydrological extremes with potentially severe impacts on human societies and natural landscapes. Rainfall erosivity density (RED), i.e. rainfall erosivity (MJ mm hm(-2) h(-1) yr(-1)) per rainfall unit (mm), is a measure of rainstorm aggressiveness and a proxy indicator of damaging hydrological events. METHODS AND FINDINGS: Here, using downscaled RED data from 3,625 raingauges worldwide and log-normal ordinary kriging with probability mapping, we identify damaging hydrological hazard-prone areas that exceed warning and alert thresholds (1.5 and 3.0 MJ hm(-2) h(-1), respectively). Applying exceedance probabilities in a geographical information system shows that, under current climate conditions, hazard-prone areas exceeding a 50% probability cover ~31% and ~19% of the world’s land at warning and alert states, respectively. CONCLUSION: RED is identified as a key driver behind the spatial growth of environmental disruption worldwide (with tropical Latin America, South Africa, India and the Indian Archipelago most affected). Public Library of Science 2022-08-24 /pmc/articles/PMC9401149/ /pubmed/36001546 http://dx.doi.org/10.1371/journal.pone.0272161 Text en © 2022 Diodato et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Diodato, Nazzareno Borrelli, Pasquale Panagos, Panos Bellocchi, Gianni Global assessment of storm disaster-prone areas |
title | Global assessment of storm disaster-prone areas |
title_full | Global assessment of storm disaster-prone areas |
title_fullStr | Global assessment of storm disaster-prone areas |
title_full_unstemmed | Global assessment of storm disaster-prone areas |
title_short | Global assessment of storm disaster-prone areas |
title_sort | global assessment of storm disaster-prone areas |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9401149/ https://www.ncbi.nlm.nih.gov/pubmed/36001546 http://dx.doi.org/10.1371/journal.pone.0272161 |
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