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A global empirical typology of anthropogenic drivers of environmental change in deltas
It is broadly recognized that river delta systems around the world are under threat from a range of anthropogenic activities. These activities occur at the local delta scale, at the regional river and watershed scale, and at the global scale. Tools are needed to support generalization of results fro...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Japan
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6106097/ https://www.ncbi.nlm.nih.gov/pubmed/30174737 http://dx.doi.org/10.1007/s11625-016-0357-5 |
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author | Tessler, Zachary D. Vörösmarty, Charles J. Grossberg, Michael Gladkova, Irina Aizenman, Hannah |
author_facet | Tessler, Zachary D. Vörösmarty, Charles J. Grossberg, Michael Gladkova, Irina Aizenman, Hannah |
author_sort | Tessler, Zachary D. |
collection | PubMed |
description | It is broadly recognized that river delta systems around the world are under threat from a range of anthropogenic activities. These activities occur at the local delta scale, at the regional river and watershed scale, and at the global scale. Tools are needed to support generalization of results from case studies in specific deltas. Here, we present a methodology for quantitatively constructing an empirical typology of anthropogenic change in global deltas. Utilizing a database of environmental change indicators, each associated with increased relative sea-level rise and coastal wetland loss, a clustering analysis of 48 global deltas provides a quantitative assessment of systems experiencing similar or dissimilar sources and degrees of anthropogenic stress. By identifying quantitatively similar systems, we hope to improve the transferability of scientific results across systems, and increase the effectiveness of delta management best practices. Both K-Means and Affinity Propagation clustering algorithms find similar clusters, with relative stability across small changes in K-Means cluster number. High-latitude deltas appear similar, in terms of anthropogenic environmental stress, to several low-population, low-latitude systems, including the Amazon delta, despite substantially different climatic regimes. Highly urbanized deltas in Southeast Asia form a distinct cluster. By providing a quantitative boundary between groups of delta systems, this approach may also be useful for assessing future delta change and sustainability given projected population growth, urbanization, and economic development trends. |
format | Online Article Text |
id | pubmed-6106097 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Springer Japan |
record_format | MEDLINE/PubMed |
spelling | pubmed-61060972018-08-30 A global empirical typology of anthropogenic drivers of environmental change in deltas Tessler, Zachary D. Vörösmarty, Charles J. Grossberg, Michael Gladkova, Irina Aizenman, Hannah Sustain Sci Special Feature: Original Article It is broadly recognized that river delta systems around the world are under threat from a range of anthropogenic activities. These activities occur at the local delta scale, at the regional river and watershed scale, and at the global scale. Tools are needed to support generalization of results from case studies in specific deltas. Here, we present a methodology for quantitatively constructing an empirical typology of anthropogenic change in global deltas. Utilizing a database of environmental change indicators, each associated with increased relative sea-level rise and coastal wetland loss, a clustering analysis of 48 global deltas provides a quantitative assessment of systems experiencing similar or dissimilar sources and degrees of anthropogenic stress. By identifying quantitatively similar systems, we hope to improve the transferability of scientific results across systems, and increase the effectiveness of delta management best practices. Both K-Means and Affinity Propagation clustering algorithms find similar clusters, with relative stability across small changes in K-Means cluster number. High-latitude deltas appear similar, in terms of anthropogenic environmental stress, to several low-population, low-latitude systems, including the Amazon delta, despite substantially different climatic regimes. Highly urbanized deltas in Southeast Asia form a distinct cluster. By providing a quantitative boundary between groups of delta systems, this approach may also be useful for assessing future delta change and sustainability given projected population growth, urbanization, and economic development trends. Springer Japan 2016-03-19 2016 /pmc/articles/PMC6106097/ /pubmed/30174737 http://dx.doi.org/10.1007/s11625-016-0357-5 Text en © The Author(s) 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided 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. |
spellingShingle | Special Feature: Original Article Tessler, Zachary D. Vörösmarty, Charles J. Grossberg, Michael Gladkova, Irina Aizenman, Hannah A global empirical typology of anthropogenic drivers of environmental change in deltas |
title | A global empirical typology of anthropogenic drivers of environmental change in deltas |
title_full | A global empirical typology of anthropogenic drivers of environmental change in deltas |
title_fullStr | A global empirical typology of anthropogenic drivers of environmental change in deltas |
title_full_unstemmed | A global empirical typology of anthropogenic drivers of environmental change in deltas |
title_short | A global empirical typology of anthropogenic drivers of environmental change in deltas |
title_sort | global empirical typology of anthropogenic drivers of environmental change in deltas |
topic | Special Feature: Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6106097/ https://www.ncbi.nlm.nih.gov/pubmed/30174737 http://dx.doi.org/10.1007/s11625-016-0357-5 |
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