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Global Dam Tracker: A database of more than 35,000 dams with location, catchment, and attribute information
We present one of the most comprehensive geo-referenced global dam databases to date. The Global Dam Tracker (GDAT) contains 35,000 dams with cross-validated geo-coordinates, satellite-derived catchment areas, and detailed attribute information. Combining GDAT with fine-scaled satellite data spannin...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9950439/ https://www.ncbi.nlm.nih.gov/pubmed/36823207 http://dx.doi.org/10.1038/s41597-023-02008-2 |
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author | Zhang, Alice Tianbo Gu, Vincent Xinyi |
author_facet | Zhang, Alice Tianbo Gu, Vincent Xinyi |
author_sort | Zhang, Alice Tianbo |
collection | PubMed |
description | We present one of the most comprehensive geo-referenced global dam databases to date. The Global Dam Tracker (GDAT) contains 35,000 dams with cross-validated geo-coordinates, satellite-derived catchment areas, and detailed attribute information. Combining GDAT with fine-scaled satellite data spanning three decades, we demonstrate how GDAT improves upon existing databases to enable the inter-temporal analysis of the costs and benefits of dam construction on a global scale. Our findings show that over the past three decades, dams have contributed to a dramatic increase in global surface water coverage, especially in developing countries in Asia and South America. This is an important step toward a more systematic understanding of the worldwide impact of dams on local communities. By filling in the data gap, GDAT would help inform a more sustainable and equitable approach to energy access and economic development. |
format | Online Article Text |
id | pubmed-9950439 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-99504392023-02-25 Global Dam Tracker: A database of more than 35,000 dams with location, catchment, and attribute information Zhang, Alice Tianbo Gu, Vincent Xinyi Sci Data Data Descriptor We present one of the most comprehensive geo-referenced global dam databases to date. The Global Dam Tracker (GDAT) contains 35,000 dams with cross-validated geo-coordinates, satellite-derived catchment areas, and detailed attribute information. Combining GDAT with fine-scaled satellite data spanning three decades, we demonstrate how GDAT improves upon existing databases to enable the inter-temporal analysis of the costs and benefits of dam construction on a global scale. Our findings show that over the past three decades, dams have contributed to a dramatic increase in global surface water coverage, especially in developing countries in Asia and South America. This is an important step toward a more systematic understanding of the worldwide impact of dams on local communities. By filling in the data gap, GDAT would help inform a more sustainable and equitable approach to energy access and economic development. Nature Publishing Group UK 2023-02-23 /pmc/articles/PMC9950439/ /pubmed/36823207 http://dx.doi.org/10.1038/s41597-023-02008-2 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/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/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Data Descriptor Zhang, Alice Tianbo Gu, Vincent Xinyi Global Dam Tracker: A database of more than 35,000 dams with location, catchment, and attribute information |
title | Global Dam Tracker: A database of more than 35,000 dams with location, catchment, and attribute information |
title_full | Global Dam Tracker: A database of more than 35,000 dams with location, catchment, and attribute information |
title_fullStr | Global Dam Tracker: A database of more than 35,000 dams with location, catchment, and attribute information |
title_full_unstemmed | Global Dam Tracker: A database of more than 35,000 dams with location, catchment, and attribute information |
title_short | Global Dam Tracker: A database of more than 35,000 dams with location, catchment, and attribute information |
title_sort | global dam tracker: a database of more than 35,000 dams with location, catchment, and attribute information |
topic | Data Descriptor |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9950439/ https://www.ncbi.nlm.nih.gov/pubmed/36823207 http://dx.doi.org/10.1038/s41597-023-02008-2 |
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