Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Zhang, Alice Tianbo, Gu, Vincent Xinyi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
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
_version_ 1784893164322029568
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
work_keys_str_mv AT zhangalicetianbo globaldamtrackeradatabaseofmorethan35000damswithlocationcatchmentandattributeinformation
AT guvincentxinyi globaldamtrackeradatabaseofmorethan35000damswithlocationcatchmentandattributeinformation