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A comprehensive data set of physical and human-dimensional attributes for China’s lake basins
Lakes provide water-related ecosystem services that support human life and production. Nevertheless, climate changes and anthropogenic interventions remarkably altered lake and basin hydrology in recent decades, which pose a significant threat to lacustrine ecosystems. Therefore, assessments of lacu...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9411201/ https://www.ncbi.nlm.nih.gov/pubmed/36008422 http://dx.doi.org/10.1038/s41597-022-01649-z |
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author | Chen, Tan Song, Chunqiao Fan, Chenyu Cheng, Jian Duan, Xuejun Wang, Lei Liu, Kai Deng, Shulin Che, Yue |
author_facet | Chen, Tan Song, Chunqiao Fan, Chenyu Cheng, Jian Duan, Xuejun Wang, Lei Liu, Kai Deng, Shulin Che, Yue |
author_sort | Chen, Tan |
collection | PubMed |
description | Lakes provide water-related ecosystem services that support human life and production. Nevertheless, climate changes and anthropogenic interventions remarkably altered lake and basin hydrology in recent decades, which pose a significant threat to lacustrine ecosystems. Therefore, assessments of lacustrine ecosystems require the spatial and temporal characteristics of key physical and human-dimensional attributes for lakes and lake basins. To facilitate stakeholders obtaining comprehensive data of lake basins in China, we compiled the comprehensive data set for China’s lake basins (CODCLAB) mostly from publicly available data sources based on spatial analysis and mathematical statistics methods in this study. The CODCLAB is available in three data formats, including raster layers (Level 1) in “tiff” format, vector shapefiles (Level 2), and attribute tables (Level 3). It covers 767 lakes (>10 km(2)) in China and their basin extent associating with 34 variables organized into five categories: Hydrology, Topography, Climate, Anthropogenic, and Soils. This unique database will provide basic data for research on the physical processes and socioeconomic activities related to these lakes and their basins in China and expect to feed a broad user community for their application in different areas. |
format | Online Article Text |
id | pubmed-9411201 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-94112012022-08-27 A comprehensive data set of physical and human-dimensional attributes for China’s lake basins Chen, Tan Song, Chunqiao Fan, Chenyu Cheng, Jian Duan, Xuejun Wang, Lei Liu, Kai Deng, Shulin Che, Yue Sci Data Data Descriptor Lakes provide water-related ecosystem services that support human life and production. Nevertheless, climate changes and anthropogenic interventions remarkably altered lake and basin hydrology in recent decades, which pose a significant threat to lacustrine ecosystems. Therefore, assessments of lacustrine ecosystems require the spatial and temporal characteristics of key physical and human-dimensional attributes for lakes and lake basins. To facilitate stakeholders obtaining comprehensive data of lake basins in China, we compiled the comprehensive data set for China’s lake basins (CODCLAB) mostly from publicly available data sources based on spatial analysis and mathematical statistics methods in this study. The CODCLAB is available in three data formats, including raster layers (Level 1) in “tiff” format, vector shapefiles (Level 2), and attribute tables (Level 3). It covers 767 lakes (>10 km(2)) in China and their basin extent associating with 34 variables organized into five categories: Hydrology, Topography, Climate, Anthropogenic, and Soils. This unique database will provide basic data for research on the physical processes and socioeconomic activities related to these lakes and their basins in China and expect to feed a broad user community for their application in different areas. Nature Publishing Group UK 2022-08-25 /pmc/articles/PMC9411201/ /pubmed/36008422 http://dx.doi.org/10.1038/s41597-022-01649-z Text en © The Author(s) 2022 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 Chen, Tan Song, Chunqiao Fan, Chenyu Cheng, Jian Duan, Xuejun Wang, Lei Liu, Kai Deng, Shulin Che, Yue A comprehensive data set of physical and human-dimensional attributes for China’s lake basins |
title | A comprehensive data set of physical and human-dimensional attributes for China’s lake basins |
title_full | A comprehensive data set of physical and human-dimensional attributes for China’s lake basins |
title_fullStr | A comprehensive data set of physical and human-dimensional attributes for China’s lake basins |
title_full_unstemmed | A comprehensive data set of physical and human-dimensional attributes for China’s lake basins |
title_short | A comprehensive data set of physical and human-dimensional attributes for China’s lake basins |
title_sort | comprehensive data set of physical and human-dimensional attributes for china’s lake basins |
topic | Data Descriptor |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9411201/ https://www.ncbi.nlm.nih.gov/pubmed/36008422 http://dx.doi.org/10.1038/s41597-022-01649-z |
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