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A comprehensive data set of physical and human-dimensional attributes for China's lake basins.

Authors :
Chen, Tan
Song, Chunqiao
Fan, Chenyu
Cheng, Jian
Duan, Xuejun
Wang, Lei
Liu, Kai
Deng, Shulin
Che, Yue
Source :
Scientific Data; 8/25/2022, Vol. 9 Issue 1, p1-15, 15p
Publication Year :
2022

Abstract

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<superscript>2</superscript>) 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. Measurement(s) Hydrology, Topography, Climate, Anthropogenic, and Soils for China's lake basins Technology Type(s) spatial analysis and mathematical statistics Factor Type(s) CODCLAB_Level1 • CODCLAB_Level2 • CODCLAB_Level3 Sample Characteristic - Location China's lake basins [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
20524463
Volume :
9
Issue :
1
Database :
Complementary Index
Journal :
Scientific Data
Publication Type :
Academic Journal
Accession number :
158726192
Full Text :
https://doi.org/10.1038/s41597-022-01649-z