Earth System Science Data
Earth System Science Data
Earth System Science Data
Articles | Volume 16, issue 3
Articles | Volume 16, issue 3
https://doi.org/10.5194/essd-16-1559-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-16-1559-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 16, issue 3
Data description paper
 | 
25 Mar 2024
Data description paper |  | 25 Mar 2024

A synthesis of Global Streamflow Characteristics, Hydrometeorology, and Catchment Attributes (GSHA) for large sample river-centric studies

Ziyun Yin, Peirong Lin, Ryan Riggs, George H. Allen, Xiangyong Lei, Ziyan Zheng, and Siyu Cai

Viewed

Total article views: 3,718 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
2,684 926 108 3,718 114 114
  • HTML: 2,684
  • PDF: 926
  • XML: 108
  • Total: 3,718
  • BibTeX: 114
  • EndNote: 114
Views and downloads (calculated since 10 Jul 2023)
Cumulative views and downloads (calculated since 10 Jul 2023)

Viewed (geographical distribution)

Total article views: 3,718 (including HTML, PDF, and XML) Thereof 3,575 with geography defined and 143 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 30 Jul 2025
Download
Short summary
Large-sample hydrology (LSH) datasets have been the backbone of hydrological model parameter estimation and data-driven machine learning models for hydrological processes. This study complements existing LSH studies by creating a dataset with improved sample coverage, uncertainty estimates, and dynamic descriptions of human activities, which are all crucial to hydrological understanding and modeling. 
Read more
Share
Altmetrics
Final-revised paper
Preprint