Articles | Volume 16, issue 3
https://doi.org/10.5194/essd-16-1559-2024
https://doi.org/10.5194/essd-16-1559-2024
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

Data sets

A Synthesis of Global Streamflow characteristics, Hydrometeorology, and catchment Attributes (GSHA) for Large Sample River-Centric Studies Ziyun Yin et al. https://doi.org/10.5281/zenodo.8090704

A Synthesis of Global Streamflow characteristics, Hydrometeorology, and catchment Attributes (GSHA) for Large Sample River-Centric Studies V1.1 (1.3) Ziyun Yin et al. https://doi.org/10.5281/zenodo.10433905

R-ArcticNET Water Systems Analysis Group http://www.r-arcticnet.sr.unh.edu/v4.0/AllData/index.html

Australian Bureau of Meteorology waterdata Australian Bureau of Meteorology http://www.bom.gov.au/waterdata/

National water and sanitation agency (ANA) Agência Nac Águas E Saneam. Básico ANA Brazil National Water Agency https://www.snirh.gov.br/hidroweb/serieshistoricas

National water data archive HYDAT Canada National Water Data Archive https://www.canada.ca/en/environment-climate-change/services/water-overview/quantity/monitoring/survey/data-products-services/national-archive-hydat.html

Center for climate and resilience research CR2 Explorator Chile Center for Climate and Resilience Research https://explorador.cr2.cl/

The global runoff data centre GRDC Data Portal The Global Runoff Data Centre https://portal.grdc.bafg.de/applications/public.html?publicuser=PublicUser

India Water Resources Information System India Water Resources Information System https://indiawris.gov.in/wris/#/RiverMonitoring

Japanese Water Information System Ministry of Land, Infrastructure, Transport and Tourism http://www.river.go.jp/

Spain Anuario de Aforos 2022 Anuario de Aforos Anuario de Aforos Digital -- datos.gob.esm http://datos.gob.es/es/catalogo/e00125801-anuario-de-aforos/resource/4836b826-e7fd-4a41-950c-89b4eaea0279

RID River Discharge Data Thailand Royal Irrigation Department http://hydro.iis.u-tokyo.ac.jp/GAME-T/GAIN-T/routine/rid-river/disc_d.html

Gages Through the Ages U.S. Geological Survey https://labs.waterdata.usgs.gov/visualizations/gages-through-the-ages

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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. 
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