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

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Cited articles

Addor, N., Newman, A. J., Mizukami, N., and Clark, M. P.: The CAMELS data set: catchment attributes and meteorology for large-sample studies, Hydrol. Earth Syst. Sci., 21, 5293–5313, https://doi.org/10.5194/hess-21-5293-2017, 2017. 
Addor, N., Nearing, G., Prieto, C., Newman, A. J., Le Vine, N., and Clark, M. P.: A ranking of hydrologicalsignatures based on their predictability in space, Water Resour. Res., 54, 8792–8812, https://doi.org/10.1029/2018WR022606, 2018. 
Addor, N., Do, H. X., Alvarez-Garreton, C., Coxon, G., Fowler, K., and Mendoza, P. A.: Large-sample hydrology: recent progress, guidelines for new datasets and grand challenges, Hydrolog. Sci. J., 65, 712–725, https://doi.org/10.1080/02626667.2019.1683182, 2020. 
Aerts, J. P. M., Hut, R. W., van de Giesen, N. C., Drost, N., van Verseveld, W. J., Weerts, A. H., and Hazenberg, P.: Large-sample assessment of varying spatial resolution on the streamflow estimates of the wflow_sbm hydrological model, Hydrol. Earth Syst. Sci., 26, 4407–4430, https://doi.org/10.5194/hess-26-4407-2022, 2022. 
AghaKouchak, A., Chiang, F., Huning, L. S., Love, C. A., Mallakpour, I., Mazdiyasni, O., Moftakhari, H., Papalexiou, S. M., Ragno, E., and Sadegh, M.: Climate Extremes and Compound Hazards in a Warming World, Annu. Rev. Earth Pl. Sc., 48, 519–548, https://doi.org/10.1146/annurev-earth-071719-055228, 2020. 
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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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