Preprints
https://doi.org/10.5194/essd-2023-256
https://doi.org/10.5194/essd-2023-256
10 Jul 2023
 | 10 Jul 2023
Status: a revised version of this preprint was accepted for the journal ESSD and is expected to appear here in due course.

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

Abstract. Our understanding and predictive capability of streamflow processes largely rely on high-quality datasets that depict a river’s upstream basin characteristics. Recent proliferation of large sample hydrology (LSH) datasets has promoted model parameter estimation and data-driven analyses of the hydrological processes worldwide, yet existing LSH is still insufficient in terms of sample coverage, uncertainty estimates, and dynamic descriptions of anthropogenic activities. To bridge the gap, we contribute the Synthesis of Global Streamflow characteristics, Hydrometeorology, and catchment Attributes (GSHA) to complement existing LSH datasets, which covers 21,568 watersheds from 13 agencies for as long as 43 years based on discharge observations scraped from web. In addition to annual streamflow indices, each basin’s daily meteorological variables (i.e., precipitation, 2 m air temperature, longwave/shortwave radiation, wind speed, actual and potential evapotranspiration), daily-weekly water storage terms (i.e., snow water equivalence, soil moisture, groundwater percentage), and yearly dynamic descriptors of the land surface characteristics (i.e., urban/cropland/forest fractions, leaf area index, reservoir storage and degree of regulation) are also provided by combining openly available remote sensing and reanalysis datasets. The uncertainties of all meteorological variables are estimated with independent data sources. Our analyses revealed the following insights: (i) the meteorological data uncertainties vary across variables and geographical regions, and the prominent patterns revealed should be accounted for by LSH users, (ii) ~6 % watersheds shifted between human managed and natural states during the GSHA time span, which may be useful for hydrologic analysis that takes the changing land surface characteristics into account, and (iii) GSHA watersheds observed a more widespread declining trend in runoff coefficient than an increasing trend, which warrants further studies on water availability. Overall, GSHA is expected to serve hydrological model parameter estimation and data-driven analyses as it continues to improve. GSHA v1.0 can be accessed at https://doi.org/10.5281/zenodo.8090704 (Yin et al., 2023).

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

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CC1: 'Comment on essd-2023-256', Ather Abbas, 01 Aug 2023
    • CC2: 'Reply on CC1', Ziyun Yin, 01 Aug 2023
      • CC3: 'Reply on CC2', Ather Abbas, 08 Aug 2023
        • CC6: 'Reply on CC3', Ziyun Yin, 10 Sep 2023
  • RC1: 'Comment on essd-2023-256', Anonymous Referee #1, 22 Aug 2023
    • AC1: 'Reply on RC1', Peirong Lin, 14 Nov 2023
  • CC4: 'Comment on essd-2023-256', Xinli Bai, 01 Sep 2023
    • CC5: 'Reply on CC4', Ziyun Yin, 03 Sep 2023
  • RC2: 'Comment on essd-2023-256', Anonymous Referee #2, 16 Oct 2023
    • AC2: 'Reply on RC2', Peirong Lin, 15 Nov 2023

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CC1: 'Comment on essd-2023-256', Ather Abbas, 01 Aug 2023
    • CC2: 'Reply on CC1', Ziyun Yin, 01 Aug 2023
      • CC3: 'Reply on CC2', Ather Abbas, 08 Aug 2023
        • CC6: 'Reply on CC3', Ziyun Yin, 10 Sep 2023
  • RC1: 'Comment on essd-2023-256', Anonymous Referee #1, 22 Aug 2023
    • AC1: 'Reply on RC1', Peirong Lin, 14 Nov 2023
  • CC4: 'Comment on essd-2023-256', Xinli Bai, 01 Sep 2023
    • CC5: 'Reply on CC4', Ziyun Yin, 03 Sep 2023
  • RC2: 'Comment on essd-2023-256', Anonymous Referee #2, 16 Oct 2023
    • AC2: 'Reply on RC2', Peirong Lin, 15 Nov 2023
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; Peirong Lin; Ryan Riggs; George H. Allen; Xiangyong Lei; Ziyan Zheng; Siyu Cai https://doi.org/10.5281/zenodo.8090704

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

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Short summary
Large-sample hydrology (LSH) datasets have been the backbone of the hydrological model parameter estimation as well as data-driven machine learning models for hydrological processes. Existing LSH datasets are still insufficient in terms of sample coverage, uncertainty estimates, and dynamic descriptions of human activities, which are all crucial to hydrological understanding and modeling, as highlighted by a recent LSH review article. Therefore, we contribute GHSA to address these limitations.
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