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.
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.
the Creative Commons Attribution 4.0 License.
Data description article
| 
25 Mar 2024
Data description article |
| 25 Mar 2024
| 25 Mar 2024
A synthesis of Global Streamflow Characteristics, Hydrometeorology, and Catchment Attributes (GSHA) for large sample river-centric studies
Ziyun Yin
Institute of Remote Sensing and GIS, School of Earth and Space Sciences, Peking University, Beijing, China
Institute of Remote Sensing and GIS, School of Earth and Space Sciences, Peking University, Beijing, China
International Research Center for Big Data for Sustainable Development Goals, Beijing, China
Southwest United Graduate School, Kunming, Yunnan, China
Ryan Riggs
Department of Geography, Texas A&M University, College Station, Texas, USA
George H. Allen
Department of Geosciences, Virginia Polytechnic Institute and State University, Blacksburg, Virginia, USA
Xiangyong Lei
Institute of Remote Sensing and GIS, School of Earth and Space Sciences, Peking University, Beijing, China
Ziyan Zheng
Key Laboratory of Regional Climate-Environment Research for Temperate East Asia, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing, China
Siyu Cai
State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing, China
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Cited
14 citations as recorded by crossref.
- Process diagnostics of snowmelt runoff in global hydrological and land surface models – Part 1: A systematic evaluation across basins of increasing complexity X. Lei et al. https://doi.org/10.5194/hess-30-5343-2026
- CONCN: a high-resolution, integrated surface water–groundwater ParFlow modeling platform of continental China C. Yang et al. https://doi.org/10.5194/hess-29-2201-2025
- Technical note: High Nash–Sutcliffe Efficiencies conceal poor simulations of interannual variance in seasonal regimes S. Ruzzante et al. https://doi.org/10.5194/hess-30-2337-2026
- Long-term global trends and influencing factors of surface urban cool and heat islands R. Qiu et al. https://doi.org/10.1080/17538947.2025.2489731
- Three-dimensional canopy morphology and wind dynamics govern global rainfall interception Q. Li et al. https://doi.org/10.1038/s43247-026-03694-7
- Unveiling the impact of potential evapotranspiration method selection on trends in hydrological cycle components across Europe V. Thakur et al. https://doi.org/10.5194/hess-29-4395-2025
- Comprehensive Global Assessment of 24 Gridded Precipitation Datasets Across 18 428 Catchments Using Hydrological Modeling A. Abbas et al. https://doi.org/10.5194/hess-30-3399-2026
- Flood regulation as a multivariate challenge in the Anthropocene: a systematic modeling approach K. Zheng & P. Lin https://doi.org/10.1088/1748-9326/ae08ce
- A data-driven gap-filling approach to assess long-term streamflow trends across Colombia D. Jimenez Osorio et al. https://doi.org/10.1080/02626667.2026.2707173
- Enhancing Streamflow Prediction Using Cutting-edge Deep Learning Models and Seasonal-Trend Decomposition Y. Jia et al. https://doi.org/10.1007/s11269-025-04447-5
- Human perturbations reshape hydrological responses in riverine systems: Insights from a reach-level quantification framework to the Pearl River Basin H. Lin et al. https://doi.org/10.1016/j.horiz.2025.100174
- Identifying Dominant Drivers of Wind Erosion and Assessing Ecological Risk on the Qinghai-Tibet Plateau Using an XGBoost-SHAP Model S. Ouyang et al. https://doi.org/10.1051/e3sconf/202671101014
- Interannual variability of surface urban Heat Islands across global cities: Patterns and climatic drivers R. Qiu et al. https://doi.org/10.1016/j.uclim.2026.102772
- Global Attribution of Anthropogenic Climate Change to Terrestrial Long-Term Droughts M. Nouri & M. Khorsandi https://doi.org/10.1007/s11269-025-04455-5
14 citations as recorded by crossref.
- Process diagnostics of snowmelt runoff in global hydrological and land surface models – Part 1: A systematic evaluation across basins of increasing complexity X. Lei et al. https://doi.org/10.5194/hess-30-5343-2026
- CONCN: a high-resolution, integrated surface water–groundwater ParFlow modeling platform of continental China C. Yang et al. https://doi.org/10.5194/hess-29-2201-2025
- Technical note: High Nash–Sutcliffe Efficiencies conceal poor simulations of interannual variance in seasonal regimes S. Ruzzante et al. https://doi.org/10.5194/hess-30-2337-2026
- Long-term global trends and influencing factors of surface urban cool and heat islands R. Qiu et al. https://doi.org/10.1080/17538947.2025.2489731
- Three-dimensional canopy morphology and wind dynamics govern global rainfall interception Q. Li et al. https://doi.org/10.1038/s43247-026-03694-7
- Unveiling the impact of potential evapotranspiration method selection on trends in hydrological cycle components across Europe V. Thakur et al. https://doi.org/10.5194/hess-29-4395-2025
- Comprehensive Global Assessment of 24 Gridded Precipitation Datasets Across 18 428 Catchments Using Hydrological Modeling A. Abbas et al. https://doi.org/10.5194/hess-30-3399-2026
- Flood regulation as a multivariate challenge in the Anthropocene: a systematic modeling approach K. Zheng & P. Lin https://doi.org/10.1088/1748-9326/ae08ce
- A data-driven gap-filling approach to assess long-term streamflow trends across Colombia D. Jimenez Osorio et al. https://doi.org/10.1080/02626667.2026.2707173
- Enhancing Streamflow Prediction Using Cutting-edge Deep Learning Models and Seasonal-Trend Decomposition Y. Jia et al. https://doi.org/10.1007/s11269-025-04447-5
- Human perturbations reshape hydrological responses in riverine systems: Insights from a reach-level quantification framework to the Pearl River Basin H. Lin et al. https://doi.org/10.1016/j.horiz.2025.100174
- Identifying Dominant Drivers of Wind Erosion and Assessing Ecological Risk on the Qinghai-Tibet Plateau Using an XGBoost-SHAP Model S. Ouyang et al. https://doi.org/10.1051/e3sconf/202671101014
- Interannual variability of surface urban Heat Islands across global cities: Patterns and climatic drivers R. Qiu et al. https://doi.org/10.1016/j.uclim.2026.102772
- Global Attribution of Anthropogenic Climate Change to Terrestrial Long-Term Droughts M. Nouri & M. Khorsandi https://doi.org/10.1007/s11269-025-04455-5
Saved (final revised paper)
Latest update: 22 Sep 2026
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.
Large-sample hydrology (LSH) datasets have been the backbone of hydrological model parameter...
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