Articles | Volume 15, issue 7
https://doi.org/10.5194/essd-15-2781-2023
© Author(s) 2023. 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-15-2781-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description article
| 
05 Jul 2023
Data description article |
| 05 Jul 2023
| 05 Jul 2023
Res-CN (Reservoir dataset in China): hydrometeorological time series and landscape attributes across 3254 Chinese reservoirs
Youjiang Shen
CORRESPONDING AUTHOR
Department of Civil Engineering, Graduate School of Engineering, The University of Tokyo, Tokyo, 113-0033, Japan
Karina Nielsen
DTU Space, National Space Institute, Technical University of Denmark, 2800, Kongens Lyngby, Denmark
Menaka Revel
Global Hydrological Prediction Center, Institute of Industrial Science, The University of Tokyo, Tokyo, 153-8505, Japan
Dedi Liu
State Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan, 430072, China
Dai Yamazaki
Department of Civil Engineering, Graduate School of Engineering, The University of Tokyo, Tokyo, 113-0033, Japan
Global Hydrological Prediction Center, Institute of Industrial Science, The University of Tokyo, Tokyo, 153-8505, Japan
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Hydrol. Earth Syst. Sci., 29, 7149–7172, https://doi.org/10.5194/hess-29-7149-2025, https://doi.org/10.5194/hess-29-7149-2025, 2025
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Earth Syst. Sci. Data, 17, 6669–6680, https://doi.org/10.5194/essd-17-6669-2025, https://doi.org/10.5194/essd-17-6669-2025, 2025
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Hydrol. Earth Syst. Sci., 29, 3315–3339, https://doi.org/10.5194/hess-29-3315-2025, https://doi.org/10.5194/hess-29-3315-2025, 2025
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Earth Syst. Sci. Data, 15, 3483–3511, https://doi.org/10.5194/essd-15-3483-2023, https://doi.org/10.5194/essd-15-3483-2023, 2023
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Yuki Kimura, Yukiko Hirabayashi, Yuki Kita, Xudong Zhou, and Dai Yamazaki
Hydrol. Earth Syst. Sci., 27, 1627–1644, https://doi.org/10.5194/hess-27-1627-2023, https://doi.org/10.5194/hess-27-1627-2023, 2023
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Monica Coppo Frias, Suxia Liu, Xingguo Mo, Karina Nielsen, Heidi Ranndal, Liguang Jiang, Jun Ma, and Peter Bauer-Gottwein
Hydrol. Earth Syst. Sci., 27, 1011–1032, https://doi.org/10.5194/hess-27-1011-2023, https://doi.org/10.5194/hess-27-1011-2023, 2023
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Dirk Eilander, Anaïs Couasnon, Tim Leijnse, Hiroaki Ikeuchi, Dai Yamazaki, Sanne Muis, Job Dullaart, Arjen Haag, Hessel C. Winsemius, and Philip J. Ward
Nat. Hazards Earth Syst. Sci., 23, 823–846, https://doi.org/10.5194/nhess-23-823-2023, https://doi.org/10.5194/nhess-23-823-2023, 2023
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Menaka Revel, Xudong Zhou, Dai Yamazaki, and Shinjiro Kanae
Hydrol. Earth Syst. Sci., 27, 647–671, https://doi.org/10.5194/hess-27-647-2023, https://doi.org/10.5194/hess-27-647-2023, 2023
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The capacity to discern surface water improved as satellites became more available. Because remote sensing data is discontinuous, integrating models with satellite observations will improve knowledge of water resources. However, given the current limitations (e.g., parameter errors) of water resource modeling, merging satellite data with simulations is problematic. Integrating observations and models with the unique approaches given here can lead to a better estimation of surface water dynamics.
Youjiang Shen, Dedi Liu, Liguang Jiang, Karina Nielsen, Jiabo Yin, Jun Liu, and Peter Bauer-Gottwein
Earth Syst. Sci. Data, 14, 5671–5694, https://doi.org/10.5194/essd-14-5671-2022, https://doi.org/10.5194/essd-14-5671-2022, 2022
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A data gap of 338 Chinese reservoirs with their surface water area (SWA), water surface elevation (WSE), and reservoir water storage change (RWSC) during 2010–2021. Validation against the in situ observations of 93 reservoirs indicates the relatively high accuracy and reliability of the datasets. The unique and novel remotely sensed dataset would benefit studies involving many aspects (e.g., hydrological models, water resources related studies, and more).
Robert J. Parker, Chris Wilson, Edward Comyn-Platt, Garry Hayman, Toby R. Marthews, A. Anthony Bloom, Mark F. Lunt, Nicola Gedney, Simon J. Dadson, Joe McNorton, Neil Humpage, Hartmut Boesch, Martyn P. Chipperfield, Paul I. Palmer, and Dai Yamazaki
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Yujie Zeng, Dedi Liu, Shenglian Guo, Lihua Xiong, Pan Liu, Jiabo Yin, and Zhenhui Wu
Hydrol. Earth Syst. Sci., 26, 3965–3988, https://doi.org/10.5194/hess-26-3965-2022, https://doi.org/10.5194/hess-26-3965-2022, 2022
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The sustainability of the water–energy–food (WEF) nexus remains challenge, as interactions between WEF and human sensitivity and water resource allocation in water systems are often neglected. We incorporated human sensitivity and water resource allocation into a WEF nexus and assessed their impacts on the integrated system. This study can contribute to understanding the interactions across the water–energy–food–society nexus and improving the efficiency of resource management.
Toby R. Marthews, Simon J. Dadson, Douglas B. Clark, Eleanor M. Blyth, Garry D. Hayman, Dai Yamazaki, Olivia R. E. Becher, Alberto Martínez-de la Torre, Catherine Prigent, and Carlos Jiménez
Hydrol. Earth Syst. Sci., 26, 3151–3175, https://doi.org/10.5194/hess-26-3151-2022, https://doi.org/10.5194/hess-26-3151-2022, 2022
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Reliable data on global inundated areas remain uncertain. By matching a leading global data product on inundation extents (GIEMS) against predictions from a global hydrodynamic model (CaMa-Flood), we found small but consistent and non-random biases in well-known tropical wetlands (Sudd, Pantanal, Amazon and Congo). These result from known limitations in the data and the models used, which shows us how to improve our ability to make critical predictions of inundation events in the future.
Dirk Eilander, Willem van Verseveld, Dai Yamazaki, Albrecht Weerts, Hessel C. Winsemius, and Philip J. Ward
Hydrol. Earth Syst. Sci., 25, 5287–5313, https://doi.org/10.5194/hess-25-5287-2021, https://doi.org/10.5194/hess-25-5287-2021, 2021
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Digital elevation models and derived flow directions are crucial to distributed hydrological modeling. As the spatial resolution of models is typically coarser than these data, we need methods to upscale flow direction data while preserving the river structure. We propose the Iterative Hydrography Upscaling (IHU) method and show it outperforms other often-applied methods. We publish the multi-resolution MERIT Hydro IHU hydrography dataset and the algorithm as part of the pyflwdir Python package.
Daisuke Tokuda, Hyungjun Kim, Dai Yamazaki, and Taikan Oki
Geosci. Model Dev., 14, 5669–5693, https://doi.org/10.5194/gmd-14-5669-2021, https://doi.org/10.5194/gmd-14-5669-2021, 2021
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We developed TCHOIR, a hydrologic simulation framework, to solve fluvial- and thermodynamics of the river–lake continuum. This provides an algorithm for upscaling high-resolution topography as well, which enables the representation of those interactions at the global scale. Validation against in situ and satellite observations shows that the coupled mode outperforms river- or lake-only modes. TCHOIR will contribute to elucidating the role of surface hydrology in Earth’s energy and water cycle.
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Short summary
Res-CN fills a gap in a comprehensive and extensive dataset of reservoir-catchment characteristics for 3254 Chinese reservoirs with 512 catchment-level attributes and significantly enhanced spatial and temporal coverage (e.g., 67 % increase in water level and 225 % in storage anomaly) of time series of reservoir water level (data available for 20 % of 3254 reservoirs), water area (99 %), storage anomaly (92 %), and evaporation (98 %), supporting a wide range of applications and disciplines.
Res-CN fills a gap in a comprehensive and extensive dataset of reservoir-catchment...
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