Articles | Volume 15, issue 8
https://doi.org/10.5194/essd-15-3483-2023
© 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-15-3483-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
08 Aug 2023
Data description paper |
| 08 Aug 2023
| 08 Aug 2023
Lake-TopoCat: a global lake drainage topology and catchment database
Department of Geography and Geospatial Sciences, Kansas State University, Manhattan, KS, USA
Department of Geography and Geographic Information Science, University of Illinois Urbana-Champaign, Urbana, IL, USA
Department of Geography and Geospatial Sciences, Kansas State University, Manhattan, KS, USA
George H. Allen
Department of Geosciences, Virginia Polytechnic Institute and State University, Blacksburg, VA, USA
Yongwei Sheng
Department of Geography, University of California, Los Angeles, CA, USA
Dai Yamazaki
Institute of Industrial Science, The University of Tokyo, Tokyo, Japan
Chunqiao Song
Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing, China
University of Chinese Academy of Sciences, Nanjing (UCASNJ), Nanjing, China
Meng Ding
Department of Geography and Geospatial Sciences, Kansas State University, Manhattan, KS, USA
Jean-François Crétaux
Laboratoire d'Études en Géophysique et Océanographie Spatiales (LEGOS), Centre National d'Études Spatiales (CNES), Toulouse, France
Tamlin M. Pavelsky
Department of Earth, Marine and Environmental Sciences, University of North Carolina, Chapel Hill, NC, USA
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Earth Syst. Sci. Data, 14, 1869–1899, https://doi.org/10.5194/essd-14-1869-2022, https://doi.org/10.5194/essd-14-1869-2022, 2022
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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.
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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.
Xudong Zhou, Wenchao Ma, Wataru Echizenya, and Dai Yamazaki
Nat. Hazards Earth Syst. Sci., 21, 1071–1085, https://doi.org/10.5194/nhess-21-1071-2021, https://doi.org/10.5194/nhess-21-1071-2021, 2021
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This article assesses different uncertainties in the analysis of flood risk and found the runoff generated before the river routing is the primary uncertainty source. This calls for attention to be focused on selecting an appropriate runoff for the flood analysis. The uncertainties are reflected in the flood water depth, inundation area and the exposure of the population and economy to the floods.
Claire E. Simpson, Christopher D. Arp, Yongwei Sheng, Mark L. Carroll, Benjamin M. Jones, and Laurence C. Smith
Earth Syst. Sci. Data, 13, 1135–1150, https://doi.org/10.5194/essd-13-1135-2021, https://doi.org/10.5194/essd-13-1135-2021, 2021
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Sonar depth point measurements collected at 17 lakes on the Arctic Coastal Plain of Alaska are used to train and validate models to map lake bathymetry. These models predict depth from remotely sensed lake color and are able to explain 58.5–97.6 % of depth variability. To calculate water volumes, we integrate this modeled bathymetry with lake surface area. Knowledge of Alaskan lake bathymetries and volumes is crucial to better understanding water storage, energy balance, and ecological habitat.
Yanbin Lei, Tandong Yao, Lide Tian, Yongwei Sheng, Lazhu, Jingjuan Liao, Huabiao Zhao, Wei Yang, Kun Yang, Etienne Berthier, Fanny Brun, Yang Gao, Meilin Zhu, and Guangjian Wu
The Cryosphere, 15, 199–214, https://doi.org/10.5194/tc-15-199-2021, https://doi.org/10.5194/tc-15-199-2021, 2021
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Two glaciers in the Aru range, western Tibetan Plateau (TP), collapsed suddenly on 17 July and 21 September 2016, respectively, causing fatal damage to local people and their livestock. The impact of the glacier collapses on the two downstream lakes (i.e., Aru Co and Memar Co) is investigated in terms of lake morphology, water level and water temperature. Our results provide a baseline in understanding the future lake response to glacier melting on the TP under a warming climate.
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Short summary
We introduce Lake-TopoCat to reveal detailed lake hydrography information. It contains the location of lake outlets, the boundary of lake catchments, and a wide suite of attributes that depict detailed lake drainage relationships. It was constructed using lake boundaries from a global lake dataset, with the help of high-resolution hydrography data. This database may facilitate a variety of applications including water quality, agriculture and fisheries, and integrated lake–river modeling.
We introduce Lake-TopoCat to reveal detailed lake hydrography information. It contains the...
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