Articles | Volume 14, issue 4
https://doi.org/10.5194/essd-14-1869-2022
© Author(s) 2022. 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-14-1869-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
21 Apr 2022
Data description paper |
| 21 Apr 2022
| 21 Apr 2022
GeoDAR: georeferenced global dams and reservoirs dataset for bridging attributes and geolocations
Department of Geography and Geospatial Sciences, Kansas State
University, Manhattan, Kansas, USA
Blake A. Walter
Department of Geography and Geospatial Sciences, Kansas State
University, Manhattan, Kansas, USA
Fangfang Yao
Cooperative Institute for Research in Environmental Sciences (CIRES),
University of Colorado Boulder, Boulder, Colorado, USA
Chunqiao Song
Nanjing Institute of Geography and Limnology, Chinese Academy of
Sciences, Nanjing, China
Meng Ding
Department of Geography and Geospatial Sciences, Kansas State
University, Manhattan, Kansas, USA
Abu Sayeed Maroof
Department of Geography and Geospatial Sciences, Kansas State
University, Manhattan, Kansas, USA
Jingying Zhu
Nanjing Institute of Geography and Limnology, Chinese Academy of
Sciences, Nanjing, China
Chenyu Fan
Nanjing Institute of Geography and Limnology, Chinese Academy of
Sciences, Nanjing, China
Jordan M. McAlister
Department of Geography, Oklahoma State University, Stillwater,
Oklahoma, USA
Safat Sikder
Department of Geography and Geospatial Sciences, Kansas State
University, Manhattan, Kansas, USA
Yongwei Sheng
Department of Geography, University of California, Los Angeles (UCLA),
Los Angeles, California, USA
George H. Allen
Department of Geography, Texas A&M University, College Station,
Texas, USA
Jean-François Crétaux
Laboratoire d'Études en Géophysique et Océanographie
Spatiales (LEGOS), Centre National d'Études Spatiales (CNES), Toulouse,
France
Yoshihide Wada
International Institute for Applied Systems Analysis (IIASA),
Laxenburg, Austria
Data sets
Georeferenced global Dams And Reservoirs (GeoDAR) dataset Jida Wang, Blake A. Walter, Fangfang Yao, Chunqiao Song, Meng Ding, Abu S. Maroof, Jingying Zhu, Chenyu Fan, Jordan M. McAlister, Md Safat Sikder, Yongwei Sheng, George H. Allen, Jean-François Crétaux, and Yoshihide Wada https://doi.org/10.5281/zenodo.6163413
Short summary
Improved water infrastructure data on dams and reservoirs remain to be critical to hydrologic modeling, energy planning, and environmental conservation. We present a new global dataset, GeoDAR, that includes nearly 25 000 georeferenced dam points and their associated reservoir boundaries. A majority of these features can be linked to the register of the International Commission on Large Dams, extending the potential of registered attribute information for spatially explicit applications.
Improved water infrastructure data on dams and reservoirs remain to be critical to hydrologic...
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