TY - JOUR
T1 - GeoDAR
T2 - georeferenced global dams and reservoirs dataset for bridging attributes and geolocations
AU - Wang, Jida
AU - Walter, Blake A.
AU - Yao, Fangfang
AU - Song, Chunqiao
AU - Ding, Meng
AU - Maroof, Abu Sayeed
AU - Zhu, Jingying
AU - Fan, Chenyu
AU - McAlister, Jordan M.
AU - Sikder, Safat
AU - Sheng, Yongwei
AU - Allen, George H.
AU - Crétaux, Jean François
AU - Wada, Yoshihide
N1 - Funding Information:
Acknowledgements. This research was in part supported by the NASA Surface Water and Ocean Topography (SWOT) grant (grant no. 80NSSC20K1143) and the Kansas State University faculty start-up fund. We would like to acknowledge ICOLD for providing the WRD and the central office of ICOLD for providing information on data dissemination policies and for allowing us to release the position information of the WRD we georeferenced. We thank Aote Xin at Kansas State University for assisting in data harmonization, quality control, and validation and for providing comments on the manuscript. We thank Yao Li at Texas A&M University for providing information on incomplete reservoir polygons and Elizabeth M. Prior at Virginia Tech for providing information on some of the duplicate dam points in the US during the open-discussion process. The authors are also grateful to Bernhard Lehner at McGill University for his constructive suggestions and comments on data curation, usage, and dissemination. We also acknowledge Google Maps Platform (https://cloud.google.com/maps-platform, last access: 14 February 2022) for providing the geocoding API.
Funding Information:
This research was in part supported by the NASA Surface Water and Ocean Topography (SWOT) grant (grant no. 80NSSC20K1143) and the Kansas State University faculty start-up fund. We would like to acknowledge ICOLD for providing the WRD and the central office of ICOLD for providing information on data dissemination policies and for allowing us to release the position information of the WRD we georeferenced. We thank Aote Xin at Kansas State University for assisting in data harmonization, quality control, and validation and for providing comments on the manuscript. We thank Yao Li at Texas A&M University for providing information on incomplete reservoir polygons and Elizabeth M. Prior at Virginia Tech for providing information on some of the duplicate dam points in the US during the open-discussion process. The authors are also grateful to Bernhard Lehner at McGill University for his constructive suggestions and comments on data curation, usage, and dissemination. We also acknowledge Google Maps Platform (https://cloud.google.com/maps-platform, last access: 14 February 2022) for providing the geocoding API. This research has been supported by the NASA Surface Water and Ocean Topography (SWOT) grant (grant no. 80NSSC20K1143).
Publisher Copyright:
© 2022 Jida Wang et al.
PY - 2022/4/21
Y1 - 2022/4/21
N2 - Dams and reservoirs are among the most widespread human-made infrastructures on Earth. Despite their societal and environmental significance, spatial inventories of dams and reservoirs, even for the large ones, are insufficient. A dilemma of the existing georeferenced dam datasets is the polarized focus on either dam quantity and spatial coverage (e.g., GlObal geOreferenced Database of Dams, GOODD) or detailed attributes for a limited dam quantity or region (e.g., GRanD (Global Reservoir and Dam database) and national inventories). One of the most comprehensive datasets, the World Register of Dams (WRD), maintained by the International Commission on Large Dams (ICOLD), documents nearly 60g000 dams with an extensive suite of attributes. Unfortunately, the WRD records provide no geographic coordinates, limiting the benefits of their attributes for spatially explicit applications. To bridge the gap between attribute accessibility and spatial explicitness, we introduce the Georeferenced global Dams And Reservoirs (GeoDAR) dataset, created by utilizing the Google Maps geocoding application programming interface (API) and multi-source inventories. We release GeoDAR in two successive versions (v1.0 and v1.1) at 10.5281/zenodo.6163413 (Wang et al., 2022). GeoDAR v1.0 holds 22g560 dam points georeferenced from the WRD, whereas v1.1 consists of (a) 24g783 dam points after a harmonization between GeoDAR v1.0 and GRanD v1.3 and (b) 21g515 reservoir polygons retrieved from high-resolution water masks based on a one-to-one relationship between dams and reservoirs. Due to geocoding challenges, GeoDAR spatially resolved g1/4g40g% of the records in the WRD, which, however, comprise over 90g% of the total reservoir area, catchment area, and reservoir storage capacity. GeoDAR does not release the proprietary WRD attributes, but upon individual user requests we may provide assistance in associating GeoDAR spatial features with the WRD attribute information that users have acquired from ICOLD. Despite this limit, GeoDAR, with a dam quantity triple that of GRanD, significantly enhances the spatial details of smaller but more widespread dams and reservoirs and complements other existing global dam inventories. Along with its extended attribute accessibility, GeoDAR is expected to benefit a broad range of applications in hydrologic modeling, water resource management, ecosystem health, and energy planning.
AB - Dams and reservoirs are among the most widespread human-made infrastructures on Earth. Despite their societal and environmental significance, spatial inventories of dams and reservoirs, even for the large ones, are insufficient. A dilemma of the existing georeferenced dam datasets is the polarized focus on either dam quantity and spatial coverage (e.g., GlObal geOreferenced Database of Dams, GOODD) or detailed attributes for a limited dam quantity or region (e.g., GRanD (Global Reservoir and Dam database) and national inventories). One of the most comprehensive datasets, the World Register of Dams (WRD), maintained by the International Commission on Large Dams (ICOLD), documents nearly 60g000 dams with an extensive suite of attributes. Unfortunately, the WRD records provide no geographic coordinates, limiting the benefits of their attributes for spatially explicit applications. To bridge the gap between attribute accessibility and spatial explicitness, we introduce the Georeferenced global Dams And Reservoirs (GeoDAR) dataset, created by utilizing the Google Maps geocoding application programming interface (API) and multi-source inventories. We release GeoDAR in two successive versions (v1.0 and v1.1) at 10.5281/zenodo.6163413 (Wang et al., 2022). GeoDAR v1.0 holds 22g560 dam points georeferenced from the WRD, whereas v1.1 consists of (a) 24g783 dam points after a harmonization between GeoDAR v1.0 and GRanD v1.3 and (b) 21g515 reservoir polygons retrieved from high-resolution water masks based on a one-to-one relationship between dams and reservoirs. Due to geocoding challenges, GeoDAR spatially resolved g1/4g40g% of the records in the WRD, which, however, comprise over 90g% of the total reservoir area, catchment area, and reservoir storage capacity. GeoDAR does not release the proprietary WRD attributes, but upon individual user requests we may provide assistance in associating GeoDAR spatial features with the WRD attribute information that users have acquired from ICOLD. Despite this limit, GeoDAR, with a dam quantity triple that of GRanD, significantly enhances the spatial details of smaller but more widespread dams and reservoirs and complements other existing global dam inventories. Along with its extended attribute accessibility, GeoDAR is expected to benefit a broad range of applications in hydrologic modeling, water resource management, ecosystem health, and energy planning.
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U2 - 10.5194/essd-14-1869-2022
DO - 10.5194/essd-14-1869-2022
M3 - Article
AN - SCOPUS:85129124005
SN - 1866-3508
VL - 14
SP - 1869
EP - 1899
JO - Earth System Science Data
JF - Earth System Science Data
IS - 4
ER -