24 Mar 2021

24 Mar 2021

Review status: this preprint is currently under review for the journal ESSD.

GeoDAR: Georeferenced global dam and reservoir dataset for bridging attributes and geolocations

Jida Wang1, Blake A. Walter1, Fangfang Yao2, Chunqiao Song3, Meng Ding1, Abu S. Maroof1, Jingying Zhu3, Chenyu Fan3, Aote Xin1, Jordan M. McAlister4, Safat Sikder1, Yongwei Sheng5, George H. Allen6, Jean-François Crétaux7, and Yoshihide Wada8 Jida Wang et al.
  • 1epartment of Geography and Geospatial Sciences, Kansas State University, Manhattan, Kansas, USA
  • 2Cooperative Institute for Research in Environmental Sciences (CIRES), University of Colorado Boulder, Boulder, Colorado, USA
  • 3Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing, China
  • 4Department of Geography, Oklahoma State University, Stillwater, Oklahoma, USA
  • 5Department of Geography, University of California, Los Angeles (UCLA), Los Angeles, California, USA
  • 6Department of Geography, Texas A&M University, College Station, Texas, USA
  • 7Laboratoire d'Études en Géophysique et Océanographie Spatiales (LEGOS), Centre National d'Études Spatiales (CNES), Toulouse, France
  • 8International Institute for Applied Systems Analysis (IIASA), Laxenburg, Austria

Abstract. Dams and reservoirs are among the most widespread human-made infrastructure 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., GOODD) or detailed attributes for limited dam quantity or regions (e.g., GRanD 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 60,000 dams with an extensive suite of attributes. Unfortunately, WRD records are not georeferenced, 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 Dam And Reservoir (GeoDAR) dataset, created by utilizing online geocoding API and multi-source inventories. We release GeoDAR in two successive versions (v1.0 and v1.1) at GeoDAR v1.0 holds 21,051 dam points georeferenced from WRD, whereas v1.1 consists of a) 23,680 dam points after a careful harmonization between GeoDAR v1.0 and GRanD and b) 20,214 reservoir polygons retrieved from high-resolution water masks. Due to geocoding challenges, GeoDAR spatially resolved 40 % of the records in WRD which, however, comprise over 90 % 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 can assist in associating GeoDAR spatial features with the WRD attribute information that users have acquired from ICOLD. With a dam quantity triple that of GRanD, GeoDAR 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 modelling, water resource management, ecosystem health, and energy planning.

Jida Wang et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2021-58', Anonymous Referee #1, 01 Apr 2021
    • AC1: 'Reply on RC1', Jida Wang, 03 Apr 2021
    • AC3: 'Reply on RC1', Jida Wang, 13 Oct 2021
  • RC2: 'Comment on essd-2021-58', Anonymous Referee #2, 22 Apr 2021
    • AC2: 'Reply on RC2', Jida Wang, 22 Apr 2021
    • AC4: 'Reply on RC2', Jida Wang, 13 Oct 2021

Jida Wang et al.

Data sets

Georeferenced global Dams And Reservoirs (GeoDAR) Jida Wang, Blake A. Walter, Fangfang Yao, Chunqiao Song, Meng Ding, Md Abu Sayeed Maroof, Jingying Zhu, Chenyu Fan, Aote Xin, Jordan M. McAlister, Md Safat Sikder, Yongwei Sheng, George H. Allen, Jean-François Crétaux, and Yoshihide Wada

Jida Wang et al.


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