Preprints
https://doi.org/10.5194/essd-2023-216
https://doi.org/10.5194/essd-2023-216
14 Jul 2023
 | 14 Jul 2023
Status: this preprint was under review for the journal ESSD but the revision was not accepted.

A Global Lake/Reservoir Surface Extent Dataset (GLRSED): An integration of HydroLAKES, GRanD and OpenStreetMap

Bingxin Bai, Lixia Mu, Ge Chen, and Yumin Tan

Abstract. Global lake/reservoir surface water extent is the basic input data for many studies. Although there are some datasets at present, there are problems such as incomplete or spatial inconsistency exist among them due to various reasons like different data sources and dynamic change characteristics of the surface water. In this paper, a new Global Lake/Reservoir Surface Extent Dataset (GLRSED) that contains spatial extent and basic attributes (e.g., name, area, lake type and source) of 2.17 million lakes/reservoirs was produced based on HydroLAKES, GRanD and OpenStreetMap. In addition, by overlaying with mountain data, we identified the lakes/reservoirs located in mountain areas. By overlaying with the Global geReferenced Database of Dams (GOODD) and Georeferenced Global Dams and Reserves (GeoDAR) dataset, we partitioned human-managed reservoirs from natural lakes. Lakes/reservoirs on the rivers were identified by overlaying with the SWOT Mission River Database (SWORD). Using the same method, we identified endorheic, glacier-fed and permafrost-fed lakes. Furthermore, the coverage of Surface Water and Ocean Topography (SWOT) ground track to each lake/reservoir in GLRSED was calculated to explore the potential of SWOT for monitoring lakes. These datasets could provide basic data for global lake/reservoir monitoring, enabling the study on the impact of human actions and climate changes on lake/reservoir freshwater availability. The GLRSED database is available at https://doi.org/10.5281/zenodo.8121174 (Bai et al., under review, 2023).

Bingxin Bai, Lixia Mu, Ge Chen, and Yumin Tan

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2023-216', Anonymous Referee #1, 29 Aug 2023
    • AC1: 'Reply on RC1', B. X. Bai, 25 Sep 2023
  • CC1: 'Comment on essd-2023-216', Yaohui Liu, 01 Oct 2023
  • RC2: 'Comment on essd-2023-216', Anonymous Referee #2, 06 Oct 2023

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2023-216', Anonymous Referee #1, 29 Aug 2023
    • AC1: 'Reply on RC1', B. X. Bai, 25 Sep 2023
  • CC1: 'Comment on essd-2023-216', Yaohui Liu, 01 Oct 2023
  • RC2: 'Comment on essd-2023-216', Anonymous Referee #2, 06 Oct 2023
Bingxin Bai, Lixia Mu, Ge Chen, and Yumin Tan

Data sets

A Global Lake/Reservoir Surface Extent Dataset (GLRSED) Bingxin Bai, Lixia Mu, Ge Chen, Yumin Tan https://doi.org/10.5281/zenodo.8121174

Bingxin Bai, Lixia Mu, Ge Chen, and Yumin Tan

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Short summary
Global lake/reservoir surface water extent is the basic input data for many studies.But incomplete or spatial inconsistency problems exist in existing datasets. Fully utilizing HydroLakes, OpenStreetMap and GRanD, we produced a global dataset of lakes/reservoirs with 2.17 million individual features, called GLRSED. By spatially overlaying GLRSED with other auxiliary data, we identified mountain lakes, endorheic lakes, reservoirs, glacier-fed and permafrost-fed lakes, etc.
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