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<p>Quantification of the water storage dynamics in global lakes and reservoirs is pivotal for understanding the roles of surface water in regional climatology, mitigating natural disasters, and preserving ecosystems. However, the ability to accurately comprehend these storage dynamics is significantly hindered by the lack of reliable and cost-effective global bathymetry information. This study introduces the 3D-LAKES dataset, which contains the area-elevation (A-E) relationship and three-dimensional (3D) bathymetry information for 510,530 global lakes and reservoirs, representing 98.9% of global surface water storage capacity. This dataset was validated using 214 A-E relationships and 12 bathymetry maps collected from <i>in-situ</i> measurements, showing strong agreement. The A-E relationships yield an RMSE of 0.60 m, a NRMSE of 0.14, and an R<sup>2</sup> of 0.61; while the 3D bathymetry maps have an RMSE of 1.37 m and a NRMSE of 0.26. This dataset has the potential to support many applications, from monitoring lake/reservoir storage variations to parameterizing hydraulic/hydrological models. Such integration provides essential information for global hydrological studies, water management programs, and disaster mitigation.</p>

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3D-LAKES: Three-Dimensional Global Lake and Reservoir Bathymetry from ICESat-2 Altimetry and Landsat Imagery

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年10月8日

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  • Chi Hsiang Huang,
  • Shuai Zhang,
  • Deep Shah,
  • Anshul Yadav,
  • Yao Li,
  • Gang Zhao,
  • Huilin Gao

摘要

Quantification of the water storage dynamics in global lakes and reservoirs is pivotal for understanding the roles of surface water in regional climatology, mitigating natural disasters, and preserving ecosystems. However, the ability to accurately comprehend these storage dynamics is significantly hindered by the lack of reliable and cost-effective global bathymetry information. This study introduces the 3D-LAKES dataset, which contains the area-elevation (A-E) relationship and three-dimensional (3D) bathymetry information for 510,530 global lakes and reservoirs, representing 98.9% of global surface water storage capacity. This dataset was validated using 214 A-E relationships and 12 bathymetry maps collected from in-situ measurements, showing strong agreement. The A-E relationships yield an RMSE of 0.60 m, a NRMSE of 0.14, and an R2 of 0.61; while the 3D bathymetry maps have an RMSE of 1.37 m and a NRMSE of 0.26. This dataset has the potential to support many applications, from monitoring lake/reservoir storage variations to parameterizing hydraulic/hydrological models. Such integration provides essential information for global hydrological studies, water management programs, and disaster mitigation.