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

IF 6.9 2区综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES

Scientific Data Pub Date : 2025-10-08 DOI:10.1038/s41597-025-05911-y

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

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引用次数: 0

Abstract

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.

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3D-LAKES：基于ICESat-2测高和陆地卫星图像的三维全球湖泊和水库测深。

全球湖泊和水库蓄水动态的量化对于理解地表水在区域气候学、减轻自然灾害和保护生态系统中的作用至关重要。然而，由于缺乏可靠且具有成本效益的全球水深测量信息，准确理解这些存储动态的能力受到严重阻碍。本研究引入了3D- lakes数据集，该数据集包含全球510,530个湖泊和水库的面积-高程（A-E）关系和三维（3D）测深信息，占全球地表水储存量的98.9%。该数据集使用214个A-E关系和从现场测量收集的12个测深图进行了验证，显示出很强的一致性。a - e关系的RMSE为0.60 m， NRMSE为0.14，R2为0.61；而三维测深图的均方根误差为1.37 m， NRMSE为0.26。该数据集具有支持许多应用的潜力，从监测湖泊/水库存储变化到参数化水力/水文模型。这种整合为全球水文研究、水管理计划和减灾提供了必要的信息。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Scientific Data Social Sciences-Education

CiteScore

11.20

自引率

4.10%

发文量

689

审稿时长

16 weeks

期刊介绍： Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.