Published on 23 July 2025

Global lakes depth-area-volume relationship with 0.1m resolution

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Yu, Shengde;Van Cappellen, Philippe

Description

Our research presents comprehensive approaches to modeling the depth-area-storage relationships of over 1.4 million global water bodies with a high accuracy of 0.1 meters. It addresses critical ecological challenges exacerbated by human activities, like climate change and industrial impacts on freshwater systems. The study employs a dual methodology, combining polynomial equations (first to fifth order) and a power function, offering representations of area, depth, and volume relationships in water bodies. Our advanced approaches enhance the precision in predicting water bodies’ bathymetric dynamics, crucial for ecological modeling and sustainable water management. The research's significant contribution lies in laying the foundation for advanced hydrodynamic and water quality models, important for monitoring and predicting ecological changes in freshwater ecosystems.

Citations (0)

Mentions (0)

Metrics

Dataset Index

1.6

FAIR Score

65%

Citations

0

Mentions

0

Metrics Over Time

Publication Details

DOI

Publisher

Federated Research Data Repository / dépôt fédéré de données de recherche

Assigned Domain

Subfield

Water Science and Technology

Field

Environmental Science

Domain

Physical Sciences

Confidence Score

53%

Source

Scholar Data Model

Keywords

depth-area-volumemachine learningglobal lakeswaterbodiesHydrogeologyHydrogéologie

Normalization Factors

FT

13.46

CTw

1.00

MTw

1.00