Articles | Volume 17, issue 6
https://doi.org/10.5194/essd-17-2277-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-17-2277-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
04 Jun 2025
Data description paper |
| 04 Jun 2025
| 04 Jun 2025
Mapping the world's inland surface waters: an upgrade to the Global Lakes and Wetlands Database (GLWD v2)
Bernhard Lehner
CORRESPONDING AUTHOR
Department of Geography, McGill University, Montreal, Quebec H3A 0B9, Canada
Mira Anand
Department of Geography, McGill University, Montreal, Quebec H3A 0B9, Canada
Etienne Fluet-Chouinard
Earth System Science Division, Pacific Northwest National Laboratory, Richland, WA, USA
Florence Tan
Department of Geography, McGill University, Montreal, Quebec H3A 0B9, Canada
Department of Earth and Environmental Sciences, KU Leuven, Leuven, Belgium
Filipe Aires
LERMA, Observatoire de Paris, CNRS, Paris, France
George H. Allen
Department of Geosciences, Virginia Polytechnic Institute and State University, Blacksburg, VA, USA
Philippe Bousquet
Laboratoire des Sciences du Climat et de l'Environnement, Université Paris-Saclay, CEA, CNRS, UVSQ, Gif sur Yvette, France
Josep G. Canadell
Global Carbon Project, CSIRO Environment, Canberra, ACT 2601, Australia
Nick Davidson
Nick Davidson Environmental, Queens House, Ford Street, Wigmore HR6 9UN, UK
Gulbali Institute for Agriculture, Water & Environment, Charles Sturt University, Albury, NSW, Australia
Meng Ding
Department of Geography and Geospatial Sciences, Kansas State University, Kansas 66506, USA
C. Max Finlayson
Gulbali Institute for Agriculture, Water & Environment, Charles Sturt University, Albury, NSW, Australia
Centre for Ecosystem Science, School of Biological, Earth and Environmental Sciences, UNSW Sydney, Sydney, NSW, Australia
Thomas Gumbricht
Department of Physical Geography, Stockholm University, 10691 Stockholm, Sweden
Lammert Hilarides
Wetlands International, Global Office, 6700 AL Wageningen, the Netherlands
Gustaf Hugelius
Department of Physical Geography and Bolin Centre for Climate Research, Stockholm University, 106 91 Stockholm, Sweden
Robert B. Jackson
Department of Earth System Science, Woods Institute for the Environment, and Precourt Institute for Energy, Stanford University, Stanford, CA 94305, USA
Maartje C. Korver
Department of Geography, McGill University, Montreal, Quebec H3A 0B9, Canada
Liangyun Liu
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Peter B. McIntyre
Department of Natural Resources and the Environment, Cornell University, Ithaca, NY 14853, USA
Szabolcs Nagy
Wetlands International, Global Office, 6700 AL Wageningen, the Netherlands
David Olefeldt
Department of Renewable Resources, University of Alberta, Alberta T6G 2G7, Canada
Tamlin M. Pavelsky
Department of Earth, Marine and Environmental Sciences, University of North Carolina, Chapel Hill, NC 27599, USA
Jean-Francois Pekel
European Commission, Joint Research Centre (JRC), Ispra, Italy
Benjamin Poulter
NASA Goddard Space Flight Center, Biospheric Sciences Lab, Greenbelt, MD 20771, USA
Catherine Prigent
LERMA, Observatoire de Paris, CNRS, Paris, France
Jida Wang
Department of Geography and Geographic Information Science, University of Illinois, Urbana–Champaign, IL 61801, USA
Department of Geography and Geospatial Sciences, Kansas State University, Kansas 66506, USA
Thomas A. Worthington
Conservation Science Group, Department of Zoology, University of Cambridge, Cambridge, UK
Dai Yamazaki
Institute of Industrial Science, The University of Tokyo, Tokyo, 153-8505, Japan
Xiao Zhang
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Michele Thieme
World Wildlife Fund, Washington, DC 20037, USA
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Cited
4 citations as recorded by crossref.
- WetCH4: a machine-learning-based upscaling of methane fluxes of northern wetlands during 2016–2022 Q. Ying et al. 10.5194/essd-17-2507-2025
- A novel framework for accurate, automated and dynamic global lake mapping based on optical imagery T. Zhou et al. 10.1016/j.isprsjprs.2025.02.008
- A probabilistic map of Costa Rican peatlands based on vegetation, ecosystem, and soil inventories E. Rabel et al. 10.1038/s41598-025-03126-0
- Monte Carlo Guidance for Better Imaging of Boreal Lakes in the Wavelength Region of 400–800 nm V. Du Le 10.3390/s25041020
1 citations as recorded by crossref.
3 citations as recorded by crossref.
- A novel framework for accurate, automated and dynamic global lake mapping based on optical imagery T. Zhou et al. 10.1016/j.isprsjprs.2025.02.008
- A probabilistic map of Costa Rican peatlands based on vegetation, ecosystem, and soil inventories E. Rabel et al. 10.1038/s41598-025-03126-0
- Monte Carlo Guidance for Better Imaging of Boreal Lakes in the Wavelength Region of 400–800 nm V. Du Le 10.3390/s25041020
Latest update: 26 Jun 2025
Short summary
The Global Lakes and Wetlands Database (GLWD) version 2 distinguishes a total of 33 non-overlapping wetland classes, providing a static map of the world’s inland surface waters. It contains cell fractions of wetland extents per class at a grid cell resolution of ~500 m. The total combined extent of all classes including all inland and coastal waterbodies and wetlands of all inundation frequencies – that is, the maximum extent – covers 18.2 × 106 km2, equivalent to 13.4 % of total global land area.
The Global Lakes and Wetlands Database (GLWD) version 2 distinguishes a total of 33...
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