Surface water transitions 1984–2022: a global dataset at annual resolution
Surface water transitions 1984–2022: a global dataset at annual resolution
Recent advances in satellite technology and cloud computing have enabled global-scale monitoring oflong-term surface water changes. The dynamic nature of surface water, driven by seasonal fluctuationsand climatic events, presents challenges for accurately interpreting these dynamics. Here, we introducethe first global dataset that identifies the timing, at annual resolution, of surface water advance or recession from 1984 to 2022. Our approach focuses on identifying persistent changes in surface water features by filtering out seasonal or shorter-term fluctuations. Using a novel algorithm, we mapped the timing of surface water transitions globally, including rivers, lakes, reservoirs, flooded agriculture, and coastal regions. In the dataset each 30 m × 30 m pixel records whether water advance or recession occurred and specifies the year of transition. This dataset enables users to visualize the location, type, and magnitude of changes, while its focus on timing provides new insights into the drivers of water dynamics. Designed for accessibility, the dataset supports scientific research as well as NGOs, policymakers, and water managers in addressing surface water-related challenges.
Nagel, Gustavo
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Darby, Stephen E.
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Leyland, Julian
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31 October 2025
Nagel, Gustavo
cd476661-a65b-4fe1-8a48-756636015e6e
Darby, Stephen E.
4c3e1c76-d404-4ff3-86f8-84e42fbb7970
Leyland, Julian
6b1bb9b9-f3d5-4f40-8dd3-232139510e15
Nagel, Gustavo, Darby, Stephen E. and Leyland, Julian
(2025)
Surface water transitions 1984–2022: a global dataset at annual resolution.
Scientific Data, 12 (1), [1729].
(doi:10.1038/s41597-025-06013-5).
Abstract
Recent advances in satellite technology and cloud computing have enabled global-scale monitoring oflong-term surface water changes. The dynamic nature of surface water, driven by seasonal fluctuationsand climatic events, presents challenges for accurately interpreting these dynamics. Here, we introducethe first global dataset that identifies the timing, at annual resolution, of surface water advance or recession from 1984 to 2022. Our approach focuses on identifying persistent changes in surface water features by filtering out seasonal or shorter-term fluctuations. Using a novel algorithm, we mapped the timing of surface water transitions globally, including rivers, lakes, reservoirs, flooded agriculture, and coastal regions. In the dataset each 30 m × 30 m pixel records whether water advance or recession occurred and specifies the year of transition. This dataset enables users to visualize the location, type, and magnitude of changes, while its focus on timing provides new insights into the drivers of water dynamics. Designed for accessibility, the dataset supports scientific research as well as NGOs, policymakers, and water managers in addressing surface water-related challenges.
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s41597-025-06013-5
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Accepted/In Press date: 24 September 2025
e-pub ahead of print date: 31 October 2025
Published date: 31 October 2025
Identifiers
Local EPrints ID: 507262
URI: http://eprints.soton.ac.uk/id/eprint/507262
ISSN: 2052-4463
PURE UUID: 29dc394d-6827-46dc-a3a4-05bdd7f0bf81
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Date deposited: 02 Dec 2025 18:07
Last modified: 03 Dec 2025 03:12
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Author:
Gustavo Nagel
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