Evidence map›Paper›PMID 41173900›Full record

ArticleScientific data2025

Surface Water Transitions 1984-2022: A Global Dataset at Annual Resolution.

Gustavo Willy Nagel, Stephen E Darby, Julian Leyland

Abstract read
In one paragraph

Article in Scientific data, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Gustavo Willy NagelSchool of Geography and Environmental Sciences, University of Southampton, Southampton, SO17 1BJ, UK. gwn1n21@soton.ac.uk.ORCID http://orcid.org/0000-0002-8060-0772
Stephen E DarbySchool of Geography and Environmental Sciences, University of Southampton, Southampton, SO17 1BJ, UK.ORCID http://orcid.org/0000-0001-8778-4394
Julian LeylandSchool of Geography and Environmental Sciences, University of Southampton, Southampton, SO17 1BJ, UK.ORCID http://orcid.org/0000-0002-3419-9949

Funding

RCUK | Natural Environment Research Council (NERC) NE/S007210/1
6 · The paper itself

Abstract

Recent advances in satellite technology and cloud computing have enabled global-scale monitoring of long-term surface water changes. The dynamic nature of surface water, driven by seasonal fluctuations and climatic events, presents challenges for accurately interpreting these dynamics. Here, we introduce the 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.

Identifiers

PMID41173900
PMCPMC12578977

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.