Evidence map›Paper›PMID 41360817›Full record

ArticleScientific data2025

Global dataset of sand dam features and geographical distribution across drylands.

Luigi Piemontese, Lorenzo Villani, Natalia Limones, Jeroen C J H Aerts, Giulio Castelli, Jessica A Eisma, Bongani Mpofu, Doug Graber Neufeld, Hannah Ritchie, Cate Ryan and 3 more

Abstract read
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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. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

13 authors.

Luigi PiemonteseDepartment of Agriculture, Food, Environment and Forestry (DAGRI), University of Florence, Florence, Italy. luigi.piemontese@unifi.it.ORCID http://orcid.org/0000-0002-1600-5450
Lorenzo VillaniDepartment of Agriculture, Food, Environment and Forestry (DAGRI), University of Florence, Florence, Italy.
Natalia LimonesDepartment of Physical Geography and Regional Geographical Analysis, University of Seville, Seville, Spain.
Jeroen C J H AertsInstitute for Environmental Studies (IVM), VU University Amsterdam, Deltares Institute, Delft, Netherlands.ORCID http://orcid.org/0000-0002-2162-5814
Giulio CastelliDepartment of Agriculture, Food, Environment and Forestry (DAGRI), University of Florence, Florence, Italy.ORCID http://orcid.org/0000-0002-0209-0869
Jessica A EismaDepartment of Civil Engineering, University of Texas at Arlington, Arlington, Texas, USA.
Bongani MpofuDabane Water Workshops, Bulawayo, Zimbabwe.
Doug Graber NeufeldDepartment of Biology, Eastern Mennonite University, Harrisonburg, Virginia, USA.
Hannah RitchieSchool of Water, Energy and Environment, Cranfield University, Cranfield, UK.
Cate RyanDepartment of Environmental Science, Auckland University of Technology, Aotearoa, New Zealand.
Ruth QuinnDepartment of Civil Engineering and Construction Studies, Atlantic Technological University Sligo, Sligo, Ireland.
Christine WhinneySand Dams Worldwide, London, UK.
Elena BresciDepartment of Agriculture, Food, Environment and Forestry (DAGRI), University of Florence, Florence, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Sand dams are water infrastructure, built across ephemeral sandy rivers, that increase water supply by creating an artificial sandy aquifer upstream of the dam. Despite their effectiveness and recent traction in the research and development arena, empirical data on their distribution and characteristics are scattered and largely unreported. This gap represents a major barrier for understanding the large-scale potential of such a Nature-based Solution and for planning new installations. This paper presents a global dataset of sand dam locations and dimensions, developed collaboratively by research and development experts. We collected sand dam information from several sources, including local sand dam organizations. The data was reviewed and integrated through visual inspection in Google Earth. Although most georeferenced sand dams are from Eastern and Southern Africa, this dataset is a first global inventory and represents an invitation for others working in sand dams around the world to contribute their data. The dataset supports research on the effectiveness of sand dams and can aid practitioners with science-based criteria for sand dam development.

Identifiers

PMID41360817
PMCPMC12686441

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LicenceCC BY
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Registered trials

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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.