Piemontese et al. (2025) Global dataset of sand dam features and geographical distribution across drylands

Identification

• Journal: Scientific Data
• Year: 2025
• Date: 2025-12-08
• Authors: Luigi Piemontese, Lorenzo Villani, Natalia Limones, Jeroen C. J. H. Aerts, Giulio Castelli, Jessica Eisma, Bongani Mpofu, Doug Graber Neufeld, Hannah Ritchie, Cate Ryan, Ruth Quinn, Christine Whinney, Elena Bresci
• DOI: 10.1038/s41597-025-06197-w

Research Groups

• Department of Agriculture, Food, Environment and Forestry (DAGRI), University of Florence, Florence, Italy
• Department of Water and Climate (HYDR), Vrije Universiteit Brussels, Brussels, Belgium
• Department of Physical Geography and Regional Geographical Analysis, University of Seville, Seville, Spain
• Institute for Environmental Studies (IVM), VU University Amsterdam, Deltares Institute, Delft, Netherlands
• UNESCO Chair in Hydropolitics, University of Geneva, Geneva, Switzerland
• Institute for Environmental Sciences (ISE), University of Geneva, Geneva, Switzerland
• Department of Civil Engineering, University of Texas at Arlington, Arlington, Texas, USA
• Dabane Water Workshops, Bulawayo, Zimbabwe
• Department of Biology, Eastern Mennonite University, Harrisonburg, Virginia, USA
• School of Water, Energy and Environment, Cranfield University, Cranfield, UK
• Department of Environmental Science, Auckland University of Technology, Aotearoa, New Zealand
• Department of Civil Engineering and Construction Studies, Atlantic Technological University Sligo, Sligo, Ireland
• Sand Dams Worldwide, London, UK

Short Summary

This paper introduces the Global Sand Dams Dataset (GSDD), the first comprehensive global inventory of 1006 sand dam locations and dimensions, developed to address the lack of empirical data on this crucial dryland water infrastructure. The dataset aims to support research on sand dam effectiveness and aid practitioners in planning new installations.

Objective

• To present a global dataset of sand dam locations and dimensions to overcome the barrier of scattered and largely unreported empirical data, thereby enabling a better understanding of their large-scale potential as a Nature-based Solution and aiding the planning of new installations.

Study Configuration

• Spatial Scale: Global, covering 15 countries across Africa, Asia, and South America.
• Temporal Scale: Sand dams constructed between 1952 and 2023.

Methodology and Data

• Models used: RStudio and QGIS for data processing and mapping. Google Earth for visual inspection, data review, integration, and estimation of dam characteristics and construction dates.
• Data sources:
  • Local sand dam organizations (NGOs): Sasol Foundation, Sand Dams Worldwide (previously Africa Sand Dams Foundation), Dabane Trust, No One Out.
  • Working group of international researchers and collaborators.
  • Scientific and grey literature.
  • Online open-access sources: Global Database on Sustainable Land Management (WOCAT), Global Inventory of Managed Aquifer Recharge applications by the International Groundwater Resources Assessment Centre (UN-IGRAC).

Main Results

• The Global Sand Dams Dataset (GSDD) contains 1006 georeferenced records of sand dams.
• These sand dams are distributed across 15 countries and 3 continents (Africa, Asia, and South America), built over an 80-year period (1952–2023).
• The majority of records (892) are from Kenya, reflecting existing literature on sand dam distribution, but the dataset also includes records from Angola, India, Tanzania, and Zimbabwe (each with over 10 dams).
• Median dimensions of the inventoried sand dams are: 32 meters (m) for dam crest length, 17 m for stream width upstream, and 130 m for throwback.
• The GSDD shows no overlap with the Global Dam Watch (GDW) database, indicating its unique contribution.

Contributions

• Provides the first extensive and revised global dataset of sand dam locations and dimensions, addressing a significant data gap for this critical dryland water infrastructure.
• Offers open-access data to stimulate research on sand dam hydrological, ecological, and socio-economic impacts, suitability, and effectiveness.
• Supports practitioners with science-based criteria for sand dam development, planning, and implementation.
• Establishes a platform for systematically collecting and harmonizing sand dam reporting standards globally.
• Serves as an example for storing and sharing research-quality data from rural areas in the Global South, adhering to FAIR data principles.

Funding

• Funding for publication was provided by J.A.E. (Jessica A. Eisma).

Citation

@article{Piemontese2025Global,
  author = {Piemontese, Luigi and Villani, Lorenzo and Limones, Natalia and Aerts, Jeroen C. J. H. and Castelli, Giulio and Eisma, Jessica and Mpofu, Bongani and Neufeld, Doug Graber and Ritchie, Hannah and Ryan, Cate and Quinn, Ruth and Whinney, Christine and Bresci, Elena},
  title = {Global dataset of sand dam features and geographical distribution across drylands},
  journal = {Scientific Data},
  year = {2025},
  doi = {10.1038/s41597-025-06197-w},
  url = {https://doi.org/10.1038/s41597-025-06197-w}
}