Nazari, Sara; Reinecke, Robert; Moosdorf, Nils (2025): Global sectoral groundwater withdrawal: estimates and uncertainty analysis [dataset]. PANGAEA, https://doi.org/10.1594/PANGAEA.982842
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Published: 2025-08-07 • DOI registered: 2025-08-20
Abstract:
Groundwater, Earth's largest source of liquid freshwater, is vital for sustaining ecosystems and meeting societal needs. However, quantifying global groundwater withdrawals remains a challenge due to significant uncertainties. This dataset provides global groundwater withdrawal estimates from 2001 to 2020, derived using the data-driven Global Groundwater Withdrawal (GGW) model. The GGW model estimates annual groundwater withdrawals across domestic, industrial, and agricultural sectors at a 0.1° spatial resolution. Implemented in Python, it integrates reported country-level data with global grid-based datasets to generate sectoral withdrawal estimates. Additionally, this dataset includes an uncertainty assessment based on key input variables, such as total country-level withdrawals, sector-specific fractions, European sectoral data, irrigation efficiency, and return flow fractions. The uncertainty analysis employs Latin Hypercube Sampling (LHS), with 1000 Monte Carlo simulations to quantify variability.
Supplement to:
Nazari, Sara; Reinecke, Robert; Moosdorf, Nils (2025): Global estimates of groundwater withdrawal trends and uncertainties. Environmental Research Letters, 20(9), 094043, https://doi.org/10.1088/1748-9326/adf6ca
Related to:
Maus, Victor; Giljum, Stefan; Gutschlhofer, Jakob; da Silva, Dieison M; Probst, Michael; Gass, Sidnei L B; Luckeneder, Sebastian; Lieber, Mirko; McCallum, Ian (2020): Global-scale mining polygons (Version 1) [dataset]. PANGAEA, https://doi.org/10.1594/PANGAEA.910894
References:
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Global Human Settlement Layer: Population and built-up estimates, and degree of urbanization settlement model grid (Version 1) (2021). Joint Research Centre - JRC - European Commission; Center for International Earth Scienec Information Network - CIESIN - Columbia University; NASA Socioeconomic Data and Applications Center, https://doi.org/10.7927/H4154F0W
Global Groundwater Information System (GGIS) (2024). International Groundwater Resources Assessment Centre (IGRAC), Delft, the Netherlands, https://ggis.un-igrac.org/
AQUASTAT Core Database [dataset]. Food and Agriculture Organization of the United Nations, https://www.fao.org/aquastat/en/databases/maindatabase/index.html
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Reinecke, Robert; Gnann, Sebastian; Stein, Lina; Bierkens, Marc F P; de Graaf, Inge E M; Gleeson, Tom; Oude Essink, Gualbert H P; Sutanudjaja, Edwin H; Ruz Vargas, Claudia; Verkaik, Jarno; Wagener, Thorsten (2024): Uncertainty in model estimates of global groundwater depth. Environmental Research Letters, 19(11), 114066, https://doi.org/10.1088/1748-9326/ad8587
Rohwer, Janine; Gerten, Dieter; Lucht, Wolfgang (2007): Development of functional irrigation types for improved global crop modelling (PIK Report 104). Potsdam-Institut für Klimafolgenforschung, https://publications.pik-potsdam.de/pubman/faces/ViewItemOverviewPage.jsp?itemId=item_14687
Siebert, Stefan; Henrich, Verena; Frenken, Karen; Burke, Jacob (2013): Global Map of Irrigation Areas version 5 [dataset]. Rheinische Friedrich-Wilhelms-University, Bonn, Germany / Food and Agriculture Organization of the United Nations, Rome, Italy, https://www.fao.org/aquastat/en/geospatial-information/global-maps-irrigated-areas/latest-version/index.html
Comment:
Source "Global Human Settlement Layer: Population and built-up estimates, and degree of urbanization settlement model grid (Version 1.00)" last accessed: 2025-08-06
Parameter(s):
| # | Name | Short Name | Unit | Principal Investigator | Method/Device | Comment |
|---|---|---|---|---|---|---|
| 1 | netCDF file | netCDF | Nazari, Sara | |||
| 2 | netCDF file (File Size) | netCDF (Size) | Bytes | Nazari, Sara | ||
| 3 | netCDF file (Media Type) | netCDF (Type) | Nazari, Sara | |||
| 4 | netCDF file (MD5 Hash) | netCDF (Hash) | Nazari, Sara |
License:
Creative Commons Attribution 4.0 International (CC-BY-4.0)
Size:
9 data points
Data
All files referred to in data matrix can be downloaded in one go as ZIP or TAR. Be careful: This download can be very large! To protect our systems from misuse, we require to sign up for an user account before downloading.
| 1 netCDF | 2 netCDF (Size) [Bytes] | 3 netCDF (Type) | 4 netCDF (Hash) |
|---|---|---|---|
| 5th_Percentile_Global_Ave_An_Dom_GWW_2001_2020.nc | 49.5 MBytes | application/x-hdf | 42c03a9269171063851e820f1608d172 |
| 5th_Percentile_Global_Ave_An_Ind_GWW_2001_2020.nc | 49.5 MBytes | application/x-hdf | 3d7fe6e771fe630b63dc5015976f72dc |
| 5th_Percentile_Global_Ave_An_Net_Agr_GWW_2001_2020.nc | 49.5 MBytes | application/x-hdf | 925926e96e95a1f71855aeb2111f5b11 |
| 95th_Percentile_Global_Ave_An_Dom_GWW_2001_2020.nc | 49.5 MBytes | application/x-hdf | 4cd51b626a8c43f79dc125d02916f59e |
| 95th_Percentile_Global_Ave_An_Ind_GWW_2001_2020.nc | 49.5 MBytes | application/x-hdf | fc0f0558798b4abaa06075bfb676724b |
| 95th_Percentile_Global_Ave_An_Net_Agr_GWW_2001_2020.nc | 49.5 MBytes | application/x-hdf | 649bae8f2d2b1713b5d76697e8d912c9 |
| Global_Ave_An_Dom_GWW_2001_2020.nc | 49.5 MBytes | application/x-hdf | f40c6ec16759b96cf3cc2552dc5a47f0 |
| Global_Ave_An_Ind_GWW_2001_2020.nc | 49.5 MBytes | application/x-hdf | edc62ce151f9171d024a8acdfcdcf6bd |
| Global_Ave_An_Net_Agr_GWW_2001_2020.nc | 49.5 MBytes | application/x-hdf | aaf20fb838c3498b952167225457ef10 |