| _version_ | 1867171052326486016 |
|---|---|
| author | Nazari, Sara Reinecke, Robert Moosdorf, Nils |
| author_facet | Nazari, Sara Reinecke, Robert Moosdorf, Nils |
| collection | Datos científicos de ciencias marinas y ambientales |
| contents | 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. |
| format | Dataset Open Access |
| id | pangaea_https___doi_org_10_1594_PANGAEA_982842 |
| institution | PANGAEA |
| language | en |
| publishDate | 2025 |
| publisher | PANGAEA |
| record_format | pangaea |
| spellingShingle | Global sectoral groundwater withdrawal: estimates and uncertainty analysis Nazari, Sara Reinecke, Robert Moosdorf, Nils global; global groundwater withdrawal; hydrological modelling; netCDF file; netCDF file (File Size); netCDF file (MD5 Hash); netCDF file (Media Type) 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. |
| title | Global sectoral groundwater withdrawal: estimates and uncertainty analysis |
| topic | global; global groundwater withdrawal; hydrological modelling; netCDF file; netCDF file (File Size); netCDF file (MD5 Hash); netCDF file (Media Type) |
| url | https://doi.org/10.1594/PANGAEA.982842 |