estimates and uncertainty analysis

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.