A Global-Scale Time Series Dataset for Groundwater Studies within the Earth System

Research output: Contribution to journalResearch articleContributedpeer-review

Contributors

  • Annemarie Bäthge - , Johannes Gutenberg University Mainz (Author)
  • Claudia Ruz Vargas - , International Groundwater Resources Assessment Centre (IGRAC) (Author)
  • Gunnar Lischeid - , Leibniz Centre for Agricultural Landscape Research, University of Potsdam (Author)
  • Raoul Collenteur - , Swiss Federal Institute of Aquatic Science and Technology (Author)
  • Mark Cuthbert - , Cardiff University (Author)
  • Jan Fleckenstein - , Helmholtz Centre for Environmental Research (Author)
  • Martina Flörke - , Ruhr University Bochum (Author)
  • Inge de Graaf - , Wageningen University & Research (WUR) (Author)
  • Sebastian Gnann - , University of Freiburg (Author)
  • Andreas Hartmann - , Institute of Groundwater Management, Chair of Groundwater Systems, TUD Dresden University of Technology (Author)
  • Xander Huggins - , University of British Columbia, Princeton University, Stockholm Resilience Centre (Author)
  • Nils Moosdorf - , Leibniz Center for Tropical Marine Research, Kiel University (Author)
  • Yoshihide Wada - , King Abdullah University of Science and Technology (Author)
  • Thorsten Wagener - , University of Potsdam (Author)
  • Robert Reinecke - , Johannes Gutenberg University Mainz (Author)

Abstract

Groundwater is a central component of the Earth system. However, our understanding of how it is dynamically interlinked with the atmosphere, hydrosphere, cryosphere, biosphere, geosphere, and anthroposphere remains limited. In the pursuit of understanding groundwater dynamics across diverse global settings, we present GROW (the global-scale integrated GROundWater package). This analysis-ready, quality-controlled dataset combines depth to groundwater and level time series from 55 countries, 91% from North America, India, Europe, and Australia, with associated Earth system variables. The dataset contains >200,000 time series with either daily, monthly, or yearly temporal resolution, accompanied by 36 time series or static attributes of meteorological, hydrological, geophysical, vegetation, and anthropogenic variables (e.g., precipitation, drainage density, rock type, NDVI, land use). 34 data flags regarding well features (e.g., coordinates and country), as well as time series characteristics (e.g., gap fraction or autocorrelation), facilitate quick data filtering. GROW provides a foundation for understanding large-scale groundwater processes in space and time, as well as for calibrating and evaluating models that simulate groundwater dynamics within the Earth system.

Details

Original languageEnglish
Article number401
JournalScientific data
Volume13
Issue number1
Publication statusPublished - Dec 2026
Peer-reviewedYes

External IDs

PubMed 41803136