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

Publikation: Beitrag in FachzeitschriftForschungsartikelBeigetragenBegutachtung

Beitragende

  • Annemarie Bäthge - , Johannes Gutenberg-Universität Mainz (Autor:in)
  • Claudia Ruz Vargas - , International Groundwater Resources Assessment Centre (IGRAC) (Autor:in)
  • Gunnar Lischeid - , Leibniz-Zentrum für Agrarlandschaftsforschung, Universität Potsdam (Autor:in)
  • Raoul Collenteur - , Eawag - das Wasserforschungsinstitut des ETH-Bereichs (Autor:in)
  • Mark Cuthbert - , Cardiff University (Autor:in)
  • Jan Fleckenstein - , Helmholtz-Zentrum für Umweltforschung (UFZ) (Autor:in)
  • Martina Flörke - , Ruhr-Universität Bochum (Autor:in)
  • Inge de Graaf - , Wageningen University & Research (WUR) (Autor:in)
  • Sebastian Gnann - , Albert-Ludwigs-Universität Freiburg (Autor:in)
  • Andreas Hartmann - , Institut für Grundwasserwirtschaft, Professur für Grundwassersysteme, Technische Universität Dresden (Autor:in)
  • Xander Huggins - , University of British Columbia, Princeton University, Stockholm Resilience Centre (Autor:in)
  • Nils Moosdorf - , Leibniz-Zentrum für Marine Tropenforschung, Christian-Albrechts-Universität zu Kiel (CAU) (Autor:in)
  • Yoshihide Wada - , King Abdullah University of Science and Technology (Autor:in)
  • Thorsten Wagener - , Universität Potsdam (Autor:in)
  • Robert Reinecke - , Johannes Gutenberg-Universität Mainz (Autor:in)

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

OriginalspracheEnglisch
Aufsatznummer401
FachzeitschriftScientific data
Jahrgang13
Ausgabenummer1
PublikationsstatusVeröffentlicht - Dez. 2026
Peer-Review-StatusJa

Externe IDs

PubMed 41803136