- 1Johannes Gutenberg University Mainz, Geography, Earth System Modelling, Germany (annemarie.bathge@gmail.com)
- 2IGRAC, Delft, The Netherlands
- 3Leibniz-Centre for Agricultural Research, Müncheberg, Germany
- 4Eawag - Swiss Federal Institute of Aquatic Science and Technology, Department Water Resources & Drinking Water, Dübendorf, Switzerland
- 5School of Earth and Ocean Sciences, Cardiff University, Cardiff, UK
- 6Department of Hydrogeology, Helmholtz-Centre for Environmental Research—UFZ, Leipzig, Germany
- 7Institute of Engineering Hydrology and Water Resources Management, Ruhr University Bochum, 44801 Bochum, Germany
- 8Earth Systems and Global Change group, Wageningen University and Research, Wageningen, The Netherland
- 9Chair of Hydrology, Faculty of Environment and Natural Resources, University of Freiburg, Freiburg, Germany
- 10Institute of Groundwater Management, TU Dresden, Dresden, Germany
- 11Institute for Resources, Environment and Sustainability, University of British Columbia, Vancouver, Canada
- 12Leibniz centre for tropical Marine Research (ZMt), Bremen, Germany
- 13Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology, Thuwal, 23955-6900, Saudi Arabia
- 14Institute of Environmental Science and Geography, University of Potsdam, Potsdam, Germany
Groundwater is a central component of social-ecological systems. However, our understanding of how it is dynamically interlinked with the atmosphere, hydrosphere, cryosphere, biosphere, geosphere, and anthroposphere is limited. Existing datasets lack features that enable us to better understand groundwater functions and how they are affected by anthropogenic change. Specifically, there remains no large-scale groundwater dataset that provides analysis-ready groundwater time series alongside groundwater-associated variables and attributes. In the pursuit of understanding the planet's groundwater dynamics, we present GROW (global GROundWater analysis package). This user-friendly, quality-controlled dataset combines groundwater depth and level time series from around the world with associated social-ecological variables. GROW is designed to enable large-sample spatio-temporal groundwater analysis without much further preprocessing. The dataset contains more than 180,000 time series from 41 countries – whereby over 90 % of the time series are from either North America, Australia or Europe - in a daily, monthly, or yearly temporal resolution. Most of them are between 10 and 20 years long, from 01/1888 to 04/2024, and have a median depth to the water table of 8 metres. Groundwater data is paired with a total of 37 time series or attributes of meteorological, hydrological, geophysical, botanical, and anthropogenic variables (e.g., precipitation, ground elevation, aquifer type, NDVI, land use). More than 20 data flags about well features (e.g., location coordinates and license), as well as time series characteristics (e.g., gap fraction or length), simplify a quick data filtering tailored to specific needs. GROW provides an essential foundation understanding large-scale groundwater processes and provides a robust resource for calibrating and validating models that address groundwater dynamics in social-ecological systems. Gaining an enhanced insight in these processes is essential for managing groundwater resources and ensuring their long-term sustainability.
How to cite: Bäthge, A., Ruz Vargas, C., Lischeid, G., Collenteur, R., Cuthbert, M., Fleckenstein, J., Flörke, M., de Graaf, I., Gnann, S., Hartmann, A., Huggings, X., Moosdorf, N., Wada, Y., Wagener, T., and Reinecke, R.: GROW: A Global Time Series Dataset for Large-Sample Groundwater Studies, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17713, https://doi.org/10.5194/egusphere-egu25-17713, 2025.
