GROW-Qual: A global observational groundwater quality dataset linked to water table dynamics and Earth system variables
Year: 2026
Type: preprint
Abstract: Groundwater underpins freshwater supply, food production and ecosystem health across the globe. Yet, groundwater levels and quality are increasingly threatened by overextraction, contamination, and climate change. Understanding how water table dynamics and quality are interlinked and affected by these pressures requires integrated observational groundwater datasets combined with Earth system variables that represent potential environmental drivers. Here we present GROW-Qual, a global dataset that standardises, harmonises and combines ~25 million groundwater quality observations across 92 constituents from 68 countries. Groundwater quality observations are linked to co-located water table timeseries (i.e. depth or level), in addition to a diverse range of contextual Earth system datasets spanning meteorological, hydrological, geophysical, biological and anthropogenic variables. GROW-Qual can support a broad range of applications, from empirical analysis to large-scale groundwater modelling, while its structural consistency with the companion river water quality dataset (Caravan-Qual) enables integrated study of groundwater-river water quantity and quality dynamics.
DOI: 10.31223/x55f8g
Language: English
Subfield: Water Science and Technology
Field: Environmental Science
Domain: Physical Sciences
Open Access status: green
Award: G7079439188
OpenAlex ID: W7223175809