GROW-Qual: A global observational groundwater quality dataset linked to water table dynamics and Earth system variables

Edward Russell Jones , Annemarie Bäthge , Andrew Pearson, Nicole Gyakowah Otoo, Moritz Heinle, Robert Reinecke, Michelle T H van Vliet

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.