Open-source data: A Global-Scale Time Series Dataset for Groundwater Studies within the Earth System

2026-05-05 · Source: A Geodyssey – Geoscience Text Analytics and Enterprise Search Research · Field: Science & Research — Environmental Science & Earth Systems · Depth: Intermediate, quick

Summary

The GROW (Global-scale integrated GROundWater dataset) dataset, introduced by Bäthge et al. (2026) in Nature, provides a large, standardized, and analysis-ready collection of groundwater time series. This dataset aims to enhance the understanding of groundwater dynamics within the Earth system, addressing the current poor understanding of its interactions with climate, ecosystems, geology, and human activity. GROW comprises 204,292 groundwater time series from 55 countries, with 85% of the series having yearly temporal resolution, 9% monthly, and 6% daily. Over half (51%) of these series extend beyond 10 years. It also includes 36 associated Earth system variables, such as climate data (precipitation, evapotranspiration), hydrology, geology (rock type), biosphere (vegetation, NDVI), cryosphere (snow cover), and human activity (land use, water withdrawal). The dataset features 34 data-quality flags to facilitate filtering for gaps, trends, and outliers.

Key takeaway

For Earth system modelers and hydrologists studying global water cycles, the GROW dataset provides an unprecedented resource. You should integrate this standardized, quality-flagged data into your research to improve models of groundwater-climate interactions and human impacts. Utilizing its extensive temporal and spatial coverage, alongside 36 associated Earth system variables, can lead to more robust and comprehensive analyses of critical freshwater resources.

Key insights

GROW offers a global, standardized groundwater dataset to advance Earth system understanding.

Principles

• Groundwater is 99% of accessible freshwater.
• Interactions with Earth systems are poorly understood.

In practice

• Filter data using 34 quality flags.
• Integrate 36 Earth system variables.

Topics

• GROW Dataset
• Groundwater Dynamics
• Earth System Variables
• Time Series Data
• Hydrology

Code references

• EarthSystemModelling/GROW

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Editorial summary, takeaway, and curation by AIssential. Original article published by A Geodyssey – Geoscience Text Analytics and Enterprise Search Research.