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

GROW (the global-scale integrated GROundWater package) is a global-scale, analysis-ready, quality-controlled dataset that combines groundwater depth or level time series from around the world with associated Earth system variables. The dataset contains > 200,000 time series from 55 countries, 91% from North America, India, Europe, and Australia,  in a 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.The dataset is organized in two files: A table containing time series (grow_timeseries.csv/ .parquet) and one table with static attributes (grow_attributes.csv/.parquet). The columns of the attributes and time series table are described in Table 1 & 2 in the Readme file. Additionally, the attributes are given as json-file (grow_attributes.json) with well locations as geometry. This file can be opened directly in GIS programs.The time series with different temporal resolutions are stored in a single table. Time-resolved variables with unit of quantity are all given in mm/year regardless of the temporal resolution of the time series. This enables the straightforward derivation of further aggregations across different time series resolutions, subsets, and statistics. When the user wants to use only daily or monthly data, GROW can be easily subset and the unit can be transferred to the target resolution. An example of use that demonstrates how the data is subset and prepared is given on GitHub (https://github.com/EarthSystemModelling/GROW/blob/main/usage_example.py) and Zenodo (python_scripts).CitationThis dataset is licensed under Creative Commons Attribution Non-Commercial ShareAlike 4.0 International License (CC-BY-NC-SA 4.0; https://creativecommons.org/licenses/by-nc-sa/4.0/)When using the data, please cite Bäthge et al. “GROW: A Global-Scale Time Series Dataset for Groundwater Studies within the Earth System” (full citation available on Zenodo)