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

作者：Annemarie Bäthge, Claudia Ruz Vargas, Gunnar Lischeid, Raoul Collenteur, Mark Cuthbert, Jan H. Fleckenstein, Martina Flörke, Inge De Graaf, Sebastian Gnann, Andreas Hartmann, Xander Huggins, Nils Moosdorf, Yoshihide Wada, Thorsten Wagener, Robert Reinecke · 发表于：Zenodo (CERN European Organization for Nuclear Research) · 年份：2026 · DOI：10.5281/zenodo.15149480 · 被引用次数：1 · 研究领域：Data mining、Computer science、Remote sensing、Environmental science、Geology

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 ac...