Contact Details
sayantan.majumdar@dri.edu
Dataset description
A 1-arcsec (~30 m) resolution water table depth (WTD) map for the contiguous United States](https://hf-hydrodata.readthedocs.io/en/latest/gen_ma_2025.html) using machine learning methods trained on over one million well observations compiled from multiple groundwater databases spanning 1914-2023. A random forest model with 300 decision trees was trained on 80% of these data using input variables including climatology (precipitation, temperature, PME), subsurface properties (hydraulic conductivity, soil texture), and topographic features (elevation, slope, distances to streams), achieving test performance of r = 0.79, RMSE = 14.94 m, and NSE = 0.62.
Ma, Y., Condon, L.E., Koch, J. et al. High resolution US water table depth estimates reveal quantity of accessible groundwater. Commun Earth Environ 7, 45 (2026). https://doi.org/10.1038/s43247-025-03094-3
Data also accessible via the HydroData platform https://hydroframe.org/hydrodata
Earth Engine Snippet if dataset already in GEE
GEE asset: projects/nmose-openet/assets/WTD-US
CONUS WTD explorer: https://code.earthengine.google.com/87c965aae54facde740850b0949fc3b4
Enter license information
CC BY-NC-ND 4.0 (https://creativecommons.org/licenses/by-nc-nd/4.0/deed.en)
Keywords
hydrology; groundwater; machine learning; parflow; conus
Code of Conduct
Contact Details
sayantan.majumdar@dri.edu
Dataset description
A 1-arcsec (~30 m) resolution water table depth (WTD) map for the contiguous United States](https://hf-hydrodata.readthedocs.io/en/latest/gen_ma_2025.html) using machine learning methods trained on over one million well observations compiled from multiple groundwater databases spanning 1914-2023. A random forest model with 300 decision trees was trained on 80% of these data using input variables including climatology (precipitation, temperature, PME), subsurface properties (hydraulic conductivity, soil texture), and topographic features (elevation, slope, distances to streams), achieving test performance of r = 0.79, RMSE = 14.94 m, and NSE = 0.62.
Ma, Y., Condon, L.E., Koch, J. et al. High resolution US water table depth estimates reveal quantity of accessible groundwater. Commun Earth Environ 7, 45 (2026). https://doi.org/10.1038/s43247-025-03094-3
Data also accessible via the HydroData platform https://hydroframe.org/hydrodata
Earth Engine Snippet if dataset already in GEE
GEE asset:
projects/nmose-openet/assets/WTD-USCONUS WTD explorer: https://code.earthengine.google.com/87c965aae54facde740850b0949fc3b4
Enter license information
CC BY-NC-ND 4.0 (https://creativecommons.org/licenses/by-nc-nd/4.0/deed.en)
Keywords
hydrology; groundwater; machine learning; parflow; conus
Code of Conduct