Journal Highlights

Mapping the global depth to bedrock for land surface modeling

Editors’ Highlight

The work provides a global dataset of depth to bedrock in its hydrologic/geologic sense, distinct from the "soil depth" parameter typically used in the land surface schemes of Earth system models for evapotranspiration parameterizations. A machine learning technique using borehole data, soil and geological surveys generates spatial predictions of depth to bedrock.  The technique does a good job in data-rich areas, including interpolation between measurements as shown by data-denial tests.  Extrapolation to large data void areas remains a problem, but the potential of the framework should motivate more nations to make well data available to improve regional and global hydrologic and hydroclimatological modeling.