New Groundwater Map
Measuring groundwater across a landscape presents a significant challenge because only about 1% of Earth's freshwater is on the surface. Subsurface water measurements vary based on unseen water table depth and ground porosity.
Unveiling Subsurface Waters: A High-Resolution Map
A new high-resolution map, developed by Reed Maxwell, a hydrologist at Princeton University, and his team, provides the most detailed estimate of groundwater in the contiguous United States to date.
The map estimates approximately 306,500 cubic kilometers of groundwater, a volume 13 times greater than all the Great Lakes combined.
This estimate, made at a 30-meter resolution, accounts for groundwater down to a depth of 392 meters. Previous estimates for groundwater quantity, using similar constraints, have varied significantly, from 159,000 to 570,000 cubic kilometers.
Methodology and Resolution
Historically, groundwater quantity estimations have relied primarily on localized well observations, which offer limited spatial representation. The new research integrated a vast dataset, combining about one million well measurements taken between 1895 and 2023 with satellite data, alongside crucial environmental factors such as precipitation, temperature, hydraulic conductivity, soil texture, elevation, and stream proximity.
This comprehensive dataset was then used to train a machine learning model. The team demonstrated the critical importance of high resolution in these estimations.
Reducing the map's resolution from 30 meters to 100 kilometers resulted in an 18% underestimation of groundwater, totaling approximately 252,000 cubic kilometers.
Key Findings and Implications
The high-resolution map offers more detailed insights into groundwater sources and their interactions.
It indicates that roughly 40% of the land in the contiguous United States has a water table shallower than 10 meters.
This depth range is particularly significant for groundwater-plant-land surface interactions, highlighting the strong connectivity of these vital systems.
Notably, the machine learning approach incorporated data that reflected groundwater pumping and depletion by humans, implicitly learning these biases. Researchers anticipate that this map will serve as a valuable resource for regional water management decision-makers and farmers planning irrigation strategies. The work also aims to increase general awareness of groundwater resources and their pivotal role.