Rempe, Daniella | Search NASA

DOE OSTI2021

An AI-Enabled MODEX Framework for Improving Predictability of Subsurface Water Storage across Local and Continental Scales

Focal Area: (2) Predictive modeling through the use of AI techniques and AI-derived model components. (3) Insight gleaned from complex data using AI, big data analytics, and other advanced methods. We propose an AI-enabled model-experiment (MODEX) framework to improve the predictability of subsurface water storage (SWS) from local to conus scales in a changing environment by taking advantage of DOE’s observation and simulation capabilities, as well as to inform the model and the observation development.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Quantifying Dynamic Water Storage in Unsaturated Bedrock with Borehole Nuclear Magnetic Resonance

Quantifying the volume of water that is stored in the subsurface is critical to studies of water availability to ecosystems, slope stability, and water-rock interactions. In a variety of settings, water is stored in fractured and weathered bedrock as rock moisture. However, few techniques are available to measure rock moisture in unsaturated rock, making direct estimates of water storage dynamics difficult to obtain. Here, we use borehole nuclear magnetic resonance (NMR) at two sites in seasonally dry California to quantify dynamic rock moisture storage. Additionally, we show strong agreement between NMR estimates of dynamic storage and estimates derived from neutron logging and mass balance techniques. The depths of dynamic storage are up to 9 m and likely reflect the depth extent of root water uptake. To our knowledge, these data are the first to quantify the volume and depths of dynamic water storage in the bedrock vadose zone via NMR.

58 GEOSCIENCES↗