Projects at LM Sites Aim to Improve Soil Moisture Monitoring While Lowering Costs

Office of Legacy Management (LM) scientists are investigating groundbreaking options for the future management of disposal sites containing uranium mill tailings.

LM Support Partners with the Applied Studies & Technology team (AS&T) are testing a technique to manage conventional rock-armor covers more like evapotranspiration covers at the disposal sites LM manages.

Evapotranspiration (ET) is a process by which water is naturally removed from soil via root uptake by plants (transpiration) and evaporation. The concept is fairly straightforward: ET covers employ naturally occurring vegetation, which takes up water through its roots, reducing soil moisture. Less moisture in the soil means a lower probability that water will accumulate, begin to flow through the disposal cell cover, and transport contaminants buried under the cell cover into groundwater.

Relatively simple methods, such as strategic revegetation techniques, can be used to enhance these natural processes on rock-armored covers at many LM sites, which were originally designed without vegetation. Ultimately, managing ET helps LM maintain the performance of the remedy/cell by minimizing percolation of water through the cover system, ensuring compliance with Nuclear Regulatory Commission standards.

AS&T scientists and their collaborators have found that increasing ET can effectively prevent rain and snowmelt from traveling through disposal covers, thereby reducing the potential of that water to transport contaminants or compromise the disposal cells’ integrity.

“LM’s mission is inherently long-term, and what better model to follow than Mother Nature? We are developing simple ways to enhance beneficial natural processes on our engineered disposal cells,” said AS&T Senior Scientist David Holbrook. “Technology has helped us quantify their effectiveness, and as a result, these approaches have become accepted compliance strategies. Plus, it can be a very cost-effective alternative.”

The key performance parameter in ET covers is soil moisture. If soil moisture is too high, water could percolate into tailings; if it is too low, radon gas diffusion and flux could increase through the cover. AS&T and their collaborators at the University of Montana are currently combining spaceborne satellite (remote sensing) data, ground-based sensor measurements, and machine learning to build highly accurate models capable of predicting soil moisture in near real-time within the disposal cell cover at the Monticello, Utah, Disposal Site.

Monticello is an internationally unique test site, where these models are developed and refined using one of the largest and longest ongoing ET cover studies in the world, with more than 25 years of continuous data collection. 

After the models have been calibrated and optimized using the Monticello test site, they will be applied to LM’s disposal cell near Grand Junction, Colorado. These data will be used in several ways to directly improve and reduce costs of long-term surveillance and maintenance at LM. They will be used to detect if and when soil-water content exceeds a particular threshold, indicating where and when water is draining through the cover system; they will be used to identify and target specific areas where cover enhancement efforts should be focused (i.e., areas that are too moist or too dry); and they will be used to track horizontal and vertical “wetting fronts” to determine subsurface movement of water and to identify the source of subsurface water. 

The models and methods the AS&T team is developing are also being applied more broadly within LM at sites with no direct means of measuring the local water balance (e.g., ET and drainage of water through the cover). LM typically uses hydrologic instruments known as lysimeters to accurately measure these parameters, but that method is expensive and may not capture important spatial differences within individual sites and across the LM portfolio.

“Advances in satellite technology, combined with the powerful capabilities of machine learning, gives us the unprecedented ability to monitor the three-dimensional water balance that was previously not possible with traditional, more expensive instruments like lysimeters,” said AS&T Senior Scientist Chris Jarchow.

The AS&T team will apply the machine learning models to LM’s disposal site in Rifle, Colorado, where pore water had been accumulating and causing concern for the cell’s integrity.  Following immediate mitigation measures implemented in 2024 (i.e. upgraded pump and evaporation systems), LM initiated a pilot test to increase ET and monitor potential reductions in pore water accumulation utilizing this technology.

The results will be used to inform a targeted implementation strategy geared toward providing long-term pore water management. Using the data from Monticello and Grand Junction, remote sensing technology, and emerging data-driven modeling techniques, AS&T is developing cost-effective, minimally invasive, and accurate methods to enhance and track the water balance performance in LM’s disposal cells.

Darina Castillo, who leads the Applied Studies and Technology Program for LM, is proud of the work AS&T is doing and believes it will lead to better management of LM’s engineered disposal cells.

“Our scientists are using cutting-edge technology for maximum gain,” she said. “We’re not only improving the accuracy of our data, we’re lowering the cost to compile it. And that’s exactly the point.”