In the vast, intricate world beneath our feet, a phenomenon known as preferential flow (PF) is reshaping our understanding of how water, nutrients, and contaminants move through soil. This process, which causes rapid transport into the subsurface, has significant implications for groundwater recharge, streamflow, and water quality. Until recently, data scarcity has hindered the quantification of PF occurrence and the identification of its drivers across diverse ecoregions. However, a groundbreaking study published in the journal *Geophysical Research Letters* (translated as *Letters on Geophysical Research*) is shedding new light on this critical process.
Led by Bonan Li, a researcher at the College of Earth, Ocean, and Atmospheric Sciences at Oregon State University, the study analyzed high-frequency, multi-depth soil moisture data across 17 ecoregions in the USA. Using approximately 1,500 sensors at 40 sites, the team discovered that PF is more widespread than previously thought. “We found that PF occurs in up to 60% of rainfall events that are 2 millimeters or larger,” Li explained. This widespread occurrence suggests that PF is a significant factor in the rapid transport of water and contaminants into the subsurface, influencing groundwater recharge and streamflow.
The study identified several key drivers of PF, including increased peak rainfall intensity, finer textured soil material, low soil moisture variability, humid climate, and higher net primary productivity. These findings suggest that PF patterns could shift with projected climate changes, increasing uncertainty in predictions of groundwater recharge, water quality, and streamflow generation.
For the energy sector, these findings are particularly relevant. Understanding PF can help in the management of water resources, which is crucial for energy production, especially in regions where water scarcity is a growing concern. “As climate change alters rainfall patterns and intensity, the energy sector needs to be prepared for potential shifts in groundwater availability and quality,” Li noted. This research could guide the development of more accurate models for predicting water availability, ultimately supporting more sustainable energy production.
The study’s comprehensive approach, utilizing data from the National Ecological Observatory Network (NEON), provides a robust foundation for future research. By identifying the key drivers of PF, the study opens doors for further investigation into how these factors interact and evolve under different climatic conditions.
As we look to the future, the insights gained from this research could shape the way we manage water resources and develop energy policies. By understanding and predicting PF, we can better prepare for the challenges posed by climate change and ensure the sustainable use of our precious water resources. This study not only advances our scientific understanding but also underscores the importance of interdisciplinary collaboration in addressing global environmental challenges.