Understanding preferential flow in the Vadose Zone: Recent advances and future prospects
Preferential flow is broadly defined as the channeling of water and solutes through a small fraction of the pore space in the vadose zone. Among other consequences, preferential flow has significant implications for the hydrological response to rainfall and also plays an important role in determining the risk of contaminant transport to surface waters and groundwater. The study of preferential flow phenomena was stimulated by pioneering work in the 1970s, and it has become a major topic of research during the past 40 years.
An article in Vadose Zone Journal summarizes recent work that is leading to an improved understanding of preferential flow processes across a wide spectrum of scales, from pore to core, through field, hillslope, catchment, and global scales.
At the pore-to-core scale, non-invasive measurement techniques such as X-ray imaging, neutron radiography, magnetic resonance imaging, and PET/SPECT scanning are helping to reveal the complex nature of pore networks and flow pathways in soil. These observations are being interpreted using mathematical theories that describe the connectivity and continuity of pore networks.
At larger spatial scales, dye-staining experiments give a picture of the flow pathways over distances up to several meters—scales at which the observational techniques mentioned above cannot be used. Ground-penetrating radar and electrical resistivity tomography are being used at soil profile-to-field scales to map the heterogeneity of water flow and solute transport processes.
At the landscape scale, application of machine-learning techniques to global meta-datasets has led to some new insights into the influence of site attributes such as climate, land use, and soil type on preferential flow.
Progress in understanding may also result from interpreting the results of experimental studies with models. However, this review suggests that current generation simulation models do not fully reflect the present state of process understanding and empirical knowledge of preferential flow. Future advances in computational techniques, computer hardware, and measurement technologies should, however, lead to better models and increasingly reliable predictions of the impacts of preferential flow, even at the larger scales relevant for management.