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Perspective: Preferential Flow in Riparian Buffers: Current Research and Future Needs  Public Access

Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan

Citation:  Transactions of the ASABE. 64(6): 1907-1911. (doi: 10.13031/trans.14732) @2021
Authors:   Derek M. Heeren, Lucie Guertault, Kyle Mankin
Keywords:   Best management practice, Buffer strip, Agricultural conservation practice, Filter strip, Macropore, Nonpoint-source pollution.


Preferential flow (PF) can critically reduce riparian buffer contaminant removal efficiency.

This collection presents research on PF measurement, visualization, modeling, and contaminant transport impacts.

Future needs include tools to identify landscape-scale PF areas and conservation practices.

Future models for research and practice should account for PF in riparian buffers.

Abstract. Preferential flow in riparian buffers can substantially compromise their effectiveness in reducing contaminants from overland runoff. The objective of this article is to introduce a collection of five articles on current research into subsurface preferential flow measurement, visualization, modeling, and impacts on contaminant fate and transport at scales ranging from the subsurface pore scale to the plot scale to the watershed scale. This collection presents selected works from a broader invited session on “Preferential flow and piping in riparian buffers” at the 2020 ASABE Annual International Meeting. Major findings include: new methodologies, such as light transmission and geophysics, to characterize subsurface preferential flow; an infiltration partitioning approach to quantify preferential flow from field experiments; a kinematic dispersive wave model to effectively simulate subsurface preferential flow; and the significant impact of surface concentrated flow pathways on pesticide fate and transport both upstream and within a riparian buffer. Future work is needed to develop methods and tools to identify PF areas and management solutions within a landscape, and to update both research and design models to better quantify and account for PF processes.

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