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Identifying preferential flow from soil moisture time series: Review of methodologies

Authors

Nimmo,  John R.
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Wiekenkamp,  Inge       
1.4 Remote Sensing, 1.0 Geodesy, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

Araki,  Ryoko
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Groh,  Jannis
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Singh,  Nitin K.
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Crompton,  Octavia
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Wyatt,  Briana M.
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Ajami,  Hoori
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Giménez,  Daniel
External Organizations;

Hirmas,  Daniel R.
External Organizations;

Sullivan,  Pamela L.
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Sprenger,  Matthias
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5035368.pdf
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Citation

Nimmo, J. R., Wiekenkamp, I., Araki, R., Groh, J., Singh, N. K., Crompton, O., Wyatt, B. M., Ajami, H., Giménez, D., Hirmas, D. R., Sullivan, P. L., Sprenger, M. (2025): Identifying preferential flow from soil moisture time series: Review of methodologies. - Vadose Zone Journal, 24, 2, e70017.
https://doi.org/10.1002/vzj2.70017


Cite as: https://gfzpublic.gfz.de/pubman/item/item_5035368
Abstract
Identifying and quantifying preferential flow (PF) through soil—the rapid movement of water through spatially distinct pathways in the subsurface—is vital to understanding how the hydrologic cycle responds to climate, land cover, and anthropogenic changes. In recent decades, methods have been developed that use measured soil moisture time series to identify PF. Because they allow for continuous monitoring and are relatively easy to implement, these methods have become an important tool for recognizing when, where, and under what conditions PF occurs. The methods seek to identify a pattern or quantification that indicates the occurrence of PF. Most commonly, the chosen signature is either (1) a nonsequential response to infiltrated water, in which soil moisture responses do not occur in order of shallowest to deepest, or (2) a velocity criterion, in which newly infiltrated water is detected at depth earlier than is possible by nonpreferential flow processes. Alternative signatures have also been developed that have certain advantages but are less commonly utilized. Choosing among these possible signatures requires attention to their pertinent characteristics, including susceptibility to errors, possible bias toward false negatives or false positives, reliance on subjective judgments, and possible requirements for additional types of data. We review 77 studies that have applied such methods to highlight important information for readers who want to identify PF from soil moisture data and to inform those who aim to develop new methods or improve existing ones.