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United States Department of Agriculture

Agricultural Research Service

Title: Preferential Flow: Identification and Quantification

Authors
item Shirmohammadi, Adel - UNIV. OF MARYLAND
item Montas, Hubert - UNIV. OF MARYLAND
item Bergstorm, Lars - SWEDISH UNIV. OF AG. SCI.
item Sadeghi, Ali
item Bosch, David

Submitted to: Book Chapter
Publication Type: Book / Chapter
Publication Acceptance Date: April 28, 2004
Publication Date: November 15, 2004
Citation: Shirmohammadi, A., Montas, H., Bergstorm, L., Sadeghi, A.M., Bosch, D.D. 2004. Preferential flow: identification and quantification. In: Alvarez-Benedi, J., Munoz-Carpena, R., editors. Soil-Water-Solute Process Characterization. Washington, DC: CRC Press. p. 289-308.

Interpretive Summary: Speedy arrival of water and chemicals through preferential pathways has attracted the attention of researchers in its quantification through both experimental and theoretical approaches. This chapter provided a review of some of the experimental and mathematical approaches used to quantify flow of water and chemicals in soils displaying preferential transport. It reviewed the experimental techniques such as staining, tension infiltrometer, and lysimeter for identification of quantification of preferential flow and solute transport. This chapter also reviewed the state-of-the-art mechanistic methods considering hydrodynamic principles. Mechanistic-empirical and stochastic approaches were also discussed. The review provided a glance at the manipulated Darcian approaches and provided a new 3-Domain concept in quantifying the infiltration rate for soils displaying preferential flow. The chapter concluded that our handling of the preferential flow either fails the proper mathematical representation or it fails the proper parameterization for proper representation of the system. One may use limited data methods such as artificial neural networks (ANN) to provide a proper parameter distribution for use in either mechanistic or stochastic models. However, using a black-box approach such as the method used in ANN may limit our understanding of the dynamic processes. Admittedly, our present state of knowledge and handling of preferential flow is at the state of "wondering" at best, thus further attention is needed to develop appropriate mathematical algorithms and easy and cost effective parameter quantification techniques.

Technical Abstract: Concern over chemical loadings to unconfined aquifers and into surface water resources through drain tiles and subsurface groundwater flow has directed researchers to focus on the pathways that speed up the pollutant arrival to such sources. This chapter provided a review of some of the experimental and mathematical approaches used to quantify flow of water and chemicals in soils displaying preferential transport. It reviewed the experimental techniques such as staining, tension infiltrometer, and lysimeter for identification of quantification of preferential flow and solute transport. This chapter also reviewed the state-of-the-art mechanistic methods considering hydrodynamic principles. Mechanistic-empirical and stochastic approaches were also discussed. The review provided a glance at the manipulated Darcian approaches and provided a new 3-Domain concept in quantifying the infiltration rate for soils displaying preferential flow. The chapter concluded that our handling of the preferential flow either fails the proper mathematical representation or it fails the proper parameterization for proper representation of the system. One may use limited data methods such as artificial neural networks (ANN) to provide a proper parameter distribution for use in either mechanistic or stochastic models. However, using a black-box approach such as the method used in ANN may limit our understanding of the dynamic processes! It may be appropriate to quote the old Indian metaphor that states "The one who rides tiger feels strong but can not get down; you wonder whether he is the captor or the captive." Similarly, our state of knowledge and handling of preferential flow is at the state of "wondering" at best, thus further attention is needed to develop appropriate mathematical algorithms and easy and cost effective parameter quantification techniques.

Last Modified: 10/23/2014
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