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Hydraulic Conductivity Measurement for Three Frozen and Unfrozen Soils in the Red River of the North Basin

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

Citation:  Transactions of the ASABE. 64(3): 761-770. (doi: 10.13031/trans.14224) @2021
Authors:   Debjit Roy, Xinhua Jia, Xuefeng Chu, Jennifer M. Jacobs
Keywords:   Frozen soil, Hydraulic conductivity, Mini disk infiltrometer, Red River Valley.


Hydraulic conductivity was measured in frozen and unfrozen soil conditions by a minidisk infiltrometer.

In the RRB, frozen sandy loam and silty clay soils had the highest and lowest hydraulic conductivity, respectively.

Three simple equations were developed for the three soils to predict frozen soil hydraulic conductivity.

Freeze-thaw cycles reduced soil hydraulic conductivity.

Abstract. Hydraulic conductivity (k) is a key parameter in describing water movement through a soil profile. In the Red River of the North basin (RRB), the hydraulic properties of frozen soils vary with temperature, water content, and other factors. In this study, a minidisk infiltrometer was used to measure the k values of three soils from the RRB: Colvin silty clay loam, Fargo silty clay, and Hecla sandy loam. The k values were measured for frozen and unfrozen soils with five different initial soil water contents: oven dry, permanent wilting point, field capacity, midway between permanent wilting point and field capacity, and saturation. The results showed that the mean k value of a frozen soil increased with an increase in initial soil water contents. Hecla soil had the highest k values and Fargo soil had the lowest k values for frozen soils. Three equations were fitted with the measured k values of Colvin silty clay loam, Fargo silty clay, and Hecla sandy loam soils. The k values were also estimated using the Motovilov model. When evaluating model performance, the fitted regression models agreed more closely with the measured k values (index of agreement, d, values of 0.96, 0.94, and 0.94 for Colvin, Fargo, and Hecla soils, respectively) than Motovilov models. Based on overall considerations of statistical measures, the fitted regression models predicted the k values better than Motovilov models for all three frozen soils. It was also found that the k values decreased with an increase in the number of the freeze and thaw cycles that changed the soil properties.

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