Open AccessSoil Water Repellency Index Prediction Using the Molarity of Ethanol Droplet Test
Author(s) -
Moody David R.,
Schlossberg Maxim J.
Publication year - 2010
Publication title -
vadose zone journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.036
H-Index - 81
ISSN - 1539-1663
DOI - 10.2136/vzj2009.0119
Subject(s) - wetting , sorptivity , soil water , soil science , chemistry , surface tension , vapor pressure , vadose zone , penetration (warfare) , infiltration (hvac) , analytical chemistry (journal) , mathematics , mineralogy , chromatography , thermodynamics , environmental science , physics , organic chemistry , operations research , porosity
The profound impact of soil water repellency (WR) on vadose zone processes makes accurate characterization of this phenomenon paramount. Numerous WR measurement techniques exist, each having advantages and disadvantages with regard to laboriousness, resolution, and accuracy. The molarity of ethanol droplet (MED) test quantifies WR as the lowest ethanol concentration permitting droplet penetration within 5 s, or alternatively, the 90° liquid surface tension of the infiltrating droplet (γ ND ). This method is simple and rapid but poorly represents soil wetting behavior across measurement intervals. Although time consuming, water/ethanol sorptivity ratio calculation of the repellency index ( R ) generates a continuous, linear scale of WR that intrinsically isolates the effect of WR on infiltration. This study compared MED and R measurements of sand samples displaying varying degrees of WR. Each technique was performed at 20°C and 1.78 kPa H 2 O vapor pressure using duplicate subsamples of oven‐dried (55°C) sands. A nonlinear association between R and γ ND or MED was observed. Regressing log 10 R by γ ND revealed a statistically significant model, yet the 95% log 10 R prediction interval included values less than the theoretical lower limit of R Alternatively, regressing log 10 R by MED generated the following model ( P < 0.0001, r 2 = 0.727): log 10 R = 0.705 + 0.5144(MED), capable of predicting R within the operation bounds of R theory. While the predicted R values are distributed across a wide interval, their availability offers cautious users an intuitive scale for enhanced interpretation of more commonly generated MED data.