|GHANBARIAN, BEHZAD - University Of Texas|
|TASLIMITEHRANI, VAHID - Wright State University|
|DONG, GUOZHU - Wright State University|
Submitted to: Journal of Hydrology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 8/3/2015
Publication Date: 9/21/2015
Citation: Ghanbarian, B., Taslimitehrani, V., Dong, G., Pachepsky, Y.A. 2015. Sample dimensions effect on prediction of soil water retention curve and saturated hydraulic conductivity. Journal of Hydrology. 528:127-137.
Interpretive Summary: The ability of soils to conduct and retain water is critical for a multitude of projects in agronomy, hydrology, meteorology, and environmental engineering. Measuring soil water retention and conductivity is time- and labor consuming, and for practical purpose these properties have to be estimated from readily available soil data. Both properties are scale-dependent; that is their values are dependent on the size of the soil sample or field soil volume used in measurements. Unfortunately, available data on soil water retention and soil hydraulic conductivity have been obtained with very different soil volumes. So, to use the entire wealth of data in soil hydraulic property estimation, the size of soil volume has to be incorporated into the estimation procedure. Nobody has done this so far, and therefore it is not know what type of dependence on scale has to be incorporated. We applied a novel machine learning technique that automatically determines dependencies of soil hydraulic properties on both easily available soil data and on scale. Introduction f the dependencies on scale radically improved the accuracy of predictions. We expect results of this work to be widely used by soil scientists, agronomists, hydrologists, micrometeorologists, environmental engineers, and other professionals and researchers who use soil hydraulic properties in environmental assessments and predictions.
Technical Abstract: Soil water retention curve (SWRC) and saturated hydraulic conductivity (SHC) are key hydraulic properties for unsaturated zone hydrology and groundwater. Not only are the SWRC and SHC measurements time-consuming, their results are scale dependent. Although prediction of the SWRC and SHC from available parameters, such as textural data, organic matter, and bulk density have been under investigation for decades, up to now no research has focused on the effect of measurement scale on the soil hydraulic properties pedotransfer functions development. The main purpose here was to investigate the effect of measurement scale on the prediction of the soil water retention curve and the saturated hydraulic conductivity. We develop pedotransfer functions using a novel approach called contrast pattern aided regression (CPXR) and consider the measurement scale parameters as input variables. Two datasets including 210 and 213 soil samples were extracted from the UNSODA database to develop and evaluate pedotransfer functions for the SWRC and SHC, respectively. The 10-fold cross-validation method was applied to evaluate the accuracy and reliability of the proposed regression-based models. Our results show that including measurement scale parameters, such as sample internal diameter and length, could substantially improve the accuracy of the SWRC and SHC pedotransfer functions developed using the CPXR method.