Measurement Scale Effect on Prediction of Soil Water Retention Curve and Saturated Hydraulic Conductivity

TL;DR

This study investigates how measurement scale influences the prediction accuracy of soil water retention and hydraulic conductivity, introducing a novel contrast pattern aided regression method that outperforms traditional linear regression.

Contribution

The paper develops and compares a new pedotransfer function using CPXR with traditional methods, highlighting the importance of measurement scale parameters for improved predictions.

Findings

CPXR significantly improves prediction accuracy over MLR.

Including scale parameters enhances model performance.

Results demonstrate the importance of measurement scale in soil hydraulic property prediction.

Abstract

Soil water retention curve (SWRC) and saturated hydraulic conductivity (SHC) are key hydraulic properties for unsaturated zone hydrology and groundwater. In particular, SWRC provides useful information on entry pore-size distribution, and SHC is required for flow and transport modeling in the hydrologic cycle. Not only the SWRC and SHC measurements are time-consuming, but also scale dependent. This means as soil column volume increases, variability of the SWRC and SHC decreases. 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. In the literature, several data mining approaches have been applied, such as multiple linear regression, artificial neural networks, group method of data handling. However, in this study we develop pedotransfer functions using a novel approach called contrast pattern aided regression (CPXR) and compare it with the multiple linear regression method. For this purpose, two databases including 210 and 213 soil samples are collected to develop and evaluate pedotransfer functions for the SWRC and SHC, respectively, from the UNSODA database. The 10-fold cross-validation method is 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, while this is not the case when MLR is used. Moreover, the CPXR method yields remarkably more accurate soil water retention curve and saturated hydraulic conductivity predictions than the MLR approach.