Developing Pseudo Continuous Pedotransfer Functions for International Soils Measured with the Evaporation Method and the HYPROP System: II. The Soil Hydraulic Conductivity Curve

Amninder Singh,Wolfgang Durner,Amir Haghverdi,Hasan Sabri Öztürk

doi:10.3390/w13060878

Amninder Singh, Wolfgang Durner + Show 2 more

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https://doi.org/10.3390/w13060878

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Abstract

Direct measurement of unsaturated hydraulic parameters is costly and time-consuming. Pedotransfer functions (PTFs) are typically developed to estimate soil hydraulic properties from readily available soil attributes. For the first time, in this study, we developed PTFs to estimate the soil hydraulic conductivity (log(K)) directly from measured data. We adopted the pseudo continuous neural network PTF (PCNN-PTF) approach and assessed its accuracy and reliability using two independent data sets with hydraulic conductivity measured via the evaporation method. The primary data set contained 150 international soils (6963 measured data pairs), and the second dataset consisted of 79 repacked Turkish soil samples (1340 measured data pairs). Four models with different combinations of the input attributes, including soil texture (sand, silt, clay), bulk density (BD), and organic matter content (SOM), were developed. The best performing international (root mean square error, RMSE = 0.520) and local (RMSE = 0.317) PTFs only had soil texture information as inputs when developed and tested using the same data set to estimate log(K). However, adding BD and SOM as input parameters increased the reliability of the international PCNN-PTFs when the Turkish data set was used as the test data set. We observed an overall improvement in the performance of PTFs with the increasing number of data points per soil textural class. The PCNN-PTFs consistently performed high across tension ranges when developed and tested using the international data set. Incorporating the Turkish data set into PTF development substantially improved the accuracy of the PTFs (on average close to 60% reduction in RMSE). Consequently, we recommend integrating local HYPROPTM (Hydraulic Property Analyzer, Meter Group Inc., USA) data sets into the international data set used in this study and retraining the PCNN-PTFs to enhance their performance for that specific region.

Highlights

IntroductionDirect measurements of soil hydraulic properties in the field and laboratory can be tedious, laborious, and often expensive due to their significant inherent spatial variability
Schaap and Leij [6] reported root mean square error (RMSE) values ranging from 1.12 to 1.76 for calibration subset and from 1.18 to 1.77 for an independent validation data set for their hydraulic conductivity pedotransfer functions (PTFs), indicating lower accuracy and reliability than the PCNN -PTF developed in this study
These results concur with the results we reported in the companion paper (Singh et al [27]), where high performance was observed for the dominant soil textures for the SWRC estimations

Summary

Introduction

Direct measurements of soil hydraulic properties in the field and laboratory can be tedious, laborious, and often expensive due to their significant inherent spatial variability. Pedotransfer functions (PTFs) are often developed and used to indirectly estimate these properties by establishing empirical relationships based on the readily available soil properties such as soil texture, bulk density (BD), and soil organic matter content (SOM) [1]. The primary soil hydraulic properties include the soil water retention and hydraulic conductivity curves (SWRC and SHCC) that define the volumetric water content’s nonlinear relationships with the soil tension and the soil hydraulic conductivity, respectively. The hydraulic conductivity decreases as the volumetric water content decreases because of a reduction in the cross-sectional area of water flow and increased tortuosity and drag forces [2,3]

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