Pedotransfer functions estimating soil hydraulic properties using different soil parameters

Christen D. Børgesen, Bo V. Iversen, Ole H. Jacobsen, Marcel G. Schaap

Research output: Contribution to journalArticlepeer-review

31 Scopus citations

Abstract

Estimates of soil hydraulic properties using pedotransfer functions (PTF) are useful in many studies such as hydrochemical modelling and soil mapping. The objective of this study was to calibrate and test parametric PTFs that predict soil water retention and unsaturated hydraulic conductivity parameters. The PTFs are based on neural networks and the Bootstrap method using different sets of predictors and predict the van Genuchten/Mualem parameters. A Danish soil data set (152 horizons) dominated by sandy and sandy loamy soils was used in the development of PTFs to predict the Mualem hydraulic conductivity parameters. A larger data set (1618 horizons) with a broader textural range was used in the development of PTFs to predict the van Genuchten parameters. The PTFs using either three or seven textural classes combined with soil organic mater and bulk density gave the most reliable predictions of the hydraulic properties of the studied soils. We found that introducing measured water content as a predictor generally gave lower errors for water retention predictions and higher errors for conductivity predictions. The best of the developed PTFs for predicting hydraulic conductivity was tested against PTFs from the literature using a subdata set of the data used in the calibration. The test showed that the developed PTFs gave better predictions (lower errors) than the PTFs from the literature. This is not surprising since the developed PTFs are based mainly on hydraulic conductivity data near saturation and sandier soils than the PTFs from the literature.

Original languageEnglish (US)
Pages (from-to)1630-1639
Number of pages10
JournalHydrological Processes
Volume22
Issue number11
DOIs
StatePublished - May 30 2008
Externally publishedYes

Keywords

  • Hydraulic properties
  • Neural network
  • Pedotransfer functions

ASJC Scopus subject areas

  • Water Science and Technology

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