Elsevier

Geoderma

Volume 93, Issues 3–4, December 1999, Pages 225-253
Geoderma

Comparison of different approaches to the development of pedotransfer functions for water-retention curves

https://doi.org/10.1016/S0016-7061(99)00061-0Get rights and content

Abstract

Pedotransfer functions (PTFs) for estimating water-retention from particle-size and bulk density are presented for Australian soil. The water-retention data sets contain 733 samples for prediction and 109 samples for validation. We present both parametric and point estimation PTFs using different approaches: multiple linear regression (MLR), extended nonlinear regression (ENR) and artificial neural network (ANN). ENR was found to be the most adequate for parametric PTFs. Multiple linear regression cannot be used to predict van Genuchten parameters as no linear relationship was found between soil properties and the curve shape parameters. Using the prediction set, ANN performance was similar to the ENR performance for the prediction dataset, but ENR performed better on the validation set. Since ANN is still considered as a black-box approach, the ENR approach which has a more physical basis is preferred. Point estimation PTFs were estimated for water contents at −10, −33 and −1500 kPa. Multiple linear regression performed better for point estimation. An exponential increase trend was found between particles <2 μm and water contents held at −10, −33 and −1500 kPa. The point estimation ANN did not improve prediction compared to MLR. Increasing the number of functions and parameters in developing PTFs does not necessary improve the prediction. The effect of parameter uncertainty, differences in texture determination and spatial variability on the error in prediction is also discussed.

Introduction

The water-retention curve, which defines the relationship between soil water content (θ) and hydraulic potential (h) is an important physical property of soil material. Determining this property directly can be expensive and time consuming. Since water retention by soil is affected by other physical properties, such as texture and structure, it is possible to develop empirical relationships to predict soil water retention. Many attempts have been made to determine the water retention curve indirectly from easily measured properties or properties available from routine soil survey data. Bouma (1989) introduced the term pedotransfer function (PTF), which he described as translating data we have into what we need, i.e., predictive functions of certain soil properties from other easily, routinely, or cheaply measured properties.

In the past, attempts were made to correlate basic soil properties, such as percentage of sand, silt, clay and organic carbon, with the water content held at certain hydraulic potentials (usually at −33 kPa and −1500 kPa). This was made in order to estimate water content at field capacity, permanent wilting point and the availability of soil water to plants Briggs and Shantz, 1912, Salter et al., 1966. Developments in computer modelling of water and solute transport in soil is advancing rapidly, as speed of computation increases and complexity of models expand. The models are used to solve both production and environmental problems. With this advance, the need for appropriate θ(h) data as an input to the models is becoming more and more critical. Many hydraulic properties pedotransfer functions have been developed in an attempt to accommodate this need Gupta and Larson, 1979, Rawls et al., 1982.

Pedotransfer functions for predicting the water-retention curve can be divided into 3 types.

(1) Point estimation: This PTF is an empirical function that predicts the water content at a pre-defined potential. The most frequently estimated θ are at −10, −33 kPa (corresponding to field capacity) and at −1500 kPa (corresponding to permanent wilting point), which are needed to determine plant available water content.

(2) Parametric estimation: Parametric PTFs are based on the assumption that the θ(h) relationship can be described adequately by a hydraulic model that is a closed-form equation with a certain number of parameters, for example Brooks and Corey, 1964, Campbell, 1974 and van Genuchten (1980). The parametric approach is preferred in soil–water transport modelling as it yields a continuous function of the θ(h) relationship. Empirical functions are developed to estimate parameters of the hydraulic model from easily measured properties.

(3) Physico-empirical model: In this approach, the water-retention curve is derived from physical attributes. Arya and Paris (1981) translated the particle-size distribution into a water-retention curve by converting solid mass fractions to water content, and pore-size distribution into hydraulic potential by means of a capillary equation. The problem with this method is the need for information about the packing of soil particles.

Comprehensive soil hydraulic properties databases have been developed in the USA (UNSODA, Leij et al. 1996) and Europe (HYPRES, Wösten et al., 1999) while in Australia there is still little published data available and collation of a national database has just begun (Cresswell et al., 1997). Because of the supposed distinctive properties of Australian soil, PTFs developed elsewhere might not be directly applicable. As noted by Bastet et al. (1997), the performance of published PTFs varied according to the pedological origin of the soil on which they were developed. Consequently PTFs should not be extrapolated beyond their geographical training area without first assessing their general validity. Previous attempts at developing PTFs for predicting soil water-retention for Australian soils from physical and morphological data have been described by Williams et al., 1992, Cresswell and Paydar, 1996, Smettem and Gregory, 1996 and Bristow et al. (1997).

In this paper we compare different approaches for deriving point estimation and parametric PTFs from particle-size and bulk density data. The performances of developed PTFs for estimating water retention for Australian soil is compared, and the effect of parameter uncertainty and alternate textural determinations evaluated.

Section snippets

Multiple linear regression

The most common method used in point estimation PTF is to employ multiple linear regression. For example:θp=asand+bsilt+cclay+dorganicmatter+ebulkdensitywhere θp is the water content (m3m−3) at potential p and a, b, c, d, e are regression coefficients (Gupta and Larson, 1979).

Multiple linear regression is also used in parametric PTFs. Parameters of the hydraulic models are estimated by fitting the model to water-retention data with nonlinear regression and empirical relationships between basic

Data set

Six previously published water-retention datasets from across Australia are used in this paper (Table 1). The particle-size data are given in Fig. 1.

As can be seen from Fig. 1b the texture range for the Australian data is largely similar to that for European data contained in HYPRES (Nemes et al., 1999), except that there are more points in the clay–clay loam range in the Australian data. This suggests that different PTFs might be required.

A subset of data from Prebble, Forrest, Geeves, Smettem

Parametric PTFs

The results of different PTFs' performance for predicting θ for the prediction and validation data sets are shown in Table 5. Using the QCV to compare the performance of the ENR models for the prediction set, it was found that most of the models performed similarly except for ENR6 which does not use PWP to predict θr. Similarly, RMSR for all ENR models have values less than 0.04 m3m−3 except for ENR6. This indicates that prior information on the smaller potential θ is needed to obtain

Conclusions

Point estimation and parametric pedotransfer functions have been developed for Australian soil (Table 8). Because of unique soil properties, different cutoffs used in particle-size classification, and unavailability of data on some soil properties, most PTFs developed elsewhere cannot be applied directly. Using particle-size distribution and bulk density data, the PTFs developed predict water content at different potentials with reasonable accuracy. Parametric PTFs is useful as an input to soil

Uncited references

Vereecken et al., 1989

Acknowledgements

The authors would like to thank Dr. Hamish Cresswell, CSIRO Land and Water Canberra, for providing the Geeves and the Forrest data set, Mr. Damien J. Field, Department of Agricultural Chemistry and Soil Science, for his helpful suggestions.

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