Abstract
Direct measurements of soil hydraulic properties are time-consuming, challenging, and often expensive. Therefore, their indirect estimation via pedotransfer functions (PTFs) based on easily collected properties like soil texture, bulk density, and organic matter content is desirable. This study was carried out to assess the accuracy of the pseudo continuous neural network PTF (PCNN-PTF) approach for estimating the soil water retention curve of 153 international soils (a total of 12,654 measured water retention pairs) measured via the evaporation method. In addition, an independent data set from Turkey (79 soil samples with 7729 measured data pairs) was used to evaluate the reliability of the PCNN-PTF. The best PCNN-PTF showed high accuracy (root mean square error (RMSE) = 0.043 cm3 cm−3) and reliability (RMSE = 0.061 cm3 cm−3). When Turkish soil samples were incorporated into the training data set, the performance of the PCNN-PTF was enhanced by 33%. Therefore, to further improve the performance of the PCNN-PTF for new regions, we recommend the incorporation of local soils, when available, into the international data sets and developing new sets of PCNN-PTFs.
Highlights
Pedotransfer functions (PTFs) are statistical tools used in soil science to estimate soil hydraulic properties, mainly the soil water retention curve (SWRC), based on the collected basic soil properties, available from most regional and national databases [1]
Parametric pedotransfer functions (PTFs) [11,12,13,14] estimate the parameters of a soil hydraulic function that
The specific objectives of this paper are to (I) develop water retention PCNN -PTFs by utilizing the international data set from evaporation experiments, (II) evaluate the accuracy and reliability of the PCNN -PTFs using the international data set from evaporation experiments and an independent Turkish data set and (III) determine whether incorporating the Turkish soils into the development data set improves the reliability of the PTFs
Summary
Pedotransfer functions (PTFs) are statistical tools used in soil science to estimate soil hydraulic properties, mainly the soil water retention curve (SWRC), based on the collected basic soil properties, available from most regional and national databases [1]. Parametric PTFs [11,12,13,14] estimate the parameters of a soil hydraulic function that. Parametric PTFs are more prevalent because of their continuous representation of SWRC and their ability to provide soil hydraulic parameter estimates for use in hydrological models. Developing parametric PTFs involves fitting a soil hydraulic model to individual water-retention points and subsequently estimating the parameters of that model using basic soil properties. The widely used parametric PTFs such as Rosetta [15,16] and Neuro-m [17]
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