Most pedotransfer functions (PTFs) have adopted soil texture information as the main predictor to estimate
soil hydraulic properties, whether inputs are defined in terms of the relative proportion of different grain size
particles or texture-based classifications. The objective of this studywas to develop ternary diagrams for estimating
soil water retention (θ) at−33 and−1500 kPa matric potentials, corresponding to the field capacity and wilting
point, respectively, from particle size distribution using two geostatistical approaches. The texture triangle was
divided into a 1% grid of soil texture composition resulting in 4332 different soil textures. Measured soil water
retention values determined in 742 soil horizons/layers located in Portugal were then used to develop and
validate the hydraulic ternary diagrams. The development subset included two-thirds of the data, and the
validation subset the remaining samples. The measured soil water content values were displayed in the ternary
diagram according to the coordinates given by the particles size distribution determined in the same soil
samples. The volumetric water content values were then predicted for the entire ternary diagram using two
different geostatistical interpolation algorithms (ordinary kriging and the empirical best linear unbiased
predictor). Uncertainty analysis resulted in a root mean square error below 0.040 and 0.034 cm3 cm−3 when
comparing the interpolated water contents at −33 and −1500 kPa matric potential values, respectively, with
the measured ones included in the validation dataset. The estimation variance calculated with both methods
was also considered to access the uncertainty of the predictions. The available water content of Portuguese
soils was then derived from θ−33 kPa and θ−1500 kPa ternary diagrams developed with both approaches. The
hydraulic ternary diagrams may thus serve as simplified tools for estimating water retention properties from
particle size distribution and eventually serve as an alternative to the traditional statistical regression and data
mining techniques used to derive PTFsinfo:eu-repo/semantics/publishedVersio