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Modelling water and solute transport through soil requires the characterisation of the soil hydraulic functions; however, determining these functions based on measurements is time-consuming and costly. Pedotransfer functions (PTFs), which make use of easily measurable soil properties to predict the hydraulic functions, have been proposed as an alternative to measurements. The better known and more widely used PTFs were developed in the USA or Europe, where large datasets exist. No specific PTFs have been published for New Zealand soils. To address this gap, we evaluated a range of published PTFs against an available dataset comprising a range of different soils from New Zealand and selected the best PTFs to construct an ensemble PTF (ePTF). Assessment (and adjustment when required) of published PTFs was done by comparing measurements and estimates of soil water content and the hydraulic conductivity at selected matric suction values. For each point, the best two or three PTFs were chosen to compose the ePTF, with correcting constants if needed. The outputs of the ePTF are the hydraulic properties at selected matric suctions, akin to obtaining measurements, thus allowing the fit of different equations as well as combining any available measurements. Testing of the ePTF showed promising performance, with reasonably accurate estimates of the water retention of an independent dataset. Root mean square error values averaged 0.06 m3 m–3 for various New Zealand soils, which is within the accuracy level of published PTF studies. The largest errors were found for soils with high clay content, for which the ePTF should be used with care. The performance of the ePTF for estimating soil hydraulic conductivity was not as reliable as for water content, exhibiting large scatter. Predictions of saturated hydraulic conductivity were of the same magnitude as the measurements, whereas the unsaturated values were generally under-predicted. The conductivity data available for this study were limited and highly variable. The estimates for hydraulic conductivity should therefore be used with much care, and future research should address measurements and analysis to improve the predictions. The ePTF was also used to parameterise the SWIM soil module for use in Agricultural Production Systems Simulator (APSIM) simulations. Comparisons of drainage predicted by APSIM against results from lysimeter experiments suggest that the use of the derived ePTF is suited for the estimation of soil parameters for use in modelling. The ePTF is not envisaged as a substitute for measurements but is a useful tool to complement datasets with limited amounts of measured data.

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Lack of accurate data to estimate soil physical properties for soil types is limiting the wide application of simulation models to address modern environmental and land-use issues. In this study, systematic sampling of soil profiles for soil physical characteristics has provided an improved basis upon which to estimate a number of soil physical properties for 4 soil series. The selected soils form a soil drainage sequence on the post-glacial surface of the Canterbury Plains and vary from shallow sandy loam, well-drained soils to deep clay loam, poorly drained soils. Three profiles within 3 map units were sampled for each of 4 soil series. Three horizons in each soil profile were sampled for soil porosity values, particle size, and saturated and near-saturated hydraulic conductivity. Variability in all data, as shown by coefficient of variation, increased in the order: total porosity = field capacity < wilting point < total available water = clay content < readily available water < macroporosity < sand content < hydraulic conductivity. Hydraulic conductivity exhibited high variability within horizons, between profiles, and within soil series. Temuka subsoils had extremely high variability in saturated hydraulic conductivity and this could be explained by their coarse prismatic structure. Analysis of variance identified horizons that differed in soil physical properties between soil series. Horizons that do not differ between series may be given pooled soil property values for the pooled series. Total porosity, field capacity, wilting point, clay content, and near-saturated hydraulic conductivity had the greatest number of differences (60–70%) between series comparisons, while total available water had fewest differences (5%). The series with greatest differences in drainage class (Temuka compared with Eyre or Templeton soils) recorded the largest number of differences in water release characteristics and particle size. There were few differences between well-drained Eyre and moderately well-drained Templeton series. Subsoils of Eyre series differed in hydraulic conductivity from subsoils for the other 3 series, but few differences in hydraulic conductivity were found between horizons of Templeton, Wakanui, and Temuka series. Hydraulic conductivity estimates for these series can therefore be pooled.

In New Zealand, occurrence of loess often determines the spatial pattern of soil depth, and influences droughtiness, leaching potential, organic matter accumulation, nutrient retention, and natural plant-species distribution. Understanding loess distribution is therefore a major prerequisite for soil and land resource management. Although New Zealand's soil scientists have accumulated a good empirical knowledge of loess distribution through several decades of field investigation, only some of this knowledge is recorded in papers and reports. This study estimates loess thickness and percent cover, and provides loess landscape models for the internal loess distribution of all land units in the South Island based on expert knowledge. We derived loess depth classes and percent cover classes and assembled land units with similar loess distribution patterns. The soil sets underpinning the map units of the New Zealand Land Resource Inventory (NZLRI) were classified according to loess depth, loess cover, and loess pattern. New loess maps of the South Island were produced from those classifications, displaying loess coverage, thickness, loess pattern, and loess landscapes. These maps present our current knowledge of the coarse-scale loess distribution and provide a framework for fine-scale loess landscape modelling.