Predicting the water retention characteristics of UK mineral soils

Abstract

The management of water resources in compliance with regulatory requirements increasingly uses national environmental impact assessments incorporating models of soil solute transport. Most such models use either continuous functions designed to solve the Richards equation, or capacity-type functions based on water contents between specified soil water pressure heads. The work described here has used three separate sets of measured water-release data representative of the whole of the UK to develop a set of pedotransfer functions (PTFs) for predicting volumetric water contents at seven pressure heads on the water release curve ranging from 0 to −1500 kPa. A theoretically-based structural stratification of the data was proposed and tested. Within each grouping, multiple regression analysis was used to derive equations for predicting the water contents at each pressure head, based on a selected set of predictors. The resulting PTFs gave a significantly better prediction than PTFs derived from the unstratified data and also than the widely used HYPRES PTFs. For all non-sandy horizons not subject to regular cultivation, additional PTFs incorporating a climatic variable, the average annual potential soil moisture deficit (PSMD) gave a further improvement in prediction in drier areas of the UK with PSMD greater than 130 mm. Mean errors associated with using the PTFs to parameterize continuous function models and a capacity model for calculating water availability for cereals (AP cereals ) are ±16.4 and 16.9%, respectively.

Simulating a century of soil erosion for agricultural catchment management

“…In the three upland catchments moorland is the dominant land cover. The predicted saturated hydraulic conductivities ( K sat ) based on pedotransfer functions (Hollis et al ., ) range 33–235 mm h ‐1 with the highest K sat values in the Brotherswater and Loch of the Lowes catchments that contain the largest area of moorland. Notably, the range in K sat in the Loch of the Lowes catchment (66–202 mm h ‐1 ) is consistent with the reported range in mean K sat (56–224 mm h ‐1 ) for similar soils under woodland in the same region (Archer et al ., ).…”

“…Soil data for the catchments in England was supplied by the National Soil Resources Institute (NSRI, ) and by the James Hutton Institute for the catchments in Scotland (James Hutton Institute, ). Soil hydraulic parameters were estimated using pedotransfer functions based on soil texture (Hollis et al ., ). The soil parameters used in MMF‐TWI are summarized for each catchment in the Appendix (Table ).…”

Assessing the performance of a physically based hydrological model using a proxy‐catchment approach in an agricultural environment

“…Five soil types were identified in the Blackwater catchment; the one with the largest extent (40%), namely, WICKHAM (Eutric Luvic Planosols), had a soil texture of 21% sand, 41% silt, and 30% clay. Each soil is characterized by five depth layers between 0 and 1.5 m; and the properties for each soil type and layer vary depending on the land use (Hollis et al, ). Six soil classifications with five depth layers (0–1.5 m) were identified in the Kit Brook catchment; BATCOMBE (18% sand, 58% silt, and 24% clay) and CHARITY (16% sand, 58% silt, and 26% clay) soils cover the 37% and 31% of the catchment, respectively.…”

Derivation and predictive performance of pedotransfer functions: an empirical investigation

“…Hollis et al, 2015) and (ii) the observed strength and nature (linear or quadratic) of the relations with FC and PWP (the Y variables) (Figures 2, 3) and the X variables (not shown). Bulk density and organic carbon content were included because of their importance and effect on water retention (Rawls et al, 2003;Hollis et al, 2015). Of the PSD fractions, clay and silt contents were included because they affect water retention (Hollis et al, 2015), and fine sand because it decreases water retention.…”

“…Direct measurements of many soil properties, such as field capacity (FC) and permanent wilting point (PWP), are time consuming and expensive. Pedotransfer functions (PTFs) (Hollis et al , ) help to overcome these limitations through the derivation of an empirical predictive relation, in many cases regression based, of any such costly‐to‐measure soil property with other correlated soil properties that are more easily and economically measured. The data for derivation of these PTFs come from soil samples taken using various sampling designs that aim to identify the inherent spatial range and distribution of properties in a region.…”