Submitted to: Water Resources Research
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 11/6/2003
Publication Date: 2/27/2004
Citation: Leij, F.J., Romano, N., Palladino, M., Schaap, M.G., Coppola, A. 2004. Topographical attributes to predict soil hydraulic properties along a hillslope transect. Water Resources Research. 40:1:15. Interpretive Summary: Soil hydraulic properties are needed for a wide array of hydrological, agricultural, and agronomic purposes. Measurement of such properties, however, is difficult and expensive. For this reason so-called pedotransfer functions are being developed that utilize correlations between soil hydraulic properties and widely available soils data such as texture and soil bulk density. The current paper posits that terrain topography may also play a role and explores a data set from Italy to study the correlations between slope, slope orientation and other terrain-dependent non-soil properties. It was found that the estimation of water retention could be improved with 10% by including topography. A further improvement was possible if solar radiation input was taken into account. Results are relevant for large-scale studies such as GIS-based hydrological programs where topography is important. More research is needed to extrapolate the findings from the studied research area to other situations.
Technical Abstract: Basic soil properties have long been used to predict unsaturated soil hydraulic properties with pedotransfer function (PTFs). Implementation of such PTFs is usually not feasible for catchment-scale studies because of the experimental effort that would be required. On the other hand, topographical attributes are often readily available. This study therefore examines how well PTFs perform that use both basic soil properties and topographical attributes for a hillslope in Basilicata, Italy. Basic soil properties and hydraulic data were determined on soil samples taken at 50-m intervals along a 5-km hillslope transect. Retention parameters were somewhat correlated with topographical attributes z, slope, aspect, and potential solar radiation. Water contents were correlated most strongly with elevation (coefficient between 0.38 and 0.48) and aspect during "wet" conditions. Artificial neural networks were developed for 21 different sets of predictors to estimate retention parameters, saturated hydraulic conductivity, and water contents at capillary heads h = 50 cm and 12 bar. The prediction of retention parameters could be improved with 10% by including topography (RMSE = 0.0327 cm3 cm-3) using textural fractions, density organic carbon, elevation and slope. Furthermore, organic carbon became a better predictor when the PTF also used elevation as predictor. The water content at h = 50 cm could be predicted 26% more accurately (RMSE = 0.0231 cm3cm-3) using texture, density, organic carbon, elevation, slope, and potential solar radiation as input. Predictions of ANNs with and without topographical attributes were most accurate in the wet range (0 < h < 250 cm). Semivariograms of the hydraulic parameters and their residuals showed that the ANNs could explain part of the (spatial) variability.